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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

25 ENERGY STORAGE↗
DOE OSTI2024

An Investigation of LES Wall Modeling for Rayleigh–Bénard Convection via Interpretable and Physics-Aware Feedforward Neural Networks with DNS

Abstract The traditional approach of using the Monin–Obukhov similarity theory (MOST) to model near-surface processes in large-eddy simulations (LESs) can lead to significant errors in natural convection. In this study, we propose an alternative approach based on feedforward neural networks (FNNs) trained on output from direct numerical simulation (DNS). To evaluate the performance, we conduct both a priori and a posteriori tests. In the a priori (offline) tests, we compare the statistics of the surface shear stress and heat flux, computed from filtered DNS input variables, to the stress and flux obtained from the filtered DNS. Additionally, we investigate the importance of various input features using the Shapley additive explanations value and the conditional average of the filter grid cells. In the a posteriori (online) tests, we implement the trained models in the System for Atmospheric Modeling (SAM) LES and compare the LES-generated surface shear stress and heat flux with those in the DNS. Our findings reveal that vertical velocity, a traditionally overlooked flow quantity, is one of the most important input features for determining the wall fluxes. Increasing the number of input features improves the a priori test results but does not always improve the model performance in the a posteriori tests because of the differences in input variables between the LES and DNS. Last, we show that physics-aware FNN models trained with logarithmic and scaled parameters can well extrapolate to more intense convection scenarios than in the training dataset, whereas those trained with primitive flow quantities cannot. Significance Statement The traditional near-surface turbulence model, based on a shear-dominated boundary layer flow, does not represent near-surface turbulence in natural convection. Using a feedforward neural network (FNN), we can construct a more accurate model that better represents the near-surface turbulence in various flows and reveals previously overlooked controlling factors and process interactions. Our study shows that the FNN-generated models outperform the traditional model and highlight the importance of the near-surface vertical velocity. Furthermore, the physics-aware FNN models exhibit the potential to extrapolate to convective flows of various intensities beyond the range of the training dataset, suggesting their broader applicability for more accurate modeling of near-surface turbulence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration

Abstract The isotopic composition of evapotranspirationδ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculateδ ET , though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested theδ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. Theσ ET (mean) would be greater thanσ ET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter ofr 2 > 0.8, around 70% ofσ ET (mean) was greater thanσ ET (median). This phenomenon might be due to the normality of the vapor concentrationC υ producing the asymmetric distribution of 1/C υ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentrationC υ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils

The peroxide value of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine peroxide values using field portable and process instrumentation; those efforts presented “best-case scenarios” with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique or combination thereof is best for predicting peroxide values. Following peroxide value assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression calibration models to predict the peroxide value of unknown oil samples. A global peroxide value model based on near-infrared (8 mm optical path length) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm optical path length near infrared (5.1), Raman (6.9) and 50 µm optical path length mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global peroxide value models based on low-level fusion of the NIR (8 and 24 mm optical path length) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes. This work demonstrates the difficulty of developing a truly global method for determining peroxide value of oils.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Graphene SHDMC Data

The data used to produce all of the figures and tables in the manuscript titled "Highly Accurate Many-Body Theory Reaches 2D Materials" can be found here. This data set includes: -SHDMC results for graphene -selected CI with and without re-normalized second-order perturbation (rPT2) theory corrections for graphene -data demonstrating that SHDMC displays an exponential rate of convergence -data used for sCI + rPT2 complete basis set extrapolation -data used to extrapolate SHDMC energies to the infinite basis limit -data used to demonstrate compactness of SHDMC wavefunction

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Two-stage formation-energy correction (NbZr, TaZr, VZr)

This bundle contains the scripts, the raw and corrected per-structure data, and the manuscript plots for the NbZr / TaZr / VZr BCC binary formation energies and the associated RMSDs. Why a two-stage correction is necessary: The "raw" formation energy of every relaxed VASP configuration is computed in the usual way, FE_raw(c) = E_alloy(c) - sum_i x_i * E_pure_i , where E_pure_i are the per-atom total energies of the pure-element reference structures (Nb, Ta, V, Zr in the same BCC supercell, with identical INCAR / KPOINTS / PAW choices). With perfectly consistent reference runs the raw FE should vanish at the two pure-element endpoints (x = 0 and x = 1) by construction. In practice this does not hold for two reasons that are present in our dataset: 1. Reference-energy inconsistency (composition-dependent bias). Even with identical input parameters, the pure-element runs (stored in `corrected_DFT_pure_element_runs/`) differ slightly from the values that would be implied by the alloy runs at near-pure compositions (a few meV/atom). This bias is approximately linear in concentration, because the residual error in E_pure_Nb (or E_pure_Ta / E_pure_V) propagates into FE_raw(c) as (1 - x) * dE_pure_1, and the corresponding error in E_pure_Zr propagates as x * dE_pure_2. Left uncorrected, this produces a non-physical "tilt" of FE_raw(x) and shifts the entire FE-vs-x cloud away from zero at the endpoints. 2. Endpoint anchoring against the audited true endpoints. The strict endpoint values (FE_x0_meVatom, FE_x1_meVatom in `corrected_fe_strict_endpoints_20260518/strict_endpoint_check_20260518.csv`) were re-derived from an independent cross-check of the pure-element runs. After stage 1 removes the linear bias, the near-pure compositions in the alloy dataset still extrapolate to values that differ slightly from these audited endpoints — because stage 1 is fit from a few near-end alloy bins, not from the audited pure-element references themselves. The README.txt file discusses how these issues are addressed by the two-stage correction, and describes folder layout, pipeline summary, and how to re-run.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Learning genetic perturbation effects with variational causal inference

Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit in the lack of enough data and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict responses to unseen perturbations. However, their application has been limited to small studies due to overly simplistic assumptions, making them less effective in handling noisy, large-scale single-cell data. We propose a hybrid approach that combines a mechanistic causal model with variational deep learning, termed Single Cell Causal Variational Autoencoder (SCCVAE). The mechanistic model employs a learned regulatory network to represent perturbational changes as shift interventions that propagate through the learned network. SCCVAE integrates this mechanistic causal model into a variational autoencoder, generating rich, comprehensive transcriptomic responses. Our results indicate that SCCVAE exhibits superior performance over current state-of-the-art baselines for extrapolating to predict unseen perturbational responses. Additionally, for the observed perturbations, the latent space learned by SCCVAE allows for the identification of functional perturbation modules and simulation of single-gene knockdown experiments of varying penetrance, presenting a robust tool for interpreting and interpolating perturbational responses at the single-cell level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Scripts and data associated with a manuscript linking soil and sediment elemental composition with dissolved organic matter chemistry across CONUS

This data package provides scripts and geochemical data for a manuscript titled “Linkages between mineral element composition of soils and sediments with hyporheic zone dissolved organic matter chemistry across the contiguous United States” (preprint: doi: 10.22541/essoar.169447343.31694990/v1). This data is associated with the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS, https://whondrs.pnnl.gov) and is an extension of the Summer 2019 Sampling campaign which crowdsourced samples from rivers and sediment across the continental United States. Data from this study can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719. The main objective of this manuscript was to couple sediment water extractable dissolved organic matter chemistry, defined by ultra-high resolution mass spectrometry, with localized sediment elemental composition and watershed scale soil elemental characteristics. This data package contains one main folder with four subfolders. The main data folder contains (1) readme; (2) data dictionary (dd); (3) file-level metadata (flmd); (4) an R markdown to reproduce manuscript figures and analyses; (5) a pdf of instructions to reproduce NGS interpolations with ArcGIS software; and (6) a python script to reproduce NGS extrapolations with python. The four subfolders contain files required to reproduce NGS extrapolations include (1) ‘CONUS_boundaries’ containing boundary layers (.shp) for the Continental United States; (2) ‘ngs_project’ containing files (.shp) with point level NGS soil elemental data (Grossman et al., 2004); (3) ‘raster_outputs’ containing the interpolated raster output files for various soil elements; and (4) ‘NGS_Chemistry_Final’ contain final extracted soil elemental data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Status of permanent magnet radiation resiliency studies at CEBAF

An ongoing investigation for the future of Jefferson Lab’s Continuous Electron Beam Accelerator Facility (CEBAF) lies in upgrading its maximum nominal energy using Fixed-Field Alternating-gradient (FFA) technology for its recirculating arcs, using permanent magnets for the FFA arcs. A common concern among the community is the degradation of these permanent magnets during operation due to the radiation environment in which they will be present. This work, funded by a Laboratory Directed R&D grant, aims to measure the permanent magnet degradation in the CEBAF tunnel enclosure, and extrapolate to the energies expected from the upgrade. We present the latest results of this study, as well as plans moving forward.

Accelerator Physics↗
DOE OSTI2020

Progression to Compatibility Evaluations in Flowing Molten Salts

Molten salt compatibility with structural alloys has been identified as a key issue for the development of Generation 3 concentrating solar power (CSP) systems with thermal storage. To accelerate this evaluation to pumped systems, the goal of this project was to conduct thermal convection loop (TCL) experiments with a peak temperature of ≥700°C and a typical temperature gradient of ~100°C. The experiments indicated that conventional ~16wt.%Cr Ni-based alloys are compatible up to 700°C with purified (i.e. low O) or dried (low H₂O) industrial-sourced Mg-K-Na chloride salt with <10 μm/yr loss. This two-year project was conducted based on the experimental and mechanistic understanding developed more than 60 years ago at Oak Ridge National Laboratory (ORNL). The first TCL experiment met the <15 μm/yr corrosion metric for this project with specimens of Ni-based alloy 600 exposed at 580°-700°C for 1000 h and post-exposure room temperature tensile tests showed minimal degradation. The second TCL experiment successfully deployed an electrochemical sensor from Argonne National Laboratory and had a peak temperature of 750°C but only ran for ~110 h due to a furnace failure. Both experiments used highly purified industrial-sourced salt with an O content of ~3 μg O/g salt and a Mg addition of 0.04 wt.%. In the second year, the Chloride Collective developed a more economical drying procedure such that the O content was much higher (>20,000 μg O/g salt). A third TCL experiment was conducted with a sensor and a peak temperature of 700°C using dried salt from the same industrial source and increased NaCl content (~20 wt.%). In addition to a 0.05% Mg addition to the salt, a Mg coupon was added in the coldest part of the loop which dissolved during the experiment. Again, small mass changes were noted for specimens of alloys 600 and C276 but the values were slightly higher than those measured in purified salt with Mg. A thin non-continuous and non-adherent oxide layer was deposited on most specimens containing Mg, Si and Al but Cr depletion also was observed. Both years included facilities qualification crucible experiments and then capsule experiments to confirm a baseline isothermal reaction rate. The first year capsule experiment led to the conclusion that the two-stage ORNL purification process using NH₄Cl and CCl₄ left the salt with a high Cl potential and a Mg addition (~0.05wt.%) was needed to lower the potential. The second year capsule experiments at 600° and 700°C found little difference in depth of attack for 0-0.25%Mg additions. In general, the complex reactions where salt can be trapped in the porous surface layer of metal indicated that mass change is an unreliable metric and average depth of Cr depletion is a better metric for assessing the extent of attack. These results have created a new baseline that is contrary to the recent published literature for chloride salts where mass losses have been reported that can be extrapolated to very significant annual metal loss rates. Chloride salt corrosion can be controlled and the results also indicate that salt purification to low O levels may not be necessary. However, additional TCL experiments are needed at different times and temperatures to generate important engineering information such as temperature dependent corrosion rates and reaction rate laws for extrapolation to long-term behavior and isolate the effect of salt additives and impurities.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Model 9975 O-Ring Fixture Long-Term Leak Performance (FY2020 Status Report)

Leak testing experiments to monitor the aging performance of Viton GLT and GLT-S O-rings used in the model 9975 shipping package has been ongoing since 2004 at Savannah River National Laboratory. Seventy tests using mock-up 9975 primary containment vessels (PCVs) with GLT O-rings were assembled and heated to temperatures ranging from 200 to 450 °F. In addition, fourteen tests with GLT-S O-rings were initiated in 2008 and heated to temperatures ranging from 200 to 400 °F. The conditioning temperatures are elevated compared to the calculated maximum O-ring temperature in a 9975 package in storage, 158 °F, to accelerate aging and enable observations of O-ring failures in a reasonable time frame. The fixtures are leak tested periodically and all GLT O-ring fixtures aging at 350 °F or above have failed to maintain a leak-tight seal. Eight GLT O-ring fixtures aging at 300 °F have failed after 2.8 to 5.7 years at temperature while the remaining fixtures at 300 °F were retired from testing following more than five years without failure. No failures have yet been observed in GLT O-ring fixtures aging at 200 °F for 12 to 14 years or aging at 270 °F for 8.5 years. All GLT-S O-ring fixtures aging at 300 °F or above have failed their leak test. No failures have yet been observed in GLT-S O-ring fixtures aging at 200 and 250 °F for 10 to 10.5 years. Data from the O-ring fixtures are generally consistent with results from compression stress-relaxation testing, and provide confidence in the predictive models based on those results. However, uncertainty exists in extrapolating these elevated temperature results to the lower temperatures of interest for normal storage in K-Area Complex (KAC). Oxygen consumption testing, which includes results from temperatures near KAC normal storage temperatures, is ongoing to provide further confidence and corroborate these extrapolations. The collective data from these test efforts suggest the minimum O-ring service life at KAC normal storage conditions should be at least 34 years for GLT and GLT-S O-rings. Measurement of compression set in O-rings removed from failed fixtures, compared to that from KAC surveillance O-rings, indicates margin remains for O-rings still in service. Aging and periodic leak testing will continue for the remaining PCV fixtures.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

National Ignition Campaign Data Analysis by an ICF Outsider

A simple DT burn model, with no alpha-particle warming feedback, using a combination of ICF inputs and the measured ion temperature, T, volume compression, C, ablator velocity, v, and burn width time, Δt, can reproduce the national-ignition campaign (NIC) fusion yields with a rms percent difference of ~40%. Analysis suggestion the fusion-yield reproducibility of NIC events with nominally identically measured T, C, v, and Δt is <30%. The highest fusion yields, at a fixed laser energy are obtained with depleted-uranium (DU) hohlraums and high-density carbon (HDC) ablators. Small diameter fit tubes (≤5 μm) enhance fusion yields by an average of 26±15% relative to 10-μm diameter fit tubes. A dependence of fusion yield on the diameter of the capsule relative to the diameter of the hohlraum is inferred for fixed T, C, v, and Δt. No evidence of conditions close to ignition, or alpha-particle warming is found in the existing NIC data with LEH< 2MJ. To obtain a predictive model for extrapolation proposes we map the T, C, and v into power laws as a function of LEH. The above mentioned extrapolatable model has a rms percent difference from the yields of the second half of the NIC of (56 ± 4)%. Higher (and significantly lower) DT temperatures are obtained with HDC ablators (and low-foot drives). The highest compressions are obtained via low-foot drives, but this does not compensate for the corresponding lower temperatures. An extrapolation of the presented model suggests significant alpha-particle heating at an LEH of ~5 MJ. It is hoped that others more familiar it the conditions needed for ignition will use to presented model to estimate the LEH required for ignition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

An initial framework for the rapid qualification of long-term creep rupture strength via microstructural modeling

This report describes the development and testing of a new method for extrapolating short-term creep rupture test data to predict long-term rupture strength. The goal of this work is to reduce the time required to qualify new materials for nuclear service by reducing the lead time required for dedicated, long-term material testing to establish key long-term material properties. The new approach described here uses a physics-based model to predict the long-term creep rupture strength of 316H stainless steel using only short-term test data. The key idea is to use Bayesian inference to find the statistical distribution of the model parameters that best explain the short-term rupture data. Because the model is physics-based these parameters are all microstructural quantities that can be measured through detailed material characterization experiments. The Bayesian prior distributions provide a means for incorporating this characterization data into the final model to improve the accuracy of the long-term model predictions. However, where such data is not available the process still produces an accurate model based on an uniformed prior. Our hypothesis is that this approach more accurately extrapolates the short-term test data when compared to current, empirical methods. The report proves this hypothesis using actual long-term rupture data available for 316H, including tests with rupture times greater than 200,000 hours. The general approach developed here could be applied to other materials and other time-dependent material properties. Applying this new technique to develop long-term qualified material properties, potentially in conjunction with other accelerated qualification approaches like staggered qualification test programs, could greatly reduce the time required to qualify new materials for nuclear service.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Model 9975 O Ring Fixture LongTerm Leak Performance (FY2021 Status Report)

Leak testing experiments to monitor the aging performance of Viton® GLT and GLT-S O-rings used in the model 9975 shipping package has been ongoing since 2004 at Savannah River National Laboratory. Seventy tests using mock-up 9975 primary containment vessels (PCVs) with GLT O-rings were assembled and heated to temperatures ranging from 200 to 450 °F. Due to material substitution, fourteen tests with GLT-S O-rings were initiated in 2008 and heated to temperatures ranging from 200 to 400 °F. The conditioning temperatures are elevated compared to the calculated maximum O-ring temperature in a 9975 package in storage, 158 °F, to accelerate aging and enable observations of O-ring failures in a reasonable time frame. The fixtures are leak tested periodically and all GLT O-ring fixtures aging at 350 °F or above have failed to maintain a leak-tight seal. Eight GLT O-ring fixtures aging at 300 °F have failed after 2.8 to 5.7 years at temperature while the remaining fixtures at 300 °F were retired from testing following more than five years without failure. There has been one GLT O-ring fixture which failed after 13.4 years aging at 200 °F. However, 19 other GLT O-rings aging at 200 °F have remained leak-tight for over 14 years and remain in test. No failures have yet been observed in GLT O-ring fixtures aging at 270 °F for 9.5 years. All GLT-S O-ring fixtures aging at 300 °F or above have failed their leak test. No failures have yet been observed in GLT-S O-ring fixtures aging at 200 and 250 °F for 11.5 years. Data from the O-ring fixtures are generally consistent with results from compression stress-relaxation testing, and provide confidence in the predictive models based on those results. However, uncertainty exists in extrapolating these elevated temperature results to the lower temperatures of interest for normal storage in K-Area Complex (KAC). Oxygen consumption testing, which includes results from temperatures near KAC normal storage temperatures, is ongoing to provide further confidence and corroborate these extrapolations. The collective data from these test efforts suggest the minimum O-ring service life at KAC normal storage conditions should be at least 34 years for GLT and GLT-S O-rings. Measurement of compression set in O-rings removed from failed fixtures, compared to that from KAC surveillance O-rings, indicate significant margin remains for O-rings still in service in 9975 packages in KAC. Aging and periodic leak testing will continue for the remaining 26 mock-up PCV fixtures.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Assessing the ASME Section III, Division 5, Class A Primary Load Design Rules Against Creep Notch Effects

This report assesses the ASME Section III, Division 5, Subsection HB, Subpart B rules covering the design and construction of high temperature Class A nuclear reactor components for their robustness against creep notch effects. The creep notch effect combines the effect of multiaxial stresses on material creep deformation and damage. This report considers both effects, including the potential for creep mechanism shifts affecting the extrapolation of design data from high stress, short-time experimental data to low stress, long time operating component conditions. The report summarizes the history of and literature on multiaxial creep and surveys the current ASME design rules dealing with multiaxial effects. The report then describes the results of two dedicated numerical studies, one assessing the robustness of the ASME rules against uncertainty in extrapolating from uniaxial creep test data to multiaxial component conditions and a second study examining the potential effects of a mechanism shift from high stress, dislocation-mediated creep to diffusion-dominated creep at lower stresses. The final conclusion of the report is that the ASME rules adequately guard against multiaxial creep failure, though there are several aspects of the Code that could be optimized to provide less over-conservative design predictions or to provide a more consistent design margin as a function of temperature and stress. In a few areas, particularly the potential for mechanism shifts outside the currently-available experimental data, the Code rules should be revaluated as additional experimental data and new modeling and simulation results become available

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Model 9975 O-Ring Fixture Long-Term Leak Performance (FY2022 Status Report)

Leak testing experiments to monitor the aging performance of Viton® GLT and GLT-S O-rings used in the model 9975 shipping package has been ongoing since 2004 at Savannah River National Laboratory. Seventy tests using mock-up 9975 primary containment vessels (PCVs) with GLT O-rings were assembled and heated to temperatures ranging from 200 to 450 °F. Due to material substitution, fourteen tests with GLT-S O-rings were initiated in 2008 and heated to temperatures ranging from 200 to 400 °F. The conditioning temperatures are elevated compared to the calculated maximum O-ring temperature in a 9975 package in storage, 158 °F, to accelerate aging and enable observations of O-ring failures in a reasonable time frame. The fixtures are leak tested periodically and all GLT O-ring fixtures aging at 350 °F or above have failed to maintain a leak-tight seal. Eight GLT O-ring fixtures aging at 300 °F have failed after 2.8 to 5.7 years at temperature while the remaining fixtures at 300 °F were retired from testing following more than five years without failure. There has been one GLT O-ring fixture which failed after 13.4 years aging at 200 °F. However, 20 other GLT O-rings aging at 200 °F have remained leak-tight for over 14 years and remain in test. No failures have yet been observed in GLT O-ring fixtures aging at 270 °F for 10.4 years. All GLT-S O-ring fixtures aging at 300 °F or above have failed their leak test. No failures have yet been observed in GLT-S O-ring fixtures aging at 250 °F for 12.5 years. This year, one GLT-S O-ring fixture failed after 12.4 years aging at 200 °F. Data from the O-ring fixtures are generally consistent with results from compression stress-relaxation testing, and provide confidence in the predictive models based on those results. However, uncertainty exists in extrapolating these elevated temperature results to the lower temperatures of interest for normal storage in K-Area Complex (KAC). Oxygen consumption testing, which includes results from temperatures near KAC normal storage temperatures, is ongoing to provide further confidence and corroborate these extrapolations. The collective data from these test efforts suggest the minimum O-ring service life at KAC normal storage conditions should be at least 34 years for GLT and GLT-S O-rings. Measurement of compression set in O-rings removed from failed fixtures, compared to that from KAC surveillance O-rings, indicate significant margin remains for O-rings still in service in 9975 packages in KAC. Aging and periodic leak testing will continue for the remaining 25 mock-up PCV fixtures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Disruption Mitigation Solutions for Long Pulse Tokamaks (Final Technical Report)

The project "Disruption Mitigation Solutions for Long Pulse Tokamaks" was a collaborative effort between ORNL, GA, and UCSD. The main goal of the project was to study the injection of shattered cryogenic pellets (SPI) as a method of reducing wall damage from global tokamak plasma instabilities called disruptions. This work was primarily carried out in the JET tokamak in the UK. The team already had extensive experience studying PSI in the medium sized DIII-D tokamak in the US, but adding data from the larger JET tokamak enabled more confident extrapolation to the even larger planned ITER tokamak in France. Each of the collaborating institutions focused on different aspects of SPI disruption mitigation. The UCSD collaboration focused on the volume recombination of post-disruption runaway electron (RE) beams by massive hydrogen injection. Disruptions can sometimes result in the formation of toroidal (ring shaped) beams of highly energetic (relativistic) electron beams, which can cause serious local wall damage, even in present-day tokamaks. A promising method to reduce wall damage from the RE beams is to inject massive amounts of H 2 or D 2 into the beams using SPI. although the precise method by which the injected H 2 reducses RE wall damage is still being investigated, it seems clear that a necessary and required step for the reduction to occur is for the H 2 injection to cause volume recombination (free electron density reduction) of the background cold plasma surrounding the fast electron beam. The UCSD collaboration focused on answering the question of if this recombination could be achievable in the future reactor-size tokamak ITER using realistic levels of H 2 injection. A model capturing the most essential physics was developed and was constrained using both DIII-D and JET data. This model was then used to extrapolate to ITER. It was predicted that RE beam recombination should be achievable in ITER, although in the worst-case scenari (a 10 MA Re beam in ITER), recombination is barely achieved within the maximum allowed levels of H 2 injection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Modeling and Simulation Development Pathways to Accelerating KP-FHR Licensing (Final Report)

This project assembles a strong U.S. industry and national laboratory team to complete scope of work. Kairos Power (KP), headquartered in Alameda, CA, is the leader of this effort and has built an internal team of highly competent engineers and managers with extensive combined experience in nuclear power, conventional power, product development, and licensing. INL, ANL, and LANL bring unique capabilities in advanced reactor R&D and licensing. The project funding source is the result of FOA No. 0001817, U. S. Industry Opportunities for Advanced Nuclear Technology Development. There has been on-going work and this Access Cooperative Research and Development Agreement (CRADA) will cover the remaining work scope of FOA 0001817. KP is implementing innovative strategies that can reduce the cost and accelerate the initial demonstration of the Kairos Power Fluoride-salt-cooled, High-temperature Reactor (KP-FHR) to meet the needs of the U.S. electricity market by 2030. Licensing of the KP-FHR could be significantly accelerated using advanced computing methods with sufficient predictive capabilities to be able to extrapolate potential response of the structure in different scenarios. However, currently used computational methods heavily rely on empirical fits and cannot be used for extrapolation. The scope focuses on improving the modeling capability in the NEAMS Grizzly structural mechanics code and the NEAMS BISON fuel performance code and the NEAMS SAM systems analysis code. This work leverages the expertise and know-how gathered in three DOE National Laboratories – INL, ANL, and LANL.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗