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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Reservoir Temperature Estimator (RTEst): A multicomponent geothermometry tool

The Reservoir Temperature Estimator (RTEst) is a multicomponent geothermometry tool for estimating reservoir geochemical parameters including reservoir temperature, CO 2 fugacity, mass of water lost or gained, and a reaction factor. It estimates these parameters and their associated uncertainties by minimizing an objective function that is the weighted sum of squares of the saturation indices of a user-selected set of minerals believed to be equilibrated with the reservoir fluid. RTEst accomplishes these estimates by combining the geochemical modeling capabilities of The Geochemist’s Workbench® with the optimization/parameter estimation resources of PEST®. An included interface aids the user in selecting plausible mineral phases to comprise the objective function and calculates their weighting factors. The working principles of RTEst are described and its efficacy is illustrated by presenting results of its application to various geothermal fields with known conditions. These examples show RTEst can account for the alteration of ascending reservoir fluid by mineral (calcite) re-equilibration with changes in temperature, reconstruct waters with CO 2 loss, correct for the deficit of water and other volatiles (CO 2,gas , H 2 S gas ) from boiling, and determine the amount of mixing of thermal and non-thermal waters. RTEst can use data with basis species below detection limit, missing, or unreliable either by assuming equilibrium with a controlling mineral (fixed-analyte method) or by treating the analyte concentration as an optimization parameter. The inverse of variance weighting method included in RTEst provides more representative results than either the normalization or unit weighting methods. Finally, the ability of RTEst to calculate reservoir temperatures, gas fugacity, and mixing fractions demonstrates its usefulness as a tool for evaluating geothermal systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluation of the anisotropic grain boundaries and surfaces of α -U via molecular dynamics

Alloys based on uranium-zirconium are gaining renewed interest as fuels for the Versatile Test Reactor and a number of microreactor designs. Implementing metallic fuel in reactors creates the need for robust descriptive and predictive fuel performance modeling. Here, the current state of metallic fuel performance modeling relies on empirical equations derived from historical experiments, which may be unreliable when applied outside of their temperature, power, and composition phase space. One area where such data is lacking is the irradiation behavior of alpha -U, specifically tearing and porosity formation at the early stages of irradiation. While grain boundaries likely play a key role in this fuel behavior, relatively little is known about grain boundaries in alpha-U. Thus, we evaluate the grain boundary, surface energy, and work of adhesion of alpha -U utilizing molecular dynamics. Symmetric tilt grain boundaries (STGBs) are analyzed with the tilt plane oriented along each major crystallographic axis, for a total of eighty unique grain boundaries. The effect of temperature, tilt plane, and misorientation angle on interfacial energies are analyzed. The interfacial energies typically increase with temperature and there is significant variance as a function of misorientation angle, irrespective of the tilt plane. At 500 K, the average surface energy (1.23 J/m 2 ) is approximately 1.5 times the grain boundary energy (0.79 J/m 2 ), and the work of adhesion is approximately twice the grain boundary energy (1.68 J/m 2 ). Orientations for the likely formation of twins and likely failure planes are identified.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗
DOE OSTI2022

Multiscale spatial analysis of fracture arrangement and pattern reconstruction using Ripley's K-function

This work presents novel multiscale spatial data analytics using Ripley's K-function, as a measure of spatial interaction, to study one-dimensional arrangement of fractures. Fracture spatial arrangements are classified into clustered, anticlustered, or indistinguishable from random by testing statistical significance of the calculated Ripley's K-function. Characterizations of fracture arrangements are performed as a function of length scale and position. Analysis of the K-function along the study interval identifies where fracture clustering and anticlustering occur. Here, a simulation technique is also introduced here to statistically reconstruct spatial arrangements and to generate fracture realizations that are spatially similar to the fractures observed in the field. With this simulation technique, one can also fill spatial gaps in fracture measurements where data are absent, unreliable, or unused. Synthetic as well as field-measured 1D fracture datasets are used for testing and demonstration. Methods introduced in this work can be readily applied to fracture datasets observed in outcrops, borehole image logs, and cores.

58 GEOSCIENCES↗
DOE OSTI2025

Leveraging explainable AI to characterize floating-point exceptions in linear solvers

Linear solver packages are central to many scientific, engineering, and machine learning applications. When floating-point exceptions occur in these solvers, e.g., division by zero or overflow, numerical results are compromised and become unreliable. Existing static and dynamic analysis tools can detect such exceptions, but they do not explain why the exceptions occur in terms of the solver inputs. Here, we present a study to characterize the inputs that cause numerical exceptions in linear solver packages. Our approach uses explainable AI (XAI) to find the most relevant characteristics of input matrices that explain the occurrence of exceptions in the solvers. Since training data in this domain is scarce, we perform extensive data gathering and data augmentation to obtain exception-inducing inputs. Our approach uses a repair strategy on the features blamed by XAI to validate that such features indeed explain the exceptions. We compare the LIME and SHAP XAI techniques using a dozen matrix features with three classifiers. We evaluate the approach on three widely used linear solver packages and find that some input characteristics can explain the occurrence of exceptions 100% of the time, in specific solvers and preconditioners.

Explainable AI↗
DOE OSTI2024

Generating synthetic signaling networks for in silico modeling studies

Predictive models of signaling pathways have proven to be difficult to develop. Reasons include the uncertainty in the number of species, the complexity in species’ interactions, and the sparseness and uncertainty in experimental data. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. For example, uncertainty and sparseness of the data often result in overfitted models that have little predictive value beyond recapitulating the experimental data itself. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. However, to determine the effectiveness of any new algorithm, a baseline model is needed to test its performance. To meet this need, we developed a method for generating artificial synthetic networks that are reasonably realistic and thus can be treated as ground truth models. These synthetic models can then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. Here, we describe a simple approach for generating synthetic signaling networks that can be used for this purpose.

42 ENGINEERING↗
DOE OSTI2025

Development and validation of a software for simulating γ-γ coincidence emission and detection probabilities

Gamma-gamma coincidence spectrometers have the potential to significantly enhance detection sensitivity for ultra-trace radionuclide measurements. The implementation of these spectrometers, however, is limited by the complexity of acquisition hardware, data processing and quantification. This work reports development of a novel radionuclide quantification software for γ-γ coincidence measurements. For any radionuclide, the software parses the Evaluated Nuclear Structure Data File (ENSDF) database, recursively simulating all possible γ-γ coincidence signatures and their respective emission and detection probabilities. Implemented using Python programming language, the software employs several strategies to boost overall computational performance. Since coincidence-based spectrometers are of notable interest in monitoring compliance for the Comprehensive Nuclear-Test-Ban Treaty (CTBT), the software’s execution was tested for 84 CTBT-relevant radionuclides. To date, the software has been experimentally validated for 15 radionuclides using the Advanced Radionuclide Gamma spectrOmeter (ARGO) at Pacific Northwest National Laboratory, USA (PNNL). Notably, the software can be operated in convergence mode, whereby coincidence detection efficiency’s convergence behavior can help avoid unreliable radionuclide activity estimates. With growing number of coincidence spectrometers worldwide, this paper aims to assist the radiation metrology community in developing similar software for their system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Results of the AGR-5/6/7 UCO TRISO fuel irradiation test in the Advanced Test Reactor

AGR-5/6/7 was the last in a series of irradiation experiments sponsored by the U.S. Department of Energy in support of the development and qualification of TRISO coated particle fuel for use in a high-temperature gas-cooled reactor. This experiment was conducted to verify the performance of the reference-design TRISO-coated low-enriched UCO fuel for modular high-temperature gas-cooled reactor normal operating conditions and to explore fuel performance at temperatures substantially beyond those typical of normal operation. A total of 194 UCO fuel compacts in five capsules were irradiated in the Advanced Test Reactor for 360.9 effective full-power days, achieving final burnup ranging from 5.66% to 15.26% fissions per initial heavy metal atom and fast neutron fluence ranging from 1.62 × 10 25 to 5.55 × 10 25 n/m 2 (E > 0.18 MeV). Calculated time-averaged fuel temperatures ranged from 467 °C to 1432 °C, with a peak fuel temperature of 1536 °C. During the first five irradiation cycles (∼180 effective full-power days), 85m Kr fission gas release-rate-to-birth-rate ratios were 10 −7 –10 −6 , indicating no particle failures. Near the end of the sixth cycle, a substantial number of in-pile particle failures occurred in Capsule 1. The fission gas release from this capsule impacted the readings from the other four capsules and led to unreliable fission gas release measurements for all capsules during the last four cycles. A preliminary post-irradiation examination of Capsule 1 fuel and internal components revealed the in-pile particle failures resulted from operational issues with the capsule, not subpar performance of the fuel particles.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. Here, we implement our approach in the XSCOPE framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. XSCOPE is able to identify inputs that trigger exceptions in about 73% of the tested functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

A fast and robust computational modeling approach for density and shape predictions in powder metallurgy hot isostatic pressing

Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near-net-shape parts with high material utilization and uniform microstructures. PM-HIP is frequently used for producing small-scale parts with complicated geometries and is potentially economical for producing large-scale parts. However, excessive post-HIP shape distortions can reduce its effectiveness and economic advantage, especially for larger parts. A PM-HIP computational model can predict and help mitigate these distortions. However, due to complex deformation mechanisms and thermo-mechanical coupling present in PM-HIP processes, these non-linear computational models sometimes become numerically unstable. The numerical instabilities in these models can lead to very slow convergence or no convergence at all, which often translates to slow and unreliable models. These limitations are more pronounced in large models with complicated geometries. Hence, in this work, an alternative modeling approach is presented that improves numerical stability and computational performance. The presented approach achieves these improvements through approximating the fully coupled thermo-mechanical PM-HIP model as a decoupled model and adding inertial damping to the model’s mechanical part. In conclusion, a comparison with the fully coupled model indicated a slight dip in prediction accuracy (<5% error) but significant improvements in numerical stability (>20 times larger time step size) and computational performance (5-10 times speed-up with less computational resource usage) when using the presented approach.

Hot isostatic pressing↗
DOE OSTI2023

Parallel evaluation of alternative skin barrier models and excised human skin for dermal absorption studies in vitro

Skin permeation is a primary consideration in the safety assessment of cosmetic ingredients, topical drugs, and human users handling veterinary medicinal products. While excised human skin (EHS) remains the ‘gold standard’ for in vitro permeation testing (IVPT) studies, unreliable supply and high cost motivate the search for alternative skin barrier models. In this study, a standardized dermal absorption testing protocol was developed to evaluate the suitability of alternative skin barrier models to predict skin absorption in humans. Under this protocol, side-by-side assessments of a commercially available reconstructed human epidermis (RhE) model (EpiDerm-200-X, MatTek), a synthetic barrier membrane (Strat-M, Sigma-Aldrich), and EHS were performed. The skin barrier models were mounted on Franz diffusion cells and the permeation of caffeine, salicylic acid, and testosterone was quantified. Transepidermal water loss (TEWL) and histology of the biological models were also compared. EpiDerm-200-X exhibited native human epidermis-like morphology, including a characteristic stratum corneum, but had an elevated TEWL as compared to EHS. The mean 6 h cumulative permeation of a finite dose (6 nmol/cm2) of caffeine and testosterone was highest in EpiDerm-200-X, followed by EHS and Strat-M. Salicylic acid permeated most in EHS, followed by EpiDerm-200-X and Strat-M. Altogether, evaluating novel alternative skin barrier models in the manner outlined herein has the potential to reduce the time from basic science discovery to regulatory impact.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Poor reliability of public charging stations can impede the growth of the electric vehicle market

How does the reliability of public charging infrastructure affect electric vehicle (EV) adoption? Substantial public and private investments are expanding EV charging networks, but concerns are growing about the poor reliability of existing chargers and its potential impacts on EV adoption. Using data from a nationwide survey, we employ a choice model to quantify the effects of perceived charging reliability on Americans’ intentions to purchase new or used EVs. By randomly assigning participants to receive information characterizing public charging as either very reliable or very unreliable, we show a causal effect of reliability perceptions on EV purchase intentions. In conclusion, we find that differences in perceived reliability are equivalent to changing price by 32 % of purchasing budget or changing range by 366 miles, underscoring the importance of reliable public charging.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. Here, we develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. Further, the accuracy of the force field is demonstrated by calculating rate constants for the H + HO 2 → H 2 + 3 O 2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Comparison of Electronic Structure Methods for Predicting the Hydrogenation Energies of Candidate Molecules for Hydrogen Storage

The development of novel energy materials and fuels is required to expand current available energy sources. Aiming to reach this goal, there is growing interest in using molecular hydrogen as an energy carrier due to its abundance and high energy density. Liquid organic hydrogen carriers (LOHCs) are a promising route to the large-scale storage and transport of hydrogen for use in the energy economy. The search for thermodynamically viable LOHC molecules for real world use has led to a set of constraints on the dehydrogenation enthalpy and the minimum gravimetric hydrogen capacity. These constraints allow one to formulate the search for an ideal LOHC candidate molecule as an optimization problem well suited to the strengths of machine learning and artificial intelligence computational approaches. A critical barrier to a large-scale, high-throughput screening of LOHC candidate molecules is the lack of reliable training data. Computational electronic structure methods including density functional theory, coupled cluster approximations, and diffusion Monte Carlo can be used to provide training data where experimental data are either unreliable or do not exist. In this work, we use these methods to calculate the dehydrogenation energies and enthalpies of candidate LOHC molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗