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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Thermal Analysis on Large Format Lithium-Ion Batteries in Thermal Runaway

Lithium-ion (Li-ion) batteries can deliver electrical power across a large range of applications, and large format versions of these batteries are common in aerospace for their low mass and energy dense properties. Li-ion cells usage has increased as they are more power dense than fuel cells and have overall higher performance over lead-acid/NiCd batteries. However, Li-ion cells can experience Thermal Runaway (TR) via electro-chemical, mechanical, or thermal abuse. TR is a phenomenon where the stored energy in a cell is rapidly released along with vented gases and other effluents. A cell is generally determined to be in TR if its internal heat generation has surpassed its ability to dissipate that heat. If a Li-ion cell enters TR, propagation within the battery may occur. Propagation is the chain reaction that can occur to nearby cells due to a cell that has entered TR initially. Understanding a battery’s susceptiveness to propagation is necessary to evaluate risks associated with TR phenomenon. To determine if a battery is designed in a sufficient manner to prevent battery propagation if single cell TR occurs, testing and thermal analysis of the battery must be conducted. The presentation for TFAWS 2026 will cover considerations for battery TR thermal analysis, such as: testing methods, influences on the cells, failure mechanisms, and TR parameters. This presentation will include a walkthrough of an example battery TR thermal analysis, and the results thereof.

Lithium Ion Batteries↗
DOE OSTI2023

Advanced Reactor Cyber Analysis and Development Environment (ARCADE) for System-Level Design Analysis

Cybersecurity is a persistent concern to the safety and security of Nuclear Power Plants (NPPs), but has lacked data-driven, evidence-based research. Rigorous cybersecurity analysis is critical for the licensing of advanced reactors using a performance-based approach. One tool that enables cybersecurity analysis is modeling and simulation. The nuclear industry makes extensive use of modeling and simulation throughout the decision process but lacks a method to incorporate cybersecurity analysis with existing models. To meet this need, the Advanced Reactor Cyber Analysis and Development Environment (ARCADE) was developed. ARCADE is a suite of publicly available tools that can be used to develop emulations of industrial control system devices and networks and integrate those emulations with physics simulators. This integration of cyber emulations and physics models enables rigorous cyber-physical analysis of cyber-attacks on NPP systems. This report provides an overview of key considerations for using ARCADE with existing physics models and demonstrates ARCADE’s capabilities for cybersecurity analysis. Using a model of the Small Modular Advanced High Temperature Reactor (SmAHTR), ARCADE was able to determine the sensitivity of the primary heat exchangers (PHX) to coordinated cyber-attacks. The analysis determined that while the PHX’s failures cause disruption to the reactor, they did not cause any safety limits to be exceeded because of the plant design, including passive safety features. Further development of ARCADE will enable rigorous, repeatable, and automated cyber-physical analysis of advanced reactor control systems. These efforts will also help reduce regulatory uncertainty by presenting similar types of cybersecurity analyses in a common format, driving standard approaches and reporting.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRSDate not supplied

Machine Learning Enabled Quantitative Risk Assessment of Aerial Wildfire Response

Aerial wildfire operations are high risk and account for a large number of firefighter deaths. Increasing intensity of wildfires is driving a surge in aerial operations, while simultaneously there is growing interest in improving system safety and performance. In this work, wildfire aviation mishaps documented using the SAFECOM system are analyzed using a previously developed framework for hazard extraction and analysis of trends (HEAT). Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard are calculated, including frequency, rate, and severity. We examine whether these metrics change over time, and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on rate of occurrence and average severity. Results identify jumper operations hazards as high-risk, in addition to bucket drop failures, cargo let down failures, and severe weather as medium risk.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Enabled Quantitative Risk Assessment of Aerial Wildfire Response

Aerial wildfire operations are high risk and account for a large number of firefighter deaths. Increasing intensity of wildfires is driving a surge in aerial operations, while simultaneously there is growing interest in improving system safety and performance. In this work, wildfire aviation mishaps documented using the SAFECOM system are analyzed using a previously developed framework for hazard extraction and analysis of trends (HEAT). Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard are calculated, including frequency, rate, and severity. We examine whether these metrics change over time, and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on rate of occurrence and average severity. Results identify jumper operations hazards as high-risk, in addition to bucket drop failures, cargo let down failures, and severe weather as medium risk.

machine learning↗
NASA NTRS2002

Reliability Analysis of Brittle Material Structures - Including MEMS(?) - With the CARES/Life Program

Brittle materials are being used, or considered, for a wide variety of high tech applications that operate in harsh environments, including static and rotating turbine parts. thermal protection systems, dental prosthetics, fuel cells, oxygen transport membranes, radomes, and MEMS. Designing components to sustain repeated load without fracturing while using the minimum amount of material requires the use of a probabilistic design methodology. The CARES/Life code provides a general-purpose analysis tool that predicts the probability of failure of a ceramic component as a function of its time in service. For this presentation an interview of the CARES/Life program will be provided. Emphasis will be placed on describing the latest enhancements to the code for reliability analysis with time varying loads and temperatures (fully transient reliability analysis). Also, early efforts in investigating the validity of using Weibull statistics, the basis of the CARES/Life program, to characterize the strength of MEMS structures will be described as as well as the version of CARES/Life for MEMS (CARES/MEMS) being prepared which incorporates single crystal and edge flaw reliability analysis capability. It is hoped this talk will open a dialog for potential collaboration in the area of MEMS testing and life prediction.

Nemeth, Noel N.↗
NASA NTRS1993

Back Propagation Neural Networks for Predicting Ultimate Strengths of Unidirectional Graphite/Epoxy Tensile Specimens

The research presented herein demonstrates the feasibility of predicting ultimate strengths in simple composite structures through a neural network analysis of their acoustic emission (AE) amplitude distribution data. A series of eleven ASTM D-3039 unidirectional graphite/epoxy tensile samples were loaded to failure to generate the amplitude distributions for this analysis. A back propagation neural network was trained to correlate the AE amplitude distribution signatures generated during the first 25% of loading with the ultimate strengths of the samples. The network was trained using two sets of inputs: (1) the statistical parameters obtained from a Weibull distribution fit of the amplitude distribution data, and (2) the event frequency (amplitude) distribution itself. The neural networks were able to predict ultimate strengths with a worst case error of -8.99% for the Weibull modeled amplitude distribution data and 3.74% when the amplitude distribution itself was used to train the network. The principal reason for the improved prediction capability of the latter technique lies in the ability of the neural network to extract subtle features from within the amplitude distribution.

Walker, James L.↗
NASA NTRS1992

Fault detection and fault tolerance in robotics

Robots are used in inaccessible or hazardous environments in order to alleviate some of the time, cost and risk involved in preparing men to endure these conditions. In order to perform their expected tasks, the robots are often quite complex, thus increasing their potential for failures. If men must be sent into these environments to repair each component failure in the robot, the advantages of using the robot are quickly lost. Fault tolerant robots are needed which can effectively cope with failures and continue their tasks until repairs can be realistically scheduled. Before fault tolerant capabilities can be created, methods of detecting and pinpointing failures must be perfected. This paper develops a basic fault tree analysis of a robot in order to obtain a better understanding of where failures can occur and how they contribute to other failures in the robot. The resulting failure flow chart can also be used to analyze the resiliency of the robot in the presence of specific faults. By simulating robot failures and fault detection schemes, the problems involved in detecting failures for robots are explored in more depth.

Visinsky, Monica↗
NASA NTRS1993

Reliability studies of integrated modular engine system designs

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Hardy, Terry L.↗
NASA NTRS1993

Reliability studies of Integrated Modular Engine system designs

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Hardy, Terry L.↗
NASA NTRS1995

Static stability of the Space Station solar array FASTMast structure

The combined loads test of the 3-Bay FASTMast marks the end of the Lewis Research Center (LeRC) effort to characterize the behavior of the principal Space Station solar array support structure. The primary objective of this test and analysis effort was to develop a method to predict structural stability failure modes under flight-like applied loads. Included at the beginning of this report is a brief historical perspective of the hardware design development and FASTMast structural stability problem evolution. Once an understanding of the solution process has been established, test and analysis details are presented and related to the postulated failure theories. The combined load test series subjected the structure to a combination of transverse, moment, and torsion loads similar to that expected in the service environment. Nonlinear finite element (FE) models were developed and large displacement analyses were performed to support the test effort and failure mode predictions. Details of the test configuration as well as test and analysis results are presented. The results were then critiqued to establish valid and successful support of the failure mode assessments. Finally, study conclusions are drawn and recommendations for safe operation of the FASTMast structure are presented for consideration.

Shaker, John F.↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Hygrothermal aging and recycling effects on mechanical and thermal properties of recyclable thermoplastic glass fiber composites

Abstract Glass fiber‐reinforced polymer composites are widely used in marine applications, including renewable energy devices and submarines, due to their strength‐to‐weight ratios and corrosion resistance. As sustainability becomes more important, recyclable thermoplastic composites are gaining attention in marine energy applications. These materials face harsh conditions such as high chloride water, UV radiation, pressurization, and thermal cycling, which can affect their durability and recyclability. This study examines the impact of hygrothermal aging on the recyclability of an Elium glass fiber (GF) composite. Samples were mechanically ground for recycling, and mechanical and thermal properties were compared between aged and unaged samples. After seawater aging, the tensile strength and modulus of the unaged composite dropped by 25% and 13% respectively, due to water ingress along the fiber‐matrix interface. The recycled composite showed reduced flexural strength as the long fiber composite was converted into short fibers during the process. Differential scanning calorimetry (DSC) revealed no change in glass transition temperature after aging. Weight loss (25%) was attributed to the thermal stability of the glass fibers. Dynamic mechanical analysis (DMA) showed that the storage modulus increased with aging, and fracture analysis confirmed a mixed mode of failure for aged composites. Highlights Hygrothermal aging lowers mechanical performance of Elium® GF composites. Failure mechanism tends to be matrix cracking and interface bonding. Glass transition temperature is not reduced by hygrothermal aging. Fractured composite was successfully recycled via thermoforming. Flexural strength of the recycled composite is lower.

17 WIND ENERGY↗
DOE OSTI2020

Degradation in photovoltaic encapsulation strength of attachment: Results of the first PVQAT TG5 artificial weathering study

Delamination of the encapsulant in photovoltaic (PV) module technology results in immediate optical loss and may enable subsequent corrosion or mechanical damage. The effects of artificial weathering were not previously known; therefore, an empirical study was performed to survey the factors most affecting adhesion, including the ultraviolet (UV) source (ie, Xe or fluorescent lamp[s]), the optical filters for the lamp, the chamber temperature, and the relative humidity. Natural weathering was also performed at locations, including the following: Golden, Colorado; Miami, Florida; Phoenix, Arizona; QiongHai, China; Riyadh, Saudi Arabia; and Turpan, China. Specimens were constructed using a laminated glass/poly (ethylene-co-vinyl acetate)/glass geometry. The compressive shear test (CST) was used to quantify the mechanical strength of attachment, taken as a proxy for adhesion. The fractography of select specimens (including cross-sectional optical microscopy) was used to verify the failure mode (delamination or decohesion). Additional analysis of the local solar spectrum as well as the specimen temperature was performed to interpret the results of natural weathering. The goals of this study include to identify the most significant stressors, clarify where strong coupling may occur between stressors, and validate accelerated test results relative to natural weathering. The importance of specimen conditioning (for moisture) and hygrometric degradation as well as the effects of UV degradation are identified in this paper.

14 SOLAR ENERGY↗
DOE OSTI2020

Mechanical and Corrosion Assessment of Friction Self-Piercing Rivet Joint of Carbon Fiber-Reinforced Polymer and Magnesium Alloy AZ31B

In the present work, thermoset carbon fiber–reinforced polymer (CFRP) was spot joined to magnesium alloy AZ31B by a friction self-piercing riveting (F-SPR) process. Lap shear tensile and cross-tension testing were used to evaluate the mechanical joint performance. An average lap shear tensile load of 5.18 kN was achieved, while an average of 2.81 kN was found from cross-tension testing. All F-SPR samples showed a pullout of AZ31B after mechanical testing, indicating good mechanical interlocking between the steel rivet and AZ31B. Corrosion potential was measured for each material to establish the galvanic corrosion characteristics. As expected, AZ31B was found to be the most active, while thermoset CFRP was the most noble. The steel rivet fell between the AZ31B (active) and the thermoset CFRP (noble). Salt fog corrosion testing (ASTM B-117) was performed to evaluate the corrosion performance of the uncoated F-SPR joint. With up to 200 h of exposure, the post-corroded F-SPR joint integrity retained 81.2% of the pre-exposure F-SPR joint strength with AZ31B pullout failure mode. From cross-sectional analysis of the F-SPR joint, extensive corrosion of AZ31B was observed at the joint and other exposure areas. However, steel rivet was not significantly corroded due to sacrificial anode effect by which AZ31B corroded first in the galvanic couple.

36 MATERIALS SCIENCE↗
DOE OSTI2020

MECHANICAL AND CORROSION ASSESSMENT OF FRICTION SELF-PIERCING RIVET JOINT OF CARBON FIBER-REINFORCED POLYMER AND MAGNESIUM ALLOY AZ31B

In the present work, thermoset carbon fiber–reinforced polymer (CFRP) was spot joined to magnesium alloy AZ31B by a friction self-piercing riveting (F-SPR) process. Lap shear tensile and cross-tension testing were used to evaluate the mechanical joint performance. An average lap shear tensile load of 5.18 kN was achieved, while an average of 2.81 kN was found from cross-tension testing. All F-SPR samples showed pull-out of AZ31B after mechanical testing, indicating good mechanical interlocking between the steel rivet and AZ31B. Corrosion potential was measured for each material to establish the galvanic corrosion characteristics. As expected, AZ31B was found to be the most active, while thermoset CFRP was the most noble. The steel rivet fell between the AZ31B (active) and the thermoset CFRP (noble). Salt fog corrosion testing (ASTM B-117) was performed to evaluate the corrosion performance of the uncoated F-SPR joint. With up to 200 h of exposure, the post-corroded F-SPR joint integrity retained 81.2% of the pre-exposure F-SPR joint strength with AZ31B pull-out failure mode. From cross-sectional analysis of the F-SPR joint, extensive corrosion of AZ31B was observed at the joint and other exposure areas. However, steel rivet was not significantly corroded potentially due to sacrificial anode effect by which AZ31B corroded first in the galvanic couple.

Lim, Yong Chae↗
NASA NTRS1974

Asymmetrical booster guidance and control system design study. Volume 3: Space shuttle vehicle SRB actuator failure study

The investigation of single actuator failures on the space shuttle solid rocket booster required the analysis of both square pattern and diamond pattern actuator configurations. It was determined that for failures occuring near or prior to the region of maximum dynamic pressure, control gain adjustments can be used to achieve virtually nominal mid-boost vehicle behavior. A distinct worst case failure condition was established near staging that could significantly delay staging. It is recommended that the square pattern be retained as a viable alternative to the baseline diamond pattern because the staging transient is better controlled resulting in earlier staging.

Williams, F. E.↗