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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A State-Space Model for Stability Boundary Analysis of Grid-Following Voltage Source Converters Considering Grid Conditions

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification.

grid-connected inverters↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗
NASA NTRS2019

Air Traffic Management TestBed Simulation Architect: User's Guide

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform provides cloud services including back-end big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various concepts such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. The Simulation Architect application provides a graphical user interface tool for designing traffic scenarios and simulations using blocks representing components and links representing message channels linking them. This guide describes a high-level user interface design of Simulation Architect and provides information for a new user to compose traffic scenarios and simulations.

Software User Guide↗
NASA NTRS2020

Air Traffic Management TestBed Traffic Viewer: Developer's Guide

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform is designed to provide cloud services including back-end, big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various methods such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. The Traffic Viewer application provides a graphical user interface tool for visualizing real and simulated air traffic as well as airspace definition in two-dimensional space. This guide describes a high-level design and implementation of Traffic Viewer and provides information for a new developer or a user to add new capabilities by following the software design and leveraging existing capabilities.

Lai, Chok Fung↗
NASA NTRS2020

Air Traffic Management TestBed Data Exchange Model

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform is designed to provide cloud services including back-end, big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various methods such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. In order to allow easier access to simulation components, TestBed provides a messaging support layer for connectivity using a consistent set of input/output interfaces. In addition, a standard data format is introduced to facilitate communication between the components. The data exchange model, supported in the messaging support layer, standardizes the format of the information to be exchanged among the components. This document describes the messaging data model currently developed in TestBed and provides data dictionaries for references to component developers as well as simulation engineers.

air traffic simulation↗
NASA NTRSDate not supplied

An Approach to Reasoning Service Migration in Data and Reasoning Fabric (DRF) Implementation

In this paper we consider service placement problem for Data and Reasoning Fabric (DRF) enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage and power resources, where cloud/edge servers, and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, we estimate for each potential location the response time, consumed energy and total cost; formulate an optimization problem for cost minimization given the users QoS constraints and network infrastructure limitations; and solve it using nonlinear programming tools. The approach is applied to trajectory re-planning use case to handle a no-fly zone contingency in real time.

Vahram Stepanyan↗
NASA NTRS2021

An Approach to Reasoning Service Migration in Data and Reasoning Fabric (DRF) Implementation

In this paper we consider service migration problem for Data and Reasoning Fabric (DRF) enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage and power resources, where cloud/edge servers, and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints, network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory re-planning use case to avoid a collision with an emergency vehicle in real time.

Air mobility↗
NASA NTRSDate not supplied

Comparisons of Mixing Efficiency for the Strut Fuel Injector Obtained from Large-Eddy and Reynolds-Averaged Simulations, and Experiments

Mixing efficiency is obtained for a strut fuel injector at hypervelocity flow conditions by using large-eddy simulations (LES), Reynolds-averaged simulations (RAS), and experiments. The injector and flow conditions have been previously investigated by using RAS and experiments as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). Because the fidelity of LES is a strong function of the grid, the mixing efficiency is obtained on two grids, the coarser of which is a factor of two coarser in each of the three dimensions with respect to the fine grid. The RAS uses the two-equation linear eddy viscosity and diffusivity modeling of Menter. In RAS, the species diffusivity model exhibits a strong dependence on the turbulent Schmidt number, which is often adjusted until some metric of engineering interest, such as the mixing efficiency, matches the experimental data. In the absence of experimental data, scale-resolving simulations, such as LES, have been proposed as surrogates for experiments that could provide the data needed to “calibrate” the turbulent Schmidt number in the RAS models. This approach is followed because LES requires significantly more computational resources (CPU, data storage, and time) than RAS, making it prohibitive for use in many engineering applications and specifically for parameter exploration or optimization. Here we examine the mixing efficiency obtained from several RAS with different values of the turbulent Schmidt number, and compare the results with those obtained from the LES and experiments. In addition, the least squares fitting approach was used to demonstrate how to obtain an estimate for the turbulent Schmidt number from LES analytically. These estimates were then used together with prior knowledge about RAS model sensitivity to select a turbulence model that was expected to best match the LES data.

LES↗
NASA NTRSDate not supplied

Comparisons of Mixing Efficiency for the Strut Fuel Injector Obtained from Large-Eddy and Reynolds-Averaged Simulations, and Experiments

Mixing efficiency is obtained for a strut fuel injector at hypervelocity flow conditions by using large-eddy simulations (LES), Reynolds-averaged simulations (RAS), and experiments. The injector and flow conditions have been previously investigated by using RAS and experiments as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). Because the fidelity of LES is a strong function of the grid, the mixing efficiency is obtained on two grids, the coarser of which is a factor of two coarser in each of the three dimensions with respect to the fine grid. The RAS uses the two-equation linear eddy viscosity and diffusivity modeling of Menter. In RAS, the species diffusivity model exhibits a strong dependence on the turbulent Schmidt number, which is often adjusted until some metric of engineering interest, such as the mixing efficiency, matches the experimental data. In the absence of experimental data, scale-resolving simulations, such as LES, have been proposed as surrogates for experiments that could provide the data needed to “calibrate” the turbulent Schmidt number in the RAS models. This approach is followed because LES requires significantly more computational resources (CPU, data storage, and time) than RAS, making it prohibitive for use in many engineering applications and specifically for parameter exploration or optimization. Here we examine the mixing efficiency obtained from several RAS with different values of the turbulent Schmidt number, and compare the results with those obtained from the LES and experiments. In addition, the least squares fitting approach was used to demonstrate how to obtain an estimate for the turbulent Schmidt number from LES analytically. These estimates were then used together with prior knowledge about RAS model sensitivity to select a turbulence model that was expected to best match the LES data.

LES↗
DOE OSTI2021

Wellbore integrity assessment with casing-based advanced sensing

Wellbore integrity is of paramount importance to subsurface resource extraction, energy storage 1 and waste disposal. After installation, well casing and cement are subject to mechanical stress due to near-well pressure changes and fluid induced corrosion. This is exacerbated for geothermal wells where produced fluid is at high temperature and corrosive. The current state-of-the-art technologies for wellbore integrity assessments are an array of cased hole logging tools. Wireline deployed acoustic, electromagnetic and mechanical tools are all available to inspect steel casing corrosion and casing-cement bond and these tools can provide high-resolution assessment of borehole conditions. They are intrusive, however, in terms of borehole preparation and interruption to the normal operation of the wells, and not suitable for high temperature or highly deviated well deployments. In addition, these measurements are performed infrequently due to high cost, and are therefore incapable of providing frequent data to better predict borehole degradation trajectory, which can help provide early warning of potential borehole failures. For this project we are developing a suite of novel, non-invasive, casing based tools for wellbore integrity assessment, combining fast/low cost screening with higher-precision investigation. Our approach is based on monitoring the response of the casing when energized at the wellhead, thereby interrogating the casing without well intervention. Lab, field and numerical approaches are used in our study. During the early stage of the research, we focus on numerical simulations, which have shown the sensitivity of the low frequency electromagnetic (EM) signals to changes in borehole depths and have successfully tested the concept at a field site with different length well casings. Initial seismic modeling efforts have also demonstrated our capability to simulate seismic tube wave and seismic field alterations due to borehole breakage and associated fluid leakage. Further numerical, laboratory and field experiments are underway for additional technology sensitivity analysis, particularly the transient EM/Seismic reflectometry methods, data acquisition optimization, and numerical simulation improvements.

Wilt, Michael↗
DOE OSTI2020

Optimal Sizing of Resilience Solutions for the U.S. Army Reserve

Power or water outages in buildings threaten the ability of the military to support surrounding communities during natural disasters. Outages can last for days, weeks or months. Typical solutions include very expensive batteries and onsite generation that are sized based on historical power needs. The U.S. Army Reserve (USAR) has teamed up with the Pacific Northwest National Laboratory (PNNL) to develop a simulation framework that optimizes future power needs to reduce the cost of resilience solutions. The approach starts with the building. Power needs during an emergency event are simulated at the building end-use level, then loads are reduced through the selection of life cycle cost-effective building-level technology improvements. These new optimized loads are then fed into a microgrid sizing tool that dynamically constructs many different combinations of solar photovoltaic, battery storage, and generator resources to meet the load for hundreds of statistically generated outage scenarios. Six site assessments completed by USAR and PNNL in 2019 have resulted in a 1-14% reduction in overall investment when optimizing the buildings first before determining the generation requirements. This approach helps the Army secure critical missions and provide a 14 day-minimum supply of necessary energy and water in the most efficient manner.

buidlings, efficiency, resilience, FEDS, MCOR↗
DOE OSTI2023

Microgrid Tiered Circuits Effects for a Planned Housing Community in Puerto Rico

Puerto Rico faced a double strike from hurricanes Irma and Maria in 2017. The resulting damage required a comprehensive rebuild of electric infrastructure. There are plans and pilot projects to rebuild with microgrids to increase resilience. This paper provides a techno-economic analysis technique and case study of a potential future community in Puerto Rico that combines probabilistic microgrid design analysis with tiered circuits in building energy modeling. Tiered circuits in buildings allow electric load reduction via remote disconnection of non-critical circuits during an emergency. When coupled to a microgrid, tiered circuitry can reduce the chances of a microgrid's storage and generation resources being depleted. The analysis technique is applied to show 1) Approximate cost savings due to a tiered circuit structure and 2) Approximate cost savings gained by simultaneously considering resilience and sustainability constraints in the microgrid optimization. The analysis technique uses a resistive capacitive thermal model with load profiles for four tiers (tier 1-3 and non-critical loads). Three analyses were conducted using: 1) open-source software called Tiered Energy in Buildings and 2) the Microgrid Design Toolkit. For a fossil fuel based microgrid 30% of the total microgrid costs of 1.18 million USD were calculated where the non-tiered case keeps all loads 99.9% available and the tiered case keeps tier 1 at 99.9%, tier 2 at 95%, tier 3 at 80% availability, with no requirement on non-critical loads. The same comparison for a sustainable microgrid showed 8% cost savings on a 5.10 million USD microgrid due to tiered circuits. The results also showed 6-7% cost savings when our analysis technique optimizes sustainability and resilience simultaneously in comparison to doing microgrid resilience analysis and renewables net present value analysis independently. Though highly specific to our case study, similar assessments using our analysis technique can elucidate value of tiered circuits and simultaneous consideration of sustainability and resilience in other locations.

building energy modeling↗
DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗
DOE OSTI2023

Control of Behind-the-Meter Resources for Enhancing the Resilience of Residential Buildings

There is increased concern about the impacts of frequent power outages, caused primarily by extreme weather conditions. With increasing behind-the-meter resources such as solar photovoltaics (PV), battery energy storage, and controllable loads, these resources - if properly coordinated - can meet critical loads even during an outage. Resilience building controls can coordinate and operate these resources to enhance the resiliency of buildings supporting critical loads for longer duration. In this paper, we present two resilience building controls: rule-based control and model predictive control (MPC). We simulated various scenarios considering different locations, seasons, outage types, and times of outages to evaluate the performance of resilience controls. The results show that MPC-based control can enhance resilience up to 65% compared to rule-based control. Similarly, PV self-consumption and occupant thermal comfort both increase during outages with MPC-based control.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2026

Assessment of Energy Technology Options for the Island of Molokai, Hawaii: Analysis of Floating Solar, Pumped Storage Hydropower, and Backup Energy Systems [Slides]

This presentation summarizes analysis done by researchers at the National Laboratory of the Rockies and Pacific Northwest National Laboratory to evaluate the potential for and explore project concepts of electricity generation and storage additions on the island of Molokai, Hawaii, as identified in the Community Energy Resilience Action Plan (CERAP) by the Molokai Clean Energy Hui (MCEH), Sustainable Molokai, and the Hawaii Natural Energy Institute (HNEI). These electricity generation and storage additions include distributed photovoltaics (PV), battery energy storage, and generators for critical facilities on the island that can provide backup energy to the facilities during grid disruptions and outages, a floating PV (FPV) system on Kualapuu Reservoir, and pumped storage hydropower (PSH) systems scaled to act as a significant or primary source of energy storage on the Molokai grid. This presentation accompanies the full technical report published under the same title.

14 SOLAR ENERGY↗
DOE OSTI2026

Assessment of Energy Technology Options for the Island of Molokai, Hawaii: Analysis of Floating Solar, Pumped Storage Hydropower, and Backup Energy Systems

This report documents analysis done by researchers at the National Laboratory of the Rockies and Pacific Northwest National Laboratory to evaluate the potential for and explore project concepts of electricity generation and storage additions on the island of Molokai, Hawaii, as identified in the Community Energy Resilience Action Plan (CERAP) by the Molokai Clean Energy Hui (MCEH), Sustainable Molokai, and the Hawaii Natural Energy Institute (HNEI). These electricity generation and storage additions include distributed photovoltaics (PV), battery energy storage, and generators for critical facilities on the island that can provide backup energy to the facilities during grid disruptions and outages, a floating PV (FPV) system on Kualapuu Reservoir, and pumped storage hydropower (PSH) systems scaled to act as a significant or primary source of energy storage on the Molokai grid.

13 HYDRO ENERGY↗