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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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DOE OSTI2024

Learnability Transitions in Monitored Quantum Dynamics via Eavesdropper’s Classical Shadows

Monitored quantum dynamics—unitary evolution interspersed with measurements—has recently emerged as a rich domain for phase structure in quantum many-body systems away from equilibrium. Here we study monitored dynamics from the point of view of an eavesdropper who has access to the classical measurement outcomes, but not to the quantum many-body system. We show that a measure of information flow from the quantum system to the classical measurement record—the —undergoes a phase transition in correspondence with the measurement-induced phase transition (MIPT). This transition determines the eavesdropper’s (in)ability to learn properties of an unknown initial quantum state of the system, given a complete classical description of the monitored dynamics and arbitrary classical computational resources. We make this learnability transition concrete by defining classical shadow protocols that the eavesdropper may apply to this problem, and show that the MIPT manifests as a transition in the sample complexity of various shadow-estimation tasks, which become harder in the low-measurement phase. We focus on three applications of interest: Pauli expectation values (where we find the MIPT appears as a point of optimal learnability for typical Pauli operators), many-body fidelity, and global charge in U ( 1 ) -symmetric dynamics. Our work unifies different manifestations of the MIPT under the umbrella of and gives this notion a general operational meaning via classical shadows. Published by the American Physical Society 2024

Physics↗
DOE OSTI2026

Quantitative measurements of dislocations in metals for advancing predictive simulations

LLNL applications require scientists to predict how materials evolve under various thermomechanical conditions. While this is achieved through physics-based simulations, uncertainty in the predictions of mechanical properties remains a serious challenge that limits the predictive capabilities of models because we lack methods to compare predictions of atomic-scale defects (dislocations) with experimental measurements. High energy X-ray diffraction (HEXRD) is the most relevant technique that can provide the necessary statistical information on dislocations. However, this technique is not yet quantitative because we lack a precise understanding of the relationship between X-ray diffraction patterns and the underlying material dislocation content and arrangements. To address this need, we used our novel computational X-ray diffraction method to simulate the effect of dislocations on the diffraction patterns. We compared virtual and experimental diffraction patterns. Results allowed us to clearly establish the relationship between X-ray diffraction patterns and the underlying dislocation structures, proving that it is feasible to quantitatively measure dislocation statistics with HEXRD. This project delivered a method that can provide the missing piece to LLNL’s mechanical property simulations in advanced metals by obtaining experimentally long-needed quantitative dislocation data, which could fully enable predictive capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics

The Simulation EnviRonment for Geomorphology, Hydrodynamics, and Ecohydrology in Integrated form (SERGHEI) is a multi-dimensional, multi-domain, and multi-physics model framework for environmental and landscape simulation, designed with an outlook towards Earth system modelling. At the core of SERGHEI's innovation is its performance-portable high-performance parallel-computing (HPC) implementation, built from scratch on the Kokkos portability layer, allowing SERGHEI to be deployed, in a performance-portable fashion, in graphics processing unit (GPU)-based heterogeneous systems. In this work, we explore combinations of MPI and Kokkos using OpenMP and CUDA backends. In this contribution, we introduce the SERGHEI model framework and present with detail its first operational module for solving shallow-water equations (SERGHEI-SWE) and its HPC implementation. This module is designed to be applicable to hydrological and environmental problems including flooding and runoff generation, with an outlook towards Earth system modelling. Its applicability is demonstrated by testing several well-known benchmarks and large-scale problems, for which SERGHEI-SWE achieves excellent results for the different types of shallow-water problems. Finally, SERGHEI-SWE scalability and performance portability is demonstrated and evaluated on several TOP500 HPC systems, with very good scaling in the range of over 20 000 CPUs and up to 256 state-of-the art GPUs.

58 GEOSCIENCES↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
DOE OSTI2020

My vehicle is a data mine

In this talk we explore how analysis of vehicle data provides information of individual vehicle behaviors, information of other vehicles in the flow of traffic, and insights into the behavior of drivers. Over the last two decades traditional passenger vehicles have been transformed from integrated two-port electrical nodes to cyber-physical systems of communicating computational nodes whose individual state and control variables are shared on a standard controller area network (CAN) bus. As driver assistance systems have crept into vehicles as safety features, driver behaviors can be observed through analysis of the data streams on the CAN bus as these new nodes communicate with one another. The properties of these data streams, as well as architectures and approaches to gather the data, are important to consider when drawing conclusions on the relevance of the data in making decisions at varying levels of the information hierarchy. We will demonstrate several technical challenges associated with these data collection processes, as well as preliminary results that demonstrate application relevance of the data to behavior, traffic, and systems domains.

42 ENGINEERING↗
DOE OSTI2024

Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

Abstract The work function is the key surface property that determines the energy required to extract an electron from the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. This work presents a high‐throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that are created from 3,716 bulk materials. The number of calculated surface properties surpasses the previously largest database by a factor of ≈27. Several surfaces with an ultra‐low (<2 eV) and ultra‐high (>7 eV) work function are identified. Specifically, the (100)‐Ba‐O surface of BaMoO 3 and the (001)‐F surface of Ag 2 F have record‐low (1.25 eV) and record‐high (9.06 eV) steady‐state work functions. Based on this database a physics‐based approach to featurize surfaces is utilized to predict the work function. The random forest model achieves a test mean absolute error (MAE) of 0.09 eV, comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function (≈ 10 5 faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic device applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology

Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol–disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol–disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. Furthermore, as an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol–disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

GADRAS-DRF for Safeguards (FY 2025 Mid-Year/Annual Report)

Task 1 – Extract GADRAS-DRF capabilities for a workflow intuitive to safeguards analysis. This task is almost complete. Significant improvements have been made to the functionality of customizing peak fits for use in the isotopics application, as well as peak-based FSA model analysis. The remaining tasks revolve around bug fixes, testing the application, and adding a density scroll bar to isotopics for real time analysis updates. The density scroll bar values will be reflected in summary tables and the self-shielding form accessed within isotopics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8,000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

An Evaluation of The Dynamic Physical Security Risk Assessment Methodology for Fleet-Wide Applications

The requirements for U.S. nuclear power plants to maintain a large onsite physical security force contribute to their high operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability (LWRS) program’s physical security pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. The conservatisms built into current security postures may be analyzed and minimized to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within LWRS program’s physical security pathway has successfully developed a dynamic force-on-force modeling framework using various computer simulation tools and integrating them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This integrated process for physical security analysis is named Modeling and Analysis for Safety Security using Dynamic EMRALD Framework (MASS-DEF). This document provides an update on the progress in applying the MASS-DEF process to an operating commercial nuclear power plant as well as additional industry feedback regarding use of the tool for other physical security risk-informed topics. This report is only a summary of the progress and does not contain specific modeling results as those contain sensitive security information. Previous reports described how a user could integrate their plant-specific force-on-force models with the dynamic simulation tool EMRALD, model operator actions, and integrate with probabilistic risk assessment tools, such as CAFTA (Computer Aided Fault Tree Analysis System) or SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations), and with thermal-hydraulic tools, such as RELAP-5 or MAAP. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report is an update the progress of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. This report also provides an update to the procedural guidance for the MASS-DEF process and an overview of the generic models available for use by utilities. This report does not contain any plant’s sensitive information and/or safeguards information. This study’s purpose was to verify that the results achieved using generic models are similar to actual plant results and refine our guidance on the use of the framework. This assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Resonance Self-Shielding: Why it is so Important

This paper is one of a series that I am writing to document my 58 years of experience with ENDF and Neutron Transport calculations, beginning when I worked at the National Nuclear Data Center (NNDC), Brookhaven National Laboratory (BNL), from 1967 to 1972. During those years I was the head of the computer unit of NNDC, assigned to develop computer codes to pre-process, view and test ENDF/B data. Since then, I have continued to support the ENDF effort without any official position or monetary compensation, because I realized how important accurate nuclear data is for use in use in our Engineering applications. It is so important to realize that regardless of how accurate or even perfect our application codes may be to transport particles, without accurate nuclear data we are in a “Garbage In = Garbage Out” situation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

An efficient reconstruction algorithm for diffusion on triangular grids using the nodal discontinuous Galerkin method

High-energy-density (HED) hydrodynamics studies such as those relevant to inertial confinement fusion and astrophysics require highly disparate densities, temperatures, viscosities, and other diffusion parameters over relatively short spatial scales. This presents a challenge for high-order accurate methods to effectively resolve the hydrodynamics at these scales, particularly in the presence of highly disparate diffusion. A significant volume of engineering and physics applications use an unstructured discontinuous Galerkin (DG) method developed based on the finite element mesh generation and algorithmic framework. This work discusses the application of an affine reconstructed nodal DG method for unstructured grids of triangles. Solving the diffusion terms in the DG method is non-trivial due to the solution representations being piecewise continuous. Hence, the diffusive flux is not defined on the interface of elements. The proposed numerical approach reconstructs a smooth solution in a parallelogram that is enclosed by the quadrilateral formed by two adjacent triangle elements. The interface between these two triangles is the diagonal of the enclosed parallelogram. Similar to triangles, the mapping of parallelograms from a physical domain to a reference domain is an affine mapping, which is necessary for an accurate and efficient implementation of the numerical algorithm. Thus, all computations can still be performed on the reference domain, which promotes efficiency in computation and storage. This reconstruction does not make assumptions on choice of polynomial basis. Reconstructed DG algorithms have previously been developed for modal implementations of the convection–diffusion equations. However, to the best of the authors’ knowledge, this is the first practical guideline that has been proposed for applying the reconstructed algorithm on a nodal discontinuous Galerkin method with a focus on accuracy and efficiency. As a result, the algorithm is demonstrated on a number of benchmark cases as well as a challenging substantive problem in HED hydrodynamics with highly disparate diffusion parameters.

Computational efficiency↗
DOE OSTI2025

Quantum computing for energy correlators

In recent years, energy correlators have emerged as powerful observables for probing the fragmentation dynamics of high-energy collisions. We introduce the first numerical strategy for calculating energy correlators using the Hamiltonian lattice approach, providing access to the intriguing nonperturbative dynamics of these observables. Furthermore, motivated by rapid advances in quantum computing hardware and algorithms, we propose a quantum algorithm for calculating energy correlators in quantum field theories. This algorithm includes ground state preparation, the application of source, sink, energy flux and real-time evolution operators, and the Hadamard test. We validate our approach by applying it to the SU(2) pure gauge theory in 2 + 1 dimensions on 3 × 3 and 5 × 5 honeycomb lattices with 𝑗 max = $\frac{1}{2}$ at various couplings, utilizing both classical methods and the quantum algorithm, the latter tested using the IBM emulator for specific configurations. The results are consistent with the expected behavior of the strong coupling regime and motivate a more comprehensive study to probe the confinement dynamics across the weak and strong coupling regimes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Frost prediction using machine learning and deep neural network models

This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantum surrogate models for uncertainty quantification

Surrogate models are a critical ingredient to computation-based design and validation of many DOE mission-relevant physical systems. When first-principles computation of properties of a physical systems becomes pro hibitive, surrogate models are the only path towards achieving tasks such as uncertainty quantification (UQ), exploration of design space, and validation of design choices. In this project we have developed and demonstrated a new surro gate modeling paradigm for complex models that is data-driven, non-intrusive, and has the potential to be versatile and equipped with performance guaran tees. This combination of features is absent in existing surrogate modeling tools. The framework we have developed in this project exploits a quantum-classical correspondence to establish a quantum system that mimics the dynamics of the classical Hamiltonian system from which data in the form of temporal snapshots is provided. Since quantum dynamics propagates distributions over observables, the framework is naturally suited to propagation of epistemic uncertainties in the form of distributions over initial state and parametric uncertainties. In this project, we take the first step in establishing this novel framework by deriving a quantization and de-quantization procedure, demonstrating the accuracy of the quantum surrogate models these define using two model systems, and defining the next steps in maturing the framework towards a tool applicable to Sandia mission-relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

$t\overline{t}b\overline{b}$ at NLO precision in a variable flavor number scheme

Top-quark pair production in association with two $b$-jets is computed at next-to-leading order QCD precision, including effects of the $b$-quark mass, and matched to a $t\overline{t}$+jets simulation in a variable flavor number scheme. The Monte Carlo realization of this method, called fusing, consistently embeds the four-flavor calculation in a particle-level event generator. As a first phenomenological application, we present observables relevant to the data-driven estimation of irreducible backgrounds to $t\overline{t}H$ -production.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Building a Long-Lived 3D Multimode Quantum Processing Unit with TESLA Cavities

Superconducting radio-frequency (SRF) cavities, such as TESLA-shaped cavities, are excellent candidates for long-lived quantum memories. Integrating these cavities with nonlinear elements, such as transmons, enables universal quantum control; however, this integration can often introduce unwanted losses. In this talk, we present the successful integration of a long-lived 2-cell TESLA cavity with an ancillary qubit, forming a quantum processing unit (QPU) that allows for robust control while maintaining coherence. We demonstrate universal control of the QPU through various schemes, including ancilla-cavity sideband and echoed conditional displacement (ECD) methods, enabling fault-tolerant high-fidelity preparation of large Fock states and fast entanglement generation. These results illustrate the versatility and efficiency of our 3D quantum processing unit, paving the way for advanced quantum computing applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗