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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
NASA NTRS2018

Observing System Simulation Experiments

Observing System Simulation Experiments can be powerful tools for evaluating and exploring both the behavior of data assimilation systems and the potential impacts of future observing systems. With great power comes great responsibility - given a pure modeling framework, how can we be sure our results are meaningful? The challenges and pitfalls of OSSE calibration and validation will be addressed, as well as issues of incestuousness, selection of appropriate metrics, and experiment design. The use of idealized observational networks to investigate theoretical ideas in a fully complex modeling framework will also be discussed.

Prive, Nikki↗
NASA NTRSDate not supplied

Resiliency in Future Cislunar Space Architectures

Resiliency is an aspirational metric, and it is a common goal for complex systems – be they engineered space architectures, geopolitical networks, or the human immune system. However, despite the ubiquitous desire for resiliency, the concept is surprisingly difficult to define and apply to future planning efforts. We assert that resiliency can emerge from a layered approach of deliberately chosen capabilities with overlap and flexibility that, in aggregate, result in a resilient system. The challenge is to identify capabilities that contribute to resiliency and to accurately characterize their value. Resiliency is discussed through the lens of future architecture planning, outlining how the National Aeronautics and Space Administration (NASA) can benefit from a shift in approach when transitioning focus to the cislunar environment.

Jason Hay↗
DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Effective many-body interactions in reduced-dimensionality spaces through neural network models

Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate complex correlation effects in many-body systems is crucial to advance the understanding of these complicated problems. However, a numerical evaluation of these predictive models can still be associated with a significant computational overhead. To address this challenge, in this paper we discuss a combined framework that integrates recent advances in the development of active-space representations of coupled cluster (CC) downfolded Hamiltonians with neural network approaches. The primary objective of this effort is to train neural networks to eliminate the computationally expensive steps required for evaluating hundreds or thousands of Hugenholtz diagrams, which correspond to multidimensional tensor contractions necessary for evaluating a many-body form of downfolded effective Hamiltonians. Using small molecular systems (the H 2 O and HF molecules) as examples, we demonstrate that training neural networks employing effective Hamiltonians for a few nuclear geometries of molecules can accurately interpolate or extrapolate their forms to other geometrical configurations characterized by different intensities of correlation effects. We also discuss differences between effective interactions that define CC downfolded Hamiltonians with those of bare Hamiltonians defined by Coulomb interactions in the active spaces. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Scale-bridging Optimization Framework for Desalination Integrated Produced Water Networks

Produced water (PW) management poses a major challenge to U.S. oil and gas development. Due to freshwater scarcity and PW injection curtailments, desalination may become a necessity in some parts of the country. Integrating rigorous desalination process models within a produced water network for co-optimization of desalination design and network operation can be challenging to scale-up due to computational complexity. In this work we use the trust region filter framework to decompose the integrated optimization problem into an optimization master problem consisting of the network model and simplified surrogates for the desalination units and an optimization sub problem for the rigorous desalination unit model. The approach is demonstrated on a multiperiod produced water network from the PARETO library with thermal desalination units.

Naik, Sakshi↗
DOE OSTI2020

Macromolecular gelatin properties affect fibrin microarchitecture and tumor spheroid behavior in fibrin-gelatin gels

The biophysical properties of extracellular matrices (ECM) are known to regulate cell behavior, however decoupling cell behavior changes due to the relative contributions of material microstructure versus biomechanics or nutrient permeability remains challenging, especially within complex, multi-material matrices. We developed four gelatin-fibrin interpenetrating network (IPN) formulations which are identical in composition but possess variable gelatin molecular weight distributions, and display differences in microstructure, biomechanics, and diffusivity. In this work we interrogate the response of multicellular tumor spheroids to these IPN formulations and found that a high stiffness, gelatin-network dominated IPNs impeded remodeling and invasion of multicellular tumor spheroids; whereas relatively lower stiffness, fibrin-network dominated IPNs permitted protease-dependent remodeling and spheroid invasion. Cell proliferation correlated to nutrient diffusivity across tested IPN formulations. These findings demonstrate the complexity of ECM IPNs, relative to single polymer matrices, and highlight that cell response does not derive from a single aspect of the ECM, but rather from the interplay of multiple biomechanical properties. The methodology developed in this study represents a framework for future studies which aim to characterize cellular phenotypic responses to biophysical cues present within complex, multi-material matrices.

36 MATERIALS SCIENCE↗
NASA NTRS2000

Channels and Valleys

More than 200 channels and valleys have been identified on the Magellan images of Venus. These are classified, on the basis of morphology. as simple channels (including sinuous rilles, simple channels with flow margins. and canali), complex channels (with or without flow margins), compound channels, and valley networks (including rectangular, labyrinthic, and pitted or irregular networks). Sinuous rilles closely resemble their lunar counterparts. Canali are exceptional for their remarkably constant width along very extenuated flow paths, exceeding 500 km. One of the compound channels, the outflow complex of Kallistos Vallis. extends over 1200 km and is up to 30 km wide. Venusian channels are globally distributed, but each class has a preferential topographic association. The canali are developed on the volcanic plains. while sinuous rilles occur at higher elevations, associated with volcanic complexes and coronae. Both canali and sinuous rilles have been deformed by post-emplacement tectonism. Highly fluid lavas, erupted at sustained, high discharges seem best to explain many of the channel features, particularly for the canali and the compound channels. Explanation of the canali morphologies may also involve unusual low-viscosity lavas, perhaps of exotic composition.

Baker, Victor R.↗
DOE OSTI2023

CCSI Toolset 3.19 Release

CCSI Toolset 3.19 Release Highlights A gradient generation tool was developed to support GENN models in FOQUS. Certain machine learning tools train gradient-enhanced neural network (GENN) models which can be more accurate for complex datasets given a priori knowledge of model derivatives. However, the derivatives must be known beforehand and are not often available for process data. This tool automatically predicts the gradients for a training dataset in a form usable by common GENN trainers, such as Surrogate Modeling Toolbox. Support was added for Surrogate Modeling Toolbox GENN models in FOQUS, including updates to the run methods, node properties, test framework, documentation and optional dependencies list. Users can train/save Surrogate Modeling Toolbox gradient-enhanced neural network (GENN) models with custom objects and produce .pkl files compatible with the Machine Learning/Artificial Intelligence Plugin in FOQUS. A simpler implementation of the ordering algorithm in the Sequential Design of Experiments (SDOE) module was included. The SDOE examples documentation was updated. The Optimality-Based Design of Experiments was updated to improve the error handling when the results are None.

AS↗
DOE OSTI2022

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.

aerodynamics↗
DOE OSTI2024

Multiple Lattice Instabilities and Complex Ground State in Cs 2 Ag Bi Br 6

Metal-halide perovskites (MHPs) are attracting considerable interest for optoelectronic applications, with Cs 2 Ag Bi Br 6 one of the main contenders among lead-free systems. Cs 2 Ag Bi Br 6 crystallizes in a nominally double-perovskite structure, but exhibits a soft lattice with large atomic fluctuations characteristic of MHPs. While crucial to understand electron-phonon and phonon-phonon couplings, the spatiotemporal correlations of these fluctuations remain largely unknown. Here, we reveal these correlations using comprehensive neutron and x-ray scattering measurements on Cs 2 Ag Bi Br 6 single crystals, complemented with first-principles simulations augmented with machine-learned neural-network potentials. We report the discovery of an unexpected complex modulated ground-state structure containing several hundred atoms, arising from a soft-phonon instability of the low-temperature tetragonal phase. Further, our experiments and simulations both reveal extensive correlated two-dimensional fluctuations of Br octahedra at finite temperature, arising from soft optic phonons that are strongly broadened by anhamonicity, reflecting very shallow potential wells. These results provide new insights into the atomic structure and fluctuations in MHPs, critical to understand and control their thermal and optoelectronic properties. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2001

Comparison of Analytical and Numerical Performance Predictions for a Regenerative Heat Exchanger in the International Space Station Node 3 Internal Active Thermal Control System

The complexity of International Space Station (ISS) systems modeling often necessitates the concurrence of various dissimilar, parallel analysis techniques to validate modeling. This was the case with a feasibility and performance study of the ISS Node 3 Regenerative Heat Exchanger (RHX). A thermo-hydraulic network model was created and analyzed in SINDA/FLUINT. A less complex, closed form solution of the system dynamics was created using Excel. The purpose of this paper is to provide a brief description of the modeling processes utilized, the results and benefits of each to the ISS Node 3 RHX study.

Wise, Stephen A.↗
NASA NTRS2002

Comparison of Analytical and Numerical Performance Predictions for an International Space Station Node 3 Internal Active Thermal Control System Regenerative Heat Exchanger

The complexity of International Space Station (ISS) systems modeling often necessitates the concurrence of various dissimilar, parallel analysis techniques to validate modeling. This was the case with a feasibility and performance study of the ISS Node 3 Regenerative Heat Exchanger (RHX). A thermo-hydraulic network model was created and analyzed in SINDA/FLUINT. A less complex, closed form solution of the systems dynamics was created using an Excel Spreadsheet. The purpose of this paper is to provide a brief description of the modeling processes utilized, the results and benefits of each to the ISS Node 3 RHX study.

Wise, Stephen A.↗
NASA NTRS2019

Space Communications and Navigation Validation: Extracting Data for the Strategic Center for Networking, Integration, and Communications Scheduling Algorithms

Efficiency in communication system architecture performance between Space Communications and Navigation (SCaN) assets and missions is crucial, as space communication is varied, complex, and often not utilized to its full potential. The SCaN Strategic Center for Networking, Integration, and Communications (SCENIC) new scheduling algorithms, which are designed to simulate the allocation of resources between SCaN assets and missions, have the potential to simulate an increase of this efficiency; however, they require real-world data to be validated against. The purpose of this project was to extract said validation data, which details the frequency and duration of utilized contact windows between missions and assets in the Near Earth Network (NEN), Space Network (SN), and Deep Space Network (DSN). Stored as images in daily operations summaries (DOSs), the tabular data existed in a variety of file formats such as.pdf, .docx, and .doc. Since the tables were stored as images, ABBYY® FineReader® (ABBYY Software Ltd.) optical character recognition (OCR) was implemented, which is a proprietary software that reads images from text. The comma separated value (CSV) output was utilized as input to a series of MATLAB® (The MathWorks, Inc.) methods for reformatting, at which point it was ready to be machine-read. Finally, the results were converted to a Microsoft Excel format for human readability. Along with being used for validation purposes, the data will also be used to map equipment degradation as a function of time to analyze the reliability of network assets.

Kontur, Noah P.↗
NASA NTRS1994

Reconfigurable optical interconnections via dynamic computer-generated holograms

A system is proposed for optically providing one-to-many irregular interconnections, and strength-adjustable many-to-many irregular interconnections which may be provided with strengths (weights) w(sub ij) using multiple laser beams which address multiple holograms and means for combining the beams modified by the holograms to form multiple interconnections, such as a cross-bar switching network. The optical means for interconnection is based on entering a series of complex computer-generated holograms on an electrically addressed spatial light modulator for real-time reconfigurations, thus providing flexibility for interconnection networks for largescale practical use. By employing multiple sources and holograms, the number of interconnection patterns achieved is increased greatly.

Liu, Hua-Kuang↗
NASA NTRS1996

Reconfigurable Optical Interconnections Via Dynamic Computer-Generated Holograms

A system is presented for optically providing one-to-many irregular interconnections, and strength-adjustable many-to-many irregular interconnections which may be provided with strengths (weights) w(sub ij) using multiple laser beams which address multiple holograms and means for combining the beams modified by the holograms to form multiple interconnections, such as a cross-bar switching network. The optical means for interconnection is based on entering a series of complex computer-generated holograms on an electrically addressed spatial light modulator for real-time reconfigurations, thus providing flexibility for interconnection networks for large-scale practical use. By employing multiple sources and holograms, the number of interconnection patterns achieved is increased greatly.

Liu, Hua-Kuang↗
DOE OSTI2023

Efficient solutions of fermionic systems using artificial neural networks

In this study, we explore the similarities and differences between variational Monte Carlo techniques that employ conventional and artificial neural network representations of the ground-state wave function for fermionic systems. Our primary focus is on shallow neural network architectures, specifically the restricted Boltzmann machine, and we examine unsupervised learning algorithms that are appropriate for modeling complex many-body correlations. We assess the advantages and drawbacks of conventional and neural network wave functions by applying them to a range of circular quantum dot systems. Our findings, which include results for systems containing up to 90 electrons, emphasize the efficient implementation of these methods on both homogeneous and heterogeneous high-performance computing facilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Communications among data and science centers

The ability to electronically access and query the contents of remote computer archives is of singular importance in space and earth sciences; the present evaluation of such on-line information networks' development status foresees swift expansion of their data capabilities and complexity, in view of the volumes of data that will continue to be generated by NASA missions. The U.S.'s National Space Science Data Center (NSSDC) manages NASA's largest science computer network, the Space Physics Analysis Network; a comprehensive account is given of the structure of NSSDC international access through BITNET, and of connections to the NSSDC available in the Americas via the International X.25 network.

Green, James L.↗