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DOE OSTI2021

Recent Advances for Improving the Accuracy, Transferability, and Efficiency of Reactive Force Fields

Reactive force fields provide an affordable model for simulating chemical reactions at a fraction of the cost of quantum mechanical approaches. However, classically accounting for chemical reactivity often comes at the expense of accuracy and transferability, while computational cost is still large relative to nonreactive force fields. Here, we summarize recent efforts for improving the performance of reactive force fields in these three areas with a focus on the ReaxFF theoretical model. To improve accuracy, we describe recent reformulations of charge equilibration schemes to overcome unphysical long-range charge transfer, new ReaxFF models that account for explicit electrons, and corrections for energy conservation issues of the ReaxFF model. To enhance transferability we also highlight new advances to include explicit treatment of electrons in the ReaxFF and hybrid nonreactive/reactive simulations that make it possible to model charge transfer, redox chemistry, and large systems such as reverse micelles within the framework of a reactive force field. To address the computational cost, we review recent work in extended Lagrangian schemes and matrix preconditioners for accelerating the charge equilibration method component of ReaxFF and improvements in its software performance in LAMMPS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Prototype-Wise Sensitivity Analysis of Urban Building Energy Simulation Surrogate Modeling Accuracy

Urban Building Energy Modeling (UBEM) is an important reference for urban energy-related policymaking. Because of the significant impact of urban microclimates on the energy simulation, UBEM requires simulations of many microclimate-prototype pairs. Surrogate modeling is commonly used to reduce the cost of simulation computations. In UBEM surrogate modeling, it is important to determine the percentage of microclimates related to a prototype used for generating surrogate model training data. This study analyzes the prototype-wise variations and sensitivities of surrogate model estimation accuracy to the microclimate sampling ratios. The results of the study can help determine the number of simulations used for generating surrogate modeling data, avoid redundant simulations, and reduce the computational cost for UBEM surrogate modeling and its time.

Pan, Xiyu↗

DOE OSTI2024

IBR Bulk System Protection Issues and Modeling Challenges

Issue to be Address: Difficulty in predicting Inverter (IBR) generation dynamic response characteristics. Main challenges are unavailability of proprietary control details for modeling and the aggregate mixed IBRs with different dynamic response characteristics all contributing during system faults. Framework for Discussion: Identifying issues by sharing experience Reviewing options to improve IBR modeling accuracy, computation speed, use of test results. Presenting related research by EPRI and PG&E (DOE project) Desired Outcomes: Provide technical update for participants Get feedback and suggestions for ongoing research Improve research collaboration

IBR, Protection Challenges, dynamic response, IBR ↗

DOE OSTI2021

A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA

The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Measurement accuracy and spatial resolution of a distributed temperature sensor based on a two-pulse differential coherent reflectometer

We present a model and numerical simulation of a distributed temperature sensor based on a two-pulse differential coherent optical time-domain reflectometer (COTDR). The differential phase measured using a phase-sensitive Rayleigh reflectometer is shown to have a regular component, which is a linear function of temperature, and a random component, which is related to a random distribution of scattering centres in the fibre and restricts the accuracy of measurements of variations in temperature. Measurement accuracy can be improved by reducing the relative contribution of the random component via a decrease in pulse duration and/or an increase in the time delay between pulses. The spatial resolution of a differential two-pulse phase-sensitive reflectometer is shown to be determined by the time delay between pulses and to vary little with pulse duration. At a typical pulse duration (200 ns) and delay time (300 ns), the accuracy in measurements of variations in temperature in the 0.1-K range is 2 % and the spatial resolution is about 30 m. (fibre-optic sensors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Deep energy-pressure regression for a thermodynamically consistent EOS model

Abstract In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

NLML: A Deep Neural Network Emulator for the Exact Nonlinear Interactions in a Wind Wave Model

Nonlinear wave interactions describe the resonant energy transfer between wave components, playing a fundamental role in the evolution of ocean wave spectra. Nonlinear wave interactions significantly influence wave growth and development, making them essential for accurate wave modeling. However, resolving the full six-dimensional Boltzmann integral of the exact nonlinear wave interactions (Webb-Resio-Tracy method, WRT) is computationally expensive, limiting its application in real-time operational wave forecasting and for research purposes. Current approximations, such as the Discrete Interaction Approximation (DIA), prioritize computational speed over accuracy, resulting in significant errors in wave mean parameters. Here, we introduce NLML, a machine learning (ML) emulator designed to approximate the exact nonlinear wave interactions within WAVEWATCH III (WW3), with the goal of achieving the accuracy of WRT while maintaining the stability and computational speed of DIA. By leveraging GPU capabilities such as half precision inference, we achieved substantial speedups, up to 136x mathematical equation faster than the WRT and only a modest 1.04x mathematical equation slowdown relative to DIA, while achieving 2x mathematical equation the accuracy of DIA in global wave spectral energy and mean wave parameters, with up to 7x mathematical equation higher accuracy in some regions. Unlike previous ML approaches, NLML maintained inherent stability throughout model integration in a standalone, year-long WW3 simulation, without requiring additional constraints. Our new ML parameterization bridges the gap between accuracy and efficiency, offering a promising alternative for improving wave modeling in operational settings and research purposes.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

Abstract Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Energy & Fuels↗

DOE OSTI2022

Experimental validation of a high fidelity Monte Carlo neutron transport model of the MIT graphite exponential pile

High-fidelity modeling and simulation were performed for the MIT graphite exponential pile (MGEP) using Monte Carlo neutron transport codes OpenMC and MCNP, and the results were validated by experimental data. The MGEP is being used as the test bed for the design of an autonomous control system for the pile's neutron flux distribution. The main contribution of this work is to generate the training data sets of neutron flux distributions with different locations of control rods that perturb the neutron flux profiles. First, code -to-code cross verification between OpenMC and MCNP was performed to ensure consistency of the numerical modeling within statistical uncertainties. To validate the accuracy of this high-fidelity model, a series of neutron flux measurements were conducted using a Helium-3 (He-3) neutron detector on a mobile platform that is placed inside the pile. Second, the neutron flux profiles were measured in four vertical layers of interest, and compared to the corresponding simulation results. The comparison results shows that the root mean square error is less than 2.5% in the two upper layers, and less than 4.5% in all four measured layers. Here the results validated the accuracy of the modeling and simulation. Finally, the relative change of the neutron flux profiles from moving control rods was analyzed, which identified the layer that has the best sensitivity regarding the control rods movements. Thus, this work identified and provided training data sets of both simulated and experimental neutron flux profiles in the most sensitive layer, paving the path forward to the real-time experimental demonstration of the autonomous control system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Valuable knowledge of catalysis is often hidden in a large amount of scientific literature. There is an urgent need to extract useful knowledge to facilitate scientific discovery. Here this work takes the first step toward the goal in the field of catalysis. Specifically, we construct the first information extraction benchmark data set that covers the field of catalysis and also develop a general extraction framework that can accurately extract catalysis-related entities from scientific literature with 90% extraction accuracy. We further demonstrate the feasibility of leveraging the extracted knowledge to help users better access relevant information in catalysis through an entity-aware search engine and a correlation analysis system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Analytic model of electron transport through and over non-linear barriers

Tunneling barriers are an essential component of electron sources, sensors, detectors, and vacuum nanoelectronics, and a pivotal factor in their performance, but the barriers themselves routinely depart from the analytic models used to model their behavior. Here, a new formalism is developed to analytically and accurately model emission through and over barriers associated with depletion layers, nanotip barriers, and MIM structures. The transmission probability for depletion layer and metal-insulator-metal (MIM) and metaloxide-semiconductor (MOS) barriers is accurately modeled as the electron energy exceeds the barrier height using approaches designed for rapid implementation demanded by simulation codes, and extensible to general barriers. The models supersede conventional thermal and field models in depletion and MIM/MOS barrier studies. Thermal-field methods are used to treat the transmission probability, and shape factor methods to treat the tunneling factor. Analytic formulae for current density are obtained. The methods ease device simulation and characterization of current-voltage relations for emerging technologically interesting barriers with better accuracy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Neural network surrogate models for equations of state

Equation of state (EOS) data provide necessary information for accurate multiphysics modeling, which is necessary for fields such as inertial confinement fusion. Here, we suggest a neural network surrogate model of energy and entropy and use thermodynamic relationships to derive other necessary thermodynamic EOS quantities. We incorporate phase information into the model by training a phase classifier and using phase-specific regression models, which improves the modal prediction accuracy. Our model predicts energy values to 1% relative error and entropy to 3.5% relative error in a log-transformed space. Although sound speed predictions require further improvement, the derived pressure values are accurate within 10% relative error. Our results suggest that neural network models can effectively model EOS for inertial confinement fusion simulation applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Machine learning of consistent thermodynamic models using automatic differentiation

In this study, we propose a data-driven method to describe consistent equations of state (EOS) for arbitrary systems. Complex EOS are traditionally obtained by fitting suitable analytical expressions to thermophysical data. A key aspect of EOS is that the relationships between state variables are given by derivatives of the system free energy. In this work, we model the free energy with an artificial neural network and utilize automatic differentiation to directly learn the derivatives of the free energy. We demonstrate this approach on two different systems, the analytic van der Waals EOS and published data for the Lennard-Jones fluid, and we show that it is advantageous over direct learning of thermodynamic properties (i.e., not as derivatives of the free energy but as independent properties), in terms of both accuracy and the exact preservation of the Maxwell relations. Furthermore, the method implicitly provides the free energy of a system without explicit integration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Numerical simulations of noisy quantum circuits for computational chemistry

The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the behavior of these noisy quantum circuits using numerical simulations to estimate the accuracy and fidelity of the prepared quantum states relative to the ground truth obtained by conventional means. We implement several different types of ansatz circuits derived from unitary coupled cluster theory for the purposes of estimating the ground-state energy of sodium hydride using the variational quantum eigensolver algorithm. We show how relative error in the energy and the fidelity scale with the levels of gate-based noise, the internuclear configuration, the ansatz circuit depth, and the parameter optimization methods.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Predicting partner fitness based on spatial structuring in a light-driven microbial community

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology ( e . g ., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Structure-preserving numerical discretizations for domains with boundaries

This SAND report documents Exploratory Express LDRD Project 223790, "Structure-preserving numerical discretizations for domains with boundaries", which developed a method to incorporate consistent treatment of domain boundaries and arbitrary boundary conditions in discrete exterior calculus (DEC) for arbitrary polygonal (2D) and tensor-product structure prism (3D) grids. The new DEC required the development of novel discrete exterior derivatives, boundary operators, wedge products and Hodge stars. This was accomplished through the use of boundary extension and the blending of known 2D operators on the interior with 1D operators on the boundary. The Hodge star was based on the Voronoi Hodge star, and retained the limitation of a triangular circumcentric primal or dual grid along with low-order accuracy. In addition to the new DEC, two related software packages were written: one for the study of DEC operators on arbitrary polygonal and polyhedral grids using both symbolic and numerical approaches and one for a (thermal) shallow water testbed using TRiSK-type numerics. Immediately relevant (already funded, through CANGA) followup work is the development of a high-order, geometrically flexible Hodge star and structure-preserving, high-order, oscillation-limiting transport operators (using WENO) for n-forms on arbitrary 2D and 3D grids. This will provide all of the machinery required for a high-order version of TRiSK with boundaries on arbitrary 2D and tensor-product 3D grids, which is applicable to both the atmospheric (CRM in E3SM-MMF) and oceanic (MPAS-O) components of E3SM.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Synthetic Aperture Radar Image Geolocation Using Fiducial Images

Synthetic Aperture Radar (SAR) creates imagery of the earth’s surface from airborne or spaceborne radar platforms. However, the nature of any radar is to geolocate its echo data, i.e., SAR images, relative to its own measured radar location. Acceptable accuracy and precision of such geolocation can be quite difficult to achieve, and is limited by any number of parameters. However, databases of geolocated earth imagery do exist, often using other imaging modalities, with Google Earth being one such example. These can often be much more accurate than what might be achievable by the radar itself. Consequently, SAR images may be aligned to some higher accuracy database, thereby improving the geolocation of features in the SAR image. Examples offer anecdotal evidence of the viability of such an approach.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

HLPW-4/GMGW-3: High Order Discretization Technology Focus Group Workshop Summary

This paper summarizes the High-Order Technical Focus Group (HO-TFG) submissions for the joint 4th AIAA High Lift Prediction and 3rd Geometry and Mesh Generation Workshop. The goal of the workshop was to assess the state-of-the-art in mesh generation and computational fluid dynamics software. The Common Research Model High-Lift (CRM-HL) variant served as the primary focus of the workshop, and a 2D airfoil section from the CRM-HL was used as a verification test case. The HO-TFG was tasked with generating high-order curved meshes for both geometries, and computing high-order solutions using both Reynolds Averaged Navier-Stokes and scale resolving formulations. While limited by computational resources, the final results demonstrate the potential of higher-order methods to increase solution accuracy with a lower degree of freedom count relative to second-order discretizations.

Galbraith, Marshall C.↗