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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Energy Resilience for Mission Assurance: Agile Co-simulation for Cyber Energy System Security (ACCESS), Model Advancements for Resilience Analysis

Agile Co-simulation for Cyber Energy System Security (ACCESS) is a co-simulation platform developed by Lawrence Livermore National Laboratory (LLNL). The primary high-level use-case for ACCESS is to study existing or new cyber-physical critical infrastructure systems, with a heavy emphasis on 1) systems that utilize communication networks, and 2) studies that seek to understand cyber-related system impacts. ACCESS is currently used for several energy system resilience projects at LLNL. In the Energy Resilience for Mission Assurance (ERMA) project, ACCESS is used in the Modeling for Metric Calculation task (specifically, subtask 4.3, Communications and Cyber Modeling) to model and simulate the cyber and communication system aspects of Defense Critical Electric Infrastructure (DCEI) systems, with a focus on computing specific communication system metrics that can impact system resilience and mission performance. Simulated communication system performance will be fed back to other ERMA system components so that mission performance can be evaluated holistically. This report describes several enhancements to the ACCESS platform that were implemented during the execution of the ERMA project in support of reslience analysis. This includes the addition of new models and subsystems, enhancements to existing models, and integration with external systems. The remainder of this report is structured as follows. In Section 2, a brief background description of the ACCESS platform is provided, including an outline of ACCESS components, example usecases, and a set of communication network resilience metrics that can be computed with ACCESS. Section 3 describes the ACCESS model enhancements for ERMA in detail. Finally, Section 4 briefly outlines future integration opportunities between ACCESS and project participant capabilities identified during the progression of the project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Energy Resilience for Mission Assurance, Agile Co-simulation for Cyber Energy System Security (ACCESS) Model Advancements for Resilience Analysis (Version 3, July 2023)

Agile Co-simulation for Cyber Energy System Security (ACCESS) is a co-simulation platform developed by Lawrence Livermore National Laboratory (LLNL). The primary high-level use-case for ACCESS is to study existing or new cyber-physical critical infrastructure systems, with a heavy emphasis on 1) systems that utilize communication networks, and 2) studies that seek to understand cyber-related system impacts. ACCESS is currently used for several energy system resilience projects at LLNL. In the Energy Resilience for Mission Assurance (ERMA) project, ACCESS is used in the Mod eling for Metric Calculation task (specifically, subtask 4.3, Communications and Cyber Modeling) to model and simulate the cyber and communication system aspects of Defense Critical Electric Infrastructure (DCEI) systems, with a focus on computing specific communication system metrics that can impact system resilience and mission performance. Simulated communication system per formance will be fed back to other ERMA system components so that mission performance can be evaluated holistically. This report describes several enhancements to the ACCESS platform that were implemented during the execution of the ERMA project in support of reslience analysis. This includes the addition of new models and subsystems, enhancements to existing models, and integration with external systems. The remainder of this report is structured as follows. In Section 2, a brief background description of the ACCESS platform is provided, including an outline of ACCESS components, example use cases, and a set of communication network resilience metrics that can be computed with ACCESS. Section 3 describes the ACCESS model enhancements for ERMA in detail. Finally, Section 4 briefly outlines future integration opportunities between ACCESS and project participant capabilities identified during the progression of the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Real-Time Optimization Workflow Status Update

Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2025

Model Scripts for "Old-Aged Groundwater Contributes to Mountain Hillslope Hydrologic Dynamics"

The partitioning of water inputs between deep and shallow groundwater flow paths is a fundamental processes, yet is challenging to observe. Numerical models provide a valuable tool to further develop insights on these groundwater mixing processes. This repository contains scripts to run the ParFlow-CLM and EcoSLIM integrated hydrologic models along the Pumphouse Hillslope in the East River Watershed and associated python scripts to process model outputs. Files includes input decks for the associated models, python scripts, and text and css files to support the model runs and processing. The 2-D hillslope model simulates from 2000 to 2021 using transient forcing conditions from the publicly available NLDAS-2 dataset. We develop an ensemble of models with variable hydrogeologic and soil parameters. Model outputs from the ensemble of runs are compared to to water level observations at the PLM1 and PLM6 wells published at (https://data.ess-dive.lbl.gov/view/doi:10.15485/1866836) and groundwater mean ages published at (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1960042. The model results are used to evaluate hillslope water mass-balance transience and mixing dynamics between groundwater flow paths with young (<10 year) and old (>10 year) ages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

U.S. Offshore Pipeline and Reported Incident Datasets

The U.S. Offshore Pipeline and Reported Incident Datasets provide a compilation of data from a variety of credible resources, spatially-temporally integrated into multivariate resources. This spatial resource includes more than 80,000 points along existing and abandoned pipelines in the Gulf with matched incidents based on similar lease blocks and temporal timelines (e.g., the incident date occurs within reported pipeline lifespan), structural characteristics, geologic and seafloor data, and meteorological, oceanographic, and biochemical statistics spatially and temporally matched to each point. This is provided as both a feature class in a file geodatabase, as well as a CSV file for ease of use. The pipeline incidents table is a CSV file containing more than 900 reported incidents from 1986 to 2021, including incident date, area (Outer Continental Shelf (OCS) lease block and area code), reported causes, reported incident information, and results (i.e., cost, repairs, inspections), along with quantitative severity metrics. Field dictionaries are included for both the pipeline locations and incidents datasets, which detail field definitions. The pipeline locations field dictionary includes original resource reference information.

Advanced Infrastructure Integrity Model↗
DOE OSTI2025

Modular Hydronic Room Conditioning System (CRADA NFE-24-10120 Final Report)

This project presents the development and validation of high-fidelity numerical models for fin-tube heat exchangers to enable accurate performance prediction and informed design optimization. The modeling framework integrates detailed geometric specifications, thermophysical property data, and system-level constraints to simulate the heat exchanger’s behavior under a range of operating conditions. The model is calibrated using real-world product specifications and validated against experimental data collected from controlled cooling and heating tests. In cooling mode, the model captures the overall trends in capacity and outlet air temperature but tends to underpredict latent effects, especially at lower air flow rates. In heating mode, the simulation consistently overestimates both the thermal capacity and outlet air temperature, indicating the need for refinement in air-side heat transfer assumptions. Despite these deviations, the model provides a solid foundation for optimization, allowing key design variables to be tuned within physical and performance-based constraints. This research advances the ability to simulate, validate, and optimize fin-tube heat exchanger designs with greater confidence and efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Modular Hydronic Room Conditioning System

This project presents the development and validation of high-fidelity numerical models for fin-tube heat exchangers to enable accurate performance prediction and informed design optimization. The modeling framework integrates detailed geometric specifications, thermophysical property data, and system-level constraints to simulate the heat exchanger’s behavior under a range of operating conditions. The model is calibrated using real-world product specifications and validated against experimental data collected from controlled cooling and heating tests. In cooling mode, the model captures the overall trends in capacity and outlet air temperature but tends to underpredict latent effects, especially at lower air flow rates. In heating mode, the simulation consistently overestimates both the thermal capacity and outlet air temperature, indicating the need for refinement in air-side heat transfer assumptions. Despite these deviations, the model provides a solid foundation for optimization, allowing key design variables to be tuned within physical and performance-based constraints. This research advances the ability to simulate, validate, and optimize fin tube heat exchanger designs with greater confidence and efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2021

Enhancing integrated analysis of national and global goal pursuit by endogenizing economic productivity

Analysis with integrated assessment models (IAMs) and multisector dynamics models (MSDs) of global and national challenges and opportunities, including pursuit of Sustainable Development Goals (SDGs), requires projections of economic growth. In turn, the pursuit of multiple interacting goals affects economic productivity and growth, generating complex feedback loops among actions and objectives. Yet, most analysis uses either exogenous projections of productivity and growth or specifications endogenously enriched with a very small set of drivers. Extending endogenous treatment of productivity to represent two-way interactions with a significant set of goal-related variables can considerably enhance analysis. Among such variables incorporated in this project are aspects of human development (e.g., education, health, poverty reduction), socio-political change (e.g., governance capacity and quality), and infrastructure (e.g. water and sanitation and modern energy access), all in conditional interaction with underlying technological advance and economic convergence among countries. Using extensive datasets across countries and time, this project broadly endogenizes total factor productivity (TFP) within a large-scale, multi-issue IAM, the International Futures (IFs) model system. We demonstrate the utility of the resultant open system via comparison of new TFP projections with those produced for Shared Socioeconomic Pathways (SSP) scenarios, via integrated analysis of economic growth potential, and via multi-scenario analysis of progress toward the SDGs. We find that the integrated system can reproduce existing SSP projections, help anticipate differential economic progress across countries, and facilitate extended, integrated analysis of trade-offs and synergies in pursuit of the SDGs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Development of Integrated Mechanical Pods

This presentation highlights early wins, updated progress, and upcoming developments on ‘national-scale shared development platform’ for rapid prototyping, testing and validation of various integrated Mechanical Pod solutions and form factors. Such pod solutions consist of a set of all-electric heat pump mechanical equipment that have integrated functionalities through built-in controls, with heating, cooling, hot water, ventilation (including energy recovery), electrical management, and battery storage within a single package. The presentation draws inspiration from the success of bathroom pods in the US modular construction industry, UK’s efforts with unitizing mechanical systems as ‘utility cupboards’, and VEIC’s early wins in design-build of all-electric Mechanical Pod solutions in Vermont. The presentation includes researchers and partners involved with NREL in Design for Manufacturing and Assembly (DfMA), Virtual Design and Construction (VDC), and digital twin based process optimization modeling of integrated Mechanical Pod solutions. The presentation aims to highlight early wins from such a platform and how various physical and virtual tools are currently being employed as part of NREL’s ongoing multi-year project funded by US DOE. Streamlined procurement, coordination, installation, and O&M of Mechanical Pods such that the majority of work is delegated to the off-site modular factory implies monetary savings. Such a seemingly basic shift in location of the construction process leads to great reduction in complexity, first cost, lead time, and waste, and greater opportunities for innovative compartmentalization and integration of mechanical systems appropriately sized for each apartment or hotel guest room. However, past studies on unitized combination systems show that high installation costs, maintenance issues, challenges with system integration, limitations in existing electrical infrastructure, and lack of architecturally appealing solutions are key barriers. NREL and partners aim to address key barriers through DfMA approach, rapid prototyping and testing, and digital twin process optimization modeling. The presentation is also a call for interested entities to partner with NREL as part of the national-scale development platform, help drive both product and process innovation, and encourage open source sharing of learnings. Learning objectives include (1) learn about the vision of national-scale shared development platform for process-product innovation on integrated mechanical pod solutions and how to get involved, (2) gain an understanding of the components of an all-electric, high performance home, design characteristics and equipment included in an all-electric mechanical pod, integration of mechanical systems within a modular factories’ assembly line, and the system’s commissioning, operation and maintenance. The pre-planning and coordination with the factory and sub-contractors are also highlighted, (3) gain an understanding of using process modeling tools to quantify resource-constrained performance of operations (such as integration of energy efficiency strategies) to manufacture modules of varying design, (4) gain insights on virtual design, rapid prototyping, and emulated testing of various form factors across different climatic conditions. The need for such preliminary testing with open source sharing of learnings will also be highlighted.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data

Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress–strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this study, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure α-Ti are identified. Remarkably, using texture as an objective in combination with stress–strain objectives constrains the model of Ti to fully reproduce not only stress–strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted.

42 ENGINEERING↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
DOE OSTI2021

Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model

This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.

biofuel sustainability↗
DOE OSTI2021

Solitons in lattice field theories via tight-binding supersymmetry

Reflectionless potentials play an important role in constructing exact solutions to classical dynamical systems (such as the Korteweg-de Vries equation), non-perturbative solutions of various large-N field theories (such as the Gross-Neveu model), and closely related solitonic solutions to the Bogoliubov-de Gennes equations in the theory of superconductivity. These solutions rely on the inverse scattering method, which reduces these seemingly unrelated problems to identifying reflectionless potentials of an auxiliary one-dimensional quantum scattering problem. There are several ways of constructing these potentials, one of which is quantum mechanical supersymmetry (SUSY). In this paper, motivated by recent experimental platforms, we generalize this framework to develop a theory of lattice solitons. We first briefly review the classical inverse scattering method in the continuum limit, focusing on the Korteweg-de Vries (KdV) equation and SU(N) Gross-Neveu model in the large N limit. We then generalize this methodology to lattice versions of interacting field theories. Our analysis hinges on the use of trace identities, which are relations connecting the potential of an equation of motion to the scattering data. For a discrete Schrödinger operator, such trace identities had been known as far back as Toda; however, we derive a new set of identities for the discrete Dirac operator. We then use these identities in a lattice Gross-Neveu and chiral Gross-Neveu (Nambu-Jona-Lasinio) model to show that lattice solitons correspond to reflectionless potentials associated with the discrete scattering problem. These models are of significance as they are equivalent to a mean-field theory of a lattice superconductor. To explicitly construct these solitons, we generalize supersymmetric quantum mechanics to tight-binding models. We show that a matrix transformation exists that maps a tight-binding model to an isospectral one which shares the same structure and scattering properties. The corresponding soliton solutions have both modulated hopping and onsite potential, the former of which has no analogue in the continuum limit. We explicitly compute both topological and non-topological soliton solutions as well as bound state spectra in the aforementioned models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Multiphysics Analysis of Li Cooled Divertor Substrate During Loss of Coolant Accident (LOCA)

In the ongoing study of potential designs for liquid metal (LM) plasma-facing components (PFCs), so-called “slow” and “fast” Li flow divertor concepts are under investigation. In the previous studies on design and analysis of the slow Li flow divertor and comparison with the fast Li flow divertor, the magnetohydrodynamics (MHD)/heat transfer effects of the Li flowing inside the substrate as a second coolant were comprehensively investigated under the normal steady-state operation conditions. Here, in the present study, the multiphysics analysis is extended to the unsteady abnormal divertor scenario where the Li layer on top of the substrate does not provide full coverage or even totally disappears for a certain period of time, so that the substrate becomes directly exposed to the incident high plasma heat flux. Such an unwanted event may happen regardless of the concept of the divertor and is worth detailed investigations, typically referred to as a loss of coolant accident (LOCA). To address this situation, a simplified scoping analysis is conducted first in 2-D, and then an integrated 3-D modeling is performed using a time-dependent multiphysics model in COMSOL Multiphysics that integrates LM MHD, heat transfer, and solid mechanics. The main goal is to evaluate conditions under which the major material limits, such as the maximum allowable temperature, stress, and displacement of the substrate, can still be met. It was shown that the maximum time over which the substrate of RAFM steel can retain structural integrity during the LOCA is around 0.2 ∼ 0.3 s. Any divertor concept that utilizes RAFM steel as a substrate material and liquid Li as a second coolant should take such a permitted time into consideration.

divertor↗
DOE OSTI2025

Thermal Analysis of a Solid Particle Light-Trapping Planar Cavity Receiver Using Computational Fluid Dynamics

Concentrated solar power (CSP) is one of the most effective ways of harnessing solar power to create efficient, durable, and resilient energy systems. This study entails thermal modeling and analysis of a novel central tower receiver configuration. This receiver uses solid particles as the heat transfer fluid (HTF), a promising option for third-generation CSP systems. The configuration considered here is the light-trapping planar cavity receiver (LTPCR) introduced by the National Renewable Energy Laboratory. While heat transfer studies of various LTPCR subsystems have been done, system-level thermal analysis of the LTPCR receiver has not been attempted. This study also presents important sensitivity analyses of the operating parameters of the CSP system, which can help guide the design of future central tower receivers. This study employs Ansys Fluent as a computational fluid dynamics (CFD) tool to model fluid dynamics and heat transfer in the receiver, intending to quantify its thermal performance. The model seamlessly integrates Monte Carlo ray tracing data, which generates absorbed solar flux profiles from the heliostat field design, with the heat transfer characteristics of the fluidized particle bed. This unified model is designed to accurately predict the thermal behavior of the LTPCR. Analysis of preliminary results reveals that the primary loss mechanisms are radiative and natural convective losses, in that order. Based on observations from a baseline case, several strategies are suggested and numerically tested. These solutions include selective cooling of high-temperature regions and manipulation of particle bed parameters. Selective cooling of high-temperature regions reduced the peak temperature by 151 degrees C and decreased thermal losses by 0.9%. Improving the particle-wall heat transfer coefficient (P-W HTC) of the particle bed decreased the thermal losses by 1.7% and decreased the peak temperatures by 57 degrees C. Decreasing the particle inlet temperature (PIT) also reduced thermal losses by 3.5% and decreased peak temperatures by 29 degrees C. Compounding these strategies improved the thermal losses of the receiver from 13.5% in the baseline case to 7.5%. Additionally, the study explores the variation in thermal performance across different locations of the receiver, where a variation of thermal losses from 12.9% to 17.3% is found. This allows a comprehensive evaluation of potential improvements in efficiency and temperature management.

computational fluid dynamics↗