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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗
DOE OSTI2016

Cost‐effective monitoring of biological invasions under global change: a model‐based framework

Summary Ecological monitoring programmes are designed to detect and measure changes in biodiversity and ecosystems. In the case of biological invasions, they can contribute to anticipating risks and adaptively managing invaders. However, monitoring is often expensive because large amounts of data might be needed to draw inferences. Thus, careful planning is required to ensure that monitoring goals are realistically achieved. Species distribution models ( SDM s) can provide estimates of suitable areas to invasion. Predictions from these models can be applied as inputs in optimization strategies seeking to identify the optimal extent of the networks of areas required for monitoring risk of invasion under current and future environmental conditions. A hierarchical framework is proposed herein that combines SDM s, scenario analysis and cost analyses to improve invasion assessments at regional and local scales. We illustrate the framework with Acacia dealbata Link. (Silver‐wattle) in northern Portugal. The framework is general and applicable to any species. We defined two types of monitoring networks focusing either on the regional‐scale management of an invasion, or management focus within and around protected areas. For each one of these two schemes, we designed a hierarchical framework of spatial prioritization using different information layers (e.g. SDM s, habitat connectivity, protected areas). We compared the performance of each monitoring scheme against 100 randomly generated models. In our case study, we found that protected areas will be increasingly exposed to invasion by A. dealbata due to climate change. Moreover, connectivity between suitable areas for A. dealbata is predicted to increase. Monitoring networks that we identify were more effective in detecting new invasions and less costly to management than randomly generated models. The most cost‐efficient monitoring schemes require 18% less effort than the average networks across all of the 100 tested options. Synthesis and applications . The proposed framework achieves cost‐effective monitoring networks, enabling the interactive exploration of different solutions and the combination of quantitative information on network performance with orientations that are rarely incorporated in a decision support system. The framework brings invasion monitoring closer to European legislation and management needs while ensuring adaptability under rapid climate and environmental change.

Vicente, Joana R.↗
DOE OSTI2018

Iodine in Metal–Organic Frameworks at High Pressure

Capture of highly volatile radioactive iodine is a promising application of metal–organic frameworks (MOFs), thanks to their high porosity with flexible chemical architecture. Specifically, strong charge-transfer binding of iodine to the framework enables efficient and selective iodine uptake as well as its long-term storage. As such, precise knowledge of the electronic structure of iodine is essential for a detailed modeling of the iodine sorption process, which will allow for rational design of iodophilic MOFs in the future. Here we probe the electronic structure of iodine in MOFs at variable iodine···framework interaction by Raman and optical absorption spectroscopy at high pressure (P). The electronic structure of iodine in the straight channels of SBMOF-1 (Ca- sdb , sdb = 4,4'-sulfonyldibenzoate) is modified irreversibly at P > 3.4 GPa by charge transfer, marking a polymerization of iodine molecules into a 1D polyiodide chain. In contrast, iodine in the sinusoidal channels of SBMOF-3 (Cd- sdb ) retains its molecular (I 2 ) character up to at least 8.4 GPa. Such divergent high-pressure behavior of iodine in the MOFs with similar port size and chemistry illustrates adaptations of the electronic structure of iodine to channel topology and strength of the iodine···framework interaction, which can be used to tailor iodine-immobilizing MOFs.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Evaluation of copper, aluminum, and nickel interatomic potentials on predicting the elastic properties

Choice of appropriate force field is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to yield reliable results. Since investigations on the mechanical behavior of materials at micro/nanoscale have been becoming much more widespread, it is necessary to determine an adequate potential which accurately models the interaction of the atoms for desired applications. In this framework, reliability of multiple embedded atom method based interatomic potentials for predicting the elastic properties was investigated. Assessments were carried out for different copper, aluminum, and nickel interatomic potentials at room temperature which is considered as the most applicable case. Examined force fields for the three species were taken from online repositories of National Institute of Standards and Technology, as well as the Sandia National Laboratories, the LAMMPS database. Using molecular dynamic simulations, the three independent elastic constants, C{sub 11}, C{sub 12}, and C{sub 44}, were found for Cu, Al, and Ni cubic single crystals. Voigt-Reuss-Hill approximation was then implemented to convert elastic constants of the single crystals into isotropic polycrystalline elastic moduli including bulk modulus, shear modulus, and Young's modulus as well as Poisson's ratio. Simulation results from massive molecular dynamic were compared with available experimental data in the literature to justify the robustness of each potential for each species. Eventually, accurate interatomic potentials have been recommended for finding each of the elastic properties of the pure species. Exactitude of the elastic properties was found to be sensitive to the choice of the force fields. Those potentials that were fitted for a specific compound may not necessarily work accurately for all the existing pure species. Tabulated results in this paper might be used as a benchmark to increase assurance of using the interatomic potential that was designated for a problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2010

Risk-Informed Safety Assurance and Probabilistic Assessment of Mission-Critical Software-Intensive Systems

This report validates and documents the detailed features and practical application of the framework for software intensive digital systems risk assessment and risk-informed safety assurance presented in the NASA PRA Procedures Guide for Managers and Practitioner. This framework, called herein the "Context-based Software Risk Model" (CSRM), enables the assessment of the contribution of software and software-intensive digital systems to overall system risk, in a manner which is entirely compatible and integrated with the format of a "standard" Probabilistic Risk Assessment (PRA), as currently documented and applied for NASA missions and applications. The CSRM also provides a risk-informed path and criteria for conducting organized and systematic digital system and software testing so that, within this risk-informed paradigm, the achievement of a quantitatively defined level of safety and mission success assurance may be targeted and demonstrated. The framework is based on the concept of context-dependent software risk scenarios and on the modeling of such scenarios via the use of traditional PRA techniques - i.e., event trees and fault trees - in combination with more advanced modeling devices such as the Dynamic Flowgraph Methodology (DFM) or other dynamic logic-modeling representations. The scenarios can be synthesized and quantified in a conditional logic and probabilistic formulation. The application of the CSRM method documented in this report refers to the MiniAERCam system designed and developed by the NASA Johnson Space Center.

Guarro, Sergio B.↗
DOE OSTI2015

Computer model for electrochemical cell performance loss over time in terms of capacity, power, and conductance (CPC)

Available capacity, power, and cell conductance figure centrally into performance characterization of electrochemical cells (such as Li-ion cells) over their service life. For example, capacity loss in Li-ion cells is due to a combination of mechanisms, including loss of free available lithium, loss of active host sites, shifts in the potential-capacity curve, etc. Further distinctions can be made regarding irreversible and reversible capacity loss mechanisms. There are tandem needs for accurate interpretation of capacity at characterization conditions (cycling rate, temperature, etc.) and for robust self-consistent modeling techniques that can be used for diagnostic analysis of cell data as well as forecasting of future performance. Analogous issues exist for aging effects on cell conductance and available power. To address these needs, a modeling capability was developed that provides a systematic analysis of the contributing factors to battery performance loss over aging and to act as a regression/prediction platform for cell performance. The modeling basis is a summation of self-consistent chemical kinetics rate expressions, which as individual expressions each covers a distinct mechanism (e.g., loss of active host sites, lithium loss), but collectively account for the net loss of premier metrics (e.g., capacity) over time for a particular characterization condition. Specifically, sigmoid-based rate expressions are utilized to describe each contribution to performance loss. Through additional mathematical development another tier of expressions is derived and used to perform differential analyses and segregate irreversible versus reversible contributions, as well as to determine concentration profiles over cell aging for affected Li+ ion inventory and fraction of active sites that remain at each time step. Reversible fade components are surmised by comparing fade rates at fast versus slow cycling conditions. The model is easily utilized for predictive calculations so that future capacity performance can be estimated. The invention covers mathematical and theoretical frameworks, and demonstrates application to various Li-ion cells covering test periods that vary in duration, and shows model predictions well past the end of test periods. Version 2.0 Enhancements: the code now covers path-dependent aging scenarios, wherein the framework allows for arbitrarily-chosen aging conditions over a timeline to accommodate prediction of battery aging over a multiplicity of changing conditions. The code framework also allows for cell conductance and power loss evaluations over cell aging, analysis of series strings that contain a thermal anomaly (hot spot), and evaluation of battery thermal management parameters that impact battery lifetimes. Lastly, a comprehensive GUI now resides in the Ver. 2.0 code.

Gering, KevinL.↗
DOE OSTI2024

Integrating Quantum Computing with High-Performance Computing: A Streamlined Approach

In recent years, quantum computing has demon-strated the potential to revolutionize specific algorithms and applications by solving problems exponentially faster than classical computers. However, its widespread adoption for general computing remains a future prospect. This paper discusses the integration of quantum computing within High-Performance Computing (HPC) environments, focusing on a resource management framework designed to streamline quantum simulators' use and enhance runtime performance and efficiency. The proposed framework facilitates hybrid applications' transition from simulation backends to real quantum hardware, optimizing resource utilization and providing a flexible infrastructure for developing and testing quantum algorithms.

Shehata, Amir↗
DOE OSTI2024

Ecologically inspired metrics for transitioning to a sustainable and resilient circular economy with application to multilayer plastic films

Current Circular Economy (CE) frameworks applied to product chains exhibit notable shortcomings. These include neglecting the resilience and robustness of design, requiring detailed economic and environmental data for impact assessment, and relying on qualitative rather than quantitative metrics capturing certain CE design aspects. In the current contribution, we addressed these shortcomings by developing an Ecologically inspired (Eco-inspired) Framework using the mathematical foundations of Ecological Network Analysis (ENA). While ENA metrics have previously found application in designing circular economies, particularly in Industrial Symbiosis (IS) networks, our adaptation tailors these metrics for use in product-level CE, recognizing the inherent distinctions between product-level CE and IS. Our Eco-inspired Framework comprises three key categories to provide holistic and granular-level metrics for designing product-level CE. The first set of metrics assesses circularity and resource efficiency. The second set gauges network intensity and robustness as complementary indicators ensuring a CE is both sustainable and resilient. The third group of metrics evaluates enhancement potential of CE strategies through introducing quantitative metrics for measuring the degree of closed-loop strategies and average circularity level of a CE design. The three comprehensive set of indicators within the Eco-inspired Framework uniquely captures various facets of circular design, whether originating from technological innovations and recovery improvement at the end of life (EoL), shifts in human consumption patterns, alterations in product design, or changes in business models. The framework’s application is tested in designing a CE for multilayer Polyethylene-Polyamide (PE-PA) films. Using the Eco-inspired Framework, we identified the best strategy for designing a resilient and sustainable CE for PE-PA films. A diverse set of EoL strategies along with a reduction in product consumption can improve circularity and resilience by 650% and 255%, respectively, and mitigate greenhouse gas emissions by 90%. The framework minimizes trade-offs between sustainability and circularity goals and offers insights on how to enhance each strategy for achieving a resilient and sustainable CE for products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancement of risk informed validation framework for external hazard scenario

In recent years, the U.S. Nuclear Regulatory Commission (USNRC) and the International Atomic Energy Agency (IAEA) have developed methodologies to assess the vulnerabilities of nuclear plants against site specific extreme hazards. In many cases, advanced simulation tools are being considered to simulate multi-physics, multi-scale phenomena and to evaluate vulnerability of nuclear facilities. The credibility of advanced simulation tools is assessed based on a formal verification, validation, and uncertainty quantification procedure. One of the key limitations in validation is the lack of relevant experimental data at system-level. This limitation leads to a decrease in the confidence of system-level risk predictions. Therefore, a robust validation framework is needed to formalize the confidence in predictive capability of advanced simulation results. Additionally, this study enhances the existing risk informed validation methodology, originally proposed by Kwag et al. [1] and Bodda et al. [2], by developing additional attributes and a new set of validation indicies for a complete and wider applicability of the framework. In this manuscript, the methodology to identify the critical path that leads to the system-level failure is illustrated. The overall validation is checked for completeness and consistency by comparing the critical path for both the system-level simulation and experimental models. The applicability of the code for an intended application is represented in terms of various maturity levels and helps in the process of decision making.

42 ENGINEERING↗
DOE OSTI2019

Solvent-Free Synthesis of CuO/HKUST-1 Composite and Its Photocatalytic Application

A metal–organic framework (MOF) is one kind of crystalline microporous material and is increasingly used as a host of catalytically active guests. Nanostructured materials supported on MOFs have presented enhanced catalytic activity and stability. Templates or several steps are essential to the synthesis of MOF composites. Simple and effective MOF synthesis methods are still challenging. In this work, nanosized copper oxide particles in MOF composites, described as nanosized CuO@HKUST-1, were prepared by a facile solvent-free reaction. These series of CuO@HKUST-1 composites exhibited excellent photocatalytic activity for production of hydrogen and methylene blue (MB) degradation under visible light. This synthesis method provides an effective way to fabricate MOF-related nanocomposite catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Scenario Storyline Discovery for Planning in Multi‐Actor Human‐Natural Systems Confronting Change

Scenarios have emerged as valuable tools in managing complex human-natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced “forensic”): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of scenario storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify drought scenario storylines, that can be used to inform future adaptation planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Scalable Multigrid Reduction Framework for Multiphase Poromechanics of Heterogeneous Media

Simulation of multiphase poromechanics involves solving a multiphysics problem in which multiphase flow and transport are tightly coupled with the porous medium deformation. To capture this dynamic interplay, fully implicit methods, also known as monolithic approaches, are usually preferred. The main bottleneck of a monolithic approach is that it requires solution of large linear systems that result from the discretization and linearization of the governing balance equations. Because such systems are nonsymmetric, indefinite, and highly ill-conditioned, preconditioning is critical for fast convergence. Recently, most efforts in designing efficient preconditioners for multiphase poromechanics have been dominated by physics-based strategies. Current state-of-the-art “black-box” solvers such as algebraic multigrid (AMG) are ineffective because they cannot effectively capture the strong coupling between the mechanics and the flow subproblems, as well as the coupling inherent in the multiphase flow and transport process. In this work, we develop an algebraic framework based on multigrid reduction (MGR) that is suited for tightly coupled systems of PDEs. Using this framework, the decoupling between the equations is done algebraically through defining appropriate interpolation and restriction operators. One can then employ existing solvers for each of the decoupled blocks or design a new solver based on knowledge of the physics. We demonstrate the applicability of our framework when used as a “black-box” solver for multiphase poromechanics. Here, we show that the framework is flexible to accommodate a wide range of scenarios, as well as efficient and scalable for large problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation

Model-based and data-driven methods are combined to develop a hierarchical decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Assessment Framework of Marine Hydrokinetic Technologies for Microgrid Applications

Communities in Alaska that are not connected to a regional grid (referred to hereafter as grid-islanded Alaskan communities) rely heavily on stand-alone generators with imported diesel fuel as the primary source of energy. Several of these grid-islanded Alaskan communities have the potential to harness significant wave, tidal, and hydrokinetic power; many also have hydropower or wind potential that could complement these resources. The implementation of marine and hydrokinetic energy focused microgrids in these communities would diversify their energy profiles, with the potential in many communities to keep costs flat while reducing dependance on diesel. This would enhance resilience and reduce environmental impacts. This seedling proposal will systematically identify grid-islanded coastal communities in Alaska with wave, tidal, and hydrokinetic energy potential to add fuel diversity to generation, applying microgrid integration methods and strategies that will meet the community’s needs. This proposal is in partnership with the Alaska Center for Energy and Power (ACEP) and XENDEE Corporation who bring extensive expertise in Alaskan communities and microgrid technoeconomic assessment, respectively. This project will deliver: (1) a database of grid-islanded communities that are candidates for MHK based microgrids, (2) an assessment of possible microgrid integration methods and strategies related to tidal, wave, and hydrokinetic technologies, and (3) a MHK development plan for the XENDEE Microgrid design platform and planning tool that can serve these grid-islanded communities and project developers. In the final stage, the planning tool will allow for the comprehensive comparison between diesel based and MHK based renewable generation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

An Explainable Classification Framework for Determining and Understanding the Suitability of Solvent Extraction for Bioproduct Recovery

Lignocellulosic biomass is an abundant feedstock for producing sustainable fuels and chemicals. However, a key challenge in most biomass utilization strategies is the recovery of products from a dilute, typically aqueous, phase. In this respect, liquid–liquid extraction, which relies on a solvent to transfer a product of interest from one liquid phase to another (solvent-rich) phase, is a technology that can reduce the energy requirements for product recovery. To reduce solvent consumption, liquid–liquid extraction needs to be combined with another separation method (e.g., distillation) to recycle the solvent. Despite the research on solvent extraction, there are limited system-wide methods that allow us to determine when extraction is well suited to carry out a specific separation. Accordingly, we present a classification framework to predict whether extraction, coupled with distillation, is feasible and more economical than distillation. Our framework is based on features such as feed composition, liquid–liquid equilibrium constants, relative volatilities, and solvent price, and leads to trained classifiers that show good prediction accuracy. We further study how specific features influence the suitability of extraction. Furthermore, to showcase the applicability of the framework, we use it to analyze the separation of acetic acid from water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗