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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models

We present a method for autogenerating a large domain-specific question-answering (QA) dataset from a thermoelectric materials database. We show that a small language model, BERT, once fine-tuned on this automatically generated dataset of 99,757 QA pairs about thermoelectric materials, affords better performance in the field of thermoelectric materials compared to a BERT model fine-tuned on the generic English-language QA data set, SQuAD-v2. We further show that mixing the two data sets (ours and SQuAD-v2), which have significantly different syntactic and semantic scopes, allows the BERT model to achieve even better performance. The best-performing BERT model fine-tuned on the mixed data set outperforms the models fine-tuned on the other two data sets by scoring an exact match of 67.93% and an F1 score of 72.29% when evaluated on our test data set. This has important implications as it demonstrates the ability to realize high-performing small language models, with modest computational resources, empowered by domain-specific materials data sets which can be generated according to our method.

biological databases↗
NASA NTRS2015

An Overview of the Human Systems Integration Division

This presentation will provide an overview of the Human Systems Integration Division, and will highlight some of the human performance modeling efforts undertaken in previously presented MIDAS human performance modeling efforts.

human systems integration↗
NASA NTRS2000

Modeling and Performance Simulation of the Mass Storage Network Environment

This paper describes the application of modeling and simulation in evaluating and predicting the performance of the mass storage network environment. Network traffic is generated to mimic the realistic pattern of file transfer, electronic mail, and web browsing. The behavior and performance of the mass storage network and a typical client-server Local Area Network (LAN) are investigated by modeling and simulation. Performance characteristics in throughput and delay demonstrate the important role of modeling and simulation in network engineering and capacity planning.

Kim, Chan M.↗
NASA NTRS2008

The Application of the Human Engineering Modeling and Performance Laboratory for Space Vehicle Ground Processing Tasks at Kennedy Space Center

The introduction of United Space Alliance's Human Engineering Modeling and Performance Laboratory began in early 2007 in an attempt to address the problematic workspace design issues that the Space Shuttle has imposed on technicians performing maintenance and inspection operations. The Space Shuttle was not expected to require the extensive maintenance it undergoes between flights. As a result, extensive, costly resources have been expended on workarounds and modifications to accommodate ground processing personnel. Consideration of basic human factors principles for design of maintenance is essential during the design phase of future space vehicles, facilities, and equipment. Simulation will be needed to test and validate designs before implementation.

Woodbury, Sarah K.↗
NASA NTRS2008

International Space Station Human Behavior and Performance Competency Model: Volume I

This document defines Human Behavior and Performance (HBP) competencies that are recommended to be included as requirements to participate in international long duration missions. They were developed in response to the Multilateral Crew Operations Panel (MMOP) request to develop HBP training requirements for the International Space Station (ISS). The competency model presented here was developed by the ITCB HBPT WG and forms the basis for determining the HBP training curriculum for long duration crewmembers. This document lists specific HBP competencies and behaviors required of astronauts/cosmonauts who participate in ISS expedition and other international longduration missions. Please note that this model does not encompass all competencies required. For example, outside the scope of this document are cognitive skills and abilities, including but not limited to concentration, memorization, perception, imagination, and thinking. It is assumed that these skills, which are crucial in terms of human behavior and performance, are considered during selection phase since such professionally significant qualities of the operator should be taken into consideration in order to ensure sufficient baseline levels that can be further improved during general astronaut training. Also, technical competencies, even though critical for crewmembers, are beyond the scope of this document. It should also be noted that the competencies in this model (and subsequent objectives) are not intended to limit the internal activities or training programs of any international partner.

Schmidt, Lacey↗
NASA NTRS1986

Effects of impurities on silicon solar-cell performance

Model analyses indicate that sophisticated solar cell designs (back surface fields, optical reflectors, surface passivation, and double layer antireflective coatings) can produce devices with conversion efficiencies above 20%. To realize this potential, the quality of the silicon from which the cells are made must be improved; and these excellent electrical properties must be maintained during device processing. As the cell efficiency rises, the sensitivity to trace contaminants also increases. For example, the threshold Ti impurity concentraion at which cell performance degrades is more than an order of magnitude lower for an 18% cell than for a 16% cell. Similar behavior occurs for numerous other metal species which introduce deep level traps that stimulate the recombination of photogenerated carriers in silicon. Purification via crystal growth in conjunction with gettering steps to preserve the large diffusion length of the as grown material can lead to the production of devices with efficiencies above 18%, as verified experimentally.

Hopkins, R. H.↗
DOE OSTI2024

Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks

Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

Gonzalez, Emmanuel M.↗
NASA NTRS2019

Camera modeling, centroiding performance, and geometric camera calibration on ASTERIA

The Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) is a 10-kg, 6U CubeSat in low-Earth orbit that was able to achieve subarcsecond pointing stability and repeatability. To date, this is the best pointing on a spacecraft of its size. This paper will analyze various aspects of the performance of its key piece of hardware—the payload. First,a model of the optics and imager, which is used to simulate stellar images, will be presented. The imager parameters used in this model were derived from simple ground measurements. Next, a centroiding algorithm is provided and used on the simulated images to predict centroiding performance. These results will be shown to match on-orbit telemetry of centroiding performance, validating the modeling approach. This paper will then describe an approach for and results of a geometric camera calibration algorithm to estimate the focal length,distortion,and alignment parameters. The modeling, analyses, and results presented in this paper provide key information that can be used in a time domain pointing simulation or a frequency-domain pointing error analysis.

Smith, Matthew W.↗
DOE OSTI2024

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2010

Modeling and Performance Considerations for Automated Fault Isolation in Complex Systems

The purpose of this paper is to document the modeling considerations and performance metrics that were examined in the development of a large-scale Fault Detection, Isolation and Recovery (FDIR) system. The FDIR system is envisioned to perform health management functions for both a launch vehicle and the ground systems that support the vehicle during checkout and launch countdown by using suite of complimentary software tools that alert operators to anomalies and failures in real-time. The FDIR team members developed a set of operational requirements for the models that would be used for fault isolation and worked closely with the vendor of the software tools selected for fault isolation to ensure that the software was able to meet the requirements. Once the requirements were established, example models of sufficient complexity were used to test the performance of the software. The results of the performance testing demonstrated the need for enhancements to the software in order to meet the demands of the full-scale ground and vehicle FDIR system. The paper highlights the importance of the development of operational requirements and preliminary performance testing as a strategy for identifying deficiencies in highly scalable systems and rectifying those deficiencies before they imperil the success of the project

Ferrell, Bob↗
DOE OSTI2026

Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic-Boreal Region

During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20-year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q 10 , is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Poe, Jeralyn [Northern Arizona University, Flagsta↗
DOE OSTI2026

Observation of quantum effects on radiation reaction in strong fields

Radiation reaction, the force experienced by an accelerated charge due to radiation emission, has long been the subject of extensive theoretical and experimental research. Experimental verification of a quantum, strong-field description of radiation reaction is fundamentally important, and has wide-ranging implications for astrophysics, laser-driven particle acceleration, next-generation particle colliders and inverse-Compton photon sources for medical and industrial applications. However, the difficulty of accessing regimes where strong field and quantum effects dominate inhibited previous efforts to observe quantum radiation reaction in charged particle dynamics with high significance. We report the first high significance (> 5σ) observation of strong-field radiation reaction on electron spectra where quantum effects are substantial. We obtain the first, quantitative, strong evidence favouring the quantum-continuous and quantum-stochastic models over the classical model; the quantum models perform comparably. The lower electron energy losses predicted by the quantum models accounts for their improved performance. Model comparison was performed using a novel Bayesian framework which has widespread utility for laser-particle collision experiments, including those utilising conventional accelerators, where some collision parameters cannot be measured directly.

Los, Eva E. [Imperial College London (United Kingd↗
NASA NTRS1998

Model Engine Performance Measurement From Force Balance Instrumentation

A large scale model representative of a low-noise, high bypass ratio turbofan engine was tested for acoustics and performance in the NASA Lewis 9- by 15-Foot Low-Speed Wind Tunnel. This test was part of NASA's continuing Advanced Subsonic Technology Noise Reduction Program. The low tip speed fan, nacelle, and an un-powered core passage (with core inlet guide vanes) were simulated. The fan blades and hub are mounted on a rotating thrust and torque balance. The nacelle, bypass duct stators, and core passage are attached to a six component force balance. The two balance forces, when corrected for internal pressure tares, measure the total thrust-minus-drag of the engine simulator. Corrected for scaling and other effects, it is basically the same force that the engine supports would feel, operating at similar conditions. A control volume is shown and discussed, identifying the various force components of the engine simulator thrust and definitions of net thrust. Several wind tunnel runs with nearly the same hardware installed are compared, to identify the repeatability of the measured thrust-minus-drag. Other wind tunnel runs, with hardware changes that affected fan performance, are compared to the baseline configuration, and the thrust and torque effects are shown. Finally, a thrust comparison between the force balance and nozzle gross thrust methods is shown, and both yield very similar results.

Jeracki, Robert J.↗
NASA NTRSDate not supplied

2023 Artemis Crew Health and Performance System Model Development

While the NASA Human Research Program (HRP) utilizes a Crew Health and Performance (CHP) System to represent all the Agency’s efforts to ensure the health and performance of NASA astronauts, there is no shared mental model of a CHP system at NASA. Some groups may consider a CHP system to be only a medical kit, while others may not be using the concept at all. To facilitate the integration of functions and capabilities to ensure astronaut health and performance during vehicle development, HRP has proposed a CHP Shared Mental Model derived from the NASA Human Health, Medical, and Performance Spaceflight Standards (NASA-STD-3001 Vol.1/Vol.2). [1] Even though many vehicle, ground, and communication systems as well as mission operations are modeled for the Artemis Campaigns, no mission level CHP system model was created to achieve the intent of the HRP CHP Shared Mental Model. The lack of this model renders it difficult to visualize and understand how the many programs work together to provide the necessary cross program functions and capabilities to ensure the health and performance of the crew throughout an Artemis mission. For this purpose, the Exploration Medical Capability (ExMC) element of HRP developed a CHP system model for the Artemis III and IV missions to provide a view of how each program contributes to and interacts with the overall CHP system. To develop the 2023 Artemis CHP system model, ExMC leveraged existing data and models from the Moon to Mars Program Office, the Office of the Chief Health and Medical Officer (OCHMO) and the Orion, Gateway, Extravehicular Activity and Human Surface Mobility (EHP) and Human Landing System (HLS) programs. By using a Model-Based Systems Engineering (MBSE) approach, existing requirements, functions, and concepts of operations were combined to create a single system model focused on representing CHP from the launch to the return to Earth segments of the Artemis III and IV missions. Additionally, by incorporating the HRP Systems Platform for Aggregating and Relating Capabilities, or SPARC tool, the data from the programs was also related back to the 2nd volume of the NASA Human Health, Medical, and Performance Spaceflight Standard (NASA-STD-3001, Vol.2) and the human system risks identified by the Human System Risk Board (HSRB). The first version of the 2023 Artemis CHP system model was baselined in Fall of 2023 after the model was demonstrated to be a potentially useful tool for systems engineers integrating CHP capabilities in vehicle development as well as members of the Health and Medical Technical Authority providing oversight of those programs. The model may also be useful to any stakeholder of astronaut health and performance by providing insights on how an Artemis mission satisfies the NASA Human Health, Medical, and Performance Spaceflight Standards as well as how they mitigate the HSRB Human System Risks. This presentation highlights how the model was developed and the possible benefits of the model. [1] NASA HRP (2022), Crew Health and Performance System Whitepaper

Systems engineering↗
NASA NTRS1982

Modeling lateral acceleration effects on pilot performance

Attendant to the direct side force maneuver of a Vectored Force Fighter is the transverse acceleration imposed on the pilot. This lateral acceleration (Gy), when combind with a positive Gz stress, is a potential source of pilot tracking performance impairment. A research effort to investigate these performance decrements includes experimental as well as anaytical pilot performance modeling using the Optimal Control Model.

Korn, J.↗
NASA NTRS2011

Orion Spacecraft MMOD Protection Design and Assessment

A first-principals, semi-empirical ballistic performance model has been developed for porous ceramics. Lightweight thermal protection systems protect the crew and vehicle of orbital and exo-orbital missions from the intense heat of atmospheric reentry. To maintain low launch weights these materials are their own protection from space hazards like orbital debris and meteoroids. A ballistic performance model is described here that models the performance under a variety of impact conditions. Using the model described here relative to an energy scaled model results in a significantly reduced prediction of full penetration of this material at ISS orbital parameters.

Miller, Joshua E.↗
NASA NTRS2009

Graphical User Interface for Simulink Integrated Performance Analysis Model

The J-2X Engine (built by Pratt & Whitney Rocketdyne,) in the Upper Stage of the Ares I Crew Launch Vehicle, will only start within a certain range of temperature and pressure for Liquid Hydrogen and Liquid Oxygen propellants. The purpose of the Simulink Integrated Performance Analysis Model is to verify that in all reasonable conditions the temperature and pressure of the propellants are within the required J-2X engine start boxes. In order to run the simulation, test variables must be entered at all reasonable values of parameters such as heat leak and mass flow rate. To make this testing process as efficient as possible in order to save the maximum amount of time and money, and to show that the J-2X engine will start when it is required to do so, a graphical user interface (GUI) was created to allow the input of values to be used as parameters in the Simulink Model, without opening or altering the contents of the model. The GUI must allow for test data to come from Microsoft Excel files, allow those values to be edited before testing, place those values into the Simulink Model, and get the output from the Simulink Model. The GUI was built using MATLAB, and will run the Simulink simulation when the Simulate option is activated. After running the simulation, the GUI will construct a new Microsoft Excel file, as well as a MATLAB matrix file, using the output values for each test of the simulation so that they may graphed and compared to other values.

Durham, R. Caitlyn↗