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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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All sourcesNASA NTRSDOE OSTI
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 ↗
DOE OSTI2017

Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models

The widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization. However, there are significant barriers that must be overcome to extract these insights from EMR data. First, EMR datasets consist of both static and dynamic observations of discrete and continuous-valued variables, many of which may be missing, precluding the application of standard multivariate analysis techniques. Second, clinical populations observed via EMRs and relevant to the study and management of conditions like sepsis are often heterogeneous; properly accounting for this heterogeneity is critical. Here, we describe an unsupervised, probabilistic framework called a composite mixture model that can simultaneously accommodate the wide variety of observations frequently observed in EMR datasets, characterize heterogeneous clinical populations, and handle missing observations. In conclusion, we demonstrate the efficacy of our approach on a large-scale sepsis cohort, developing novel techniques built on our model-based clusters to track patient mortality risk over time and identify physiological trends and distinct subgroups of the dataset associated with elevated risk of mortality during hospitalization.

59 BASIC BIOLOGICAL SCIENCES↗
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↗
DOE OSTI2016

Dual chain perturbation theory: A new equation of state for polyatomic molecules

In the development of equations of state for polyatomic molecules, thermodynamic perturbation theory (TPT) is widely used to calculate the change in free energy due to chain formation. TPT is a simplification of a more general and exact multi-density cluster expansion for associating fluids. In TPT, all contributions to the cluster expansion which contain chain–chain interactions are neglected. That is, all inter-chain interactions are treated at the reference fluid level. This allows for the summation of the cluster theory in terms of reference system correlation functions only. The resulting theory has been shown to be accurate and has been widely employed as the basis of many engineering equations of state. While highly successful, TPT has many handicaps which result from the neglect of chain–chain contributions. The subject of this document is to move beyond the limitations of TPT and include chain–chain contributions to the equation of state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
DOE OSTI2016

Synchronous parallel spatially resolved stochastic cluster dynamics

In this work, a spatially resolved stochastic cluster dynamics (SRSCD) model for radiation damage accumulation in metals is implemented using a synchronous parallel kinetic Monte Carlo algorithm. The parallel algorithm is shown to significantly increase the size of representative volumes achievable in SRSCD simulations of radiation damage accumulation. Additionally, weak scaling performance of the method is tested in two cases: (1) an idealized case of Frenkel pair diffusion and annihilation, and (2) a characteristic example problem including defect cluster formation and growth in α-Fe. For the latter case, weak scaling is tested using both Frenkel pair and displacement cascade damage. To improve scaling of simulations with cascade damage, an explicit cascade implantation scheme is developed for cases in which fast-moving defects are created in displacement cascades. For the first time, simulation of radiation damage accumulation in nanopolycrystals can be achieved with a three dimensional rendition of the microstructure, allowing demonstration of the effect of grain size on defect accumulation in Frenkel pair-irradiated α-Fe.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Modeling copper precipitation hardening and embrittlement in a dilute Fe-0.3at.%Cu alloy under neutron irradiation

The neutron irradiation in light water reactors can induce precipitation of nanometer sized Cu clusters in reactor pressure vessel steels. The Cu precipitates impede dislocation gliding, leading to increase of yield strength (hardening) and upward shift of ductile-to-brittle transition temperature (embrittlement). In this work, cluster dynamics modeling is used to model the entire Cu precipitation process (nucleation, growth, and coarsening) in a Fe-0.3at.%Cu alloy under neutron irradiation at 300 °C. The evolution of the Cu cluster number density and mean radius predicted by the modeling agrees well with experimental data reported in literature for the same alloy under the same irradiation conditions. The predicted precipitation kinetics are used as input for a dispersed barrier hardening model to correlate the microstructural evolution with the radiation hardening and embrittlement in this alloy. The predicted radiation hardening agrees well with the mechanical test results in literature. In conclusion, the limitation of the model and future improvement directions are also discussed in this work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Progress on Grizzly Development for Reactor Pressure Vessels and Reinforced Concrete Structures

The Grizzly code has been under development for the U.S. Department of Energy’s Light Water Reactor Sustainability program to provide predictive tools for the evolution of material degradation in critical light water reactor structural components due to long-term exposure to the environmental conditions of normal reactor operation, and for the effects of this degradation on the safety of these components. This development has primarily targeted reactor pressure vessels and reinforced concrete structures. Work performed during Fiscal Year 2019 added important features for both of these types of structures. For probabilistic fracture mechanics analysis of reactor pressure vessels, capabilities were added to account for the effects of warm prestressing and residual stresses, as well as to model crack initiation, growth, and arrest. For modeling microstructure evolution in reactor pressure vessels, a cluster dynamics model for evolution of Mn-Ni-Si phases has been implemented. For modeling degraded reinforced concrete structures, capabilities to consider the combined effects of damage and creep in the concrete constitutive model and model nonlinear behavior of reinforcing bars has been added. These newly-developed features fill important gaps in Grizzly’s feature set for engineering-scale analysis of degraded reactor pressure vessels and reinforced concrete structures. Grizzly development in both of these areas has reached a point where it is ready for extensive testing on relevant engineering problems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Energy Performance Evaluation of Aquila's Aquarius Fixed Cold Plate Cooling System at NREL's High Performance Computing Center

In the first half of 2018, as part of a partnership with Sandia National Laboratories, Aquila installed its fixed cold plate, liquid-cooled Aquarius rack solution for high-performance computing (HPC) clustering at the National Renewable Energy Laboratory's (NREL's) Energy Systems Integration Facility (ESIF). This new fixed cold plate, warm-water cooling technology together with a manifold design provides easy access to service nodes and eliminates the need for server auxiliary fans altogether. Aquila and Sandia National Laboratories chose NREL's HPC Data Center for the initial installation and evaluation because the data center is configured for liquid cooling and has the required instrumentation to measure flow and temperature differences to facilitate testing. This paper gives an overview of the Aquarius fixed cold plate cooling technology and provides results from early energy performance evaluation testing.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2016

High Resolution Model Development to Quantify the Impact of Icebergs on the Stability of the Atlantic Meridional Overturning Circulation

In the present-day North Atlantic Ocean, relatively warm and salty water moves northwards from the tropics to the high latitudes, sinks, and returns southward towards the equator as North Atlantic Deep Water, forming the so called Atlantic Meridional Overturning Circulation (AMOC). It has been found that the stability of the AMOC is non-linearly related to the freshwater budget of the North Atlantic. In this way, additional fresh water can be added to the ocean with little impact, until a tipping point is reached that causes the AMOC to suddenly weaken and the Northern Hemisphere to abruptly cool. A great deal of uncertainty still remains over the sensitivity of the AMOC to changes in freshwater discharge as a result of the unrealistic manner in which freshwater has historically been added to climate models. Frequently, freshwater is discharged in ocean models entirely as liquid water, but in reality a large fraction of freshwater entering the ocean is ice calving from marine glaciers (half for Antarctica and two-thirds for Greenland). To more accurately quantify AMOC sensitivity to past and future changes in freshwater input, this project developed a comprehensive iceberg model to more realistically simulate the interaction between the cryosphere and the oceans at high-latitudes. The iceberg model created is written in Fortran90 and designed to scale efficiently on High Performance Computing (HPC) clusters so that tens-of-thousands of icebergs can be simulated at any time. Experiments performed with our model showed that in the Pleistocene there would have been enormous floods of freshwater released into the North Atlantic that would have transported icebergs and meltwater along the entire east coast of the United States, as far south as Florida Keys. In addition, high-resolution, modern-day, model simulations showed that if the Greenland Ice Sheet continues to melt at its current rate then there will be a 6-fold increase in the number of icebergs drifting in the North Atlantic in the next 30 years, and that the strength of the AMOC could weaken by 3.5%.

54 ENVIRONMENTAL SCIENCES↗
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↗