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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Evaluating the Thermal Performance of Mass Walls Compared to Wood-Framed and Steel-Framed Walls in Residential Buildings using EnergyPlus

While homes across the United States are traditionally built using wood-frame construction, mass or concrete walls are popular in certain regions of the country. While, mass walls have certain obvious advantages in terms of energy efficiency compared to the lighter wood-frame or steel-frame walls, conducting hotbox tests and specialized thermal modelling analyses of these walls to evaluate energy performance is often time-consuming and cost-prohibitive. More importantly, heat transfer in real buildings is a dynamic process consisting of the effects of outdoor air temperature and solar gains in conjunction with infiltration and internal gains. These are not captured through isolated hotbox tests or modelling techniques. The latest state-of-art building energy simulation tools can be utilized to overcome these constraints. This paper describes a comprehensive analysis of different wall types using EnergyPlus. The methodology for modelling various wood, steel and mass walls and the development of material properties for use in simulation is described. An overview of the large-scale simulation structure used in the analysis is provided. Resulting energy consumption for various mass walls is included along with a comparison of select mass walls and wood-frame or steel-frame walls.

Mass walls, Large-scale simulation, residential bu↗
DOE OSTI2021

Physics-Informed Machine Learning-Aided System Space Discretization

Decision-making is the process of identifying and choosing alternatives based on an agreed-upon set of metrics and preferences established by the decision-maker. There are options to be considered during the decision-making process and each option offers a different trajectory and associated success profile in moving from a given system state to the desired system state. The decision-making process typically involves uncertainties associated with the current component and system states. In this sense, probabilistic risk assessment (PRA) can be an analytical method and tool for accomplishing the probabilistic aspect of the decision-making process. Dynamic PRA is an evolution of conventional PRA methodology in which driving forces on modeled plant elements and the element behaviors are explicitly modeled over time. In the recent past, risk assessment methodologies have evolved to address risk issues in a continuously evolving environment and a novel probabilistic dynamics framework in continuous time and state-space discretization forms has been proposed. While state-space discretization has shown its strength in both consequence and causal reasoning modes, several challenges, including the computational requirement and physically meaningful system state identification, exist. Conventional system space discretization has usually been done by either the equal width discretization method or a data-driven method. Those methods naturally possess challenges coming from the physical understanding of discretized system space (i.e., system state) and the trajectory moving from a given system state to another system state. The purpose of this paper is to present a physics-based and data-driven system state discretization method such that one can justify what the discretized system space implies and understand the state trajectory from the viewpoint of operational actions.

Kim, Junyung↗
DOE OSTI2022

Characterizing Dynamic Structure in Battery Electrodes by Time-Resolved Cryo-TEM

In recent years, cryogenic transmission electron microscopy (cryo-TEM) has enabled high-resolution characterization of sensitive battery materials by minimizing electron beam-induced artifacts and damage. Success of this technique relies on the preparation of thin, rapidly frozen samples, generally by disassembling batteries under inert atmosphere, transferring materials of interest to a TEM grid, and finally plunge freezing into a cryogen. In material degradation studies, this extensive time between electrochemical cycling and cryo-TEM characterization leaves room for structural relaxation, diffusion, and other dynamic processes that make it difficult to precisely correlate the imaged structure with the native structure that evolves during battery cycling or aging. Here, we present a method to integrate battery cycling with fast preparation of electrode samples for cryo-TEM. This enables higher fidelity between the structures characterized and the electrochemical state of interest, which we use to study deformation in silicon nanoparticle anodes for lithium-ion batteries.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS↗
DOE OSTI2023

Enhanced microfabrication using electrochemical techniques

A method is provided for subtractively processing a layer of etchable material formed over an electrically conductive surface region of a workpiece. The workpiece is immersed in a liquid solution, generally but not exclusively a conductive solution, that comprises an etchant for the etchable material, so that etching of the etchable material is initiated. An electric circuit is connected to include a control electrode, a reference electrode, and the electrically conductive surface region of the workpiece. The electric circuit is used to monitor the development process dynamically at each of a plurality of intervals during the etching. The etching is terminated when the electrochemical signal satisfies a criterion indicating that the etching is complete.

Arrington, Christian↗
DOE OSTI2023

Digital Twin for Optimizing Real-time Economy of the Integrated Energy Systems

Economic and safe operation of integrated energy systems (IES) requires real-time optimization (RTO) of the control and actions conducted on each system component. In this regard, digital twins (DTs), which consist of a physical system, a virtual system, and the data communication that occurs between the two, are essential for effective RTO. Through the data warehouse, the virtual system is constantly updated with real-time data from the physical system, and functions as the model in the optimization framework. The reduced-order model of the dynamic process model in the virtual system is used in the optimization framework. The optimization results are then returned, via the data warehouse, as control actions to the physical system. This work demonstrates the software capabilities of DT assets for an IES in the context of preparing a DT for an experimental system comprised of Idaho National Laboratory (INL)’s Thermal Energy Delivery System and battery system. For the virtual demonstration, the DTs encompass (1) a physical system, including the Modelica models of the Thermal Energy Delivery System and the battery system; (2) virtual optimization via the Optimization of Real-Time Capacity Allocation (ORCA) platform; and (3) the open-source data warehouse software DeepLynx. This work assesses the performance of ORCA, which utilizes a reduced-order model built using the Risk Analysis Virtual Environment (RAVEN) and trained on the Modelica models and real-time data pipeline through the graph database hosted in DeepLynx. The proposed optimization workflow will be an RTO model based on DTs and the data they generate.

25 ENERGY STORAGE↗
DOE OSTI2022

Framework for Optimization, Quantification of Uncertainty and Surrogates – Updated Capabilities

This poster presents updated capabilities within the FOQUS Toolset, covering core features as well as project-based applications. Specifically, the poster highlights advancements in FOQUS integration with cloud-based clients, plugin support for machine learning tools and external simulations, sequential design of experiments, and advanced support for dynamic process models. The applications demonstrate the breadth of FOQUS predictive capabilities for chemical, energy and economic system analysis.

Paul, Brandon↗
DOE OSTI2024

From Resilient and Ready to Used and Useful: Managing Temporal and Locational Uncertainty in Electrification, DER Adoption, and Climate Adaptation

Grid planning decisions involve weighing risks against benefits. The best decisions will facilitate development of electrical infrastructure that minimizes risk and maximizes benefits. With the rapidly evolving energy landscape, today's planner must discern new loads and demand cycles; embrace the operational complexity of climate risk; revise settled standards; and anticipate and manage the system impacts of distributed energy resource (DER) adoption. Only by managing the combined uncertainty of these dynamic processes can a planner hope to make effective decisions. Our analysis focuses on the management of temporal and locational uncertainty, and particularly on the risks presented to customers by the mismanagement of the factors that are the sources of these uncertainties.

climate↗
DOE OSTI2025

A DATA EFFICIENT SPARSE MODELING FRAMEWORK FOR POWER ESTIMATION IN WATER TREATMENT SENSING OPERATIONS

With increasing freshwater scarcity, advanced process design mechanisms such as Closed-Circuit Reverse Osmosis (CCRO) and Digital/Physical Twin systems are gaining traction in water treatment and reuse operations. While digital and physical twin models enable improved system insight and control, their development is often expensive and computationally intensive, requiring large volumes of synthetic or experimental data to characterize underlying process dynamics. This work introduces a sparse surrogate modeling framework to estimate power consumption from measured flow and pressure variables, along with their nonlinear polynomial and interaction expansions. To ensure model reliability and reduce overfitting, a two-stage pipeline is proposed. First, a dynamic data filtering algorithm is employed to remove uninformative observations and transient operational states. Second, a sparse penalized regression technique is applied to select a minimal set of parsimonious features. The proposed model achieves high sparsity, retaining only 7 out of 34 candidate features (≈79.41% sparsity) while delivering a root mean square error (RMSE) of 0.072 on the test dataset.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2026

Neural-network quantum states for the nuclear many-body problem

A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.

Lovato, Alessandro [Argonne; TIFPA-INFN, Trento; V↗
NASA NTRS2012

DDS-Suite - A Dynamic Data Acquisition, Processing, and Analysis System for Wind Tunnel Testing

Wind Tunnels have optimized their steady-state data systems for acquisition and analysis and even implemented large dynamic-data acquisition systems, however development of near real-time processing and analysis tools for dynamic-data have lagged. DDS-Suite is a set of tools used to acquire, process, and analyze large amounts of dynamic data. Each phase of the testing process: acquisition, processing, and analysis are handled by separate components so that bottlenecks in one phase of the process do not affect the other, leading to a robust system. DDS-Suite is capable of acquiring 672 channels of dynamic data at rate of 275 MB / s. More than 300 channels of the system use 24-bit analog-to-digital cards and are capable of producing data with less than 0.01 of phase difference at 1 kHz. System architecture, design philosophy, and examples of use during NASA Constellation and Fundamental Aerodynamic tests are discussed.

Burnside, Jathan J.↗
DOE OSTI2015

Dynamics of ionization processes in high-pressure nitrogen, air, and SF{sub 6} during a subnanosecond breakdown initiated by runaway electrons

The dynamics of ionization processes in high-pressure nitrogen, air, and SF{sub 6} during breakdown of a gap with a nonuniform distribution of the electric field by nanosecond high-voltage pulses was studied experimentally. Measurements of the amplitude and temporal characteristics of a diffuse discharge and its radiation with a subnanosecond time resolution have shown that, at any polarity of the electrode with a small curvature radius, breakdown of the gap occurs via two ionization waves, the first of which is initiated by runaway electrons. For a voltage pulse with an ∼500-ps front, UV radiation from different zones of a diffuse discharge is measured with a subnanosecond time resolution. It is shown that the propagation velocity of the first ionization wave increases after its front has passed one-half of the gap, as well as when the pressure in the discharge chamber is reduced and/or when SF{sub 6} is replaced with air or nitrogen. It is found that, at nitrogen pressures of 0.4 and 0.7 MPa and the positive polarity of the high-voltage electrode with a small curvature radius, the ionization wave forms with a larger (∼30 ps) time delay with respect to applying the voltage pulse to the gap than at the negative polarity. The velocity of the second ionization wave propagating from the plane electrode is measured. In a discharge in nitrogen at a pressure of 0.7 MPa, this velocity is found to be ∼10 cm/ns. It is shown that, as the nitrogen pressure increases to 0.7 MPa, the propagation velocity of the front of the first ionization wave at the positive polarity of the electrode with a small curvature radius becomes lower than that at the negative polarity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Targeted quantification of functional enzyme dynamics in environmental samples for microbially mediated biogeochemical processes: Targeted quantification of functional enzyme dynamics

Microbially mediated biogeochemical processes are catalyzed by enzymes that control the transformation of carbon, nitrogen, and other elements in environment. The dynamic linkage between enzymes and biogeochemical species transformation has, however, rarely been investigated because of the lack of analytical approaches to efficiently and reliably quantify enzymes and their dynamics in soils and sediments. Herein, we developed a signature peptide-based technique for sensitively quantifying dissimilatory and assimilatory enzymes using nitrate-reducing enzymes in a hyporheic zone sediment as an example. Moreover, the measured changes in enzyme concentration were found to correlate with the nitrate reduction rate in a way different from that inferred from biogeochemical models based on biomass or functional genes as surrogates for functional enzymes. This phenomenon has important implications for understanding and modeling the dynamics of microbial community functions and biogeochemical processes in environments. Our results also demonstrate the importance of enzyme quantification for the identification and interrogation of those biogeochemical processes with low metabolite concentrations as a result of faster enzyme-catalyzed consumption of metabolites than their production. The dynamic enzyme behaviors provide a basis for the development of enzyme-based models to describe the relationship between the microbial community and biogeochemical processes.

Li, Minjing↗
DOE OSTI2025

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗
DOE OSTI2018

Membrane cholesterol modulates STEAP2 conformation during dynamic intracellular trafficking processes leading to broad subcellular distribution

Highlights: • STEAP2 distributes broadly into multiple subcellular compartments at steady state. • Cell surface STEAP2 is a minor constituent of the total cellular STEAP2 protein pool. • STEAP2 conformation is modulated within the membrane by its cholesterol content. • STEAP2's metalloreductase activities are suppressed at the plasma membrane. • STEAP2 properties are controlled in a localization-dependent manner by cholesterol. STEAP2 is a member of the Six-Transmembrane Epithelial Antigen of the Prostate (STEAP) protein family that is proposed to function as metalloreductase. While STEAP2 shows a complex subcellular distribution pattern localizing to both secretory and endocytic pathway organelles, how such broad steady-state distribution is maintained is unknown. Similarly, whether STEAP2 undergoes any compartment-specific modulation during intracellular trafficking has not been reported. Leveraging a newly-identified monoclonal antibody that recognizes a conformation-sensitive epitope nested in the second extracellular loop of STEAP2, we demonstrate that the epitope formation was dependent on the cholesterol content of the membrane in which STEAP2 was embedded. Monitoring the STEAP2-dependent internalization of this antibody uncovered STEAP2's rapid internalization from the cell surface and their subsequence trafficking to the Golgi region and endosome-like puncta. Acute inhibition of endocytosis also increased the detectable amount of STEAP2 at the plasma membrane. Collectively, these experiments demonstrate that an intricate balance of membrane flux between the secretory and endocytic pathways underlies the characteristic broad subcellular localization of STEAP2. By using a cell-based assay that detects the metalloreductase functions of cell surface-localizing STEAP4, STEAP2's metalloreductase activities were not detectable, suggesting that its enzymatic function is suppressed at the plasma membrane. The conformational modulation of STEAP2 by the local membrane cholesterol content can therefore serve as a potential mechanism to modulate STEAP2 function in a compartment-restricted manner, by coupling a pre-existing difference in cholesterol content among different cellular membranes to a dynamic trafficking process leading to broad subcellular distribution.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

Parallel processing for nonlinear dynamics simulations of structures including rotating bladed-disk assemblies

The principal objective of this research is to develop, test, and implement coarse-grained, parallel-processing strategies for nonlinear dynamic simulations of practical structural problems. There are contributions to four main areas: finite element modeling and analysis of rotational dynamics, numerical algorithms for parallel nonlinear solutions, automatic partitioning techniques to effect load-balancing among processors, and an integrated parallel analysis system.

Hsieh, Shang-Hsien↗
DOE OSTI2020

TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units

Developed over the past decade, TeraChem is an electronic structure and ab initio molecular dynamics software package designed from the ground up to leverage graphics processing units (GPUs) to perform large-scale ground and excited state quantum chemistry calculations in the gas and the condensed phase. TeraChem’s speed stems from the reformulation of conventional electronic structure theories in terms of a set of individually optimized high-performance electronic structure operations (e.g., Coulomb and exchange matrix builds, one- and two-particle density matrix builds) and rank-reduction techniques (e.g., tensor hypercontraction). Recent efforts have encapsulated these core operations and provided language-agnostic interfaces. Finally, this greatly increases the accessibility and flexibility of TeraChem as a platform to develop new electronic structure methods on GPUs and provides clear optimization targets for emerging parallel computing architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Investigation of Dynamic and Physical Processes in the Upper Troposphere and Lower Stratosphere

Research under this Cooperative Agreement has been funded by several NASA Earth Science programs: the Atmospheric Effects of Radiation Program (AEAP), the Upper Atmospheric Research Program (UARP), and most recently the Atmospheric Chemistry and Modeling Assessment Program (ACMAP). The purpose of the AEAP was to understand the impact of the present and future fleets of conventional jet traffic on the upper troposphere and lower stratosphere, while complementary airborne observations under UARP seek to understand the complex interactions of dynamical and chemical processes that affect the ozone layer. The ACMAP is a more general program of modeling and data analysis in the general area of atmospheric chemistry and dynamics, and the Radiation Sciences program.

Selkirk, Henry B.↗