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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
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 OSTI2022

Co-extrusion of dissimilar Al alloys via shear assisted processing and extrusion

Cladded tubes are generally used where two distinct properties are required at the outer and inner part of a tube, for instance, strength and corrosion resistance. In this investigation, shear assisted processing and extrusion (ShAPE) was employed to fabricate bi-metallic tubes from 1100 and 7075, and 1100 and 2024 dissimilar Al alloy combinations. Surface defect-free bi-metallic tubes were fabricated via the ShAPE process. Detailed microstructural characterization of the interface and various locations of the cladded tubes were carried out using light and scanning electron microscopy techniques. A defect-free, metallurgically bonded interface between the cladded layer and high strength Al (7075 and 2024 Al) was observed. Furthermore, hardness and tensile properties of the cladded ShAPE tubes were examined in the as-extruded conditions.

Komarasamy, Mageshwari↗
DOE OSTI2024

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. Here, in this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R 2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Corrosion evaluation of Ti–6Al–4V manufactured by electron beam melting in Ringer’s physiological solution: an in vitro study of the passive film

We report that electrochemical characteristics and semiconducting behavior of additively manufactured electron beam melted (EBM) and wrought (WR) Ti–6Al–4V (Ti-G5) are compared in Ringer’s physiological solution. X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) confirmed the α+β structure of the tested materials, with two different microstructure types of “bimodal” and “basket-weave” for WR and EBM, respectively. Potentiodynamic polarization (PDP) revealed that the corrosion current density for EBM (i corr =0.27±0.06 μA cm -2 ) is less than the WR (i corr =0.70±0.05 μA cm -2 ). Moreover, potentiostatic polarization (PS) that was employed to form the passive layers at three different potentials of 300, 500, and 700 mV Ag/AgCl , showed that the passive films on the EBM sample are thinner. This finding was confirmed by electrochemical impedance spectroscopy (EIS). Furthermore, through Mott–Schottky (M–S) analysis, donor densities on WR passive films were found to be ~1.5 times larger than EBM. Although PS and EIS confirmed that the passive layer on EBM is thinner, it provides higher corrosion resistance than WR. The passive layer on both samples were found to have n-type characteristics with a duplex structure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling in High Temperature Corrosion: A Review and Outlook

Realizing higher operating temperatures to increase efficiency of future applications for energy conversion and storage while minimizing cost is a challenge for development of high-temperature materials. Simultaneous optimization of mechanical properties and corrosion resistance continues to be a difficult task but is essential due to the need to significantly accelerate the transition between technology readiness levels in the future. Furthermore, oxidation-induced degradation will be a critical life-limiting mechanism at increased operating temperatures. Suitable high-temperature materials cannot be solely identified by time-consuming experiments and reliable computational methods incorporating the relevant physics of processes must be considered to complement the experimental efforts. In the present work, a review of the methods employed to model oxidation-induced material degradation described in literature will be discussed. Furthermore, their capability to predict lifetime and aid in material selection will be evaluated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Revisiting Wagner’s Criteria to Predict Establishment and Retention of Alumina Scales on Ternary NiCrAl Alloys

Despite the advent of numerical modeling approaches and high-performance computing infrastructure, the design and development of corrosion-resistant high temperature alloys (> 500 °C) continue to be largely empirical and typically involve extensive experimentation. This is mainly due to the lack of a single unified physics-based model that can address the impact of multiple competing factors such as time, environment, alloy composition, microstructure, and geometry. The classical Wagner’s criteria have been foundational to estimate the minimum concentrations required of an oxide-forming element to establish and sustain a protective oxide scale. However, the formulation is primarily limited to lower-order alloy systems (binary alloys) and ignores the time dependence of subsurface compositional changes in the alloy. The lack of key data on the temperature and composition dependence of the solubility and transport of oxidants in multicomponent-multiphase alloys further exacerbates the problem. In the present work, a few of these limitations were addressed using a flux-based approach (FLAP) which tracks the spatiotemporal evolution of the fundamental flux balance between oxygen and the oxide-forming elements to enable the prediction of the formation of an external alumina scale in ternary NiCrAl alloys. In conclusion, the modeling results were validated with the literature findings and additional experimental work conducted in the present work.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Temperature-Dependent Thermal and Mechanical Properties of a Wire Arc Additively Manufactured Low Transformation Temperature Steel

Recent research has studied the use of low transformation temperature (LTT) martensite steel as feedstock for wire arc additive manufacturing (WAAM) and low tensile residual stresses or compressive residual stresses were detected in the printed walls. These residual stress states help to improve printed product properties such as fatigue strength and corrosion resistance. However, the thermal and mechanical properties of WAAM printed LTT martensite steel walls are largely unknown. In this work, a printed LTT martensite steel was characterized for its thermal, metallurgical, and mechanical behavior at room and elevated temperatures. The temperature-dependent specific heat capacity, thermal expansion, atomic lattice spacing, and tensile properties were measured during both heating and cooling and related to observed microstructural features and computational thermodynamics predictions. These results revealed a large hysteresis in the martensitic transformation, with a martensite start temperature of 240 °C and austenite start temperature of 680 °C. Additional thermal cycles and specimen orientation did not affect the printed specimen austenite and martensite transformations. However, it was observed that the printed metal may exhibit tempering embrittlement at about 350 °C but further studies are needed to confirm that. Further, these results suggest that a temperature control of 250 °C to 350 °C during WAAM is needed to maximize the stress reduction potential of the LTT250 martensite steel. Opportunities for future implementation of LTT martensite steels and optimization of additive manufacturing process conditions are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advancing Development and Application of Superalloys

This JOM topic is sponsored by the High Temperature Alloys Committee of TMS and addresses advances in the development and application of superalloys. Each year, the High Temperature Alloys Committee organizes symposia at the TMS and Materials Science and Technology annual meetings. In 2020, two symposia were organized for the TMS annual meeting on February 23–27 in San Diego, CA: Materials Design Approaches & Experiences V, co-sponsored by the Integrated Computational Materials Engineering Committee and Advancing Current and State-of-the-Art Applications of Ni- and Co-base Superalloys, co-sponsored by the Corrosion and Environmental Effects Committee. Later this year, the 14th International Symposium on Superalloys will be held on September 13-17 in Seven Springs, PA. The conference, held every 4 years, convenes members of the superalloy community to discuss recent challenges and discoveries affecting future performance and use of superalloys. The TMS High Temperature Alloys Committee meets at both the TMS Annual Meeting & Exhibition and the annual Materials Science and Technology conference. The meetings are open to anyone interested in high-temperature alloys. This special topic on Advancing Development and Application of Superalloys includes a range of sub-topics including mechanical performance, corrosion resistance, processing and advanced manufacturing techniques, which were submitted by research groups from industry, national laboratories and universities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2022

Ab Initio Study of Energetics, Charge Transfer, and Atomic Structures of FCC Fe/NbC Interfaces with and Without N Doping: From Coherent to Semi-coherent Interfaces

Nitrogen is added to stainless steels to improve their toughness and corrosion resistance. However, it is not well understood how nitrogen may impact the precipitate/matrix interfacial properties. In this work, we consider the (FCC) Fe (001)/NbC (001) interface as a model system to study how interfacial structure, energy, and electron charge are affected by nitrogen using DFT calculations. We compare the structures and energetics of coherent and semi-coherent interfaces by including the elastic contribution component. It is found that nitrogen does not have a significant effect on either the interfacial energy or the atomic arrangement near the interface region. A highly intricate bonding feature is revealed near heterophase interfaces between alloy elements, in which metallic and covalent features are present together with charge transfer. Additionally, the work on determining accurate interfacial energies is at the core of all quantitative precipitation modeling efforts (in particular, in the XMAT Program). In turn, nucleation, growth/dissolution, and coarsening of precipitates contribute critically to the material’s ability to withstand creep, creep fatigue, and other detrimental processes reducing its service life. It is for this reason that developing quantitative understanding of interfaces and their energetics in materials is so important for their development and further improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cladding and Butt-Joining Dissimilar Aluminum Alloys Simultaneously via Shear Assisted Processing and Extrusion

Shear assisted processing and extrusion has been used to fabricate multi-metallic tubing that incorporates regions with clad and butt-joined structures. In this work, aluminum alloys 6061, 7075, and 1100 are combined to create a monolithic extruded tubing with weldable ends (6061), a high strength center (7075), and a corrosion-resistant cladding (1100). Optical microscopy shows a gradual, defect-free, transition between alloys at the butt-joint interfaces and a sharp transition between alloys at the clad interface. Tensile testing proved the mechanical coherency of the joint, with failure occurring outside the tri-metallic structure. In conclusion, smoothed particle hydrodynamic simulations identified a spiral pattern of the cladding alloy that leads to inconsistent cladding thickness, and identified a simplified 2-alloy combination of aluminum alloys 6082 and 2014 that accomplished the design objectives of the tri-metallic structure.

Coatings↗
DOE OSTI2025

Experimental Evaluation of Interfacial Bonding Strength Between 304 Stainless Steel Substrate and Electrodeposited Nickel Coating Using Mesoscale Mechanical Testing Methods

Electrodeposition is a commonly used method for depositing a metal layer on a metallic substrate, to provide a decorative finish or for functional purposes including wear and corrosion resistance. The interfacial bonding strength is a critical factor in determining the quality of electrodeposition, as it ensures the adhesion of the deposited layer to the substrate. Despite its importance, there has not previously been a suitable mechanical testing method to quantitatively characterize the bonding strength between the electrodeposited layers and the substrate, due to the high strength of the materials and to restrictions imposed by the geometry (due to the small thickness of the plating layer) to manufacture tensile bars. In this study, we introduce a novel mesoscale mechanical testing method to overcome these limitations. This technique was applied to assess the bonding strength between a 304 stainless steel (SS) substrate and electrodeposited nickel with three different plating formulae (nickel sulfamate, Watts bath, and hard nickel). The thickness of the pure nickel coatings achieved for each of the three electrolyte solutions was approximately 1 mm on each side of the substrate. Mesoscale tensile bars of 1 mm length were manufactured by a femtosecond laser, with the material interface at the center of the gauge section, and then tensile-tested with digital image correlation. Further, the results proved that the electrodeposition technique is able to produce a very high bonding strength that is close to the yield strength of both the substrate material and the electrodeposited nickel layer. Additionally, in the case of the Watts bath formula, the interfacial bonding strength between the SS 304 and electrodeposited nickel can exceed that of the nickel layer. This method is expected to be useful for quantifying the interfacial bonding strength in numerous applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A thermochemical database from high-throughput first-principles calculations and its application to analyzing phase evolution in AM-fabricated IN718

A comprehensive thermochemical database is constructed based on high–throughput first-principles phonon calculations of over 3000 atomic structures in limited concentrations in Ni, Fe, and Co alloys involving a total of 26 elements including Al, B, C, Cr, Cu, Hf, La, Mn, Mo, N, Nb, O, P, Re, Ru, S, Si, Ta, Ti, V, W, Y, and Zr, providing thermochemical data largely unavailable from existing experiments. Here, the database can be employed to predict the equilibrium phase compositions and fractions directly from first-principles by minimizing the chemical potential of a multicomponent system with a fixed overall chemical composition and a fixed temperature. It is applied to the additively manufactured nickel-based IN718 superalloy to analyze the phase evolution with temperature. IN718 is known for its great performance in tensile, fatigue, creep, and rupture strength, combined with easy fabrication and corrosion resistance. In particular, we successfully predicted the formation of L1 0 -FeNi, γ’-Ni 3 (Fe,Al), α-Cr, δ-Ni 3 (Nb,Mo), γ”-Ni 3 Nb, and η-Ni 3 Ti at low temperatures (below 680 K), γ’-Ni 3 Al, δ-Ni 3 Nb, γ”-Ni 3 Nb, α-Cr, and γ-Ni(Fe,Cr,Mo) at intermediate temperatures (between 680 and 1140 K), and δ-Ni 3 Nb and γ-Ni(Fe,Cr,Mo) at high temperatures (above 1140 K) in IN718. These predictions are validated by EDS mapping of compositional distributions and corresponding identifications of phase distributions. The database is expected to be a valuable source for future thermodynamic analysis and microstructure prediction of alloys involving the 26 elements.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The effect of copper additives on hybrid Zr-based chemical conversion coating morphology and chemical compositions

In the realm of corrosion protection, Zr-based conversion coatings offer an environmentally friendly, chromate-free alternative to conventional coating. This study uses advanced X-ray, electron microscopy and electrochemical testing techniques to better understand the impact of varying Cu²⁺ ion concentrations on the characteristics of Zr-based coatings on Fe substrates. Our findings demonstrate that within the tested conditions, higher Cu²⁺ additive concentrations at 40 ppm enhance surface characteristics, increasing stability towards anti-corrosion capability, particularly under NaCl treatment. Conversely, at a lower Cu²⁺ concentration of 20 ppm, coatings exhibited more significant dissolution of Cu clusters and increased vulnerability to chloride-induced degradation. X-ray photoelectron spectroscopy and synchrotron X-ray fluorescence (XRF) and X-ray absorption near edge structure (XANES) spectroscopy analyses revealed Cu²⁺ formation in Cu20, while Cu40 retained its metallic state (Cu⁰) with a slower reaction rate. Cu20 offers some protection but lacks durability, whereas in Cu40, lower I corr , and enhance corrosion resistance, making it ideal for protective coatings in salt-water environments. This study underscores the importance of balancing Cu²⁺ ions concentration in the coating solution to optimize performance, highlighting the role of Cu in enhancing both surface properties and long-term stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Understanding non-stochiometric deposition of multi-principal elemental NiCoCr thin films

Multi-principal elemental NiCoCr thin films with compositional flexibility have potential applications as a thermal barrier, corrosion-resistant coatings, and functional energy materials. In this study, the non-stochiometric multi-principal element alloy thin film formation behavior from an equiatomic ablation target of NiCoCr using pulsed laser deposition has been reported. Here, the effect of varying laser fluence on compositions of constituent elements in the NiCoCr thin films is systematically investigated. The increase in laser fluence typically increases the likelihood of acquiring undesirable particulates in thin films. Therefore, to maintain the film quality the laser fluences are adjusted within the range of 1.3–3.3 J·cm -2 , while the film thicknesses are kept constant at ~30 nm. Results show that the constituent elements are well-distributed in the formed NiCoCr thin films, however, having average atomic percentages of Ni and Cr twice as high as Co irrespective of the equiatomic composition of individual elements in the ablation target. This stochiometric alteration in the as-deposited thin films is governed by the rate of ablation and subsequent evaporation of each element, which is facilitated by the optical properties and vapor pressure of individual constituent elements. Additionally, electron energy-loss spectroscopy and x-ray absorption spectroscopy analysis reveal that the thin films have partially oxidized, with the Cr oxidation contributing the most to the O-K edge in comparison to Ni and Co. Finally, this study presents a laser-assisted pathway for fabricating compositionally flexible NiCoCr thin films and develops an understanding of the non-equilibrium laser-material interactions and how the intrinsic properties of constituent elements affect the stoichiometric flow of materials from the target to the final film deposition.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Carbon aerogels with integrated engineered macroporous architectures for improved mass transport

Carbon aerogels (CAs) combine unique properties including ultra-high surface area, high electrical conductivity, corrosion resistance, and robust mechanical properties making them ideal materials for electrochemical applications. Traditional CA synthesis results in isotropic, random nanoporous networks that work well for applications relying on diffusional mass transport. However, many applications would benefit from integration of engineered macroporous network structures that enable directed pressure-gradient-driven mass transport. In this study, we report on using 3D-printed sacrificial polymeric templates to generate templated CAs (t-CAs) with integrated engineered nonrandom macroporous network structures. Specifically, we used projection micro-stereo-lithography (PμSL) and two-photon polymerization direct laser writing (2PP-DLW) to fabricate millimeter-to-centimeter-sized 3D sacrificial polymeric templates with features ranging from tens of microns (PμSL) to 100s of nanometers (2PP-DLW). T-CAs were fabricated by infiltrating the templates with resorcinol-formaldehyde (RF) precursor solution, followed by carbonization at 1050 °C to simultaneously convert the RF gel to a CA and decompose the 3D-printed template, leaving an embedded templated macroporous network structure behind. X-ray computer tomography confirms integration of the macroporous architecture defined by the template. The templated macroporous architecture improves mass transport in t-CAs compared to traditional bulk CA as demonstrated by more uniform activation and their response in electrochemical cyclic voltammetry and galvanostatic charge-discharge tests.

36 MATERIALS SCIENCE↗