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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 NTRS2026

A Study of Space Environment Effect on Highly Thermally Conductive Hybrid Carbon Fiber Polymer Composites

A set of novel highly thermally conductive hybrid carbon fiber (CF) polymer composites has been developed for lightweight thermal radiator applications in space missions. This study investigates the effects of the space environment on these materials following exposure in low-Earth orbit (LEO) during the Materials International Space Station Experiment (MISSE)-17 flight mission, where samples experienced 159 days of combined atomic oxygen (AO), ultraviolet (UV) radiation, high vacuum, space radiation and thermal cycling. Post-flight characterization included weight loss, surface morphology, thermo-optical properties, molecular structures, glass transition temperature, thermal degradation and thermal conductivity analyses. The pyrolytic graphite sheet (PGS) samples exhibited negligible weight loss, stable thermo-optical property, and only minor oxidation signature on the exposed surface. Although AO erosion of the epoxy polymer matrix was evident, carbon nanotube (CNT)-infused PGS/CF epoxy composites retained high thermal emissivity and preserved their high thermal conductivities. These results demonstrate that the novel highly thermally conductive hybrid CF composites possess strong environmental resilience and are promising candidates for lightweight thermal radiators and thermal management components in future space exploration missions.

Space Environment↗
NASA NTRS1986

A high reliability battery management system

Over a period of some 5 years Canadian Astronautics Limited (CAL) has developed a system to autonomously manage, and thus prolong the life of, secondary storage batteries. During the development, the system was aimed at the space vehicle application using nickel cadmium batteries, but is expected to be able to enhance the life and performance of any rechargeable electrochemical couple. The system handles the cells of a battery individually and thus avoids the problems of over, and under, drive that inevitably occur in a battery of cells managed by an averaging system. This individual handling also allow cells to be totally bypassed in the event of failure, thus avoiding the losses associated with low capacity, partial short circuit, and the catastrophe of open circuit. The system has an optional capability of managing redundant batteries simultaneously, adding the advantage of on line reconditioning of one battery, while the other maintains the energy storage capability of the overall system. As developed, the system contains a dedicated, redundant, microprocessor, but the capability exists to have this computing capability time shared, or remote, and operating through a data link. As adjuncts to the basic management system CAL has developed high efficiency, polyphase, power regulators for charge and discharge power conditioning.

Moody, M. H.↗
NASA NTRS2011

Exploring the Model Design Space for Battery Health Management

Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries have been studied in detail in literature, an accurate run-time battery life prediction algorithm has eluded us. Current reliability-based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This paper presents a Particle Filter (PF) based BHM framework with plug-and-play modules for battery models and uncertainty management. The batteries are modeled at three different levels of granularity with associated uncertainty distributions, encoding the basic electrochemical processes of a Lithium-polymer battery. The effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle (UAV) application with emulated flight profiles.

Saha, Bhaskar↗
NASA NTRS1991

Augmentation of the Space Station Module Power Management and Distribution Breadboard

The space station module power management and distribution (SSM/PMAD) breadboard models power distribution and management, including scheduling, load prioritization, and a fault detection, identification, and recovery (FDIR) system within a Space Station Freedom habitation or laboratory module. This 120 VDC system is capable of distributing up to 30 kW of power among more than 25 loads. In addition to the power distribution hardware, the system includes computer control through a hierarchy of processes. The lowest level consists of fast, simple (from a computing standpoint) switchgear that is capable of quickly safing the system. At the next level are local load center processors, (LLP's) which execute load scheduling, perform redundant switching, and shed loads which use more than scheduled power. Above the LLP's are three cooperating artificial intelligence (AI) systems which manage load prioritizations, load scheduling, load shedding, and fault recovery and management. Recent upgrades to hardware and modifications to software at both the LLP and AI system levels promise a drastic increase in speed, a significant increase in functionality and reliability, and potential for further examination of advanced automation techniques. The background, SSM/PMAD, interface to the Lewis Research Center test bed, the large autonomous spacecraft electrical power system, and future plans are discussed.

Bryan Walls↗
DOE OSTI2026

Optimization of the FRIB beam dump: a hybrid genetic algorithm and reinforcement learning approach

The operational envelope of high-power-density systems, such as particle accelerators and advanced nuclear energy systems, is critically constrained by the need to manage extreme thermal loads. To address this, we present a novel hybrid optimization framework combining a genetic algorithm (GA) with a soft actor-critic (SAC) deep reinforcement learning agent. This framework was applied to a practical high-heat-flux problem: redesigning the beam dump at the Facility for Rare Isotope Beams (FRIB) for a power upgrade from 20 kW to 50 kW. The resulting design, validated by three-dimensional conjugate heat transfer simulations, suppresses hazardous hot spots and yields a markedly more uniform temperature distribution. This provides a robust operating margin, increasing the average power-handling capability by 72% relative to the current design, demonstrating the framework’s potential to solve complex thermal management challenges in both accelerator technology and advanced nuclear systems.

Accelerator↗
DOE OSTI1991

Nuclear Safety [Vol. 32, No. 1, January-March 1991]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 The Safety Review and Approval Process for Space Nuclear Power Sources, G. L. Bennett; 19 Report on the American Nuclear Society International Topical Meeting: "The Safety, Status, and Future of Non-Commercial Reactors and Irradiation Facilities", E. G. Silver; 35 Book Review: Fission Product Transport Processes in Reactor Accidents, Proceedings to the International Centre tor Heat and Mass Processes T. S. Kress; 38 Fast Reactor Technology in the 1990s: A Summary of the 1990 International Fast Reactor Safety Meeting, A. E. Levin; ACCIDENT ANALYSIS: 56 Effects of Chemical Phenomena on LWR Severe Accident Fission Product Behavior, A. P. Malinauskas and T. S. Kress; CONTROL AND INSTRUMENTATION: 65 Technical Note: Safety Parameter Display Systems—10 Years Later, R. J. Eckenrode; 68 Potential Application of Neural Networks to the Operation of Nuclear Power Plants, R. E. Uhrig; DESIGN FEATURES: 80 Twenty-First DOE/NRC Nuclear Air-Cleaning Conference, R. R. Bellamy, D. W. Moeller, and M. W. First; 91 Impact of an Apparent Radiation Embrittlement Rate on the Life Expectancy of PWR Vessel Supports, R. D. Cheverton, G. C. Robinson, W. E. Pennell, and R. K. Nanstad; ENVIRONMENTAL EFFECTS: 103 Technical Note: The Impact of Offsite Factors on the Safety Performance of Small Nuclear Power Plants, Yu. D. Baranaev and A. N. Viktorov; WASTE AND SPENT FUEL MANAGEMENT: 109 Activities Related to Waste Management, Compiled by E. G. Silver; OPERATING EXPERIENCES: 118 Reactor Shutdown Experience, Compiled by J. W. Cletcher; 121 Selected Safety-Related Events, Compiled by G. A. Murphy; 123 Operating U.S. Power Reactors, Compiled by E. G. Silver; RECENT DEVELOPMENTS: 140 General Administrative Activities, Compiled by E. G. Silver; 150 Reports, Standards, and Safety Guides, D. S. Queener; 155 Status of Power-Reactor Licensing Activities, Compiled by E. G. Silver; 157 Proposed Rule Changes as of Sept. 30, 1990; ANNOUNCEMENTS: 79 MIT Offers Summer Program on Nuclear Power Reactor Safety; 108 Harvard School of Public Health Offers Several Short Courses; 139 Short Course and Workshop on Nuclear Criticality Safety at University of New Mexico; 160 SCK/CEN Announces Training Course on Emergency Planning and Response; 161 The Authors; 164 Indexes to Nuclear Safety, Volume 31

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2012

Analysis of Screen Channel LAD Bubble Point Tests in Liquid Methane at Elevated Temperature

This paper examines the effect of varying the liquid temperature and pressure on the bubble point pressure for screen channel Liquid Acquisition Devices in cryogenic liquid methane using gaseous helium across a wide range of elevated pressures and temperatures. Testing of a 325 x 2300 Dutch Twill screen sample was conducted in the Cryogenic Components Lab 7 facility at the NASA Glenn Research Center in Cleveland, Ohio. Test conditions ranged from 105 to 160K and 0.0965 – 1.78 MPa. Bubble point is shown to be a strong function of the liquid temperature and a weak function of the amount of subcooling at the LAD screen. The model predicts well for saturated liquid but under predicts the subcooled data.

Reaction Control System↗
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 NTRSDate not supplied

Battery Evaluation Profiles for X-57 and Future Urban Electric Aircraft

Battery energy density is one of the most critical design parameters for sizing all-electric aircraft, however it’s easily overestimated. Establishing the effective, usable energy density is confused by varying degrees of margin needed to account for structural and thermal management between different cell chemistry and pack designs. Therefore, a better methodology is needed to fairly compare emerging battery technologies for electric aircraft. Currently, there is a loss of critical information when vehicle trade studies are performed using “nominal” published cell-level performance metrics. Aircraft power demands rarely match these nominal power profiles, and aircraft designers lack the ability to accurately simulate the battery performance and temperature off-nominally unless the battery chemistry is well established. Conversely, battery suppliers have no generalized reference cases to publish more realistic performance metrics. This can lead to poor assumptions, such as aircraft studies assuming a fixed discharge efficiency of a battery, when in reality the usable energy in a pack is dependent on the power and thermal profile. Information needed to properly assess weight penalties for thermal management is also typically poorly characterized when assessing candidate batteries. This paper serves to better inform battery development, and similarly, provide aircraft designers with more realistic assumptions for applying knockdown margins in their designs. Detailed power and thermal performance estimates are provided, which provide a starting point for sizing power and thermal budgets using experimentally derived battery models. Results show that the X-57 battery-to-shaft efficiency is 77.3% for a particular optimized mission. Considering a 25% reserve on the battery capacity, this means that only roughly half of the original 55.3kWh ‘nominal’ pack energy can be converted to useful work during a mission. Further estimates on a clean-sheet VTOL optimization show an average 82.7% battery-to-shaft efficiency, using 98% peak efficiency inverters and 97.4% peak efficiency motors. Although higher battery efficiencies are possible, the resulting weight penalty negates improvement in vehicle performance. These trade-offs and resulting power profiles are provided as a starting point to better assess future battery designs.

Battery↗
NASA NTRS2026

Thermal Cycle and Capacitance Testing of Mezzo Industries Phase Changer Material Heat Capacitor

Effective Thermal management is essential for the safety and performance of human spacecraft, especially during long-duration missions with fluctuating heat loads. Phase Change Material (PCM) heat exchangers offer a compact, efficient solution by storing and releasing thermal energy through latent heat during phase transitions. This allows PCM systems to buffer transient thermal loads without immediate heat rejection, reducing the need for oversized radiators – particularly critical during ascent and re-entry when radiators are stowed. By maintaining stable temperatures in crew cabins and equipment bays, PCM technology enhances crew comfort, protects sensitive systems, and supports long-term mission sustainability with minimal mass and volume impact. A phase change material heat capacitor prototype, designed and built under a NASA Small Business Innovation Research grant by Mezzo Technologies, utilizes a laser welding microtube manufacturing process that promises increased reliability and performance over current manufacturing methods like brazing. This heat exchanger was tested through coolant thermal cycling within the expected flow rates and temperature range of the Orion spacecraft, the crewed spacecraft taking NASA astronauts to the moon under the Artemis Program. The useful heat storage capacitance was measured and compared to Orion spacecraft requirements.

Active Thermal Control Systems↗
NASA NTRSDate not supplied

Engineering the Interface: Advanced Surface Technologies for Lunar Dust Management and Equipment Longevity

Through the Artemis program, NASA intends to develop a sustainable human foothold on the Moon, ultimately paving the way for crewed exploration of Mars. The Moon's hostile environment poses numerous obstacles, including exposure to radiation, temperature extremes, micrometeoroid threats, and particularly the persistent problem of lunar dust. Lunar dust impacts nearly every aspect of surface operations through adhesion and abrasion mechanisms, with contamination from anthropogenic activities (landing, rovers) far outweighing natural phenomena. Multiple adhesion pathways contribute to surface contamination in the lunar environment, including van der Waals forces, electrostatic forces, chemical reaction, and magnetic forces from elemental iron deposits. Sharp asperities from micrometeoroid bombardment and atmospheric absence increase interaction potential and enable mechanical interlocking. Low cohesion between dust particles exacerbates these challenges, as minimal interaction potential between dust and nearby surfaces overcomes particle cohesion, causing contamination. Lunar dust adhesion mitigation technologies can be categorized as either active, requiring external energy, or passive, relying on intrinsic material properties. Ultrasonic and electrodynamic technologies have been developed to the highest technology readiness level for active approaches. Passive strategies primarily focus on surface chemistry and topography modifications. At NASA Langley Research Center, approaches include surface migration agents to reduce surface energy, topographical modification using laser ablation patterning, and tailored surface conductivity to reduce intrinsic adhesion force. Performance has been evaluated using custom-built ultrasonic and centrifuge instruments. Plume-surface interactions from lunar landers can propel micrometer-sized particles at velocities up to 1000 m s-1.8 These particles pose risks to landers, habitats and infrastructure, leading to erosion, degradation, and reduced component lifespan. A panel recovered from Surveyor III was determined to have been severely abraded because of lunar dust displaced from the Apollo 12 lunar module that landed 160 m away. The performance of metallic surfaces has been evaluated via high velocity single particle impact using the laser-induced project impact test (LIPIT) facility at the University of Utah. Peridynamics modeling, a form of continuum mechanics that uses a nonlocal approach enabling greater simulation capabilities of crack initiation and fracture, has also been utilized to gain greater insight into material response during impact events. Lunar dust contamination challenges extend to power generation systems and moving equipment. Cables, rotation stages, and other mechanisms may experience limited range of motion and reduced lifetime due to dust infiltration. NASA Langley Research Center has evaluated traditional aerospace alloys, softgoods, wear resistant ceramics, and several polymer and polymer composite materials. Test methods have included traditional techniques like Taber abrasion testing, as well as designed test configurations developed in the DUSTE (dust, ultraviolet radiation, and space thermal environmental) chamber that reproduce mechanism functions in operational environment. Beyond laboratory experiments, several flight experiments have been conducted. Materials were exposed to the low Earth orbit environment on the Materials International Space Station Experiment (MISSE) and to the lunar surface environment through the Aegis Aerospace Regolith Adherence Characterization (RAC) payload and the Honeybee Robotics PlanetVac payload. Determining lunar dust's impact on surface exploration and habitation requires comprehensive experimental and computational capabilities combined with lessons learned from initial lunar activities. Identifying the greatest environmental challenges and developing mitigation technologies provides the clearest path toward successfully, expeditiously, and efficaciously completing NASA's mission. This presentation will discuss ongoing efforts at NASA Langley Research Center and collaborator contributions to these critical objectives.

Surface Engineering↗
DOE OSTI2026

Challenges for Megawatt-Scale Artificial Intelligence Rack Infrastructure

As artificial intelligence (AI) computing densities continue to increase, industry is pursuing megawatt-scale rack architectures that require tightly coordinated advances in electrical power delivery, thermal management, operations, and infrastructure integration. This document summarizes the primary technical challenges for achieving this target.

Nawaz, Kashif [ORNL] (ORCID:0000000251612491)↗
NASA NTRSDate not supplied

Battery Evaluation Profiles for X-57 and Future Urban Electric Aircraft

Battery energy density is one of the most critical design parameters for sizing all-electric aircraft, however it’s easily overestimated. Establishing the effective, usable energy density is confused by varying degrees of margin needed to account for structural and thermal management between different cell chemistry and pack designs. Therefore, a better methodology is needed to fairly compare emerging battery technologies for electric aircraft. Currently, there is a loss of critical information when vehicle trade studies are performed using “nominal” published cell-level performance metrics. Aircraft power demands rarely match these nominal power profiles, and aircraft designers lack the ability to accurately simulate the battery performance and temperature off-nominally unless the battery chemistry is well established. Conversely, battery suppliers have no generalized reference cases to publish more realistic performance metrics. This can lead to poor assumptions, such as aircraft studies assuming a fixed discharge efficiency of a battery, when in reality the usable energy in a pack is dependent on the power and thermal profile. Information needed to properly assess weight penalties for thermal management is also typically poorly characterized when assessing candidate batteries. This paper serves to better inform battery development, and similarly, provide aircraft designers with more realistic assumptions for applying knockdown margins in their designs. Detailed power and thermal performance estimates are provided, which provide a starting point for sizing power and thermal budgets using experimentally derived battery models. Results show that the X-57 battery-to-shaft efficiency is 77.3% for a particular optimized mission. Considering a 25% reserve on the battery capacity, this means that only roughly half of the original 55.3kWh ‘nominal’ pack energy can be converted to useful work during a mission. Further estimates on a clean-sheet VTOL optimization show an average 82.7% battery-to-shaft efficiency, using 98% peak efficiency inverters and 97.4% peak efficiency motors. Although higher battery efficiencies are possible, the resulting weight penalty negates improvement in vehicle performance. These trade-offs and resulting power profiles are provided as a starting point to better assess future battery designs.

Battery Electric Aircraft↗
DOE OSTI2026

Generalizable Web User Interface for Scalable and Streamlined Deployment of Building Energy Management Systems in Small and Medium-Sized Commercial Buildings

Small and medium-sized commercial buildings (SMCBs) comprise 94% of US commercial buildings yet face significant barriers to implementing building energy management systems despite advances in smart device technology. Existing solutions present critical limitations: cloud-based API solutions simplify deployment but create vendor lock-in constraints; commercial integrated software solutions ensure compatibility via standardized protocols but require substantial cost and technical expertise; open-source IoT platforms offer cost-effective vendor independence but provide insufficient standardized protocol support for commercial building automation. This research presents a generalizable web user interface framework that bridges the gap between evolving smart device capabilities and lagging software infrastructure for SMCBs. The proposed system integrates VOLTTRON open-source middleware with an automated configuration converter that transforms unified specifications written in YAML, a human-readable data-serialization format, into system-specific files, streamlining manual setup processes. The vendor-agnostic architecture supports industry-standard protocols (BACnet and Modbus) and semantic building models while providing adaptive web interfaces that dynamically adjust to various building configurations. Demonstrations through simulation-based testing and a field deployment show automatic interface adaptation across heterogeneous HVAC systems and multizone monitoring. The automated configuration converter also substantially reduces labor-intensive setup.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
DOE OSTI2026

Used Nuclear Fuel Management Using the Next Generation System Analysis Model

The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.

Craig, Brian [Argonne National Laboratory (ANL)]↗