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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 OSTI2022

Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach

Severe geomagnetic disturbances (GMDs) increase the magnitude of the electric field on the Earth’s surface (E-field) and drive geomagnetically-induced currents (GICs) along the transmission lines in electric grids. These additional currents can pose severe risks, such as current distortions, transformer saturation and increased reactive power losses, each of which can lead to system unreliability. Today several mitigation actions (e.g., changing grid topology) exist that can reduce the harmful GIC effects on the grids. Making such decisions can be challenging, however, because the magnitude and direction of the E-field are uncertain and non-stationary. In this paper, we model uncertain E-fields using the distributionally robust optimization (DRO) approach that determines optimal transmission grid operations such that the worst-case expectation of the system cost is minimized. We also capture the effect of GICs on the nonlinear AC power flow equations. For solution approaches, we develop an accelerated column-and-constraint generation (CCG) algorithm by exploiting a special structure of the support set of uncertain parameters representing the E-field. Extensive numerical experiments based on “epri-21” and “uiuc-150” systems, designed for GMD studies, demonstrate (i) the computational performance of the accelerated CCG algorithm, (ii) the superior performance of distributionally robust grid operations that satisfy nonlinear, nonconvex AC power flow equations and GIC constraints, in comparison with standard stochastic programming-based methods during the out-of-sample testing.

42 ENGINEERING↗
DOE OSTI2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

Bi, Sirui↗
DOE OSTI2024

Deep learning-based predictive models for laser direct drive at the Omega Laser Facility

The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Observation-Based Evaluation of Flood Estimation Techniques for a Small Snow-Dominated Basin in the Washington Cascade Mountains

Here, this study examines the application of the recently developed next-generation intensity-duration-frequency (NG-IDF) curves in hydrological design, focusing on a small snow-dominated basin in Washington state. Four methods are used to assess the NG-IDF performance: (1) basin outlet flood frequency analysis, (2) use of standard precipitation-based intensity-duration-frequency (PREC-IDF) curves following surface water design manuals typically used in the basin, (3) utilization of locally constructed PREC-IDF curves, and (4) use of locally constructed NG-IDF curves. The standard PREC-IDF design method assumes precipitation as rainfall and neglecting snowmelt or rain-on-snow (ROS) events that can lead to significant flood underestimations in the test basin. In contrast, the NG-IDF method incorporates snow processes and shows promising improvements in accurately estimating design floods. The validation analysis demonstrates the superiority of NG-IDF over standard and local PREC-IDF in this test basin. The study highlights the necessity for adjustments in current design manuals to ensure sufficient flood protection, particularly in light of the planned substantial investments in US infrastructure.

13 HYDRO ENERGY↗
DOE OSTI2026

Electrochemical dynamics of imidazolium ionic liquids at graphene electrodes for energy storage applications

Electric double layer capacitors (EDLCs) are prominent energy storage systems that constitute the foundation of more reliable and sustainable energy infrastructures. Modern EDLCs often incorporate ionic liquids (ILs) as a key component in their electrolytes, leveraging the high electrochemical stability of ILs to enhance device performance. The performance and functionality of these capacitors also hinge on the interfacial behavior of ILs at electrode surfaces, which remain insufficiently understood. Here, we performed synchrotron infrared nanospectroscopy (SINS) in combination with density functional theory (DFT) calculations to investigate the electric double layers (EDLs) of three imidazolium-based ILs in a custom-designed graphene liquid cell. This approach revealed new insights into the dynamics of IL EDLs and the underpinning factors originating from the IL structures. Variations in anion size and structure were found to tune the ILs’ ability to form EDLs with compact and closely-correlated ion arrangements, which are critical for enhancing their capacitive performance. These findings highlight the intricate interactions between ions governed by their structures and charge behaviors, which underscores the opportunities for targeted design of IL-based electrolytes to optimize EDLC functionality.

Electric double layers↗
DOE OSTI2023

Modelling integrated quantum frequency processors towards robust quantum networks

Frequency-encoded quantum information offers intriguing opportunities for quantum communications networks, with the quantum frequency processor (QFP) paradigm promising scalable construction of quantum gates. Yet all experimental demonstrations to date have relied on discrete fiber-optic components that occupy significant physical space and impart appreciable loss. We introduce a model for designing QFPs comprising microring resonator-based pulse shapers and integrated phase modulators. We estimate the performance of frequency-bin Hadamard gates, finding high fidelity values sustained for relatively wide-bandwidth frequency bins. Our simple model and can be extended to other material platforms, providing a design tool for future frequency processors in integrated photonics.

Nussbaum, Benjamin↗
DOE OSTI2024

Development and Validation of a Process Model and Open-Source Process Simulator for Microalgae-Based Tertiary Phosphorus Recovery

Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In this study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM 2 ), including both photoautotrophic and heterotrophic metabolisms of microalgae, was developed to predict effluent phosphorus concentration and biomass yield in response to dynamic influent and varying environmental conditions. The model was implemented in QSDsan – an open-source, Python-based design and simulation platform – for robust simulation under uncertainty. A global sensitivity analysis was performed to prioritize model parameters for calibration. The model was then calibrated and validated using batch experimental data and 45 days of continuous online monitoring data from a full-scale (568 m 3 ·d -1 ) microalgae-based tertiary wastewater treatment plant (EcoRecover process). In particular, along with dynamic influent composition, temperature and light intensity data with diel variation were provided as model inputs to reflect the microalgal behavior under day-night cycling. Overall, the QSDsan-based microalgae process simulator was able to predict effluent phosphorus within 0.02–0.04 mg-P·L -1 , while also capturing the general trends of state variables according to nutrient availability.

Lumped pathway metabolic model↗
DOE OSTI2021

Performance Evaluation of Distributed Energy Resource Management Algorithm in Large Distribution Networks

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of a full-scale distribution system of 11,000 nodes, the DERMS software controller, and 90 power hardware photovoltaics (PVs) and battery inverters. The applied DERMS algorithm is designed based on a realtime optimal power flow algorithm and implemented with acceleration design that performs fast dispatch of simulated PVs and real physical hardware DER devices every 4 seconds.

DERMS↗
DOE OSTI2024

Multiphysics Co-Optimization Design and Analysis of Double-Side Cooled Silicon Carbide-Based Power Module: Preprint

With the rapid growth of Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs), much more rigorous design targets have been set for automotive power electronics, including high power density, high reliability, and low cost. Novel power module and inverter technologies based on wide bandgap (WEG) semiconductors have been developed to meet these design targets, while providing optimal power semiconductor operating temperature and promising thermomechanical performance. Compared with conventional cooling techniques which are normally applied only on one side of power module, double-side cooling approach is now believed to be the solution to enable high power density and low thermal resistance of WEG semiconductor-based power electronics. In this work, we develop a three-phase power module that is double-sided cooled using dielectric fluid jet impingement. In each phase, four silicon carbide (SiC) power semiconductors are bonded to copper busbars without electrical insulation layers. A finite element analysis (FEA) model is created for thermal and thermomechanical analysis. Based on FEA modeling results, we select particular dimensions for a parametric study to optimize thermal and mechanical performance. Using a multi-objective genetic algorithm (MOGA)-based optimization method, we have minimized the maximum junction temperature and thermal stresses within the power module. The multiphysics co-optimization approach has enabled an efficient design process of power modules with greatly reduced computational cost, as compared to conventional processes that rely on exhaustive numerical simulations and iterations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A System-Level Model for Estimating Residual Strain and Life of Nuclear Reactor Coolant System Components Under Connected-System-Thermal–Mechanical Boundary Conditions

Background: Environmental-assisted fatigue (EAF) is a major issue for the long-term survival of nuclear power plant fleets in the U.S. and worldwide. Multi-material welded regions (e.g., nozzles) and other high-stress regions of reactor coolant system (RCS) components are prone to EAF-related damage. Objective: The discussed work describes a system-level finite element (FE) model of RCS components of a pressurized water reactor (PWR). This is with the goal of predicting the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Methods: Herein, the FE model was developed considering system-level loading conditions (under connected system thermal-mechanical boundary conditions). Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the FE model results, the strain residuals, strain amplitudes and resulting fatigue lives of RCS components were predicted. Results: The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. Higher residual strain can lead to accelerated cyclic hardening of material and the associated effect of EAF. The simulated component-level strain profile (under realistic multi-axial-multi-physics loading cycle) can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests, which is a focus of future works. Conclusions: Despite similar geometry and material the RCS component can have significantly different strain profiles and resulting fatigue lives. The discussed approach can help to identify and prioritize the RCS components for conducting expensive nondestructive evaluation (NDE) inspections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Chemically and mechanically recyclable polyester-based multilayer plastics

Approximately 100 million tons of multilayered plastics (MLPs) are produced each year worldwide but are not recycled due to their complex structure. Here, this work aims to design polyester-based multilayer plastics (80–100 % polyester) that provide barrier performance comparable to typical 9–12-layer commercial MLPs, while also enabling both chemical recycling (back to parent monomer) and mechanical recycling (grind-and-melt reprocessing). Such dual recyclability is not achievable with non-polyester multilayers, such as all-polyolefin systems. Furthermore, we emphasize how the multilayer architecture was tailored to balance barrier properties, mechanical integrity, and end-of-life recyclability for both flexible and rigid packaging applications. Two main categories of polyester-based MLPs are reported; in the first type, poly(butylene adipate-co-terephthalate) (PBAT)-70 % polyglycolic acid (PGA) is used as middle barrier layer, while in second type, middle barrier layer is Ethylene-vinyl alcohol (EVOH) copolymers. Polyethylene terephthalate (PET) was used as a structural layer, while either PBAT or poly(butylene succinate) (PBS) was used to enable thermal sealing and serve as the product contact layer. These MLPs are recycled by both chemical and mechanical recycling processes. Techno-economic analysis (TEA) shows that MLPs incorporating EVOH as barrier layer have similar or lower selling costs (0.32 $\$$/m 2 ) than commercial MLPs. Life cycle assessment (LCA) indicates EVOH-based MLPs have a lower carbon footprint and lower energy consumption relative to commercial MLP benchmarks. This work offers simplified MLPs that are easy to manufacture and ready to recycle, which will significantly reduce environmental impact of MLP packaging while also providing a cost-effective and practical solution for industry.

Barrier properties↗
DOE OSTI2020

Molecular-Level Behavior of Imidazolium-Based Ionic Liquid Mixtures

Understanding the molecular-level organization of anions and cations in a mixture of different ionic liquids (ILs) is challenging, but it can be very important for designing solvents and IL-based membrane materials. In this work, we perform all-atom molecular dynamics (MD) simulations to understand the intermolecular interactions and structuring of both neat and mixed IL compositions containing 1-benzyl-3-methylimidazolium [ZMIM + ], 1-butyl-3-methylimidazolium [BMIM + ], bistriflimide [Tf 2 N – ], and tetrafluoroborate [BF 4 – ]. We observe very strong underlying molecular-level migrations in these mixtures, and this tends to mainly involve the anion species. As compared to the systems with a single anion, the mixed anions lead to large increases/decreases in the radial distribution function peak heights, whereas the cation mixing is relatively benign. The [BF 4 – ] anions have a strong tendency to displace the [Tf 2 N – ] anions in the mixtures, and this is seen with respect to both [BMIM + ] and [ZMIM + ].

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Computational flow modeling of triply periodic minimal surfaces as feed channel spacers in ultra-high pressure reverse osmosis applications

Triply periodic minimal surfaces (TPMS) are a special class of mathematical surfaces characterized by a high surface area-to-volume ratio. They have generated considerable interest in fields such as acoustics, heat transfer, and membrane-based filtration processes. This study evaluates the performance of four different TPMS designs—Schoen Gyroid, Schoen Crossed Layers of Parallels (CLP), Schoen Transverse Crossed Layers of Parallels (tCLP), and Schwarz-Primitive—when used as feed channel spacers under ultra-high pressure reverse osmosis (UHPRO) conditions, at approximately 200 bar. Our experimentally validated computational fluid dynamics model reveal different flow patterns within the feed channels for each of the four TPMS designs, leading to varying hydrodynamic and permeation properties. Under the simulated UHPRO conditions, the Gyroid and tCLP designs yield up to a 23% increase in average permeate velocity and a 14% reduction in average membrane-surface concentration relative to a non-woven spacer of the same porosity. Furthermore, the enhanced performance comes with an increased feed channel pressure drop, although it only constitutes less than 4% of the operating pressure when extrapolated for a meter-long membrane module. Additionally, the study analyzes the effects of varying inlet velocity and spacer porosity on membrane performance. Overall, this research provides valuable insights into the potential use of TPMS spacers in UHPRO applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures—comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers)—as well as 100 non-equilibrium structural variations thereof to reach a total of ≈4.2 million molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Retrofit Energy Analysis and Central Thermal modeling (REACT) v1.0

REACT is a website designed to simplify the analysis and decision-making process for retrofitting existing central plant heating and cooling systems with advanced heat pump technologies. The tool evaluates the technical and economic viability of replacing traditional boilers with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. Retrofitting traditional boiler and chiller systems with water-to-water or air-to-water heat pumps can significantly reduce energy consumption and lower lifecycle costs. The REACT provides: User-Friendly Tools: A user friendly web interface for quick, intuitive analysis accessible to non-experts. Advanced Modeling: A Python-powered engine for detailed parametric studies, optimization, and research applications. Comprehensive Analysis: Lifecycle cost evaluation, energy consumption modeling, and environmental impact assessment. Visual Insights: A variety of plots to visualize system performance and design trade-offs. The engine for the website (REACT) bases on several Python libraries, and the website will be hosted on an ETA server.

Kim, Donghun [Lawrence Berkeley National Laborator↗
DOE OSTI2020

Project 57 Air Monitoring Report: (Jan 1 - Dec 31 2018)

During the late 1950s, the Atomic Energy Commission (AEC) (now the U.S. Department of Energy [DOE]) conducted a series of safety experiments to determine if a nuclear device subjected to a large conventional explosives detonation would result in a nuclear yield. The AEC obtained temporary use of a large portion of western Emigrant Valley from the U.S. Department of Defense (DOD) for one of these experiments: Project 57. Following the Project 57 safety experiment, the AEC fenced the contaminated area and returned control of the surrounding land to the DOD. Emigrant Valley is part of the Nevada Test and Training Range (NTTR). For safety and security reasons, access to the NTTR is controlled using both physical (i.e., fences) and administrative (e.g., signs and postings) controls. Therefore, the public cannot access the Project 57 site and there are no known human receptors that routinely access the site. Project 57 was detonated on April 24, 1957, in Emigrant Valley approximately 13 mi (21 km) northeast of the north end of Yucca Flat. This test was undertaken to develop: (1) a means of estimating immediate distribution and long-term redistribution of plutonium dispersed during a nonnuclear detonation; (2) biomedical evaluation techniques for use in likely plutonium-laden environments; (3) methods of decontaminating ground areas, pavements, and building materials; and (4) alpha survey instruments and field monitoring procedures to promptly estimate contaminant deposition. Although the test did not result in the fission of nuclear materials, it did disseminate plutonium across the ground surface. Various radiological surveys have been performed in the area since Project 57 was conducted. The AEC delineated the area containing radioactive material based on radioactivity surveys performed shortly after the test was conducted. A designated Contamination Area (CA), marked by a fence, was established later. The distribution of americium-241 (Am-241) in the area was determined again in a 1997 flyover, which showed Am-241 ranging from as much as 70,000 counts per second (cps) at ground zero to background (<70 cps). This survey also documented Am-241 radioactivity on the ground surface beyond the east side CA fence at levels of up to 150 cps. In 2007, the DOE expanded the CA by posting “Contamination Area” signs 200 ft to 400 ft (60 m to 120 m) beyond the original fence, which formed a new CA boundary.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An introduction to neuromorphic computing and its potential impact for unattended ground sensors

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

97 MATHEMATICS AND COMPUTING↗