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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Geophysical data analysis and visualization using the Grid Analysis and Display System

Several problems posed by the rapidly growing volume of geophysical data are described, and a selected set of existing solutions to these problems is outlined. A recently developed desktop software tool called the Grid Analysis and Display System (GrADS) is presented. The GrADS' user interface is a natural extension of the standard procedures scientists apply to their geophysical data analysis problems. The basic GrADS operations have defaults that naturally map to data analysis actions, and there is a programmable interface for customizing data access and manipulation. The fundamental concept of the GrADS' dimension environment, which defines both the space in which the geophysical data reside and the 'slice' of data which is being analyzed at a given time, is expressed The GrADS' data storage and access model is described. An argument is made in favor of describable data formats rather than standard data formats. The manner in which GrADS users may perform operations on their data and display the results is also described. It is argued that two-dimensional graphics provides a powerful quantitative data analysis tool whose value is underestimated in the current development environment which emphasizes three dimensional structure modeling.

Doty, Brian E.↗
DOE OSTI2026

GAT (Grid Analysis Toolkit) [SWR-25-41]

Grid Analysis Toolkit (GAT) is a unified Python API and plotting for power system PCM and CEM results (Sienna, PLEXOS, ReEDS™). It's a toolkit for wrangling data for Bulk Grid Dispatch and Transmission Analysis. GAT aims to provide simplified access to PCM and CEM results in a standard format while also allowing raw data access to underlying datasets specific to the model. This software can also be found on PyPI at For plotting, GAT defaults to standard National Lab of the Rockies (NLR) color schemes and standard styles while allowing customization.

Webb, Micah [National Laboratory of the Rockies (N↗
DOE OSTI2024

GRid Analysis and Visualization Interface (GRAVI) [SWR-24-16]

GRAVI (GRid Analysis and Visualization Interface) is a web application for viewing and analyzing nodal Production Cost Model (PCM) and Capacity Expansion Model (CEM) simulations. The web application provides the ability to animate geospatially coupled timeseries data in an agnostic way regardless of the underlying simulation tool used to generate the data. GRAVI also provides capabilities to animate non-geospatial data relevant to a PCM or CEM model. Furthermore, this web application can be tailored as an real-time operational tool to better understand a live grid.

Webb, Micah↗
NASA NTRS1994

The Grid Analysis and Display System (GrADS)

During the period 1 September 1993 - 31 August 1994, further development of the Grid Analysis and Display System (GrADS) was conducted at the Center for Ocean-Land-Atmosphere Studies (COLA) of the Institute of Global Environment and Society, Inc. (IGES) under subcontract 5555-31 from the University Space Research Association (USRA) administered by The Center of Excellence in Space Data and Information Sciences (CESDIS). This final report documents progress made under this subcontract and provides directions on how to access the software and documentation developed therein. A short description of GrADS is provided followed by summary of progress completed and a summary of the distribution of the software to date and the establishment of research collaborations.

Kinter, James L., III↗
DOE OSTI2025

Scalable Hybrid Large-Scale dc-ac Grid Analysis Methods (Phase II)

The goals of the project included the identification and evaluation of a voltage source converter multiterminal high-voltage direct current (VSC-MTdc) system architecture suitable for a high amount of power transfer through long transmission lines.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Discharge Hollow Cathode and Extraction Grid Analysis for the MiXI Ion Thruster

Miniature ion thrusters are well-suited future space missions such as Terrestrial Planet Finder - Interferometer (TPF-I), where high efficiency thrusters using non-contaminating noble gas propellant are desirable. Transient dynamic and orbital analyses have shown that the low-noise, continuous thrust of the Miniature Xenon Ion (MiXI) thruster is desirable for TPF-I formation rotation maneuvers when compared with other thruster options [1], [2]. The 3cm diameter MiXI thruster, Figure 1, was originally designed using experimental methods and is capable of high Isp (> 3,000 sec), propellant efficiency > 80%, and thrust from <0.1 mN to >1.5 mN [3]. The MiXI thruster must demonstrate high levels of thrust resolution and a low minimum impulse bit to ensure it meets the precision formation flying needs of missions such as TPF-I. A novel concept for controlling the ion extraction voltages yields the necessary thrust characteristics for the MiXI thruster. Experiments verify these techniques and two dimensional computational models show that such techniques should have minimal effect on the lifetime of the thruster. During this effort, the MiXI thruster incorporates, for the first time, flight like hollow cathodes for both the discharge chamber and beam neutralization.

ions↗
DOE OSTI2023

A Transductive Graph Neural Network learning for Grid Resilience Analysis

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to largescale power systems.

grid resilience, graph neural networks, transducti↗
DOE OSTI2022

Forced Oscillation Grid Vulnerability Analysis and Mitigation Using Inverter-Based Resources: Texas Grid Case Study

Forced oscillation events have become a challenging problem with the increasing penetration of renewable and other inverter-based resources (IBRs), especially when the forced oscillation frequency coincides with the dominant natural oscillation frequency. A severe forced oscillation event can deteriorate power system dynamic stability, damage equipment, and limit power transfer capability. This paper proposes a two-dimension scanning forced oscillation grid vulnerability analysis method to identify areas/zones in the system that are critical to forced oscillation. These critical areas/zones can be further considered as effective actuator locations for the deployment of forced oscillation damping controllers. Additionally, active power modulation control through IBRs is also proposed to reduce the forced oscillation impact on the entire grid. The proposed methods are demonstrated through a case study on a synthetic Texas power system model. The simulation results demonstrate that the critical areas/zones of forced oscillation are related to the areas that highly participate in the natural oscillations and the proposed oscillation damping controller through IBRs can effectively reduce the forced oscillation impact in the entire system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2013

Evaluation of Grid Modification Methods for On- and Off-Track Sonic Boom Analysis

Grid modification methods have been under development at NASA to enable better predictions of low boom pressure signatures from supersonic aircraft. As part of this effort, two new codes, Stretched and Sheared Grid - Modified (SSG) and Boom Grid (BG), have been developed in the past year. The CFD results from these codes have been compared with ones from the earlier grid modification codes Stretched and Sheared Grid (SSGRID) and Mach Cone Aligned Prism (MCAP) and also with the available experimental results. NASA's unstructured grid suite of software TetrUSS and the automatic sourcing code AUTOSRC were used for base grid generation and flow solutions. The BG method has been evaluated on three wind tunnel models. Pressure signatures have been obtained up to two body lengths below a Gulfstream aircraft wind tunnel model. Good agreement with the wind tunnel results have been obtained for both on-track and off-track (up to 53 degrees) cases. On-track pressure signatures up to ten body lengths below a Straight Line Segmented Leading Edge (SLSLE) wind tunnel model have been extracted. Good agreement with the wind tunnel results have been obtained. Pressure signatures have been obtained at 1.5 body lengths below a Lockheed Martin aircraft wind tunnel model. Good agreement with the wind tunnel results have been obtained for both on-track and off-track (up to 40 degrees) cases. Grid sensitivity studies have been carried out to investigate any grid size related issues. Methods have been evaluated for fully turbulent, mixed laminar/turbulent and fully laminar flow conditions.

Nayani, Sudheer N.↗
NASA NTRS1991

A Graphically Interactive Approach to Structured and Unstructured Surface Grid Quality Analysis

A graphically interactive approach to structured and unstructured surface grid quality analysis is described. A Surface Analysis Code SurfACe) is developed to help a user analyze surface grid quality based on surface normal vectors, first and second derivatives of these vectors, normal, Gaussian, and mean curvatures, orthogonality, aspect ratio, and cell area. The results demonstrate how this approach can be used to reduce surface grid generation errors and increase the cost effectiveness of Computational Fluid Dynamics as an aircraft design tool.

Stewart, John E.↗
DOE OSTI2024

Probabilistic Grid Reliability Analysis with Energy Storage Systems

SAND2025-12025O The Probabilistic Grid Reliability Analysis with Energy Storage Systems (ProGRESS) software tool is an open-source tool for assessing the resource adequacy of the evolving electric power grid integrated with energy storage systems (ESS). This tool uses a simulation engine to create diverse scenarios that test the limits of the modern power grid consisting of a high-volume ESS and variable energy resources (VER). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Nguyen, Tu↗
NASA NTRS1999

Grid Stiffened Structure Analysis Tool

The Grid Stiffened Analysis Tool contract is contract performed by Boeing under NASA purchase order H30249D. The contract calls for a "best effort" study comprised of two tasks: (1) Create documentation for a composite grid-stiffened structure analysis tool, in the form of a Microsoft EXCEL spread sheet, that was developed by originally at Stanford University and later further developed by the Air Force, and (2) Write a program that functions as a NASTRAN pre-processor to generate an FEM code for grid-stiffened structure. In performing this contract, Task 1 was given higher priority because it enables NASA to make efficient use of a unique tool they already have; Task 2 was proposed by Boeing because it also would be beneficial to the analysis of composite grid-stiffened structures, specifically in generating models for preliminary design studies. The contract is now complete, this package includes copies of the user's documentation for Task 1 and a CD ROM & diskette with an electronic copy of the user's documentation and an updated version of the "GRID 99" spreadsheet.

Source record↗
DOE OSTI2022

Grid Value Analysis of Medium-Voltage Back-to-Back Converter on DER Hosting Enhancement

This paper presents an analysis of the value that can be realized by medium-voltage back-to-back (MVB2B) converters in terms of the increased utilization rate of distributed energy resources (DERs) and the improvement in operational conditions. A systematic, transferrable, and scalable methodology has been designed to analyze and quantify the increased DER value from three perspectives: 1) curtailment reduction of the DER generation, 2) size reduction of the energy storage needed to otherwise realize DER hosting levels, and 3) hosting capacity improvement of the DERs compared to the base distribution circuit capability. In the case study, the proposed methodology is applied to two utility distribution systems for analysis and quantification of the grid value of the MVB2B converter, installed in the distribution circuit, and provided to the solar photovoltaic (PV) DERs. Here, the analysis results demonstrate that the MVB2B converter can deliver significant value to the PV hosting enhancement of two adjacent distribution systems when they are connected by the MVB2B converter. Based on this case study, this paper analyzes and summarizes the approximate realized grid value of the MVB2B converter for distribution systems dominated by different shares of customer classes.

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DOE OSTI2025

Improving Trustworthiness of Data-Driven Power Grid Contingency Analysis With Bayesian Residual Graph Neural Networks

The evolving energy landscape requires novel tools to efficiently perform contingency analysis and reliability assessment of power grids, potentially in real-time. The high computational cost of traditional power flow solvers limits their applicability in practice. Machine learning (ML) surrogates such as deep neural networks (NNs) accelerate power flow solvers computations, enabling high-order contingency analysis and real-time decision-making by learning highly nonlinear functions and integrating grid topology via graph architectures. However, (graph) NNs lack predictive power away from training data and do not provide predictive confidence estimates. Here, we present a Bayesian residual graph NN that integrates knowledge from low-fidelity data via residual training and embeds granular quantification of uncertainties, improving trustworthiness critical for high-consequence decision-making. Applying Bayesian concepts to NNs is challenging due to the high-dimensionality of both the parameter space, complicating derivation of a meaningful prior, and the output space in large grid systems, requiring enhanced techniques to assess the predicted high-dimensional uncertainties. Our contributions include: (1) Deriving a prior for fully connected and graph NNs that leverages low-fidelity data to guide mean predictions and appropriately control prior predictive uncertainty. (2) Integrating this prior within an ensembling with anchoring scheme for efficient approximate posterior inference. (3) Deriving enhanced metrics to assess accuracy of both the mean and uncertainty predictions in high dimensions, appropriately accounting for correlations propagated through graph layers. The resulting Bayesian residual graph NN is tested on a contingency analysis task for 14-bus and 118-bus grids.

24 - POWER TRANSMISSION AND DISTRIBUTION↗