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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Goal Directed Model Inversion: A Study of Dynamic Behavior

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome 0 "would have been right if the outcome had been the desired one." The algorithm then proceeds as follows: (1) store the action that produced the wrong outcome as a "target" (2) redefine the wrong outcome as a desired goal (3) submit the new desired goal to the system (4) compare the new action with the target action and modify the system by using a suitable algorithm for credit assignment (Back propagation in our example) (5) resubmit the original goal. Prior publications by our group in this area focused on demonstrating empirical results based on the inverse kinematic problem for a simulated robotic arm. In this paper we apply the inversion process to much simpler analytic functions in order to elucidate the dynamic behavior of the system and to determine the sensitivity of the learning process to various parameters. This understanding will be necessary for the acceptance of GDMI as a practical tool.

Colombano, Silvano P.↗
DOE OSTI2026

Quantum Sensing of Displacements with Stabilized Gottesman-Kitaev-Preskill States

We demonstrate how recent protocols developed for the stabilization of Gottesman-Kitaev-Preskill states can be used for the estimation of two-quadrature displacement sensing, with sensitivities approaching the multivariate quantum Cramer-Rao bound. Thanks to the stabilization, this sensor is backaction evading and can function continuously without reset, making it well suited for the detection of itinerant signals. Additionally, we provide numerical simulations showing that the protocol can unconditionally surpass the Gaussian limit of displacement sensing with prior information, even in the presence of realistic noise. Our work shows how reservoir engineering in bosonic systems can be leveraged for quantum metrology, with potential applications in force sensing, waveform estimation, and quantum channel learning.

Labarca, Lautaro [Univ. of Sherbrooke, QC (Canada)↗
NASA NTRS1997

Lessons learned studying design issues for lunar and Mars settlements

In a study of lunar and Mars settlement concepts, an analysis was made of fundamental design assumptions in five technical areas against a model list of occupational and environmental health concerns. The technical areas included the proposed science projects to be supported, habitat and construction issues, closed ecosystem issues, the "MMM" issues (mining, material processing, and manufacturing), and the human elements of physiology, behavior, and mission approach. Four major lessons were learned. First it is possible to relate public health concerns to complex technological development in a proactive design mode, which has the potential for long-term cost savings. Second, it became very apparent that prior to committing any nation or international group to spending the billions to start and complete a lunar settlement, over the next century, that a significantly different approach must be taken from those previously proposed, to solve the closed ecosystem and "MMM" problems. Third, it also appears that the health concerns and technology issues to be addressed for human exploration into space are fundamentally those to be solved for human habitation of the Earth (as a closed ecosystem) in the 21st century. Finally, it is proposed that ecosystem design modeling must develop new tools, based on probabilistic models as a step up from closed circuit models.

Life Support Systems↗
NASA NTRSDate not supplied

Thermal, Structural, and Rotordynamics Refinements to the Superconducting Rotor of A 1.4 Mw Partially Superconducting Machine for Electrified Aircraft

NASA is developing the high efficiency megawatt motor (HEMM), a 1.4 MW partially superconducting machine, to ad-dress the need for highly efficient, lightweight, MW-class ma-chines to support aviation sustainability efforts. This paper presents progress on the thermal, structural, and rotordynamics aspects of HEMM’s superconducting rotor. Emissivity measurements of gold coated cobalt iron and titanium parts are presented and then used to correlate a thermal model of the rotor to existing data from a thermo-electrical test of the full-scale rotor. Lessons learned from the correlated model are presented, including a rank ordering of the contributors to the temperature of the superconducting coils. An updated structural design of the HEMM rotor is presented and compared to the prior design. Finite element analysis demonstrates that the updated design improves stress margins while reducing magnetic flux leakage and retaining thermal performance. The key conclusions of the rotordynamics design are presented.

electrified aircraft propulsion↗
DOE OSTI2025

An MLCommons Scientific Benchmarks Ontology

Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and less clear in pathways to impact. This paper introduces an ontology for scientific benchmarking developed through a unified, community-driven effort that extends the MLCommons ecosystem to cover physics, chemistry, materials science, biology, climate science, and more. Building on prior initiatives such as XAI-BENCH, FastML Science Benchmarks, PDEBench, and the SciMLBench framework, our effort consolidates a large set of disparate benchmarks and frameworks into a single taxonomy of scientific, application, and system-level benchmarks. New benchmarks can be added through an open submission workflow coordinated by the MLCommons Science Working Group and evaluated against a six-category rating rubric that promotes and identifies high-quality benchmarks, enabling stakeholders to select benchmarks that meet their specific needs. The architecture is extensible, supporting future scientific and AI/ML motifs, and we discuss methods for identifying emerging computing patterns for unique scientific workloads. The MLCommons Science Benchmarks Ontology provides a standardized, scalable foundation for reproducible, cross-domain benchmarking in scientific machine learning. A companion webpage for this work has also been developed as the effort evolves: https://mlcommons-science.github.io/benchmark/

Hawks, Ben [Fermilab] (ORCID:0000000157000288)↗
DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2011

Lessons Learned from Shuttle Payload Verification Loads Analysis

When a system experiences a loading environment characterized by rapidly varying forces, such as a rocket launch, a transient analysis is used to analyze the response of the system. The most common transient analysis methodology is the Coupled Loads Analysis (CLA). CLAs are used by the automotive and aerospace industry to analyze cars, trucks, planes, helicopters, spacecraft, etc. The Space Shuttle program also uses the CLA methodology to assess the compatibility of the payload with the Orbiter and the flight environment. The Space Shuttle Verification Loads Analysis (VLA) was a standardized process that started between ten and thirteen months prior to launch, and included several meetings as well as analysis by both the Shuttle Program and the payload developers. Over the course of the Space Shuttle Program, many improvements were made to the process which helped to reduce cycle time and improve manifest flexibility. There were also several issues which were never properly addressed, but a work-around would be developed to keep the process flowing. The lessons learned included automation of some processes and standardization of others, early assessments, improved documentation and better coordination with all stakeholders in the process. Lessons learned also included the limitations in the current process, and what needs to be planned for in the future to avoid the same issues.

Bruno, Erica↗
DOE OSTI2024

Probabilistic Programming for Transportable Source Characterization and Uncertainty Quantification of the North Korean Nuclear Tests 2006–2017

Here, we introduce a transportable technique to determine the yield and depth of burial (DOB) from seismic source spectra of underground nuclear explosions. We demonstrate this technique on the six declared North Korean nuclear tests. This approach derives source spectra in absolute units from regional phase (Pg) amplitudes by correcting the observations for geometric spreading, attenuation, and site amplification. We couple the source spectra and explosion source models with a probabilistic programming framework that integrates deep learning techniques and Bayesian modeling. This approach permits the exchange of information across various data categories to quantify both the data and model uncertainty. This technique stands out as an innovative use of broad‐area propagation models, making it transportable across various geologic settings. This method proves to be effective in scenarios with diverse and/or limited observational data, even when the source depth is unknown. We present new independent estimates of absolute yield and DOB that are consistent with the prior assessments, underscoring the potential of this method in enhancing transportable nuclear explosion monitoring capabilities.

58 GEOSCIENCES↗
DOE OSTI2025

Machine learning interatomic potential for predicting the thermal properties of uranium nitride

We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor potential framework. The MLIP was trained on density functional theory (DFT) data and validated against various quantities including energies, forces, elastic constants, phonon dispersion, and defect formation energies, achieving excellent agreement with DFT calculations, prior experimental results, and our thermal conductivity measurement. The potential was then employed in molecular dynamics simulations to predict key thermal properties such as melting point, thermal expansion, specific heat, and lattice thermal conductivity. To further assess model accuracy, we fabricated a UN sample and performed new thermal conductivity measurements representative of single-crystal properties, which showed strong agreement with the MLIP predictions. This work confirms the reliability and predictive capability of the developed potential for determining the thermal properties of UN.

36 - MATERIALS SCIENCE↗
NASA NTRS1997

Comparative Modeling Studies of Boreal Water and Carbon Balance

The coordination of the modeling and field efforts for an Intensive Field Campaign (IFC) may resemble the chicken and egg dilemma. This session's theme advocates that early and proactive involvement by modeling teams can produce a scientific and operational benefit for the IFC and Experiment. This talk will provide some examples and suggestions originating from the NASA funded IFC's of the FIFE First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment, Oregon Transect Ecosystem Research (OTTER) and predominately Boreal Ecosystem-Atmosphere Study (BOREAS) Experiments. In February 1994 and prior to the final selection of the BOREAS study sites, a group of funded BOREAS investigators agreed to run their models with data for five community types representing the proposed tower flux sites. All participating models were given identical initial values and boundary conditions and driven with identical climate data. The objectives of the intercomparison exercise were: 1) compare simulation results of participating terrestrial, hydrological, and atmospheric models over selected time frames; 2) learn about model behavior and sensitivity to estimated boreal site and vegetation definitions; 3) prioritize BOREAS field data collection efforts supporting modeling studies; 4) identify individual model deficiencies as early as possible. Out of these objectives evolved some important coordination and science issues for the BOREAS Experiment that can be generalized to IFCs and long term archiving of the data. Some problems are acceptable because they are endemic to maintaining fair and open competition prior to the peer review process. Others are logistical and addressable through application of planning, management, and information sciences. This investigator has identified one source of measurement and model incompatibility that is manifest in the IFC scaling approach. Although intuitively obvious, scaling problems are already more formally defined in the Geography literature. An example of the scaling problem will be demonstrated with Vegetation/Ecosystem Mapping and Analysis Project (VEMAP) and OTTER data.

Coughlan, J.↗
DOE OSTI2025

Coincident learning for beam-based rf station fault identification using phase information at the SLAC linac coherent light source

Anomalies in radio-frequency (rf) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC’s Linac Coherent Light Source (LCLS). Detecting these anomalies is challenging due to the complexity of accelerator systems, high data volume, and scarcity of labeled fault data. Prior work identified faults using beam-based detection, combining rf amplitude and beam position monitor data. Due to the simplicity of the rf amplitude data, classical methods are sufficient to identify faults, but the recall is constrained by the low-frequency and asynchronous characteristics of the data. In this work, we leverage high-frequency, time-synchronous rf phase data to enhance anomaly detection in the LCLS accelerator. Due to the complexity of phase data, classical methods fail, and we instead train deep neural networks within the Coincident Anomaly Detection (CoAD) framework. We find that applying CoAD to phase data detects nearly 3 times as many anomalies as when applied to amplitude data, while achieving broader coverage across rf stations. Furthermore, the rich structure of phase data enables us to cluster anomalies into distinct physical categories. Through the integration of auxiliary system status bits, we link clusters to specific fault signatures, providing additional granularity for uncovering the root cause of faults. We also investigate interpretability via Shapley values, confirming that the learned models focus on the most informative regions of the data and providing insight for cases where the model makes mistakes. This work demonstrates that phase-based anomaly detection for rf stations improves both diagnostic coverage and root cause analysis in accelerator systems and that deep neural networks are essential for effective analysis.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2024

Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data

Abstract Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum‐group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 fcc slabs with those compositions including all three low‐index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine‐tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy.

Chemistry↗
DOE OSTI2026

Structure prediction of porous organic crystals

In this work, we explore the possibility of applying automated crystal structure prediction to reproduce the experimentally identified metastable porous polymorphs. Using our recently developed High-Throughput Organic Crystal Structure Prediction ( HTOCSP ) framework, we conducted a systematic study on five representative organic crystalline systems including hydrogen-bonded frameworks (HOFs), featured by the presence of significant porosity, in conjunction with different choices of energy models from classical, machine learning force fields, tight binding to density functional theory. Our results suggest that the current structure generation framework, with careful selection of symmetry conditions, is likely to generate rather complex and abundant metastable crystal candidates for porous crystals. In conjunction with the recent advance in universal machine learning force fields, it becomes possible to identify experimental structures as the energetically favorable candidates from a simple energy versus density analysis, thus paving the way for computational design of complex porous materials with the target systems prior to the experimental synthesis and characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An evaluation of GPT models for phenotype concept recognition

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation. The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations. The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches. Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Lost in Translation: the Case for Integrated Testing

The building of a spacecraft is complex and often involves multiple suppliers and companies that have their own designs and processes. Standards have been developed across the industries to reduce the chances for critical flight errors at the system level, but the spacecraft is still vulnerable to the introduction of critical errors during integration of these systems. Critical errors can occur at any time during the process and in many cases, human reliability analysis (HRA) identifies human error as a risk driver. Most programs have a test plan in place that is intended to catch these errors, but it is not uncommon for schedule and cost stress to result in less testing than initially planned. Therefore, integrated testing, or "testing as you fly," is essential as a final check on the design and assembly to catch any errors prior to the mission. This presentation will outline the unique benefits of integrated testing by catching critical flight errors that can otherwise go undetected, discuss HRA methods that are used to identify opportunities for human error, lessons learned and challenges over ownership of testing will be discussed.

Young, Aaron↗
DOE OSTI2021

Machine Learning Using Open Data Sources for Detection of Nuclear Proliferation Activities (U)

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics (SCAIDA) at Virginia Polytechnic Institute and State University (VT) and funded by the Department of Energy’s (DOE) Defense Nuclear Nonproliferation Research and Development, began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on large-scale open data sources to identify new, developing, and/or undeclared nuclear programs. Using the announcement in May 2018 of the proposed Savannah River Plutonium Processing Facility (SRPPF) as a test subject, the goal of this 2-year project is to forecast the SRPPF using only data prior to May 2018. The project work is split into a preliminary prototype development for the first year with an initial evaluation of viability followed by the second year of development to create an integrated prototype system and more extensive performance evaluation. This report documents the results of the preliminary-phase tasks.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.

42 ENGINEERING↗