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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 109 records · Page 6

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DOE OSTI2023

Statistical analysis and degradation pathway modeling of photovoltaic minimodules with varied packaging strategies

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems.

electrical measurements↗

DOE OSTI2020

Hydration and reaction mechanisms on sodium silicate glass surfaces from molecular dynamics simulations with reactive force fields

Recent development of reactive force fields have enabled molecular dynamics simulations of interactions between silicate glasses and water at the atomistic scale. While multicomponent silicate glasses encompass a wide variety of compositions and properties, one common structural feature in these glasses is the combination of the network structure that is made up of silica tetrahedra linked through corner sharing interspersed with network modifiers like alkali and alkaline-earth ions that break up the Si–O–Si linkages by forming nonbridging oxygen. In reactions with water, ion exchange between alkali ions in the glass and proton or hydronium in the solution, as well as hydrolysis reaction of the Si–O–Si linkages and subsequent silanol formation, is observed and well documented. Furthermore, we have used a set of recently developed reactive force field to investigate the reactions between water and the surfaces of silica and sodium silicate glasses of different compositions for reactions up to 8 nanoseconds. Our results indicate sodium leaching into water and diffusion of water molecules up to 25 Å into the glass surface. We examined the structural and compositional changes inside the glass and around the diffused ions and use these to explain the rates of silanol formation at the surface. We also observed proton transport in the glass which has an indirect influence on the silanol formation rates. While the surface of the glass was rough to start with, it undergoes further modification into a hydrated gel-like structure in the glass for up to 5 Å in the higher alkali containing glasses. It was found that the leached sodium ions remain close to the interface and that fragments of silicate network from the surface is capable of dislodging from the bulk glass and enter the aqueous solution. These simulations thus provide insights into the formation and structure of an alteration layers commonly observed in multicomponent silicate glasses corroded in aqueous solutions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective

Here we present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core units. A performance of over 100 teraFLOPs is achieved for half-precision floating point operations on Nvidia’s A100 tensor core units. The second-order recursive Fermi-operator scheme is formulated in terms of a generalized, differentiable deep neural network structure, which solves the quantum mechanical electronic structure problem. We demonstrate how this network can be accelerated by optimizing the weight and bias values to substantially reduce the number of layers required for convergence. We also show how this machine learning approach can be used to optimize the coefficients of the recursive Fermi-operator expansion to accurately represent the fractional occupation numbers of the electronic states at finite temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent advancements have explored alternative neural network structures. One such innovation is the Kolmogorov–Arnold Network (KAN), which has demonstrated benefits over traditional MLPs, including faster neural scaling and better interpretability. The application of KANs to physics-informed learning has led to the development of Physics-Informed KANs (PIKANs), enabling the use of KANs to solve PDEs. However, despite their advantages, KANs often suffer from slower training speeds, particularly in higher-dimensional problems where the number of collocation points grows exponentially with the dimensionality of the system. To address this challenge, we introduce Separable Physics-Informed Kolmogorov–Arnold Networks (SPIKANs). This novel architecture applies the principle of separation of variables to PIKANs, decomposing the problem such that each dimension is handled by an individual KAN. This approach drastically reduces the computational complexity of training without sacrificing accuracy, facilitating their application to higher-dimensional PDEs. Through a series of benchmark problems, we demonstrate the effectiveness of SPIKANs, showcasing their superior scalability and performance compared to PIKANs and highlighting their potential for solving complex, high-dimensional PDEs in scientific computing.

Kolmogorov-Arnold networks↗

DOE OSTI2021

Data-Driven Learning of Nonautonomous Systems

In this work, we present a numerical framework for recovering unknown nonautonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the nonautonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Adaptive Recovery Model: Designing Systems for Testing Tracing and Vaccination to Support COVID-19 Recovery Planning.

This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Data-Driven Learning of Non-Autonomous Systems

We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Minimizing distortion in truss structures -- a Hopfield network solution

Distortions in truss structures can result from random errors in elemental lengths that are typical of a manufacturing process. These distortions may be minimized by an optimal selection of elements from those available for placement between the prescribed nodes -- a combinatorial optimization problem requiring significant investment of computational resource for all but the smallest problems. The present paper describes a formulation in which near-optimal element assignments are obtained as minimum energy, stable states, of an analogous Hopfield neural network. This requires mapping of the optimization problem into an energy function of the appropriate Lyapunov form. The computational architecture is ideally suited to a parallel processor implementation and offers significant savings in computational effort. A numerical implementation of the approach is discussed with reference to planar truss problems.

Fu, B.↗

NASA NTRS1992

Minimizing distortion in truss structures - A Hopfield network solution

Distortions in truss structures can result from random errors in element lengths that are typical of a manufacturing process. These distortions may be minimized by an optimal selection of elements from those available for placement between the prescribed nodes - a combinatorial optimization problem requiring significant investment of computational resource for all but the smallest problems. The present paper describes a formulation in which near-optimal element assignments are obtained as minimum-energy stable states, of an analogous Hopfield neural network. This requires mapping of the optimization problem into an energy function of the appropriate Liapunov form. The computational architecture is ideally suited to a parallel processor implementation and offers significant savings in computational effort. A numerical implementation of the approach is discussed with reference to planar truss problems.

Fu, B.↗

DOE OSTI2023

Water Network Tool for Resilience (WNTR)

The Water Network Tool for Resilience (WNTR) is an open source Python package designed to simulate and analyze resilience of water distribution networks. The United States Environmental Protection Agency, in partnership with Sandia National Laboratories, developed WNTR to integrate critical aspects of resilience modeling for water distribution networks into a single software framework. The software includes capability to: • Generate water network models • Modify network structure and operations • Assign fragility and survival curves to network components • Model disruptive events such as power outages, earthquakes, fires, pipe breaks, and contamination incidents • Model response and repair strategies • Simulate hydraulics and water quality • Evaluate resilience using a wide range of metrics • Integrate dependency with other critical infrastructure and supply chains • Analyze results and generate graphics SAND2019-450 M

Villa, Daniel↗

DOE OSTI2025

Experimental Characterization of Hydrogen Diffusion in Shale Rocks for Geologic Storage Applications

As global energy systems undergo a transition to cleaner alternatives, geologic hydrogen storage has emerged as a promising solution for large-scale energy storage. A critical factor in determining the feasibility of this approach is the effectiveness of caprock formations, such as shale, in preventing hydrogen migration. This study investigates the diffusion behavior of hydrogen through shale to assess its suitability as a caprock for geologic hydrogen storage. Using a novel double-seal core holder design and a through-diffusion apparatus, hydrogen diffusion was measured through shale rock from the Eagle Ford and Wolfcamp Formations under dry conditions. These measurements were complemented by microstructural and mineralogical analyses using low-pressure nitrogen adsorption and X-ray diffraction. The effective diffusion coefficient of hydrogen in these shale caprocks ranged from 2.51 × 10 –8 to 9.85 × 10 –8 m 2 /s. Notably, we observed that the diffusion behavior was more related to the pore network structure and could not be attributed to differences in the total pore volume between shale types alone. Here, to further understand the role of pore network complexity, a fractal pore model was developed to correlate tortuosity with the fractal dimension of the pore structure (a measure of pore network complexity). The proposed model closely matched tortuosity values obtained from diffusion experiments, outperforming existing theoretical tortuosity–porosity correlations. These findings provide key quantitative parameters needed to assess the feasibility of geologic hydrogen storage as well as insights that can be applied to hydrogen storage in a range of geologic formations.

08 HYDROGEN↗

DOE OSTI2020

Stress effects on flow and transport in three-dimensional fracture networks

We investigate the effects of various external stress regimes on fracture apertures, fluid flow, and solute transport in three-dimensional fracture networks. We use well-established geomechanics equations coupled with discrete fracture network modeling to characterize changes in primary flow paths within a complex network as a function of stress magnitude and orientation. These changes manifest in the alterations of the fluid flow field and are measured in terms of Eulerian and Lagrangian flow observables including solute transport, which is a key problem in many hydrologic applications. Changes in primary flow paths affect the solute transport in the network by promoting anomalously early arrival or long tailing behavior. However, early time arrival is not ubiquitous in anisotropically stressed networks and in most cases there is a delayed arrival of solute, which is attributed to (i) the presence of low-velocity zones normal to the flow direction, (ii) the angle between flow direction and major compressive principal stress directions, and (iii) changes in the primary flow paths (i.e., increases in tortuosity and active network structure). Overall, flows become more channelized in anisotropically stressed fracture networks than in unstressed and isotropically stressed networks.

58 GEOSCIENCES↗

DOE OSTI2020

Determining flow directions in river channel networks using planform morphology and topology

Abstract. The abundance of global, remotely sensed surface water observations has accelerated efforts toward characterizing and modeling how water moves across the Earth's surface through complex channel networks. In particular, deltas and braided river channel networks may contain thousands of links that route water, sediment, and nutrients across landscapes. In order to model flows through channel networks and characterize network structure, the direction of flow for each link within the network must be known. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely sensed imagery and knowledge of the network's inlet and outlet locations. We designed a suite of direction-predicting algorithms (DPAs), each of which exploits a particular morphologic characteristic of the channel network to provide a prediction of a link's flow direction. DPAs were chained together to create “recipes”, or algorithms that set all the flow directions of a channel network. Separate recipes were built for deltas and braided rivers and applied to seven delta and two braided river channel networks. Across all nine channel networks, the recipe-predicted flow directions agreed with expert judgement for 97 % of all tested links, and most disagreements were attributed to unusual channel network topologies that can easily be accounted for by pre-seeding critical links with known flow directions. Our results highlight the (non)universality of process–form relationships across deltas and braided rivers.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Developing Visualization Techniques for Semantics-based Information Networks

Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

Keller, Richard M.↗

DOE OSTI2021

Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments

Remote sensing (RS) scene classification has wide applications in the environmental monitoring and geological survey. In the real-world applications, the RS scene images taken by the satellite might have two scenarios: clear and cloudy environments. However, most of existing methods did not consider these two environments simultaneously. In this paper, we assume that the global and local features are iscriminative in either clear or cloudy environments. Many existing Convolution Neural Networks (CNN) based models have made excellent achievements in the image classification, however they somewhat ignored the global and local features in their network structure. In this paper, we propose a new CNN based network (named GLNet) with the Global Encoder and Local Encoder to extract the discriminative global and local features for the RS scene classification, where the constraints for inter-class dispersion and intra-class compactness are embedded in the GLNet training. The experimental results on two publicized RS scene classification datasets show that the proposed GLNet could achieve better performance based on many existing CNN backbones under both clear and cloudy environments.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-based Feedback Control

This research delves into applying the Macroscopic Fundamental Diagram (MFD) concept to structured airspace networks for comprehensive aggregate modeling and introduces a feedback-based departure function aimed at optimizing traffic flow. Previous studies have rarely examined structured airspace networks featuring non-stationary vehicles through the MFD perspective. We devised a scenario grounded in practical applications, featuring a multi-lane network with explicit lane-changing behavior. The MFD effectively captured the open-loop response, displaying a low-scatter, unimodal curve on the flow versus occupancy plot. Drawing inspiration from the ground transportation ramp-metering strategies, a proportional-integral-based controller was developed. Extensive simulation outcomes suggest that feedback control, informed by MFD, holds significant potential for managing traffic flow in Urban Air Mobility (UAM) environments; a reduction of 80% in the peak number of vehicles in a holding pattern was observed for a slight reduction in throughput in this study.

MFD↗

NASA NTRSDate not supplied

Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-based Feedback Control

MFD↗