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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit(CPU)on the order of ~30 s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗
NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit (CPU) on the order of ~30s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗
DOE OSTI2024

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1998

The Application of Beowulf-Class Computing to Computational Electromagnetics

Current computational developments at the Jet Propulsion Laboratory (JPL) are motivated by the NASA/JPL goal of reducing payload in future space missions while increasing mission capability through miniaturization of active and passive sensors, analytical instruments and communication systems.

beowulf-class↗
DOE OSTI2021

Synthase-Selective Exploration of a Tunicate Microbiome by Activity-Guided Single-Cell Genomics

While thousands of environmental metagenomes have been mined for the presence of novel biosynthetic gene clusters, such computational predictions do not provide evidence of their in vivo biosynthetic functionality. Using fluorescent in situ enzyme assay targeting carrier proteins common to polyketide (PKS) and nonribosomal peptide synthetases (NRPS), we applied fluorescence-activated cell sorting to tunicate microbiome to enrich for microbes with active secondary metabolic capabilities. Single-cell genomics uncovered the genetic basis for a wide biosynthetic diversity in the enzyme-active cells and revealed a member of marine Oceanospirillales harboring a novel NRPS gene cluster with high similarity to phylogenetically distant marine and terrestrial bacteria. Interestingly, this synthase belongs to a larger class of siderophore biosynthetic gene clusters commonly associated with pestilence and disease. Furthermore, this demonstrates activity-guided single-cell genomics as a tool to guide novel biosynthetic discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.

Dash, Sajal↗
DOE OSTI2024

ELECTRONIC STRUCTURE METHODS AND PROTOCOLS WITH APPLICATION TO DYNAMICS, KINETICS AND THERMOCHEMISTRY

Hydrocarbon combustion involves the reaction dynamics of a tremendous number of species beginning with many-component fuel mixtures and proceeding via a complex system of intermediates to form primary and secondary products. Combustion conditions corresponding to new advanced engines and/or alternative fuels rely increasingly on autoignition and low-temperature-combustion chemistry. In these regimes various transient radical species such as HO2, ROO·, ·QOOH, HCO, NO2, HOCO, and Criegee intermediates play important roles in determining the detailed as well as more general dynamics. A clear understanding and accurate representation of these processes is needed for effective modeling. Given the difficulties associated with making reliable experimental measurements of these systems, computation can play an important role in developing these energy technologies. Accurate calculations have their own challenges since even within the simplest dynamical approximations such as transition state theory, the rates depend exponentially on critical barrier heights and these may be sensitive to the level of quantum chemistry. Moreover, it is well-known that in many cases it is necessary to go beyond statistical theories and consider the dynamics. Quantum tunneling, resonances, radiative transitions, and non-adiabatic effects governed by spin-orbit or derivative coupling can be determining factors in those dynamics. Building upon progress made during a period of prior support through the DOE Early Career Program, this project combines developments in the areas of potential energy surface (PES) fitting and multistate multireference quantum chemistry to allow spectroscopically and dynamically/kinetically accurate investigations of key molecular systems (such as those mentioned above), many of which are radicals with strong multireference character and have the possibility of multiple electronic states contributing to the observed dynamics. An ongoing area of investigation is to develop general strategies for robustly convergent electronic structure theory for global multichannel reactive surfaces including diabatization of energy and other relevant surfaces such as dipole transition. Combining advances in ab initio methods with automated interpolative PES fitting allows the construction of high-quality PESs (incorporating thousands of high-level data) to be done rapidly through parallel processing on high-performance computing (HPC) clusters. In addition, new methods and approaches to electronic structure theory will be developed and tested through applications. This project will explore limitations in traditional multireference calculations (e.g., MRCI) such as those imposed by internal contraction, lack of high-order correlation treatment and poor scaling. Methods such as DMRG-based extended active-space CASSCF and various Quantum Monte Carlo (QMC) methods will be applied (including VMC/DMC and FCIQMC). Insight into the relative significance of different orbital spaces and the robustness of application of these approaches on leadership class computing architectures will be gained. Synergy with other components of this research program such as automated PES fitting and multireference quantum chemistry will be used to address challenges encountered by the standard approaches to computational thermochemistry (those being single-reference quantum chemistry and perturbative treatments of the anharmonic vibrational energy, which break down for some cases of electronic structure or floppy strongly coupled vibrational modes).

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Summary of Development for Structural Component Modeling in Fiscal Year 2022

This report summarizes efforts performed during Fiscal Year 2022 to develop capabilities for modeling structural component degradation in support of the U.S. Department of Energy's Nuclear Energy Advanced Modeling and Simulation Program. These efforts were centered around development of capabilities for the Grizzly code. Efforts focused both on foundational engineering-scale analysis capabilities for damage and shell formulations and on material-scale tools for accounting for irradiation effects in Grade 91 steel. A major outcome of this effort was the development of a general-purpose tool for nonlocal averaging of material properties, which enabled nonlocal damage models. In addition, the applicability of the shell elements in MOOSE has been expanded to allow modeling a wider range of component geometries and using a wide variety of material models. For irradiation effects on materials, a previously developed cluster dynamics model for light water reactor pressure vessel steels has been adapted for application to Grade 91 alloy. This work builds on prior efforts to build a flexible, capable code for addressing a variety of aging and component performance issues in nuclear power plant structural components.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Grizzly Development for Light Water Reactor and Advanced Reactor Applications in Fiscal Year 2021

This report summarizes work performed during Fiscal Year (FY) 2021 at Idaho National Laboratory (INL) for the U.S. Department of Energy’s Nuclear Engineering Advanced Modeling and Simulation (NEAMS) program for the Structural Materials and Chemistry Technical Area in the work package entitled “MS-21IN050106 - Structural Materials - INL.” This effort mainly focused on development and application of capabilities for engineering-scale analysis of nuclear reactor structural components in the Grizzly and BlackBear codes. These efforts include performance improvements and application of models for high-temperature component response, cluster dynamics modeling of precipitation in light water reactor pressure vessel steel, engineering-scale capability development in the areas of concrete simulation and reactor pressure vessel analysis, and preparation for and conducting an independent assessment of the adherence of these codes to software quality standards.

36 MATERIALS SCIENCE↗
NASA NTRS2008

NASA Tech Briefs, August 2008

Customizable Digital Receivers for Radar Two-Camera Acquisition and Tracking of a Flying Target Visual Data Analysis for Satellites A Data Type for Efficient Representation of Other Data Types Hand-Held Ultrasonic Instrument for Reading Matrix Symbols Broadband Microstrip-to-Coplanar Strip Double-Y Balun A Topographical Lidar System for Terrain-Relative Navigation Programmable Low-Voltage Circuit Breaker and Tester Electronic Switch Arrays for Managing Microbattery Arrays Topics covered include: Lower-Dark-Current, Higher-Blue-Response CMOS Imagers; Fabricating Large-Area Sheets of Single-Layer Graphene by CVD; Support for Diagnosis of Custom Computer Hardware; Providing Goal-Based Autonomy for Commanding a Spacecraft; Dynamic Method for Identifying Collected Sample Mass; Optimal Planning and Problem-Solving; Attitude-Control Algorithm for Minimizing Maneuver Execution Errors; Grants Document-Generation System; Heat-Storage Modules Containing LiNO3 3H2O and Graphite Foam; Precipitation-Strengthened, High-Temperature, High-Force Shape Memory Alloys; Improved Relief Valve Would Be Less Susceptible to Failure; Safety Modification of Cam-and-Groove Hose Coupling; Using Composite Materials in a Cryogenic Pump; Using Electronic Noses to Detect Tumors During Neurosurgery; Producing Newborn Synchronous Mammalian Cells; Smaller, Lower-Power Fast-Neutron Scintillation Detectors; Rotationally Vibrating Electric-Field Mill; Estimating Hardness from the USDC Tool-Bit Temperature Rise; Particle-Charge Spectrometer; Automated Production of Movies on a Cluster of Computers; FIDO-Class Development Rover; and Tone-Based Command of Deep Space Probes Using Ground Antennas.

Source record↗
DOE OSTI2021

High-level coupled-cluster energetics by Monte Carlo sampling and moment expansions: Further details and comparisons

We recently proposed a novel approach to converging electronic energies equivalent to high-level coupled-cluster (CC) computations by combining the deterministic CC(P;Q) formalism with the stochastic configuration interaction (CI) and CC Quantum Monte Carlo (QMC) propagations. This article extends our initial study [J. E. Deustua, J. Shen, and P. Piecuch, Phys. Rev. Lett. 119, 223003 (2017)], which focused on recovering the energies obtained with the CC method with singles, doubles, and triples (CCSDT) using the information extracted from full CI QMC and CCSDT-MC, to the CIQMC approaches truncated at triples and quadruples. It also reports our first semi-stochastic CC(P;Q) calculations aimed at converging the energies that correspond to the CC method with singles, doubles, triples, and quadruples (CCSDTQ). The ability of the semi-stochastic CC(P;Q) formalism to recover the CCSDT and CCSDTQ energies, even when electronic quasi-degeneracies and triply and quadruply excited clusters become substantial, is illustrated by a few numerical examples, including the F–F bond breaking in F 2 , the automerization of cyclobutadiene, and the double dissociation of the water molecule.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Scalable Generation of High-fidelity Synthetic Population Ensembles

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the U.S. via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. The study involves two scenarios: creating ensembles for (1) 17 U.S. metropolitan areas in 2019 and (2) full U.S. Census Divisions in 2023, with each scenario consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system within a research cloud, comprised of virtual containerizations, GPU-enhanced functionality, and orchestrated deployments of UrbanPop’s maturing Likeness Python ecosystem. Results demonstrate we maintained high-fidelity approximations of residential totals by areas of interest and the demographic characteristics of neighborhoods while reducing manual workflow burdens. Finally, we discuss plans to fine-tune and further develop our automated workflows for truly distributed job orchestration to increase computational efficiency, as well as provide an outlook for broadening applications of the ensembles.

Cluster computing↗
NASA NTRS2006

Processing EOS MLS Level-2 Data

A computer program performs level-2 processing of thermal-microwave-radiance data from observations of the limb of the Earth by the Earth Observing System (EOS) Microwave Limb Sounder (MLS). The purpose of the processing is to estimate the composition and temperature of the atmosphere versus altitude from .8 to .90 km. "Level-2" as used here is a specialists f term signifying both vertical profiles of geophysical parameters along the measurement track of the instrument and processing performed by this or other software to generate such profiles. Designed to be flexible, the program is controlled via a configuration file that defines all aspects of processing, including contents of state and measurement vectors, configurations of forward models, measurement and calibration data to be read, and the manner of inverting the models to obtain the desired estimates. The program can operate in a parallel form in which one instance of the program acts a master, coordinating the work of multiple slave instances on a cluster of computers, each slave operating on a portion of the data. Optionally, the configuration file can be made to instruct the software to produce files of simulated radiances based on state vectors formed from sets of geophysical data-product files taken as input.

Snyder, W. Van↗
DOE OSTI2022

Defect Structure in Quantum-Cutting Yb3+-Doped CsPbCl3 Perovskites Probed by X-Ray Absorption and Atomic Pair Distribution Function Analysis

Ytterbium-doping in all-inorganic lead-halide perovskites (CsPb(Cl1-xBrx)3) generates novel properties including quantum cutting and narrow line emission, making these materials attractive spectral down-converters for solar photovoltaics. The relationship between this optical efficiency and the defect structure(s) associated with Yb3+ dopants within perovskites is not well understood. Various charge-neutral doping motifs have previously been proposed and studied computationally, including clusters involving two substitutional Yb3+ ions charge-compensated by a single local Pb2+ vacancy. Near-band-edge defect states associated with such motifs are believed to play an important mechanistic role in quantum cutting itself. Here, we report the results of X-ray absorption and X-ray total-scattering measurements on ytterbium-doped CsPbCl3. XANES shows that the dopant oxidation state is exclusively Yb3+, and a combination of Yb L3 and Pb L3 EXAFS shows that this Yb3+ substitutes exclusively at Pb2+ sites, where it adopts a pseudo-octahedral [YbCl6]3- coordination environment. Shell-by-shell fits to the data show a short Yb-Cl bond distance of 2.58 Å compared to the Pb-Cl bond distance of 2.83 Å. We confirm this finding by X-ray pair distribution function analysis, which also shows evidence of additional Pb2+ vacancy formation induced by Yb3+ doping. We evaluate whether this is the primary mechanism of charge compensation using simulated EXAFS and pair distribution function data for several computed defect structures. Together, these results resolve the local dopant structures and charge-compensation mechanisms in lanthanide-doped all-inorganic lead-halide perovskites, and, as such, significantly advance the understanding of structure-function relationships in this important class of materials.

Kluherz, Kyle (ORCID:0000000279865167)↗
DOE OSTI2024

The Data Mine model for accessible partnerships in data science

Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

Betz, Margaret A.↗
DOE OSTI2025

A bi-level spatiotemporal clustering approach and its application to drought extraction

We present a novel flexible bi-level spatiotemporal clustering algorithm to extract events based on their intensity and spatiotemporal structures. Our algorithm consists of using (i) a novel space-time k-means clustering to obtain spatiotemporally coherent intensity clusters, and (ii) a density-based spatial clustering of applications with noise (DBSCAN) to spatiotemporally section the intensity clusters into individual events. We discuss the development of the algorithm, the selection, tuning and meaning of the parameters within each step, as well as its validation. Finally, we apply the algorithm to a spatiotemporal drought index, standardized vapor pressure deficit drought index (SVDI), over the continental United States (US) from 1980–2021 and show that it captures historical drought events over the continental United States and their spatiotemporal extents.

17 WIND ENERGY↗
DOE OSTI2023

Microstructure modeling of nuclear structural materials: Recent progress and future directions

Modeling and simulation of microstructures are essential to understand the complex responses and behaviors of nuclear materials in extreme environments. The needs to assess the extended life operation as well as the growing interest in accelerating nuclear materials development and qualification have stimulated the use of high-fidelity multiscale models aided by empirical and ab initio data. This paper reviews the role of various models across different length and time scales in investigating irradiation effects on microstructure evolution and degradation, in particular the embrittlement caused by radiation induced or enhanced formation of nanoscale chemical heterogeneities. The strength and limitations of these models, including classical rate theories, cluster dynamics, phase-field methods, and atomistic models informed by ab initio energies, are discussed with seminal examples. Challenges regarding the lack of thermo-kinetic data and theoretical treatments considering chemical complexities and magnetic excitations, as well as the stabilizing effect by excess point defects in nuclear structural materials are presented, along with potential solutions based on ab initio informed surrogate energy models and statistical sampling by Monte Carlo simulations. Further, the review then highlights the opportunities to leverage the advantages of different methods by establishing hybrid models by shared variables or coupled codes and applications. Finally, the review concludes with forward-looking remarks on how the use of physics-based models can aid the improvement of machine-learning models of property degradation and vice versa.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analysis of spin frustration in an Fe III 7 cluster using a combination of computational, experimental, and magnetostructural correlation methods

The synthesis, structure, and magnetic properties are reported for [Fe 7 O 3 (O 2 C t Bu) 9 (mda) 3 (H 2 O) 3 ] ( 1 ), where mdaH 2 is N -methyldiethanolamine. 1 was prepared from the reaction of [Fe 3 O(O 2 C t Bu) 6 (H 2 O) 3 ](NO 3 ) with mdaH 2 in a 1:~3 ratio in MeCN. The core of 1 consists of a central octahedral Fe III ion held within a non-planar Fe 6 loop by three μ 3 -O 2- and three μ 2 -RO - arms from the three mda 2- chelates. Variable-temperature dc and ac magnetic susceptibility studies revealed dominant antiferromagnetic coupling, leading to a ground state spin of S = 5 / 2 . The ground state was confirmed by a fit of magnetization data collected in the 0.1–7.0 T and 1.8–10.0 K ranges. The four Fe 2 pairwise exchange parameters ( J 1 - J 4 ) were estimated by independent methods: theoretical calculations using either broken symmetry energy differences (-46.3, -16.2, -3.9, and - 28.1 cm -1 , respectively) or Green’s function approximation methods (-41.4, -14.8, -13.2, and - 24.7 cm -1 ), and a magnetostructural correlation (MSC) previously developed for high nuclearity Fe III /O complexes (-39.5, -13.8, -6.7, and - 23.5 cm -1 ). Additionally, the J 1 - J 4 obtained from the MSC and theoretical methods were used with the program PHI to both simulate χ M T vs T as well as to serve as reasonable input values to fit the experimental data (-41.0, -11.4, -5.0, and - 27.3 cm -1 ). Analysis of the J ij led to identification of the spin frustration effects operative and the resultant spin vector alignments at each Fe III ion, thus allowing for the rationalization of the experimental ground state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗