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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. Here, we propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.

image segmentation↗
DOE OSTI2024

A mechanistic interpretation of Nelson curves for PVP failures under high temperature hydrogen attack

As an empirically established design criterion, Nelson curves that relate the service temperature and the allowable hydrogen partial pressure have been developed and utilized for more than sixty years in pressure vessels and piping (PVP) safety design. Despite a relatively clear thermodynamic understanding of the high-temperature-hydrogen-attack (HTHA) problem, the detailed fracture process on the microstructural length scales, however, remains elusive, and a quantitative assessment of the PVP lifetime under HTHA from the available creep fracture dataset is still not possible. This work develops a microstructure-informed and micromechanics-based model by incorporating a synergy between hydrogen transport and intergranular-cavity-based fracture process. Based on the available creep lifetime data of C-0.5Mo steels, we are able to calibrate material constitutive parameters, and then conduct nonlinear finite element simulations that reveal a real-time stress-induced hydrogen diffusional transport along grain boundaries, coupled with a microstructure-explicit failure process, from which Nelson curves can be computed. Such failure analyses allow us to delineate two distinct regimes on the Nelson curves, i.e., dislocation-creep-controlled or grain boundary diffusion-assisted cavity growth. More importantly, we found that a small change of the pipe thickness and applied stresses can significantly shift these lifetime curves. However, these two parameters are usually not provided in Nelson curves, thus limiting their usage in material selection and safety design. In conclusion, this discrepancy can clearly be mitigated by extensive parametric studies from our micromechanical modeling/simulation framework.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ice sculpting: An artificial spin ice Tutorial on controlling microstate and geometry for magnonics and neuromorphic computing

Artificial spin ice, arrays of strongly interacting nanomagnets, are complex magnetic systems with many emergent properties, rich microstate spaces, intrinsic physical memory, high-frequency dynamics in the GHz range, and compatibility with a broad range of measurement approaches. This Tutorial article aims to provide the foundational knowledge needed to understand, design, develop, and improve the dynamic properties of artificial spin ice. Special emphasis is placed on introducing the theory of micromagnetics, which describes the complex dynamics within these systems, along with their design, fabrication methods, and standard measurement and control techniques. The article begins with a review of the historical background, introducing the underlying physical phenomena and interactions that govern artificial spin ice. We then explore the standard experimental techniques used to prepare the microstate space of the nanomagnetic array and to characterize magnetization dynamics, both in artificial spin ice and more broadly in ferromagnetic materials. Finally, we introduce the basics of neuromorphic computing applied to the case of artificial spin ice systems with a goal to help researchers new to the field grasp these exciting new developments.

Sultana, Rawnak [Univ. of Delaware, Newark, DE (Un↗
DOE OSTI2024

Accelerating the design of lattice structures using machine learning

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R 2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Miniaturized, High-Bandwidth Optical Fiber Fabry–Perot Cavity Vibration Sensor Demonstrated up to 800 °C

A typical structural health monitoring technique involves measuring the vibrational characteristics of components or systems to detect signs of degradation or damage. Many industrial applications require engineered systems to safely operate under extreme, high-temperature environments that pose challenges not only to materials but also to sensors that would be used for structural health monitoring. Here, in this study, miniaturized optical Fabry-Perot cavities (FPCs) were developed and tested as a means of measuring the resonant frequencies of metal components that are most relevant to extreme-environment applications. Two of the three candidate FPC designs tested up to 800 ° C provided accurate measurements (validated by theoretical models and laser Doppler vibrometry) of the fundamental vibrational mode of the specimen to which each was bonded, although both sensors failed during thermal cycling. An analysis of the reflected optical spectrum from the FPC and X-ray computed tomography revealed two opportunities to improve the sensor reliability. First, the Cu optical fiber coating that was used could either be replaced with a more oxidation-resistant material or protected with commercially available films. Second, the adhesives used to bond the fibers to metal capillaries and establish the FPC could be replaced with a more robust solution, although the Resbond 907TS adhesive appeared to outperform Resbond 907.

Birri, Anthony [Oak Ridge National Laboratory (ORN↗
DOE OSTI2022

Pit rim decomposition into multiple quantum dots on surfaces of epitaxial thin films grown on pit-patterned substrates

Here, we report results of dynamical simulations according to an experimentally validated surface morphological evolution model on the formation of regular arrays of quantum dot molecules (QDMs) consisting of 1D arrays of smaller interacting quantum dots (QDs). These QD arrays form along the sides of each pit rim on the surface of a coherently strained thin film epitaxially deposited on a semiconductor substrate, the surface of which consists of a periodic pattern of inverted prismatic pits with rectangular pit openings. We find that this complex QDM pattern results from the decomposition of the pit rim from a “quantum fortress” with four elongated QDs into four 1D arrays of multiple smaller QDs arranged along each side of the pit rim. Systematic parametric analysis indicates that varying the pit opening dimensions and the pit wall inclination directly impacts the number of QDs in the resulting QDM pattern, while varying the pit depth only affects the dimensions of the QDs in the nanostructure pattern. Therefore, the number, arrangement, and sizes of QDs in the resulting pattern of QDMs on the film surface can be engineered precisely by proper tuning of the pit design parameters. Our simulation results are supported by predictions of morphological stability analysis, which explains the pit rim decomposition into multiple QDs as the outcome of a tip-splitting instability and provides a fundamental characterization of the post-instability nanostructure pattern. Our theoretical findings can play a vital role in designing optimal semiconductor surface patterns toward enabling future nanofabrication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Towards 20 T Hybrid Accelerator Dipole Magnets

We report the most effective way to achieve very high collision energies in a circular particle accelerator is to maximize the field strength of the main bending dipoles. In dipole magnets using Nb-Ti superconductor the practical field limit is considered to be 8-9 T. When Nb 3 Sn superconductor material is utilized, a field level of 15-16 T can be achieved. To further push the magnetic field beyond the Nb 3 Sn limits, High Temperature Superconductors (HTS) need to be considered in the magnet design. The most promising HTS materials for particle accelerator magnets are Bi2212 and REBCO. However, their outstanding performance comes with a significantly higher cost. Therefore, an economically viable option towards 20 T dipole magnets could consist in an “hybrid” solution, where both HTS and Nb3Sn materials are used. We discuss in this paper preliminary conceptual designs of various 20 T hybrid magnet concepts. After the definition of the overall design criteria, the coil dimensions and parameters are investigated with finite element models based on simple sector coils. Preliminary 2D cross-section computation results are then presented and three main layouts compared: cos-theta, block, and common-coil. Both traditional designs and more advanced stress-management options are considered.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Los Alamos National Laboratory R&D Intern

This report details the responsibilities, outcomes, and project details of a summer R&D internship at Los Alamos National Laboratory (LANL). LANL is a multidisciplinary laboratory that focuses on current cutting-edge research in many fields such as national security, engineering, materials science, computational modeling, and advanced manufacturing. The goal of the internship project was to work with lab engineers and resources to develop an energy absorbing structure for high-velocity impact applications. The successful development of this technology and methodology would not only positively impact future project funding but also contribute to the laboratory's commitment to solve national security challenges through simultaneous excellence. Such devices would also support efforts surrounding the research and development of energy absorbing structures and would provide new vital information backed by experimentation. Different computational and modeling methods were used to design these structures, in addition to qualitative background information provided by past literature. The resultant designs were successfully tested, and the test results were successfully quantified. From these results, new computational methods were developed through python programming and modeling to predict ideal materialistic properties for these structures given a sufficiently defined application.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs 0.4 La 0.6 Mn 0.25 Co 0.75 O 3 , Cs 0.3 La 0.7 NiO 3 , SrNi 0.75 Co 0.25 O 3 , and Sr 0.25 Ba 0.75 NiO 3 , are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Design Methodologies for Integrated Quantum Frequency Processors

We report frequency-encoded quantum information offers intriguing opportunities for quantum communications and networking, with the quantum frequency processor paradigm—based on electro-optic phase modulators and Fourier-transform pulse shapers—providing a path for scalable construction of quantum gates. Yet all experimental demonstrations to date have relied on discrete fiber-optic components that occupy significant physical space and impart appreciable loss. In this article, we introduce a model for the design of quantum frequency processors comprising microring resonator-based pulse shapers and integrated phase modulators. We estimate the performance of single and parallel frequency-bin Hadamard gates, finding high fidelity values that extend to frequency bins with relatively wide bandwidths. By incorporating multi-order filter designs as well, we explore the limits of tight frequency spacings, a regime extremely difficult to obtain in bulk optics. Overall, our model is general, simple to use, and extendable to other material platforms, providing a much-needed design tool for future frequency processors in integrated photonics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

A practical extension of the recursive multi-fidelity model for the emulation of hole closure experiments

We report in regimes of high strain rate, the strength of materials often cannot be measured directly in experiments. Instead, the strength is inferred based on an experimental observable, such as a change in shape, that is matched by simulations supported by a known strength model. In hole closure experiments, the rate and degree to which a central hole in a plate of material closes during a dynamic loading event are used to infer material strength parameters. Due to the complexity of the experiment, many computationally expensive, three-dimensional simulations are necessary to train an emulator for calibration or other analyses. These simulations can be run at multiple grid resolutions, where dense grids are slower but more accurate. In an effort to reduce the computational cost, a combination of simulations with different resolutions can be combined to develop an accurate emulator within a limited training time. We explore the novel design and construction of an appropriate functional recursive multi-fidelity emulator of a strength model for tantalum in hole closure experiments that can be applied to arbitrarily large training data. Hence, by formulating a multi-fidelity model to employ low-fidelity simulations, we were able to reduce the error of our emulator by approximately 81% with only an approximately 1.6% increase in computing resource utilization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing Header Shape Through Computational Fluid Dynamics for Improved Performance

Shape optimization in power plant design is crucial for maximizing efficiency and minimizing energy losses It impacts performance, cost effectiveness, and environmental sustainability Our project focuses on optimizing header pipe geometry using a method that considers temperature, flow, and pressure distributions, along with structural analysis This approach ensures structural integrity while minimizing material costs

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Mechanistic insights into metal, nitrogen doped carbon catalysts for oxygen reduction: progress in computational modeling

We report metal and nitrogen doped carbon materials (denoted as M–N–C) synthesized through high-temperature pyrolysis have been found to exhibit activity for oxygen reduction reaction (ORR) approaching that of Pt and electrochemical stability higher than previous MN 4 -containing macrocyclic molecular catalysts. Tremendous efforts have thus been devoted to the advancement of M–N–C catalysts as an economical alternative to Pt-based catalysts for proton exchange membrane fuel cell cathodes with a focus on simultaneously improving activity and stability. To this end, novel computational modeling techniques have been developed and applied to acquire knowledge crucial for accelerating the pace of M–N–C catalyst development. In this review, recent progress in computational method development, as well as the predictions of chemical structure of active sites, reaction pathways, ORR kinetics, and catalyst stability in electrochemical environments, are critically surveyed. Moreover, the crucial role of computational modeling to elucidate the functional mechanism of M–N–C catalysts for ORR in acid media and enable rational design of M–N–C catalysts is discussed with a visionary outlook for the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Enhanced Electrolyte Transport and Kinetics Mitigate Graphite Exfoliation and Li Plating in Fast–Charging Li–Ion Batteries

Despite significant progress in energy retention, lithium-ion batteries (LIBs) face untenable reductions in cycle life under extreme fast-charging (XFC) conditions, which primarily originate from a variety of kinetic limitations between the graphite anode and the electrolyte. Through quantitative Li + loss accounting and comprehensive materials analyses, it is directly observed that the operation of LIB pouch cells at 4 C||C/3 (charging||discharging) results in Li plating, disadvantageous solid-electrolyte-interphase formation, and solvent co-intercalation leading to interstitial decomposition within graphite layers. It is found that these failure modes originate from the insufficient properties of conventional electrolytes, where employing a designed ester-based electrolyte improved the capacity retention of these cells from 55.9% to 88.2% after 500 cycles when operated at the aforementioned conditions. These metrics are the result of effective mitigation of the aforementioned failure modes due to superior Li + transport and desolvation characteristics demonstrated through both experimental and computational characterization. Finally, this work reveals the vital nature of electrolyte design to XFC performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Inverse design of pore wall chemistry and topology through active learning of surface group interactions

Design of next-generation membranes requires a nanoscopic understanding of the effect of biologically inspired heterogeneous surface chemistries and topologies (roughness) on local water and solute behavior. In particular, the rejection of small, neutral solutes, such as boric acid, poses a heretofore unsolved challenge. In prior work, a computational inverse design technique using an evolutionary optimization successfully uncovered new surface design strategies for optimized transport of water over solutes in smooth, model pores consisting of two surface chemistries. However, extending such an approach to more complex (and realistic) scenarios involving many surface chemistries as well as surface roughness is challenging due to the expanded design space. In this work, we develop a new approach that uses active learning to optimize in a reduced feature space of surface group interactions, finding parameters that lead to their assembly into ordered, optimal patterns. This approach rapidly identifies novel surface functionalizations that maximize the difference in water and boric acid transport through the nanopore. Moreover, we find that the roughness of the nanopore wall, independent of its chemistry, can be leveraged to enhance transport selectivity: oscillations in the pore wall diameter optimally inhibit boric acid transport by creating energetic wells from which the solute must escape to transport down the pore. Furthermore, this proof-of-concept demonstrates the potential for active learning strategies, in concert with molecular simulations, to rapidly navigate complex design spaces of aqueous interfaces and is promising as a tool for engineering water-mediated surface interactions for a broad range of applications.

36 MATERIALS SCIENCE↗