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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Building Climate Resilience at NASA Ames

NASA Ames Research Center, located at the southern end of the San Francisco Bay (SFB) estuary has identified three primary vulnerabilities to changes in climate. The Ames Climate Adaptation Science Investigator (CASI) workgroup has studied each of these challenges to operations and the potential exposure of infrastructure and employees to an increased frequency of hazards. Sea level rise inundation scenarios for the SFB Area generally refer to projected scenarios in mean sea level rather than changes in extreme tides that could occur during future storm conditions. In the Summer of 2014, high resolution 3-D mapping of the low lying portion of Ames was performed. Those data are integrated with improved sea level inundation scenarios to identify the buildings, basements and drainage systems potentially affected. We will also identify the impacts of sea level and storm surge effects on transportation to and from the Center. This information will help Center Management develop future Master Plans. Climate change will also lead to changes in temperature, storm frequency and intensity. These changes have potential impacts on localized floods and ecosystems, as well as on electricity and water availability. Over the coming decades, these changes are going to be imposed on top of ongoing land use and land cover changes, especially those deriving from continued urbanization and increase in impervious surface areas. These coupled changes have the potential to create a series of cascading impacts on ecosystems, including changes in primary productivity and disturbance of hydrological properties and increased flood risk.The majority of the electricity used at Ames is supplied by hydroelectric dams, which will be influenced by reductions in precipitation or changes in the timing or phase of precipitation which reduces snow pack. Coupled with increased demand for summertime air conditioning and other cooling needs, NASA Ames is at risk for electricity shortfalls. To assess the anticipated energy usage as climate changes, the Ames CASI team is collecting historical energy usage data from Ames facilities, historical weather data, and projected future weather parameters from the CASI Climate subgroup. This data will be incorporated into the RETScreen model to predict how energy usage at Ames will change over the coming century.

climate↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
DOE OSTI2020

SN 2018fif: The Explosion of a Large Red Supergiant Discovered in Its Infancy by the Zwicky Transient Facility

High-cadence transient surveys are able to capture supernovae closer to their first light than ever before. Applying analytical models to such early emission, we can constrain the progenitor stars' properties. Here, we present observations of SN 2018fif (ZTF 18abokyfk). The supernova was discovered close to first light and monitored by the Zwicky Transient Facility (ZTF) and the Neil Gehrels Swift Observatory. Early spectroscopic observations suggest that the progenitor of SN 2018fif was surrounded by relatively small amounts of circumstellar material compared to all previous cases. This particularity, coupled with the high-cadence multiple-band coverage, makes it a good candidate to investigate using shock-cooling models. We employ the SOPRANOS code, an implementation of the model by Sapir & Waxman and its extension to early times by Morag et al. Compared with previous implementations, SOPRANOS has the advantage of including a careful account of the limited temporal validity domain of the shock-cooling model as well as allowing usage of the entirety of the early UV data. We find that the progenitor of SN 2018fif was a large red supergiant with a radius of $R={744.0}_{-128.0}^{+183.0}\,{R}_{\odot }$ and an ejected mass of ${M}_{\mathrm{ej}}={9.3}_{-5.8}^{+0.4}\,{M}_{\odot }$. Our model also gives information on the explosion epoch, the progenitor's inner structure, the shock velocity, and the extinction. The distribution of radii is double-peaked, with smaller radii corresponding to lower values of the extinction, earlier recombination times, and a better match to the early UV data. If these correlations persist in future objects, denser spectroscopic monitoring constraining the time of recombination, as well as accurate UV observations (e.g., with ULTRASAT), will help break the extinction/radius degeneracy and independently determine both.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)

Abstract. Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes (i.e., phenomena) such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.

58 GEOSCIENCES↗
NASA NTRS2020

SN 2018fif: The Explosion of a Large Red Supergiant Discovered in its Infancy by the Zwicky Transient Facility

High-cadence transient surveys are able to capture supernovae closer to their first light than ever before. Applying analytical models to such early emission, we can constrain the progenitor stars' properties. In this paper, we present observations of SN 2018fif (ZTF 18abokyfk). The supernova was discovered close to first light and monitored by the Zwicky Transient Facility (ZTF) and the Neil Gehrels Swift Observatory. Early spectroscopic observations suggest that the progenitor of SN 2018fif was surrounded by relatively small amounts of circumstellar material compared to all previous cases. This particularity, coupled with the high-cadence multiple-band coverage, makes it a good candidate to investigate using shock-cooling models. We employ the SOPRANOS code, an implementation of the model by Sapir & Waxman and its extension to early times by Morag et al. Compared with previous implementations, SOPRANOS has the advantage of including a careful account of the limited temporal validity domain of the shock-cooling model as well as allowing usage of the entirety of the early UV data. We find that the progenitor of SN 2018fif was a large red supergiant with a radius of R = 744.0 (sup +183.0) (sub -128.0) R⨀ and an ejected mass of M(sub ej) = 9.3 (sup +0.4)(sub -5.8) M⨀. Our model also gives information on the explosion epoch, the progenitor's inner structure, the shock velocity, and the extinction. The distribution of radii is double-peaked, with smaller radii corresponding to lower values of the extinction, earlier recombination times, and a better match to the early UV data. If these correlations persist in future objects, denser spectroscopic monitoring constraining the time of recombination, as well as accurate UV observations (e.g., with ULTRASAT), will help break the extinction/radius degeneracy and independently determine both.

Supernovae↗
DOE OSTI2021

Empirical Estimation of Shortest Route Length along U.S. Interstate Highways Based on Great Circle Distance

In this study, 98 regression models were specified for easily estimating shortest distances based on great circle distances along the U.S. interstate highways nationwide and for each of the continental 48 states. This allows transportation professionals to quickly generate distance, or even distance matrix, without expending significant efforts on complicated shortest path calculations. For simple usage by all professionals, all models are present in the simple linear regression form. Only one explanatory variable, the great circle distance, is considered to calculate the route distance. For each geographic scope (i.e., the national or one of the states), two different models were considered, with and without the intercept. Based on the adjusted R-squared, it was observed that models without intercepts generally have better fitness. Additionally, all these models generally have good fitness with the linear regression relationship between the great circle distance and route distance. At the state level, significant variations in the slope coefficients between the state-level models were also observed. Furthermore, a preliminary analysis of the effect of highway density on this variation was conducted.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

The role of above-code labeling programs in reducing CO 2 e emissions in residential buildings

Residential buildings account for 15 % of total U.S. carbon dioxide (CO 2 ) emissions. Voluntary, above-code labeling programs, such as the ENERGY STAR and Zero Energy Ready Home programs, can play a crucial role in reducing the carbon footprint of the new construction residential building sector by encouraging adoption of more energy-efficient and lower emissions building practices and technologies. It's important for the industry to understand the carbon dioxide equivalent (CO 2 e) and energy cost savings potential of these programs. Here, to estimate the potential CO 2 e emissions savings for homes built to above-code labeling program levels, it is imperative to have reliable and accurate tools for estimating emissions from buildings. The open-source OpenStudio-ERI workflow employs detailed physics-based building energy modeling (BEM) to estimate energy usage and operational CO 2 e emissions. In this study we use the OpenStudio-ERI workflow and regional, time-varying emissions factors from the Cambium database to compare the CO 2 e emissions and energy costs of homes built to performance levels required by these above-code labeling programs relative to homes built to model energy codes. We present the results of our analysis and provide a range of emissions and annual energy cost savings for the above-code labeling programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A phenomenological rate of injection model for predicting fuel injection with application to mixture formation in light-duty diesel engines

Fuel injection rate laws are one of the most important pieces of information needed when modeling engine combustion with computational fluid dynamics. In this study, a simple phenomenological model of a common-rail injector was developed and calibrated for the Bosch CRI2.2 platform. The model requires three tunable parameter fits, making it relatively easy to calibrate and suitable for injector modeling when high-fidelity information about the internal injector’s geometry and electrical circuit details are not available. Each injection pulse is modeled as a sequence of up to four stages: an injection needle mechanical opening transient, a full-lift viscous flow inertial transient, a Bernoulli steady-state stage, and a needle descent transient. Parameters for each stage are obtained as polynomial fits from measured injection rate properties. The model enforces total injected mass, and the intermediate stages are only introduced if the injection pulse duration is long enough. Experimental rates of injection from two separate campaigns on the same injector were used to calibrate the model. The model was first validated against measured injection rate laws featuring pilot injections, short partially premixed combustion pulses, and conventional diesel combustion injection strategies. Then, it was employed as an input to engine computational fluid dynamics simulations, which were run to simulate experiments of mixture formation in an optically accessible light-duty diesel engine. It was found that, though simple, this model is capable of predicting both pilot and main injection pulse mass flow rates well: the simulations yielded accurate predictions of in-cylinder equivalence ratio distributions from injection strategies for both partially premixed combustion and pilot injections. Also, once calibrated, the model produced appropriate results for a wide range of injected mass and rail pressure values. Finally, it was observed that usage of such a relatively simple model can be a good choice when high-fidelity injection rate input and highly detailed information of the injector’s geometry and operation are not available, particularly as noticeable discrepancies can be present also among different experimental campaigns on similar hardware.

42 ENGINEERING↗
NASA NTRS1987

File Usage Analysis and Resource Usage Prediction: a Measurement-Based Study

A probabilistic scheme was developed to predict process resource usage in UNIX. Given the identity of the program being run, the scheme predicts CPU time, file I/O, and memory requirements of a process at the beginning of its life. The scheme uses a state-transition model of the program's resource usage in its past executions for prediction. The states of the model are the resource regions obtained from an off-line cluster analysis of processes run on the system. The proposed method is shown to work on data collected from a VAX 11/780 running 4.3 BSD UNIX. The results show that the predicted values correlate well with the actual. The coefficient of correlation between the predicted and actual values of CPU time is 0.84. Errors in prediction are mostly small. Some 82% of errors in CPU time prediction are less than 0.5 standard deviations of process CPU time.

Devarakonda, Murthy V.-S.↗
NASA NTRS1987

Predictability of process resource usage: A measurement-based study of UNIX

A probabilistic scheme is developed to predict process resource usage in UNIX. Given the identity of the program being run, the scheme predicts CPU time, file I/O, and memory requirements of a process at the beginning of its life. The scheme uses a state-transition model of the program's resource usage in its past executions for prediction. The states of the model are the resource regions obtained from an off-line cluster analysis of processes run on the system. The proposed method is shown to work on data collected from a VAX 11/780 running 4.3 BSD UNIX. The results show that the predicted values correlate well with the actual. The correlation coefficient between the predicted and actual values of CPU time is 0.84. Errors in prediction are mostly small. Some 82% of errors in CPU time prediction are less than 0.5 standard deviations of process CPU time.

Devarakonda, Murthy V.↗
NASA NTRS1989

Predictability of process resource usage - A measurement-based study on UNIX

A probabilistic scheme is developed to predict process resource usage in UNIX. Given the identity of the program being run, the scheme predicts CPU time, file I/O, and memory requirements of a process at the beginning of its life. The scheme uses a state-transition model of the program's resource usage in its past executions for prediction. The states of the model are the resource regions obtained from an off-line cluster analysis of processes run on the system. The proposed method is shown to work on data collected from a VAX 11/780 running 4.3 BSD UNIX. The results show that the predicted values correlate well with the actual. The correlation coefficient betweeen the predicted and actual values of CPU time is 0.84. Errors in prediction are mostly small. Some 82 percent of errors in CPU time prediction are less than 0.5 standard deviations of process CPU time.

Devarakonda, Murthy V.↗
DOE OSTI2025

Core-edge integrated predictive studies of ST40 and NSTX plasmas with the scrape-off layer box model

The ability to model the interplay between the core and edge of tokamak plasmas is crucial to designing both the plasma operating scenario of a fusion pilot plant and the design of the tokamak itself. Scrape-off-layer (SOL) models that are tailored to integrated scenario modeling need to have fast turn-around time and minimal computational burden to enable wide parameter-space coverage for design scoping. The SOL 0-D Box model is a reduced SOL model based on global power and particle balance that captures the essential physics of SOL transport with little computational cost. The usage of the 0-D Box model in core-edge coupled simulations has been demonstrated in both interpretive and predictive modes on a variety of devices. This paper presents a sensitivity study of the 0-D Box model to the input SOL heat-flux width for an ST40 plasma. This study demonstrates that accurate prediction of this width is crucial to predicting global performance parameters of a plasma scenario, such as energy confinement time and flux consumption. We also present an extension of the Box model to 1-D to allow for parallel variation of plasma parameters along the magnetic field lines. The 1-D Box model is then compared with SOLPS-ITER simulations of an NSTX plasma. Advantages and limitations of the Box model are discussed, and future directions are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Hydropower Advantages over Batteries in Energy Storage of Off-Grid Systems: A Case Study

Microgrids are decentralized power production systems, where the energy production and consumption are very close to each other. Microgrids generally exploit renewable energy sources, encountering a problem of storage, as the power production from solar and wind is intermittent. This research presents a new integrated methodology and discusses a comparison of batteries and pumped storage hydropower (PSH) as energy storage systems with the integration of wind and solar PV energy sources, which are the major upcoming technologies in the renewable energy sector. We implemented the simulator and optimizer model (HOMER), which develops energy availability usage to obtain optimized renewable energy integration in the microgrid, showing its economic added value. Two scenarios are run with this model—one considers batteries as an energy storage technology and the other considers PSH—in order to obtain the best economic and technical results for the analyzed microgrid. The economic analysis showed a lower net present cost (NPC) and levelized cost of energy (LCOE) for the microgrid with PSH. The results showed that the microgrid with the storage of PSH was economical, with an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh, in comparison with the scenario with batteries, which had an NPC of 95.2 M€ and an LCOE of 0.786 €/kWh. The role of storage was understood by differentiating the data into different seasons, using a Python model. Furthermore, a sensitivity analysis was conducted by varying the capital cost multiplier of solar PV and wind turbines to obtain the best optimal economic solutions.

Guruprasad, Prajwal↗
NASA NTRS1995

On the Importance of the Dynamics of Discretizations

It has been realized recently that the discrete maps resulting from numerical discretizations of differential equations can possess asymptotic dynamical behavior quite different from that of the original systems. This is the case not only for systems of Ordinary Differential Equations (ODEs) but in a more complicated manner for Partial Differential Equations (PDEs) used to model complex physics. The impact of the modified dynamics may be mild and even not observed for some numerical methods. For other classes of discretizations the impact may be pronounced, but not always obvious depending on the nonlinear model equations, the time steps, the grid spacings and the initial conditions. Non-convergence or convergence to periodic solutions might be easily recognizable but convergence to incorrect but plausible solutions may not be so obvious - even for discretized parameters within the linearized stability constraint. Based on our past four years of research, we will illustrate some of the pathology of the dynamics of discretizations, its possible impact and the usage of these schemes for model nonlinear ODEs, convection-diffusion equations and grid adaptations.

Sweby, Peter K.↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
DOE OSTI2024

Association Kinetics for Perfluorinated n -Alkyl Radicals

Radical-radical reaction channels are important in the pyrolysis and oxidation chemistry of perfluoroalkyl substances (PFAS). In particular, unimolecular dissociation reactions within unbranched n-perfluoroalkyl chains, and their corresponding reverse barrierless association reactions, are expected to be significant contributors to the gas-phase thermal decomposition of families of species such as perfluorinated carboxylic acids and perfluorinated sulfonic acids. Unfortunately, experimental data for these reactions are scarce and uncertain. Furthermore, obtaining reliable theoretical predictions for such reactions is a laborious and computationally intensive task. Here, in this work, the chemical kinetics of the various association/decomposition reactions producing/decomposing the C 2 -C 4 series of unbranched n-perfluoroalkanes (C 2 F 6 , C 3 F 8 , and C 4 F 10 ) are examined using state-of-the-art ab initio transition-state-theory-based master-equation calculations. The variable-reaction-coordinate transition-state theory (VRC-TST) formalism is employed in computing the microcanonical and canonical rates for the association reactions. Reaction thermochemistry is obtained via composite quantum chemistry calculations and the laddering of error-canceling reaction schemes via a connectivity-based hierarchy approach employing ANL1/ANL0-style reference energies. Lennard-Jones collision model parameters for the considered systems were estimated by a direct dynamics approach, and collisional energy transfer parameters were obtained from analogies to systems of similar size and heavy-atom connectivity. A one-dimensional master equation approach was used to convert the microcanonical rate coefficients from the VRC-TST analysis into temperature- and pressure-dependent rate constants for the association reactions and the reverse dissociation reactions. The data are reported in standardized formats for usage in comprehensive chemical kinetic models for PFAS thermal destruction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗