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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

NISAR's Capabilities in Support of the Applications Community

The NISAR mission will provide data covering nearly all the Earth's land areas and sea ice designed specifically to meet requirements of the NISAR science disciplines. However, for each science use case, the same data could potentially be used by one or more applications, often with little or no change in the core observables. Here we describe NISAR's capabilities for supporting a wide range of applications and the activities undertaken to inform, engage, and train the community that will increase the mission's societal impact.

NISAR↗
DOE OSTI2021

Expanding Accessibility of Energy Modeling in Hawaii

Engage is a free, publicly available web application for energy system modeling. It can augment the energy system planning process by empowering more stakeholders to understand energy system planning options. Engage is user-friendly, free accessible, collaborative, and communicative. The Hawaii Advanced Visualization Environment Nexus (HAVEN) is an outputs-based application for rendering high-impact communicative visualizations of complex data. Engage and HAVEN were developed in collaboration with the U.S. Department of Energy's National Renewable Energy Laboratory and the Hawaii State Energy Office. This use case provides an overview of Hawaii's use of Engage and HAVEN concurrently in working to meet the state's energy goals.

48 EE - Weatherization and Intergovernmental Progr↗
NASA NTRS2020

Simulation of a Representative Future Trajectory-Based Operations Environment

Trajectory-Based Operations in the National Airspace System is a key aspect of advanced air traffic management research. Trajectory-Based Operations focuses on modernizing the current operating paradigm to increase efficiency, predictability, resilience, and flexibility while migrating toward greater operational autonomy across the airspace. Research conducted at the National Aeronautics and Space Administration supports the transition from current airspace operations to Trajectory-Based Operations targeting a 2035-2045 implementation timeframe. Simulation scenarios that demonstrate a representative Trajectory-Based Operations environment in that timeframe, and enable the evaluation of advanced airborne tools such as strategic airborne trajectory management services, are necessary to support this research effort. This report describes characteristics and assumptions made about the future operating environment that were applied to a scenario development methodology to study a representative 2040 Trajectory-Based Operations environment in a simulation use case. This report also includes descriptions of the study design and analysis approach, discussion of the simulation results, and application of these scenarios to future research activities.

Trajectory Based Operations↗
NASA NTRS2011

Validation and Simulation of Ares I Scale Model Acoustic Test - 3 - Modeling and Evaluating the Effect of Rainbird Water Deluge Inclusion

The Ares I Scale Model Acoustics Test (ASMAT) is a series of live-fire tests of scaled rocket motors meant to simulate the conditions of the Ares I launch configuration. These tests have provided a well documented set of high fidelity measurements useful for validation including data taken over a range of test conditions and containing phenomena like Ignition Over-Pressure and water suppression of acoustics. Building on dry simulations of the ASMAT tests with the vehicle at 5 ft. elevation (100 ft. real vehicle elevation), wet simulations of the ASMAT test setup have been performed using the Loci/CHEM computational fluid dynamics software to explore the effect of rainbird water suppression inclusion on the launch platform deck. Two-phase water simulation has been performed using an energy and mass coupled lagrangian particle system module where liquid phase emissions are segregated into clouds of virtual particles and gas phase mass transfer is accomplished through simple Weber number controlled breakup and boiling models. Comparisons have been performed to the dry 5 ft. elevation cases, using configurations with and without launch mounts. These cases have been used to explore the interaction between rainbird spray patterns and launch mount geometry and evaluate the acoustic sound pressure level knockdown achieved through above-deck rainbird deluge inclusion. This comparison has been anchored with validation from live-fire test data which showed a reduction in rainbird effectiveness with the presence of a launch mount.

Strutzenberg, Louise L.↗
NASA NTRS2015

ESIP Earth Sciences Data Analytics (ESDA) Cluster - Work in Progress

The purpose of this poster is to promote a common understanding of the usefulness of, and activities that pertain to, Data Analytics and more broadly, the Data Scientist; Facilitate collaborations to better understand the cross usage of heterogeneous datasets and to provide accommodating data analytics expertise, now and as the needs evolve into the future; Identify gaps that, once filled, will further collaborative activities. Objectives Provide a forum for Academic discussions that provides ESIP members a better understanding of the various aspects of Earth Science Data Analytics Bring in guest speakers to describe external efforts, and further teach us about the broader use of Data Analytics. Perform activities that:- Compile use cases generated from specific community needs to cross analyze heterogeneous data- Compile sources of analytics tools, in particular, to satisfy the needs of the above data users- Examine gaps between needs and sources- Examine gaps between needs and community expertise- Document specific data analytics expertise needed to perform Earth science data analytics Seek graduate data analytics Data Science student internship opportunities.

science data analysis↗
NASA NTRS2019

Realized Benefits from the Model-Based Systems Engineering Infusion and Modernization Initiative

Although Model-Based Systems Engineering (MBSE) as a concept has existed for over a decade, overall acceptance within the National Aeronautics and Space Administration (NASA) has been slow and is now growing. Since 2016, NASA’s MBSE Infusion And Modernization Initiative (MIAMI) has proven MBSE’s value to and increased its adoption at NASA. MBSE Pathfinder projects provided focused use cases that demonstrated both qualitative and quantitative benefits for systems engineering activities, and demonstrated the ability to connect MBSE models with discipline models such as structural loads and safety and mission assurance. MIAMI assisted NASA’s field centers to establish or enhance an MBSE presence. MIAMI partners with JAXA’s Systems Technology Unit to share lessons learned and demonstrate how MBSE can be used across organizations. Following its successful test cases, MIAMI is using design thinking, lean startup, and high technology marketing methodologies to implement a targeted deployment of its Community of Practice and other resources.

MBSE↗
NASA NTRS1999

Advanced Subsonic Technology (AST) Area of Interest (AOI) 6: Develop and Validate Aeroelastic Codes for Turbomachinery

AlliedSignal Engines, in cooperation with NASA GRC (National Aeronautics and Space Administration Glenn Research Center), completed an evaluation of recently-developed aeroelastic computer codes using test cases from the AlliedSignal Engines fan blisk and turbine databases. Test data included strain gage, performance, and steady-state pressure information obtained for conditions where synchronous or flutter vibratory conditions were found to occur. Aeroelastic codes evaluated included quasi 3-D UNSFLO (MIT Developed/AE Modified, Quasi 3-D Aeroelastic Computer Code), 2-D FREPS (NASA-Developed Forced Response Prediction System Aeroelastic Computer Code), and 3-D TURBO-AE (NASA/Mississippi State University Developed 3-D Aeroelastic Computer Code). Unsteady pressure predictions for the turbine test case were used to evaluate the forced response prediction capabilities of each of the three aeroelastic codes. Additionally, one of the fan flutter cases was evaluated using TURBO-AE. The UNSFLO and FREPS evaluation predictions showed good agreement with the experimental test data trends, but quantitative improvements are needed. UNSFLO over-predicted turbine blade response reductions, while FREPS under-predicted them. The inviscid TURBO-AE turbine analysis predicted no discernible blade response reduction, indicating the necessity of including viscous effects for this test case. For the TURBO-AE fan blisk test case, significant effort was expended getting the viscous version of the code to give converged steady flow solutions for the transonic flow conditions. Once converged, the steady solutions provided an excellent match with test data and the calibrated DAWES (AlliedSignal 3-D Viscous Steady Flow CFD Solver). However, efforts expended establishing quality steady-state solutions prevented exercising the unsteady portion of the TURBO-AE code during the present program. AlliedSignal recommends that unsteady pressure measurement data be obtained for both test cases examined for use in aeroelastic code validation.

Gardner, Kevin D.↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRSDate not supplied

Design, Development, and Use of a Lunar Lander Simulation for NASA's Artemis Program

This paper describes the design, development, and initial use of a generalized and configurable lunar lander simulation to support NASA’s Artemis Program. This simulation is being developed for the Crew Compartment Office (CrewCo) in the Human Landing Systems (HLS) program and is called the HLS CrewCo Lander Simulation (HCLS). The HCLS provides insight into the challenges associated with returning humans to the Moon and the particular difficulties of operating at the Lunar South Pole. The simulation models a generalized integrated lunar landing spacecraft based on a government reference design but can and has been adapted to model vendor specific designs as well. The simulation architecture and tool sets provide a flexible framework that allows for quickly prototyping and evaluating various aspects of a piloted lunar landing system. This includes the modeling of all principal human controlled flight phases: rendezvous and docking with crew transfer systems; docked orbital outpost operations; undocking and lunar transfer; lunar orbital operations; lunar deorbit, descent, and landing (DDL); lunar surface operations; lunar ascent; and return to the orbital outpost. The flexibility of the simulation allows for the integrated evaluation of potential vehicle subsystems, crew displays, guidance and control modes, and trajectory designs. The purpose of the HCLS is not to design the ideal lunar lander, but rather to understand and the strengths and weaknesses of vehicle design choices. This paper describes the basic simulation architecture and discusses the tool sets used to develop the HCLS. This includes a number of open source tool sets and publicly available data sets. This paper also describes some of the early use cases and findings. The primary focus of early HCLS use has been on the manual piloting task to safely get the crew to the desired landing site in case of a failure in the automated guidance system. Several guidance modes and cues have been implemented which allow a pilot to manually fly the vehicle to the targeted landing site starting anywhere from powered descent initiation. It has been a valuable tool for understanding what is required to manually fly a lunar lander as well as giving a sense of the vehicle dynamics, which are much different than anything that is flown on Earth. A number of experienced test pilots have participated in manual piloting evaluations using the HCLS and provided valuable feedback on the piloting task which has been very beneficial to NASA and the HLS program.

James Michael Gentile↗
DOE OSTI2025

sourcePy

Pollutant source identification techniques (of which there are many variations) are either locked behind researchers writing their own code for each use case or GUI platforms that are easy to use but inflexible and opaque. The Python package sourcePy brings together many of the pollutant source identification algorithms, giving the user full control out of the box. It aims to create a platform for source identification experiments where the full analysis from beginning to end can be done in Python, with a level of specificity in design that isn't available in the GUI options. sourcePy provides users with a few key features: -A Python interface with HYSPLIT, which can be used to generate trajectories and concentration plumes -Several Python classes which standardize the preparation and processing of data related to source identification experiments -Example scripts and notebooks that allow even new python users to get started with their own experiments quickly -Visualization methods

Arseneau, Isaac [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Business Case Analysis for Artificial Intelligence-Large Language Model Technology Integration

AI-assisted processes are expected to enhance operational efficiency and improve decision-making, supporting the long-term economic viability of nuclear power plants. However, detailed business analyses of AI-generated cost savings are rarely performed. Given the recent industry interest in Large Language Model (LLM), the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has conducted a comprehensive business case analysis of LLM Artificial Intelligence (AI) implementation in nuclear plant engineering workflows. The research employed three complementary business case approaches to evaluate impact of an LLM, using three representative engineering processes as use-cases: Boric Acid Corrosion (BAC) Evaluations, Maintenance Rule Evaluations, and 10 CFR 50.59 Screenings. Through detailed workload analyses and structured interviews, the study quantified significant efficiency improvements ranging from 11% to 59% across these processes. The research further considers how these efficiency gains could translate into tangible reliability improvements through enhanced engineering capacity. Analysis of historical plant trip data indicates that enabling engineers to focus on proactive reliability activities could provide substantial financial benefits through avoided outages, potentially generating greater value than the direct efficiency improvements alone. By documenting successful applications, implementation challenges, and strategic opportunities, this research provides nuclear utilities with a practical framework for evaluating the value of AI technology to support long-term operations through advanced digital technologies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Optimal low thrust geocentric transfer

A computer code which will rapidly calculate time-optimal low thrust transfers is being developed as a mission analysis tool. The final program will apply to NEP or SEP missions and will include a variety of environmental effects. The current program assumes constant acceleration. The oblateness effect and shadowing may be included. Detailed state and costate equations are given for the thrust effect, oblateness effect, and shadowing. A simple but adequate model yields analytical formulas for power degradation due to the Van Allen radiation belts for SEP missions. The program avoids the classical singularities by the use of equinoctial orbital elements. Kryloff-Bogoliuboff averaging is used to facilitate rapid calculation. Results for selected cases using the current program are given.

Edelbaum, T. N.↗
NASA NTRS2014

Development and Implementation of Dynamic Scripts to Execute Cycled WRF/GSI Forecasts

Automating the coupling of data assimilation (DA) and modeling systems is a unique challenge in the numerical weather prediction (NWP) research community. In recent years, the Development Testbed Center (DTC) has released well-documented tools such as the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) DA system that can be easily downloaded, installed, and run by researchers on their local systems. However, developing a coupled system in which the various preprocessing, DA, model, and postprocessing capabilities are all integrated can be labor-intensive if one has little experience with any of these individual systems. Additionally, operational modeling entities generally have specific coupling methodologies that can take time to understand and develop code to implement properly. To better enable collaborating researchers to perform modeling and DA experiments with GSI, the Short-term Prediction Research and Transition (SPoRT) Center has developed a set of Perl scripts that couple GSI and WRF in a cycling methodology consistent with the use of real-time, regional observation data from the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC). Because Perl is open source, the code can be easily downloaded and executed regardless of the user's native shell environment. This paper will provide a description of this open-source code and descriptions of a number of the use cases that have been performed by SPoRT collaborators using the scripts on different computing systems.

Zavodsky, Bradley↗
DOE OSTI2021

The Importance of Freeze/Thaw Cycles on Lateral Transport in Ice-Wedge Polygons: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "The importance of freeze/thaw cycles on lateral transport in ice-wedge polygons". The dataset includes xml input/configuration files. These files are compatible with the ATS version 1.0 and higher. The mesh folder contains mesh files used for high-centered polygon (hcp) and low-centered polygon (lcp). The mesh files represent the transect of the polygonal tundra (Fig 1). Here we used two types of mesh with impermeable layer and without. The mesh with an impermeable layer corresponds to the synthetic permafrost (no freezeup case). The freeze up case uses mesh without impermeable layer to simulated frozen ground start at the same depth where the impermeable layer is for the no freezeup case. The meteorological data used drive the model saved in the "inputs" folder. The processed tracer flow rates are saved in the "tracer-flow-ratesCfolder. To plot figures 1 and 2, we used VisIt software. To plot all the flow rates, we used ipython notebook script. All required inputs are saved in "freezeup" and "no freezeup" folders. Each folder includes the corresponding "lcp" and "hcp" folders. The "freezeup" folder has also flat-centered polygon (fcp) case, low porosity "lpor", and low permeability "lper" cases. Included are *.xml, *.exo, *.h5, *pdf, *.ipynb, *.sh, *.py, and *.out files. NGEE Arctic Project Summary: The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

[Activities of Bay Area Research Corporation]

During the final year of this effort the HALFSHEL code was converted to work on a fast single processor workstation from it s parallel configuration. This was done because NASA Ames NAS facility stopped supporting space science and we no longer had access to parallel computer time. The single processor version of HALFSHEL was upgraded to address low density cells by using a a 3-D SOR solver to solve the equation Delta central dot E = 0. We then upgraded the ionospheric load packages to provide a multiple species load of the ionosphere out to 1.4 Rm. With these new tools we began to perform a series of simulations to address the major topic of this research effort; determining the loss rate of O(sup +) and O2(sup +) from Mars. The simulations used the nominal Parker spiral field and in one case used a field perpendicular to the solar wind flow. The simulations were performed for three different solar EUV fluxes consistent with the different solar evolutionary states believed to exist before today. The 1 EUV case is the nominal flux of today. The 3 EUV flux is called Epoch 2 and has three times the flux of todays. The 6 EUV case is Epoch 3 and has 6 times the EUV flux of today.

Source record↗
DOE OSTI2020

URBANopt: An Open-Source Software Development Kit for Community and Urban District Energy Modeling: Preprint

Urban building modeling tools are developing rapidly; these tools use emerging simulation workflows for specific urban environmental design tasks, such as assessing the impacts of energy efficiency technologies at a district scale. However, with the emergence of new environmental design tasks, addressing all possible use cases and tasks is challenging and cannot be covered by a single tool. Urban-scale analysis at this level of complexity often requires linking multiple emerging tools, rather than using a single tool, to adequately evaluate a variety of possible fields in urban environmental design. To achieve this, flexible platforms are needed to support multiple input formats (e.g., geometric and non-geometric building properties), enabling the mapping of such inputs to underlying simulation engines. This paper provides an overview of the open-source URBANopt Software Development Kit (SDK) for modeling high-performance buildings and energy systems at a district scale. URBANopt's flexible SDK is composed of several modules that can be customized to integrate with other tools and generate new workflows to perform urban environmental design tasks, such as capturing interactions between individual buildings, district energy systems, distributed energy resources, and the electric distribution grid. We describe the functionality of the core SDK modules in URBANopt (called Core Gem, GeoJSON Gem, and Scenario Gem) and discuss the flexibility of these modules as a means of integration with a variety of tools. We also document and demonstrate technical details of writing and combining new modules to create customized workflows. Finally, we present a case study that uses the URBANopt SDK to model a hypothetical mixed-use urban project and simulate various scenarios to meet district energy performance goals.

buildings↗