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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

HPC-Enabled Optimization of High Temperature Heat Exchangers (CRADA Final Report)

This project was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Materials Sciences, LLC, to develop a technology for design and optimization of heat exchangers using powerful desktop and laptop computers. The project was originally designated as a 12-month project, and consisted of 3# major tasks and the following 8# major deliverables: 1) CFD models of 3D heat exchangers based on existing and new geometry. 2) Validation against experimental data provided by MSC and published in the literature. 3) CFD models of 3D unit cells based on TPMS. 4) Surrogate models capable of delivering the gradients of the homogenized properties with respect to the parametrization. 5) 3D design methodology using TO algorithms. 6) Conventional reference and topology optimized designs. 7) 3D optimized designs stored in a 3D printer build format. 8) Verification of the improved performance. All of the deliverables for this project were successfully completed with two no-cost time extensions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

HPC-Enabled Optimization of High Temperature Heat Exchangers (CRADA Final Report)

This project was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Materials Sciences, LLC, to develop a technology for design and optimization of heat exchangers using powerful desktop and laptop computers. The project was originally designated as a 12-month project, and consisted of 3# major tasks and the following 8# major deliverables: 1) CFD models of 3D heat exchangers based on existing and new geometry. 2) Validation against experimental data provided by MSC and published in the literature. 3) CFD models of 3D unit cells based on TPMS. 4) Surrogate models capable of delivering the gradients of the homogenized properties with respect to the parametrization. 5) 3D design methodology using TO algorithms. 6) Conventional reference and topology optimized designs. 7) 3D optimized designs stored in a 3D printer build format. 8) Verification of the improved performance. All of the deliverables for this project were successfully completed with two no-cost time extensions.

13 HYDRO ENERGY↗
NASA NTRS1992

Correlation, Cost Risk, and Geometry

The geometric viewpoint identifies the choice of a correlation matrix for the simulation of cost risk with the pairwise choice of data vectors corresponding to the parameters used to obtain cost risk. The correlation coefficient is the cosine of the angle between the data vectors after translation to an origin at the mean and normalization for magnitude. Thus correlation is equivalent to expressing the data in terms of a non orthogonal basis. To understand the many resulting phenomena requires the use of the tensor concept of raising the index to transform the measured and observed covariant components into contravariant components before vector addition can be applied. The geometric viewpoint also demonstrates that correlation and covariance are geometric properties, as opposed to purely statistical properties, of the variates. Thus, variates from different distributions may be correlated, as desired, after selection from independent distributions. By determining the principal components of the correlation matrix, variates with the desired mean, magnitude, and correlation can be generated through linear transforms which include the eigenvalues and the eigenvectors of the correlation matrix. The conversion of the data to a non orthogonal basis uses a compound linear transformation which distorts or stretches the data space. Hence, the correlated data does not have the same properties as the uncorrelated data used to generate it. This phenomena is responsible for seemingly strange observations such as the fact that the marginal distributions of the correlated data can be quite different from the distributions used to generate the data. The joint effect of statistical distributions and correlation remains a fertile area for further research. In terms of application to cost estimating, the geometric approach demonstrates that the estimator must have data and must understand that data in order to properly choose the correlation matrix appropriate for a given estimate. There is a general feeling by employers and managers that the field of cost requires little technical or mathematical background. Contrary to that opinion, this paper demonstrates that a background in mathematics equivalent to that needed for typical engineering and scientific disciplines at the masters or doctorate level is appropriate within the field of cost risk.

Dean, Edwin B.↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
DOE OSTI2020

Borehole Disposal of Radioactive Waste in Israel - 20388

Israel is assessing borehole disposal of radioactive waste. With limited geological options for disposal, intermediate-depth borehole disposal is being considered in the arid Yamin Plain region of the northeastern Negev desert at depths of several hundred meters below ground surface in the vadose zone. Unlike deep borehole disposal of several kilometers, which relies on emplacement below the depth of recirculating groundwater, the safety case for intermediate-depth borehole disposal relies more on the aridity of the vadose zone, the robustness of the waste package, and the other engineered barriers (e.g., seals, backfill materials) in the disposal borehole. As a tradeoff, the shallower depth may allow the use of a larger diameter borehole than would be possible for a deeper borehole. To study the suitability of the Yamin Plain region for borehole disposal, a small-diameter characterization borehole is being planned to retrieve core samples and to better understand the vadose zone geomechanical and hydrogeochemical properties and percolation flux. The information from the characterization borehole will inform the safety case, and together with performance assessment analyses will help to identify key areas of uncertainty and guide future research and development activities aimed at demonstrating the feasibility of the intermediate-depth borehole disposal concept in Israel. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Moisture-driven CO 2 pump for direct air capture

Direct Air Capture, the chemical separation of carbon dioxide (CO 2 ) from air, is considered a necessary approach to generate negative carbon emissions and in turn limit global warming, though energetic and monetary costs must continue to decline to meet the needed gigaton scale. The promise of energy-efficient and continuous carbon dioxide membrane separation has only recently been investigated with the development of facilitated chemical transport and novel electrochemically driven methods. This work demonstrates the potential for continuous pumping of CO 2 from air using commercial anion exchange membranes driven solely by a water vapor gradient. A reactive transport model that couples ionic transport with moisture sensitive bicarbonate chemistry in charged polymers, also termed moisture swing sorption, was developed alongside experimentally measured equilibrium and transport properties of CO 2 and water vapor in Fumasep FAA-3 to determine the mass transport limits of such a system. The results show that the commercial membrane is kinetically limited by the moisture sensitive chemistry, enabling a CO 2 current of 1.1 μmol/m 2 • s. In diffusion limited materials, a CO 2 pumping flux can reach 11 μmol/m 2 • s in and peak performance is expected with a dry air relative humidity around 50% pumping into a saturated water vapor permeate. In all configurations, water loss becomes the major cost in such a separation and must be managed through optimal polymer design or water recovery mechanisms. The work demonstrates the applicability of a new driving force, the evaporation of water, to meet the energetic demand of CO 2 separation from air.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Divertor Plasma Detachment Control Neural Network

DivControlNN is a state-of-the-art software tool that leverages advanced machine learning techniques to predict and control divertor plasma behavior in fusion reactors. Plasma, a highly energetic and electrically charged gas, requires meticulous management to protect reactor components and maintain optimal energy production. Conventional simulation methods, although extremely detailed, typically demand extensive computational time-making them unsuitable for real-time control scenarios. DivControlNN addresses this challenge by learning from tens of thousands of high-fidelity simulations, thereby creating a rapid surrogate model that can deliver near-instantaneous predictions. At the core of its functionality is a sophisticated technique known as latent space mapping, which condenses complex, high-dimensional plasma data into a compact, lower-dimensional representation. This streamlined representation enables the system to quickly forecast essential plasma properties and determine the precise conditions required for effective detachment. Detachment is a crucial process in which the plasma is cooled before reaching the divertor plates, thereby reducing heat loads and mitigating material erosion. In recent experiments conducted on the KSTAR tokamak in South Korea, DivControlNN successfully guided the detachment process without any fine-tuning-even when applied to a new tungsten divertor configuration. By achieving a computational speed-up of over one hundred million times compared to traditional simulation methods while maintaining low prediction errors, DivControlNN stands to significantly enhance real-time control and diagnostic capabilities in future fusion reactors. This breakthrough paves the way for safer, more reliable reactor operation and represents a major advancement toward realizing fusion energy as a practical, sustainable, and clean power source.

Xu, Xueqiao [Lawrence Livermore National Laborator↗
NASA NTRS2001

Passive and Active Stabilization of Liquid Bridges in Low Gravity

Tests are planned in the low gravity environment of the International Space Station (ISS) of new methods for the suppression of the capillary instability of liquid bridges. Our suppression methods are unusual in that they are not limited to liquid bridges having very special properties and may impact a variety of low-gravity and earth-based technologies. There are two main approaches to be investigated: (1) Passive Acoustic Stabilization (PAS); and (2) Active Electrostatic Stabilization (AES). In PAS, the suppression of the mode growth is accomplished by placing the bridge in an acoustic field having the appropriate properties such that the acoustic radiation pressure automatically pulls outward on the thinnest portion of the bridge. In AES, the bridge deformation is sensed optically and counteracted by actively adjusting the electrostatic Maxwell stresses via two ring electrodes concentric with the slightly conducting bridge to offset the growth of the unstable mode. While the present work emphasizes cylindrical bridges, the methods need not be restricted to that case. The methods to be explored are relevant to the suppression of capillary instabilities in floating zone crystal growth, breakup of liquid jets and columns, bubbles, and annular films as well as the management of coolants or propellants in low-gravity.

Marston, Philip L.↗
NASA NTRSDate not supplied

Visualization and Analysis of Near Real-Time Global Cloud Composites (GCC): Integration in a Geospatial Web Mapping Application

The NASA Langley Satellite ClOud and Radiation Property retrieval System (SatCORPS) team provides tools for retrieving cloud information from operational and research meteorological imager data. LEO and GEO satellite imagers are used for fusing hourly mosaics which create the Global Cloud Composite (GCC). The GCC provides global cloud products with a low latency which can help satisfy the growing needs of the research, modeling and business communities. The Esri ArcGIS system is used to transform the GCC data into Analysis Ready Data (ARD) which can be utilized for a variety of visualization and analysis activities in support of weather diagnoses/forecasting, Earth Sciences remote sensing applications, and disaster management. Additionally, the data is enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services to be consumed via API within a web mapping application. We will present a preview of the new interactive SatCORPS GCC web mapping application and its capabilities in allowing users to access and use GIS data within it.

Web Mapping Application↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific ap- plications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Net- works (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate band- widths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer perfor- mance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application- level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher pre- diction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
DOE OSTI2022

Structural Changes in Molten Salt Fuel and/or Waste Stream Compounds Cs 2 UCl 6 and Cs 2 UO 2 Cl 4 from Room Temperature to Melting and Related Materials [Slides]

Perovskite phases have been explored in recent literature as waste forms for long term storage of radioactive waste from molten salt reactors specifically. Synthesis of new halide perovskite phases can inform these efforts as well as molten salt reactor chemistry in general. Inorganic perovskites have also shown promise as new scintillating materials for detection of X-rays and gamma rays. Modifying the structure and chemistry of these compounds, perhaps with organic components, could be used to synthesize new scintillating compounds or modify the properties of existing ones. Both U 6+ and U 4+ readily form compounds with a striking similarity to vacancy ordered perovskites (Cs 2 UO 2 Cl 4 and Cs 2 UCl 6 ). Each of these compounds is relevant to molten salt reactor chemistry and/or waste streams. These phases could from in uranium chloride salt systems or waste streams: Cs being a fission product.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

LAW Glass Formulation to Support Melter Runs with Simulants

The major objective of this work was to prepare and characterize glass formulations in order to develop and select formulations that are compliant with contractual and processing requirements to support melter tests with each of the LAW Sub-Envelopes. Glasses that cover composition ranges of interest were prepared and characterized to ensure that selected glass compositions can tolerate expected composition variations resulting from process errors without adversely affecting processing characteristics or product quality. Glass compositions that are expected to result during transitions from one waste stream to another were also prepared and characterized. Other objectives of the work included preparation and characterization of glasses with respect to the properties of interest, optimization of sulfate loading in the glasses, evaluation of ability to achieve waste loading limits, testing to demonstrate compatibility of glass melts with melter materials of construction, and identification of glass formulation issues with respect to contract specifications and processing requirements.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Sister Rod Destructive Examinations (FY23)

As a part of DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rod’s condition before dry storage and are focused on understanding overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This report provides a cumulative status report of the testing progress, data accumulated, and conclusions reached as a part of the program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Radiative cooling textiles using industry-standard particle-free nonporous micro-structured fibers

Abstract Thermal radiation is a major dissipative pathway for heat generated by the human body and offers a significant thermoregulation mechanism over a wide range of conditions. We could use this in garment design to enhance personal cooling, which can improve the wearing comfort of garments or even result in energy savings in buildings. At present, however, radiative cooling has received insufficient attention in commercial design and production of textiles for wearable garments. Textiles that efficiently transmit the radiative heat were recently demonstrated, but either do not utilize standard weaving and knitting processes for wearable garments or require substantial process modifications. Here, we demonstrate the design and implementation of large-scale radiative cooling textiles for localized cooling management and enhanced thermal comfort using industry-standard particle-free nonporous micro-structured fibers that are fully compatible with standard textile materials and production methods. The micro-structured fibers, yarns and fabrics are part of a hierarchical photonic structure design that renders the textiles highly infrared transparent (up to > 0.8) while assuring visual opacity (up to 0.99). We design radiative cooling textiles with first-principles electromagnetic methods and fabricate them using commercial textile materials and formation facilities. Our “fabless” approach is confirmed by very good quantitative agreement between design and measurements. The resulting fabrics exhibit wearability properties expected of wearable textiles, and lower skin temperature by ≥ 3 °C compared to conventional textiles, which offers the potential for > 30 % energy savings in buildings and increases wearing comfort by significantly reducing the reliance on latent heat dissipation for thermoregulation.

Catrysse, Peter B.↗
DOE OSTI2020

Interdisciplinary Workshop on the Impacts of Land Use and Land Management on Earth System Evolution, Biogeochemical Cycles, Extremes, and Inter-Sectoral Dynamics (Final Report)

The Aspen Global Change Institute (AGCI) convened a week-long interdisciplinary science workshop from September 15-20, 2019 entitled, “Impacts of Land Use and Land Management on Earth System Evolution, Biogeochemical Cycles, Extremes, and Inter-Sectoral Dynamics.” The purpose of the workshop was to advance research on the impacts of land use and land management on climate, carbon emissions, and extremes, as well as the impact of land-use change on important societal issues such as food, water, and energy security. The workshop was co-chaired by Kate Calvin (PNNL), George Hurtt (University of Maryland), and Dave Lawrence (National Center for Atmospheric Research). Co-funders of the workshop included NASA, NOAA, and travel support for European colleagues through CRESCENDO. This workshop brought together 31 professionals with expertise across biogeochemistry, climate impacts, mitigation, land use datasets, and societal impacts to coordinate on upcoming research and publications on the role of biogeochemical and biophysical properties of the Earth surface and their implications for climate, as conducted through the Land Use model Intercomparison Project (LUMIP), which is part of Phase 6 of the Coupled Model Intercomparison Project (CMIP6). A primary outcome of the meeting was the definition and planning of a series of LUMIP papers that will rely on the LUMIP and CMIP6 simulations. By the end of the meeting more than twenty papers and paper lead authors had been identified and scoped out. Around ten of these papers were submitted by the December 31, 2019 IPCC deadline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Summary of US DOE R&D Activities on Graphite Oxidation (2006–2021)

The objective of the international collaboration between United States Department of Energy (U.S.-DOE) and Generation IV International Forum (GIF) is the development of the next generation of nuclear energy systems. The current GIF Project Arrangement (PA) on Materials (2018-2022) was revised in 2019 and extended for another 10 years (2020-2030). The Work Package 1 (“Graphite”) of the extended Project Plan (PP) on Materials specifies technical tasks and High Level Deliverables for research and development (R&D) activities related to using graphite in fuel elements, reflectors, and support structures of Very High Temperature Reactors (VHTR). The graphite tasks include specification and acquisition, qualification and development of new grades, characterization of properties, and development of behavior models. Specifically, Task 1.4 (“Graphite Oxidation Behavior”) outlines planned activities related to acute oxidation by air and chronic oxidation by impurities in the helium coolant. A final report on experimental data regarding graphite oxidation behavior is scheduled for 2022 (deliverable 3.1.1.4.a). In preparation of this deliverable, this document summarizes the R&D activities funded by U.S.-DOE from 2006 (the inception of the VHTR system arrangement) through present (2021). This report is being submitted to the GIF Graphite Working Group (GWG) to serve as input for the GWG high-level deliverable to the Project Management Board (PMB) of PA on Materials. Besides U.S.-DOE, other organizations participating to Task 1.4 of the current PA on Materials are: European Commission’s Joint Research Center (JRC), Korea Atomic Energy Research Institute (KAERI), and Japan Atomic Energy Agency (JAEA). U.S.-DOE is the main contributor on graphite oxidation R&D, with 85 % commitment of total funding during 2018-2022.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2008

Self-Powered Wireless Sensors

NASA's integrated vehicle health management (IVHM) program offers the potential to improve aeronautical safety, reduce cost and improve performance by utilizing networks of wireless sensors. Development of sensor systems for engine hot sections will provide real-time data for prognostics and health management of turbo-engines. Sustainable power to embedded wireless sensors is a key challenge for prolong operation. Harvesting energy from the environment has emerged as a viable technique for power generation. Thermoelectric generators provide a direct conversion of heat energy to electrical energy. Micro-power sources derived from thermoelectric films are desired for applications in harsh thermal environments. Silicon based alloys are being explored for applications in high temperature environments containing oxygen. Chromium based p-type Si/Ge alloys exhibit Seebeck coefficients on the order of 160 micro V/K and low thermal conductance of 2.5 to 5 W/mK. Thermoelectric properties of bulk and thin film silicides will be discussed

Dynys, Fred↗