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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reconstructing porous media using generative flow networks

One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize porous media volumes using a so-called generative flow model trained on x-ray microscope images of rock texture and pore structure. These models are optimized on a log-likelihood objective and they synthesize large and realistic three-dimensional images. Further, we demonstrate the rapid generation of sandstone image volumes that display realism as gauged by quantitative comparison of topological features using Minkowski functionals of porosity, specific surface area, and the Euler–Poincaré characteristic (i.e., pore connectivity). We also evaluate the single-phase permeability using Navier–Stokes and lattice Boltzmann methods and show that transport properties of the generated samples match measured trends.

58 GEOSCIENCES↗
NASA NTRS2022

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Bayesian Deep Learning↗
DOE OSTI2022

Dark Energy Survey Year 3 Results: Measuring the Survey Transfer Function with Balrog

Abstract We describe an updated calibration and diagnostic framework, Balrog , used to directly sample the selection and photometric biases of the Dark Energy Survey (DES) Year 3 (Y3) data set. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful diagnostic and calibration tool for a variety of DES Y3 science, particularly for the calibration of the photometric redshifts of distant “source” galaxies and magnification biases of nearer “lens” galaxies. The recovered Balrog injections are shown to closely match the photometric property distributions of the Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ∼1–8 mmag, but for a small subset of objects, we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. We discuss approaches to extend the current methodology to capture more aspects of the transfer function and reach full coverage of the survey footprint for future analyses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.

42 ENGINEERING↗
DOE OSTI2021

LeptonInjector and LeptonWeighter: A neutrino event generator and weighter for neutrino observatories

In this work, we present a high-energy neutrino event generator, called LeptonInjector, alongside an event weighter, called LeptonWeighter. Both are designed for large-volume Cherenkov neutrino telescopes such as IceCube. The neutrino event generator allows for quick and flexible simulation of neutrino events within and around the detector volume, and implements the leading Standard Model neutrino interaction processes relevant for neutrino observatories: neutrino-nucleon deep-inelastic scattering and neutrino-electron annihilation. In this paper, we discuss the event generation algorithm, the weighting algorithm, and the main functions of the publicly available code, with examples. Program summary: Program Titles: LeptonInjector and LeptonWeighter CPC Library link to program files: https://doi.org/10.17632/662gkpjfd9.1 Developer's repository links: https://github.com/icecube/LeptonInjector and https://github.com/icecube/LeptonWeighter Licensing provisions: GNU Lesser General Public License, version 3. Programming Language: C++11 External Routines: • Boost • HDF5 • nuflux (https://github.com/icecube/nuflux) • nuSQuIDS (https://github.com/arguelles/nuSQuIDS) • Photospline (https://github.com/icecube/photospline) • SuiteSparse (https://github.com/DrTimothyAldenDavis/SuiteSparse) Nature of problem: LeptonInjector: Generate neutrino interaction events of all possible topologies and energies throughout and around a detector volume. LeptonWeighter: Reweight Monte Carlo events, generated by a set of LeptonInjector Generators, to any desired physical neutrino flux or cross section. Solution method: LeptonInjector: Projected ranges of generated leptons and the extent of the detector, in terms of column depth, are used to inject events in and around the detector volume. Event kinematics follow distributions provided in cross section files. LeptonWeighter: Event generation probabilities are calculated for each Generator, which are then combined into a generation weight and used to calculate an overall event weight.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Evolution of thermally pulsing asymptotic giant branch stars. IV. Constraining mass loss and lifetimes of low mass, low metallicity AGB stars

The evolution and lifetimes of thermally pulsating asymptotic giant branch (TP-AGB) stars suffer from significant uncertainties. In this work, we analyze the numbers and luminosity functions of TP-AGB stars in six quiescent, low metallicity ([Fe/H] ≲ –0.86) galaxies taken from the ACS Nearby Galaxy Survey Treasury sample, using Hubble Space Telescope (HST) photometry in both optical and near-infrared filters. The galaxies contain over 1000 TP-AGB stars (at least 60 per field). We compare the observed TP-AGB luminosity functions and relative numbers of TP-AGB and red giant branch (RGB) stars, N{sub TP-AGB}/N{sub RGB}, to models generated from different suites of TP-AGB evolutionary tracks after adopting star formation histories derived from the HST deep optical observations. We test various mass-loss prescriptions that differ in their treatments of mass loss before the onset of dust-driven winds (pre-dust). These comparisons confirm that pre-dust mass loss is important, since models that neglect pre-dust mass loss fail to explain the observed N{sub TP-AGB}/N{sub RGB} ratio or the luminosity functions. In contrast, models with more efficient pre-dust mass loss produce results consistent with observations. We find that for [Fe/H] ≲ –0.86, lower mass TP-AGB stars (M ≲ 1 M{sub ☉}) must have lifetimes of ∼0.5 Myr and higher masses (M ≲ 3 M{sub ☉}) must have lifetimes ≲ 1.2 Myr. In addition, assuming our best-fitting mass-loss prescription, we show that the third dredge-up has no significant effect on TP-AGB lifetimes in this mass and metallicity range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images

Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as “black boxes”, lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method across multiple deep learning–based WSI analysis tasks spanning different tissue types. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the model’s predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.

Lou, Peiliang↗
DOE OSTI2025

Evaluating pulse-shaping capabilities of next-generation pulsed power architectures

This project evaluated the pulse shaping capabilities of next-generation pulsed power (NGPP) architectures. NGPP architectures share several common attributes including multiple independent pulse-generation lines, a radial water-insulated impedance transformer, and a central vacuum insulated load region. A multi-module circuit model was developed, incorporating independent pulse-generation lines and a 2-D transmission line mesh of the radial impedance transformer to assess the effects of azimuthal asymmetry in pulse-shaped experiments. Circuit model simulations demonstrated that NGPP architectures are able to produce the the desired current pulse shapes for exemplar NGPP experiments. Additionally, the project explored automated methods for experiment design, including derivative -ree optimization and machine learning. Pulse-shaped experiments require designers to determine machine parameters that reliably produce the desired current pulse at the load, a process that typically relies on expert knowledge and iterative adjustments using the Z circuit model. Given the increased complexity of NGPP systems, this manual approach may be impractical. While the evaluated methods do not eliminate the need for manual iteration, they can reduce the time required for experiment design. Derivative-free optimization automates much of the trial-and-error process, providing a close starting point for manual adjustments or making small modifications to near-final designs. Meanwhile, deep neural network methods can generate a good qualitative match to the desired current pulse in under one second without requiring circuit model simulations.

42 ENGINEERING↗
DOE OSTI2021

No Galaxy Left Behind: Measuring the Transfer Function of the Dark Energy Survey with Balrog

In this dissertation, we describe a calibration and diagnostic framework called Balrog which was used to directly sample the selection and photometric biases of the Dark Energy Survey (DES) Year 3 (Y3) dataset. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with traditional generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful diagnostic and calibration tool for a variety of DES Y3 science, particularly for the calibration of the photometric redshifts of distant "source" galaxies and magnification biases of nearer "lens" galaxies. The recovered Balrog injections are shown to closely match the photometric property distributions of the fiducial Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ~1-8 millimagnitudes, but for a small subset of objects we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. Finally, we discuss approaches to extend the current methodology to capture more aspects of the transfer function for future analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Implementing Artificial Thinking Autonomy with Model-Based System Engineering

Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.

Artificial Thought↗
DOE OSTI2021

An adaptive adversarial domain adaptation approach for corn yield prediction

Recently, statistical machine learning and deep learning methods have been widely explored for corn yield prediction. Though successful, machine learning models generated within a specific spatial domain often lose their validity when directly applied to new regions. To address this issue, we designed an unsupervised adaptive domain adversarial neural network (ADANN). Specifically, through domain adversarial training, the ADANN model reduced the impact of domain shift by projecting data from different domains into the same subspace. Also, the ADANN model was designed to be trained in an adaptive way, which guaranteed the model can learn the domain-invariant features and perform accurate yield prediction simultaneously. Informative variables including time-series vegetation indices and sequential weather observations were first collected from multiple data sources and aggregated to the county level. Then, we trained the ADANN model with the extracted features and corresponding reported county-level corn yield from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated in four testing years 2016–2019. The U.S. corn belt was used as the study area and counties under study were grouped into two diverse ecological regions. Overall, the experimental results showed that the developed ADANN model had better performance than three other state-of-the-art machine learning models in both local experiments (train and test in the same region) and transfer experiments (train and test in different regions). As the first study using adversarial learning for crop yield prediction, this research demonstrates a novel solution for improving model transferability on crop yield prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Voltage-Induced Charge Redistribution in Cu(In,Ga)Se 2 Devices Studied With High-Speed Capacitance–Voltage Profiling

Devices made from Cu(In, Ga)Se 2 (CIGS) solar cell material have been evaluated with high-speed capacitance-voltage profiling after stepwise voltage changes. The changes primarily affect near-interface charge at deep acceptors within the CIGS absorber layer and generate temperature-dependent capacitance changes observed in deep-level transient spectroscopy measurements. SCAPS device modeling indicates that the deep acceptor concentration is up to the two orders of magnitude higher than the shallow doping level. High deep acceptor concentrations are found in all materials studied here. In conclusion, the large deep defect levels are high enough to limit minority carrier lifetime and cell efficiency.

14 SOLAR ENERGY↗
NASA NTRS2014

Advanced Mirror & Modelling Technology Development

The 2020 Decadal technology survey is starting in 2018. Technology on the shelf at that time will help guide selection to future low risk and low cost missions. The Advanced Mirror Technology Development (AMTD) team has identified development priorities based on science goals and engineering requirements for Ultraviolet Optical near‐Infrared (UVOIR) missions in order to contribute to the selection process. One key development identified was lightweight mirror fabrication and testing. A monolithic, stacked, deep core mirror was fused and replicated twice to achieve the desired radius of curvature. It was subsequently successfully polished and tested. A recently awarded second phase to the AMTD project will develop larger mirrors to demonstrate the lateral scaling of the deep core mirror technology. Another key development was rapid modeling for the mirror. One model focused on generating optical and structural model results in minutes instead of months. Many variables could be accounted for regarding the core, face plate and back structure details. A portion of a spacecraft model was also developed. The spacecraft model incorporated direct integration to transform optical path difference to Point Spread Function (PSF) and between PSF to modulation transfer function. The second phase to the project will take the results of the rapid mirror modeler and integrate them into the rapid spacecraft modeler.

Effinger, Michael↗
DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗
DOE OSTI2021

Deep Symbolic Optimization for Electric Component Sizing in Fixed Topology Power Converters

Power converters (PC) are a major component in any current electronic hardware device. The development and design is usually guided by expert knowledge and heavily relies on human intuition and experience. The process is a very time consuming and costly activity and it is generally hard to improve upon current designs. As a first step towards autonomous PC design, we are here proposing a new framework for the sizing of components for fixed topology PCs based on given design requirements. To this end, we developed surrogate models for rapid evaluation of new topologies and adapt the deep symbolic optimization (DSO) framework to generate new topologies guided by a reinforcement learning training signal. In an empirical evaluation, we show that our DSO based approach is able to find the optimal configuration for all investigated topologies, while reducing the learning time by at least a factor of 100 compared to popular RL algorithms.

Xue, Lincoln↗