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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MULTISTEP AND CONTINUOUS PHYSICS-INFORMED NEURAL NETWORK METHODS FOR LEARNING GOVERNING EQUATIONS AND CONSTITUTIVE RELATIONS

In this work, we investigate the applicability and relative merit of discrete and continuous versions of physics-informed neural network (PINN) methods for learning unknown governing equations or constitutive relations in a nonlinear dynamical system. In the case of unknown dynamics, entire right-hand-side (RHS) equations of the ordinary differential equations are unknown. In the case of unknown constitutive relations, however, the RHS equations are known up to the specification of constitutive relations (that may depend on the state of the system). We use a deep neural network to model unknown governing equations or constitutive relations. The discrete PINN approach combines classical multistep discretization methods for dynamical systems with neural-network-based machine learning methods. On the other hand, the continuous versions utilize deep neural networks to minimize the residual function for the continuous governing equations. We use the case of a fedbatch bioreactor system to study the effectiveness of these approaches and discuss conditions for their applicability. Our results indicate that the accuracy of the trained neural network models is much higher for the cases where we only have to learn a constitutive relation instead of all dynamics. This finding corroborates the well-known fact from scientific computing that building as much structural information as is available into an algorithm can enhance its efficiency and/or accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Open Call LDRD: Physically Informed Autoencoders for Galactic Redshift Regression

Physical constraints have been suggested to make neural network models more generalizable, act scientifically plausible, and be more data-efficient over unconstrained baselines. In this report, we present preliminary work on evaluating the effects of adding soft physical constraints to computer vision neural networks trained to estimate the conditional density of redshift on input galaxy images for the Sloan Digital Sky Survey. We introduce physically motivated soft constraint terms that are not implemented with differential or integral operators. We frame this work as a simple ablation study where the effect of including soft physical constraints is compared to an unconstrained baseline. We compare networks using standard point estimate metrics for photometric redshift estimation, as well as metrics to evaluate how faithful our conditional density estimate represents the probability over the ensemble of our test dataset. We find no evidence that the implemented soft physical constraints are more effective regularizers than augmentation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

The women in science and engineering scholars program

The Women in Science and Engineering Scholars Program provides scientifically talented women students, including those from groups underrepresented in the scientific and technical work force, with the opportunity to pursue undergraduate studies in science and engineering in the highly motivating and supportive environment of Spelman College. It also exposes students to research training at NASA Centers during the summer. The program provides an opportunity for students to increase their knowledge of career opportunities at NASA and to strengthen their motivation through exposure to NASA women scientists and engineers as role models. An extensive counseling and academic support component to maximize academic performance supplements the instructional and research components. The program is designed to increase the number of women scientists and engineers with graduate degrees, particularly those with an interest in a career with NASA.

Falconer, Etta Z.↗
NASA NTRSDate not supplied

Sample Return and Preliminary Examination Timeline for the OSIRIS-REx Mission

NASA’s OSIRIS-REx spacecraft collected a sample of asteroid (101955) Bennu on October 20, 2020, using the Touch-and-Go Sample Acquisition Mechanism (TAGSAM) [1,2,3]. After sample acquisition, the TAGSAM collection head was stowed in the Sample Return Capsule (SRC; Fig. 1) for transport back to Earth. On September 24, 2023, the OSIRIS-Rex spacecraft will fly by Earth and release the SRC, which will land at the Utah Test and Training Range (UTTR). The sample will be transported to the OSIRIS-REx curation facility, located within NASA’s Johnson Space Center (JSC), where it will be examined, cataloged, and allocated for scientific analyses. Here we describe the timeline of operations for the return, recovery, and initial characterization of the Bennu sample.

Asteroid↗
DOE OSTI2023

Data-driven Whitney forms for structure-preserving control volume analysis

Control volume analysis models physics via the exchange of generalized fluxes between subdomains. Here, we introduce a scientific machine learning framework adopting a partition of unity architecture to identify physically-relevant control volumes, with generalized fluxes between subdomains encoded via Whitney forms. The approach provides a differentiable parameterization of geometry which may be trained in an end-to-end fashion to extract reduced models from full field data while exactly preserving physics. The architecture admits a data-driven finite element exterior calculus allowing discovery of mixed finite element spaces with closed form quadrature rules. An equivalence between Whitney forms and graph networks reveals that the geometric problem of control volume learning is equivalent to an unsupervised graph discovery problem. The framework is developed for manifolds in arbitrary dimension, with examples provided for H(div) problems in $\mathbb{R}$ establishing convergence and structure preservation properties. Finally, we consider a lithium-ion battery problem where we discover a reduced finite element space encoding transport pathways from high-fidelity microstructure resolved simulations. The approach reduces the 5.89M finite element simulation to 136 elements while reproducing pressure to under 0.1% error and preserving conservation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Growth of New High-Quality and Large-Size Single Crystals via High Pressure Floating-Zone Technique

This award supports experimental materials physics research on a class of technologically relevant materials that cannot form under ambient conditions. With the complementary expertise at Louisiana State University (LSU) and infrastructure at Oak Ridge National Laboratory (ORNL), single crystals of these materials will be synthesized using the high-pressure floating-zone technique. Our ability to grow high-quality single crystals will facilitate advanced characterization of these materials and improve the scientific outcomes. In addition to in-house material characterization, the principle investigator will partner with scientists at ORNL and Brookhaven National Laboratory (BNL) to tackle materials problems using the state-of-the-art characterization techniques. This will allow us to explore the fundamental physics underlying the emergent phenomena only seen in materials prepared under extreme conditions. Our ultimate goal is to address one of the grand challenges of materials science: how do complex phenomena emerge from simple ingredients? The proposed project will also spearhead a unique opportunity to train “new breed” scientists, co-advised by experts at both LSU and U. S. Department of Energy National Laboratories. They will be essential for expanding materials physics research with ability to grow crystals, characterize their properties, and design new materials -- an imperative skill that is scarce in the U. S. scientific community.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Life Sciences Data Archive Scientific Development

The Life Sciences Data Archive will provide scientists, managers and the general public with access to biomedical data collected before, during and after spaceflight. These data are often irreplaceable and represent a major resource from the space program. For these data to be useful, however, they must be presented with enough supporting information, description and detail so that an interested scientist can understand how, when and why the data were collected. The goal of this contract was to provide a scientific consultant to the archival effort at the NASA-Johnson Space Center. This consultant (Jay C. Buckey, Jr., M.D.) is a scientist, who was a co-investigator on both the Spacelab Life Sciences-1 and Spacelab Life Sciences-2 flights. In addition he was an alternate payload specialist for the Spacelab Life Sciences-2 flight. In this role he trained on all the experiments on the flight and so was familiar with the protocols, hardware and goals of all the experiments on the flight. Many of these experiments were flown on both SLS-1 and SLS-2. This background was useful for the archive, since the first mission to be archived was Spacelab Life Sciences-1. Dr. Buckey worked directly with the archive effort to ensure that the parameters, scientific descriptions, protocols and data sets were accurate and useful.

Buckey, Jay C., Jr.↗
DOE OSTI2021

Evaluating causal‐based feature selection for fuel property prediction models

Abstract In‐silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench‐scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation‐based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal‐based feature selection might have on both model performance and identification of key molecular substructures. We found that causal‐based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.

Nguyen, Bernard↗
DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗
DOE OSTI2022

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗
DOE OSTI2025

Retrospective on decadal progress of the NOAA/NPS ocean noise reference station network

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the U.S. National Park Service (NPS), established the Ocean Noise Reference Station Network (NRS) in 2014 as a foundational component of NOAA’s Ocean Noise Strategy. This long-term effort aims to characterize baseline ocean ambient sound conditions across diverse marine environments and to inform management of noise impacts on protected species and habitats within U.S. waters. The NRS is now composed of 13 autonomous passive acoustic monitoring stations strategically positioned across the U.S. Exclusive Economic Zone (EEZ), extending from Arctic regions to tropical waters in depths ranging from 33 to 4,790 m. These locations include several National Marine Sanctuaries and National Parks, such as the recently designated Chumash Heritage National Marine Sanctuary off the coast of California. Each station is equipped to continuously sample low-frequency underwater sound at five kHz, enabling the detection of anthropogenic, geophysical, and biological acoustic signals. To date the network has sampled over 72 years of calibrated acoustic data. The spatial breadth and consistent methodology of the NRS allow for comparative acoustic assessments across diverse marine ecosystems. In addition to applied research functions, the NRS has served as a platform for education and training, offering opportunities for students to develop skills for marine science and data analysis. Looking forward, the NRS project team is focused on network expansion, improved data delivery, and broader integration with collaborative scientific initiatives. NRS recordings are being archived in partnership with NOAA’s National Centers for Environmental Information to enhance accessibility and long-term utility. Efforts are underway to develop standardized metadata and summary products to accompany raw audio files, making the data more usable for a wide range of stakeholders in the ocean science community. The NRS is evolving into a fully integrated national framework for ocean sound monitoring that supports scientific inquiry, management decision-making, national security interests, and public engagement with ocean acoustic environments.

Long-term monitoring↗
NASA NTRS1985

Psychological and interpersonal adaptation to Mars missions

The crucial importance of a thorough understanding of the psychological and interpersonal dimensions of Mars flights is indicated. This is necessary both to reduce the chances that psychological problems or interpersonal frictions will threaten the success of Mars missions and to enhance the quality of life of the people involved. Adaptation to interplanetary flight will depend on an interplay of the psychological stresses imposed by the missions and the psychological strengths and vulnerabilities of the crewmembers involved. Stresses may be reduced through environmental engineering, manipulating crew composition, and the structuring of situations and tasks. Vulnerabilities may be reduced through improving personnel selection procedures, training personnel in psychological and group dynamics, and providing mechanisms for emotional support. It is essential to supplement anecdotal evidence regarding the human side of space travel with the results of carefully conducted scientific research.

Harrison, A. A.↗
NASA NTRS1990

The 11 July 1988 Denver windshear encounters

On July 11, 1988, between 2207 and 2213 UTC (16:07 to 16:13 MDT), four successive United flights had inadvertent encounters with microburst windshear conditions while on final approach to Denver Stapleton Airport (DEN), each resulting in a missed approach, subsequent delay, and uneventful arrival. A fifth flight executed a missed approach without encountering the phenomena. There was no damage to aircraft and no passenger injuries were incurred. The term inadvertent is used within United Airlines' windshear training materials and the Federal Aviation Administration (FAA) Windshear Training Aid to connote an encounter with windshear after vigilance and cautionary practices fail to identify and afford complete avoidance of the hazardous area. No crew culpability is implied. A comprehensive investigation for scientific purposes in the study of windshear phenomenon is being conducted separately under the guidance of the FAA with involvement and cooperation from United, the National Transportation Safety Board (NTSB), National Center for Atmospheric Research (NCAR), Boeing, Douglas, NASA, and others.

Ireland, Robert L.↗
NASA NTRS2005

Genesis Recovery Processing

The Genesis spacecraft, launched in August 2001 to collect samples of the solar wind, returned to Earth on 8 September 2004. The Sample Return Capsule (SRC) failed to deploy its drogue parachute and parafoil and subsequently impacted the Utah Test and Training Range (UTTR) at an estimated 310 kph (193 mph). The goal of the Genesis mission to collect and return samples of the solar wind for precise elemental and isotopic analysis provides the scientific community with a unique set of materials to aid in understanding the origin of our solar system. The spacecraft orbited the Earth-Sun L1 point for 29 months exposing a suite of fifteen types of ultrapure, ultraclean materials in several different locations. Most of the materials were mounted on fixed or deployable wafer panels called collector arrays . A few materials were mounted as targets in the focal spot of an electrostatic mirror (the concentrator ). Other materials were strategically placed to maximize the area for solar-wind collection.

Stansbery, E. K.↗
NASA NTRS2013

Evaluating the High School Lunar Research Projects Program

The Center for Lunar Science and Exploration (CLSE), a collaboration between the Lunar and Planetary Institute and NASA s Johnson Space Center, is one of seven member teams of the NASA Lunar Science Institute (NLSI). In addition to research and exploration activities, the CLSE team is deeply invested in education and outreach. In support of NASA s and NLSI s objective to train the next generation of scientists, CLSE s High School Lunar Research Projects program is a conduit through which high school students can actively participate in lunar science and learn about pathways into scientific careers. The objectives of the program are to enhance 1) student views of the nature of science; 2) student attitudes toward science and science careers; and 3) student knowledge of lunar science. In its first three years, approximately 168 students and 28 teachers from across the United States have participated in the program. Before beginning their research, students undertake Moon 101, a guided-inquiry activity designed to familiarize them with lunar science and exploration. Following Moon 101, and guided by a lunar scientist mentor, teams choose a research topic, ask their own research question, and design their own research approach to direct their investigation. At the conclusion of their research, teams present their results to a panel of lunar scientists. This panel selects four posters to be presented at the annual Lunar Science Forum held at NASA Ames. The top scoring team travels to the forum to present their research in person.

Shaner, A. J.↗
DOE OSTI2024

Speeding up and reducing memory usage for scientific machine learning via mixed precision

Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets require significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. Here, we also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nuclear Criticality Experiments Research Center Futures: A Report of a Workshop held September 6-9, 2022, Los Alamos, NM, November, 2022

Experiments and training with critical assemblies and fissionable material (at or near the critical state) that explore reactivity phenomena are central to a number of national security challenges. From fission energy to nuclear weapons to a broad suite of scientific challenges, it is clear that additional capacity and capability are needed. The National Criticality Experiments Research Center (NCERC) marked 10 years of operations in 2021. This anniversary was an opportune time to celebrate our successes and progress, and to evaluate the remaining and emergent challenges. Against this backdrop, a workshop of approximately 140 national and international leaders in nuclear research was convened in Los Alamos, New Mexico to explore “NCERC Futures.” Therefore, the present workshop focused specifically on needed capabilities and tools to meet the research challenges in eight topical mission areas served by NCERC. As each Topical Group summarized their discussions in the out brief, it was recognized that key challenges could be met through enabling infrastructure investments and new critical assemblies. Enabling infrastructure includes staffing, additional space/buildings, developing an agile bounding safety basis, the ability to keep pace with technological advances in detectors and data acquisition systems (allowing use of those with Bluetooth™ and similar technologies), an expanded set of materials options (especially plutonium), a “Plug and Play” design and implementation mindset, and a facility that enabled free-field measurements. The new critical assemblies that were identified as having the most impact were a bare plutonium (Pu) Critical Assembly, a Horizontal Split Table (HST), a Super Comet, and a Uranium Solution Burst Assembly. NECRC is a unique, one-of-a-kind facility in the United States. If all the improvements were to be made, NCERC would enable the United States and its partners to address many important research questions related to criticality. These include but are not limited to: (1) Covering the entire neutron energy spectrum for both highly enriched uranium (HEU) and Pu in configurations for virtually all conceivable applications; (2) Performing multi-physics solution experiments and irradiations with a Uranium Solution Burst Assembly, which more closely resembles actual criticality accidents; (3) Conducting free-field experiments to make basic fission physics measurements and much cleaner benchmarks with various experimental observables; and (4) Providing more training classes and more experiments annually at greater cost efficiency enabled by additional buildings and machines and an agile, bounding, risk-balanced Safety Basis. In the end, workshop attendees enthusiastically concluded that NCERC Futures are bright and the workshop helped to identify a roadmap of capability gaps that need to be addressed. This report documents the results of those efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

97 MATHEMATICS AND COMPUTING↗