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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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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Myna

The additive manufacturing (AM) community has been developing digital factory tools over the past decade to better leverage the multi-modal process data coming out of the advanced manufacturing process. As a result, numerous databases of additive manufacturing process data exist in the literature and in the archival storage of disparate research groups. While some efforts have been made to create a standard ontology for storing and sharing AM data, in practice a variety of data structures are used to store AM build data, even within a single institution. This causes many problems for maintainability and extensibility when attempting to integrate computational modeling tools with experimental data to either validate models or to provide further insight into results and trends. Myna is a Python-based framework that aims to decrease the effort needed to connect individual computational models to the variety of AM process data that exist in different research groups and institutions. This type of software is sometimes referred to as "middleware" or “glueware,” in that it connects disparate databases and applications into a single computational ecosystem. Instead of maintaining unique interfaces between each application and each database, developers can create a single interface from each application to Myna and thereby gain access to the implemented database connections. Similarly, developing a database connection in Myna provides access to the developed simulation applications. This framework greatly simplifies the maintainability of model applications that rely on experimental data. Using external simulation tools, users will also be able to run pre-configured workflows using the built-in workflow manager. Several examples of input files are provided with Myna for different workflows, including melt pool geometry predictions and detailed melt pool and solidification microstructure predictions.

Knapp, GerryL. [Oak Ridge National Laboratory (ORN↗
DOE OSTI2020

Assessing impacts of selective logging on water, energy, and carbon budgets and ecosystem dynamics in Amazon forests using the Functionally Assembled Terrestrial Ecosystem Simulator

Abstract. Tropical forest degradation from logging, fire, and fragmentation not only alters carbon stocks and carbon fluxes, but also impacts physical land surface properties such as albedo and roughness length. Such impacts are poorly quantified to date due to difficulties in accessing and maintaining observational infrastructures, as well as the lack of proper modeling tools for capturing the interactions among biophysical properties, ecosystem demography, canopy structure, and biogeochemical cycling in tropical forests. As a first step to address these limitations, we implemented a selective logging module into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) by mimicking the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events, splitting the logged forest patch into disturbed and intact patches; determine the survivorship of cohorts in the disturbed patch; and modifying the biomass and necromass (total mass of coarse woody debris and litter) pools following logging. We parameterized the logging module to reproduce a selective logging experiment at the Tapajós National Forest in Brazil and benchmarked model outputs against available field measurements. Our results suggest that the model permits the coexistence of early and late successional functional types and realistically characterizes the seasonality of water and carbon fluxes and stocks, the forest structure and composition, and the ecosystem succession following disturbance. However, the current version of FATES overestimates water stress in the dry season and therefore fails to capture seasonal variation in latent and sensible heat fluxes. Moreover, we observed a bias towards low stem density and leaf area when compared to observations, suggesting that improvements are needed in both carbon allocation and establishment of trees. The effects of logging were assessed by different logging scenarios to represent reduced impact and conventional logging practices, both with high and low logging intensities. The model simulations suggest that in comparison to old-growth forests the logged forests rapidly recover water and energy fluxes in 1 to 3 years. In contrast, the recovery times for carbon stocks, forest structure, and composition are more than 30 years depending on logging practices and intensity. This study lays the foundation to simulate land use change and forest degradation in FATES, which will be an effective tool to directly represent forest management practices and regeneration in the context of Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Computationally Efficient Control Co-Design Optimization Framework with Mixed-Fidelity Fluid and Structure Analysis: OpenTurbineCoDe (OTCD) - ARPA-E ATLANTIS Phase I Project (Final Technical Report)

The goal of the Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control (ATLANTIS) Program is to develop new technologies for floating offshore wind turbines, or wind farms, using the discipline of control co-design (CCD.) In this context, our goal is to develop a computationally efficient optimization framework for design of floating offshore wind turbines. Our specific aim is to utilize high-fidelity structural, aerodynamic, aero-structural tools, and to derive control-oriented reduced- or low-order models directly from the high-fidelity tools. We proposed a mixed-fidelity modeling approach which means that we are also using low- and mid-fidelity tools when necessary. This research is conducted by a multidisciplinary team consisting of Rutgers University, University of Michigan, Brigham Young University, and the National Renewable Energy Laboratory (NREL). The computational framework, called OpenTurbineCoDe, is designed to integrate, where possible, traditional structural, aerodynamic, aeroelastic models (e.g., OpenFAST) and advanced control algorithms with higher fidelity simulation tools including Reynolds-averaged Navier–Stokes (RANS) solvers, and three-dimensional structural finite element solvers. All the high-fidelity tools used in this research provide numerically exact gradients to facilitate both efficient optimization and local linearization for control implementation.

17 WIND ENERGY↗
DOE OSTI2022

An agent-based modeling tool supporting bioenergy and bio-product community communication regarding cellulosic bioeconomy development

The emergence of the cellulosic bioeconomy requires not only adequate technological, economic, and policy advances, but also effective communication and coordination among the multiple stakeholders in the bioeconomy community. Aiming to facilitate stakeholder communication and collaboration for cellulosic bioeconomy growth, an agent-based model is developed as a community communication tool in this study. The tool simulates the behaviors and interactions of key stakeholders, as well as the responses and interactions between the stakeholders and biophysical, socioeconomic, and environmental conditions. The agent behavior rules are developed and validated based on behavior and economic theories and information attained from a stakeholder focus group study and a land use survey. A pilot study in a Central Illinois watershed shows stakeholder synergies, i.e., positive feedback loops across multiple stakeholders. The modeling tool's role in facilitating community communication is demonstrated via the focus group study. A comparison of policy scenarios suggests that the cellulosic bioeconomy development is fastest under a portfolio of policies combining the views of the multiple stakeholders. Critically, subsidy for small-scale bio-facilities is essential as it rapidly initializes an early market for cellulosic feedstocks and the adoption of Miscanthus. As a result, the communication tool is applied to bringing stakeholders and researchers together to develop shared visions, assess barriers, and explore solution portfolios to initialize and speed up the regional cellulosic bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2026

Demand response event simulator and risk-aware bidding tool for industrial customers

Incentive Based Demand Response (IBDR) program participation delivers financial benefits to the consumers and resiliency benefits to the electricity grid. Effectively participating in these programs as an industrial consumer requires bidding strategies that balance financial risk with operational constraints. Existing bidding tools tend not to fully incorporate stochastic IBDR event modeling, program specific baseline and payment/penalty calculations, or demand reduction process control schemes that account for the cascading impacts of shutdown in complex facilities. Here, this work presents an IBDR event simulator and risk-aware bidding framework tool integrating three key components: a flexible, parameterized demand response event generator that rigorously accounts for program structures and stochasticity, a demand response operational simulation model that generates explicit control strategies for load reduction, and a Monte Carlo simulator to evaluate financial risk for varied capacity bids. A case study at a wastewater treatment plant participating in PG&E's Capacity Bidding Program demonstrates the framework's utility. In the peak capacity price month of August, optimal bidding by the wastewater treatment plant nets a mean IBDR benefit of $101,000 (67% of the August electricity bill) with 0.4% probability of a financial loss. This framework enables industrial operators to make informed bidding decisions, negotiate better program terms with demand response load aggregators, and analyze energy flexibility investments at their facilities. Ultimately, this work reduces participation barriers in IBDR programs and supports the broader goal of enhancing grid reliability and renewable energy integration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning

Unsteady aerodynamics will be an important part of the floating wind turbines of the future operating under high shear across the rotor disk coupled with platform motion and atmospheric turbulence. We develop an unsteady aerodynamics and dynamic stall model using a long short-term memory variant of recurrent neural networks. The neural network model is trained using the oscillating airfoil data set from Ohio State University. The predictions from our machine learning (ML)-based model show good agreement with the experimental data and other state-of-the-art dynamic stall models for a wide range of airfoils, Reynolds numbers and reduced frequencies. In some cases the predictions are better than the Beddoes-Leishman model implementation in OpenFAST, when using the default coefficients. The ML-based model is also able to capture the key physics associated with dynamic stall, such as the precedence of moment stall before lift stall and cycle-to-cycle variations in the aerodynamic response. The new unsteady aerodynamics model is expected to improve prediction of fatigue loads for yaw-based wake-steering control scenarios in actuator-line and actuator-disc simulations of wind farms. Our methodology for training the ML-model provides a pathway for improving design level tools using high-fidelity computational fluid dynamics (CFD) simulations in the future.

17 WIND ENERGY↗
DOE OSTI2024

Polaris Studio

SF-24-040 Polaris-studio is the Python entry point/front end for using the Polaris transportation simulator. The package contains the code used to create new models from open and user-provided data, an extensive range of convenience tools for data preparation and result analysis and the ability of designing, launching and tracking studies combining multiple individual Polaris simulations from a Python terminal. The Polaris Studio software contains several modules for accomplishing the above tasks, including the PolarisLib - the Polaris object model, Polaris Manager - code for setting up and running Polaris studies, QPOLARIS - an add-on for the QGis software to allow model files to be created and edited, PolarisVis - a juptyer-based postprocessing library, and Polaris Tools - a useful collection of supplemental tools for interacting with Polaris files.

Auld, Joshua↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗
DOE OSTI2024

The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Inverter-Based Operation of Maui: Electromagnetic Transient Simulations

As larger and larger power systems approach and reach 100% inverter-based resource (IBR) operation during some hours of the year, questions arise regarding the stability of such extremely-high IBR power systems, the potential need for grid-forming (GFM) inverter technology, and the potential need for synchronous condensers. Relatedly, questions also arise about the ability of conventional positive sequence power system modeling tools to capture high-IBR system dynamics. This presentation introduces electromagnetic transient (EMT) simulations in PSCAD of the near-future (year 2023) Maui power system at and near 100% IBR operation and compares those simulations to positive sequence (PSSE) simulations. The Maui PSCAD model is parallelized on 30 cores and includes the entire transmission system (>200 three-phase buses), >170 individual and aggregate IBR models, four wind plants, and three synchronous generators plus six synchronous condensers at two locations. We investigate system stability with varying levels of inertia using conventional grid-following IBR controls, and then we investigate the impact of GFM controls on stability. Results suggest that: 1) positive sequence simulations can miss key dynamics in extremely high IBR cases; 2) EMT simulations can also miss key dynamics if inverter inner control dynamics are not modeled; 3) synchronous condensers can stabilize a system in which 100% of the energy is supplied by IBRs, even conventional grid-following IBRs; 4) GFM controls on just some of the IBRs can stabilize a 100% IBR power system, even if that system has zero inertia (i.e. no synchronous condensers, though synchronous condensers may be needed for other purposes such as protection system operations).

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

PV Hosting Capacity Estimation: Experiences with Scalable Framework; Preprint

Hosting capacity is an indication of the amount of photovoltaics (PV) can be hosted in a distribution system. This paper presents a framework for estimating distributed PV hosting capacity at scale. We first analyze the key challenges of performing relevant large scale simulation including computational and modeling challenges. Then, we develop two python-based software tools in order to conduct repeatable distribution analyses: Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations, and JADE for parallelizing jobs on HPC clusters. A case study of hosting capacity estimation for SMART-DS SFO 2000+ synthetic feeders is used to demonstrate the capability of the developed framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, through which the overall upgrade cost can be better planned in order to integrate more PV in the future.

distributed energy resources↗
DOE OSTI2023

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Single Primary Heat Extraction and Removal Emulator (SPHERE) Long Duration Testing

For the development of heat-pipe cooled microreactors, it is crucial to thoroughly understand the characteristics and functioning of heat pipes across a wide spectrum of operating conditions. Passive heat removal and its long-term performance stability are critical factors in this context. Enhanced experimental data is vital for evaluating the operational lifespan of alkali metal heat pipes. Idaho National Laboratory (INL) has successfully conducted an extended duration test on a high-performance sodium-filled heat pipe, closely monitoring the axial temperature profile, power supplied by the heaters, and heat removed by a gas-gap calorimeter. The results from this testing provide valuable data that are instrumental in supporting heat pipe validation efforts. Specifically, this data aids in the development and validation of Sockeye, the Multiphysics Object-Oriented Simulation Environment (MOOSE) tool under the US-DOE NEAMS program designed for heat pipe modeling. By comparing experimental results with Sockeye’s predictions, the tool's accuracy and reliability can be assessed and improved, thereby enhancing its capability to simulate heat pipe operations under various conditions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Validation of the ERO2.0 code using W7-X and JET experiments and predictions for ITER operation

Abstract The paper provides an overview of recent modelling of global material erosion and deposition in the fusion devices Wendelstein 7-X (W7-X), JET and ITER using the Monte-Carlo code ERO2.0. For validating the modelling tool in a three-dimensional environment, W7-X simulations are performed to describe carbon erosion from the graphite test divertor units, which were equipped in operational phase OP 1.2 and analysed post-mortem. Synthetic spectroscopy of carbon line emission is compared with experimental results from the divertor spectrometer measurement system, showing a good agreement in the e-folding lengths in the radial intensity profiles of carbon. In the case of metallic wall materials, earlier modelling of the Be/W environment in JET and ITER is revisited and extended with an updated set of sputtering and reflection data, as well as including the mixing model for describing the Be/W dynamics in the divertor. Motivated by recent H/D/T isotope experiments in JET, limited and diverted configuration pulses are modelled, showing the expected trend of both Be and W erosion increasing with isotope mass. For the JET diverted configuration pulses, it is shown that Be migrates predominantly to the upper part of the inner divertor where it initially leads to strong W erosion. With longer exposure time, the growth of a Be deposited layer leads to a reduction of W erosion in that region. A similar trend is observed in simulations of the ITER baseline Q = 10 scenario, however with a more symmetric Be migration pattern leading to deposition also on the outer divertor.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗