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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quasi-free scattering in inverse kinematics as a tool to unveil the structure of nuclei: A tribute to Mahir S. Hussein

Abstract Quasi-free scattering of electrons and protons has been extensively utilized in the past to study the single-particle structure of nuclei, clustering in light nuclei, and short-range correlated nucleon–nucleon pairs in nuclei. Recently, this approach has been applied in inverse kinematics using hydrogen targets. The characteristic features of this reaction and the experimental challenges and advantages of inverse-kinematics experiments are summarized. The applicability to radioactive beams opens a large research potential to study a variety of properties of neutron-to-proton asymmetric nuclei. Applications of quasi-free scattering in inverse kinematics and its potential are reviewed based on recent and ongoing research programs at different accelerator facilities worldwide.

Physics↗
DOE OSTI2025

VAIM-CFF: a variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted CFFs, use of uncertainty quantification technique, and inclusion of prior physics information. We then use dimensionality reduction techniques such as principal component analysis to visualize the missing physics information tracked in the latent space of the VAIM framework. Through re-framing the extraction of CFFs as an inverse problem, we gain access to fundamental properties of the problem not comprehensible in standard fitting methodologies: exploring the limits of the information encoded in deeply virtual exclusive experiments.

Accelerator Physics↗
DOE OSTI2022

Superconductivity and Local Inversion-Symmetry Breaking

Inversion and time reversal are essential symmetries for the structure of Cooper pairs in superconductors. The loss of one or both leads to modifications to this structure and can change the properties of the superconducting phases in profound ways. Superconductivity in materials lacking inversion symmetry, or noncentrosymmetric materials, has become an important topic. These materials show unusual magnetic and magnetoelectric properties and can host topological superconductivity. Recently, crystal structures with local, but not global, inversion-symmetry breaking have attracted attention. Here, superconductivity can exhibit phenomena not naively expected in centrosymmetric materials. In this review, we first introduce the concept of locally noncentrosymmetric crystals and different material realizations. We then discuss consequences of such local symmetry breaking on the normal state electronic structure and the classification of superconducting order parameters. Finally, we review the expected and, in parts, already observed phenomenology of unconventional superconductivity and possible topological superconducting phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Temperature Inversions below 1 km from a V-Band Scanning Radiometer at the North Slope of Alaska

A single-channel (56.7 GHz) scanning radiometer was deployed in August 2022 at the Atmospheric Radiation Measurement (ARM) North Slope of Alaska site near Utqiaġvik. The radiometer is designed to provide temperature profiles between 0 and 1 km every 5 min. Averaging kernels show that this single-channel radiometer, taking observations at 10 discrete elevation angles, yields approximately the same information as a seven-channel V-band radiometer scanning three elevation angles. The instrument is able to reproduce the occurrence of temperature inversions between the surface and 1 km and their strength showing a correlation of 0.85, bias of −0.6 K, and slope of 1.04 with respect to radiosondes. Uncertainty in the inversion base height varies from 50 m near the surface to ∼300 m above 0.4 km when compared with radiosondes. Conversely, the inversion top height is overestimated and has higher uncertainty due to the degrading effects of the averaging kernels on the vertical resolution of the retrievals. Thanks to the high temporal resolution of the retrievals, the diurnal cycle of boundary layer temperature was evaluated showing that the radiometer can capture some aspects of the boundary layer thermal structure. The present analysis provides an overview of the capabilities of this simple observing configuration for selected applications.

Atmospheric profilers↗
DOE OSTI2020

Hessian-based multiparameter fractional viscoacoustic full-waveform inversion

Recent progress on fractional modeling enables incorporating seismic attenuation into wavefield simulation in an accurate and efficient way. But its inverse problem, i.e., the multiparameter viscoacoustic full waveform inversion (FWI), still suffers from various issues, especially the crosstalk between velocity and attenuation. In this study, we integrate the Hessian information via the Newton-CG framework and develop the multiparameter fractional viscoacoustic FWI algorithm. It significantly mitigates the crosstalk problems and sheds light upon simultaneous inversion for both velocity and Q models.

Xing, Guangchi↗
DOE OSTI2020

Improving inverse Compton sources by avoiding non-linearities

We present a new, more nuanced understanding of non-linear effects in inverse Compton sources. Deleterious non-linear effects can arise even at low laser intensities, a regime previously viewed as linear. After laying out a survey of non-linear phenomena which degrade the effectiveness of inverse Compton sources, we discuss two powerful techniques designed to avoid these non-linearities. Starting with the known technique of non-linear longitudinal chirping of the laser pulse in the high laser field regime, we show that the simple stretching of the laser pulse, while keeping the energy constant, can significantly increase the spectral density of the scattered radiation in many operating regimes. Our numerical simulations show that combining these two techniques avoids detrimental non-linearities and improves the performance of inverse Compton sources over an order of magnitude.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Inverse design for waveguide dispersion with a differentiable mode solver

Inverse design of optical components based on adjoint sensitivity analysis has the potential to address the most challenging photonic engineering problems. However, existing inverse design tools based on finite-difference-time-domain (FDTD) models are poorly suited for optimizing waveguide modes for adiabatic transformation or perturbative coupling, which lies at the heart of many important photonic devices. Among these, dispersion engineering of optical waveguides is especially challenging in ultrafast and nonlinear optical applications involving broad optical bandwidths and frequency-dependent anisotropic dielectric material response. In this work, we develop gradient back-propagation through a general-purpose electromagnetic eigenmode solver and use it to demonstrate waveguide dispersion optimization for second harmonic generation with maximized phase-matching bandwidth. This optimization of three design parameters converges in eight steps, reducing the computational cost of optimization by ∼100x compared to exhaustive search and identifying new designs for broadband optical frequency doubling of laser sources in the 1.3–1.4 µm wavelength range. Furthermore, we demonstrate that the computational cost of gradient back-propagation is independent of the number of parameters, as required for optimization of complex geometries. This technique enables practical inverse design for a broad range of previously intractable photonic devices.

Gray, Dodd (ORCID:000000030469599X)↗
DOE OSTI2026

Improved Regional Moment Tensor Inversion for Moderately Large Earthquakes in the Western United States Using a 3D Earth Model Based on Full Waveform Tomography

The nature of seismic sources for moderately large (moment magnitude, M w 5.0–6.5) events are commonly characterized by their moment tensor (MT) solutions and obtained by inversion of regional distance (200–1600 km) long‐period (20–50 s) waveforms. Regional MT estimates are often calculated from average plane‐layered, one‐dimensional (1D) velocity models. However, 1D model calculations can produce misfits in the arrival times and waveform shapes that introduce errors, particularly at longer distances or for shorter periods, which are necessary for analyzing lower magnitude events. Approximate Earth models (e.g., 1D) representing broad areas may be inadequate, particularly in the crust and uppermost mantle of tectonically complex regions. In this study, we show how a three‐dimensional (3D) Earth model obtained from full waveform inversion tomography can improve waveform fits and decrease phase errors. We developed a platform and workflow to perform routine 3D MT inversions and inverted MTs for 25 earthquakes in the western United States and seven nuclear explosions using an average 1D and a recent 3D Earth model, WUS256 (Rodgers et al., 2022). Using the 3D model improves waveform fits (variance reduction and phase time shifts) compared with the 1D model, and the 3D MT solutions are stable across large distances. This study shows that 3D models obtained from full waveform tomography can improve MTs and source characterization especially at far regional distances (>800 km).

Geosciences↗
DOE OSTI2025

AK112: Full Waveform Inversion Tomography of Alaska Improves Waveform Fits While Imaging Crustal, Mantle, and Slab Structure

We report a full waveform inversion tomography model of Alaska and the surrounding regions, inferring radially anisotropic shear and isotropic compressional wavespeeds by fitting complete waveforms from 120 regional earthquakes. Our multiscale approach inverted time–frequency phase misfits (maximum period of 100 s), starting with a minimum period of 40 s and ending at 20 s in 7 stages and 112 total iterations. The model (AK112) was evaluated by computing the misfits for 36 independent validation events. We find that misfit reductions were large and equal (∼55%) for both the inversion and validation data sets, providing confidence in the model. AK112 also provides much better waveform fits compared to other reported models for the region, including an isotropic version of itself, highlighting the importance of anisotropy. The model resolves known crustal, upper mantle, and slab structure to depths of 100 km with new detail: sedimentary basins in the Alaskan Shelf, Cook Inlet, and Colville basins, among others; discontinuous lithospheric structure across major terrane boundaries; and subducting slab geometry and back‐arc volcanic sources. In addition to tectonic interpretations, the model enables full waveform simulations for long‐period earthquake ground motions and source characterization (e.g., moment tensor and finite‐fault inversion).

Rodgers, Arthur [Lawrence Livermore National Labor↗
DOE OSTI2022

Surface 3D Electrical Resistivity Tomography Inversion of 2005 BC Cribs and Trenches Datasets

Hydrogeophysics, Inc. (HGI) conducted an electrical resistivity tomography (ERT) dataset at the BC Cribs and Trenches site, located in the Central Plateau of the Hanford Site. The 20 trenches and 6 cribs received large volumes of liquid inorganic waste in the 1950s, resulting in a large inventory of contaminants in the vadose zone. The objective of the ERT survey was to map plume extents resulting from the legacy discharges. The HGI interpretation of the resistivity data was performed using geometric inversion to interpolate 2D lines into a 3D image. To demonstrate a newly developed geophysical code capability (E4D), the resistivity data were re-processed to fit a full 3D model of the bulk electrical conductivity. This proof-of-concept model inversion was executed in calendar year 2011, as the large dataset was well-suited for the use of high-performance computing. The 3D re-processing of the BC Cribs and Trenches ERT data conducted in 2011 resolved the true bulk electrical conductivity. This means that all of the resistivity data were fit to a single model of the bulk electrical conductivity, with true horizontal and vertical dimensions. This differed from the HGI data interpretation approach that used geometric inversion to process 2D lines independently, which were then interpolated into a 3D image. Both the full 3D re-processing and the 2D interpolation to a 3D image demonstrated a higher electrical conductivity observed immediately beneath the trenches and cribs. The electrical conductivity is strongly correlated with nitrate concentrations, indicating the presence of nitrate and other co-located contaminants.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Context-Aware Learning for Inverse Design in Photovoltaics

This document describes progress in the ARPA-E DIFFERENTIATE project titled “Context-Aware Learning for Inverse Design in Photovoltaics” during the period of May 2019 to May 2022. This project is being performed at Iowa State University, New York University, Stanford University, and National Renewable Energy Laboratory. The project aims to develop a new machine learning (ML) framework to significantly accelerate the design of organic microstructures for improved organic photovoltaic performance. In this project, we had developed an inverse design framework using Deep Learning called InvNets for generating microstructures with desired physics-driven properties. As a preliminary product, in Milestone 3, we demonstrated how InvNets show 20% improvement in the performance of the microstructures and over 100X speedup in the performance compared to traditional processes for physics-driven inverse design. Later, in Milestone 6, we demonstrated that InvNets work for more complex physics properties, specifically, generating microstructures for organic photovoltaic cells with desired current-voltage characteristics. Further, in Milestone 4, we explored the idea of using physics-aware surrogates for obtaining solutions of partial differential equations(PDE) called as DiffNets(now called as NeuFENets to avoid ambiguity of names). The connection between both frameworks is that DiffNet surrogates form the physics-aware surrogate in the InvNet framework. Finally in Milestone 8, we extend our framework for other physics domains. Specifically, we explore building geometry-aware NeuFENets by developing physics surrogates that exploit ideas from traditional immersed boundary finite element methods. With these updates, we are able to achieve all the Milestones.

36 MATERIALS SCIENCE↗
DOE OSTI2022

DIAS: A Data-Informed Active Subspace Regularization Framework for Inverse Problems

This paper presents a regularization framework that aims to improve the fidelity of Tikhonov inverse solutions. At the heart of the framework is the data-informed regularization idea that only data-uninformed parameters need to be regularized, while the data-informed parameters, on which data and forward model are integrated, should remain untouched. We propose to employ the active subspace method to determine the data-informativeness of a parameter. The resulting framework is thus called a data-informed (DI) active subspace (DIAS) regularization. Four proposed DIAS variants are rigorously analyzed, shown to be robust with the regularization parameter and capable of avoiding polluting solution features informed by the data. They are thus well suited for problems with small or reasonably small noise corruptions in the data. Furthermore, the DIAS approaches can effectively reuse any Tikhonov regularization codes/libraries. Though they are readily applicable for nonlinear inverse problems, we focus on linear problems in this paper in order to gain insights into the framework. Various numerical results for linear inverse problems are presented to verify theoretical findings and to demonstrate advantages of the DIAS framework over the Tikhonov, truncated SVD, and the TSVD-based DI approaches.

Nguyen, Hai↗
DOE OSTI2025

Multi-frequency progressive refinement for learned inverse scattering

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method — a commonly used technique for stably inverting scattered wavefield data — that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Taylor approximation variance reduction for approximation errors in PDE-constrained Bayesian inverse problems

In numerous applications, surrogate models are used as a replacement for accurate parameter-to-observable mappings when solving large-scale inverse problems governed by partial differential equations (PDEs). The surrogate model may be a computationally cheaper alternative to the accurate parameter-to-observable mappings and/or may ignore additional unknowns or sources of uncertainty. The Bayesian approximation error (BAE) approach provides a means to account for the induced uncertainties and approximation errors, i.e. the errors between the accurate parameter-to-observable mapping and the surrogate. The statistics of these errors are, however, in general unknown a priori, and are thus calculated using Monte Carlo sampling. Although the sampling is typically carried out offline, i.e. before considering the data, the process can still represent a computational bottleneck. In this work, we develop a scalable computational approach for reducing the costs associated with the sampling stage of the BAE approach. Specifically, we consider the Taylor expansion of the accurate and surrogate forward models with respect to the uncertain parameter fields either as a control variate for variance reduction or as a means to directly and efficiently approximate the mean and covariance of the approximation errors. We propose efficient methods for evaluating the expressions for the mean and covariance of the Taylor approximations based on linear(-ized) PDE solves. Furthermore, the proposed approach is independent of the dimension of the uncertain parameter, depending instead on the intrinsic dimension of the data, ensuring scalability to high-dimensional problems. The potential benefits of the proposed approach are demonstrated for two high-dimensional inverse problems governed by PDE examples, namely for the estimation of a distributed Robin boundary coefficient in a linear diffusion problem, and for a coefficient estimation problem governed by a nonlinear diffusion problem.

Bayesian approximation error↗
DOE OSTI2025

Mitigation of Boiling-Induced Thermal Degradation Using Microporous Nickel Inverse Opals Structures

Engineered microporous structures have received much attention in high-heat-flux electronics cooling due to their high thermal conductivity and permeability, and large surface area for heat transfer, but are susceptible to boiling-induced thermal degradation. Here, this study investigates the efficacy of nickel inverse opals (NiIOs) in mitigating structural degradation caused by corrosion-assisted erosion during pool boiling with water as the working fluid. First, we compared the reliability of NiIOs to copper inverse opals (CuIOs) for a 3-day pool boiling test at constant heat flux. The NiIOs demonstrated superior resistance to thermal degradation due to their inherent corrosion resistance and mechanical strength. Subsequently, we conducted a more controlled experiment to show the effect of heat flux on the degradation of the NiIOs while excluding the effect of temperature variations. Pool boiling tests of 20-μm-thickness NiIOs covering an area of ∼11 × 11 mm 2 with a 2.5 × 2.5 mm 2 heater at the center were conducted at heat flux levels of 20%, 40%, and 60% of the critical heat flux (CHF) for 3 days. The NiIOs subjected to heat flux levels of 20% and 40% CHF showed minimal degradation, while the sample subjected to 60% CHF showed erosion on the top surface due to higher bubble formation and departure rate. These results show the potential of NiIOs as a promising solution for long-term thermal management in high-power electronic devices, although design considerations for maximum allowable heat flux are necessary for reliable operation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Machine-Learning Microstructure for Inverse Material Design

Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here a successful machine-learning (ML) method is developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗