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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Electronic Neural Networks

Memory based on neural network models content-addressable and fault-tolerant. System includes electronic equivalent of synaptic network; particular, matrix of programmable binary switching elements over which data distributed. Switches programmed in parallel by outputs of serial-input/parallel-output shift registers. Input and output terminals of bank of high-gain nonlinear amplifiers connected in nonlinear-feedback configuration by switches and by memory-prompting shift registers.

Lambe, John↗
DOE OSTI2023

LCA-PyTorch

LCA-PyTorch is a code repository which contains PyTorch implementations of the Locally Competitive Algorithm (LCA), which is a biologically-plausible sparse coding model. LCA-PyTorch allows for the training, testing, and analysis of single layer LCA networks, multi-layer LCA networks, and hybrid LCA-based deep neural network models on a wide variety of applications and data types. LCA-PyTorch was developed in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning. LCA-PyTorch interfaces heavily with the open-source PyTorch Python package.

Teti, Michael↗
NASA NTRS1990

Neural Network Development Tool (NETS)

Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

Baffes, Paul T.↗
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
DOE OSTI2022

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. Here, we validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer’s disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

42 ENGINEERING↗
DOE OSTI2020

Predictive Integrated Modeling of Heated Tokamak Plasmas (Final Technical Report)

The research carried out by the Lehigh University fusion group during this reporting period: (1) Self-consistent predictive core-pedestal ITER scenario modeling; (2) Microtearing instabilities and electron thermal transport in standard and spherical tokamak discharges; (3) Neural network model of the Multi-Mode anomalous transport module; (4) Anomalous transport in KSTAR, JET, EAST, Alcator C-Mod and DIII-D discharges; (5) Theory for transport in magnetized plasmas; (6) Control-oriented modeling of the q-profile dynamic response in preparation for advanced scenario control in KSTAR and in EAST; (7) Modeling of edge harmonic oscillation in DIII-D QH mode discharges; (8) Elucidating plasma dynamics in Hasegawa-Wakatani turbulence by information geometry; (9) Non-linear Langevin and Fractional Fokker–Planck equations for anomalous diffusion by Levy stable processes; and (10) Investigation of the plasma shaping effects on the H-mode pedestal structure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2020

Convolutional Neural Network for Transition Modeling Based on Linear Stability Theory

Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally, the modeling of transition has relied on correlation-based empirical formulas based on integral quantities such as the shape factor of the boundary layer. However, in many applications of computational fluid dynamics, the shape factor is not straightforwardly available or not well-defined. We propose using the complete velocity profile along with other quantities (e.g., frequency, Reynolds number) to predict the perturbation amplification factor. While this can be achieved with regression models based on a classical fully connected neural network, such a model can be computationally more demanding. We propose a novel convolutional neural network inspired by the underlying physics as described by the stability equations. Specifically, convolutional layers are first used to extract integral quantities from the velocity profiles, and then fully connected layers are used to map the extracted integral quantities, along with frequency and Reynolds number, to the output (amplification ratio). Numerical tests on classical boundary layers clearly demonstrate the merits of the proposed method. More importantly, we demonstrate that, for Tollmien-Schlichting instabilities in two-dimensional, low-speed boundary layers, the proposed network encodes information in the boundary layer profiles into an integral quantity that is strongly correlated to a well-known, physically defined parameter – the shape factor.

Laminar-turbulent transition↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Thermal stability of electron-irradiated poly(tetrafluoroethylene) - X-ray photoelectron and mass spectroscopic study

Polytetrafluoroethylene (PTFE) was subjected to 3 keV electron bombardment and then heated in vacuum to 300 C. The behavior of the material as a function of radiation dose and temperature was studied by X-ray photoelectron spectroscopy (XPS) of the surface and mass spectroscopy of the species evolved. Lightly damaged material heated to 300 C evolved saturated fluorocarbon species, whereas unsaturated fluorocarbon species were evolved from heavily damaged material. After heating the heavily damaged material, those features in the XPS spectrum that were associated with damage diminished, giving the appearance that the radiation damage had annealed. The observations were interpreted by incorporating mass transport of severed chain fragments and thermal decomposition of severely damaged material into the branched and cross-linked network model of irradiated PTFE. The apparent annealing of the radiation damage was due to covering of the network by saturated fragments that easily diffused through the decomposed material to the surface region upon heating.

Wheeler, Donald R.↗
NASA NTRS1997

Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

Liu, Youhua↗
NASA NTRS2019

Thermal Analysis of Potted Litz Wire for High-Power-Density Aerospace Electric Machines

Increasing the power density and efficiency of electric machines (motors and generators) is integral to bringing Electrified Aircraft (EA) to commercial realization. To that end an effort to create a High Efficiency Megawatt Motor (HEMM) with a goal of exceeding 98% efficiency and 1.46 MW of power has been undertaken at the NASA Glenn Research Center. Of the motor components the resistive losses in the stator windings are by far the largest contributor (34%) to total motor loss. The challenge is the linear relationship between resistivity and temperature, making machine operation sensitive to temperature increases. In order to accurately predict the thermal behavior of the stator the thermal conductivity of the Litz wire-potting-electrical insulation system must be known. Unfortunately, this multi material system has a wide range of thermal conductivities (0.1 W/m-K – 400 W/m-K) and a high anisotropy (axial vs transverse) making the prediction of the transverse thermal conductivity an in turn the hot spot temperatures in the windings is difficult. In order to do this a device that simulates the thermal environment found in the HEMM stator was designed. This device is not unlike the motorettes (little motors) that are described in IEEE standards for testing electrical insulation lifetimes or other electric motor testing. However, because the HEMM motor design includes significant rotor electrical and thermal considerations the term motorette was not deemed appropriate. Instead statorette (or little stator) was adopted as the term for this test device. This paper discussed the design, thermal heat conjugate analysis (thermal model), manufacturing and testing of HEMM's statorette. Analysis of the results is done by thermal resistance network model and micro thermal model and is compared to analytical predictions of thermal conductivity of the insulated and potted Litz wire system.

Motor↗
NASA NTRSDate not supplied

Towards Sheaf Theoretic Analyses for Delay Tolerant Networking

The goal of Delay Tolerant Networking (DTN) is to take a collection of heterogeneous, disparate connections between satellites, space assets, ground stations, and ground infrastructure and bring it together into a cohesive, functioning overlay network. Depending on the systems being considered, one can find links with a one-way light time exceeding minutes (and hours),periodic links which can sometimes be predicted by orbital mechanics, and restrictions based on the variety of capabilities built into these systems. These characteristics preclude traditional network models and routing techniques and have classically led to either rigid routing tables or purely probabilistic models. As the deeper underlying structures remain unknown, development of more DTN-optimized algorithms has lacked the necessary foundation. In a continuation of previous work, the goal of this paper is to identify and study these fundamental structures that exist in delay tolerant networks (DTN), with a focus on space networks. The current routing methodology has been to use contact graph routing (CGR) algorithms. CGR models a series of known contacts as a static graph. For CGR to work, this graph must be globally consistent and must have an accurate picture of the network. Because this is a globally controlled structure, there is little room for flexibility in the event of changes to the network which would naturally occur as the network grows. As a response to the desire for flexibility as the network changes, we introduced the mathematical structure known as sheaves to DTNs last year. The tag-line for sheaves is that they are a mathematically precise way of gluing local data together into unique global data. Thus, sheaves lend extra power to traditional models(and routing algorithms) by taking additional information and merging it, in as consistent a manner as possible, with the representation itself. The clearest example of how Earth-bound networks exhibit behavior that is “sheafy” is link state routers, which build a local-to-global picture of their network by gluing local information together into a global network, exactly as a sheaf would do. For routing within delay tolerant networks to truly exploit this structure, a deeper structure than a graph is required. In this paper, we develop sheaves that can work over directed graphs such as temporal flow networks, we construct a sheaf representation for Dijkstra’s algorithm, and we outline a construction for routing sheaves capable of modeling multicast scenarios. Finally, there is a section of future work suggesting follow-on research.

Robert Short↗
NASA NTRS2004

Generalized Fluid System Simulation Program

A general purpose program implemented on a computer analyzes steady state and transient flow in a complex fluid network, modeling phase changes, compressibility, mixture thermodynamics and external body forces such as gravity and centrifugal force. A preprocessor provides for the inter- active development of a fluid network simulation having nodes and branches. Mass, energy, and specie conservation equations are solved at the nodes, and momentum conservation equations are solved in the branches. Contained herein are subroutines for computing "real fluid" thermodynamic and thermophysical properties for 12 fluids, and a number of different source options are provided for model- ing momentum sources or sinks in the branches. The system of equations describing the fluid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods. Application and verification of this invention are provided through an example problem, which demonstrates that the predictions of the present invention compare most reasonably with test data.

Alok Kumar Majumdar↗
NASA NTRS1986

On the magnetic structure of the quiet transition region

Existing models of the quiet chromosphere-corona transition region predict a distribution of emission measure over temperature that agrees with observation for a T greater than about 100,000 K. These 'network' models assume that all magnetic field lines that emerge from the photosphere extend into and are in thermal contact with the corona. It is shown that the observed fine-scale structure of the photospheric magnetic network instead suggests a two-component picture in which magnetic funnels that open into the corona emerge from only a fraction of the network. The gas that makes up the hotter transition region is mostly contained within these funnels, as in standard models, but, because the funnels are more constricted in our picture, the heat flowing into the cooler transition region from the corona is reduced by up to an order of magnitude. The remainder of the network is occupied by a population of low-lying loops with lengths less than about 10,000 km. It is proposed that the cooler transition region is mainly located within such loops, which are magnetically insulated from the corona and must, therefore, be heated internally. The fine-scale structure of ultraviolet spectroheliograms is consistent with this proposal, and theoretical models of internally heated loops can explain the behavior of the emission measure below a T of aobut 100,000 K.

Rabin, D.↗
DOE OSTI2021

Reactive Flows in Porous Media: Challenges in Theoretical and Numerical Methods

We review theoretical and computational research, primarily from the past 10 years, addressing the flow of reactive fluids in porous media. The focus is on systems where chemical reactions at the solid–fluid interface cause dissolution of the surrounding porous matrix, creating nonlinear feedback mechanisms that can often lead to greatly enhanced permeability. Here, we discuss insights into the evolution of geological forms that can be inferred from these feedback mechanisms, as well as some geotechnical applications such as enhanced oil recovery, hydraulic fracturing, and carbon sequestration. Until recently, most practical applications of reactive transport have been based on Darcy-scale modeling, where averaged equations for the flow and reactant transport are solved. We summarize the successes and limitations of volume averaging, which leads to Darcy-scale equations, as an introduction to pore-scale modeling. Pore-scale modeling is computationally intensive but offers new insights as well as tests of averaging theories and pore-network models. We include recent research devoted to validation of pore-scale simulations, particularly the use of visual observations from microfluidic experiments.

wormhole formation↗
DOE OSTI2023

Power system restoration incorporating diverse distributed energy resources

An example system includes an aggregator configured to receive a service collaboration request and iteratively determine, based on minimum and maximum power values for DERs under its management, an optimized operation schedule. The aggregator may also be configured to iteratively determine, based on the optimized operation schedule, an estimated flexibility range for devices under its management and output an indication thereof. The system may also include a power management unit (PMU) configured to iteratively receive the indication and determine, based on a network model that includes the estimated flexibility range, a reconfiguration plan and an overall optimized operation schedule for the network. The PMU may also be configured to iteratively cause reconfiguration of the network based on the plan. The PMU and aggregator may also be configured to iteratively, at a fast timescale, cause energy resources under their management to modify operation based on the overall optimized operation schedule.

Ding, Fei↗
NASA NTRS2023

Blade-Wake Interaction Noise for Small Hovering Rotors, Part I: Characterization Study

This work illustrates the use of artificial neural network modeling to study and characterize broadband blade-wake interaction noise from hovering small unmanned aerial systems rotors subject to varying airfoil geometries, rotor geometries, and operating conditions. Design of experiments was used to create input feature spaces, and a high-fidelity strategy was implemented at the discrete data points defined by the input feature spaces to design airfoils and rotor blades, predict the unsteady rotor aerodynamics and aeroacoustics, and isolate the blade-wake interaction noise from the acoustic broadband noise. A metric for the blade-wake interaction noise was developed, and the ANOPP2 Artificial Neural Network Tool was used to identify an optimal prediction model for the nonlinear relationship between the input features and the metric for blade-wake interaction noise. This optimal artificial neural network was then validated over training/test data and exhibited prediction accuracy over 91% for data previously unseen by the model. A sensitivity analysis was conducted, which showed that input features that directly modify the thrust coefficient had a dominant effect over blade-wake interaction noise. The optimal prediction model along with aerodynamic simulations were used to further study the effect of varying input features on blade-wake interaction noise, and three types of blade-wake interaction noise were identified

Christopher S. Thurman↗