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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals

As discussed in the previous chapter, high energy diffraction microscopy (HEDM) is used to study the micromechanical evolution of a material during in situ loading. HEDM experiments have been used to verify crystal plasticity (CP) simulations [119, 91, 90, 120], for experimental planning, material design, and to further analyze experimental results. However, Fast Fourier transform-based CP (CP-FFT) or finite element-based CP (CP-FE) methods are often too slow to be used in real-time during an experiment. CP-FFT is faster than CP-FE simulations due to the absence of meshing, but can still take hours to simulate the response of a single volume depending on the size and number of strain steps [127]. Reducing computation time would create a larger exploration space in planning and design, and enable faster analysis of experimental results and real-time feedback during an experiment. This research expands upon previous works to develop a workflow for predicting the full-field evolution of a 3D polycrystal. The workflow is simplified from previous works to predict only orientation and elastic strain tensors (from which stress tensors are calculated). The network is physically informed through loss functions and network architecture for a more robust model. The orientation predictions are informed about the cubic crystal symmetry of the material by incorporating disorientation and misorientation information into the network architecture and loss. The Von Mises stress is used to enforce the correct stress-strain trends in the strain tensor predictions. Additional total strain steps from the elastic and elastoplastic region are included to better capture the stress-strain evolution at smaller total strain steps. Material and hardening parameters are additional inputs into the networks to further inform the network and to study the network’s ability to predict different materials other than those used for training.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bimetallic Ir x Pb nanowire networks with enhanced electrocatalytic activity for the oxygen evolution reaction

Metallic nanowire networks (MNNs) have attracted increasing attention due to their high surface area and tunable compositions. Although enormous efforts have been devoted to preparing noble metal MNNs with different compositions, the composition optimization and the formation mechanism of Ir-based MNNs have not been thoroughly investigated. Here, this work presents a facile method for synthesizing robust Ir-based bimetallic MNNs by chemical reduction. As unveiled by our characterization, the network structure is constructed by ultrafine nanowires evolved from aggregated nanoparticles. The element analysis confirms the even distribution of Pb and Ir elements. The introduction of Pb is found to be beneficial to the stable formation of Ir x Pb MNNs in the sol–gel process. By tuning the precursor ratio of Ir and Pb, the optimized Ir x Pb catalyst delivers an enhanced oxygen evolution reaction (OER) performance in acid media (307 mV at 10 mA cm −2 ), which is superior to that of the commercial IrO 2 catalyst. Besides, due to the robust structure of the Ir x Pb MNNs, excellent OER durability is observed in the accelerated durability test (ADT) (2000 cycles).

Bimetallic↗
NASA NTRS2002

An Analysis for the Use of Research and Education Networks and Commercial Network Vendors in Support of Space Based Mission Critical and Non-Critical Networking

Currently, and in the past, dedicated communication circuits and "network services" with very stringent performance requirements are being used to support manned and unmanned mission critical ground operations at GSFC, JSC, MSFC, KSC and other NASA facilities. Because of the evolution of network technology, it is time to investigate using other approaches to providing mission services for space ground operations. The current NASA approach is not in keeping with the evolution of network technologies. In the past decade various research and education networks dedicated to scientific and educational endeavors have emerged, as well as commercial networking providers, that employ advanced networking technologies. These technologies have significantly changed networking in recent years. Significant advances in network routing techniques, various topologies and equipment have made commercial networks very stable and virtually error free. Advances in Dense Wave Division Multiplexing will provide tremendous amounts of bandwidth for the future. The question is: Do these networks, which are controlled and managed centrally, provide a level of service that equals the stringent NASA performance requirements. If they do, what are the implication(s) of using them for critical space based ground operations as they are, without adding high cost contractual performance requirements? A second question is the feasibility of applying the emerging grid technology in space operations. Is it feasible to develop a Space Operations Grid and/or a Space Science Grid? Since these network's connectivity is substantial, both nationally and internationally, development of these sorts of grids may be feasible. The concept of research and education networks has evolved to the international community as well. Currently there are international RENs connecting the US in Chicago to and from Europe, South America, Asia and the Pacific rim, Russia and Canada. And most countries in these areas have their own research and education network as do many states in the USA.

Bradford, Robert N.↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Automata network models of galaxy evolution

Two ideas appear frequently in theories of star formation and galaxy evolution: (1) star formation is nonlocally excitatory, stimulating star formation in neighboring regions by propagation of a dense fragmenting shell or the compression of preexisting clouds; and (2) star formation is nonlocally inhibitory, making H2 regions and explosions which can create low-density and/or high temperature regions and increase the macroscopic velocity dispersion of the cloudy gas. Since it is not possible, given the present state of hydrodynamic modeling, to estimate whether one of these effects greatly dominates the other, it is of interest to investigate the predicted spatial pattern of star formation and its temporal behavior in simple models which incorporate both effects in a controlled manner. The present work presents preliminary results of such a study which is based on lattice galaxy models with various types of nonlocal inhibitory and excitatory couplings of the local SFR to the gas density, temperature, and velocity field meant to model a number of theoretical suggestions.

Chappell, David↗
DOE OSTI2023

Evolution of Robustness in Growing Random Networks

Networks are widely used to model the interaction between individual dynamic systems. In many instances, the total number of units and interaction coupling are not fixed in time, and instead constantly evolve. In networks, this means that the number of nodes and edges both change over time. Various properties of coupled dynamic systems, such as their robustness against noise, essentially depend on the structure of the interaction network. Therefore, it is of considerable interest to predict how these properties are affected when the network grows as well as their relationship to the growth mechanism. Here, we focus on the time evolution of a network’s Kirchhoff index. We derive closed-form expressions for its variation in various scenarios, including the addition of both edges and nodes. For the latter case, we investigate the evolution where single nodes with one or two edges connecting to existing nodes are added recursively to a network. In both cases, we derive the relations between the properties of the nodes to which the new node connects along with the global evolution of network robustness. In particular, we show how different scalings of the Kirchhoff index can be obtained as a function of the number of nodes. We illustrate and confirm this theory via numerical simulations of randomly growing networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Space-based Science Operations Grid Prototype

Grid technology is the up and coming technology that is enabling widely disparate services to be offered to users that is very economical, easy to use and not available on a wide basis. Under the Grid concept disparate organizations generally defined as "virtual organizations" can share services i.e. sharing discipline specific computer applications, required to accomplish the specific scientific and engineering organizational goals and objectives. Grids are emerging as the new technology of the future. Grid technology has been enabled by the evolution of increasingly high speed networking. Without the evolution of high speed networking Grid technology would not have emerged. NASA/Marshall Space Flight Center's (MSFC) Flight Projects Directorate, Ground Systems Department is developing a Space-based Science Operations Grid prototype to provide to scientists and engineers the tools necessary to operate space-based science payloads/experiments and for scientists to conduct public and educational outreach. In addition Grid technology can provide new services not currently available to users. These services include mission voice and video, application sharing, telemetry management and display, payload and experiment commanding, data mining, high order data processing, discipline specific application sharing and data storage, all from a single grid portal. The Prototype will provide most of these services in a first step demonstration of integrated Grid and space-based science operations technologies. It will initially be based on the International Space Station science operational services located at the Payload Operations Integration Center at MSFC, but can be applied to many NASA projects including free flying satellites and future projects. The Prototype will use the Internet2 Abilene Research and Education Network that is currently a 10 Gb backbone network to reach the University of Alabama at Huntsville and several other, as yet unidentified, Space Station based science experimenters. There is an international aspect to the Grid involving the America's Pathway (AMPath) network, the Chilean REUNA Research and Education Network and the University of Chile in Santiago that will further demonstrate how extensive these services can be used. From the user's perspective, the Prototype will provide a single interface and logon to these varied services without the complexity of knowing the where's and how's of each service. There is a separate and deliberate emphasis on security. Security will be addressed by specifically outlining the different approaches and tools used. Grid technology, unlike the Internet, is being designed with security in mind. In addition we will show the locations, configurations and network paths associated with each service and virtual organization. We will discuss the separate virtual organizations that we define for the varied user communities. These will include certain, as yet undetermined, space-based science functions and/or processes and will include specific virtual organizations required for public and educational outreach and science and engineering collaboration. We will also discuss the Grid Prototype performance and the potential for further Grid applications both space-based and ground based projects and processes. In this paper and presentation we will detail each service and how they are integrated using Grid

Bradford, Robert N.↗
DOE OSTI2024

Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions

In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.

LiDAR, machine learning, neural network, object re↗
NASA NTRS2016

Evolution of the Mars Relay Network End-to-End Information System in the Mars Human Era (2030-2040)

In order to help the reader understand this evolution from the current to the future MRN state in the Mars human era, we will provide a description of the current communication infrastructure supporting Mars mission users, and how we envision it to change in order to meet the needs and challenges of supporting both human and robotic communication by 2030-2040. This includes the introduction of new data types like audio and video, and new in-situ relay services for communications and navigation services.

Kazz, Greg J.↗
NASA NTRS2016

Evolution of the Mars Relay Network End-To-End Information System in the Mars Human Era (2016-2040)

In order to help the reader understand this evolution from the current to the future MRN state in the Mars human era, we will provide a description of the current communication infrastructure supporting Mars mission users, and how we envision it to change in order to meet the needs and challenges of supporting both human and robotic communication by 2030-2040. This includes the introduction of new data types like audio and video, and new in-situ relay services for communications and navigation services.

Kazz, Greg↗
DOE OSTI2024

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Abstract Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

36 MATERIALS SCIENCE↗
NASA NTRS2002

An Analysis for an Internet Grid to Support Space Based Operations

Currently, and in the past, dedicated communication circuits and "network services" with very stringent performance requirements have been used to support manned and unmanned mission critical ground operations at GSFC, JSC, MSFC, KSC and other NASA facilities. Because of the evolution of network technology, it is time to investigate other approaches to providing mission services for space ground and flight operations. In various scientific disciplines, effort is under way to develop network/komputing grids. These grids consisting of networks and computing equipment are enabling lower cost science. Specifically, earthquake research is headed in this direction. With a standard for network and computing interfaces using a grid, a researcher would not be required to develop and engineer NASA/DoD specific interfaces with the attendant increased cost. Use of the Internet Protocol (IP), CCSDS packet spec, and reed-solomon for satellite error correction etc. can be adopted/standardized to provide these interfaces. Generally most interfaces are developed at least to some degree end to end. This study would investigate the feasibility of using existing standards and protocols necessary to implement a SpaceOps Grid. New interface definitions or adoption/modification of existing ones for the various space operational services is required for voice both space based and ground, video, telemetry, commanding and planning may play a role to some undefined level. Security will be a separate focus in the study since security is such a large issue in using public networks. This SpaceOps Grid would be transparent to users. It would be anagulous to the Ethernet protocol's ease of use in that a researcher would plug in their experiment or instrument at one end and would be connected to the appropriate host or server without further intervention. Free flyers would be in this category as well. They would be launched and would transmit without any further intervention with the researcher or ground ops personnel. The payback in developing these new approaches in support of manned and unmanned operations is lower cost and will enable direct participation by more people in organizations and educational institutions in space based science. By lowering the high cost of space based operations and networking, more resource will be available to the science community for science. With a specific grid in place, experiment development and operations would be much less costly by using standardized network interfaces. Because of the extensive connectivity on a global basis, significant numbers of people would participate in science who otherwise would not be able to participate.

Bradford, Robert↗
DOE OSTI2025

Mesoscale Modeling of Dislocation Cell Structure Evolution and Radiation-Induced Segregation in Additively Manufactured Austenitic Stainless Steel

Structural alloys under irradiation develop radiation-induced segregation (RIS) at point defect sinks, which lead to undesired changes in the alloy's properties. Additively manufactured austenitic stainless steels are expected to yield a distinct response to irradiation damage owing to their unique as-printed dislocation cell structure. In this talk, we present the development of a mesoscale model for dislocation cell structure evolution coupled with RIS using the MOOSE framework. The evolution of network dislocations within spatially distinct cells and cell walls are modeled using climb-mediated edge annihilation and generation processes. Starting with initial microstructures of as-printed non-equilibrium segregations at dislocation cell walls, our 2D and 3D simulations explore the effects of irradiation temperature, dose rate, dislocation sink bias, and specimen thickness on microstructure evolution. The results will be compared against experimental characterizations of in-situ and ex-situ ion-irradiated samples, and the implications of evolving cell structure on irradiation damage response will be discussed.

additive manufacturing↗
DOE OSTI2022

The Evolution of the Peridynamics Co-Authorship Network

We report peridynamics is a relatively new field in continuum mechanics that has developed over the past 20 years. This paper studies the evolution of collaborations in the field of peridynamics since its inception using social network analysis. For this purpose, we construct a network for each year from 2000 to 2019 describing co-authorship between scientists in peridynamics. In these networks, each node represents a scientist and each link connects two co-authoring scientists with a link weight representing the frequency and strength of their collaboration; each network as a whole can be thought of as a graph representation of the peridynamics community for the given year. By constructing a network for each year, we are able to analyze the evolution of the network in time and discuss the implications of this evolution for the peridynamics community. Our study demonstrates that the peridynamics community has been growing exponentially in size in recent years. Centrality metrics are also used to identify the most collaborative scientists in the community. Moreover, we compute link recommendations based on both elevating a scientist’s position in the network with respect to certain centrality metrics or closing structural holes in the network identified with persistent homology. We further extend the analysis to higher-order networks whose nodes represent groups of scientists in the community and whose links connect collaborating groups. In some sense, our work studies the past, present, and future of the peridynamics community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exact block encoding of imaginary time evolution with universal quantum neural networks

We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the nonunitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave-function is closed under the action of the imaginary time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the yields the ground state of the system. The number of qubits grows linearly with the number of interactions and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via midcircuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and number of interactions, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the number of interactions, and circuit depth grows linearly with imaginary time only. Every midcircuit measurement has a postselection success probability, and the overall success probability is equal to the product of the probabilities of the midcircuit measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multifold Insights for Power System Dynamics From Data Assimilation: Meeting Current Challenges

The world’s electric power grids have evolved over the last 120 years from a single power line to today’s large networks. The evolution will continue at an accelerated rate with extensive smart grid development worldwide. Here for example, the U.S. government has set a goal of reaching 100 percent carbon pollution-free electricity by 2035, while the Department of Energy (DOE) shared a goal to deploy 30 GW and 110 GW of offshore wind by 2030 and 2050, respectively. To meet such ambitious goals, in the years to come, a significant percentage of electricity will come from intermittent renewable sources, electric vehicles (EVs), and be supplied to a vast number of loads that will actively respond to grid conditions and incentive signals. This development is largely driven by environmental and economic factors, such as reducing carbon emissions and saving electricity cost for consumers. These energy resources lead to new uncertain behaviors and dynamics which the grid has never seen and were not considered in its design. Operating such a dynamic grid with sufficient reliability and efficiency is a monumental challenge.

24 POWER TRANSMISSION AND DISTRIBUTION↗