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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving bioenergy recovery from municipal wastewater with a novel cloth-filter anaerobic membrane bioreactor

Anaerobic membrane bioreactors (AnMBR) have been used for treating high-strength industrial wastewater at full-scale and the potential to use them for mainstream municipal wastewater treatment presents an important opportunity to turn energy-intensive plants into net-energy producers. However, several limitations of the AnMBR technology have prevented their adoption in the municipal wastewater industry, namely, high membrane cleaning energy demand and low membrane flux. This study demonstrated a novel AnMBR configuration that uses a commercially available cloth filter technology to address the key limitations of cleaning energy and membrane flux. The cloth filter anaerobic membrane bioreactor (CFAnMBR) is comprised of an anaerobic fixed-film bioreactor coupled with a cloth filter membrane with nominal pore size of 5 µm. The pilot CFAnMBR was operated for 150 days through the winter at a municipal wastewater plant in central Illinois (minimum/average influent temperature 5/13°C). The CFAnMBR increased membrane flux by more than 2 orders of magnitude (3,649 ± 1,246 L per meter squared per hour) and reduced cleaning energy demand by 78%—92% (0.0085 kWh/m 3 ) relative to previously reported AnMBR configurations. With the CFAnMBR, average chemical oxygen demand and total suspended solids removal were 66% and 91%, respectively, and were shown to be increased up to 88% and 96% by in-line coagulant dosing with ferric chloride. Average headspace methane yield was 154 mL CH 4 /g COD removed by the end of the study period with influent temperatures of 11°C± 4°C. The CFAnMBR resolves major limitations of AnMBR technology by employing a commercially-available technology already used for other municipal wastewater treatment applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

High performance Nb/TiNi nanocomposites produced by packaged accumulative roll bonding

Taking full advantage of intrinsic high strength of nano-reinforcements has been proven difficult in a composite until the concept of strain matching was experimental verified in a nanowire Nb/TiNi in-situ composite, which was obtained via solidifying eutectic Nb/TiNi and subsequent severe wire drawing. However, the volume fraction of the nano-reinforcement was dictated by eutectic reaction, which severely limited the development of high-performance composites. Here, the martensitic transformation nanocomposites were successfully acquired with increased volume fraction and changeable morphology of the nano-reinforcement through hot packaged ARB (PARB) and subsequent wire drawing. The nano-reinforcement with lamellar configuration in the composite could exhibit a large lattice strain of similar to 3.8%, which demonstrated the applicability and effectiveness of the strain matching principle in nano-lamellar composite. The obtained composites show a yield strength over 2.0 GPa and a quasi-linear elastic strain as larger as similar to 6.5% with adjustable apparent elastic modulus from 30 to 50 GPa.

42 ENGINEERING↗
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.

99 GENERAL AND MISCELLANEOUS↗
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 OSTI2023

Grain refinement in titanium prevents low temperature oxygen embrittlement

Interstitial oxygen embrittles titanium, particularly at cryogenic temperatures, which necessitates a stringent control of oxygen content in fabricating titanium and its alloys. Here, we propose a structural strategy, via grain refinement, to alleviate this problem. Compared to a coarse-grained counterpart that is extremely brittle at 77 K, the uniform elongation of an ultrafine-grained (UFG) microstructure (grain size ~ 2.0 µm) in Ti-0.3wt.%O is successfully increased by an order of magnitude, maintaining an ultrahigh yield strength inherent to the UFG microstructure. This unique strength-ductility synergy in UFG Ti-0.3wt.%O is achieved via the combined effects of diluted grain boundary segregation of oxygen that helps to improve the grain boundary cohesive energy and enhanced dislocation activities that contribute to the excellent strain hardening ability. The present strategy will not only boost the potential applications of high strength Ti-O alloys at low temperatures, but can also be applied to other alloy systems, where interstitial solution hardening results into an undesirable loss of ductility.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Extraordinary Strain Hardening from Dislocation Loops in Defect-Free Al Nanocubes

The complex interaction of crystalline defects leads to strain hardening in bulk metals. Metals with high stacking fault energy (SFE), such as aluminum, tend to have low strain hardening rates due to an inability to form stacking faults and deformation twins. Here, we use in situ SEM mechanical compressions to find that colloidally synthesized defect-free 114 nm Al nanocubes combine a high linear strain hardening rate of 4.1 GPa with a high strength of 1.1 GPa. These nanocubes have a 3 nm self-passivating oxide layer that has a large influence on mechanical behavior and the accumulation of dislocation structures. Post-compression TEM imaging reveals stable prismatic dislocation loops and the absence of stacking faults. MD simulations relate the formation of dislocation loops and strain hardening to the surface oxide. These results indicate that slight modifications to surface and interfacial properties can induce enormous changes to mechanical properties in high SFE metals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Alloy Partitioning Effect on Strength and Toughness of κ-Carbide Strengthened Steels

Alloy partitioning during heat treatment in a lightweight precipitation hardened steel was investigated using transmission electron microscopy and atom probe tomography. The mechanical properties are discussed as a function of the effect of solution treatment temperature and aging time, giving rise to variations in chemical modulation. A wrought lightweight steel alloy with a nominal composition of Fe-30Mn-9Al-1Si-1C-0.5Mo (wt. %) was solution-treated between 1173–1273 K and aged at 773 K. Lower solution treatment temperatures retained a finer grain size and accelerated age hardening response that also produced an improved work hardening behavior with a tensile strength of -1460 MPa at 0.4 true strain. Atom probe tomography indicated these conditions also had reduced modulation in the Si and Al content due to the reduced aging time preventing silicon from diffusing out of the κ-carbide into the austenite. This work provides the framework for heat-treating lightweight, age hardenable steels with high strength and improved energy absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Production of Creep-Resistant FeCrAl-ODS

Four ODS FeCrAl alloys with base alloy composition Fe-16Cr-4Al-2Mo-0.2Y (wt.%) were mechanically alloyed with minor additions of CeO 2 and Y 2 O 3 additions and were successfully extruded into rectangular-shaped bars. Two oxide compositions were explored, which were designated NC with oxide addition of 0.5CeO 2 plus 0.2Yin solution of the base alloy powder and PC with additions of 0.5CeO 2 and 0.7Y 2 O 3 to elevate the O level. The microstructure of both alloys revealed by SEM and EBSD to consist of very small grains with sizes of ~450-520 nm and weak texture components. Sub-size tensile specimens fabricated from both alloys were tensile tested from room temperature to 800ºC. The results for both alloys from room temperature to 400ºC showed very high strengths, but low ductilities, especially uniform strain. From 500ºC to 800ºC, the strengths decreased rapidly, and the ductilities increased significantly. The reason for the rapid decreases in tensile strengths above 500ºC for both alloys is not clear, but will require advanced characterization techniques including atom probe tomography, to determine the size and number density of the nano-size oxide particles, and TEM/EFTEM, for investigating the dispersion of nano-size oxide particles on grain boundaries to assess the Zener grain boundary pinning effect, which will hinder grain coarsening at high temperatures that may enhance the Hall Petch strengthening mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Shear assisted processing and extrusion of enhanced strength aluminum alloy tubing

Hollow extrusions are used in many industries to make strong, lightweight, and affordable structures. Shear Assisted Processing and Extrusion (ShAPE) enables the extrusion of many alloys, often with noteworthy properties. In this investigation, ShAPE was used to extrude 1 and 2 mm wall tubes of aluminum alloy 6063 measuring 12 mm in diameter at extrusion speeds up to 3.8 m/min. This speed is 10 and 15 times faster than has been reported for any alloy using ShAPE and friction stir back extrusion, respectively. Increasing the extrusion speed from 0.7 to 3.8 m/s resulted in using 68% less process energy at steady state. Microstructural analysis and tensile testing were performed on as-extruded tubes and tubes with a direct T5 heat treatment. Grain refinement from 65 µm to below 20 µm was observed. Growth of macroscale Mg 2 Si strengthening precipitates was not observed. ShAPE processing resulted in recrystallization, and extrusion speed was shown to influence final extrudate texture. Here, transmission electron microscopy analysis revealed that the as-extruded microstructure was free of nanoscale ß" strengthening precipitates. Nanoscale ß" was prominent in direct-aged (T5) ShAPE tubes, similar to a slightly over-aged conventional T6 microstructure. As-extruded tubes had ultimate tensile strengths on par with conventional T5 extrusions and approximately double the total elongation. Tubes that underwent a T5 heat treatment had yield and ultimate strengths averaging 198 and 234 MPa, respectively, with an average total elongation of 11%. The tensile test results are comparable to a conventional T6 heat treatment, but without the solutionization heat treatment step. Mechanical properties either remained flat or improved with increased extrusion speed, which suggests that ShAPE can be scaled up to an industrial process to create energy- and economically efficient high-strength extrusions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Epitaxial nanotwinned metals and alloys: synthesis-twin structure–property relations

Extensive studies have been performed to understand the structure–property relationship in nanotwinned (NT) metals, where the average twin thickness or twin spacing is less than 100 nm. NT metals may have high strength, ductility, good electrical conductivity and corrosion resistance and have promising applications in microelectronics, structural materials and coatings for gas and oil industry. Most prior studies focus on the computational and experimental investigations of NT metals with low stacking fault energy (SFE) and often in polycrystalline forms. In this highlight, we review recent findings on epitaxial growth of NT metals and alloys with low and high SFEs deposited on single crystal substrates, and focus primarily on advancing our comprehension towards tailoring the microstructures and properties of NT metals via extrinsic factors, including the synthesis technique, parametric control and growth template, and intrinsic factors, such as SFE and thermodynamic, kinetic alloying effects. The review intends to illuminate how to synthesize and design epitaxial NT structures to achieve desirable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Less Is More: Oligomer Extraction and Hydrothermal Annealing Increase PDMS Adhesion Forces for Materials Studies and for Biology-Focused Microfluidic Applications

Cues in the micro-environment are key determinants in the emergence of complex cellular morphologies and functions. Primary among these is the presence of neighboring cells that form networks. For high-resolution analysis, it is crucial to develop micro-environments that permit exquisite control of network formation. This is especially true in cell science, tissue engineering, and clinical biology. We introduce a new approach for assembling polydimethylsiloxane (PDMS)-based microfluidic environments that enhances cell network formation and analyses. We report that the combined processes of PDMS solvent-extraction and hydrothermal annealing create unique conditions that produce high-strength bonds between solvent-extracted PDMS (E-PDMS) and glass—properties not associated with conventional PDMS. Extraction followed by hydrothermal annealing removes unbound oligomers, promotes polymer cross-linking, facilitates covalent bond formation with glass, and retains the highest biocompatibility. Herein, our extraction protocol accelerates oligomer removal from 5 to 2 days. Resulting microfluidic platforms are uniquely suited for cell-network studies owing to high adhesion forces, effectively corralling cellular extensions and eliminating harmful oligomers. We demonstrate the simple, simultaneous actuation of multiple microfluidic domains for invoking ATP- and glutamate-induced Ca 2+ signaling in glial-cell networks. These E-PDMS modifications and flow manipulations further enable microfluidic technologies for cell-signaling and network studies as well as novel applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermal shock resistance of multilayer silicon carbide receiver tubes for 800°C molten salt concentrating solar power application

CSP power tower receiver systems during rapid transient weather periods can be vulnerable to thermal shock conditions from rain that which can facilitate the onset of leaks and failures that can have catastrophic consequences. Silicon carbide (SiC) materials have attractive receiver application characteristics for being light weight, having high-strength and excellent thermal shock resistance performance which make them a particularly good fit for receiver absorber materials in CSP. In this investigation, the performance characteristics of Ceramic Tubular Products (CTP) SiC ceramic matrix composite (CMC), multilayered tubes were explored with respect to thermal shock performance for solar receiver applications in next generation CSP plants. Here, thermal shock testing was performed at the Sandia National Laboratories (SNL) Solar Furnace facility using a dynamic stage and thermal shock tube test setup. The tubes tested under incident solar heat flux of 100 W/cm2 were heated with inner tube temperatures reaching approximately 800 °C, with outer temperatures exceeding or just reaching 1000 El for the multilayer and monolithic SiC tubes respectively. The tubes were then quenched with simulated rain. The tubes were then cooled and subjected to hoop stress analysis using an Instron device to assess their subsequent mechanical strength. Furthermore, the on-sun study experimental results indicate an average of 24.2% and 97% higher hoop strength for the CMC tubes than those composed of monolithic SiC and aluminum oxide (A1203) respectively.

14 SOLAR ENERGY↗
DOE OSTI2021

Microbial production of megadalton titin yields fibers with advantageous mechanical properties

Manmade high-performance polymers are typically non-biodegradable and derived from petroleum feedstock through energy intensive processes involving toxic solvents and byproducts. While engineered microbes have been used for renewable production of many small molecules, direct microbial synthesis of high-performance polymeric materials remains a major challenge. Here we engineer microbial production of megadalton muscle titin polymers yielding high-performance fibers that not only recapture highly desirable properties of natural titin (i.e., high damping capacity and mechanical recovery) but also exhibit high strength, toughness, and damping energy — outperforming many synthetic and natural polymers. Structural analyses and molecular modeling suggest these properties derive from unique inter-chain crystallization of folded immunoglobulin-like domains that resists inter-chain slippage while permitting intra-chain unfolding. These fibers have potential applications in areas from biomedicine to textiles, and the developed approach, coupled with the structure-function insights, promises to accelerate further innovation in microbial production of high-performance materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evaluating Effects of Shear Processing on 2D Crystalline Materials in 3D Metal Matrices: Atomistic Understanding of High Shear Deformation of Copper Graphene Composites

Composites made of copper and graphene demonstrate high strength, lubricity and enhanced electrical and thermal conductivity compared pure copper. However, manufacturing these composites at bulk volumes for industrial applications has been a big challenge. Shear deformation assisted processing is an effective method for manufacturing materials such as copper-graphene composites demonstrating ultra-fine grain structures and compositional homogeneity. Nevertheless, microstructural evolution of the composites and their property development under such conditions is not clearly understood currently. To rectify this gap in literature, high strain shear deformation of copper coated graphene foils was performed using a tribometer pin in this study. Changes in microstructure of the composite as well as the constituent components under shear deformation was correlated to process conditions. A sharp increase in the coefficient of friction attributed to rupture and smearing of graphene layer into copper substrate was observed during the shear processing. The coefficient of friction of the sheared copper/graphene composite was lower than that of pure copper, suggesting that partially worn graphene is effectively lubricious at the macroscale. A multimodal characterization of the processed region further revealed a shear deformation-induced ultrafine two-phase grain-structure consisting of copper and graphitic domains. Shear deformation reduced the copper grain size from around 50 – 100 µm to ~200 nm on an average and ~2 – 5 nm in some locations. The semicrystalline graphene films were observed to fracture into 10 – 50 µm size flakes. Oxygen enrichment was observed in the processed region. Finally, graphitic domain were identified for the first time in the copper matrix and not just at the grain boundaries providing evidence for a metastable composite microstructure as a result of solid phase processing at room temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Making ultrastrong steel tough by grain-boundary delamination

Developing ultrahigh-strength steels that are ductile, fracture resistant, and cost effective would be attractive for a variety of structural applications. We show that improved fracture resistance in a steel with an ultrahigh yield strength of nearly 2 gigapascals can be achieved by activating delamination toughening coupled with transformation-induced plasticity. Delamination toughening associated with intensive but controlled cracking at manganese-enriched prior-austenite grain boundaries normal to the primary fracture surface dramatically improves the overall fracture resistance. As a result, fracture under plane-strain conditions is automatically transformed into a series of fracture processes in “parallel” plane-stress conditions through the thickness. The present “high-strength induced multidelamination” strategy offers a different pathway to develop engineering materials with ultrahigh strength and superior toughness at economical materials cost.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying the Role of APB Tubes on the Work-hardening of Ordered Phases

For sixty years, it has been theorized that antiphase boundary (APB) tubes, which are defects produced by deformation, contribute to the high strength of ordered alloys or intermetallic compounds. The concept of an APB tube was originally proposed by Vidoz and Brown [A.E. Vidoz, L.M. Brown, "On work-hardening in ordered alloys", Philosophical Magazine, 7 (1962) 1167–1175] in 1962 to explain the ~50% increase in strength observed when an alloy, such as Ni 3 Fe, is chemically ordered compared to when it is disordered. The existence of APB tubes was only definitively confirmed by Chou and Hirsch in 1981 [C.T. Chou, P.B. Hirsch Antiphase domain boundary tubes in plastically deformed ordered Fe 30.5 at.% Al alloy, Philosophical Magazine, A. 44 (1981) 1415–1419] using a transmission electron microscope (TEM). Prior to the current work, there had been no studies trying to correlate the presence of APB tubes with their effect on the mechanical properties. The main finding of this project was that APB tubes do not contribute to the strength of the strongly-ordered intermetallic compounds FeAl and Ni 3 Al. This was shown by removing the APB tubes by heating the materials and showing that the mechanical properties were unaffected. In addition, in-situ deformation experiments were undertaken in a TEM on both disordered and ordered Ni 3 Fe, in which it was shown that defects (superdislocations) in the ordered alloy underwent many more interaction events than defect (dislocations) in the disordered alloy. This difference can explain the increase in the increase in strength observed when this material is ordered. The project supported a PhD student, Rachel Osmundsen, who successfully defended her thesis in April, 2022. It also supported the principal investigator, Ian Baker, two PhD exchange students and an undergraduate researcher. The work involved collaborations with the following people and use of the following facilities: Dr. Si Chen, Advanced Photon Source at Argonne National Laboratory, for synchrotron X-ray diffraction; and Dr. Katherine Shanks and Dr. Kelly Nygren, Cornell High Energy Synchrotron Source (CHESS), for synchrotron X-ray diffraction. The work led to 12 papers published in refereed journals with two papers currently submitted to journals. There were also 16 presentation made at conferences and universities on the work, ten of which were invited.

36 MATERIALS SCIENCE↗