Advisory Committee on Nuclear Waste 133rd Meeting, March 20, 2002

                Official Transcript of Proceedings


Title:                    Advisory Committee on Nuclear Waste
                               133rd Meeting

Docket Number:  (not applicable)

Location:                 Rockville, Maryland

Date:                     Wednesday, March 20, 2002

Work Order No.: NRC-283                             Pages 117-184

                   NEAL R. GROSS AND CO., INC.
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                     + + + + +
                   133RD MEETING
                     + + + + +
                  MARCH 20, 2002
                     + + + + +
                 The meeting commenced at 1:00 p.m. in Conference
           Room 2B3, Two White Flint North, Rockville, Maryland,
           George M. Hornberger, ACNW Chairman, presiding.
           GEORGE M. HORNBERGER       ACNW Chairman
           B. JOHN GARRICK            ACNW Member
           MILTON N. LEVENSON         ACNW Member
           RAYMOND G. WYMER           ACNW Member

           .           STAFF PRESENT:
           JOHN T. LARKINS            Exec. Dir.-ACRS/ACNW
           SHER BADAHUR               Assoc. Dir.-ACRS/ACNW
           HOWARD J. LARSON           Spec. Asst.-ACRS/ACNW
           LYNN DEERING               ACNW Staff
           LATIF HAMDAN               ACNW Staff
           MICHAEL LEE                ACNW Staff
           RICHARD K. MAJOR           ACNW Staff

           ALSO PRESENT:

           .                                 I-N-D-E-X
           AGENDA                                          PAGE
           High-Level Waste Performance Assessment
           Sensitivity Studies
                 Sitakanta Mohanty. . . . . . . . . . . . . 120
                 Richard Codell . . . . . . . . . . . . . . 144
           Discussion . . . . . . . . . . . . . . . . . . . 166
           Adjourn. . . . . . . . . . . . . . . . . . . . . 184

           .                           P-R-O-C-E-E-D-I-N-G-S
                                                    (1:07 p.m.)
                       CHAIRMAN HORNBERGER:  The meeting will
           come to order, the afternoon session here of the 133rd
           meeting of the ACNW.  I have a note for the Committee.
           At three o'clock, we are to go over to the neighboring
           building to get new badges.  And so we have an
           appointment at three o'clock.  That shouldn't be a
           problem because we have a 2:45 to three o'clock break
           schedule and I don't think that I will steal so much
           of the time that we've given over to Dick and to
           Sitakanta to do a presentation.
                       Dick was originally scheduled to talk to
           us about the sensitivity studies for the waste
           package, and Howard tells me he's going to talk about
           anticipatory research instead.
                       Although I may be corrected and it may in
           fact revert back to the sensitivity studies.
           Sitakanta, are you going to go first or is Dick?
                       MR. MOHANTY:  I'm going first.  Good
           afternoon, ladies and gentlemen.  My name is Sitakanta
           Mohanti.  I will be -- myself and Dr. Richard Codell
           will make this presentation.  I will go over the first
           part of the presentation, and Dr. Codell will make the
           presentation on the second half of this talk.
                       The title of this presentation is,
           "Sensitivity and Uncertainty in the NRC Total System
           Performance Assessment of TPA 4.1 Code," I should add
           results.  Okay.  Here is an outline of this
           presentation.  First, we will briefly address the
           purpose of this analysis of uncertainty and
           sensitivity.  Then we will present an overview of the
           Total System Performance Assessment preliminary
           results.  And then we will talk about the sensitivity
           analysis results that have been obtained so far.  Then
           some effects of treatment of data, especially variance
           and uncertainty on the expected dose estimation.  Then
           finally we will talk about the preliminary risk
           insights from the sensitivity and uncertainty
                       Under the sensitivity analysis results,
           that is the third bullet, we have three specific
           presentations:  One is characterized as the parametric
           sensitivity analysis, then we will talk about
           distributional sensitivity analysis, then the third
           one will be with a subsystem of barrier component
           sensitivity analysis.  I will be talking about the
           first two bullets, and a portion of sensitivity
           analysis results, especially distributional
           sensitivity analysis and subsystem of barrier
           component sensitivity analysis.
                       First, here are the purposes of the
           analysis.  As you all know, NRC staff, in conjunction
           with the staff from the Center for Nuclear Waste
           Regulatory Analysis, have been involved over several
           years in developing the Total System Performance
           Assessment Code.  The TPA Code represents an
           independent approach to assist NRC's review of DOE's
           performance assessment.
                       NRC's performance assessment tools are
           intended to be used for gaining risk insights and to
           risk inform the pre-licensing and the potential
           licensing activities proactively and reactively.  For
           example, the development of the Yucca Mountain Review
           Plan that you will hear about tomorrow, the
           development of analysis tools by various key technical
           user groups, or KTIs, and the confirmatory testing,
           all these have been and will continue to be influenced
           by the analysis that is performed using the Total
           System Performance Assessment or the TPA groups of
                       As far as the reactor work is concerned,
           staff is particularly looking at improving capability
           to review license applications, such as DOE's
           performance assessment results and probe DOE's
           assertion regarding the repository performance,
           identify probabilities, such as risk dilution.  Some
           of these examples we will cover during the course of
           this presentation.
                       Staff will also look at DOE's sensitivity
           and uncertainty analysis approaches and also will try
           to identify by doing its independent analysis which
           model assumptions analysis and what is the degree of
           importance of all these to the overall performance.
           And it will also verify DOE's assertion regarding the
           barrier importance.
                       These activities will require staff's,
           one, understanding of the system as a whole, therefore
           getting into various components of the total system is
           very important.  Therefore, staff will use these tools
           and knowledge in understanding the system as a whole
           and understand the factors that are important to
           safety performance.
                       Here I will give you just a very brief
           background before we move on to the results.  These
           are also some of the caveats in the sense that you
           have heard about the results -- you have heard the
           results from TPA 3.2 sensitivity analysis in the past,
           and this represents the latest -- DOE's latest design,
           which it designed.  And we have not done any analysis
           using DOE's low temperature concept, because their
           high temperature concept is considered as the normal
                       The Total System Performance Assessment
           Code, or the TPA Code, currently has about 950
           parameters, out of which 330 parameters are samples.
           So this is a pretty large problem for any kind of
           Monte Carlo analysis on conducting sensitivity and
           uncertainty analysis.  So that means 620 parameters
           are not sample, they're fixed at constant values at
           what we believe as the best available value.  And if
           necessary, those values can be varied if we want to
           support the current sensitivity analysis.
                       The results that will be presented
           alternative conceptual models -- the results on
           conceptual model analysis will not be shown for the
           second time.  However, in the context of the Total
           System Performance Assessment, conceptual model
           studies are done on a case-by-case basis, alternative
           conceptual model studies.
                       And we would like to add the note that
           analysis are performed mainly for developing staff
           understanding, and the analysis that would be
           presented are not necessarily mandated by the
           regulatory requirements.  And the results are
           preliminary in the sense that this sensitivity
           analysis is currently under development.  The report
           is not ready.  So what you are seeing today is a
           snapshot of the results that we have come up with so
           far.  The results will be perhaps finalized in several
                       Here I will start with the performance
           assessment results.  The performance measure is the
           peak expected dose to the reasonably maximally exposed
           individual.  And the results will be shown essentially
           for two scenarios.  The first one is the nominal case
           scenario, which is characterized by the slow
           degradation over time leading to ground water release.
           And the disruptive events scenario only one we will
           present here is the igneous activity.  Other two
           disruptive event scenarios are seismic activities as
           well as faulting activity.  However, seismicity is
           included in the nominal case, whereas we are not
           presenting results on faulting because there is no
           sensitivity.  We don't see more sensitivity to
           faulting event results.
                       And as far as the nominal case scenarios
           are concerned, essentially the risk is computed by
           averaging the results from Monte Carlo realizations,
           which is in terms of dose as a function of time.  And
           then the peak is determined from the expected dose
           curve.  Whereas the disruptive event scenario requires
           some specialized calculations because of the low event
           probability.  Therefore, special convolution has been
           used to take into consideration all possible events
           prior to the event time.  Prior to the evaluation
           time, if there are events, those should be
           appropriately factored in so that we get a smooth risk
                       First, this is the result of the nominal
           case scenario.  In the figure, we are presenting dose
           versus time, but before we go through the figure, let
           me just highlight that by the time the regulation was
           out, was finalized, this work was already underway.
           Therefore, some of the things that you are seeing here
           are still different from what is mandated by the rule.
           For example, the well pumping rate is varied in these
           calculations.  The receptor group is located at 20
           kilometers.  Other than that, I think these are the
           main ones that are different compared to the rule.
                       And just to highlight, what we have seen
           so far is that there are no corrosion failures in
           10,000 years, no seismic failures in the nominal case.
           The nominal case is the one which is defined by
           probability pretty close to one, and we are presenting
           here results from 350 realizations.  So primarily the
           doses are resulting from the initially defective
           failure which is varied between one to 88 waste
           packages.  To compare that, we have a total of 8,877
           waste packages, with each waste package having about
           7.89 MTU of spent fuel.
                       CHAIRMAN HORNBERGER:  Sitakanta, what does
           that probability approximately one, what does that
                       MR. MOHANTY:  Because this is the -- okay.
           If we subtract the probability for the disruptive
           events, such as volcanism, then it's a very large
           number.  This is number is pretty close to one.
                       Also, there are several important things
           to observe in this figure.  The rate curve represents
           the expected dose curve, which is an arithmetic
           average of the individual realizations which are
           represented in this blue color.  The dark blue color
           is the 95th percentile curve, and the green color
           represents the 75th percentile curve.  What this
           entails is that until about 6,000 years the expected
           dose curve exceeds the 95th percentile.  And
           throughout the 10,000 years, the expected dose curve
           exceeds the 75th percentile.
                       So this gives some sort of indication that
           the expected dose curve appears to be quite robust in
           determining the expected dose.  And the peak expected
           dose is from the expected dose curve, and clearly this
           indicates the expected dose curve is -- expected dose
           arc is pretty close to 10,000 years.  And to be exact,
           in our calculation it is showing up at 9,769 years.
                       MEMBER GARRICK:  Now, this is just from
           defective failures.
                       MR. MOHANTY:  These are all from defective
                       MEMBER GARRICK:  Because this is not the
           peak dose for much later times.
                       MR. MOHANTY:  Right.
                       MEMBER GARRICK:  Yes.
                       MR. MOHANTY:  Okay.  Corresponding to that
           figure, here are some additional results.  The figures
           on the left represent the cumulative release from the
           saturated zone because that is the end point after the
           transport through the geosphere, and after that it is
           the biosphere.  So I'll talk first between the
           biosphere and the geosphere.  This is the release
           rate, and the release rate -- cumulative release rate
           are presented in the Y axis of this curve for 10,000
           years here and 100,000 years here.  And these values
           are presented in large scale so that you can see these
           numbers which are very small, smaller than one.  And
           because these are smaller than -- some of these are
           smaller than one, therefore the log of that is a
           negative number here.
                       Clearly, it shows that technetium is
           dominating, also iodine-129 and chlorine-36.  And here
           is the corresponding curve.  And this indicates that
           most of the dose, which is about 52 percent of the
           dose, contributes and is coming from technetium-99 and
           about 25 percent of the dose coming from iodine and 20
           percent coming from neptunium-237, and others are sort
           of insignificant in terms of dose contribution.
                       I have just put a figure from 100,000
           years just for comparison purposes.  This shows that
           if you go beyond 10,000 years, the dominant -- the
           same nuclides are dominating, but you can see some
           finite values.  You don't have to see -- there are no
           negative numbers here in the log space.
                       Next, here is the result from the
           disruptive event scenario, as I mentioned earlier.
           The faulting event -- we are not showing the results
           for faulting event, and the seismicity was included as
           part of the base case, and we did not see any failures
           in 10,000 years.  So, essentially, this is a
           comparison between the nominal case scenario and the
           igneous activity scenario.
                       So, clearly, this shows that the peak in
           the igneous activity scenario, which has a recurrence
           rate of ten to the one minus seven per year, the dose
           -- the peak expected dose occurs much earlier compared
           to the nominal case.  As I mentioned earlier, in the
           nominal case scenario, the peak expected dose occurs
           close to 10,000 years.
                       And to obtain this smooth curve, we needed
           about 4,200 realizations, coupled with the convolution
           integral approach that one was used to obtain this
           curve.  And this drop here is perhaps because we have
           not taken one step beyond 10,000 years.  Because if we
           take a step beyond 10,000 years, this line is going to
           flatten out or will perhaps slightly go up.
                       For the early release, this peak from the
           igneous activity event, which is 0.35 milirems per
           year, that occurs at 245 years.  And the dominant
           radionuclide is americium-241, and this dose is
           primarily because of high activity nuclides, which
           americium-241 is one of them.
                       Okay.  Next, I will briefly go over the
           stability of the peak expected dose.  As such, because
           it is an expected dose, we should expect a lot of
           stability in that number.  We are using 350
           realizations, and we considered that to be quite
           stable.  But I also wanted to show you some variation,
           what happens if we go much beyond 350 realizations.
           In this table, that shows that we have gone beyond
           500.  We have gone all the way to -- in fact, we have
           gone to 4,000 realizations.  Here, this one shows only
           up to 3,000 realizations.  And it varies between 2.48
           ten to the minus two milirems per year and 3.24 ten to
           the minus two.
                       So, essentially, we don't see nice and
           smooth conversions.  And we did some investigation to
           find out what might be the reason for that.  It turned
           out that when we plugged the peak dose as a function
           of the number of sampling realizations, there are some
           extreme values.  That is what is causing this kind of
           change in the peak expected dose value.  And we have
           noticed that this kind of realization shows up in
           about one to 2,000 realizations.  And this is
           something we are continuing to investigate further.
                       Next, we will start with sensitivity
           analysis.  I will be talking about the distributional
           sensitivity analysis and subsystem value components in
           sensitivity analysis.  And after me, Dr. Codell with
           start with the parametrics sensitivity analysis.
                       This distributional sensitivity analysis
           is done primarily to understand how the peak expected
           dose is going to be influenced if the distributional
           function assumption that we have received from various
           KTIs are not correct or at least to identify if there
           are some areas where staff need to focus more to
           determine if anything can be improved.
                       Two approaches we have followed.  One is
           using a fixed range from here to here for changing the
           mean of the distribution by ten percent.  So we have
           shifted the mean by ten percent.  And in the second
           approach, we have completely changed the distribution
           function type.  That means if in the nominal case we
           had a normal distribution, we changed that to a
           uniform distribution to see if that has a major
           impact.  Similarly, if the distribution had a log
           uniform distribution, we changed that to log normal,
           because in log space we thought that would capture the
                       Instead of working with all 330
           parameters, we thought maybe changing for the top ten
           influential parameters that we have identified by
           using other methods would be more appropriate, because
           those parameters are already showing a lot of
           sensitivity.  That's why in this talk we will
           primarily focus on the top ten influential parameters.
                       And we have used two different sensitivity
           measures.  One is the change to the peak expected
           dose, before and after changing the distribution
           function type and an effective distance between the
           CDFs.  CDFs are constructed by using the peak dose
           from individual realizations.
                       CHAIRMAN HORNBERGER:  Sitakanta, when you
           say you looked at the top ten percent in importance,
           how did you determine that, from a different
           sensitivity analysis?
                       MR. MOHANTY:  Yes.  Those were determined
           from parametric sensitivity analysis that Dick is
           going to talk about.
                       For the distributional sensitivity
           analysis, the two kinds that I saw, these two figures
           are showing their results.  The ten percent shift to
           the mean with a fixed range, the results are shown
           here.  And for the complete change to the distribution
           function, the results are shown by these blue curves
           -- bars.
                       Let me describe the results from the shift
           to the mean by ten percent.  Clearly, it shows that
           when the distribution function type is increased, the
           mean is increased by ten percent, there is a 150
           percent increase in the waste package flow
           multiplication -- a 150 percent increase in the peak
           expected dose because of a ten percent change for the
           waste package flow multiplication factor.
                       The second one that came out to be very
           important is the spent fuel dissolution, which is a
           pre-exponential term that defines that dissolution --
           spent fuel dissolution rate.  Fifty-seven percent
           change to the peak expected dose occurred because of
           a ten percent shift to the distribution function.
                       Similarly, when we changed the
           distribution function type we did not see that kind of
           effects for the two that showed up as important when
           the mean was shifted.  Rather, the two that turned out
           to be important are the drip shield failure time and
           the neptunium retardation in alluvium.  So, therefore,
           this clearly indicates that staff should revisit and
           determine if the input parameter distributions were
           not carefully looked at, at least the ones that are
           showing up as important in the sensitivity analysis
           should be looked at further, because these effects can
           be cumulative.  So when we add these things up for
           many parameters, many sample parameters, that could
           influence the peak expected dose that we compute from
           the nominal case.
                       Next we'll talk about the subsystem of
           barrier component sensitivity analysis.  This analysis
           is just an extension of the sensitivity analysis we
           are doing, first, to the parametric sensitivity
           analysis, distributional sensitivity analysis, then
           the system can be broken down in many different ways.
           One can break the system along the line of
           subprocesses, but here we have broken it down along
           the line of physical components, and we are primarily
           interested in seeing how much sensitivity we are
           getting from individual components.  But then breaking
           down these components are very subjective, because one
           can have more components than what we have shown here.
           But it appears to be adequate for our purpose.
                       But it is very important to highlight here
           that this analysis should not be mixed with multiple
           barrier analysis.  This is not a proposal to do
           analysis this way.  Therefore, it should be clearly
           noted that this analysis is not required by -- there
           is no regulatory requirement for this kind of
                       And I would like to draw your attention to
           the representation of the repository in this column.
           The repository can be viewed at the top as an
           unsaturated zone.  Then next to that we have -- below
           that we have the drip shield.  Below that we have the
           waste package, then waste form, invert, unsaturated
           zone and the saturated zone.  So here you are seeing
           barrier components, but in the results that I will
           present in the next two slides, we show only six
           because unsaturated zone above the repository and
           below the repository will be treated as one entity.
                       And we will also show results from the
           one-on analysis, one-off analysis and cumulative
           addition analysis, because they all provide different
           insights into the system.
                       CHAIRMAN HORNBERGER:  Sitakanta, I don't
           think I full understood something.  You said that this
           was not intended to be an analysis of barriers, but
           then I've lost track of why you're doing this.
                       MR. MOHANTY:  We are doing this purely to
           supplement the sensitivity analysis.  We are trying to
           group them together.  It's one way of looking at a
           group of parameters, so we thought maybe grouping the
           parameters along the line of a physical entity makes
           it easier to understand.
                       My purpose in showing that column in the
           previous slide was that these should be viewed as
           individual cases.  Each column here represents one
           case.  The group on the left represents the one-off
           analysis; the group on the right represents one-on
           analysis.  And the first column under the one-off
           analysis represents the nominal case.  And the row at
           the bottom these represent the percentage change.
                       I would like to draw your attention to
           these numbers in the sense that the numbers on the
           left-hand side these are changes with respect to the
           nominal case results.  The numbers on the right-hand
           side, these are with respect to the case where all
           barriers are suppressed, and the suppression of a
           barrier is represented by the gray color.  That means
           if we go to the second column here, this shows the
           drip shield as a barrier has been suppressed.  Under
           the third column, the waste package as a barrier has
           been suppressed.  So, therefore, this number -- when
           the drip shield barrier is suppressed, the number at
           the bottom shows that the peak expected dose changed
           only by a factor of 34 percent.  When the waste
           package value was suppressed, the peak expected dose
           changed by 62,200 percent.  So likewise, these numbers
           represent changes with respect to the nominal case
           result.  But these are in percentages.
                       CHAIRMAN HORNBERGER:  And can you
           enlighten me just a little bit by what you mean by
                       MR. MOHANTY:  By suppression, we mean that
           the function of the -- okay, from a purely technical
           point of view here, the drip shield fails at a certain
           time; it has a distribution.  By suppression here we
           imply that drip shield failure has been shifted back
           to time zero.  That means drip shield may have failed
           at time zero, but if there is no infiltration because
           of the thermal hydrology until 10,000 years, no water
           is going to contact the waste package.
                       CHAIRMAN HORNBERGER:  So the drip shield
           -- the suppression of the drip shield is equivalent to
           assuming that there is no drip shield.
                       MR. MOHANTY:  Right, right.
                       CHAIRMAN HORNBERGER:  Okay.  Now, if we go
           to the waste package, that's a little more difficult
           for me.  Does that mean that there is no waste
                       MR. MOHANTY:  Right.  Here, what it -- let
           me give you the detail here, if I can find my cursor
           here.  When the waste package is gone as a barrier,
           what it implies is that the waste package has two
           functions:  when the waste package fails, and the
           second is it contains -- it does not allow water to
           enter into the waste package through flow
           multiplication factor.  Only a fraction of the waste
           package surface area will contribute to the water
           getting into the waste package.  Therefore, it will
           come into contact to the spent fuel.  So when the
           waste package is gone, when the waste package is
           removed as a barrier, that implies that the waste
           package is failing at time zero.  And, also --
                       MEMBER GARRICK:  Does that also affect the
           composition of the water?
                       MR. MOHANTY:  No.  So that is an important
           point we want to make, that when we are doing this
           analysis we are not changing the physical processes
           that are going on.
                       MEMBER GARRICK:  So you're really not
           accounting for the interactive effects.
                       MR. MOHANTY:  Right, because your purpose
           is primarily the sensitivity --
                       MEMBER GARRICK:  So this is not so much an
           attempt to see the physical event and the progression,
           as it is to deal with this question of sensitivity and
                       MR. MOHANTY:  Right.  Yes.
                       MEMBER GARRICK:  Okay.
                       MR. MOHANTY:  But I think it is also
           important to point out here that this removal, this
           one-off analysis, especially when it comes to waste
           package, only affects the waste package that are
           already seeing water.  So two things are happening:
           It is seeing water early and the second, more water is
           getting into the waste package.  But there are lots of
           waste packages that do not see that water, as long as
           the unsaturated zone barrier is above -- unsaturated
           zone is above the repository horizon.
                       So, similarly, the numbers on the right
           maybe they should be viewed as a decrease, so there
           should be negative numbers.  So here it means that
           when the drip shield -- when all barriers are
           suppressed and drip shield is added as a barrier, just
           the only barrier, and the spent fuel would be in the
           waste package somewhere here, but now we have no waste
           package, in that case the drip shield has a
           performance of about 63 percent.  So this allows us to
           see how much performance individual are coming from
           these individual barrier components.
                       So here is shows that when the waste
           package barrier is added to a case where all barriers
           are suppressed, we have a 99.9 percent reduction in
           peak expected dose.  Whereas, when the unsaturated
           zone is added, we have a reduction of 96 percent, and
           when the saturated zone is added, when others are
           suppressed, it's about 94 percent.
                       So we have carried this analysis a little
           further, and we have added to the one-on analysis.
           Here we are adding those cumulatively.  The first
           column here represents when all barriers are
           suppressed.  In the second column, this shows that
           this is similar to the saturated column you saw on the
           previous page.  But when we add unsaturated zone to
           the saturated zone, it says that the peak expected
           dose has been reduced by 99.2 percent.  Of course
           there are several decimal places that I'm not showing.
                       When we add invert to the unsaturated zone
           and saturated zone, then that reduces to 99.6 percent.
           And by the time we reach the waste package, this is
           99.99, but maybe the number will change in about
           seventh or eighth decimal place.  So then when we add
           all the barriers, all the barrier components, then we
           regain the nominal case.
                       Then we grouped all these barrier
           components together to reflect the engineered barrier
           and the natural barrier.  Clearly, as expected, as we
           observed from individual component sensitivity,
           clearly, when we group them together, it shows when we
           compare that with respect to the nominal case, there
           is a -- and when the engineered barrier is suppressed,
           then we see a substantial increase in the peak
           expected dose, which is about 808,233 percent.  And
           when the engineered barrier system is there, but the
           natural barrier is suppressed, it's about 58,233
                       I think that ends my presentation.  Now
           Dr. Richard Codell will take over.
                       DR. CODELL:  Please don't adjust your
           sets.  We're experiencing technical difficulties here.
                       CHAIRMAN HORNBERGER:  While we're waiting,
           I'll interject and try to ask Sitakanta a tough
           question.  Having looked at all of that one-on and
           one-off analysis, I'm not sure what message I'm
           supposed to take from that.
                       MR. MOHANTY:  We are continuing to conduct
           this analysis.  These results are quite fresh.  We are
           also trying to figure out how they are going to
           contribute to the risk significance.  The main reason
           we did this kind of analysis is to see if any barrier
           component in suppressing is shadowing the effect of
           other barriers.  So to determine how these individual
           barrier components are performing, we had to separate
           those out to individual ones.
                       For example, if we go to -- I think it
           should be Slide Number 13, the one-on analysis for the
           drip shield, we see that when all other barriers are
           suppressed, the drip shield reduces dose by 63
           percent.  That number -- we have not devised any
           better method at this time to determine whether the
           effect of drip shield is 63 percent or something else.
           Simply by looking at one-off analysis we could not
           figure that out.
                       Also, another reason for doing this
           analysis is that if something is modeled, then we can
           capture that effect in the traditional sensitivity
           analysis.  But if the model doesn't represent that,
           then sensitivity analysis cannot capture it, because
           it is not in the model.  We do our best to capture
           everything possible in the model, but there are also
           uncertainty about the models themselves.
                       So there are two important aspects here.
           One is uncertainty in the model themselves, number
           one; and number two, is the shadowing effect of one
           barrier component over others.  So, therefore, by
           adding it cumulatively, starting from the saturated
           zone and coming up all the way to the level of drip
           shield or the unsaturated zone above that, that gives
           us some insights.  Simply from those numbers we can
           derive if there are any shadowing effects.
                       MEMBER GARRICK:  I guess the thing that --
                       DR. CODELL:  Maybe I should, to save time,
           just work from the viewgraphs.  Hopefully we'll be --
           in a minute or two we'll have the presentation, and
           I'll be able to put it up on the screen.
                       I cover parametric sensitivity analysis.
           We're talking about the nominal -- we're on Slide 15.
           We're covering nominal scenario only.  And the purpose
           is to determine sensitivity of parameters singly and
           also in groups.  The grouping is something new this
           year.  We're using -- there are two methods we're
           using for parametric sensitivity.  The first is
           statistical methods that evaluate sensitivity to a
           previously calculated pool of vectors that were
           generated by the TPA 4.1 Code.  In this case, we're
           using generally 4,000 vectors cover the range of the
           parameters.  And then there are non-statistical
           techniques that get to a sensitivity a second way,
           which is to redirect the calculations to get the
           maximum -- extract the maximum sensitivities from the
                       We generally look at the peak of each
           realization and look at the sensitivity of that, even
           though the standard is based on something else; that
           is, the peak of the mean dose.  Starting on the next
           slide, the statistical methods, these include
           primarily regression on raw and transformed variables,
           non-parametric tests, like Kolmogorov-Smirnoff tests
           and assigned tests, the parameter tree approach, and
           there's some new work on cumulative distribution
           functions sensitivity method and some other work
           recently developed by Sitakanta Mohanti and Justin Wu
           at the Center.
                       Another method along these lines is a
           method which is based on the mean -- calculating the
           sensitivity of the mean dose directly with respect to
           the means of the independent parameters and also the
           variance of the independent parameters.  These are too
           new to really go into any detail but they're
                       The non-statistical methods include
           differential analysis, Morris method and FAST method.
           These are things that we covered before.  There's one
           new method that -- factorial design of experiments
           which has the unfortunate acronym DOE, it's usually
           called DOE.
                       This is something that John Telford, of
           the Office of Research, and I have been working on for
           several months with some pretty impressive results, I
           feel.  I'll go into that in a little more detail.
                       Let me just take one second here to open
           up the correct file.
                       Okay.  So we're at the bottom of Slide 16
           now, starting at 17.  The next slide shows -- this is
           a tried and true method that we've been using for
           several years now.  We call it a composite statistical
           method.  This is to look at, in this case, six
           statistical tests of various kinds, looking at
           transformed and untransformed variables.  And it's
           really a seat-of-the-pants kind of method but works
           quite well.
                       We used six statistical tests with 4,000
           realizations.  And then looking at each test and
           factor the number of times the variable in question is
           statistically significant in each test and its rank
           and then develop a single list of parameters, top
           parameters from the number of times they appear and
           the ranks of the six tests.  And when you do that you
           come up with a list arbitrarily cut off at ten
           variables for 10,000 and 100,000 years, showing that
           a lot of the parameters that show up have to do with
           how much water gets in contact with the waste.
                       I'll show some comparisons of the methods
           later, but to get into the new work we've done, John
           Telford and I, the factorial design of experiments.
           Basically, factorial design, in the simplest form, is
           to look at two values of each of the variables, a high
           and a low value.  We took fifth and 95th percentile of
           each distribution, and since there were 330 variables,
           if you looked at all possible combinations, you'd have
           two to the 303 runs required, which at the present
           rate would take ten to the 94 years.  And, of course,
           in maybe 1,000 years this will be --
                       MEMBER GARRICK:  Are you looking for
           permanent employment?
                       DR. CODELL:  In 1,000 years things might
           be much better, but right now it's out of the
           question.  So the fractional factorial design is what
           you have to use, but it gives you reasonable time
           estimates, but it's somewhat ambiguous.
                       So the way we did it is looking at the
           sampling iteratively, that is running a fractional
           factorial and then using other information from the
           runs to refine the list and then repeating on the
           refined list several times until we're quite sure that
           we've gotten most of the important variables.  And
           this took a lot of trial and error, but we think we
           hit on a good procedure for doing this.
                       The advantages of this technique is it's
           systematic and potentially precise.  It's easily
           interpreted with a powerful, statistical techniques
           like analysis of variance and trees.  And it does
           reveal interaction among variables instead of looking
           only at sensitivity of single variables.  I think this
           is an important point.  Disadvantages are it's still
           costly and difficult to implement.  And looking at
           only the high and the low value, you're not looking at
           the range of entire variable.
                       So this is how we went through the
           procedure.  For the 10,000 year case, first looked at
           a design set using some statistical software of 2,048
           variables, and we identified 100 potentially sensitive
           variables.  We reduced that list to 37 on the basis of
           other information.  For example, even though some of
           these variables appeared to be sensitive like seismic
           parameters, you could see from other results that
           there weren't any failures, so you knew those
           variables were confounded and could be eliminated from
                       And the second screening -- that was the
           first screening -- the second identified ten variables
           and then we went into a full factorial with only ten
           variables, which is a reasonable number to deal with,
           and identified six to eight sensitive variables.  When
           you do this and you go through the analysis of
           variance, one of the byproducts is a tree diagram.
           And this shows very clearly that if you follow the
           path of the cursor here, a low value of drip shield
           failure time gives you -- and a high value of the flow
           multiplying factor, the diversion factor and the fuel
           dissolution factor and the waste package effective
           fraction leads to the highest dose.
                       So this kind of information is much more
           revealing than looking at sensitivity of single
           variables at a time.  This same sort of information,
           incidentally, comes out of what we call the parameter
           tree method, which is a statistical technique, but
           this is much more precise, whereas there's a lot of
           uncertainty in the parameter tree approach.
                       The next slide shows the same sort of
           result for the 100,000-year run, and it also shows the
           high and the low value of each variable contributing
           to the highest dose.
                       Just to show that we think we've captured,
           with a very small number of variables, most of the
           uncertainty, the next few slides reconstruct some of
           the results of the original run with the 330 variables
           and the reduced set, both either from regression or
           from fractional factorials.  This is a cumulative
           distribution function of the peak doses showing that
           especially for the high end of the dose curve that
           we've captured most of the uncertainty just with ten
           variables from the regression analysis or eight
           variables from the factorial design method.  The lower
           curve shows the mean dose for the same calculations,
           330 versus ten for regression and eight for the
           factorial design.  So even though it's not perfect,
           we've captured most of the uncertainty with a very few
           number of variables.  And the same is true for the
           100,000-year result.
                       Now, the next set of slides, moving away
           from the factorial design now, there were two options
           for looking at sensitivities.  The first one is that
           we have -- we can look at the peak of each individual
           run and look at the sensitivity based on the number or
           we could look at the time of the occurrence of the
           peak of the mean and look at that.  The upper graph
           shows the sensitivity result looking at the mean dose;
           the bottom is looking at the peak of the individual
           doses.  Except for the first two columns here, the
           results are quite different, and we're tempted to say
           that the sensitivity measure and statistical parlance
           has more power using the peak dose from each
           individual run rather than the mean.
                       But there's one interesting factor here.
           If you look at this particular variable, drip shield
           failure time came out about number 20 on this measure
           using the mean of the peak dose, and yet it came out
           quite high looking at the individual peak doses.  This
           is not an error.  This is -- there's a real reason for
           this that isn't obvious, and the next couple of slides
           really get to this -- why does drip shield failure
           time differ?
                       MEMBER GARRICK:  Dick, could you say
           something about the sensitivity measure, what it
           really is?
                       DR. CODELL:  Well, I'll let Sitakanta
           address that.  He prepared these slides.
                       MR. MOHANTY:  For the sensitivity
           analysis, we need a point value.  That means we have
           dose as a function of time, but when we do the
           analysis it has to be a point value that represents
           for the 10,000 years.  So it's a matter of whether we
           should choose the peak from that realization or should
           we choose the value, dose value corresponding to the
           peak expected dose?  That will be the point value.
                       So in other words, the red bars, those are
           showing the sensitivity analysis using the dose values
           corresponding to the time when the peak expected dose
           occurred.  Whereas the figure at the bottom that is
           indicating that peak can occur any time in 10,000
           years so that it is independent of the time of
           occurrence.  So, therefore, it reflects sort of the
           whole time domain, whereas the one at the top
           represents a particular time.
                       CHAIRMAN HORNBERGER:  But what are the
           units on that sensitivity measure?
                       MR. MOHANTY:  Oh.  We have different
           sensitivity measures from different methods.  This
           particular one is representing one method that we have
           used for the two graphs.  So that measure is
           essentially, but it's kind of hard to explain.  This
           is extracted from the Morris method in which we take
           the sensitivity from individual points and average
           that, and we determined the mean of the del Y over del
           X where X is the variable that is being changed.  Del
           Y is the dose value that is being changed.  So this is
           an ensemble statistics so that sensitivity measure is
           based on the ensemble statistics, both mean and
                       MEMBER GARRICK:  But it is in a change in
           dose per unit change in parameter.
                       MR. MOHANTY:  In the parameter, right.
                       DR. CODELL:  Okay.  Thanks, Sitakanta.
                       CHAIRMAN HORNBERGER:  But you must
           normalize somehow.
                       MR. MOHANTY:  Yes.  The parameters are
                       CHAIRMAN HORNBERGER:  Okay.
                       DR. CODELL:  On the next slide, I wanted
           to talk about the treatment of data variability and
           performance assessment modeling.  This particular
           piece of work came up during the SAS review of the
           TSPASR and the SSPA, and it was called the galsean
           variance partitioning.  It was basically how you treat
           data in the model.
                       It isn't exactly how DOE is doing it, but
           it leads us to some interesting conclusion on how we
           should deal with experimental data uncertainty either
           because of lack of knowledge or variability.  And the
           difference between these two kinds of uncertainty,
           epistemic and aleatory, is often blurred, for example,
           treatment of corrosion rate data for the waste
           packages and its effects on dose.
                       To get at this phenomenon, we put together
           a model based very loosely on NRC's model and DOE's
           model, but it's a separate model.  NRC's TPA model we
           represent variability and waste package corrosion by
           a few representative waste packages -- only one per
           subarea, ten in all.  Whereas DOE has in its Total
           System Performance Assessment uses the patch failure
           model that allows significant spatial variability of
                       We could look at the data on corrosion,
           and we could say it's either -- this is real data from
           the corrosion experiments on the coupons and say it's
           either a fixed but uncertain rate or a spatially
           varying rate due to the material and environmental
                       On the next slide, I showed this very
           simple model that deals only with a few parts of the
           model, particularly the waste package corrosion and
           the dissolution of waste in a fixed number of years
           once the waste package has failed.  Now, there are
           three possible models.  Model 1 is the whole
           repository.  That's where you take this corrosion rate
           data, shown here as a density function, and you apply
           it to each and every waste package identically; that
           is, they'll all fail at exactly the same rate, pretty
           much, there is some slight variation, but at the same
           time.  Whereas the other extreme is Model 3 where each
           patch of each waste package is sampled from the
           distribution so that each and every waste package and
           each and every patch has a different failure time.
           And Model 2 is in between those two extremes.
                       Now, if you take this model and you just
           look at, for the present time, five realizations, as
           shown in this figure, you'd see that Model 1, where
           every waste package fails at about the same time,
           gives you five individual peaks, and they're all
           rather high, because when they fail at the same time
           you get a big release and therefore a big dose.
           Whereas Model 3, where you have this patch failure,
           they're all pretty much the same and smaller.
                       The interesting thing is that I wanted to
           point out here is that the dose and the way the NRC
           has defined in the rule as the peak of the mean is
           very sensitive to the timing of the peaks, so that
           even though these individual peaks are high, when you
           look at the way that Model 3 always fails the same,
           each new realization looks pretty much like the last
           one, these all line up.  So when you take the peak of
           the mean dose, Model 3 actually gives you a higher
           dose, which I will show.
                       And how does that relate to a few slides
           before where I showed the drip shield failure time
           being an important parameter?  Well, drip shield
           failure time determines the timing of the dose.  If it
           fails early, then the release is early.  If it fails
           late, the release is late.  That's the same effect as
           changing Model 1 to Model 3.  And that's why it showed
           up in one way when you look at the peak of the mean
           dose and another way when you looked at the peak to
           the individual doses.  But that was an interesting
                       MEMBER GARRICK:  Again, that's dependent
           upon the corrosion model that you --
                       DR. CODELL:  Well, yes.  And the way we've
           treated drip shield failure time in the TPA model is
           just a sample failure time.  It just relates -- it's
           just a -- it could have been another example of the
           same phenomenon.
                       MEMBER GARRICK:  Right.  And the other
           thing here is that there's going to much greater
           variability in the setting than in the waste packages.
           So whatever you take advantage of with respect to the
           similarity of the waste packages could be very offset
           by the variability because of spatial considerations.
                       DR. CODELL:  It could be but we don't have
           enough information from the corrosion rate data to
           know which is which, and that's a dilemma.  A very
           important factor in our analysis is how quickly the
           waste packages will corrode.  And even though it seems
           to be a very long time, if it were not a long time,
           then we'd want to know whether the variability in the
           data was due to real spatial differences or
           experimental -- or other unquantifiable errors.
                       MEMBER GARRICK:  And, of course, in the
           DOE model, they'd decouple the drip shield
           contribution from the diffusive transport out of the
           waste package.
                       DR. CODELL:  Yes.
                       MEMBER GARRICK:  So it really depends upon
           how you structure the thing.  I'm curious about how
           you screened your parameters.
                       DR. CODELL:  I'm sorry, which slide were
                       MEMBER GARRICK:  Well, when you go from
           900 to 330, to 100, to 37.
                       DR. CODELL:  Well, actually, the 990 were
           screened by experience.  We've, at various times in
           the past, looked at all those variables varying and
           decided that most of them didn't contribute anything
           to the results.  So those were held at fixed values.
           The screening that took place in the factorial design
           was more systematic, because we started with 330 and
           worked our way down.  And it was either based on the
           sensitivities we observed in the analysis or variance
           or --
                       One of the problems with fractional
           factorial design is a problem called confounding, and
           that's where a variable can be mistaken -- sensitivity
           in a variable can look like it's sensitive but it's
           actually a combination of several other variables.
           And it's just a numerical combinatorial problem, not
           a real physical problem.  But by looking at the
           physical outputs of the code, for example, seeing that
           the factors that looked sensitive, that had to do with
           seismicity, couldn't have been because there weren't
           any failures due to seismicity.  So we could eliminate
           those.  So it took a little bit of a combination of
           the silicon and the carbon computers to reach this
                       MEMBER GARRICK:  Did you call this the
           confounding phenomenon?
                       DR. CODELL:  Yes.
                       MEMBER GARRICK:  That's a good name.
                       DR. CODELL:  It's not my -- that's what
           it's called in the factorial design method.
                       CHAIRMAN HORNBERGER:  How are you sure at
           the end of the day that you don't have some aliasing
           left in your final ten or whatever --
                       DR. CODELL:  Well, there can't be any when
           you do the full factorial.
                       CHAIRMAN HORNBERGER:  Oh, right.
                       DR. CODELL:  That's the --
                       CHAIRMAN HORNBERGER:  So once you choose
           the ten it's okay.
                       DR. CODELL:  Right.  Yes.  And the final
           test was seeing how well you did by comparing it to
           the original.
                       Well, getting back to this little
           experiment on the two kinds of uncertainty, the
           epistemic and aleatory, the first result shows for a
           full set of realizations that the peak of the mean
           dose the Model 3, where you have the patch failure,
           gives you the highest result.  This may seem
           counterintuitive, but as it turns out, if you're
           sampling each and every patch, you end up getting the
           similar kinds of failures each new realization.  And
           that's why they look identical and fall on top of each
           other, leading to a high peak of the mean dose.  And
           the other models give you much lower doses.
                       However, this is sensitive to other
           parameters in the model, and what we determined when
           all was said and done was that if you look at a much
           slower release, say 100 times slower than what we used
           in this example, all three models pretty much fall on
           top of each other.  And when looking at the ranges of
           parameters in the Department of Energy model, probably
           it is more like the case on the right more than the
           left.  But it's still an interesting phenomenon and
           explains some other interesting features like the drip
           shield failure time result we got.
                       Something related to this previous
           exploitation is risk dilution.  This is something we
           worry about.  It's not good enough just to increase
           the range of distribution if you don't know it.
           There's some cases if you do that, you'll end with
           actually a lower dose, which isn't what you wanted at
           all.  And here's an example.  Once again, drip shield
           failure time.  If you have a narrow range, this green
           curve, or a wide range, the blue curve, you'll get
           different results.  And the narrow range gives you a
           higher dose than the wide range.  Once again, this is
           one of the parameters that has to do with the timing
           of the doses, and when you increase the range of that,
           you're going to end up with a lower result.
                       So summing this all up --
                       CHAIRMAN HORNBERGER:  Dick, let me -- I
           don't know, I think I'd like to challenge you on that
           one, because you said you put in a wide range and you
           may get a lower dose, and that isn't what you want.
           If in fact you have a broad uncertainty range, why
           isn't it what I would want?
                       DR. CODELL:  Well, I think the --
                       CHAIRMAN HORNBERGER:  I mean if you really
           -- if your uncertainty and failure times for drip
           shield really is -- I mean I suppose it ties back into
           your aleatory versus epistemic, because if you really
           believed that every single drip shield was going to
           fail on day 372, then it really matters.  Is that what
           you're saying?
                       DR. CODELL:  That's right.  That's right.
           Yes.  And this is interesting because I think prior to
           NRC's regulations for high-level waste, I think most
           people considered looking at the peak of the
           individual doses as a factor.  And automatically if
           you put a wider distribution in, you're going to get
           a higher -- one of those is going to be a higher peak.
           But it doesn't work that way when you look at the peak
           of the mean curve; it's just the opposite.
                       CHAIRMAN HORNBERGER:  Which I assume is
           why even though the regulation calls for the peak of
           the mean dose, you will require the potential licensee
           to display all sorts of things, including all of the
                       DR. CODELL:  Well, I wouldn't go too far
           there.  I think I'd be stepping out of bounds.  I
           don't think we would require anything like that.  If
           Tim is in the audience, he could probably rescue me
           right now.
                       CHAIRMAN HORNBERGER:  Maybe I could frame
           it another way.  The ACNW will want to see that.
                       DR. CODELL:  Yes.
                       MR. McCARTEN:  Well, as Dick's slide
           indicates, I mean the key there is the inappropriate
           use of a wider distribution.  We are clearly
           interested in the distribution.  And if the
           uncertainty is there, we're not saying don't include
           the uncertainty you have.
                       If there are some arbitrary decisions that
           are done and sometimes done in the sense of
           conservatism, let me make this bigger, I'm uncertain
           about it, we want to look at that to make sure, well,
           you may think it's conservative to make it bigger but
           you've actually, in essence, produced a lower dose.
           And so you want to have an appropriate range.  As Dick
           indicated, I think we are going to look at all the
           information the Department gives us.
                       MEMBER GARRICK:  Let me understand
           something.  Is this distribution a random variable?
                       DR. CODELL:  Yes.
                       MEMBER GARRICK:  Because what we're really
           interested in is our uncertainty about a fixed
                       DR. CODELL:  Well, it actually is an
           uncertainty about a fixed variable in almost every
           case.  I think in every case in the TPA Code it's
           uncertainty about a fixed variable.  I would consider
           that a definition.
                       MEMBER GARRICK:  And if that's the case,
           then of course you know want to be very careful about
           manipulating a broad distribution into -- or a peak
           distribution into a broad distribution as to what
           information you might be losing in that process.
                       DR. CODELL:  Right.  Well, these density
           functions that we use are based on either data or
           people's idea of what the data should look like.  And
           it isn't always -- they aren't always precise.
                       MEMBER GARRICK:  Well, as George says,
           we're going to be very interested in following this.
                       DR. CODELL:  Preliminary insights from the
           sensitivity analyses for 10,000 years, factors that
           control water/fuel contact seem to be the most
           important and most doses from low retardation, long
           half-life radionuclides, like technetium.  For 100,000
           years, it's interesting that waste packages usually
           fail by 100,000 years, so the parameters aren't always
           showing up as being conservative, because you'll
           usually have failure anyway.  Changing them isn't
           going to make any difference.
                       The fuel/water contact is still important,
           and the dominant radionuclide, neptunium-237, seems to
           be very important, so parameters associated with that
           are important.  For barrier sensitivity, the
           preliminary results that both natural barrier and
           engineering barrier make substantial contributions.
                       This is some additional work in progress
           that was too callow to talk about today, but we've
           acquired some neural network software, which seems to
           be very powerful, and this is basically doing non-
           linear regression.  We've had some limited success
           with it so far.
                       Dave Esch and I took some training in it,
           and I think you'll probably see this next we make a
           presentation.  We're looking at new sensitivity
           measures consistent with the peak expected dose, as we
           showed in the previous slide, and looking for
           efficient distributional sensitivity methods like the
           cumulative distribution function sensitivity that
           allows us to look at the sensitivity at different
           parts of the cumulative distribution; that is, high
           dose and medium dose or low dose and also the means --
           the sensitivity of the mean dose directly.
                       Some other work, we're trying to get a
           handle on barrier performance in a couple ways.  We're
           trying to define what a degraded state of a barrier
           means.  This is a very difficult problem trying to
           figure out how to define a barrier as failed, like
           what does a failed waste package mean.
                       Just looking at the kinds of barrier
           sensitivity analysis that Sitakanta presented earlier,
           there are six barriers, so two to the six is 64
           possible combinations of failures, from everything
           failed to everything working.  There has been 29 of
           the 64 analyses completed, and we've made some
           preliminary shot at making a tree structure, but it's
           possible to draw a tree with this result but looking
           more powerfully at -- looking at it with more powerful
           methods like analysis of variance there's not enough
           runs yet to do that, but we're hoping to do that in a
           future presentation.
                       In conclusion, parametric sensitivities
           provide useful risk insights.  The method we've been
           using, the sensitivity method where we're combining
           ranks of the various statistical methods still works
           very well.
                       Factorial design shows great promise and
           clearly defining the sensitivities and the
           interactions of the variables.  The distributional
           sensitivity technique that Sitakanta presented is an
           effective approach identifying the impact of the
           choice of parameter distribution and the shape and the
           shift in the mean.  We've shown that inappropriate
           parameter ranges can lead to risk dilution in some
           cases, and the treatment of uncertainty as lack of
           knowledge, epistemic or variability, can affect the
           peak risk calculation.  That's the end of our
           presentation.  We'd be happy to take additional
                       CHAIRMAN HORNBERGER:  Actually, before --
           now that John is back.  I started before you got back
           from lunch, John, but this is really your bailiwick,
           so why don't you run it.
                       MEMBER GARRICK:  All right, well, let's
           see if there's some questions.  Of course, I have a
                       MEMBER LEVENSON:  One comment.
                       MEMBER GARRICK:  Microphone.
                       MEMBER LEVENSON:  One comment and then one
           question based entirely on ignorance.  One of the
           things is that I guess I sort of disagree with your
           use of terminology because no matter what you do in
           the way of assumptions, you are not going to change
           the risk.  So you can't dilute the risk or increase
           the risk.  You may change your calculated number, but
           it's not really the risk.
                       But on Slide 13, I'm having trouble
           relating this to the physical world in that on the
           one-OFF, if you remove the waste package, you have a
           62,200 percent change.
                       MEMBER GARRICK:  Can we see that on the
           screen?  Can you put the projector back on, please?
                       On the one-OFF analysis, when you remove
           the waste package, you have a 62,200 percent change,
           but with the one-ON analysis, none of the other
           barriers are there.  You just add the waste package.
           You only have 100 percent change.  Factor 2.  It
           doesn't seem consistent with the physical world, as I
           visualize it.
                       MR. MOHANTY:  Let me explain the
           difference.  Under the one-OFF analysis, the first
           column represents the nominal case.  For the nominal
           case, the peak expected dose is .021 millirems per
           year, whereas under the one-ON analysis, we are
           determining the percent in change based on the first
           column under one-ON analysis.  And that number, I
           don't remember what that value is, but we are using
           that number to determine the change.  So that means at
           most it can be that.  So when we put the waste
           package, so 99.9 percent represents a reduction in
           what we observe under column 1.
                       CHAIRMAN HORNBERGER:  You can't take away
           any more than 100 percent.  But if you have something
           to start with, you can change it by 62,000 percent.
                       MEMBER LEVENSON:  I guess without the
           numbers, it's very difficult to determine what the
           significance of what this chart is.
                       CHAIRMAN HORNBERGER:  Even with the
           numbers, I would maintain it's very difficult to
           figure out what the significance of what this charge
                       I don't mean to be too severely because I
           know we are interested, like you are in barrier
           performance.  But it strikes me, Dick just casually
           said it's a really difficult problem to figure out
           what it means to have a barrier suppressed.  And I
           agree with that.  It just doesn't make sense to me to
           even consider changes as if all the drip shields
           failed at Time Zero.  I don't understand what you're
                       MEMBER LEVENSON:  It's not so much to
           understand what you're doing.  It isn't clear to me
           what the significance is.
                       CHAIRMAN HORNBERGER:  Right.
                       MR. HAMDAN:  One of the main objectives of
           this is one that sensitivity analysis is one that is
           not risk, but in response to your question and that is
           to test one other.  And one could argue that these
           barriers individually or in combination is to see if
           one is working and this has not been in all these
           slides clearly that you can elicit in slide 3, clearly
           the emphasis also I think he did answer and answer
           very well.  But the question as to what added value
           the sensitivity adds to the model and whether the
           model has been improved has not been addressed.
                       MEMBER GARRICK:  Yes, but the black box
           here is the degree to which the model represents
           reality and I think that's part of what Milt is
           struggling with.
                       You know, it's this question of if you
           tried to look at this as a system and you apply the
           basic equations of continuity and conservation of mass
           and momentum, etcetera, etcetera and you flow through
           the system, this model isn't doing that because the
           800-pound gorilla here is the water and the chemistry
           of the water.  And the chemistry of the water is
           extremely sensitive to each of the stages it passes
                       So we're not talking about something
           that's so much represents reality as we are talking
           about some very interesting concepts that you can
           apply in a Monte Carlo-type analysis, but at least
           that's my perspective.
                       VICE CHAIR WYMER:  I'll be a little bit
           facetious.  I certainly admire the sophistication and
           complexity of these analytical tools and the variety
           that were used in these analyses and that's
           impressive, but I couldn't lay a finger on it myself.
           But I was pleased to see that in your preliminary
           sensitivity analysis that you confirmed what I thought
           for the last two or three years that --
                       CHAIRMAN HORNBERGER:  Chemistry is
                       VICE CHAIR WYMER:  Natural version as a
           substantial contribution, that looks pretty good.
                       Waste packages fail corrosion parameter is
           not sensitive.  Fuel water contact, that's important,
           pretty good.
                       And retardation of neptunium seems to me
           like that's important.  But I thought I knew that
                       Factors controlling water fuel contact
           dominate performance.  That's right.  And most dose
           from low retardation and long half-life radionuclide,
           sure, I know that trivializes the degree to which you
           understand these things and the sophistications, but
           nonetheless the answers are sort of self-evident for
           whatever that's worth.
                       MEMBER GARRICK:  George?
                       CHAIRMAN HORNBERGER:  I just wanted to
           make a final comment on the barrier component
           sensitivity.  I really do understand what Latif said
           and that is that you do a lot of these things to try
           to understand what's going on with your modeling.  I
           certainly have no problems with this.
                       The issue that I have, the difficulty I
           have with slides like this is that there's too much
           chance for mischief making with the numbers by people
           who will want to use them for purposes that are not to
           understand how your model is working.  And I guess I"d
           just ask you to give a little thought to that as you
           present these things.
                       MR. CODALL:  We've given a great deal of
           thought to that.  In fact, at every level of review,
           we've been asked to be sensitive to this and to put
           disclaimers in that this not underlying not required
           by the regulations.
                       I think people who want to make mischief
           of this will do so regardless.
                       But this is the kind of analysis that's
           often done for safety.  You look at the failure of a
           system.  You look at what happens when an engine on an
           airplane dies.  It's nothing not wrong with it, in my
           opinion. It's just my opinion.
                       MEMBER LEVENSON:  I think there's a
           significant difference in fact, that's one of the
           points I think George made earlier that an engine is
           either on or off and you can -- this is a legitimate
           analysis for that sort of thing.  The waste container
           is not either in existence or not in existence, and
           therefore, I think you have to be very careful about
           using what is a legitimate analysis under other
           conditions or this one that might be much more
           legitimate to say what happens if 10 percent of the
           waste packages fail early, etcetera, rather than  --
           they're either on or off.
                       MEMBER GARRICK:  Yeah.  I think that
           there's no question that the modeling test exercise
           that you've done here is very interesting and very
           powerful.  As I was saying earlier thought, I think
           that what we're really interested in is information
           that would give us confidence that the models that are
           being employed are doing a reasonable job of
           representing reality in terms of what's happening.
                       Now maybe this can contribute to that, but
           what is really something that concerns us is the
           interactive effects of these different barriers and
           how the one thing that would suppress some of the
           mischief that we talk about would be to do this same
           exercise for different models.
                       Take for example, the TPA model and do the
           exercise, and then take the diffusive transport model
           of DOE and do the exercise and you would certainly see
           that things would line up differently.  And it would
           clearly indicate that how model sensitive it is.
                       But again, I guess the question I would
           ask is what contribution comes from this work towards
           creating a model that we have increased confidence in
           in terms of representing the performance of the
                       MR. MOHANTY:  Let me start with one-ON
           analysis.  What that figure tells us is that on
           saturated zone, unsaturated zone is making quite a bit
           of contribution and these individual contributions
           perhaps could not have bene seen if we did not isolate
           those from other either components or in a broad sense
                       So that tells us something.  And also when
           we compare that say with an invert, we are seeing only
           2 percent change.  Then we are going back to the total
           system for performance assessment code to determine
           why we are only getting .2 percent and we did go back
           and find that the way in what is modeled in what is
           supposed to reduce flow or delay transport, but it
           just so happens that the flow through invert is
           predominantly fracture flow.
                       So when the flow is a predominantly
           fracture, in the fractures and we are not assigning
           any retardation fractures to the fractures, therefore,
           the invert is almost completely bypassed in the TPA
           approach.  So that is a kind of insight we gain when
           we do this kind of one-OFF one-ON analysis or
           cumulative analysis.
                       VICE CHAIR WYMER:  I would have liked to
           have seen that sort of thing in your table of
           preliminary insights, the two cases you just cited are
           much more informative.
                       MR. MOHANTY:  If I can make another point
           that there are two ways we can determine all
           components for how well the components of the code is
           functioning.  To give you an example, if the packages
           were going to fail at 1 million years, then the only
           way we can find out that what is what is affecting the
           packages is we go with continual calculus for 1
           million years, are we deliberately suppressed that to
           find out what the impacts are if we are to fail early.
                       So by doing this kind of analysis that
           prevents us from going to much further into the future
           to million years because we can gain similar kind of
           insights by deliberately doing the sensitivity
           analysis by suppressing components.
                       CHAIRMAN HORNBERGER:  I have a question
           now that you have mentioned your one-ON analysis.  If
           we look at that lefthand column, okay, you have a dose
           associated with that, is that right?  What you said
           was that we could read those as 99.9 percent reduction
           in dose?
                       MR. MOHANTY:  Right.
                       CHAIRMAN HORNBERGER:  So my question is
           what is the dose in that lefthand columna nd how did
           yo calculate it?
                       MR. MOHANTY:  Under the one-OFF analysis
           or one-ON analysis?
                       CHAIRMAN HORNBERGER:  One-ON analysis.
                       MR. MOHANTY:  On the one-ON analysis, this
           is not a real dose where barriers are suppressed.  We
           do honor the various processes in the TPA code.  We do
           not veer away from the processes too far because we
           know that we are limited simply by this is just a
           technique we are using.
                       CHAIRMAN HORNBERGER:  All right, so then
           if I go to the first column, the drip shield, the 63
           percent reduction in what?
                       MR. MOHANTY:  We do have a dose value.
                       CHAIRMAN HORNBERGER:  How did you
           calculate it?
                       MR. MOHANTY:  This is by suppressing all
                       CHAIRMAN HORNBERGER:  Okay, which means
           what did you do, dissolve all the fuel instantaneously
           in the --
                       MR. MOHANTY:  Yes.
                       CHAIRMAN HORNBERGER:  And so it's a high
                       MR. MOHANTY:  It is a high dose, yes.
                       CHAIRMAN HORNBERGER:  I mean that's a high
           calculated dose.  I don't want to get in trouble with
                       So I just point out that again, even on
           this one we're saying oh yes, the natural, the insight
           that you gain, it's quite artificial and I'm much more
           comfortable with Latif's interpretation that what
           we're doing is learning how the components of the
           model are working.
                       MEMBER GARRICK:  Also, those kind of
           reductions in the kind of doses we're talking about
           are not very relevant.
                       I don't know that that really tells us a
           great deal about the protection provided by those
           components.  I'll have to think about that, a good
           deal more.  And I still worry about the fact that
           there is interaction between the barriers and what the
           waste package sees in terms of input material is
           different than what the invert sees as different what
           the unsaturated zone sees and so on.
                       And that could be a major factor in what
           really happens.  All the peer reviews of the TSPAs
           have given great attention to the importance of water
           composition because that's the mechanism by which
           everything happens and that's just the process of
           applying principles of continuity from the
           infiltration model, if you wish, namely the geology
           above the waste package through to the waste package
           and so on.
                       So again, there's no question this is an
           intriguing process and it does, as Latif says, but
           we're going to have to be a lot more diligent students
           and studiers of this before we can really see what it
           contributes to reality.
                       MR. CODALL:  In terms of projected work,
           we are getting together soon to talk about what
           degradation of barriers means.  I hope in the next few
           weeks, Tim McCarten is convening a group to better
           define in terms of what is expected in the regulations
           what barrier degradation means.
                       This one-OFF, one-ON analysis probability
                       CHAIRMAN HORNBERGER:  yes.
                       MR. CODALL:  But getting at the -- getting
           a finer level is the next step. This maybe you
           consider this a first step and gaining information
           about importance of barriers.
                       MEMBER GARRICK:  Go ahead, Latif.
                       MR. HAMDAN:  But do we need a more refined
           -- it seems to me that from this beautiful
           presentation the staff has already has all the tools
           it needs what it needs to do from the subject.  After
           all, the barrier requirements which was about 60 has
           been removed by 63.  This morning Commissioner
           McGaffigan complained about things could grow and grow
           and grow.
                       Now with the two that you have here, it
           seems that either you are doing a very nice job with
           what you have, so why bother with to come up with new
           tools that you want to try and new analysis,
           specifically tools to do the same thing again and
                       I would suggest that you rethink this
           because this is really maybe you can do what you want
           to do with what you already have and keep in mind
           again that the barrier capability performance for
           individual barriers which was about 60 is now omitted.
                       MR. McCARTEN:  I guess -- we hear what the
           Committee is saying and there's no question we've had
           a lot of discussions internally and that's the reason.
           It's clearly stated.  These types of analyses are not
           required to demonstrate compliance with the barrier
           requirements in 63.  But what we're trying to do and
           for the Committee I think Dick and Sitakanta both
           tried to give all the things we're looking at with
           potential to increase our understanding, and I think
           the concept of all these things, we're just throwing
           out where we're going.
                       There is a huge downside to doing barrier
           neutralization because people jump to those numbers at
           the bottom and the value is not in those numbers at
           the bottom.  And what we're -- I like to think that
           when this is done, it's a way to probe and test your
           own thinking of how the code is working, how you think
           the system is working and this is just another way to
           poke at you, your brain a little harder, to think a
           little more.
                       Ultimately, what you're not seeing and
           we're not there yet is what kind of information about
           the system can you pull out and that's the key.  And
           I think this is a way to kind of jiggle the system a
           little more.
                       Maybe it's not the right way to go, but I
           think it's a way to push your understanding and I
           think that's the key we need to get to as you guys are
           indicating.  And we're not there yet.  We owe you
           something more.  Where's the understanding in this?
           And right now, it clearly is not at those bottom
           numbers.  It's deeper than that.
                       MEMBER LEVENSON:  Let me ask a question
           sort of in that relationship.  In the one-ON analysis,
           the unsaturated zone and the saturated zone have for
           all practical purposes the same significance, whatever
           that is.  But in the one-OFF analysis the significance
           is different by more than an order of magnitude.  What
           do we learn from that?
                       MR. McCARTEN:  Well, these numbers in
           order of magnitude, I don't think is necessarily that
           significant, but the next step is what -- the key is
           understanding the capability of the barriers and
           what's going on and why those numbers came out.  I
           think that's what -- you may use this analysis to push
           you a little harder about the understanding of the
           capabilities of the barriers, so you clear -- oh yeah,
           that's why those numbers came out that way, but that's
           sort of the next step with this and whether this is
           the right way to go or there's other approaches re
           better or there's as Dick indicated some intermediate
           steps or this is the first step, that's where we're at
           now and we're just trying to as a group, we're always
           trying to do additional analyses to see if it's
                       CHAIRMAN HORNBERGER:  Tim, you remind me
           of one of my favorite quotes, the purpose of computing
           is insight, not numbers.  The purpose of computing
           numbers is not yet in sight.
                       I do have a question for Dick, actually.
           I was really intrigued because I hadn't thought of
           that before, but DOE's patch model really does lead
           them to get essentially the same failure rates on
           every realization.
                       Do you see this as a problem in DOE's
                       MR. CODALL:  No.  I think it's probably
           somewhat more realistic than what we chose, so that's
           a point for DOE's conservatism.
                       CHAIRMAN HORNBERGER:  But it strikes me
           that -- I mean, that's equivalent, I think to your
           saying that all of the uncertainty is aleatory.  I
           hate those terms, but environmental variation and it
           strikes me that they're probably -- potentially could
           be another component if for no other reason that you
           would have differences in fabrication of casks.
                       MR. CODALL:  Right, but the problem is
           that the data don't tell you which is which.  And then
           I think though that the answer that -- is that it
           doesn't -- it seems that for the ranges of parameters
           that we're dealing with, the results aren't too
           different no matter what you assume and that's
           somewhat reassuring.
                       CHAIRMAN HORNBERGER:  Actually, I had --
           I've often somewhat facetiously suggested that DOE
           could build a better safety case by purposefully
           damaging canisters in a certain pre-determined rate so
           that they all wouldn't fail at the same time.
                       MEMBER GARRICK:  This reminds of the old
           days of reliability analysis when they had no
           failures, someone would assume a failure, a horrible,
           horrible thing to do.
                       Well, this is very interesting and we'd
           like to continue.  I think that my perspective on this
           that what you're doing needs to contribute to a couple
           of things or we would have to challenge it.
                       It's wherewithal.  One, a better
           understanding of the contribution of the individual
           barriers.  And two, a greater confidence that we can
           build a model that represents reality a little more
           effectively after doing this work than we could
           before.  And if it doesn't do -- contribute to those
           things, then I would have to wonder.
                       MR. CODALL:  Well, I'd just like to point
           out this is not part of this presentation, but we're
           starting development on TPA 5.0 and we're putting back
           in that code, the diffusion model.  It was taken out
           earlier.  I think I was probably responsible for
           putting it in and taking it out because it didn't seem
           to make any difference, but since DOE is depending on
           that release pathway, we're putting that back in too,
           so we'll have a handle on it.  So there are changes to
           the code that will improve it.
                       MEMBER GARRICK:  I sure wish you'd put
           something in there that would account for the chemical
           effects inside the waste package.
                       MR. CODALL:  There may be something like
           that going in.  Are you aware of something, Sam?
                       Nothing comes to mind.  But where people
           are chemists here and at the center who worry about
           such things.
                       MEMBER GARRICK:  That's where the action
           is relative to the mobilization of the waste and the
           creation of the source term and I think a lot of
           attention on that would pay high dividends.
                       All right, well, we're running a little
           behind.  We thank you very much.
                       MR. CODALL:  Thank you.
                       MEMBER GARRICK:  And I think we'll adjourn
           for a recess.
                       (Off the record.)


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