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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 OSTI2022

Integrated Computational Materials and Mechanical Modeling for Additive Manufacturing of Alloys with Graded Structure Used in Fossil Fuel Power Plants

Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
DOE OSTI2023

Statistical Tool Size Study for Computer-Controlled Optical Surfacing

Over the past few decades, computer-controlled optical surfacing (CCOS) systems have become more deterministic. A target surface profile can be predictably achieved with a combination of tools of different sizes. However, deciding the optimal set of tool sizes that will achieve the target residual error in the shortest run time is difficult, and no general guidance has been proposed in the literature. In this paper, we present a computer-assisted study on choosing the proper tool size for a given surface error map. First, we propose that the characteristic frequency ratio (CFR) can be used as a general measure of the correction capability of a tool over a surface map. Second, the performance of different CFRs is quantitatively studied with a computer simulation by applying them to guide the tool size selection for polishing a large number of randomly generated surface maps with similar initial spatial frequencies and root mean square errors. Finally, we find that CFR = 0.75 achieves the most stable trade-off between the total run time and the number of iterations and thus can be used as a general criterion in tool size selection for CCOS processes. To the best of our knowledge, the CFR is the first criterion that ties tool size selection to overall efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Object-space optimization of tomographic reconstructions for additive manufacturing

Volumetric 3D printing motivated by computed axial lithography enables rapid printing of homogeneous parts but requires a high dimensionality gradient-descent optimization to calculate image sets. Here we introduce a new, simpler approach to image-computation that algebraically optimizes a model of the printed object, significantly improving print accuracy of complex parts under imperfect material and optical precision by improving optical dose contrast between the target and surrounding regions. Quality metrics for volumetric printing are defined and shown to be significantly improved by the new algorithm. The approach is extended beyond binary printing to grayscale control of conversion to enable functionally graded materials. The flexibility of the technique is digitally demonstrated with realistic projector point spread functions, printing around occluding structures, printing with restricted angular range, and incorporation of materials chemistry such as inhibition. Finally, simulations show that the method facilitates new printing modalities such as printing into flat, rather than cylindrical packages to extend the applications of volumetric printing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Compute in‐Memory with Non‐Volatile Elements for Neural Networks: A Review from a Co‐Design Perspective

Abstract Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute‐in‐memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute‐unit to memory, known as the von Neumann bottleneck. At its heart is a cross‐bar architecture with nodal non‐volatile‐memory elements that performs an analog multiply‐and‐accumulate operation, enabling the matrix‐vector‐multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system‐level characteristics and chip performance, including speed, power, and classification accuracy. With an over‐arching co‐design viewpoint, this review assesses the use of cross‐bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non‐volatile memory materials will need to demonstrate for a successful CIM technology.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A comprehensive review of dwell time optimization methods in computer-controlled optical surfacing

Dwell time plays a vital role in determining the accuracy and convergence of the computer-controlled optical surfacing process. However, optimizing dwell time presents a challenge due to its ill-posed nature, resulting in non-unique solutions. To address this issue, several well-known methods have emerged, including the iterative, Bayesian, Fourier transform, and matrix-form methods. Despite their independent development, these methods share common objectives, such as minimizing residual errors, ensuring dwell time's positivity and smoothness, minimizing total processing time, and enabling flexible dwell positions. This paper aims to comprehensively review the existing dwell time optimization methods, explore their interrelationships, provide insights for their effective implementations, evaluate their performances, and ultimately propose a unified dwell time optimization methodology.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Automated identification of deformation twin systems in Mg WE43 from SEM DIC

In this study, the application of machine learning and computer vision approaches to microscale deformation data for the automated identification of deformation twinning systems, and their associated twin area, is introduced. Deformation data was obtained during in-situ SEM compression testing of a WE43 Mg alloy using digital image correlation (DIC) modified for use with electron microscopy. A 5.7 mm × 3.4 mm area of interest was analyzed, generating ~100 million data points of deformation. Experimental twin trace directions were determined by applying k-means clustering, morphological thinning, and Hough transforms to the deformation data. The identification of deformation twinning was achieved by consideration of the twin trace direction, its strain value, and its evolution. The deformation map was divided into areas corresponding to individual active twin systems, enabling the analysis of microstructure dependence of deformation behavior and twinning activity. The performance of the proposed twinning identification approach was evaluated by accuracy, precision, and sensitivity metrics. The effect of the tuning parameters used in the algorithm on the performance is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Review of Computer-Aided Manufacturing (CAM) strategies for hybrid directed energy deposition

Hybrid additive manufacturing intertwines both additive and subtractive manufacturing layer by layer to digitally fabricate parts with complex geometries, improved surface finish and tight dimensional accuracies, the sum of which is difficult to obtain with any single process. Computer-Aided Manufacturing (CAM) software is required to orchestrate the machine toolpathing for both the deposition as well as the machining processes and is crucial for the successful fabrication of high quality structures. Additionally, CAM requires substantial operator input to account for challenging aspects of each fabricated structure. For example, deciding at which layer of deposition will the machining process continue to maintain access to complex cavities for finishing - internal features that would otherwise be unfinishable due to reach limitations or obstructions. Moreover, of the many commercially-available hybrid systems, each has a unique kinematic environment which can benefit from specific optimization of toolpath planning and substantial research has directly correlated toolpathing with the microstructure evolution, mechanical properties, porosity and residual stress state of the final fabricated part. This review explores the available strategies for CAM in the context of hybrid direct energy deposition, discusses the advantages and disadvantages of each and considers future CAM trends for this transformational digital manufacturing technology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

End–to–End Metasurface Design for Temperature Imaging via Broadband Planck‐Radiation Regression

A theoretical framework is presented for temperature imaging from long-wavelength infrared (LWIR) thermal radiation (e.g., 8–12 µm) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. A new nonlinear reconstruction algorithm, “Planck regression”, is introduced to reconstruct the temperature map from a gray scale sensor image, even in the presence of severe chromatic aberration, by exploiting black body and optical physics particular to thermal imaging. This algorithm is combined with an end-to-end approach that optimizes manufacturable, single-layer metasurfaces to yield the most accurate reconstruction. The designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. Here, it is also shown that Planck regression is much more generalizable to arbitrary images than a straightforward neural-network reconstruction, which requires a large training set of domain-specific images.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part II: comprehensive ternary design and validation

Abstract Here the discovery of refractory multi-principal element alloys (MPEAs) with high-temperature strength and stability is pursued within a constrained and application-relevant design space. A comprehensive approach is developed and applied to explore all 165 ternary systems in the Al-Ce-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr family. A subset of ternary systems that contain large areas in composition–temperature space with high strength and robust BCC phase stability is found. Twelve sets of high-performing alloys are identified, each set optimized for one combination of phase constraint, optimization target, and temperature range. Preliminary mechanical tests support the viability of the method. This work highlights the importance of considering phase stability, exploring non-equiatomic regions of composition space, and applying application-relevant constraints. Parts I and II provide three down-selection techniques for identifying high-performing BCC refractory MPEAs, design guidelines, and many candidates predicted to have BCC phase stability and strengths 2–3 times higher than any reported to date.

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

Computational discovery of two-dimensional rare-earth iodides: promising ferrovalley materials for valleytronics

Two-dimensional Ferrovalley materials with intrinsic valley polarization are rare but highly promising for valley-based nonvolatile random access memory and valley filter devices. These ferromagnetic materials exhibit valleys at or near the Fermi level with intrinsic magnetism. The strong coupling between magnetism and spin–orbit coupling induces intrinsic valley polarization. Using Kinetically Limited Minimization, an unconstrained crystal structure prediction algorithm, and prototype sampling based on first-principles calculations, we have discovered new Ferrovalley materials, rare-earth iodides RI 2 , where R is a rare-earth element belonging to Sc, Y, or La-Lu, and I is Iodine. The rare-earth iodides are layered and demonstrate either 2H, 1T, or 1T d phase as the ground state in bulk, analogous to transition metal dichalcogenides (TMDCs). The calculated exfoliation energy of monolayers (MLs) is comparable to that of graphene and TMDCs, suggesting possible experimental synthesis. The MLs in the 2H phase exhibit ferromagnetism due to unpaired electrons in d and f orbitals. Throughout the rare-earth series, d bands have valley polarization at K and $\overline{K}$ points in the Brillouin zone in the vicinity of the Fermi level. Large intrinsic valley polarization in the range of 15–143 meV without external stimuli is observed in these Ferrovalley materials, which can be enhanced further by applying an in-plane bi-axial strain. These valleys can selectively be probed and manipulated for information storage and processing, potentially offering superior performance beyond conventional electronics and spintronics. Here we further show that the 2H ferromagnetic phase of RI 2 MLs possesses non-zero Berry curvature and exhibits anomalous valley Hall effect with considerable anomalous Hall conductivity. Our work will incite exploratory synthesis of the predicted Ferrovalley materials and their application in valleytronics and beyond.

2D materials↗
DOE OSTI2022

Experimental and theoretical studies of native deep-level defects in transition metal dichalcogenides

Abstract Transition metal dichalcogenides (TMDs), especially in two-dimensional (2D) form, exhibit many properties desirable for device applications. However, device performance can be hindered by the presence of defects. Here, we combine state of the art experimental and computational approaches to determine formation energies and charge transition levels of defects in bulk and 2D MX 2 (M = Mo or W; X = S, Se, or Te). We perform deep level transient spectroscopy (DLTS) measurements of bulk TMDs. Simultaneously, we calculate formation energies and defect levels of all native point defects, which enable identification of levels observed in DLTS and extend our calculations to vacancies in 2D TMDs, for which DLTS is challenging. We find that reduction of dimensionality of TMDs to 2D has a significant impact on defect properties. This finding may explain differences in optical properties of 2D TMDs synthesized with different methods and lays foundation for future developments of more efficient TMD-based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning‐Designed Diffractive Polarization Transformer

Abstract Controlled synthesis of optical fields having nonuniform polarization distributions presents a challenging task. Here, a universal polarization transformer is demonstrated that can synthesize a large set of arbitrarily‐selected, complex‐valued polarization scattering matrices between the polarization states at different positions within its input and output field‐of‐views (FOVs). This framework comprises 2D arrays of linear polarizers positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. After its deep learning‐based training, this diffractive polarization transformer can successfully implement N i N o = 10 000 different spatially‐encoded polarization scattering matrices with negligible error, where N i and N o represent the number of pixels in the input and output FOVs, respectively. This universal polarization transformation framework is experimentally validated in the terahertz spectrum by fabricating wire‐grid polarizers and integrating them with 3D‐printed diffractive layers to form a physical polarization transformer. Through this set‐up, an all‐optical polarization permutation operation of spatially‐varying polarization fields is demonstrated, and distinct spatially‐encoded polarization scattering matrices are simultaneously implemented between the input and output FOVs of a compact diffractive processor. This framework opens up new avenues for developing novel devices for universal polarization control and may find applications in, e.g., remote sensing, medical imaging, security, material inspection, and machine vision.

Optical neural networks↗
DOE OSTI2020

The effects of introducing elasticity using different interpolation schemes to the grand potential phase field model

Introducing elastic energy in the phase field method has been shown to influence interfacial energy, depending on the elastic interpolation scheme. This study investigates the impact of the elastic energy when using a grand potential-based phase field method, comparing the result of Khachaturyan’s strain interpolation scheme (KHS) and Voight-Taylor’s elastic energy interpolation scheme (VTS). The KHS model leads to a decrease in the interfacial energy, while the VTS model leads to an increase. The change in interfacial energy is greater with the VTS model than the KHS model, which suggests that the KHS model is more appropriate to limit the artificial impact of the elastic energy on the interfacial energy. When the contribution at the interface is not negligible, it is shown that both the microstructure evolution kinetics and the equilibrium microstructure can be influenced by the choice of the elastic scheme being used. In addition, this paper shows that the grand potential model might not be appropriate when the system requires the introduction of a composition-dependent term in the elastic energy contribution. This limitation is due to the need for an explicit and invertible relation between the total potential and the composition.

36 MATERIALS SCIENCE↗
DOE OSTI2022

express: Extensible, high-level workflows for swifter ab initio materials modeling

In this work, we introduce an open-source Julia project, express, an extensible, lightweight, high-throughput, high-level workflow framework that aims to automate ab initio calculations for the materials science community. express is shipped with well-tested workflow templates, including structure optimization, equation of state (EOS) fitting, phonon spectrum (lattice dynamics) calculation, and thermodynamic property calculation in the framework of the quasi-harmonic approximation (QHA). It is designed to be highly modularized so that its components can be reused across various occasions, and customized workflows can be built on top of that. Users can also track the status of workflows in real-time, and rerun failed jobs thanks to the data lineage feature express provides. Finally, two working examples, i.e., all workflows applied to lime and akimotoite, are also presented in the code and this paper.

36 MATERIALS SCIENCE↗