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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Unraveling the transformation pathway of the 𝛽 to 𝛾 phase transition in Ga 2 ⁢O 3 from atomistic simulations

Defect spinel 𝛾−Ga 2 ⁢O 3 is the least stable polymorph of Ga 2 ⁢O 3 , so its frequent appearance as a structural defect within or on the surface of monoclinic 𝛽−Ga 2 ⁢O 3 remains a mystery. Through first-principles calculations, we explore potential pathways for the phase transition from 𝛽−Ga 2⁢ O 3 to 𝛾−Ga 2 ⁢O 3 , and examine two key driving forces: tensile strain and Ga deficiency. When configurational entropy contributions to phase energies are included, the 𝛾 phase becomes energetically competitive with the 𝛽 phase, with the free energy difference between these phases diminishing even further under Ga-deficient conditions. Notably, a stability crossover occurs at room temperature at high vacancy concentrations ([V$^{3−}_{Ga}$]>3%) . A simple model 𝛽 → 𝛾 transformation pathway is identified, comprising two primary reactions, that enables the formation of the 𝛾 phase via simultaneous migration of Ga atoms from tetrahedral lattice sites to octahedral interstitial positions. The transformation barriers are prohibitively large in pristine Ga 2 ⁢O 3 , but can be substantially reduced by: (1) the presence of Ga vacancies, (2) elongational strains along the crystallographic 𝑎-axis, and (3) when volumetric relaxations are possible during transformation. These results elucidate prior experimental observations, where 𝛾−Ga 2⁢ O 3 is seen on damaged surfaces or in highly 𝑛-type 𝛽−Ga 2⁢ O 3 environments, which support Ga deficiency and mechanical strain. The insights into the driving forces and mechanisms of 𝛾−Ga 2⁢ O 3 formation enhance understanding of how localized strain and nonequilibrium defect concentrations may facilitate its formation from the 𝛽 phase.

Defects↗
DOE OSTI2026

Extreme confinement of hydrogen gas within fullerenelike nanoporous carbon

Nanoporous carbons and carbon nanostructures can store hydrogen at cryogenic temperatures but lack the volumetric and gravimetric capacity to be industrially significant. Recent inelastic neutron scattering experiments suggest a highly dense phase of hydrogen at temperatures well above the melting point of solid hydrogen. However, it remains unclear how pore geometry and intermolecular interactions enable these dense phases to exist, with dispersion (van der Waals) or electrostatic/induction suggested to be the key effects in slit and curved pores but their relative contributions have yet to be quantified. In this paper, we perform benchmark electronic structure calculations allowing the interactions between planar and curved aromatic molecules with hydrogen to be accurately determined. Dispersion was found to dominate over electrostatic and inductive effects with some many-body charge transfer (Dobson type-A) effects needed to capture the most highly curved structures. Density functional methods that include type-A many-body effects were found to accurately describe the intermolecular interactions at a fraction of the cost of coupled-cluster simulations and these approaches were used to calculate the energies inside large carbon bowl and slit pores. The interaction energies inside the bowl pores were found to depend on the orientation of the hydrogen molecule. This rotational barrier, modeled as a quantum hindered rotor, could reproduce the peak splitting observed in inelastic neutron scattering experiments, with weak splitting arising from bowl-like fullerene pores and strong splitting from highly confining nanotubelike pores. Increasing the fraction of such curved pores in nanoporous carbons may therefore offer a pathway to enhance their hydrogen-storage capacity. Moreover, the preferential adsorption of ortho hydrogen on nanotubelike pores could enable the storage of high-density hydrogen without the need to remove heat produced during the ortho-para hydrogen conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Phase segregation and miscibility of TiO x nanocomposites in Gd-doped ceria solid electrolyte material

Electro-chemo-mechanical (ECM) coupling refers to mechanical deformation due to electrochemically driven compositional change in a solid. An ECM actuator producing micrometre-size displacements and long-term stability at room temperature was recently reported, comprising a 20 mol% Gd-doped ceria (20GDC), a solid electrolyte membrane, placed between two working bodies made of TiO x /20GDC (Ti-GDC) nanocomposites with Ti concentration of 38 mol%. The volumetric changes originating from oxidation or reduction in the local TiO x units are hypothesized to be the origin of mechanical deformation in the ECM actuator. Studying the Ti concentration-dependent structural changes in the Ti-GDC nanocomposites is therefore required for (i) understanding the mechanism of dimensional changes in the ECM actuator and (ii) maximizing the ECM response. Here, the systematic investigation of the local structure of the Ti and Ce ions in Ti-GDC over a broad range of Ti concentrations using synchrotron X-ray absorption spectroscopy and X-ray diffraction is reported. The main finding is that, depending on the Ti concentration, Ti atoms either form a cerium titanate or segregate into a TiO 2 anatase-like phase. The transition region between these two regimes with Ti(IV) concentration between 19% and 57% contained strongly disordered TiO x units dispersed in 20GDC containing Ce(III) and Ce(IV) and hence rich with oxygen vacancies. As a result, this transition region is proposed to be the most advantageous for developing ECM-active materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Mapping nanocrystal orientations via scanning Laue diffraction microscopy for multi-peak Bragg coherent diffraction imaging

The recent commissioning of a movable monochromator at the 34-ID-C endstation of the Advanced Photon Source has vastly simplified the collection of Bragg coherent diffraction imaging (BCDI) data from multiple Bragg peaks of sub-micrometre scale samples. Laue patterns arising from the scattering of a polychromatic beam by arbitrarily oriented nanocrystals permit their crystal orientations to be computed, which are then used for locating and collecting several non-co-linear Bragg reflections. The volumetric six-component strain tensor is then constructed by combining the projected displacement fields that are imaged using each of the measured reflections via iterative phase retrieval algorithms. Complications arise when the sample is heterogeneous in composition and/or when multiple grains of a given lattice structure are simultaneously illuminated by the polychromatic beam. Here, a workflow is established for orienting and mapping nanocrystals on a substrate of a different material using scanning Laue diffraction microscopy. The capabilities of the developed algorithms and procedures with both synthetic and experimental data are demonstrated. The robustness is verified by comparing experimental texture maps obtained with Laue diffraction microscopy at the beamline with maps obtained from electron back-scattering diffraction measurements on the same patch of gold nanocrystals. Such tools provide reliable indexing for both isolated and densely distributed nanocrystals, which are challenging to image in three dimensions with other techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Melt Pool characteristics on surface roughness and printability of 316L stainless steel in laser powder bed fusion

Purpose Surface quality and porosity significantly influence the structural and functional properties of the final product. This study aims to establish and explain the underlying relationships among processing parameters, top surface roughness and porosity level in additively manufactured 316L stainless steel. Design/methodology/approach A systematic variation of printing process parameters was conducted to print cubic samples based on laser power, speed and their combinations of energy density. Melt pool morphologies and dimensions, surface roughness quantified by arithmetic mean height (Sa) and porosity levels were characterized via optical confocal microscopy. Findings The study reveals that the laser power required to achieve optimal top surface quality increases with the volumetric energy density (VED) levels. A smooth top surface (Sa < 15 µm) or a rough surface with humps at high VEDs (VED > 133.3 J/mm 3 ) can serve as indicators for fully dense bulk samples, while rough top surfaces resulting from melt pool discontinuity correlate with high porosity levels. Under insufficient VED, melt pool discontinuity dominates the top surface. At high VEDs, surface quality improves with increased power as mitigation of melt pool discontinuity, followed by the deterioration with hump formation. Originality/value This study reveals and summarizes the formation mechanism of dominant features on top surface features and offers a potential method to predict the porosity by observing the top surface features with consideration of processing conditions.

Engineering↗
DOE OSTI2022

Thermal Design and Optimization of High- Power Wireless Charging System

In this paper, the thermal design and optimization of a high-power wireless charging system (WCS) is proposed. An integrated electromagnetic and thermal co-optimization is essential to design a high power-density WCS pad. This study presents the thermal analysis and the potential optimization scopes for a polyphase WCS pad. The coil and core causes most of the power losses of a WCS pad and causes thermal hotspot in the pad. In this paper, the thermally conductive epoxy is introduced to balance the effective volumetric loss density in the pad and mitigate the thermal hotspots in the coil and core. The proposed design is simulated through finite element analysis (FEA) and tested experimentally for a 50 kW three-phase WCS. Simulation and experimental results show that the conventional design has localized hotspots in the coil and core. The proposed thermal design mitigates the thermal hotspots without increasing the coil or core volume.

Mohammad, Mostak↗
DOE OSTI2023

Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

big scientific dataset↗
DOE OSTI2023

Cast or Additively Manufactured Coils with End Turn Enhanced Heat Transfer Surfaces for Spray or Jet Cooling

Cast or additively manufactured coils present a new opportunity for creativity in thermal management of electric machines. Improvements to the heat transfer of spray or jet cooling of coil end turns through the incorporation of pins or enhanced heat transfer surfaces in the cast end turn surfaces is explored. The thermal response of cast copper half-turns is measured for a range of spray/jet volumetric flow rates and ohmic loss heat fluxes. A significant reduction, ~30%, in the steady state temperature and increased spray cooling heat transfer coefficients was experimentally measured when the pins or enhanced heat transfer surfaces are incorporated.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Multi-phase Wireless Power Transfer with High Power Density Inductive Coils for Electric Drone Charging

Wireless charging of unmanned ground vehi-cles and aircraft has been proposed to increase charging reliability and security, allow for autonomous functionality, and either reduce battery size or increase continuous flight time. This paper proposes a three-phase Litz wire primary and a two-phase PCB secondary for high secondary-side power density considering misalignment tolerances, surface and volumetric power density, and coil sizing. Elec-tromagnetic 3D finite element analysis (FEA) simulations are conducted to study variation in mutual inductance and coupling coefficient with different secondary coil sizes and number of turns, horizontal and vertical misalignment between the primary and secondary, and a combination of both for a set of designs with the same rated power. Results indicate that receiver coils of reduced size relative to the primary can deliver rated power for increased power density and higher tolerance to misalignment.

Gastineau, Lucas [University of Kentucky]↗
DOE OSTI2026

SO(3)-invariant PCA with application to molecular data

Principal component analysis (PCA) is a fundamental technique for dimensionality reduction and denoising; however, its application to three-dimensional data with arbitrary orientations -- common in structural biology -- presents significant challenges. A naive approach requires augmenting the dataset with many rotated copies of each sample, incurring prohibitive computational costs. In this paper, we extend PCA to 3D volumetric datasets with unknown orientations by developing an efficient and principled framework for SO(3)-invariant PCA that implicitly accounts for all rotations without explicit data augmentation. By exploiting underlying algebraic structure, we demonstrate that the computation involves only the square root of the total number of covariance entries, resulting in a substantial reduction in complexity. We validate the method on real-world molecular datasets, demonstrating its effectiveness and opening up new possibilities for large-scale, high-dimensional reconstruction problems.

Fraiman, Michael [Tel Aviv Univ., Tel Aviv (Israel↗
DOE OSTI2026

Multi-Objective Boundary Analysis of Discrete and Integrated SiC FET Modular Non-inverting Buck and Boost Converters for Fuel Cell EVs

This paper presents a multi-objective analysis of discrete and integrated SiC FET-based non-inverting buck-boost converter modules for modular fuel cell electric vehicle (EV) systems. Two converter ratings, 60 kW and 90 kW, are evaluated for both implementations, scalable up to 420 kW and 450 kW, respectively. Performance is assessed across efficiency, volumetric and gravimetric power density, cost, thermal stress, and estimated lifetime, where lifetime is derived from SiC FET B10 power-cycling data and junction temperature variations at rated power. A normalized overall performance index combined with a Pareto-boundary framework is used to identify configurations that optimally balance competing objectives. Results show that most configurations lie on the Pareto front, providing balanced trade-offs, while certain high-power discrete (90 kW at 450 kW) and integrated (60 kW at 180−420 kW) configurations are dominated. In general, discrete modules are more favorable for lower-power modular systems due to higher power density and lower cost, whereas integrated modules become more advantageous at higher power levels due to improved thermal behavior and longer lifetime. These findings provide practical design guidance for scalable fuel cell converter architectures and highlight the importance of system-level trade-offs in modular power electronics design.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2025

Extremely Scalable Distributed Computation of Contour Trees via Pre-Simplification

Contour trees offer an abstract representation of the level set topology in scalar fields and are widely used in topological data analysis and visualization. However, applying contour trees to large-scale scientific datasets remains challenging due to scalability limitations. Recent developments in distributed hierarchical contour trees have addressed these challenges by enabling scalable computation across distributed systems. Building on these structures, advanced analytical tasks—such as volumetric branch decomposition and contour extraction—have been introduced to facilitate large-scale scientific analysis. Despite these advancements, such analytical tasks substantially increase memory usage, which hampers scalability. In this paper, we propose a pre-simplification strategy to significantly reduce the memory overhead associated with analytical tasks on distributed hierarchical contour trees. We demonstrate enhanced scalability through strong scaling experiments, constructing the largest known contour tree—comprising over half a trillion nodes with complex topology—in under 15 minutes on a dataset containing 550 billion elements.

Li, Mingzhe [University of Utah]↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
DOE OSTI2023

Evaluating Robust Entanglement on a Trapped Ion Platform

We present proof of principle results for evaluating an entanglement volumetric benchmark [1] on trapped ion platforms. The benchmark quantifies the robustness of multipartite and bipartite entanglement using stabilizer measurements and witness functions. Each n-qubit graph state is prepared and used to evaluate n state-specific stabilizer strings. These stabilizer measurements are used to evaluate entanglement witness functions. The entanglement benchmark defines families of graph states associated with an initial sub-graph of the hardware qubit connectivity - with all-to-all connectivity, trapped ion systems provide a flexibility in the choice of this initial graph, and this will affect the associated family. In this work, we present results targeting several classes of entangled states: 1) 1-D cluster states, 2) n-qubit GHZ states, and 3) cycle graph states. These states have been found in the literature as standard hardware benchmarks, and have connections to many near-term applications.

Hamilton, Kathleen↗
DOE OSTI2023

TRINIDI: Time-of-Flight Resonance Imaging With Neutrons for Isotopic Density Inference

Accurate reconstruction of 2D and 3D isotope densities is a desired capability with great potential impact in applications such as evaluation and development of next-generation nuclear fuels. Neutron time-of-flight (TOF) resonance imaging offers a potential approach by exploiting the characteristic neutron absorption spectra of each isotope. However, it is a major challenge to compute quantitatively accurate images due to a variety of confounding effects such as severe Poisson noise, background scatter, beam non-uniformity, absorption non-linearity, and extended source pulse duration. We present the TRINIDI algorithm which is based on a two-step process in which we first estimate the neutron flux and background counts, and then reconstruct the areal densities of each isotope and pixel. Both components are based on the inversion of a forward model that accounts for the highly non-linear absorption, energy-dependent emission profile, and Poisson noise, while also modeling the substantial spatio-temporal variation of the background and flux. Further, to do this, we formulate the non-linear inverse problem as two optimization problems that are solved in sequence. We demonstrate on both synthetic and measured data that TRINIDI can reconstruct quantitatively accurate 2D views of isotopic areal density that can then be reconstructed into quantitatively accurate 3D volumes of isotopic volumetric density.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Fast Hyperspectral Neutron Tomography

Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.

Chowdhury, Mohammad Samin Nur [Purdue University]↗
DOE OSTI2021

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗
DOE OSTI2020

Efficient and Flexible Sensitivity Matrix Computation for Adaptive Electrical Capacitance Volume Tomography

Electrical capacitance tomography is a widely used sensor modality for flow imaging in many industrial settings. Adaptive Electrical Capacitance Volume Tomography (AECVT) extends the capabilities of traditional ECT by enabling direct volumetric imaging and an improved resolution. Construction of the sensitivity matrix is a necessary step to obtain flow images. This step requires computation of the electric field inside the sensing domain, which is done via a typical field solver such as the finite element method. In this work, we present an efficient and flexible method to construct the sensitivity matrix for Adaptive Electrical Capacitance Volume Tomography (AECVT) based on individual electrode segment excitations and their judicious combination to form desired matrix elements. We illustrate how the proposed method yields the same sensitivity matrix as the traditional method but at a much lower computational cost. Once all segment contributions are obtained, we also indicate how the proposed method, unlike the traditional approach, can generate the sensitivity matrix on demand for an arbitrary combination of synthetic electrodes and obviating the need for any additional field computations. Finally, we present image reconstruction results for two different experimental scenarios where the mutual capacitance data and the corresponding sensitivity vectors are obtained through the proposed measurement combination scheme.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗