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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Postdoctoral insights on mentoring excellence: a framework for best practices at Sandia National labs

The Sandia National Laboratories Strategic Plan FY24-FY27, updated for FY25, outlines Sandia’s two Big Labs-wide Goals, Accelerate Innovation and Lead in Modern Engineering. The goal of Accelerate Innovation is that “by FY27, Sandia will be a leader in scientific, engineering and operational innovation and an employer of choice for highly innovative and creative talent.” Sandia’s postdocs are leaders in innovation, well-versed in emerging techniques and cutting-edge methods, and capable of acting as a highly agile technical force across domains at the lab. As a federally funded research and development center (FFRDC), Sandia National Laboratories attracts top doctoral talent by offering a unique opportunity for postdoctoral researchers to develop at the crossroads of government, academia, and industry, working in multi-disciplinary teams and performing cutting-edge, mission-specific research that responds to immediate needs of national interest. However, this creates unique opportunities and demands of both postdoctoral appointees and the Sandia staff who act as their mentors, making mentorship key to attract talent. Since 2007 the Sandia Postdoctoral Development (SPD) Board, originally Postdoc To Professional (PD2P), a networking group at Sandia composed of a voluntary board of current postdocs and two staff liaisons, has advocated for postdoctoral development within Sandia National Labs. In this white paper, SPD board members and the Sandia Postdoctoral Development Office, organized in 2019, have come together to develop a comprehensive overview of the postdoctoral mentoring landscape at Sandia National Labs as we currently know it. By scouring various forms of data from efforts since 2018, we’ve compiled a community-derived perspective on what makes postdoctoral mentorship at Sandia unique. First, we analyze working sessions held between mentors and mentees to develop a comprehensive map of who is involved in postdoctoral mentorship at the lab and how the responsibilities are divided amongst mentors and mentees. We then combine multiple forms of data, including exit surveys, annual surveys, and community workshops, to identify the specific challenges that mentors and mentees encounter at the national lab. Finally, we use text mining and sentiment analysis to analyze mentoring award data to develop an idea of what postdocs are self-identifying as excellent mentorship within the lab. It is our goal that this white paper act as an ongoing resource to the postdoc and postdoc mentoring communities and provide a firm foundation for further conversations on the future of postdoctoral mentorship at Sandia National Labs.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1985

Typography and layout of technical reports - Survey of current practices

As part of a review of the NASA Langley Research Center scientific and technical information program, 50 technical reports from industry, research institutions, and government agencies were systematically examined and analyzed to determine current usage and practice in regard to (1) typography, including composition method, type style, type size, and margin treatment; (2) graphic design, including layout and imposition of material on the page; and (3) physical media, including paper, ink, and binding methods. The results indicate that approximately 50 percent of the reports were typeset, 70 percent used Roman (serif) type, 80 percent used 10- or 11-point type for text, 60 percent used a ragged right-hand margin, slightly more than half used paragraph indentation, 75 percent used a single-column layout, 65 percent had one or more figures or tables placed perpendicular to (not aligned with) the text, and perfect binding was the most frequently used binding method.

Pinelli, T. E.↗
NASA NTRS1991

Computational aerodynamics - The next generation

Continued advances in the various elements that comprise the field of computational fluid dynamics (CFD) are promoting a radically different approach to the aerodynamic design and analysis of aerospace vehicles and systems. The elements of CFD generally include numerical algorithm development, transition and turbulence modeling, surface modeling, and grid generation, scientific visualization and validation methodologies. This paper discusses the research progress and prospects for the future in each of these elements within NASA's CFD and Experimental Validation Program. The applicability of computational methods for the purposes of understanding complex flow phenomena, exploring aerodynamic concepts, and providing vehicle-design input is also addressed.

Hessenius, Kristin A.↗
NASA NTRS1995

Updated Panel-Method Computer Program

Panel code PMARC_12 (Panel Method Ames Research Center, version 12) computes potential-flow fields around complex three-dimensional bodies such as complete aircraft models. Contains several advanced features, including internal mathematical modeling of flow, time-stepping wake model for simulating either steady or unsteady motions, capability for Trefftz computation of drag induced by plane, and capability for computation of off-body and on-body streamlines, and capability of computation of boundary-layer parameters by use of two-dimensional integral boundary-layer method along surface streamlines. Investigators interested in visual representations of phenomena, may want to consider obtaining program GVS (ARC-13361), General visualization System. GVS is Silicon Graphics IRIS program created to support scientific-visualization needs of PMARC_12. GVS available separately from COSMIC. PMARC_12 written in standard FORTRAN 77, with exception of NAMELIST extension used for input.

Ashby, Dale L.↗
NASA NTRS2004

Lunar and Planetary Science XXXV: Human Occupation of Space: Radiation, Risk, and Refuse

The titles in this section include: 1) Discrimination of Heavy Charged Particles in a Mixed Irradiation Using Optically Stimulated Luminescence Methods 2) MARIE: Current Status and Results from 20 Months of Observations at Mars 3) Mars Surface Analog Project: Preparing for Astronauts First Hours on Mars 4) The Need for Analogue Missions in Scientific Human and Robotic Planetary Exploration 5) Space Debris in the Geosynchronous Earth Orbit: Debris Environmental Asssessment and its Implications on Cost and Benefit Analysis.

Source record↗
NASA NTRS2022

ExoVista: A Suite of Planetary System Models for Exoplanet Studies

Studies of future space- and ground-based exoplanet surveys often rely on models of planetary systems to simulate instrument response, estimate scientific yields, perform trade analyses, and study efficient observation strategies. Until now, no planetary system models contained all of the basic physics necessary to enable study with all of the major exoplanet detection methods. Here we introduce a suite of such models generated by a new tool, exoVista. The exoVista tool quickly generates thousands of models of quasi-self-consistent planetary systems around known nearby stars at scattered light wavelengths and efficiently records the position, velocity, spectrum, and physical parameters of all bodies as functions of time. The modeled planetary systems can be used to simulate surveys using the direct imaging, transit, astrometric, and radial velocity techniques, as well as the overlap of these different methods.

Christopher C Stark↗
DOE OSTI2022

4.2.1.31 Integrated Life Cycle Sustainability Analysis

This project provides the Department of Energy's Bioenergy Technologies Office (BETO) with strategic decision-support for the evaluation of its R&D portfolio by developing, validating, and applying a coherent methodology and consistent model framework to quantify the net effects of an expanding US bioeconomy. The framework fills an analysis gap previously identified by Peer Review and supports a related milestone in BETO's Multi-Year Program Plan. The framework was scoped with inputs from practitioners in academia, national laboratories, and federal agencies. The model is a top-down, economy-wide framework using a coherent methodology to compute environmental and socio-economic metrics. It is purposefully complementary to existing bottom-up, process-based techno-economic and life cycle assessment BETO tools and uses their data as inputs. Presently, the model covers several commercial and near-commercial biofuel routes and an emerging pathway for plastics upcycling. It covers temporal detail across four time-steps and is currently being expanded with a prospective modeling capability. The model has provided analyses for the Third Triennial Report to Congress (RtC3) on the environmental impacts of the Renewable Fuel Standard (RFS2), among others. As part of this project, NREL also provides scientific support to BETO in the International Energy Agency's Technology Collaboration Program on Bioenergy (IEA Bioenergy) Task 45 on Sustainability. Here, NREL evaluates and synthesizes activities that develop, compare, or apply metrics, methods, and tools to quantify sustainability effects of bioeconomy products. NREL also coordinates related national lab involvement and a BETO Working Group on Sustainable Land Management.

bioeconomy↗
DOE OSTI2023

Magnetohydrodynamics with physics informed neural operators

Abstract The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators (NOs) (PINOs) to model 2D incompressible magnetohydrodynamics (MHD) simulations. Our AI models incorporate tensor Fourier NOs as their backbone, which we implemented with the TensorLY package. Our results indicate that PINOs can accurately capture the physics of MHD simulations that describe laminar flows with Reynolds numbers Re ⩽ 250 . We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of MHD simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cooperative Research and Development Agreement With Georgetown University Report: National Institutes of Health, National Center for Advancing Translational Sciences Clinical and Translational Science Award

Oak Ridge National Laboratory (ORNL) is participating with Georgetown University (GU) as a subrecipient in response to the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) (U54 Clinical Trial Optional) funding opportunity announcement. This Cooperative Research and Development Agreement is put in to place to facilitate the development and implementation of clinical interventions that demonstrably improve human health is currently a complex, recursive, and inefficient process that leads to delays of years or decades before discoveries in biomedical research result in health benefits for patients and communities. NCATS conducts and supports research in the science of translation, to discover the mechanistic and operational principles of the intervention development and dissemination process, thereby providing the scientific foundation for improvements in translational efficiency that will accelerate the realization of interventions that improve human health. Under NCATS’ leadership, the CTSA Program supports a national network of medical research institutions called hubs. GU is the lead institution in one of the NIH hubs that was created as a result of a previous NIH CTSA. The missions of the GU have historically included the advancement of health through research in the clinical and biomedical sciences, the education of future leaders in medical and nursing practice and academia, and the provision of compassionate and scientifically competent patient care and service to the Washington, DC community and the nation. GU is the lead institution for the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), a multi-institutional partnership of medical research institutions forged from a desire to promote clinical research and translational science. Through multiple collaborations among these institutions, GHUCCTS is transforming clinical research and translational science in order to bring new scientific advances to health care. Oak Ridge National Laboratory is the Department of Energy's (DOE) largest science and energy laboratory. Managed since April 2000 by a partnership of the University of Tennessee and Battelle, ORNL was established in 1943 as a part of the secret Manhattan Project to pioneer a method for producing and separating plutonium. During the 1950s and 1960s, ORNL became an international center for the study of nuclear energy and related research in the physical and life sciences. With the creation of DOE in the 1970s, ORNL's mission broadened to include a variety of energy technologies and strategies. Today the laboratory supports the nation with a peacetime science and technology mission that is just as important as, but very different from, its role during the Manhattan Project. ORNL is home to the world's premier center for high performance supercomputing to enable scientific discovery. ORNL has extensive expertise in various areas of computer science that are uniquely situated to support GU. Additionally, ORNL’s leading computational user facilities present a unique opportunity to leverage the largest scale machines for open science in support of the stated mission of the NCATS CTSA. ORNL's partnership with GU will offer unparalleled opportunity in data analytics, deep-learning, artificial intelligence, and urban dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Preliminary Insights Into the Feasibility of Determining the Purification Date of Enriched Uranium by Direct Measurement of the 230 Th/ 234 U Ratio Using an All-Faraday Detector Configuration on the Neoma MC-ICP-MS

Rationale: Mass spectrometric measurement of the 230 Th/ 234 U ratio to calculate the purification age of enriched uranium is typically conducted via a combination of ion counters and faraday detectors, thus requiring an inter-detector calibration scheme. Here, our aim is to understand whether the pursuit of a simplified measurement scheme involving only faraday detectors is feasible. Methods: We investigate the possibility of determining U-Th model ages for two enriched uranium standards (NBL U630 and U850) by direct measurement of the 230 Th/ 234 U ratio (without chromatographic separation or isotope dilution) on a ThermoFisher Scientific Neoma MC-ICP-MS utilizing both solution and laser ablation (LA)-based sampling techniques and an all-faraday detector configuration. Results: For the solution mode analyses conducted on aliquots containing sub μg/mL total U, we produce composite average 230 Th/ 234 U model dates of May 19, 1988 (± 351 days), and March 26, 1961 (± 2.5 years) using the directly measured 230 Th/ 234 U ratios for the NBL U630 and U850 uranium standards, which have certified purification dates of June 6, 1988 (± 190 days), and December 31, 1957 (± 36.5 days), respectively. The ages produced by LA-based sampling of dried residues of the same standards deposited onto cotton TexWipes are less accurate and of poorer precision (June 23, 2004 ± 8.7 years for U630 and December 21, 1965 ± 7.9 years for U850) but still yield meaningful information in regards to the purification date. Conclusions: We believe that further refinement of the all faraday detector measurement approach to include development of a more robust Th/U relative sensitivity factor determination, signal cutoff selection, and data processing protocols will allow for this approach to be confidently applied to enriched uranium materials with unknown purification histories. Potential advantages of the method include the reduced sample handling and infrastructure requirements as well as the ability to simultaneously generate a broad picture of the uranium isotopic composition in tandem with the U-Th age determination.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1979

Scientific experiments on the flight of the 1979 biological satellite, draft plan

The various physiological, biological, radiobiological, and radiation physics experiments to be conducted onboard the 1979 biological satellite are described. These experiments deal with the effects of space flight on living organisms, measurement of radiation, and possible methods of shielding spacecraft against such radiation.

Source record↗
NASA NTRS1983

Pilot climate data system: A state-of-the-art capability in scientific data management

The Pilot Climate Data System (PCDS) was developed by the Information Management Branch of NASA's Goddard Space Flight Center to manage a large collection of climate-related data of interest to the research community. The PCDS now provides uniform data catalogs, inventories, access methods, graphical displays and statistical calculations for selected NASA and non-NASA data sets. Data manipulation capabilities were developed to permit researchers to easily combine or compare data. The current capabilities of the PCDS include many tools for the statistical survey of climate data. A climate researcher can examine any data set of interest via flexible utilities to create a variety of two- and three-dimensional displays, including vector plots, scatter diagrams, histograms, contour plots, surface diagrams and pseudo-color images. The graphics and statistics subsystems employ an intermediate data storage format which is data-set independent. Outside of the graphics system there exist other utilities to select, filter, list, compress, and calculate time-averages and variances for any data of interest. The PCDS now fully supports approximately twenty different data sets and is being used on a trial basis by several different in-house research grounds.

Smith, P. H.↗
DOE OSTI2022

EdgeAI: Machine learning via direct attached accelerator for streaming data processing at high shot rate x-ray free-electron lasers

We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting

Abstract Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improves the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to achieve desirable outcomes in the healthcare field.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

In Situ Visualization of Li-Whisker with Grating-Interferometry-Based Tricontrast X-ray Microtomography

The lithium-ion battery has demonstrated tremendous economic and social impacts. Upon battery operation under different conditions, lithium changes its chemical state and physical formation, leading to undesired side reactions, e.g., lithium dendrite growth that degrades the cell performance and causes safety concerns. In situ detection and visualization of lithium metal in functional batteries could offer insights with both scientific and industrial significance but remain a frontier challenge. In this work, we demonstrate in situ three-dimensional imaging of lithium whisker using a grating-interferometry-based tricontrast X-ray microtomography method. Our approach explicitly reveals the micromorphology of the electrochemically developed porous lithium whisker with micrometer-level spatial resolution while offering sensitivity to the nanoporosity that is smaller than the nominal resolution limit. Our result reveals valuable structural information on the lithium whisker that is otherwise inaccessible. This method is also readily applicable to a broad range of battery research, including all-solid-state batteries.

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

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗