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DOE OSTI2020

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

UV-Vis spectrophotometric determination of rare earth elements (REE) speciation at near-neutral to alkaline pH. Part I: m-cresol purple properties from 25-75 °C and Er hydrolysis

The speciation and mobility of rare earth elements (REE) strongly depends on pH which controls the formation of charged aqueous hydroxyl species. The latter potentially play an important role in controlling heavy REE adsorption on clay minerals in near-neutral to alkaline waters such as in regolith-hosted REE mineral deposits. However, accurate REE hydrolysis constants are needed for developing geochemical models that can predict the role of these charged species in natural systems. Here, we develop a robust experimental UV-Vis spectrophotometric method using m-cresol purple to determine in situ pH from 25 to 75 °C. This method is used to derive the average ligand number and hydrolysis constants of erbium (Er) at 25 °C in aqueous solutions with low ionic strength (≤ 0.001 mol/L) at pH from ~7 to 9.5 and in the presence of Er concentrations from 0 to 0.057 mM. The average ligand number ranges between 1 and 3 indicating that Er(OH) 2+ , Er(OH) 2 + and Er(OH) 3 0 control speciation in the experiments. The logarithm of the Er hydrolysis constants (log*β n ° , n= 1 to 3) derived at infinite dilution for the reaction Er 3+ + nH 2 O = Er(OH) n 3-n + nH + are: *β 1 ° = –7.22 ± 0.10, *β 2 ° = –14.52 ± 0.08, *β 3 ° = –23.24 ± 0.04. Implementation of these experimental data into a geochemical model indicates that the Er(OH) 2 + and Er(OH) 3 0 species are both stable in a much wider pH range than previously predicted. Consequently, the positively charged REE hydroxyl complexes can potentially control the fractionation of light vs. heavy REE via adsorption as observed in the formation of certain regolith-hosted REE deposits.

58 GEOSCIENCES↗

DOE OSTI2023

Experimental realization of an additively manufactured monatomic lattice for studying wave propagation

Increasing interest in wave propagation in phononic systems and metamaterials motivates the development of experimental designs, measurement techniques, and fabrication methods for use in basic research and classroom demonstrations. The simplest phononic system, the monatomic chain, exhibits rich physics such as dispersion and frequency-domain filtering. However, a limited number of experimental studies showcase monatomic chains for macroscale observation of phonons. Herein, we discuss the design, fabrication, and testing of monatomic lattices as enabled by three-dimensional (3D) printing. Using this widely available technology, we provide design guidelines for realization of a monatomic chain composed of 3D printed serpentine springs and press-fitted cylindrical masses. We also present measurement techniques that record propagating waves and algorithms for the experimental determination of dispersion behavior.

Mork, Nehemiah↗

DOE OSTI2021

Gemma V&V/UQ/Credibility Activities: FY2020 Progress

This report describes the credibility activities undertaken in support of Gemma code development in FY20, which include Verification & Validation (V&V), Uncertainty Quantification (UQ), and Credibility process application. The main goal of these activities is to establish capabilities and process frameworks that can be more broadly applied to new and more advanced problems as the Gemma code development effort matures. This will provide Gemma developers and analysts with the tools needed to generate credibility evidence in support of Gemma predictions for future use cases. The FY20 Gemma V&V/UQ/Credibility activities described in this report include experimental uncertainty analysis, the development and use of methods for optimal design of computer experiments, and the development of a framework for validation. These initial activities supported the development of broader credibility planning for Gemma that continued into FY21.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Extension of the Generic Multi-Frequency Modelling Method for Type 3 Wind Turbines

Reflecting potential non-linearities of converter-based systems, especially frequency and sequence couplings, is an ongoing challenge for linearized multi-frequency models. Besides, design details are required to develop such models, which either are the intellectual property of manufacturers or require experimental tests. The generic multi-frequency modelling method has been proposed to fill this gap; however, it is only developed for converter-connected systems, e.g., Type 4 Wind Turbines (WT). This paper proposes to extend the application of the generic multi-frequency modelling method for Type 3 WTs. In this way, a theory for patterns of the couplings in Type 3 WTs is proposed. Accordingly, a group of emissions and couplings are Rotor-Speed-Dependent (RSD). The RSD emissions and couplings are particular characteristics of Type 3 WTs, which should be addressed in the generic multi-frequency models. Additionally, the proposed theory is verified by unique-worldwide experimental perturbation tests on a 2 MVA Type 3 WT using a 7 MVA grid emulator. Accordingly, a limited number of RSD couplings and emissions are observed in the test results, mainly in low frequencies (below 1 kHz). Therefore, addressing the RSD couplings is practical and important to extend the generic multi-frequency modelling for Type 3 WTs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Rapid Construction of Validated Chemistry Models for Advanced Biofuels (Co-Optima Final Report)

Computer methods were developed and demonstrated that make it possible to predict the combustion and pyrolysis chemistry of proposed biofuels, even before the biofuels are ever manufactured or experimentally tested. The methods are based on first-principles calculations of reaction rates and equilibria. A large number of complicated calculations are required, so we developed a computer workflow that automates many of the steps. With the chemical structure of the biofuel and the system conditions (temperature, pressure, compositions, etc.) as the input, the computer methods can construct a detailed kinetic model with refined parameters without intense human intervention. The automation of model construction makes it practical for a single engineer to make the predictions in a reasonable time period. The accuracy of the computer predictions was tested by measuring the time-profiles of various reaction intermediates (carbon monoxide, carbon dioxide, water, formaldehyde, methane, and ethylene) formed as the biofuels pyrolyze or ignite using sophisticated laser shock tube methods. Several biofuels, including methyl propyl ether, cyclopentanone, butyl acetate isomers, etc., were carefully studied to demonstrate the validity of the proposed computational and experimental methodology. This research project opens up the possibility for assessing some proposed biofuels on the computer without expensive experimental campaigns. The new computer methods are open source and well-documented, allowing a knowledgeable modeler to easily access the developed methods and implement them in their work routine. Moreover, they are also convenient and powerful, promising for developing the fuel chemistry models needed for use in engine simulators, facilitating co-optimization of new engine designs with new fuels.

09 BIOMASS FUELS↗

DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Coexistent quantum channel characterization using quantum process tomography with spectrally resolved detection

The coexistence of classical and quantum signals over the same optical fiber is critical for quantum networks operating within the existing communications infrastructure. Here, we characterize the quantum channel that results from distributing approximate single-photon polarization-encoded qubits simultaneously with classical light of varying intensities through a 25 km fiber-optic channel. We use spectrally resolved quantum process tomography with a newly developed Bayesian reconstruction method to estimate the quantum channel from experimental data, both with and without classical noise. Furthermore, we show that the coexistent fiber-based quantum channel has high process fidelity with an ideal depolarizing channel if the noise is dominated by Raman scattering. These results aid future development of quantum repeater designs and quantum error-correcting codes which benefit from realistic channel error models.

Chapman, Joseph↗

DOE OSTI2023

Parameter identification for symmetrical Prandtl-Ishlinskii hysteresis model using Gauss-Newton method

The Prandtl-Ishlinskii (PI) hysteresis model has been used to describe nonlinear hysteretic behaviors. In this paper, the PI model is first simplified to accommodate the symmetrical behavior of ferromagnetic hysteresis. A parameter identification method is then developed to extract the parameters of the symmetrical PI model from experimental data. Based on the Gauss-Newton method, regularization technique, and line search method, the model parameters can be retrieved efficiently from the measured data. Numerical examples are presented to demonstrate the performance of the proposed method.

Yan, Su↗

DOE OSTI2024

Experimental methods to test photoionized plasma models in emission

The expanding foil photoionized plasma platform was developed on the Z-machine at Sandia National Laboratories to produce terrestrial photoionized plasmas at some of the same conditions as those observed in astrophysical black hole accretion disks. The specific scientific goals of the platform are motivated by long-standing astrophysical puzzles related to accreting black hole systems. Ultimately, the experimental goal is to inform the supersolar Fe abundance problem by testing photoionized plasma models in emission with at-parameter laboratory data. High quality high resolution emission data with absolute intensity calibration from a laboratory photoionized plasma have never been collected prior to the experiments described here, providing broader motivation. A robust test of the model predictions requires the successful production of a laboratory photoionized plasma along with measurements of the supporting model inputs including the radiation drive spectrum used to create the photoionized plasma, the electron density, and the temperature. The measured absolute spectral radiance in emission collected from the independently diagnosed plasma can be compared against model calculations performed using the measured inputs. In this paper, we focus on the experimental platform and the model inputs, specifically detailing the methodology used to measure the plasma conditions. The moderate electron temperature of ∼41 eV ± 15 eV and electron density of ∼1e19 ± 1.6e18 e−/cm3 and the ∼3e12 W/cm2 x-ray irradiance at the sample confirm that photoionization dominates the plasma ionization and spectra.

Cho, P. B. (ORCID:0000000291632905)↗

DOE OSTI2024

Practical and Optimal Sequential Bayesian Experimental Design for Complex Systems Incorporating Human Experimenter Preferences (Final Scientific/Technical Report)

Experiments are indispensable for developing models of complex systems. Carefully designed experiments can provide substantial savings for these expensive data-acquisition opportunities. However, designs based on heuristics are often suboptimal for systems with multiphysics, nonlinear dynamics, and uncertain and noisy environments. Optimal experimental design, while leveraging predictive models, seeks to systematically quantify and maximize the value of experiments. In this project, we focused on the design of multiple experiments, where current approaches are largely suboptimal: batch-design does not adapt to new data acquired during the experiment campaign (no feedback), and greedy/myopic design ignores future dynamics and consequences (no lookahead). We developed the mathematical framework and computational methods for sequential optimal experimental design (sOED) for complex systems. We enabled tractable model-based sOED in a rigorous manner through novel algorithms based on reinforcement learning, and investigated the effects of human experimenters on the design process. Our methods are fully Bayesian, able to quantify and update uncertainty in a principled manner. The traits aimed by our approach—mathematical rigor and optimality, human effects and uncertainty quantification, computational practicality—are crucial for elevating the standards of artificial intelligence (AI) to support decision-making in scientific domains, and contribute toward trust and realistic adoption of AI in experimental design practice.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Viscous Behavior of Clay‐Rich Rocks and Its Role in Focused Fluid Flow

Abstract Focused fluid flow is common in sedimentary basins worldwide, where flow structures often penetrate through sandy reservoir rocks, and clay‐rich caprocks. To better understand the mechanisms forming such structures, the impacts of the viscoelastic deformation and strongly nonlinear porosity‐dependent permeability of clay‐rich materials are assessed from an experimental and numerical modeling perspective. The experimental methods to measure the poroviscoelastic and transport properties of intact and remolded shale have been developed, and the experimental data is used to constrain the numerical simulations. It is demonstrated that viscoelastic deformation combined with nonlinear porosity‐dependent permeability triggers the development of localized flow channels, often imaged as seismic chimneys. The permeability inside a channel increases by several orders of magnitude compared to the background values. In addition, the propagation time scale and the channel size strongly depend on the material properties of the fluid and the rock. The time‐dependent behavior of the clay‐rich rock may play a key role in the long‐term integrity of the subsurface formations.

58 GEOSCIENCES↗

DOE OSTI2023

Experimental Simulations of Field-Induced Mechanical Abuse Conditions

Mechanical failure of cell components is an important component to understanding safety outcomes for batteries subjected to external loading. Several key parameters governing the mechanical response at the component to the module level are discussed in this chapter. Specifically, nuances associated with experimental measurements of these properties – including development of test methods, fixturing the test articles, ways to minimize errors in measurements, and improving the repeatability of some of these experiments – are examined closely.

Torres-castro, Loraine↗

DOE OSTI2026

Roadmap for warm dense matter physics

This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter (WDM) physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the WDM physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations & experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments of and contemporary challenges for theoretical methods and experimental techniques needed to describe, create and diagnose WDM. A large part of this roadmap is dedicated to specific WDM systems and applications in astrophysics, inertial confinement fusion and novel material synthesis.

dense astrophysical objects↗

DOE OSTI2025

Signatures of enhanced superconducting properties in niobium cavities

Superconducting radio-frequency (SRF) niobium cavities are critical for modern particle accelerators, as well as for advancing superconducting quantum systems and enabling ultrasensitive searches for new physics. In this work, we report a systematic observation of an anomalous frequency dip in Nb cavities, which occurs at temperatures just below the critical temperature (𝑇𝑐 ), indicative of enhanced superconducting properties at 𝑇 ≪ 𝑇 𝑐 . The magnitude of this dip is strongly correlated with the rf surface resistance, impurity distribution near the surface, and 𝑇𝑐 . Additionally, we report measurements of the coherence peak in the ac conductivity of two Nb SRF cavities processed using distinct methods. By comparing recent theories developed to model this experimental data, we show that the frequency-dip feature, larger coherence peak height, and reduction in the temperature-dependent surface resistance with rf current occur at minimal but finite levels of disorder.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Bunch Shaping in Electron Linear Accelerators

Modern electron linear accelerators are often designed to produce smooth bunch distributions characterized by their macroscopic ensemble-average moments. However, an increasing number of accelerator applications call for finer control over the beam distribution, such as by requiring specific shapes for its projection along one coordinate. Ultimately, the control of the beam distribution at the single-particle level could enable new opportunities in accelerator science. This review discusses the recent progress toward controlling electron beam distributions on the “mesoscopic” scale with an emphasis on shaping the beam or introducing complex correlations required for some applications. This review emphasizes experimental and theoretical developments of electron-bunch shaping methods based on bounded external electromagnetic fields or via interactions with the self-generated velocity and radiation fields.

Ha, G.↗

DOE OSTI2022

Computational Fluid Dynamics Simulations to Predict Oxidation in Heat Recovery Steam Generator Tubes

Heat Recovery Steam Generators (HRSGs) are widely used across United States in combined cycle power plants to recover waste heat from the gas turbine (GT). HRSGs are used either to generate electricity or to produce process steam for industrial applications. The primary components of HRSG consists of a duct and a heat exchanger (HX). It is known across the power industry that the high temperature oxidation and ensuing exfoliation problem is a major cause for the damage of HX materials of HRSG. Alloys and/or coatings that can prevent or mitigate oxidation are very expensive, therefore they must be used or applied on the select regions of the HX tubes where the tendency of oxide formation is the highest. The main goal of this project is to identify such regions through Computational Fluid Dynamics (CFD) simulations. Therefore, in this work, we developed a CFD framework using commercial code StarCCM+ for the prediction of the fluid flow and heat transfer in a HRSG and associated oxidation inside the tubes of the HX. The developed CFD framework was verified and validated with experimental data before deployment. We also developed an innovative method to model the effect of the fins on the heat transfer and the pressure drop using a porous media model (PMM) approach to keep the mesh size within reasonable limits. After validation, we performed high-fidelity CFD simulations of a real-scale HRSG using the PMM, with High Performance Computing (HPC) resources of ORNL. From the simulation results, we acquired oxide thickness maps for all the tubes of the select HX sections of HRSG prone to oxidation. These oxide maps can inform regions of the HX tubes that requires oxide-resistant coatings, thereby guiding engineers for cost-efficient manufacturing of the HX that can combat oxidation in HRSGs.

36 MATERIALS SCIENCE↗