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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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NASA NTRS2026

A Modular Conjugate Heat Transfer Optimization Framework for Thermal Management of Electric Aircraft

Conjugate heat transfer (CHT) analysis and optimization is a powerful method for improving thermal management, as it simultaneously resolves the temperature distribution in both fluid and solid domains. This paper presents a modular, discrete adjoint-based CHT optimization capability integrated within the OpenMDAO/MPhys framework. A unique feature of the proposed framework is its flexibility to extend to multidisciplinary optimization, including aero-structural-thermal applications. The fluid domain is modeled using a finite-volume Computational Fluid Dynamics (CFD) solver, and the solid domain with a conduction heat transfer solver. A mixed Neumann-Dirichlet boundary condition is developed to enable full submersion of the solid geometry within the fluid domain, while ensuring consistent temperature and heat flux coupling at the CHT interface. Gradient-based optimization is performed; the gradients are efficiently computed using the discrete adjoint solvers implemented in DAFoam. To demonstrate the method, this paper considers two cases related to electric aircraft thermal management: a U-bend heat exchanger and an actively cooled battery pack. The U-bend case aims to minimize pressure loss while maximizing heat flux by changing the pipe geometry. The optimized design reduces pressure loss by 52.7% and increases total heat flux by 2.3%. In the battery pack case, a 3-by-3 cell configuration is cooled by ambient airflow, with constant heat generation prescribed in the cells. The battery casing shape serves as the design variable, and the objective function is a weighted sum of pressure loss and pack weight, subject to a maximum temperature constraint. The optimized design achieves a 44.6% reduction in pressure loss and a 1.5% reduction in weight, while satisfying the thermal constraint. To ensure the reliability of the optimized designs, this study validates coarse-mesh, steady-state predictions against fine-mesh unsteady simulations, demonstrating consistency within acceptable errors. This work demonstrates the potential of the developed framework to enable rapid, high-fidelity design of thermal management systems for electric aircraft.

heat transfer↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS1973

Terrestrial Environment (Climatic) Criteria Guidelines for Use in Aerospace Vehicle Development, 1973 Revision

This document provides guidelines on probable climatic extremes and probabilities-of-occurrence of terrestrial environment data specifically applicable for NASA space vehicles and associated equipment development. The geographic areas encompassed are The Eastern Test Range (Cape Kennedy, Florida); Huntsville, Alabama; New Orleans, Louisiana; The Space and Missile Test Center (Vandenberg AFB California); Sacramento, California; Wallops Test Range (Wallops Island, Virginia); White Sands Missile Range, New Mexico; and intermediate transportation areas. In addition, sections have been included to provide information on the general distribution of natural environment extremes in the United States (excluding Alaska and Hawaii), cloud cover, and some worldwide climatic extremes. Although all these areas are covered, the major emphasis is given to the Kennedy Space Center launch area and Vandenburg Air Force Base due to importance in NASA's future large space vehicle programs. This document presents the latest available information on probable climatic extremes, and supersedes information presented in TM X-64589. The information in this document is recommended for employment in the development of space vehicles and associated equipment design and operational criteria, unless otherwise stated in contract work specifications.

cloud cover↗
NASA NTRS2026

Artemis Suit Material Optical Property Testing of Dust Exposed Fabrics

This paper highlights one aspect of NASA’s ongoing technology-infusion effort to design, fabricate, and test a next-generation outer shell fabric for a lunar Extravehicular Activity (EVA) space suit, a critical component of sustained lunar exploration. Managing thermal loads on the Moon is essential for astronaut safety and suit performance. The suit’s exterior fabric directly influences heat gain and loss through its optical properties: low solar absorptivity minimizes sunlight absorption, while high infrared emissivity aids radiative cooling. Lunar regolith complicates this balance. Its fine, abrasive particles possess unique optical behavior that can lower reflectivity and raise emissivity when embedded in or adhered to fabric surfaces, degrading thermal control and increasing the risk of overheating or cooling inefficiency. To quantify these effects, the Artemis Suit Materials (ASM) team measured solar absorptance and infrared emissivity of clean and dust-soiled Ortho Fabric, establishing beginning-of-life (BOL) and end-of-life (EOL) benchmarks. EOL conditions were simulated with a rotary tumbler abrasion process using lunar dust simulant and ceramic media to reproduce cumulative wear expected during surface operations. Tests also included unmodified fabrics and a fabric/film laminate system containing titanium dioxide to evaluate potential improvements in dust resistance and optical performance. Results from these evaluations provide critical insight into how lunar dust alters fabric thermal behavior and inform the design of bespoke suit materials that maintain required optical properties throughout mission life, supporting safe and effective long-duration EVA on the lunar surface.

Textile↗
NASA NTRS2026

Artemis Suit Material Optical Property Testing of Dust Exposed Fabrics

This paper highlights one aspect of NASA’s ongoing technology-infusion effort to design, fabricate, and test a next-generation outer shell fabric for a lunar Extravehicular Activity (EVA) space suit, a critical component of sustained lunar exploration. Managing thermal loads on the Moon is essential for astronaut safety and suit performance. The suit’s exterior fabric directly influences heat gain and loss through its optical properties: low solar absorptivity minimizes sunlight absorption, while high infrared emissivity aids radiative cooling. Lunar regolith complicates this balance. Its fine, abrasive particles possess unique optical behavior that can lower reflectivity and raise emissivity when embedded in or adhered to fabric surfaces, degrading thermal control and increasing the risk of overheating or cooling inefficiency. To quantify these effects, the Artemis Suit Materials (ASM) team measured solar absorptance and infrared emissivity of clean and dust-soiled Ortho Fabric, establishing beginning-of-life (BOL) and end-of-life (EOL) benchmarks. EOL conditions were simulated with a rotary tumbler abrasion process using lunar dust simulant and ceramic media to reproduce cumulative wear expected during surface operations. Tests also included unmodified fabrics and a fabric/film laminate system containing titanium dioxide to evaluate potential improvements in dust resistance and optical performance. Results from these evaluations provide critical insight into how lunar dust alters fabric thermal behavior and inform the design of bespoke suit materials that maintain required optical properties throughout mission life, supporting safe and effective long-duration EVA on the lunar surface.

space↗
NASA NTRS1989

Marshall Space Flight Center CFD Overview

Computational Fluid Dynamics (CFD) activities at Marshall Space Flight Center (MSFC) have been focused on hardware specific and research applications with strong emphasis upon benchmark validation. The purpose here is to provide insight into the MSFC CFD related goals, objectives, current hardware related CFD activities, propulsion CFD research efforts and validation program, future near-term CFD hardware related programs, and CFD expectations. The current hardware programs where CFD has been successfully applied are the Space Shuttle Main Engines (SSME), Alternate Turbopump Development (ATD), and Aeroassist Flight Experiment (AFE). For the future near-term CFD hardware related activities, plans are being developed that address the implementation of CFD into the early design stages of the Space Transportation Main Engine (STME), Space Transportation Booster Engine (STBE), and the Environmental Control and Life Support System (ECLSS) for the Space Station. Finally, CFD expectations in the design environment will be delineated.

L A Schutzenhofer↗
NASA NTRS1996

Multifrequency, Multipolarization External Calibration of the SIR-C/X-SAR Radars

Progress and the future plans for the following objectives are discussed: (1) Assess the accuracy at which the SIR-C/X-SAR standard data products can be calibrated through the use of ground calibrators to estimate the end-to-end system polarization calibration constants (or distortion parameters) and incorporate the constants into the data processing; (2) Study the cross-calibration between three multipolarization systems: SIR-C, the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) DC-8 SAR, and the University of Michigan ground-based polarimetric scatterometer; (3) Evaluate the calibration "stability" of SIR-C/X-SAR (measured by variations in the calibration constants) over the range swath width and over a specified distance in azimuth. Variations over a 12-hour period (between ascending and descending passes) will also be studied; and (4) Develop a cost-effective calibration plan including development of inexpensive polarimetric active calibrators.

Anthony Freeman↗
NASA NTRS2026

NASA Agile Community of Practice 2024-2026 Report

This 2024-2026 report provides a summary of the products and activities executed by the NASA Agile Community of Practice (CoP) during its second and third years. Building on the foundation established in its inaugural year, the CoP continued to advance Agile values and principles across NASA centers. The report highlights key initiatives, including specialized framework training, AI integration in Agile toolkits, and active participation in agency-wide project management and systems engineering workshops.

Agile↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRS1992

The Utilization of Advanced Composites in Military Aircraft

The purpose of this workshop was to identify the current state of the art in key issues related to compression loading and fluid effects in composite materials. In the area of compression loading, there was considerable concern over the different results obtained from various test methods. It was agreed that failure modes produced by the various test methods along with a better fundamental understanding of compression failure were key issues in the development of compression test methods. In the area of fluid effects, a lack of a comprehensive data base hampers identification of key mechanisms leading to fluid degradation. This is further complicated by the fact that interactions depend on the fluid and composite under consideration.

Advisory Group for Aerospace Research and Developm↗
NASA NTRS1991

Battery performance simulation for the Magellan mission

A battery performance simulation for the Magellan mission to Venus has been operating at the Jet Propulsion Laboratory for nearly three years. The unique operational requirements for the Magellan batteries and the test system constructed to simulate them are described. Simulation results to date are presented and compared with actual spacecraft battery performance. Recommendations for planning of future mission simulation tests are provided.

Glueck, Peter↗
NASA NTRS1992

Fifth Annual Workshop on Space Operations Applications and Research (Soar 1991), Volume 1

More than 110 papers were presented at this Symposium, sponsored by the U.S. Air Force Phillips Laboratory, the University of Houston-Clear Lake, and NASA JSC. The technical areas covered were Intelligent Systems, Automation and Robotics, Human Factors and Life Sciences, and Environmental Interactions. The U.S. Air Force and NASA programmatic overviews and panel discussions were also held in each technical area. These proceedings, along with the comments and suggestions made by the panelists and keynote speakers, will be used in assessing the progress made in joint USAF/NASA projects and activities. Furthermore, future collaborative/joint programs will also be identified. The symposium proceedings includes papers covering various disciplines presented by experts from NASA, the Air Force, universities, and industry.

Mars missions↗
NASA NTRS2023

CFD Modeling of Bi-Directional PMD inside Cryogenic Propellant Tanks Onboard Parabolic Flights

Future cryogenic propulsion systems will require efficient methods with which to transfer cryogenic propellants from a depot storage tank to a customer receiver tank to minimize cost and maximize reusability. The Reduced Gravity Cryogenic Transfer project is currently developing advanced cryogenic fluid management technology and developing and validating new numerical models for three phases of transfer: line chilldown, tank chilldown, and tank fill. Additionally, multiple liquid nitrogen (LN 2 ) parabolic flight transfer rigs are being designed by universities and NASA to investigate the gravitational sensitivities that exist in these three technologies. In order to maximize the collection of low-g data during flights, it is required to extract as much (LN 2 as possible from the supply tank, despite variable gravity levels. The purpose of this paper is to present computational fluid dynamics (CFD) volume of fluid simulations of (LN 2 behavior in the supply tank onboard parabolic flights to validate the optimal design of a bi-directional propellant management device (PMD) using the commercial software FLOW-3D. A parametric study is conducted on the effects of gravity level, fill level, pore size, open area, thickness, and type of baffle on PMD performance. Based on results, the PMD as designed exceeds the targeted expulsion efficiency.

Jason Hartwig↗
NASA NTRS1992

Flight Testing

It is considered by the Flight Mechanics Panel of AGARD as vitally important that the NATO flight test community meet regularly so that new techniques for flight test, instrumentation and data analysis and lessons learned from past and on-going programs be disseminated to ensure that safe efficient cost-effective and timely testing is accomplished. There are many new systems being tested or planned for testing in the near future. These include programmable signal processor radars, integrated flight, fire and propulsion control systems, thrust vectoring, low observable technologies, multifunction pilot displays and multisensor integration. Acquisition and processing of large quantities of avionics multiplex data are challenges that must be met. There is a need for greater use of simulators and other hardware-in-the-loop ground test facilities.

Source record↗
NASA NTRS1989

Computational Fluid Dynamics at the Lewis Research Center: An Overview

Lewis is a multidisciplinary Center with strong research and development programs in aeronautical and space propulsion, power, space communications, space experiments and materials. Computational fluid dynamics (CFD) is playing an important and growing role in most of these areas. Described here is how CFD is integrated into these programs and highlights elements of the CFD activities. Examples are presented of codes developed to predict flow fields in advanced propulsion systems and several of the code validation experiments are described. The CFD effort at Lewis ranges from basic research on new and improved algorithms through code development to the application of these codes to specific engineering problems. Because of the substantial improvement in CFD's predictive capability, its use at Lewis is on a steep growth path, spreading rapidly into new areas which had not traditionally taken advantage of the techniques of numerical simulation. Multidisciplinary codes and the future direction of CFD at Lewis are discussed.

Robert M Stubbs↗