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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

Mechanical Properties of Carbon Fiber Reinforced Composites Exposed to Cryogenic Conditions and Space Radiation via Simulation and Testing

As NASA missions extend beyond low Earth orbit, increasing reliance is placed on carbon fiber reinforced polymer (CFRP) composites for spacecraft structures where mass efficiency, durability, and long-term reliability are critical. In service, these materials are subjected to a combination of ultraviolet radiation, vacuum, ionizing radiation, atomic oxygen, and extreme thermal excursions under sustained mechanical loading. Flight systems such as the Boeing Starliner and SpaceX Dragon employ external composite structures that will experience these environments for extended durations. Although prior spaceflight and ground studies have reported limited changes in bulk mechanical properties, the synergistic effects of these environments on composite microstructure, particularly at the fiber matrix interphase, remain insufficiently characterized and represent a potential qualification and reliability risk. This study investigates the effects of short-term cryogenic exposure on a radiation shielding carbon epoxy composite, SC2020, as a ground-based analog for space relevant thermal extremes. The SC2020 material system has previously flown on the International Space Station under the Materials International Space Station Experiment (MISSE) program. Composite specimens were exposed to liquid nitrogen for 6 and 24 hours and evaluated using a multiscale characterization framework that combined ASTM D3039 tensile testing, Atomic Force Microscopy (AFM) based interphase analysis, and helium gas permeability measurements. Tensile testing showed no statistically significant or permanent degradation in global strength or modulus following cryogenic exposure. In contrast, AFM measurements revealed reductions in interphase modulus, weakened adhesion, and increased nanoscale heterogeneity, indicating localized degradation mechanisms not captured by conventional bulk testing. Gas permeability measurements showed a progressive increase in helium diffusion with exposure duration, consistent with micro-void formation or partial interfacial debonding. The results indicate that cryogenic exposure initiates degradation at the fiber matrix interphase while leaving global mechanical properties largely unchanged over short durations. These findings underscore the importance of multiscale diagnostics for identifying early-stage damage mechanisms that may influence long term performance and qualification margins for spaceflight composite structures. The data presented establish a cryogenic baseline for comparison with forthcoming MISSE flight exposure results and support ongoing NASA Established Program to Stimulate Competitive Research (EPSCoR) efforts aimed at improving composite qualification methodologies, risk assessment, and reliability prediction for space environments.

composite reliability↗
NASA NTRSDate not supplied

Evaluating Crystallinity in Thermoplastic composites

Polymer matrix composites (PMCs) offer many benefits for the aerospace industry due to their potential for weight reduction when compared to metal or ceramic based materials. Most PMCs currently in flight use thermoset matrices, however, thermoplastic resins are being explored as alternatives due to their ability to be remelted, which is of particular interest due to the potential for in-situ repair and manufacturing required in space. Most thermoplastic resins are semicrystalline polymers. The properties of semicrystalline thermoplastics are largely influenced by their crystallinity, which can vary due to many factors including thermal treatments, environmental conditions, and mechanical deformation. Monitoring the crystallinity of thermoplastic composites is key to ensuring these materials reliably meet the high demands required by space exploration. This talk discusses the use of multiple techniques such as Polarized Light Optical Microscopy and Fourier-Transform Infrared Spectroscopy to characterize the crystallinity in various thermoplastic composites, including carbon fiber reinforced PMCs and novel bio-based Martian and Lunar regolith composites designed for in-situ manufacturing. This work aims to provide the fundamental data necessary to understand the effects of crystallinity on thermoplastic PMCs, which is key to advancing their use in space applications.

Thermoplastics↗
NASA NTRS2025

Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning

Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.

Machine Learning↗
NASA NTRS2023

Satellite characterization of global stratospheric sulfate aerosols released by Tonga volcano

Large volcanic eruptions create an enhanced layer of sulfate aerosols in the stratosphere. These sulfuric acid droplets persist for many months, altering the climate and stratospheric chemistry. Sulfate aerosols scatter sunlight back to space, cooling the surface of the Earth and absorb outgoing thermal radiation, heating the stratosphere. The calculation of the climate impact of sulfate aerosols depends on their physical properties such as droplet size and chemical composition. These properties are not well known, and this uncertainty contributes to the errors in climate model predictions. Here we derive the first empirical formula that predicts the composition of stratospheric sulfate aerosols from volcanic eruptions from the air temperature and water vapor pressure. Measurements of atmospheric infrared transmittance of the Hunga Tonga-Hunga Ha'apai sulfate aerosol plume by the Atmospheric Chemistry Experiment (ACE) satellite were analyzed to determine composition (weight percent of sulfuric acid) and median particle radius. These data are supplemented by measurements of the Raikoke and Nabro eruptions. Our analysis allows the properties of volcanic aerosols in the stratosphere to be predicted reliably in atmospheric models.

P Bernath↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
DOE OSTI2026

India Power Sector Reliability Analysis [Slides]

The objective of the study is to assess pathways for India to achieve its 2070 power sector goals reliably and cost-effectively by considering various supply- and demand-side factors to support planning by national stakeholders including the Central Electricity Authority (CEA).

08 HYDROGEN↗
NASA NTRS1998

Reliability Modeling of Brittle Anisotropic Materials

A significant effort is being made to develop testing and design methodologies that will account for the lack of ductility and the strength anisotropy exhibited by brittle single crystals such as NiAl. The present approach is to modify existing test methods and brittle design methodologies, such as those applied to ceramics and glasses, and to experimentally verify the results. This paper focuses on the basic materials properties and strength behavior of a NiAl single crystal alloy and the development of a reliability model for the design of components subjected to multiaxial stresses.

Conceivably↗
NASA NTRS2026

Reliability Models and Demonstration of a Fault-Tolerant Motor Concept for Vertical Takeoff and Landing Vehicles

This report documents the completion of the Revolutionary Vertical Lift Technology Project Annual Performance Indicator 24-3.2.4.1: “Apply and document reliability prediction for high reliability motor concept.” Two modeling tools were completed for calculation of reliability of fault-tolerant (FT) motors, and key FT operations of a modular FT motor were demonstrated experimentally. The two models are complementary tools for the stakeholder and user community. Both models employ Markov chain theory. The first model is a time-homogeneous Markov chain model, and the second is a time-inhomogeneous Markov-Weibull model. This report’s main sections are as follows: 1.0 Introduction, 2.0 Theory, 3.0 Motor Reliability Models, 4.0 Validation of FT Operation by Hardware Demonstration, and 5.0 Concluding Remarks. Novel contributions to the field include development of a modular FT motor concept for electrified vertical takeoff and landing (eVTOL) application, solution methods to solve the reliability calculations, development of figures of merit, and the introduction of “linked chains” to formulate a building-block approach for time-inhomogeneous Markov-Weibull modeling of motor reliability. Example case studies have been completed, and results are provided and discussed herein. A four-module FT motor concept was developed to a preliminary-design level of detail. This eVTOL FT motor concept was designed for galvanic, magnetic, and thermal isolation of stator winding faults. The reliability of the concept motor was calculated using a time-inhomogeneous Markov chain model. Employing average failure rate as a metric, 570 times greater reliability was achieved as compared to a baseline motor without fault tolerance. A demonstrator motor was built and tested. The testing demonstrated the key features of FT operation and validated the essential premises of the FT motor concepts presented herein. The experiments included successful demonstration of the feasibility of the following four key FT features: (1) terminal open-circuit operation, (2) thermal isolation after fault, (3) terminal short-circuit operation, and (4) internal short-circuit operation. These works indicate that FT modular motor drives offer promise for addressing the daunting reliability gap that electric aircraft propulsor drives are facing relative to the best conventional motor drive technology that is available today.

Electric Motor↗
NASA NTRSDate not supplied

Advancing the Oxygen Generation Assembly Design to Increase Reliability and Reduce Costs for a Future Long Duration Mission

The state-of-the-art Oxygen Generation Assembly (OGA) has been reliably producing breathing oxygen for the crew aboard the International Space Station (ISS) for over eight years. Lessons learned from operating the ISS OGA have led to proposing incremental improvements to advance the baseline design for use in a future long duration mission. These improvements are intended to reduce system weight, crew maintenance time and resupply mass from Earth while increasing reliability. The proposed improvements include replacing the cell stack membrane material, deleting the nitrogen purge equipment, replacing the hydrogen sensors, deleting the wastewater interface, replacing the hydrogen dome and redesigning the cell stack power supply. The development work to date will be discussed and forward work will be outlined. Additionally, a redesigned system architecture will be proposed.

Kevin C Takada↗
DOE OSTI2026

Reliability Assessment of Solid-State Circuit Breakers (CRADA CRD-21-21469, Project 4 Final Report)

Growth in power requirements and the complexity of emerging distribution systems are creating the need for new solid-state products for many applications. The qualification requirements, testing standards and life-cycle management practices of solid-state devices used in protection applications is not established. The project will leverage newly developed accelerated testing capabilities design specifically for protection applications to generate large data sets that will enable advanced digital techniques for data analysis and embedded health monitoring.

14 SOLAR ENERGY↗
NASA NTRS1995

Reliability Analysis of Single Crystal NiAl Turbine Blades

As part of a co-operative agreement with General Electric Aircraft Engines (GEAE), NASA LeRC is modifying and validating the Ceramic Analysis and Reliability Evaluation of Structures algorithm for use in design of components made of high strength NiAl based intermetallic materials. NiAl single crystal alloys are being actively investigated by GEAE as a replacement for Ni-based single crystal superalloys for use in high pressure turbine blades and vanes. The driving force for this research lies in the numerous property advantages offered by NiAl alloys over their superalloy counterparts. These include a reduction of density by as much as a third without significantly sacrificing strength, higher melting point, greater thermal conductivity, better oxidation resistance, and a better response to thermal barrier coatings. The current drawback to high strength NiAl single crystals is their limited ductility. Consequently, significant efforts including the work agreement with GEAE are underway to develop testing and design methodologies for these materials. The approach to validation and component analysis involves the following steps: determination of the statistical nature and source of fracture in a high strength, NiAl single crystal turbine blade material; measurement of the failure strength envelope of the material; coding of statistically based reliability models; verification of the code and model; and modeling of turbine blades and vanes for rig testing.

Joseph Palko↗
NASA NTRS2025

Erosion Results of the MISSE 9-15 Polymers and Composites Experiment 1-4 (PCE 1-4)

Polymers and other oxidizable materials on the exterior of spacecraft in the low Earth orbit (LEO) space environment can be eroded from reaction with atomic oxygen (AO). Therefore, in order to design durable spacecraft it is important to know the extent of erosion that will occur during a mission. This can be determined by knowing the LEO AO erosion yield, E y (volume loss per incident oxygen atom), of materials susceptible to AO reaction. In addition, recent flight experiments have shown that the AO E y can vary with the AO fluence and/or solar exposure. Therefore, obtaining AO E y data for materials flown on various spaceflight missions is important. NASA Glenn Research Center has flown numerous experiments as part of the Materials International Space Station Experiment (MISSE) missions on the exterior of the International Space Station (ISS) to characterize the LEO E y of polymers, composites, protective coatings, and other spacecraft materials. Recently, four Glenn experiments with 365 flight (F) samples were flown on ISS’s MISSE-Flight Facility (MISSE-FF). These experiments are the Polymers and Composites Experiment-1 (PCE-1) flown as part of the MISSE-9 mission, the PCE-2 flown as part of the MISSE-10 mission, the PCE-3 flown as part of the MISSE-12 and MISSE-15 missions, and the PCE-4 flown as part of the MISSE-13 mission. Although each experiment had numerous sample objectives, the primary objective was to determine the LEO AO E y of various spacecraft materials as a function of solar irradiation and AO fluence. This paper provides a summary of the erosion data for the PCE 1-4 AO E y samples. The AO E y for 150 samples flown in either the LEO ram, wake, zenith or nadir directions are provided. The AO ram fluence varied from 2.97×10 20 atoms/cm 2 after 0.89 years of direct space exposure (with relatively high levels of Si contamination) on MISSE 12 to 3.93×10 20 atoms/cm 2 after 1.17 years of direct space exposure on MISSE-10. The ram AO E y values for uncoated polymers range from 3.81×10 –25 cm 3 /atom for polytetrafluoroethylene (M9R-C20 F) exposed to an AO fluence of 3.44×10 20 atoms/cm 2 on MISSE-9 to 4.43×10 –23 cm 3 /atom for AO etched low density polyimide aerogel (M12R-C21 F) exposed to an AO fluence of 2.97×10 21 atoms/cm 2 on MISSE-12. Because of the low AO fluence and relatively high Si contamination, a number of PCE-3 wake samples experienced mass gain. Thus, AO E y values are not provided for these samples. Although there are calculated AO E y values for the zenith, wake and nadir samples, the ram AO E y for a particular material is a more reliable value in terms of AO exposure because the zenith, wake and nadir directions were exposed to either no or very little AO fluence and thus other space environmental factors (i.e. vacuum, thermal extremes and thermal cycling, and/or various types of radiation) are responsible for the mass loss.

Erosion yield↗
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 NTRSDate not supplied

Assembly and Integration Status of a High Fidelity Ground Test Bed for the Water Processor Assembly

The Water Recovery System (WRS) is a critical component of life support aboard the International Space Station (ISS) and will play an essential role in future missions beyond Low Earth Orbit (LEO). Its primary functional units – the Urine Processor Assembly (UPA), Brine Processor Assembly (BPA), and Water Processor Assembly (WPA) – must be evaluated for extended operation, dormancy resilience, material obsolescence, and reliability under exploration-driven constraints. Ground testing is vital for developing these technologies and generating statistically relevant reliability assessments, which requires extended runtime under integrated, Flight-like conditions. Currently, no high-fidelity, fully integrated WPA ground test bed exists to support these objectives. To address this gap, NASA is developing a WPA test bed at Marshall Space Flight Center (MSFC) that combines downgraded ISS flight hardware with functionally flight-like components in a cost-effective configuration while maintaining priority hardware investigations. This paper describes the current status of hardware assembly and integration, outlines key challenges such as simulating microgravity effects and mitigating obsolescence, and presents future test objectives including software development, reliability assessments, dormancy studies, and exploration-oriented upgrades.

Water Processor Assembly↗
DOE OSTI2026

Boron Coordination in Multicomponent Glasses: Analytical Models and Machine Learning With Uncertainty

Borosilicate glasses are extensively used in a variety of applications from kitchenware to nuclear waste immobilization due to the strong network formed by the Si-O-B bond that makes it resistant to chemical corrosion and gives it a low thermal expansion. Boron, however, exists in both trigonal BO3 and tetrahedral BO4 bonds in glass systems, which impacts the chemical durability and thermal resistance of the glass, amongst other properties. Boron coordination (N4), or the ratio of the amount of BO4 to BO3 within a glass, may aid in predicting these properties but is difficult to derive without experimental data due to the complexity of impacts from varied glass compositions and processing factors. For this reason, compositional models have been developed to predict boron coordination, but the models typically include a limited number of glass components. To help fill this gap in the models, in this work, a diverse multicomponent glass dataset of 809 glasses is compiled from a literature search, and then a number of analytical and machine learning (ML) models are trained on the dataset. Previously developed modified Bernstein and modified Du Stebbins analytical models were fitted to update parameters with the new dataset. Then, partially Bayesian neural networks, Gaussian process regressor, and heteroskedastic deterministic neural networks were evaluated. The ML models examined all have different strategies to overcome the potential for overfitting as a result of a limited training dataset, and return results that account for model uncertainty, which can be valuable for understanding model reliability. For the first time, cooling rate is introduced as an input parameter for ML models, showing consistent improvements in performance and solidifying the importance of including parameters outside of composition alone for N4 prediction. The machine learning models examined here show promise in accurate predictions of boron coordination in borosilicate glasses, all achieving R2 values of 0.91.

boron coordination↗