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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations

Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States (US) with a comprehensive set of extreme precipitation indices, including an assessment of precipitation frequency, intensity and spatial structure. The observations exhibit significant differences in their estimates of area-average intensity distributions and spatial patterns of the mean and extremes of precipitation over the US. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly in comparison to gauge-based data. Also, the representation of the extreme precipitation indices by the CMIP6 models is better in the summer than winter. Although the ‘standard’ horizontal-resolution can vary significantly across CMIP6 models, from ~0.7° to ~2.8°, we find that resolution is not a good indicator of model performance. Overall, our results highlight common biases in CMIP6 models and demonstrate that no single model is consistently the most reliable across all indices.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Space Suit Performance: Methods for Changing the Quality of Quantitative Data

NASA is currently designing a new space suit capable of working in deep space and on Mars. Designing a suit is very difficult and often requires trade‐offs between performance, cost, mass, and system complexity. To verify that new suits will enable astronauts to perform to their maximum capacity, prototype suits must be built and tested with human subjects. However, engineers and flight surgeons often have difficulty understanding and applying traditional representations of human data without training. To overcome these challenges, NASA is developing modern simulation and analysis techniques that focus on 3D visualization. Early understanding of actual performance early on in the design cycle is extremely advantageous to increase performance capabilities, reduce the risk of injury, and reduce costs. The primary objective of this project was to test modern simulation and analysis techniques for evaluating the performance of a human operating in extra‐vehicular space suits.

Cowley, Matthew↗
DOE OSTI2022

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning-based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non-negative matrix factorization to enable learning of a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub-images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. Importantly, this yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.

36 MATERIALS SCIENCE↗
NASA NTRS1987

A spectral study of the persistent X-ray flux from 4 U/MXB 1636-53

A spectral analysis of the persistent X-ray flux from the X-ray burst source 4 U/MXB 1636-53, which was continuously observed with EXOSAT for about 25 h in May, 1984, is presented. It is found that no standard one-component spectral model provides an acceptable representation of all the data. However, almost all 'complex one-component' and 'two-component' models do accurately fit the data. A blackbody plus a 'multicolor' disk spectrum does not fit these data. In addition, some 'two-component' spectral models can be fitted to the data with more than one set of different parameter values. Based on these results, it is suggested that the various physical scenarios that have been proposed to interpret 'two-component' spectra of low-mass X-ray binaries may be based on mathematical parameterizations which may not provide unique representations of the data. If this is the case, the reported 'two-component' mathematical fits may not have any true physical significance.

Vacca, W. D.↗
NASA NTRS1977

SAS-2 observations of the diffuse gamma radiation in the galactic latitude interval from 10 to 90 deg in both hemispheres

An analysis of all the second Small Astronomy Satellite (SAS-2) gamma-ray data for galactic latitudes higher than 10 deg in both hemispheres has shown that the intensity varies with galactic latitude, being larger near 10 deg than 90 deg. For energies above 100 MeV the gamma-ray data are consistent with a latitude distribution of the form I(b) = C1 + C2/sin b, with the second term being dominant. This result suggests that the radiation above 100 MeV is coming largely from local regions of the galactic disk. Between 35 and 100 MeV, a similar equation is also a good representation of the data, but here the two terms are comparable. These results indicate that the diffuse radiation above 35 MeV consists of two parts, one with a relatively hard galactic component and the other an isotropic steep spectral component which extrapolates back well to the low-energy (less than 10 MeV) diffuse radiation. The steepness of the diffuse isotropic component places significant constraints on possible theoretical models of this radiation.

Fichtel, C. E.↗
NASA NTRS2011

Simulation of Acoustics for Ares I Scale Model Acoustic Tests

The Ares I Scale Model Acoustics Test (ASMAT) is a series of live-fire tests of scaled rocket motors meant to simulate the conditions of the Ares I launch configuration. These tests have provided a well documented set of high fidelity acoustic measurements useful for validation including data taken over a range of test conditions and containing phenomena like Ignition Over-Pressure and water suppression of acoustics. To take advantage of this data, a digital representation of the ASMAT test setup has been constructed and test firings of the motor have been simulated using the Loci/CHEM computational fluid dynamics software. Results from ASMAT simulations with the rocket in both held down and elevated configurations, as well as with and without water suppression have been compared to acoustic data collected from similar live-fire tests. Results of acoustic comparisons have shown good correlation with the amplitude and temporal shape of pressure features and reasonable spectral accuracy up to approximately 1000 Hz. Major plume and acoustic features have been well captured including the plume shock structure, the igniter pulse transient, and the ignition overpressure.

Putnam, Gabriel↗
DOE OSTI2017

Analysis of transient fission gas behaviour in oxide fuel using BISON and TRANSURANUS

The modelling of fission gas behaviour is a crucial aspect of nuclear fuel analysis in view of the related effects on the thermo-mechanical performance of the fuel rod, which can be particularly significant during transients. Experimental observations indicate that substantial fission gas release (FGR) can occur on a small time scale during transients (burst release). To accurately reproduce the rapid kinetics of burst release in fuel performance calculations, a model that accounts for non-diffusional mechanisms such as fuel micro-cracking is needed. In this work, we present and assess a model for transient fission gas behaviour in oxide fuel, which is applied as an extension of diffusion-based models to allow for the burst release effect. The concept and governing equations of the model are presented, and the effect of the newly introduced parameters is evaluated through an analytic sensitivity analysis. Then, the model is assessed for application to integral fuel rod analysis. The approach that we take for model assessment involves implementation in two structurally different fuel performance codes, namely, BISON (multi-dimensional finite element code) and TRANSURANUS (1.5D semi-analytic code). The model is validated against 19 Light Water Reactor fuel rod irradiation experiments from the OECD/NEA IFPE (International Fuel Performance Experiments) database, all of which are simulated with both codes. The results point out an improvement in both the qualitative representation of the FGR kinetics and the quantitative predictions of integral fuel rod FGR, relative to the canonical, purely diffusion-based models, with both codes. The overall quantitative improvement of the FGR predictions in the two codes is comparable. Furthermore, calculated radial profiles of xenon concentration are investigated and compared to experimental data, demonstrating the representation of the underlying mechanisms of burst release by the new model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2011

Validation and Simulation of ARES I Scale Model Acoustic Test -1- Pathfinder Development

The Ares I Scale Model Acoustics Test (ASMAT) is a series of live-fire tests of scaled rocket motors meant to simulate the conditions of the Ares I launch configuration. These tests have provided a well documented set of high fidelity measurements useful for validation including data taken over a range of test conditions and containing phenomena like Ignition Over-Pressure and water suppression of acoustics. To take advantage of this data, a digital representation of the ASMAT test setup has been constructed and test firings of the motor have been simulated using the Loci/CHEM computational fluid dynamics software. Within this first of a series of papers, results from ASMAT simulations with the rocket in a held down configuration and without water suppression have then been compared to acoustic data collected from similar live-fire tests to assess the accuracy of the simulations. Detailed evaluations of the mesh features, mesh length scales relative to acoustic signals, Courant-Friedrichs-Lewy numbers, and spatial residual sources have been performed to support this assessment. Results of acoustic comparisons have shown good correlation with the amplitude and temporal shape of pressure features and reasonable spectral accuracy up to approximately 1000 Hz. Major plume and acoustic features have been well captured including the plume shock structure, the igniter pulse transient, and the ignition overpressure. Finally, acoustic propagation patterns illustrated a previously unconsidered issue of tower placement inline with the high intensity overpressure propagation path.

Putnam, G. C.↗
DOE OSTI2021

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences.

14 SOLAR ENERGY↗
NASA NTRS1977

SAS-2 observations of the diffuse gamma radiation in the galactic latitude interval 10 deg absolute b or equal to 90 deg

An analysis of all of the second small astronomy satellite gamma-ray data for galactic latitudes with the absolute value of b 10 deg has shown that the intensity varies with galactic latitude, being larger near 10 deg than 90 deg. For energies above 100 MeV the gamma-ray data are consistent with a latitude distribution of the form I(b) = C sub 1 + C sub 2/sin b, with the second term being dominant. This result suggests that the radiation above 100 MeV is coming largely from local regions of the galactic disk. Between 35 and 100 MeV, a similar equation is also a good representation of the data, but here the two terms are comparable. These results indicate that the diffuse radiation above 35 MeV consists of two parts, one with a relatively hard galactic component and the other an isotropic, steep spectral component which extrapolates back well to the low energy diffuse radiation. The steepness of the diffuse isotropic component places significant constraints on possible theoretical models of this radiation.

Fichtel, C. E.↗
DOE OSTI2023

Representations of Materials for Machine Learning

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Feasibility of automated dropsize distributions from holographic data using digital image processing techniques

An automated technique for measuring particle diameters and their spatial coordinates from holographic reconstructions is being developed. Preliminary tests on actual cold-flow holograms of impinging jets indicate that a suitable discriminant algorithm consists of a Fourier-Gaussian noise filter and a contour thresholding technique. This process identifies circular as well as noncircular objects. The desired objects (in this case, circular or possibly ellipsoidal) are then selected automatically from the above set and stored with their parametric representations. From this data, dropsize distributions as a function of spatial coordinates can be generated and combustion effects due to hardware and/or physical variables studied.

Feinstein, S. P.↗
DOE OSTI2019

North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defned based on the characteristic spatial variations of a meteorological feld or on the remote circulation response to a known forcing, LSMPs are defned relative to the occurrence of a specifc phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary infuence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to defnitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many diferent defnitions of “extreme” are in use, making it difcult to directly compare diferent studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identifed and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Barlow, Mathew↗
DOE OSTI2022

VizBrick: A GUI-based Interactive Tool for Authoring Semantic Metadata for Building Datasets

Brick ontology is a unified semantic metadata schema to address the stand-ardization problem of buildings' physical, logical, and virtual assets and the relationships between them. Creating a Brick model for a building dataset means that the dataset's contents are semantically described using the standard terms defined in the Brick ontology. It will enable the benefits of data standardization, without having to recollect or reorganize the data and opens the possibility of automation leveraging the machine readability of the semantic metadata. The problem is that authoring Brick models for building datasets often requires knowledge of semantic technology (e.g., on-tology declarations and RDF syntax) and leads to repeated manual trial and error processes, which can be time-consuming and challenging to do with-out an interactive visual representation of the data. We developed VizBrick, a tool with a graphical user interface that can assist users in creating Brick models visually and interactively without having to understand the Re-source Description Framework (RDF) syntax. VizBrick provides handy ca-pabilities such as keyword search for easy find of relevant brick concepts and relations to their data columns and automatic suggestions of concept mapping. In this demonstration, we present a use-case of VizBrick to show-case how a Brick model can be created for a real-world building dataset.

Lee, Sangkeun (Matt)↗
DOE OSTI2019

Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers

High–dimensional datasets are becoming ubiquitous in many applications and therefore unsupervised tensor methods to interrogate them are needed. Here, we report a new unsupervised machine learning (ML) approach (NTFk) based on nonnegative tensor factorization integrated with a custom k–means clustering. We demonstrate the ability of NTFk to extracting temporal and spatial features of phase separation of copolymers as they are modeled by self–consistent field theory. Microphase separation of block copolymers has been extensively studied both experimentally and theoretically. However, the interpretation of computer simulations and/or experimental data, representing temporal and spatial changes of molecular species concentration is still a challenging task. Thus, extracting the phase diagram from simulations or experimental data as well as the interpretation of data requires discernment of the model/experimental parameters (such as, temperature, concentrations, the number of molecular species and the interaction between species) impact on the microphase separation process. An attractive and unique aspect of the introduced ML method is that it ensures the nonnegativity of the extracted latent features. Nonnegativity is an essential constraint needed to obtain interpretable and sparse latent features that are parts–based representation of the data. The custom clustering in NTFk serves to estimate the number of latent features in the data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Dynamics and configurations of galaxy triplets

The purpose is to infer the probable dynamical states of galaxy triplets by the observed data on their configurations. Two methods are proposed for describing the distributions of the triplet configuration parameters characterizing a tendency to alignment and hierarchy: (1) obtaining a representative sample of configurations and determining its statistical parameters (moments and percentages); and (2) dividing the region of possible configurations of triple systems (Agekian and Anosova, 1967) into a set of segments and finding the probabilities for the configurations to find themselves in each of them. Both these methods allow representation of the data by numerical simulations as well as observations. The effect of projection was studied. It rather overestimates the alignment and hierarchy of the triple systems. Among the parameters of interest there are found some parameters that are least sensitive to projection effects. The samples consist of simulated galaxy triplets (with hidden mass) as well as of 46 probably physical triple galaxies (Karachentseva et al., 1979). The observed triples as well as numerical models show a tendency to alignment. The triple galaxies do not show any tendency to hierarchy (formation of the temporary binaries), but this tendency may be present for simulated triplets without significant dark matter. The significant hidden mass (of order ten times the total mass of a triplet) decreases the probability of forming a binary and so weakens the hierarchy. Small galaxy groups consisting of 3 to 7 members are probably the most prevalent types of galaxy aggregate (Gorbatsky, 1987). Galaxy triplets are the simplest groups, but dynamically nontrivial ones.

Anosova, Joanna P.↗
DOE OSTI2015

Grain boundary plane orientation fundamental zones and structure-property relationships

Grain boundary plane orientation is a profoundly important determinant of character in polycrystalline materials that is not well understood. This work demonstrates how boundary plane orientation fundamental zones, which capture the natural crystallographic symmetries of a grain boundary, can be used to establish structure-property relationships. Using the fundamental zone representation, trends in computed energy, excess volume at the grain boundary, and temperature-dependent mobility naturally emerge and show a strong dependence on the boundary plane orientation. Analysis of common misorientation axes even suggests broader trends of grain boundary energy as a function of misorientation angle and plane orientation. Due to the strong structure-property relationships that naturally emerge from this work, boundary plane fundamental zones are expected to simplify analysis of both computational and experimental data. This standardized representation has the potential to significantly accelerate research in the topologically complex and vast five-dimensional phase space of grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗