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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Scaled Vecchia Approximation for Fast Computer-Model Emulation

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia’s ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Physics through the 1990s: Elementary-particle physics

The volume begins with a non-mathematical discussion of the motivation behind, and basic ideas of, elementary-particle physics theory and experiment. The progress over the past two decades with the quark model and unification of the electromagnetic and weak interactions is reviewed. Existing theoretical problems in the field, such as the origin of mass and the unification of the fundamental forces, are detailed, along with experimental programs to test the new theories. Accelerators, instrumentation, and detectors are described for both current and future facilities. Interactions with other areas of both theoretical and applied physics are presented. The sociology of the field is examined regarding the education of graduate students, the organization necessary in large-scale experiments, and the decision-making process involved in high-cost experiments. Finally, conclusions and recommendations for maintaining US excellence in theory and experiment are given. Appendices list both current and planned accelerators, and present statistical data on the US elementary-particle physics program. A glossary is included.

Source record↗
NASA NTRS2014

MARGInS: Model-Based Analysis of Realizable Goals in Systems

Under NASAs Constellation effort, the Exploration Technology Development Program funded research toward a system validation capability that applied machine learning and test-case generation techniques to the analysis of black-box system behavior. The behavior analysis capability scaled to spaces of hundreds of input parameters and tens of thousands of test cases. Aerospace systems at the vehicle level, especially those systems which contain some level of autonomy, are best described by hybrid and non-linear mathematics. Even simplified models of such systems need parameter dimensionalities in the hundreds or thousands of parameters in order to capture sufficient fidelity. The System Safety Assessments (such as those described in the SAE ARP 4761A Safety Assessment Process guidelines) for these systems are prone to errorinteractions between the vehicles subsystems are complex, and can display emergent behaviors. NASA captured this new analysis in the Model-based Analysis of Realizable Goals in Systems (MARGInS) tool and applied it to the Pad Abort 1 (PA-1) simulation as part of the independent validation and verification cycle before the PA-1 flight test in May of 2010. MARGInS evaluated the adherence of the high-fidelity simulation to its requirements, and deter- mined the margins to failure from the expected nominal input conditions. Following the PA-1 test, the capabilities within the MARGInS framework have been extended with sophisticated statistical and white-box test case generation techniques and applied to other NASA missions. The frame- work now includes a critical factors analysis that was applied to NASAs Orion simulation and design. NASAs Aeronautics Research Mission Directorate (ARMD) leveraged the existing MARGInS framework for work on aviation safety for civil transport vehicles and for research on autonomy issues. The NASA ARMD effort created a time series output prediction capability that has been used to characterize trajectories for a plane with an adaptive control system, and a safety boundary detection capability that has been applied to an air traffic control concept of operation for the Federal Aviation Administration. The statistical and machine- learning based techniques within MARGInS have been successfully combined with concolic execution to improve the coverage of a critical unit by driving system-level inputs. The use case driving the concolic execution and MARGInS integration was inspired by the Air France 447 disaster in which the loss of a critical functionality (the airspeed calculation from the pitot tubes) led to loss of the entire plane with the people aboard. To illustrate capabilities and limitations, we will highlight the analyses for the applications listed above. We will then discuss the future plans for MARGInS and its interfaces with other tools.

Validation↗
DOE OSTI2023

Preliminary Design of Critical Experiments Involving Commercially Available B 4 C Neutron Absorber Plates with Low-Enriched UO 2 Fuel

The International Criticality Safety Benchmark Evaluation Project (ICSBEP) is an initiative to provide high-quality benchmark data in a standardized format for criticality safety analysts to use to validate calculational tools and nuclear data. In the last released 2022 version, the ICSBEP handbook contains over 5,000 critical experiment descriptions, results, and associated models. This paper describes the critical configurations proposed and calculations performed as part of the conceptual design phase of Integral Experiment Request (IER) 554 (IER-554). The goal of IER-554 is to add to the benchmark experiments available to the criticality safety community by designing critical experiments that can analyze how adding commercially available Boralcan plates to an assembly of low-enriched UO2 fuel rods affects the effective neutron multiplication factor (k eff ). Boralcan, a neutron absorber product developed by Rio Tinto, is made of B4C in an Al 1100 matrix and is commonly used for criticality suppression in fuel storage pools. In the current version of the ICSBEP handbook, only a few dated critical experiments involve B4C materials, and none involve Boralcan in a thin plate shape. The experiments designed as part of IER-554 are intended to be performed at the Sandia Pulsed Reactor Facility/Critical Experiments (SPRF/CX) apparatus at Sandia National Laboratories. SPRF/CX is a well-characterized assembly considered trustworthy by the benchmarking community because of the numerous high-quality evaluations with very low experimental uncertainties included in the ICSBEP handbook. Figure 1 illustrates the assembly for a particular configuration from LCT-078, one of the published benchmarks in the ICSBEP handbook. The experiments will be moderated and reflected by light water at ambient atmospheric pressure. Before the plates are inserted in the critical assembly, they will be characterized with x-ray computed tomography (XCT) to identify the sizes and distribution of the B 4 C powder particles inside the plates. The preliminary design calculations were performed with SCALE 6.3.0 using the KENO V.a sequence for the criticality calculations, the CE-TSUNAMI-3D sequence for the sensitivity and uncertainty studies, and the ENDF/B-VII.1 Continuous Energy cross section library. All the calculation results presented have 10 pcm statistical uncertainties. A set of critical experiments with Boralcan plates could increase the confidence of the criticality safety community in its modeling methods when using this type of neutron absorber material. Experiments would also help validate k eff calculations, and the industry could use these validations to change the B loading credit limits from the US Nuclear Regulatory Commission standard review plan for dry cask storage of spent nuclear fuel. This paper summarizes only part of the analysis documented in the preliminary design report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Optimizing Prediction Error for Time-dependent Solar Radiation Modeling

Numerical weather forecasting models and statistical methods have found wide use to help power companies estimate renewable output, but better methods are needed, particularly for extended forecasts. Machine learning approaches have been used here as well, but so far a major limitation is the ability to also predict the corresponding uncertainty in a forecast. Here we show that both can be done and demonstrate this using a long-term-short memory neural network where the difference between predicted and ground truth data are used to train a model for the corresponding forecast uncertainties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Multiphysics Multiscale Simulation Platform for Damage, Environmental Degradation, and Life Prediction of CMCs in Extreme Environments

This project successfully developed a multiphysics, multiscale computational framework to enhance the design and development of CMCs, with a focus on modeling highly nonlinear, time-dependent damage mechanisms and material degradation under extreme conditions, such as those experienced in turbine service environments. The project made significant advances in improving our understanding of progressive damage, oxidative degradation, and time-dependent inelastic deformation in CMCs, with particular attention to the role of uncertainties in predictions. Key outcomes include the integration of advanced material characterization, uncertainty quantification, and multiphysics constitutive models to predict the behavior of CMCs over their service life. A novel multiscale methodology was employed, which integrated microscale constituent behaviors with structural-scale responses, enabling the manufacturing defects in the microstructure that are prone to damage nucleation. Through the development of DL algorithms, the project advanced the prediction of damage initiation and crack propagation, taking into account the defect morphology and statistical variations across multiple scales. The framework was rigorously validated using thermomechanical experiments, which tested CMCs under various mechanical loadings at elevated temperatures, further enhancing the model's predictive capability. Overall, the research outcomes have provided a more accurate, reliable method for predicting CMC component life, significantly advancing material design, and improving component reliability in extreme environments. This work has strong implications for the optimization of turbine components and other high-performance applications where CMCs are used.

03 NATURAL GAS↗
DOE OSTI2023

Stochastic modeling and statistical calibration with model error and scarce data

This paper introduces a procedure to assess the predictive accuracy of stochastic models subject to model error and sparse data. Model error is introduced as uncertainty on the coefficients of appropriate polynomial chaos expansions (PCE). The error associated with finite sample size allows us to conceive of these coefficients as statistics of the data that we describe as random variables whose influence on output quantities of interest is evaluated through the extended polynomial chaos expansion (EPCE). A Bayesian data assimilation scheme is introduced to update these expansions by considering the resulting nested chaos expansion as a hierarchical probabilistic model. Stochastic models of quantities of interest (QoI) are thus constructed and efficiently evaluated. Here, the Metropolis–Hastings Markov chain Monte Carlo procedure is used to sample the posterior. Two illustrative analytical and numerical problems are used to demonstrate the proposed approach.

Bayesian inference↗
DOE OSTI2020

Statistical Treatment of Inverse Problems Constrained by Differential Equations-based Models with Stochastic Terms

This paper introduces a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution represented numerically by an ensemble of simulations. The goal is to formulate the inverse problem, in particular the objective function, to find the closest forward distribution (i.e., the output of the stochastic forward problem) that best explains the distribution of the observations in a certain metric. We use proper scoring rules, a concept employed in statistical forecast verification, namely energy, variogram, and hybrid (i.e., combination of the two) scores. We study the performance of the proposed formulation in the context of two applications: a coefficient field inversion for subsurface flow governed by an elliptic partial differential equation with a stochastic source and a parameter inversion for power grid governed by differential-algebraic equations. In both cases we show that the variogram and the hybrid scores produce better parameter inversion results than does the energy score, whereas the energy score leads to better probabilistic predictions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Developing and applying quantifiable metrics for diagnostic and experiment design on Z

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Partnership Center for High-Fidelity Boundary Plasma Simulation (Final Report)

Within the Partnership Center for High-Fidelity Boundary Plasma Simulation (HBPS), work at UT-Austin was aimed at improved verification, validation, and uncertainty quantification (VVUQ) for edge plasma simulations and on performing gyrokinetics simulations of pedestal instabilities and turbulence in order to expand foundational understanding of pedestal transport. Regarding VVUQ, the accomplishments can be summarized as follows. First, it was shown that the Moment Preserving Constrained Resampling technique, when applied periodically in particle-in-cell simulations in the XGC code, can dramatically improve the accuracy of the simulation at essentially equivalent computational cost. Second, a technique for estimating model correlations, which are required to solve the model selection and sample allocation problem in multifidelity UQ techniques, without sampling the highest fidelity, most computationally expensive model, was developed and demonstrated. Third, previously developed methods for estimating statistical and discretization errors were applied to numerical methods relevant to edge plasma simulations, namely in particle-in-cell-based approaches, and shown to work. Finally, benchmark studies for comparing gyrokinetic codes were developed and performed, leading to reasonable agreement between four commonly used codes. Regarding physics studies, gyrokinetic simulations to investigate microtearing modes in the DIII-D pedestal were performed using the GENE code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Cross-scale covariance for material property prediction

A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~10 8 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10 2 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data From Experiments on Bubbling Fluidization of Zeolite in a Rectangular Bubbling Fluidized Bed

Fluidization experiments were conducted in a lab-scale rectangular bubbling fluidized bed with the objective of generating a high-quality dataset for model validation and artificial intelligence/machine learning (AI/ML) training. Zeolite was chosen as the bed material, and the fluidizing medium was air as supplied by a compressor. Three different flow rates at the inlet were chosen such that the particles were fluidized but not elutriated from the system. The test matrix involved randomization and replicates to provide uncertainty estimates as well as four different batches of zeolite as the bed material. The quantities of interest obtained from this study were statistics of differential pressures, interface heights, and particle velocities. Considering all the components of the elaborate test plan, the results obtained were consistent and reproducible. Characterization tests were performed to estimate particle properties including size, density, coefficient of friction, coefficient of restitution, and minimum fluidization velocity. In addition, the angle of repose from granular discharge experiments has been reported to account for rolling friction, though its effect on the overall process is expected to be negligible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Versatile TRISO fuel particle modeling in Bison

Tri-structural isotropic (TRISO) fuel particles are a key component in several previous and current reactors as well as in a variety of novel nuclear reactor designs. Interest in TRISO fuel is on the rise, necessitating considerable computer modeling of TRISO fuel behavior in order to support related design and licensing activities. The Bison nuclear fuel performance code, which offers a full set of capabilities for modeling TRISO fuels, makes it easier to explore the various important aspects of TRISO fuel behavior. One key advantage of Bison is its ability to create meshes in 1D, 2D, and 3D. Users can customize these meshes for specific geometries, mesh densities, and use cases. This enables a wide variety of analyses, including thermal, structural, mass diffusion, homogenization, and statistical failure analyses. Furthermore, the meshing capability simplifies analysts’ workflows. The inherent mesh generation capability eliminates the need for separate mesh-generating software and mesh file management. Also, the fact that the meshes are customizable makes it straightforward to automate an investigation over a range of geometric parameters or mesh densities. Here, the present paper highlights the ease with which Bison may be used to create meshes for both simple and relatively complex TRISO fuel particles, and it explores the types of analyses enabled by these meshes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

gdess: A framework for evaluating simulated atmospheric CO 2 in Earth System Models

Atmospheric carbon dioxide (CO 2 ) plays a key role in the global carbon cycle and global warming. Climate-carbon feedbacks are often studied and estimated using Earth System Models (ESMs), which couple together multiple model components—including the atmosphere, ocean, terrestrial biosphere, and cryosphere—to jointly simulate mass and energy exchanges within and between these components. Despite tremendous advances, model intercomparisons and benchmarking are aspects of ESMs that warrant further improvement (Fer et al., 2021; Smith et al., 2014). Such benchmarking is critical because comparing the value of state variables in these simulations against observed values provides evidence for appropriately refining model components; moreover, researchers can learn much about Earth system dynamics in the process (Randall et al., 2019). We introduce `gdess` (a.k.a., Greenhouse gas Diagnostics for Earth System Simulations), which parses observational datasets and ESM simulation output, combines them to be in a consistent structure, computes statistical metrics, and generates diagnostic visualizations. In its current incarnation, `gdess` facilitates evaluating a model's ability to reproduce observed temporal and spatial variations of atmospheric CO 2 . The diagnostics implemented modularly in `gdess` support more rapid assessment and improvement of model-simulated global CO 2 sources and sinks associated with land and ocean ecosystem processes. We intend for this set of automated diagnostics to form an extensible, open source framework for future comparisons of simulated and observed concentrations of various greenhouse gases across Earth system models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Graph-based featurization methods for classifying small molecule compounds

For over a decade, drug-induced liver injury (DILI) has posed significant drawbacks in the synthesis and development of drugs and remains a consequential concern. With finite success within the existing preclinical models, DILI is one of the main causes of drug withdrawal or termination from the market. Particularly, this withdrawal occurs during the late stages of drug development (Kullak-Ublick, 2017). Since DILI is difficult to diagnose and treat, it has become an obstacle in the drug production market that in turn affects clinicians, pharmaceutical companies, and consumers. We propose a method for learning features of DILI-positive drugs based on the graphical relationships and patterns they possess within a network of biological databases. We also train various statistical and machine learning models on these learned features in order to classify the drugs as DILI-positive or negative. Our methods include Random Forest, Neural networks, and logistic regression classification. We utilize labeled DILI-positive and DILI-negative datasets, which were developed by the FDA and the National center for toxicological research, as well as additional literature datasets (Thakkar, 2020) in order to validate our results and assess our featurization and model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2016

NASA's X-Plane Database and Parametric Cost Model v 2.0

The NASA Armstrong Cost Engineering Team with technical assistance from NASA HQ (SID)has gone through the full process in developing new CERs from Version #1 to Version #2 CERs. We took a step backward and reexamined all of the data collected, such as dependent and independent variables, cost, dry weight, length, wingspan, manned versus unmanned, altitude, Mach number, thrust, and skin. We used a well- known statistical analysis tool called CO$TAT instead of using "R" multiple linear or the "Regression" tool found in Microsoft Excel(TradeMark). We setup an "array of data" by adding 21" dummy variables;" we analyzed the standard error (SE) and then determined the "best fit." We have parametrically priced-out several future X-planes and compared our results to those of other resources. More work needs to be done in getting "accurate and traceable cost data" from historical X-plane records!

life cycle cost↗
DOE OSTI2025

Uncertainty estimation of bifurcated solutions in the Rayleigh–Bénard problem for advanced nuclear reactors applications

Multiphysics models of nuclear reactors frequently comprise nonlinear systems of equations. The nonlinear nature of these models could lead to solution bifurcations, where a small change in a certain parameter, e.g., the thermophysical properties of the coolant, can lead to a sudden change in the system’s behavior. At the point in parameter space where this happens, called a critical point, the Jacobian matrix of the model’s nonlinear operator becomes singular potentially permitting multiple solutions to coexist. In this paper, we perform uncertainty estimation (UE) in a parameter range that includes bifurcated solutions within the context of Rayleigh–Bénard problem. We perform this analysis assuming uncertain temperature difference, and tilt angle for the iterative solution algorithm with a unit Prandtl number (Pr = 1). Also, we perform this analysis under uncertain thermophysical properties for both FLiBe molten salt and liquid sodium as working fluid. We deploy two approaches to compute statistical moments for the resulting distributions of selected flow-field variables. The first approach is the blind computation of the mean and the standard deviation without any consideration of solution bifurcation, while the second approach utilizes k-means clustering to cluster each branch’s solutions together and compute separate statistical moments for each branch. The statistical distributions are obtained by perturbing the selected parameters about nominal values that correspond to a solution on one of the valid branches, and that solution is used as initial guess for the iterative solution algorithm. We found that perturbation of any parameter when its nominal value is close to its critical point always leads to branch jumping, i.e., the iterations converge to a solution on a branch different from the branch of the initial guess. This produces a statistical ensemble comprised of fundamentally different solutions leading to wrong mean values and uncertainty estimates, whereas clustering provides an efficient way to deal with this type of computation. This work is important for developing Gen IV nuclear systems because many of these systems rely on natural convection for cooling especially in accident conditions.

97 - MATHEMATICS AND COMPUTING↗