SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This study combines Graph Neural Networks (GNNs) and Large Language Models (LLMs) to improve material property predictions. By leveraging both embeddings, this hybrid approach achieves up to a 25% improvement over GNN-only model in accuracy.
Rolling-element bearing material technology, examining fatigue effect of material properties and processing variables
Low & high prandtl number approaches for heat transfer in laminar flow of incompressible fluids with constant material properties
This paper describes statistical procedures and their importance in obtaining composite material property values in designing structures for aircraft and military combat systems. The property value is such that the strength exceeds this value with a prescribed probability with 95% confidence in the assertion. The survival probabilities are the 99th percentile and 90th percentile for the A and B basis values respectively. The basis values for strain to failure measurements are defined in a similar manner. The B value is the primary concern of this paper.
Relational Information Management (RIM) was selected as the data base management system for a prototype engineering materials data base. The data base provides a central repository for engineering material properties data, which facilitates their control. Numerous RIM capabilities are exploited to satisfy prototype data base requirements. Numerical, text, tabular, and graphical data and references are being stored for five material types. Data retrieval will be accomplished both interactively and through a FORTRAN interface. The experience gained in creating and exercising the prototype will be used in specifying requirements for a production system.
Tensile and strain cycling fatigue properties of test materials for predicting life of turbine blade specimens
Progress in solar energy technology is reported in the following areas: aging and life prediction methodology and devices for solar cell encapsulation; the function of adhesion chemistry, primers, and a new diagnostic technique for estimations of bond durability; a study of fire retardant formulations for decreasing the potential flammability of solar modules; initial studies of the electrical insulating properties of encapsulation materials and measurement of the intrinsic dielectric strength; antisoiling compounds for the prevention of soil build-up on the outer surface of the module; and low temperature processing encapsulants that permit module fabrication at temperatures less than 100 C. Another area of study has been added to determine the degree to which formulation and processes affect the module quality and manufacturing yield.
The physics of spacecraft charging is reviewed, and criteria for selecting and testing semiinsulating polymers (SIPs) to avoid charging are discussed and illustrated. Chapters are devoted to the required properties of dielectric materials, the charging process, discharge-pulse phenomena, design for minimum pulse size, design to prevent pulses, conduction in polymers, evaluation of SIPs that might prevent spacecraft charging, and the general response of dielectrics to space radiation. SIPs characterized include polyimides, fluorocarbons, thermoplastic polyesters, poly(alkanes), vinyl polymers and acrylates, polymers containing phthalocyanine, polyacene quinones, coordination polymers containing metal ions, conjugated-backbone polymers, and 'metallic' conducting polymers. Tables summarizing the results of SIP radiation tests (such as those performed for the NASA Galileo Project) are included.
In this study, different ratios and combinations of common epoxies, curing agents, and flexibilizers were used to find a material with desirable properties. Analysis of several material properties was conducted including glass transition temperature of each cured material which ensures high flexibility at a wide range of temperatures.
The analysis of reflected ultrasonic waves induced by oblique insonification of composite materials is a powerful tool for providing informations about defects and material properties. A device was developed to manipulate a pair of transmitting and receiving transducers at vrious angles of wave incidence and propagation with the fiber orientation.
Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.
We report using carbon fibers (<100 μm in diameter) as ultrasonic waveguides to measure spatial changes in the environment and material properties. We connected carbon fibers of different lengths to an ultrasonic transducer and measured changes in the times of flight in a pulse-echo mode in response to elevated temperatures. By simultaneously interrogating multiple fibers of different lengths and collectively analyzing the time of flight in each fiber, we demonstrated dynamic measurements of the temperature distribution along the fiber bundle during their heating.
The performance improvements obtainable from extending the traditionally thin back-surface-field (BSF) layer deep into the base of silicon solar cells under terrestrial solar illumination (AM1) are analyzed. This extended BSF cell is also known as the back-drift-field cell. About 100 silicon cells were analyzed, each with a different emitter or base dopant impurity distribution whose selection was based on physically anticipated improvements. The four principal performance parameters (the open-circuit voltage, the short-circuit current, the fill factor, and the maximum efficiency) are computed using a FORTRAN program, called Circuit Technique for Semiconductor-device Analysis, CTSA, which numerically solves the six Shockley Equations under AM1 solar illumination at 88.92 mW/cm, at an optimum cell thickness of 50 um. The results show that very significant performance improvements can be realized by extending the BSF layer thickness from 2 um (18% efficiency) to 40 um (20% efficiency).
The efficient design, service-life qualification, and reliability predictions for lightweight aerospace structures require careful mechanical properties analysis of candidate structural materials. The demand for high-quality laboratory data is particularly acute when the candidate material or the structural design has little history. The pumpkin-shaped super-pressure balloon presents both challenges. Its design utilizes load members (tendons) extending from apex to base around the gas envelope to achieve a lightweight structure. The candidate tendon material is highly weight-efficient braided HM cord. Previous mechanical properties studies of Zylon have focused on fiber and yarn, and industrial use of the material in tensile applications is limited. For high-performance polymers, a carefully plamed and executed properties analysis scheme is required to ensure the data are relevant to the desired application. Because no directly-applicable testing standard was available, a protocol was developed based on guidelines fiom professional and industry organizations. Due to the liquid-crystalline nature of the polymer, the cord is very stiff, creeps very little, and does not yield. Therefore, the key material property for this application is the breaking strength. The pretension load and gauge length were found to have negligible effect on the measured breaking strength over the ranges investigated. Strain rate was found to have no effect on breaking strength, within the range of rates suggested by the standards organizations. However, at the lower rate more similar to ULDB operations, the strength was reduced. The breaking strength increased when the experiment temperature was decreased from ambient to 183K which is the lowest temperature ULDB is expected to experience. The measured strength under all test conditions was well below that resulting from direct scale-up of fiber strength based on the manufacturers data. This expected result is due to the effects of the braiding process and material ageing.
Some of the applications to materials evaluation and property determination of thermographic NDE using digital postprocessing of sequences of thermograms are demonstrated. A generic description is given of the steps used in postprocessing for obtaining material property values.
Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.
The resistance of elastomeric materials to high-pressure hydrogen-induced damage is essential for ensuring the reliability of hydrogen infrastructure. Here, in this study, we systematically investigated the swelling behavior and hydrogen transport properties of four elastomer types – EPDM, NBR, FKM, and HNBR – using a custom in-situ view cell system capable of real-time monitoring during decompression from pressures up to 96.5 MPa. Each elastomer was formulated with and without fillers and plasticizers to assess the effects of formulation on swelling response. Thermal desorption analysis (TDA) was employed to determine equilibrium hydrogen content and diffusion coefficients, providing insight into gas uptake and mobility within each material. Correlation analyses using Pearson and Spearman coefficients revealed that the diffusion coefficient showed a stronger relationship with swelling behavior than hydrogen content, highlighting the dominant role of hydrogen mobility. Filled elastomers, particularly those with carbon black, consistently showed reduced swelling due to enhanced stiffness and reduced diffusivity. These results deepen our understanding of diffuso-mechanical interactions in elastomers and support the rational design of sealing materials for high-pressure hydrogen systems.
The influence of strength, fracture toughness, and crack growth resistance on the design stress level has been determined from a mathematical model of crack growth and failure. The results show that to select materials properly, the operating parameters of desired life and initial flaw size must be considered simultaneously with the material properties. To do this, a method for constructing an operating surface is derived, discussed, and illustrated.