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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials

Optical metamaterial device design has enjoyed a long track of success over the past 50 years leading to the manipulation of light over a wide range of wavelengths spanning the ultraviolet to the far infrared. The manipulation of light over such wavelengths has already led to many technological advancements such as the design of selective radiative absorbers for solar energy, daytime passive cooling using deep space, and optical invisibility cloaks for defense applications. Further disruptive advancements in energy, defense, computing, and biomedical fields could be enabled or enhanced by future optical metamaterial devices. These technologies could lead to increased energy efficiency and hence reduced national primary energy consumption, cheap long duration energy storage, and next generation solid-state heat engines. But historically the methods to invent and develop all of these devices have been time- consuming and based mostly on intuition and iteration. Finding an optimal design can take years.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials

36 MATERIALS SCIENCE↗

DOE OSTI2023

MrHyDE v.1.0

SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2023

Interpretable inverse-designed cavity for on-chip nonlinear photon pair generation

Inverse design is a powerful tool in wave physics for compact, high-performance devices. To date, applications in photonics have mostly been limited to linear systems and it has rarely been investigated or demonstrated in the nonlinear regime. In addition, the “black box” nature of inverse design techniques has hindered the understanding of optimized inverse-designed structures. We propose an inverse design method with interpretable results to enhance the efficiency of on-chip photon generation rate through nonlinear processes by controlling the effective phase-matching conditions. We fabricate and characterize a compact, inverse-designed device using a silicon-on-insulator platform that allows a spontaneous four-wave mixing process to generate photon pairs at a rate of 1.1 MHz with a coincidence to accidental ratio of 162. Our design method accounts for fabrication constraints and can be used for scalable quantum light sources in large-scale communication and computing applications.

42 ENGINEERING↗

DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗

DOE OSTI2023

Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

In this article, full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasingly studied with various neural network architectures to improve accuracy and computational efficiency. Nevertheless, the applicability of pre-trained neural networks is severely restricted by potential discrepancies between the source function used in the field survey and the one utilized during training. Here, we develop a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI with the generalization of seismic sources, including the frequencies and locations of sources. Specifically, we employ the Fourier neural operator as the decoder of DeepONet, and we utilize source parameters as one input of Fourier-DeepONet, facilitating the resolution of FWI with variable sources. To test Fourier-DeepONet, we develop three new and realistic FWI benchmark datasets (FWI-F, FWI-L, and FWI-FL) with varying source frequencies, locations, or both. Our experiments demonstrate that compared with existing data-driven FWI methods, Fourier-DeepONet obtains more accurate predictions of subsurface structures in a wide range of source parameters. Moreover, the proposed Fourier-DeepONet exhibits superior robustness when handling data with Gaussian noise or missing traces and sources with Gaussian noise, paving the way for more reliable and accurate subsurface imaging across diverse real conditions.

42 ENGINEERING↗

DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗

DOE OSTI2023

Laser ultrasonic imaging of subsurface defects with the linear sampling method

Laser ultrasonics is a remote nondestructive evaluation technique suitable for real-time monitoring of fabrication processes in semiconductor metrology, advanced manufacturing, and other applications where non-contact, high fidelity measurements are required. Here we investigate laser ultrasonic data processing approaches to reconstruct images of subsurface side drilled holes in aluminum alloy specimens. We demonstrate through simulation that the model-based linear sampling method (LSM) can perform accurate shape reconstruction of single and multiple holes and produce images with well-defined boundaries. We experimentally confirm that LSM produces images that represent the internal geometric features of an object, some of which may be missed by conventional imaging.

42 ENGINEERING↗

DOE OSTI2022

SREMI: Super-resolution electromagnetic imaging with single-channel ground-penetrating radar

High-resolution near-surface imaging has important applications in civil engineering, infrastructure inspection, military threat detection, geological characterization, and lunar and planetary exploration. Zero-offset, singlechannel ground penetrating radar (GPR) imaging is an established technique for near-surface target imaging and sensing but often suffers from low spatial resolution and imaging artifacts, especially of deep structures. In response, we formulate the GPR imaging as a dual-sparsity optimization problem, and develop a super-resolution electromagnetic imaging method based on a fast iterative shrinkage-thresholding algorithm. We develop our GPR imaging method in the framework of electromagnetic exploding-reflectors simulation theory, therefore the imaging method is computationally efficient. In this work, we demonstrate through synthetic and field data examples that our method can produce sharper, more reliable images with fewer artifacts compared with single-pass reverse-time migration GPR method, thus leading to improved near-surface interpretation and object identification.

58 GEOSCIENCES↗

DOE OSTI2023

Giant room-temperature nonlinearities in a monolayer Janus topological semiconductor

Abstract Nonlinear optical materials possess wide applications, ranging from terahertz and mid-infrared detection to energy harvesting. Recently, the correlations between nonlinear optical responses and certain topological properties, such as the Berry curvature and the quantum metric tensor, have attracted considerable interest. Here, we report giant room-temperature nonlinearities in non-centrosymmetric two-dimensional topological materials—the Janus transition metal dichalcogenides in the 1 T’ phase, synthesized by an advanced atomic-layer substitution method. High harmonic generation, terahertz emission spectroscopy, and second harmonic generation measurements consistently show orders-of-the-magnitude enhancement in terahertz-frequency nonlinearities in 1 T’ MoSSe (e.g., > 50 times higher than 2 H MoS 2 for 18 th order harmonic generation; > 20 times higher than 2 H MoS 2 for terahertz emission). We link this giant nonlinear optical response to topological band mixing and strong inversion symmetry breaking due to the Janus structure. Our work defines general protocols for designing materials with large nonlinearities and heralds the applications of topological materials in optoelectronics down to the monolayer limit.

42 ENGINEERING↗

DOE OSTI2023

MOOSE Optimization Module Overview: Application to Residual Stress Inversion in Nuclear Fuel Plates

Manufacturing methods, material selections and unique plate geometries used in low enriched uranium plate fuels for the High Performance Research Reactor (HPRR) have led to concerns about the residual stresses effect on bond strength of the fuel/cladding interfaces and overall fuel performance under irradiation. The current HPRR plate fuel test program performed residual stress calculations using the contour method with experimental displacement data obtained by incrementally cutting the plate fuel. In this work, we use a gradient based inverse optimization module developed within the MOOSE framework to estimate normal and shear residual stresses within the plate fuels using displacement data taken from a cut placed in the HPRR fuel plate. These results are verified against residual stress calculations obtained from the contour method. The inversion protocol used in this work utilizes two meshes to separately discretize the physics and parameter space. This allows a fine mesh to be used to fully resolve the physics and geometry of the forward problem. A coarser mesh is used to resolve the parameter space with spatially varying mesh density determined from a sensitivity analysis. The adaptive resolution of the parameter space essentially has the effect of regularization as it is designed to achieve the bias-variance tradeoff.

42 ENGINEERING↗

DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A Novel Approach for Computing Rigid Body Motion Using Linear Accelerations

Here, a novel approach is presented for computing general rigid body motion based on a few known linear accelerations. This method utilizes linear acceleration data obtained from three distinct points on the body, all within a body-fixed reference frame. The only requirement is that the three chosen points must not be collinear. A system of differential-algebraic equations is derived, combining principles of rigid body kinematics with theory of the rotation group SO(3). These equations provide a framework for numerically computing various motion parameters, including angular velocity, angular acceleration, body orientation, velocity field, acceleration field, and displacement field. By numerically solving this system of equations, we can fully characterize rigid body motion in three-dimensional space. A numerical example is provided to demonstrate the practical implementation and efficacy of the proposed technique, illustrating its potential for accurate motion computation in various applications.

42 ENGINEERING↗

DOE OSTI2024

Fiber Sorbents – A Versatile Platform for Sorption-Based Gas Separations

Increasing demand for high-purity fine chemicals and a drive for process intensification of large-scale separations have driven significant work on the development of highly engineered porous materials with promise for sorption-based separations. While sorptive separations in porous materials offer energy-efficient alternatives to longstanding thermal-based methods, the particulate nature of many of these sorbents has sometimes limited their large-scale deployment in high-throughput applications such as gas separations, for which the necessary high feed flow rates and gas velocities accrue prohibitive operational costs. These processability limitations have been historically addressed through powder shaping methods aimed at the fabrication of structured sorbent contactors based on pellets, beads or monoliths, commonly obtained as extrudates. These structures overcome limitations such as elevated pressure drops commonly recorded across powder adsorption beds but often accrue thermal limitations arising from elevated particle density and aggregation, which ultimately cap their maximum separation performance. Furthermore, the harsh mechanical strain to which powder particles are subjected during contactor fabrication, in the form of extrusion/compression forces, can result in partial pore occlusion and framework degradation, further limiting their performance. Here, we present the development of porous fiber sorbents as an alternative sorbent contactor design capable of addressing sorbent processability limitations while enabling an array of performance-maximizing heat integration capabilities. This new sorbent form factor leverages pre-existing know-how from hollow fiber spinning to produce fiber-shaped sorbent contactors through the phase inversion of known polymers in a process known as dry-jet/wet quenching. The process of phase inversion allows microporous sorbent particles to be latched onto a macroporous polymer matrix under mild processing conditions, thus making it compatible with soft porous materials prone to amorphization under traditional pelletization conditions. Sorbent fibers can be created with different geometries through control of the spinning apparatus and process, offering the possibility to produce monolithic and hollow fibers alike, the latter of which can be integrated with thermalization fluid flows. In this Account, we summarize our progress in the field of fiber sorbents from both design and application standpoints. We further guide the reader through the evolution of this field from the early inceptive work on zeolite hollow fibers to recent developments on MOF fibers. We highlight the versatile nature of fiber sorbents, both from the composition, fabrication and structure points of view, and further demonstrate how fiber sorbents offer alternative paths in tackling new and challenging chemical separation challenges like direct air capture (DAC), with a final perspective on the future of the field.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Dual Representations and H ∞ -Optimal Control of Partial Differential Equations

We consider H ∞ -optimal state-feedback control of the class of linear Partial Differential Equations (PDEs) which admit a Partial Integral Equation (PIE) representation. While linear matrix inequalities are commonly used for optimal control of Ordinary Differential Equations (ODEs), the absence of a universal state-space representation and suitable dual form prevents such methods from being applied to optimal control of PDEs. Specifically, for ODEs, the controller synthesis problem is defined in state-space, and duality is used to resolve the bilinearity of that synthesis problem. Recently, the PIE representation was proposed as a universal state-space representation for linear PDE systems. In this paper, we show that any PDE system represented by a PIE admits a dual PIE with identical stability and I/O properties. This result allows us to reformulate the stabilizing and optimal state-feedback control problems as convex optimization over the cone of positive Partial Integral (PI) operators. Operator inversion formulae then allow us to construct feedback gains for the original PDE system. The results are verified through application to several canonical problems in optimal control of PDEs and indicate the resulting bounds on H ∞ norm are not conservative.

42 ENGINEERING↗

DOE OSTI2024

Band Heater Heat Flux Characterization Using Inverse Heat Conduction Problem Models

Abstract An experimental apparatus was constructed to correlate water flowrate and temperature rise under an external band heater. Due to the physical characteristics of the band heater, its transient heating behavior is unknown. This paper investigates the application of inverse heat conduction problem (IHCP) methods to characterize the heat flux from the band heater. Three experiments with different heating times (5, 10, and 20 s) and no flowrate were conducted to measure the transient temperature under the 400 W band heater. Type-T thermocouples measure surface temperature at the centerline of the band heater. The experimental results are computed with five different heat conduction models. The models are chosen to identify how the heat flux response varies from a simplified to a realistic model. Additionally, the results of the experimental heat flux are compared to the manufacturer band heater data (58.9 kW/m2) for each model. The minimum time needed for the heater to fully energize the system is from 10 to 12 s. The residuals for each model are analyzed and used to evaluate the appropriateness of the five different models. The results show that the use of simpler models can achieve results similar to those of complex models, with less time and computational cost.

Engineering↗

DOE OSTI2023

Effective permeability of an immiscible fluid in porous media determined from its geometric state

Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process or path dependent with an underlying dependency on pore structure and wettability. For applications (fuel cells to underground CO 2 storage), it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modeling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an artificial neural network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. Finally, the developed ANN is based on a prescribed set of state variables based on physical insights that predict the effective permeability of 4500 unseen pore-scale geometrical states with R 2 = 0.98.

42 ENGINEERING↗

DOE OSTI2022

A Bayesian Approach for In-Situ Stress Prediction and Uncertainty Quantification for Subsurface Engineering

Many subsurface engineering applications require accurate knowledge of the in-situ state of stress for their safe design and operation. Existing methods to meet this need primarily include field measurements for estimating one or more of the principal stresses from a borehole, or optimization methods for constructing a 3D geomechanical model in terms of geophysical measurements. These methods, however, often contain considerable uncertainty in estimating the state of stress. Here, in this paper, we build on a Bayesian approach to quantify uncertainty in stress estimations for subsurface engineering applications. This approach can provide an estimate of the 3D distribution of stress throughout the volume of interest and provide an estimate of the uncertainty arising from the stress measurement, the rheology parameters, and a paucity of measurements. The value of this approach is demonstrated using stress measurements from the In Salah carbon storage site, which was one of the world’s first industrial carbon capture and storage projects. This demonstration shows the application of this Bayesian approach for estimating the initial state of stress for In Salah and quantifying the uncertainty in the estimated stress. Also, an assessment of a maximum injection pressure to prevent geomechanical risks from CO 2 injection pressures is provided in terms of the probability distribution of the minimum principal stress quantified by the approach. With the In Salah case study, this paper demonstrates that using the Bayesian approach can provide additional insights for site explorations and/or project operations to make informed-site decisions for subsurface engineering applications.

58 GEOSCIENCES↗