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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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425 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reversible two-way tuning of thermal conductivity in an end-linked star-shaped thermoset

Polymeric thermal switches that can reversibly tune and significantly enhance their thermal conductivities are desirable for diverse applications in electronics, aerospace, automotives, and medicine; however, they are rarely achieved. Here, we report a polymer-based thermal switch consisting of an end-linked star-shaped thermoset with two independent thermal conductivity tuning mechanisms—strain and temperature modulation—that rapidly, reversibly, and cyclically modulate thermal conductivity. The end-linked star-shaped thermoset exhibits a strain-modulated thermal conductivity enhancement up to 11.5 at a fixed temperature of 60 °C (increasing from 0.15 to 2.1 W m –1 K –1 ). Additionally, it demonstrates a temperature-modulated thermal conductivity tuning ratio up to 2.3 at a fixed stretch of 2.5 (increasing from 0.17 to 0.39 W m –1 K –1 ). When combined, these two effects collectively enable the end-linked star-shaped thermoset to achieve a thermal conductivity tuning ratio up to 14.2. Moreover, the end-linked star-shaped thermoset demonstrates reversible tuning for over 1000 cycles. The reversible two-way tuning of thermal conductivity is attributed to the synergy of aligned amorphous chains, oriented crystalline domains, and increased crystallinity by elastically deforming the end-linked star-shaped thermoset.

42 ENGINEERING↗
DOE OSTI2024

A novel digital lifecycle for Material‐Process‐Microstructure‐Performance relationships of thermoplastic olefins foams manufactured via supercritical fluid assisted foam injection molding

Abstract This research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N 2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material‐process‐microstructure‐performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material‐process‐microstructure‐performance (MP2) relationships. Highlights Establishes a material‐process‐microstructure‐performance (MP2) for TPO foams Sustainably produces TPO foams using supercritical (ScF) N 2 with 20% lightweighting Shows enhanced paintability for TPO foam improved surface aesthetics Digital lifecycle (DLC) that predicts both foam microstructure and properties DLC maps process effects & microstructure onto FEA mesh for precise prediction

Engineering↗
NASA NTRS2003

NASA Glenn/AADC-Rolls Royce Collaborated to Measure Erosion Resistance on Coated Polymer Matrix Composites

Polymer matrix composites (PMCs) are increasingly used in aerospace and automotive applications because of their light weight and high strength-to-weight ratio relative to metals. However, a major drawback of PMCs is poor abrasion resistance, which restricts their use, especially at high temperatures. Simply applying a hard coating on PMCs to improve abrasion and erosion resistance is not effective since coating durability is short lived (ref. 1). Generally, PMCs have higher coefficients of thermal expansion than metallic or ceramic coatings have, and coating adhesion suffers because of poor interfacial adhesion strength. One technique commonly used to improve coating adhesion or durability is the use of bond coats that are interleaved between a coating and a substrate with vastly different coefficients of thermal expansion. An example of this remedy is the use of bondcoats for ceramic thermal barrier coatings on metallic turbine components (ref. 2). Prior collaborative research between the NASA Glenn Research Center and the Allison Advanced Development Company (AADC) demonstrated that bond coats sandwiched between PMCs and high-quality plasma-sprayed, erosion-resistant coatings substantially improved the erosion resistance of PMCs (ref. 3). One unresolved problem in this earlier collaboration was that there was no easy, accurate way to measure the coating erosion wear scar. Coating wear was determined by both profilometry and optical microscopy. Both techniques are time consuming. Wear measurement by optical microscopy requires sample destruction and does not provide a comprehensive measure of the entire wear volume. An even more subtle, yet critical, problem is that these erosion coatings contain two or more materials with different densities. Therefore, simply measuring specimen mass loss before and after erosion will not provide an accurate gauge for coating and/or substrate volume loss. By using a noncontact technique called scanning optical interferometry, which was recently developed at Glenn, researchers can accurately determine the wear performance of erosion-coated PMCs while preserving the sample. An example of this interferometry technique is shown in the preceding figure for an erosion-coated inlet guide vane from a Rolls Royce AE3007 regional gas turbine jet engine. Erosion was conducted with coated and uncoated PMC vanes, with the abrasive material moving at a velocity of 229 m/s at impingement angles of 20 and 90 degrees. The coatings for PMCs remarkably reduced the erosion volume loss by a factor of approximately 10. Currently, several erosion coatings for PMCs are being compared and downselected for engine testing at Rolls Royce.

Miyoshi, Kazuhisa↗
NASA NTRS2010

Modeling of Failure for Analysis of Triaxial Braided Carbon Fiber Composites

In the development of advanced aircraft-engine fan cases and containment systems, composite materials are beginning to be used due to their low weight and high strength. The design of these structures must include the capability of withstanding impact loads from a released fan blade. Relatively complex triaxially braided fiber architectures have been found to yield the best performance for the fan cases. To properly work with and design these structures, robust analytical tools are required that can be used in the design process. A new analytical approach models triaxially braided carbon fiber composite materials within the environment of a transient dynamic finite-element code, specifically the commercially available transient dynamic finite-element code LS-DYNA. The geometry of the braided composites is approximated by a series of parallel laminated composites. The composite is modeled by using shell finite elements. The material property data are computed by examining test data from static tests on braided composites, where optical strain measurement techniques are used to examine the local strain variations within the material. These local strain data from the braided composite tests are used along with a judicious application of composite micromechanics- based methods to compute the stiffness properties of an equivalent unidirectional laminated composite required for the shell elements. The local strain data from the braided composite tests are also applied to back out strength and failure properties of the equivalent unidirectional composite. The properties utilized are geared towards the application of a continuum damage mechanics-based composite constitutive model available within LS-DYNA. The developed model can be applied to conduct impact simulations of structures composed of triaxially braided composites. The advantage of this technology is that it facilitates the analysis of the deformation and damage response of a triaxially braided polymer matrix composite within the environment of a transient dynamic finite-element code such as LS-DYNA in a manner which accounts for the local physical mechanisms but is still computationally efficient. This methodology is tightly coupled to experimental tests on the braided composite, which ensures that the material properties have physical significance. Aerospace or automotive companies interested in using triaxially braided composites in their structures, particularly for impact or crash applications, would find the technology useful. By the development of improved design tools, the amount of very expensive impact testing that will need to be performed can be significantly reduced.

Goldberg, Robert K.↗
DOE OSTI2025

Catalytic Oxygen Reduction for Deep O 2 Removal from CO 2 Streams

Streams of CO 2 from various capture processes may contain several impurities of which O 2 is often overlooked as a problematic impurity. Because of its reactivity, the National Energy Technology Laboratory has recommended a stringent limit, less than 10 ppm, for the O 2 level in treated CO 2 products for safe transport, storage, and utilization. Here, in this study, a variety of catalytic oxygen reduction approaches using commercial automotive exhaust emission catalysts are evaluated for O 2 removal from CO 2 streams using different reducing agents, including H 2 , CO, CH 3 OH, and CH 4 . When the amount of reductant added into the feed is carefully controlled, H 2 , CO, or CH 3 OH can effectively remove O 2 from 1.5% to below 10 ppm with a single-reactor design (>99.93% removal efficiency), while simultaneously meeting the impurity limits for other species. With CH 4 as a reductant, it is challenging to simultaneously meet the the O 2 and CO specifications in a single-reactor design because CH 4 also reacts with CO 2 to produce high levels of CO in the stream (at several hundred ppm). A dual-reactor design is developed to enable the use of CH 4 , a readily available and low-cost reductant, for the purification of CO 2 streams to meet the specifications for O 2 and other impurities.

CO2 purification↗
DOE OSTI2024

Evaluation of Corrosion and Its Impact on the Mechanical Performance of Al–Steel Joints

Aluminum–steel joints are increasingly used in the automotive industry to meet the requirements for energy saving and emission reduction. Among various joining technologies, self-pierce riveting (SPR) and resistance spot welding (RSW) are two well-established technologies for fabricating dissimilar joints with stable and high mechanical performance. However, corrosion will occur in these joints inevitably due to different electrochemical properties, which can degrade the surface quality and the mechanical performance, such as strength. This paper presents a method of understanding the corrosion mechanisms in joining aluminum and steel. For this understanding, a hybrid method combining experimental observations, mechanical properties identification, and analytical approaches was used to assess the evolution of the impact of corrosion on the joining performance, such as traction separation curves. The study was conducted on common combinations used in the vehicles, e.g., a 1.2 mm thickness aluminum alloy (AA 6022) and 2.0 mm thickness hot deep galvanized steel (HDG HSLA 340) joined by SPR and RSW. After the fabrication of these joints, accelerated cyclic corrosion tests of up to 104 cycles were performed, which reproduced the environmental conditions to which a vehicle was exposed. By investigating the microstructural evolution within the joints, the corrosion mechanisms of SPR and RSW joints were revealed, including the initiation and propagation. Moreover, the intrinsic impact of the corrosion on the mechanical performance, including the strength, axial stiffness, and crashworthiness, was analyzed by performing a lap-shear test. It showed that as corrosion proceeds, the fracture modes and mechanical performance are affected significantly.

Chemistry↗
DOE OSTI2024

Friction stir processing on a strontium modified, thin-wall, vacuum-assisted high-pressure die-cast Aural-5 alloy to improve tensile and fatigue performance

Here, this study explores the application of friction stir processing (FSP) to enhance the material properties of Sr-modified Aural-5 alloy, with a focus on improved tensile and fatigue properties. Aural-5 is a well-known vacuum-assisted high-pressure die-cast (HPDC) Al-Si7-Mg alloy used in the automotive industry to reduce vehicle weight, enhance fuel efficiency, and lower carbon emissions. This alloy modifies its material chemistry with Sr for fine fibrous networks of eutectic silicon and manganese (Mn) to reduce die soldering. It has significantly less iron (Fe) content resulting in the elimination of detrimental needle-shaped Fe-bearing ß-phase intermetallic and improving ductility. The initial microstructure of as-received HPDC Aural-5 exhibits shrinkage porosity in the middle section, a dendritic microstructure with fibrous Al-Si eutectic colonies, a shear-band structure beneath the die-wall, large dendritic externally solidified crystals (ESCs), needle-shaped Mg 2 Si phase and significant second-phase particulates. Some of those microstructural features, such as porosity, ESCs, needle-shaped Mg 2 Si phase, and large second-phase particles, serve as initiation sites for cracks under mechanical loading, resulting in adverse effects on tensile properties, particularly ductility. FSP effectively transforms the microstructure into a wrought configuration with uniform particle distribution by eliminating porosity and disintegrating dendrites, eutectic colonies, ESCs, second-phase particles, and shear-band structures. FSP-driven microstructure modification enhances yield strength and tensile ductility by ~30% and ~35%, respectively. The fatigue life of the material in a bending mode configuration (stress ratio R = 0.1) after FSP exhibits enhancements ranging from 2.0 to 3.9 times that of the original HPDC Aural-5 alloy, depending on the applied stress level.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring

We introduce a novel digital twin (DT) framework for the predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. First, to manage the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as remaining casing potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainties, providing reliable confidence intervals around predicted RCP. Second, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its lifespan with the aid of hybrid modeling and the use of the discrepancy function. Third, to assist decision-making in predictive maintenance, we implement a tire state decision algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures that our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Furthermore, our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.

advanced computing infrastructure↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2024

SMART SiC Power ICs: Scalable, Manufacturable, and Robust Technology for SiC Power Integrated Circuits (Final Technical Report)

This collaborative project was initiated with the goal of developing Scalable, Manufacturable, and Robust Technology for SiC Power Integrated Circuits (SMART SiC Power ICs). In pursuit of this objective, innovative designs and fabrication processes were implemented, enabling the development of large-scale (>1 cm²) SiC Complementary Metal-Oxide-Semiconductor (CMOS) integrated circuits and high-voltage (400–600 V) lateral power MOSFETs (HV-LDMOS) on 150 mm 4H-SiC substrates. The resulting SMART SiC Power ICs are tailored to support a wide range of applications requiring diverse voltage and power levels, including automotive systems, industrial equipment, electronic data processing, energy harvesting, and power conditioning. To achieve the proposed ‘SMART’ technology for SiC ICs, the team focused on 1) the Development of highly scalable CMOS (with high channel mobilities for n-type and p-type MOSFETs), LDMOS (~600V, 10A rated), and IC technologies, 2) Establishment of a manufacturable process baseline in a production-grade-, 150mm, SiC fabrication facility, and 3) Demonstration of SMART SiC ICs. The project initially comprised of fabricating 5 lots. In lot 1 monolithic integration using a single process was achieved. Here, we were able to successfully accomplish Integrated HV NMOSFET with LV CMOS on N-epi/N+ Substrate. The HV NMOS demonstrated a Breakdown Voltage (BV) more than 600V. Circuit demonstration of CMOS was also another achievement from this lot. In lot 2, priority was in place for isolation and integration. Here we addressed the isolation concerns and integrated the HV NMOS and LV CMOS using the N-epi/P-epi/N+ substrate. Similar to the lot 1, we were able to achieve a BV of 600 V for HV NMOS. Optimized gate oxide process with high channel mobilities, better gate oxide reliability, development of SPICE models, successful ohmic process development, novel wafer area saving design layouts, P+ isolation schemes with channeling implantations and high temperature operational circuits demonstrations are some of the key highlights from lot 1 and lot2. In lot 3, discrete device performances of HV NMOS with a BV ~700V and reliable LV CMOS performances were achieved. Also, novel architectural solutions were successfully implemented to suppress the electric field crowding at the gate oxide for reliable operations. In lot 4, half bridge power driver ICs with a conversion efficiency of (target 90% to 95%) in the 1-5MHz switching frequency range for output power between 25 W to 3 kW have been included in. However, due to the unfortunate events of sudden foundry shutdown (SiCamore Semi) the processing of lot 4 wafers came to a complete stop (January 2024). Arrangements have recently been made to shift the fabrication to another foundry, General Electric Aerospace. The fabrication process now on course (as of December 2024). Characterizations are delayed due to this unfortunate circumstance. The proposed trench architectural-based devices and ICs (lot 5) underwent modifications from the original project proposal. This change was necessitated by limitations in the availability of trench-based processes at commercial production-grade fabrication facilities in the US. Apart from above achievements, a Process Development Kit (PDK) was successfully developed for planar type SiC CMOS/LDMOS.

42 ENGINEERING↗