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Publication Information
Manuscript Completed: March 2026
Date Published: October 2026
Prepared by:
A. Zigh
J. Coury
Office of Nuclear Reactor Regulation
U.S. Nuclear Regulatory Commission
Washington, DC 20555-0001
Abstract
Applicants submit dry storage cask designs for spent nuclear fuel to the U.S. Nuclear Regulatory Commission (NRC) for certification under Title 10 of the Code of Federal Regulations (10 CFR Part 72), “Licensing Requirements for the Independent Storage of Spent Nuclear Fuel, High-Level Radioactive Waste, and Reactor-Related Greater than Class C Waste”. The NRC staff performs its technical review of these designs in accordance with 10 CFR Part 72 and NUREG-2215, “Standard Review Plan for Spent Fuel Dry Storage Systems and Facilities,” issued April 2020. To ensure the cask and fuel material temperatures of the dry cask storage system remain within the allowable limits or criteria for normal, off-normal, and accident conditions, the NRC staff performs a thermal review as part of the technical review.
Recent applications have increasingly used thermal-hydraulic analyses using computational fluid dynamics (CFD) codes (e.g., Ansys Fluent) to demonstrate the adequacy of the thermal design. The applicants are also seeking to license casks with decay heat close to 70 kilowatts (kW), resulting in a peak cladding temperature (PCT) close to the temperature limit of 400 degrees Celsius (°C) (752 degrees Fahrenheit (°F)) suggested in Interim Staff Guidance-11, “Cladding Considerations for the Transportation and Storage of Spent Fuel,” issued November 2003. It is part of CFD best practice guidelines in NUREG-2152, “Computational Fluid Dynamics Best Practice Guidelines for Dry Cask Applications,” issued March 2013, to present CFD predictions for the PCT or any other target variable supported by an uncertainty quantification (UQ) to provide assurance and confidence that the obtained margin is adequate.
The NRC recognizes that finite-volume-based CFD is one of the methods applicants use to perform dry cask thermal modeling and to demonstrate adequate margins with the temperature limits suggested in NUREG-2215. Additionally, when demonstrating the thermal margins, it is valuable to quantify the uncertainty in the simulation result as a function of the computational mesh and simulation inputs. When finite-volume-based CFD is applied carefully through the use of CFD best practice guidelines in NUREG-2152, and UQ is performed, there will be confidence and certainty in the obtained margins.
The numerical modeling validation study was based on data collected at Sandia National Laboratories (SNL) in an experiment sponsored by the U.S. Department of Energy on an Advanced Drying Cycle Simulator (ADCS) documented in report SAND2022-12078R, SAND2023-01454R, SAND2023-09746R, and SAND2024. The ADCS simulates one prototypic 17x17 pressurized-water reactor (PWR) fuel assembly, which uses electrical resistance heaters to simulate the decay heat. The ADCS testing program included two test series. The first is referred to as the dry test series with three decay heats of 1, 2, and 3 kW; four helium fill pressures of 1,200 kPa (12 bar), 800 kPa (8 bar), 220 kPa (2.2 bar), 101 kPa (1 atm); and six vacuum pressure levels of 51 kPa (383 Torr), 10.1 kPa (75.8 Torr), 3.33 kPa (25 Torr), 1.33 kPa (10 Torr), 0.133 kPa (1 Torr), and 0.0133 kPa (0.1 Torr). This first test series was planned to include the range of decay heat values and pressure levels expected in the second (i.e., wet) test series. For each test in the dry test series, the ADCS used constant helium fill-pressure and decay heat and reached steady-state conditions. In the wet test series, the ADCS simulated all the transient processes involved in a commercial dry cask drying cycle, as shown in Table ES-1, Figure ES-1, and Figure ES-2.
The ADCS experiment included extensive temperature measurements that were made throughout the cask, including throughout the axial length of the fuel assembly and on different wall surfaces of the ADCS, inside and outside the pressure vessel representing the dry cask canister. The measurements included peak guide tube temperature (PGTT, analogous and effectively identical to the PCT), canister pressure, and water vapor molar fraction. The ADCS experiment included two types of boundary conditions on the external wall of the flanges, as seen in Figure 1-8. One boundary condition involved keeping a constant temperature using heat-trace cable around the flanges and insulation on the horizontal surfaces of the flanges. The alternate boundary condition was to abandon the heat trace completely and let the flanges exchange heat with the surroundings. To avoid the uncertainty on this boundary, data from the latter were used to verify and validate the CFD method, including uncertainty quantification. The verification, validation, and uncertainty quantification (VVUQ) of the steady cases included the data for 3 kW corresponding to canister pressures of 12 bar, 1 atm, 25 Torr, 10 Torr, 1 Torr, and 0.1 Torr. For the transient wet testing case, the VVUQ was performed for a case with constant power of 1.483 kW, with pressure varying depending on the process, as shown in Figure ES-1 and Table ES-1. As the last step, several sensitivities were performed using the base case model for the transient vacuum drying using different assumptions about the fluids inside the canister. Some of these assumptions about the fluid choice inside the canister are used by dry cask applicants. This report investigates these sensitivities to show the conservatism of the model or lack thereof.
This report discusses the validation and UQ of the ADCS CFD model using SNL’s experimental data. The CFD model used both the porous media representation (used as the base model) and the explicit geometry representation of the fuel rod region without any simplifications. Uncertainty quantification was performed and evaluated for the base case porous model, following the procedures outlined in American Society of Mechanical Engineers (ASME) Verification and Validation (V&V) 20-2009, “Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer”.
The sources of uncertainty examined in the analysis of the steady-state dry test series and the transient case wet test series include iterative uncertainty, spatial discretization, experimental uncertainty, and uncertainty due to modeling input parameters. Input parameter uncertainties included uncertainties in outer wall heat transfer coefficient, decay heat, ambient temperature, emissivity values, fuel region porous media hydraulic resistance (i.e., a porous media model was used to model the fuel region), and the fuel axial power profile correlation. For the wet transient test series, water vapor molar fraction was also among the input parameter uncertainties. SNL recognized that air ingress was an issue for the experiment at very low pressure. Therefore, for the 1 Torr steady-state experiment, air ingress (i.e., air molar fraction) was considered in the uncertainty quantification to reconcile the discrepancy found between the experimental data and the CFD predictions. This report documents the ADCS CFD model validation exercise, and is not intended to be used for regulatory guidance.
The CFD predictions using the porous media model and NUREG-2152 best practice guidelines were very favorable when compared to the experimental data. The CFD predictions showed that the base case porous media and the explicit geometry representation of fuel rod region models agreed very favorably in the prediction of the temperature profile in the fuel region and the external wall of the cask. Additionally, the CFD results of the base case model and experimental data for PCT, location of PCT, and temperature profiles in the fuel region, and on the external wall, agreed very favorably for all the collected cases, falling within the calculated validation uncertainty band.
The validation uncertainty was calculated using best estimate CFD analysis that includes the most likely scenario supplemented by UQ. The uncertainty in the power profile correlation and the external heat transfer coefficient in steady-state case runs were found to be the principal sources of uncertainty in the ADCS experiment. The PCT validation uncertainty for the dry steady-state cases varied 22–25°C. For the transient wet test vacuum drying case, the decay heat power profile and the water vapor molar fraction were the input variables that contributed the most to the validation uncertainty. The PCT validation uncertainty for the wet transient vacuum drying case was 21°C.
This report also shows that when proper CFD analysis is performed using NUREG-2152 guidelines and applicants’ assumptions for a single running fluid depending on the vacuum drying processes, a conservative PCT prediction is obtained. Additionally, the VVUQ of the CFD model showed that the thermal conductivity of helium in the low vacuum pressure range of 0.1 to 25 Torr did not change with pressure. In that pressure range, the VVUQ showed that helium thermal conductivity is only temperature dependent. As such, it was concluded that helium is not in a rarefied regime in that pressure range of vacuum drying processes. Additionally, sensitivity analyses for the transient vacuum drying case using different fluids showed that when helium or combinations of helium and steam were used in draining and blowdown stages, with pure steam throughout the vacuum steps, then pure helium for the initial backfill, final vacuum, and final backfill steps, conservative results are obtained.
The results show that an Ansys Fluent thermal model using NUREG-2152 CFD best practice guidelines, including simulating the fuel assembly using a porous media approach, can demonstrate the safety of the storage of spent nuclear fuel by accurately predicting the PCT with accurate model inputs. This report also looks at the quality of the data collected in the ADCS experiment documents SAND2022-12078R, SAND2023-01454R, SAND2023-09746R, and SAND2024 using the calculated validation uncertainty. The ADCS experiment was designed to minimize validation uncertainty, the key factor and the basis for thermal model validation. Consequently, a well-validated thermal model will enable thermal reviewers to have confidence in the predictions, even with decreased margins.