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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques

We report advancements in data analytics techniques have enabled complex, disparate datasets to be leveraged for alloy design. Identifying outliers in a dataset can reduce noise, identify erroneous and/or anomalous records, prevent overfitting, and improve model assessment and optimization. In this work, two alloy datasets (9-12% Cr ferritic martensitic steels, and austenitic stainless steels) have been assessed for outliers using unsupervised techniques and supplemented with domain knowledge. Principal component analysis and k-means clustering were applied to the data, and points were assessed as outliers based on their distance away from other points in the cluster and from other points in the dataset. The outlier characteristics were investigated to determine both cluster-specific and overall trends in the properties of the outlier points. The approach demonstrated here is extensible to other alloy datasets for outlier identification and evaluation to improve the reliability of machine learning and modeling predictions for advanced alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational Design of Alloys for Energy Technologies

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

Computational Design Of Materials↗
DOE OSTI2023

Molecular Dynamics Simulation of Hygroscopic Aging Effects in Epoxy Polymer

The automobile industry is incorporating more lightweight content in car designs to boost fuel-economy. New structural adhesives are needed to mitigate the corrosion and thermal expansion issues associated with joining dissimilar lightweight materials, but adhesive developers lack a fundamental understanding of the chemistry that occurs in the adhesive as the joint ages. In this study, we developed structural adhesive molecular models and applied classical molecular dynamics simulations and density functional theory calculations to gain molecular insights into the influence of water molecules on the properties of epoxy-based adhesives (DGEBA + Jeffamine (JD230)). The simulations were complemented by experimental synthesis and characterization. Our work underscores the impact of water molecules on the local structure of the epoxy network as well as resulting mechanical properties. Water molecules were mainly coordinated with hydroxyls, primary amines and secondary amines, but also weakly coordinated with ether linkages, which were found most probable to be labile. Simulated stress–strain data indicates that increasing the water content deteriorates the mechanical properties. The Young’s modulus decreased by ~ 30% when the water content increased to 3 wt%. We conclude, this integration of molecular-level chemical insights with mechanical property simulations of the hydrated epoxy system and experimental validation holds the promise to advance lightweight joint technologies.

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 OSTI2020

Beneficiation of coal using supercritical water and carbon dioxide extraction: sulfur removal

Abstract This work explores the use of carbon dioxide, water, and their mixtures as solvent for the precombustion beneficiation of raw coal without using any toxic mineral acids in the temperature range of 200–400 °C. The fluid polarity, ionic constant, and supercritical point can be adjusted by H 2 O/CO 2 ratio and temperature. Adding carbon dioxide to hydrothermal fluid also increases the ionization by forming carbonic acid. Extractions with supercritical fluids have several benefits including enhanced mass transport, ease of separation and recycle, wide range of extractive capability and tunability, better inherent safety, and in the case of carbon dioxide and water–low cost. A semi-continuous extraction system was designed and built in which pressure, temperature and the relative flow rates of CO 2 and H 2 O can be controlled. Coal powder is kept in a packed bed and the extraction is carried out at 143 bar pressure. Using sulfur as a model heteroatom, extractive efficiency is examined as a function of the temperature, fluid composition, fluid flow, and extraction time. The results indicate that carbon dioxide, water, and supercritical water-carbon dioxide (ScWC) all can effectively extract about 50% of total sulfur from bituminous coal in 1 h. Extraction above 350 °C decreased effectiveness, and extraction above the supercritical point of pure water caused hydrothermal carbonization. ScWC extraction may provide necessary control to prevent organic dissolution while removing sulfur.

DeCuir, Matthew J.↗
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 OSTI2020

Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging

To accelerate adoption of non-residential charging for electric vehicles, sites must maximize utilization of existing electrical infrastructure. In this study we model electric vehicle charging at a workplace using real charging data and evaluate the lifetime of the site’s transformer as the number of charging stations is incrementally increased. We implement and compare a range of control schemes for workplace charging including minimizing the peak load, capping the total load, minimizing bills under different rate structures with time-of-use energy costs and demand charges, and directly minimizing the transformer’s aging. These are compared by the number of vehicles they allow the transformer to support, the transformer’s health, and the operator’s electricity bill. We draw a connection between minimizing the peak load and improving the transformer’s health. We observe that minimizing the electricity bill is the best scheme by both criteria when the bill includes a demand charge; in our experiment it allowed the infrastructure to support over 67% more cars than under uncontrolled charging. To protect the transformer we recommend that demand charges or capacity management be applied to parking lots of charging electric vehicles with high infrastructure utilization, and operators schedule charging to minimize their electricity bills.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Scalable probabilistic estimates of electric vehicle charging given observed driver behavior

To prepare for rapid growth in global electric vehicle adoption, grid and policy planners depend on detailed forecasts of future charging demand. In this paper we propose a novel holistic, scalable, probabilistic framework to produce large-scale estimates of electric vehicle charging load for long-term planning that capture real drivers’ charging patterns. Our framework captures the uncertainty and stochasticity in charging demand by taking a graphical modeling approach. It has three core elements: driver groups, charging segment choices, and charging session time and energy requirements. The framework uses hierarchical clustering to group drivers by their charging histories, capturing their heterogeneous behaviors and preferences across different segments or types of charging. The framework uses probabilistic mixture models for each driver group’s sessions to identify the unique charging behaviors observed within each segment. We illustrate its application with a large data set from California, profiling the charging patterns and unique driver clusters it identifies. Using the model knobs representing drivers’ battery capacities, behavior, and segment access we present scenarios for California’s charging demand in 2030 with 8 million passenger electric vehicles. Peak charging demand ranged from 3.3 to 8.7 GW across scenarios. Furthermore, each was calculated in under 45 s on a laptop computer.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data

Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real -world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi -physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle's Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time -resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively.

Lopez-Juarez, Marcos↗
DOE OSTI2025

Enhancing anomalous Hall effect with suppressed ferromagnetism in the SrTiO 3 -confined bilayer heterostructure of ultrathin SrRuO 3 and SrIrO 3

Ferromagnetism is an essential ingredient for anomalous Hall effect. SrRuO 3 is a representative ferromagnetic oxide that exhibits anomalous Hall effect even down to the monolayer confinement limit. Paramagnetic metal SrIrO 3 , on the other hand, becomes an antiferromagnetic insulator when confined to a monolayer. Here, in this study, we show that, by forming a confined bilayer structure of SrRuO 3 and SrIrO 3 monolayers with SrTiO 3 spacers, the anomalous Hall effect is significantly enhanced while the ferromagnetism as well as the perpendicular magnetic anisotropy are suppressed. These effects originate from interfacial Ru–Ir hybridization that modifies the electronic structure in the vicinity of the Fermi level. Our work demonstrates that confined bilayer heterostructure is an useful design for exploring the synergistic combination of interfacial coupling and quantum confinement of complex oxides.

anomalous hall effect↗
DOE OSTI2022

On the surface passivating principle of functional thiol towards efficient and stable perovskite nanocrystal solar cells

Inorganic halide perovskite nanocrystals (PNCs) have demonstrated promising potential for solar cell applications. However, the lability of photoactive CsPbI3 phase under ambient conditions, coupled with considerable amounts of surface defects induced during solidification process, have impeded achieving high performances and longevities of the PNC-based solar cells. Post-treatment of the PNCs with organic ligands has been proposed as an efficient strategy for surface passivation, which, however, still relies on the binding actions of typical functional groups towards surface defects (especially, carboxylates onto iodine vacancies). Herein, we uncover that thiolate, a deprotonated form of thiol, renders distinctive binding feasibility towards iodine vacancies at the CsPbI3 PNC surface, compared with those of typical functional groups. By treating the PNC solid with deprotonated cysteine as a ligand, the surface defects are comprehensively passivated. The solar cells with the modified PNC films demonstrate an excellent PCE of 15.5 % and improved device longevity (77 % of initial PCE over 2 months) under ambient conditions. Our work not only elucidates the chemical principles of thiol on the binding with PNC surface, but also corroborates the power of thiolate as a promising strategy to develop high performances and improved longevity of solar cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

CD63-mediated cloaking of VEGF in small extracellular vesicles contributes to anti-VEGF therapy resistance

Despite wide use of anti-vascular endothelial growth factor (VEGF) therapy for many solid 30 cancers, most of them become resistant to this therapy and develop into progressive disease. 31 Therefore, new biomarkers and strategies for blocking adaptive resistance of cancer to anti-VEGF therapy are needed. As described herein, we found that cancer-derived small extracellular vesicles (EVs) package increasing quantities of VEGF and other factors in response to anti-VEGF therapy. Notably, small EV-VEGF (eVEGF) is not recognized by bevacizumab and can be transferred to endothelial cells and trigger intracrine signaling and promote angiogenesis. Also, serum eVEGF was present at higher levels in ovarian cancer mouse models with adaptive resistance to bevacizumab than in models sensitive to it. Ovarian cancer cell-derived eVEGF increased tumor growth despite treatment with bevacizumab. Notably, the eVEGF level was elevated in patient serum after bevacizumab-containing therapy. Proteomic data of small EVs from mouse models suggested that several novel biomarkers are promising for drug response prediction. Collectively, these data demonstrated a new mechanism whereby eVEGF evades recognition by therapeutic antibodies and promotes tumor angiogenesis and progression. These findings have clinical implications for biomarkers and new therapeutic strategies for ovarian cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Detailed in situ ATR-FTIR spectroscopy study of the early stages of C-S-H formation during hydration of monoclinic C3S

The early stages of tricalcium silicate hydration has been studied by in situ ATR-FTIR spectroscopy. A method for data reduction based on background correction and subtraction of spectra of pure components is presented, which makes it possible to describe the kinetic evolution of the calcium silicate hydrate phase upon hydration with great detail. The results obtained are in full agreement with recently reported, detailed solid-state NMR spectroscopy measurements. As ATR-FTIR spectroscopy does not require any isotope enrichment in order to increase the signal intensities and is a widely available technique, we suggest that our study opens up an avenue towards detailed in situ characterization of hydrating cementitious systems and hydrating systems in general.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Elucidating key reducing species beyond ions in hydrogen plasma smelting reduction of iron ore

Hydrogen plasma smelting reduction (HPSR) of iron ore has attracted significant attention over the past decade due to its high-temperature operation, rapid plasma mediated reduction kinetics, and simpler density-based separation of molten iron product, compared to H2-based solid-state reduction. All of these attributes enable processing of low-grade ores for downstream use in electric-arc furnaces, as virgin iron with low gangue content is required for high quality steel and improved furnace operation. While positive ions exist within the plasma arc, this work demonstrates that near the anodic ore surface, hydrogen radicals and vibrationally excited hydrogen species dominate and their densities correlate well with observed reduction rates. Species concentrations in the transferred plasma arc and at the plasma-ore interface are evaluated using coupled thermal plasma and near-wall non-equilibrium plasma models. The thermal plasma model is validated against experimental voltage data and spectroscopic measurements of plasma temperature and density for varying current inputs. Modeling of the near surface thermochemical non-equilibrium and micrometer scale anode sheath layer reveals, in addition to the expected H + , significant concentrations of ArH + and H$^+_3$ ions, typically not observed in thermal plasmas under thermodynamic equilibrium. Our results show that the inverted sheath structure at the anodic ore surface strongly suppresses reactive positive ion fluxes, while non-equilibrium electron-impact processes generate abundant hydrogen radicals and vibrationally excited species. These findings highlight the critical role of non-equilibrium effects in hydrogen arc-driven iron ore reduction and advance understanding beyond prevailing hypotheses centered on hydrogen ion-driven mechanisms.

08 HYDROGEN↗
DOE OSTI2021

Topology optimization of additively manufactured fluidic components free of internal support structures

This report integrates projection-based approaches for implementing overhang constraints with fluid topology optimization to design additively manufactured fluidic components that do not require internal support structures. Internal support structures are challenging, potentially impossible to remove in complex fluid networks, yet would degrade fluidic performance if left within the part. This presents a major challenge in coupling topology optimization with additive manufacturing in the design of fluid components. The proposed approach leverages past work in overhang constraints for solid mechanics, including projection formulations and adjoint sensitivity analysis, and considers incompressible Navier–Stokes equations for laminar fluid flow. The approach is demonstrated on 2D and 3D fluid topology optimization problems to minimize pressure drop and constrain mass flow rates in pipes and manifolds. Resulting designs are crisp, logical, satisfy performance constraints, and satisfy overhang constraints, eliminating the need for internal support structures.

42 ENGINEERING↗