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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Scalable Solution for Grid Optimization and DER Integration (CRADA Final Report)

Utilidata Inc. is an energy software company that provides clean energy and digital solutions for utilities. This work was the result of Utilidata Inc. being awarded a New York State Energy Research and Development Agency award to integrate Utilidata volt-VAR optimization (VVO) software with smart inverters to explore enhanced voltage control. This project was conducted in partnership with Utilidata Inc., National Grid, the New York State Energy and Research Development Authority, and the National Renewable Energy Laboratory (NREL). As part of the project NREL conducted modeling and simulation analysis, developed cost-benefit analysis framework, and conducted laboratory testing to understand the potential for smart inverter integration to a centralized VVO control scheme. The remainder of the report provides an executive summary of the work, an introduction to the challenges, sections detailing specific aspects of the work, and the overall findings and conclusions.

14 SOLAR ENERGY↗
DOE OSTI2026

De-Risking Exploration for Geothermal Plays in Magmatic Environments Through Open-Source Tools: An Open-Source Python Framework for 2D and 3D Play Fairway Analysis

The De-Risking Exploration for Geothermal Plays in Magmatic Environments (DEEPEN) project seeks to accelerate superhot geothermal development by reducing exploration risk through advanced open-source modeling tools. This work presents a novel Python-based framework, geoPFA, for conducting 2D and 3D play fairway analysis (PFA) tailored to superhot geothermal systems. Building on previous methodologies, the framework integrates thermo-hydro-mechanical-chemical simulation outputs from TReactMech, resulting in improved representation of subsurface properties that are critical to superhot resource producibility. The workflow has been applied to the Nesjavellir field in Iceland, a candidate site for the third Iceland Deep Drilling Project's superhot production scenarios. This application demonstrates the value of modular, transparent, and extensible workflows for integrating geological, geophysical, and simulation-derived datasets in high-enthalpy environments. Preliminary results indicate favorable zones consistent with known hydrothermal activity and suggest possible upflow from the Hengill volcanic system. The geoPFA library is publicly available, offering a scalable and reproducible approach to geothermal exploration across varied geological contexts.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Catastrophic Cooling in Superwinds. III. Nonequilibrium Photoionization

Observations of some starburst-driven galactic superwinds suggest that strong radiative cooling could play a key role in the nature of feedback and the formation of stars and molecular gas in star-forming galaxies. These catastrophically cooling superwinds are not adequately described by adiabatic fluid models, but they can be reproduced by incorporating nonequilibrium radiative cooling functions into the fluid model. In this work, we have employed the atomic and cooling module maihem implemented in the framework of the flash hydrodynamics code to simulate the formation of radiatively cooling superwinds as well as their corresponding nonequilibrium ionization (NEI) states for various outflow parameters, gas metallicities, and ambient densities. We employ the photoionization program cloudy to predict radiation- and density-bounded photoionization for these radiatively cooling superwinds, and we predict UV and optical line emission. Our nonequilibrium photoionization models built with the NEI states demonstrate the enhancement of C iv, especially in metal-rich, catastrophically cooling outflows, and O vi in metal-poor ones.

Cooling flows↗
DOE OSTI2024

A multi-model assessment of the early last deglaciation (PMIP4 LDv1): a meltwater perspective

Abstract. The last deglaciation (∼20–11 ka BP) is a period of a major, long-term climate transition from a glacial to interglacial state that features multiple centennial- to decadal-scale abrupt climate variations whose root cause is still not fully understood. To better understand this time period, the Paleoclimate Modelling Intercomparison Project (PMIP) has provided a framework for an internationally coordinated endeavour in simulating the last deglaciation whilst encompassing a broad range of models. Here, we present a multi-model intercomparison of 17 transient simulations of the early part of the last deglaciation (∼20–15 ka BP) from nine different climate models spanning a range of model complexities and uncertain boundary conditions and forcings. The numerous simulations available provide the opportunity to better understand the chain of events and mechanisms of climate changes between 20 and 15 ka BP and our collective ability to simulate them. We conclude that the amount of freshwater forcing and whether it follows the ice sheet reconstruction or induces an inferred Atlantic meridional overturning circulation (AMOC) history, heavily impacts the deglacial climate evolution for each simulation rather than differences in the model physics. The course of the deglaciation is consistent between simulations except when the freshwater forcing is above 0.1 Sv – at least 70 % of the simulations agree that there is warming by 15 ka BP in most places excluding the location of meltwater input. For simulations with freshwater forcings that exceed 0.1 Sv from 18 ka BP, warming is delayed in the North Atlantic and surface air temperature correlations with AMOC strength are much higher. However, we find that the state of the AMOC coming out of the Last Glacial Maximum (LGM) also plays a key role in the AMOC sensitivity to model forcings. In addition, we show that the response of each model to the chosen meltwater scenario depends largely on the sensitivity of the model to the freshwater forcing and other aspects of the experimental design (e.g. CO2 forcing or ice sheet reconstruction). The results provide insight into the ability of our models to simulate the first part of the deglaciation and how choices between uncertain boundary conditions and forcings, with a focus on freshwater fluxes, can impact model outputs. We can use these findings as helpful insight in the design of future simulations of this time period.

Snoll, Brooke (ORCID:0000000230914394)↗
DOE OSTI2025

Raptor

Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.

Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
DOE OSTI2022

Transient convective delayed neutron precursor of {sup 235}U for the molten salt reactor experiment

This work investigates the delayed neutron precursors from the fission of {sup 235}U in molten salt reactors. The six delayed neutron groups predict the spatial distribution of the advective transport for radioactive mass transfer in the molten-salt reactor experiment (MSRE). The Mole code, using the framework of the Multiphysics Object-Oriented Simulation Environment (MOOSE), was used to compute the delayed neutron concentration of the whole system. This approach can be used for analysis of reactivity in circulating conditions. The effect of delayed neutron concentrations on the static or dynamic behavior of the system in MSRE was analyzed. Using this approach, the pattern and validation of delayed neutron for a range of parameters of interest are reported and discussed herein. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. Here, we propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota’s MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

Shayesteh Zadeh, Armin (ORCID:0000000299896710)↗
DOE OSTI2020

Climate Influences on Capacity Expansion Planning with Application to the Western U.S

Electric power system planners utilize a variety of planning tools to inform decisions concerning generation and transmission additions to the electric grid, the need for operational changes, and to evaluate potential stressors on the system. Numerous factors contribute to the planning process including projected fuel and technology costs, policy and load profiles. There is also a growing recognition of the interdependency of the electric grid with other natural and engineered systems. Here we explore how future climate change and hydropower operability might influence decisions related to electricity capacity expansion planning and operations. To do so we assemble a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on future water supply for thermoelectric and hydropower generation. Separately, temperature impacts on electricity load are evaluated. Together, these climate factors spatially constrain a capacity expansion model that projects generation and transmission additions to the grid. The projected new capacity-builds are then evaluated on their operations, reliability, and cost under average and extreme climate conditions using production cost modeling. This coupled framework is demonstrated on the electric grid in the Western U.S., supporting capacity expansion planning by WECC, the North American Electric Reliability Corporation (NERC) regional entity responsible for reliability assurance of the Western Interconnection. This region was selected in part because the West is unique in that it has high potential for renewable penetrations and is experiencing large retirements/displacements of baseload resources, primarily coal, leading to possible operational challenges in terms of changing resource mix and the need for resource flexibility. Toward this challenge, planning scenarios encompass a range of alternative energy, climate and drought futures. In this context we explore answers to two strategic questions: 1) How does changing climate influence electricity expansion planning (generation and transmission) and future operations, including type and capacity of new builds, system reliability, cost and environmental impacts? 2) How does the representation of hydropower in the modeling framework influence the evaluation of bulk power system operations? Results indicate that climate has a measurable influence on recommendations concerning the capacity, type and location of new generation and transmission additions, with up to 17 GW additional capacity needed by 2038 to meet peak loads (~6.6% increase over capacity-builds based on historical climate). The extent of additional infrastructure needs is strongly influenced by future water availability for hydropower and the potential deployment of demand response technologies. Systems designed for future climate conditions were found to maintain high system reliability under a range of electricity and water availability scenarios (including significant drought), with minimal system curtailments. Additional capacity needs due to higher load tend to increase cumulative 20-year investment and operating costs by $\$$5-$\$$17 billion and generation costs increase by 9 to 19%. Finally, changing the representation of hydropower flexibility has a relatively small influence on capacity expansion in the Western Interconnection through 2038, but hydropower flexibility impacts generation costs to a similar extent as climate.

13 HYDRO ENERGY↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Probabilistic simulation of quantum circuits using a deep-learning architecture

The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Physics–Informed Neural Networks of the Saint–Venant Equations for Downscaling a Large–Scale River Model

Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

A Review: Microstructural and Phase Evolution in Alloys during Extended Plastic Deformation

Materials in service and during processing are often subjected to plastic deformation. For multi-phase metallic alloys, simple geometric models and atomistic simulations suggest that two distinctive regimes in these materials’ evolution during deformation exist. At low strains, evolutions are often dominated by the kinetic roughening of interfaces, which results from the superdiffusive transport of matter in sheared crystals. Furthermore, at high strains and temperatures where thermal diffusion is sluggish, on the other hand, shearing- induced forced atomic mixing dominates these evolutions, resulting in significantly enhanced solubility. Distinguishing these two regimes is shown to provide a convenient framework for rationalizing and analyzing recent experiments and simulations on wear of layered structures in the low strain-regime and nonequilibrium phase co-existence and self-organization in highly immiscible or reactive alloy systems in the high-strain regime.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Analysis of RF Sheath-Driven Tungsten Erosion at RF Antenna in the WEST Tokamak

This study applies the newly developed STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to analyze tungsten (W) erosion at RF antenna structures in the WEST tokamak. STRIPE integrates SolEdge3x for edge plasma backgrounds, COMSOL for 3D RF sheath potentials, RustBCA for sputtering yields, and GITR for impurity transport and ion energy–angle distributions. Building on prior work by Kumar et al. (2025) Nuclear Fusion, 65, 076039, which validated STRIPE for WEST ICRH discharge #57877, the present study provides a spatially resolved assessment of gross W erosion at both Q2 antenna limiters under ohmic and ICRH conditions. Simulations using 2D SolEdge3x profiles in COMSOL capture rectified sheath potentials exceeding 300 V, leading to strong upper-limiter localization. Both poloidal and toroidal asymmetries are observed and attributed to RF sheath effects, with modeled erosion patterns deviating from experiment—highlighting sensitivity to sheath geometry and plasma resolution. Erosion is driven primarily by high-charge-state oxygen ions (O6+–O8+), while D+ plays a negligible role. Assuming a plasma composition of 1% oxygen and 98% deuterium, STRIPE predicts a 30-fold increase in gross W erosion from ohmic to ICRH phases, consistent with a >25-fold rise in W-I (400.9 nm) brightness. Quantitative agreement is within 5% in the ohmic phase and 30% under ICRH, demonstrating predictive capability. Importantly, the study shows that the magnitude of ICRH-driven W erosion depends strongly on the concentration of light impurities (O, B, N, C), which drive sputtering through high charge states. Cleaner plasma conditions with reduced impurity content are therefore expected to substantially mitigate antenna W sources in WEST and other toroidal fusion devices. These findings establish STRIPE as a predictive framework for RF-induced plasma–material interactions and support its application to reactor-scale antenna design.

Kumar, Atul [ORNL] (ORCID:0000000277475210)↗