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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 109 records · Page 6
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DOE OSTI2022

Reinforcement Learning for Volt- Var Control: A Novel Two-stage Progressive Training Strategy

This paper develops a reinforcement learning (RL) approach to solve a cooperative, multi-agent Volt-Var Control (VVC) problem for high solar penetration distribution systems. The ingenuity of our RL method lies in a novel two-stage progressive training strategy that can effectively improve training speed and convergence of the machine learning algorithm. In Stage 1 (individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: fconsume, generate, do-nothingg. In Stage 2 (cooperative training), all agents are trained again coordinatively to share VVC responsibility. Rewards and costs in our RL scheme include (i) a system-level reward (for taking an action), (ii) an agent-level reward (for doing-nothing), and (iii) an agent-level action cost function. This new framework allows rewards to be dynamically allocated to each agent based on their contribution while accounting for the trade-off between control effectiveness and action cost. The proposed methodology is tested and validated in a modified IEEE 123-bus system using realistic PV and load profiles. Simulation results confirm that the proposed approach is robust and computationally efficient; and it achieves desirable volt-var control performance under a wide range of operation conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Implications of an emission trading scheme for India’s net-zero strategy: a modelling-based assessment

To help meet its near-term NDC goals and long-term net-zero 2070 target, the Government of India has planned to establish a Carbon Credit Trading Scheme (CCTS), i.e. a domestic emission trading scheme (ETS). An ETS is an inherently cost-effective policy instrument for emission reduction, providing the greatest flexibility to reduce emissions from within and across sectors. An effective ETS requires design features that consider country-specific challenges and reflect its role within the larger policy package to achieve long-term emission reduction. Within the Indian context and in this study we therefore investigate—(i) what might be the role of the ETS in achieving India’s long-term mitigation targets? (ii) How might the various sectors interact under an emissions cap? (iii) How might the ETS interact with existing energy and climate policies? We do this analysis by running four main scenarios using the integrated assessment model GCAM (v6.0), adapted to India-specific assumptions and expectations. These scenarios are—(i) NZ (net-zero), (ii) NZ + ETS, (iii) NZ + CC (command and control), and (iv) NZ + RPO (renewables purchase obligations) + ETS. The NZ scenario assumes India’s near-term and long-term climate commitments of net zero by 2070. Scenarios with ETS (ii) and (iv) apply an emissions cap on four sectors—electricity, iron and steel, cement, and fertilizer. The scenario with CC applies a homogenous emission cap on each of the chosen sectors but does not allow cross-sectoral trading. The last scenario includes renewables purchase obligations (RPOs along with an ETS. We show that under a specific ETS emissions cap: (i) the electricity sector emerges as the largest source of cost-effective greenhouse gas (GHG) reduction options; (ii) ETS with trading across sectors is around 24% more cost-effective than ETS with trading only within sectors, (iii) RPOs can be complementary to an ETS although the impact of RPOs on GHG reductions in the electricity sector would need to be considered when setting the level of the ETS cap (or emissions intensity targets) or the RPO targets to avoid low carbon prices, and (iv) the direction and volume of financial transfers across sectors depends on allocation targets set by the government. Based on these results we provide design recommendations for India’s ETS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Modeling and Calibration of Supplier Selection Problem in Freight Agent-Based Simulations

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and a supplier-rating model adapted from existing literature. The model’s core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas—Atlanta, Chicago, Dallas–Fort Worth, and Los Angeles—the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world decision making, providing a robust tool for policymakers and planners to evaluate targeted interventions, assess infrastructure investments, and enhance supply chain resilience in the face of disruptions.

Ismael, Abdelrahman (ORCID:0000000303712110)↗
DOE OSTI2023

Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network

Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system’s resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose a multi-agent reinforcement learning (MARL) framework to help automate consumers’ bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.

Feng, Chen↗
DOE OSTI2021

Comparison of Radiation Models for a Turbulent Piloted Methane/Air Jet Flame: A Frozen-Field Study

Numerical modeling of radiative transfer in nongray reacting media is a challenging problem in computational science and engineering. The choice of radiation models is important for accurate and efficient high-fidelity combustion simulations. Different applications usually involve different degrees of complexity, so there is yet no consensus in the community. In this paper, the performance of different radiative transfer equation (RTE) solvers and spectral models for a turbulent piloted methane/air jet flame are studied. The flame is scaled from the Sandia Flame D with a Reynolds number of 22,400. Three classes of RTE solvers, namely the discrete ordinates method, spherical harmonics method, and Monte Carlo method, are examined. The spectral models include the Planck-mean model, the full-spectrum k-distribution (FSK) method, and the line-by-line (LBL) calculation. The performances of different radiation models in terms of accuracy and computational cost are benchmarked. The results have shown that both RTE solvers and spectral models are critical in the prediction of radiative heat source terms for this jet flame. The trade-offs between the accuracy, the computational cost, and the implementation difficulty are discussed in detail. The results can be used as a reference for radiation model selection in combustor simulations.

David, Chloe↗
DOE OSTI2025

Surrogate-driven design optimization with uncertainty constraints in Monte Carlo simulations

In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predictions, as their performance depends heavily on the quality of training data. This study investigates the impact of data uncertainty on two multi-objective design problems modelled using Monte Carlo transport simulations: a neutron moderator and an ion-to-neutron converter. For each, a grid search was performed using five different tally uncertainty levels to generate training data for neural network surrogate models. These models were then optimized using NSGA-III. The recovered Pareto-fronts were analyzed across uncertainty levels: in the moderator problem, normalized hypervolume dropped from 0.886 at 1.0% uncertainty to 0.748 at 10% uncertainty, while in the converter problem it remained near 0.50 for all cases. Average simulation times were also compared to evaluate the trade-off between accuracy and computational cost. Results show that the influence of simulation uncertainty is strongly problem-dependent. In the neutron moderator case, higher uncertainties led to exaggerated objective sensitivities and distorted Pareto-fronts, reducing normalized hypervolume. In contrast, the ion-to-neutron converter task was less affected—low-fidelity simulations produced results similar to those from high-fidelity data. These findings suggest that a fixed-fidelity approach is not optimal. Surrogate models can recover the Pareto-front under noisy conditions, and multi-fidelity studies help identify suitable uncertainty levels for each problem to balance efficiency and accuracy.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

A Blockchain and PKI-Based Secure Vehicle-to-Vehicle Energy-Trading Protocol

With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a blockchain and public key infrastructure (PKI)-based secure vehicle-to-vehicle (V2V) energy-trading protocol. A permissioned blockchain utilizing the proof of authority (PoA) consensus and smart contracts is used to securely store data. Encrypted communication is ensured through transport layer security (TLS), with PKI managing the necessary digital certificates and keys. A multi-leader, multi-follower Stackelberg game-based trade algorithm is formulated to determine the optimal energy demands, supplies, and prices. Finally, we propose a detailed communication protocol that ties all the components together, enabling smooth interaction between them. Key findings, such as system behavior and performance, scalability of the trade algorithm and the blockchain, smart contract execution costs, etc., are presented through numerical results by implementing and simulating the protocol in various scenarios. This work not only enhances local energy trading among EVs, encouraging efficient energy usage and reducing burden on the power grid, but also paves a way for future research in sustainable energy management.

Stackelberg game↗
DOE OSTI2020

Multimodel validation of single wakes in neutral and stratified atmospheric conditions

Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade-off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle-mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large-eddy simulations performing the best. In the more challenging stable case, steady-state Reynolds-averaged Navier–Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time-stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake-meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.

17 WIND ENERGY↗
DOE OSTI2025

Combining Reactive Quantum-Mechanical Molecular-Dynamics Simulations with Mutagenesis, Crystallography, and Enzyme Kinetics to Reveal Plausible Steps of Isocyanide Hydratase Catalysis

A complete understanding of enzyme mechanisms requires atomistic details of chemical reactions. Quantum-based molecular dynamics simulations (QMD) are a potential source of this information, but trade-offs between accuracy and computational cost have limited their use. We previously developed extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) methods that leverage a negligible compromise in accuracy to substantially decrease the cost of QMD simulations. Here, we develop a reactive QMD approach using the latest XL-BOMD formulation, which enables efficient simulations of highly reactive systems, and use it to investigate mechanisms of intermediate formation in isocyanide hydratase (ICH) catalysis. In QMD simulations, molecular analogs of ICH active site residues reacted with para-nitrophenyl isocyanide, forming a thioimidate. Analysis of simulated atomic configurational and charge dynamics revealed a pathway where protonation of the isocyanide carbon occurs prior to thioimidate formation and suggested a possible role of Asp17 as a proton donor in the early phase of ICH catalysis. To test whether the pathway seen using the reactive QMD approach might be relevant to ICH catalysis, we performed X-ray crystallography and pre-steady-state enzyme kinetics studies of wild-type and D17N mutant ICH. Both the structure and kinetics are sensitive to the D17N mutation in a manner that is consistent with the order of the reaction steps seen in the simulations. Mobile protons play essential roles in many enzymes, yet they are difficult to observe experimentally, making the ordering of proton-dependent steps ambiguous in many enzyme mechanisms. The ability to directly simulate model reactions for the design of experiments that provide information about enzyme mechanisms involving mobile protons demonstrates the significance of our reactive QMD approach and motivates further biological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamical structure factors of warm dense matter from time-dependent orbital-free and mixed-stochastic-deterministic density functional theory

Abstract We present the first calculations of the inelastic part of the dynamical structure factor (DSF) for warm dense matter (WDM) using time-dependent orbital-free density functional theory (TD-OF-DFT) and mixed-stochastic-deterministic (mixed) Kohn Sham TD-DFT (KS TD-DFT). WDM is an intermediate phase of matter found in planetary cores and laser-driven experiments, where the accurate calculation of the DSF is critical for interpreting x-ray Thomson scattering measurements. Traditional TD-DFT methods, while highly accurate, are computationally expensive, motivating the exploration of TD-OF-DFT and mixed TD-KS-DFT as more efficient alternatives. We applied these methods to experimentally measured WDM systems, including solid-density aluminum and beryllium, compressed beryllium, and carbon–hydrogen mixtures. Our results show that TD-OF-DFT requires a dynamical kinetic energy potential in order to qualitatively capture the plasmon response. Additionally, it struggles with capturing bound electron contributions. In contrast, mixed TD-KS-DFT offers greater accuracy in distinguishing bound and free electron effects, aligning well with experimental data, though at a higher computational cost. This study highlights the trade-offs between computational efficiency and accuracy, demonstrating that TD-OF-DFT remains a valuable tool for rapid scans of parameter space, while mixed TD-KS-DFT should be preferred for high-fidelity simulations. Our findings provide insight into the future development of DFT methods for WDM and suggest potential improvements for TD-OF-DFT.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerating Ocean-Based Renewable Energy Educational Opportunities to Achieve a Clean Energy Future

The United Nations has named 2021–2030 the Decade of Ocean Science for Sustainable Development with goals to 'strengthen the international cooperation needed to develop the scientific research and innovative technologies that can connect ocean science with the needs of society' (IOC 2019 The science we need for the ocean we want: the United Nations decade of ocean science for sustainable development (2021–2030) (Paris) p 24). Important actions that have been identified in support of sustainable development goals include capacity-building, training, and education. This includes educational opportunities for ocean-based renewable energy development in support of a healthy planet and ocean. Offshore wind is experiencing rapid development globally and, while the U.S. offshore wind market is still nascent, it is on the brink of exponential growth based on large cost reductions driven largely by European development and technology advances. Growth is also expected in wave and tidal energy with significant opportunities identified for various distributed markets through the Powering the Blue Economy™ initiative, with longer-term implications for expansion at the utility scale (LiVecchi et al 2019 Powering the blue economy; exploring opportunities for marine renewable energy in maritime markets p 207). In order to expedite progress and maximize benefits to the national, state, and local economies, these development actions will require a broad, diverse, and appropriately trained workforce. The ocean-based renewable energy workforce needs engineers and scientists to develop cost-effective technologies, as well as trade and maritime workers to eventually deploy the technologies at scale. In the United States, educational institutions, state governments, and private developers are taking action to understand job skills and capability requirements and to develop educational and training programs to meet offshore workforce needs; most are focused on offshore wind power, with gaining interest in marine energy. This article explores the workforce requirements of the growing ocean-based renewable energy industry and the current state of education and training programs to meet those requirements in order to identify gaps and make recommendations for further workforce development activities and initiatives. An international view needs to be adopted that incorporates the education and skill needs of early-stage marine energy technologies and evolving offshore wind technologies together with more market-ready offshore renewable energy markets. By accelerating educational development opportunities in ocean-based renewable energy, these growing blue economy markets can deliver significant economic and social benefits.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2025

Hardware-Efficient Quantum Phase Estimation via Local Control

Quantum phase estimation plays a central role in quantum simulation as it enables the study of spectral properties of many-body quantum systems. Most variants of the phase estimation algorithm require the application of the global unitary evolution conditioned on the state of one or more auxiliary qubits, posing a significant challenge for current quantum devices. In this work, we present an approach to quantum phase estimation that uses only locally controlled operations, resulting in a significantly reduced circuit depth. At the heart of our approach are efficient routines to measure the complex phase of the expectation value of the time-evolution operator, the so-called Loschmidt echo, for both circuit dynamics and Hamiltonian dynamics. By tracking changes in the phase during the dynamics, the routines trade circuit depth for increased sampling cost and classical postprocessing. Our approach does not rely on reference states and is applicable to any efficiently preparable state, regardless of its correlations. We provide a comprehensive analysis of the sample complexity and illustrate the results with numerical simulations. Our methods offer a practical pathway for measuring spectral properties in large many-body quantum systems using current quantum devices.

Schiffer, Benjamin F. [Max Planck Institute of Qua↗
DOE OSTI2024

Energy Storage to Enable Electricity as a Commodity

A lack of large-scale economical storage, demand-side flexibility, and the need to instantaneously balance supply and demand cause challenges for electricity as a commodity. With typical commodities, stock from periods of high production can be stored and sold during low production, smoothing demand and supply shocks. In electricity markets, high storage costs have long outweighed benefits, negating convenience yield; no-arbitrage models do not apply, and typical hedging strategies cannot be performed due to infinite carry costs. However, the mechanics of energy trading are changing due to advancing storage technologies, more price-responsive demands, and virtual bidding in electricity markets, shaping a system where electricity storage can be valued on factors other than diurnal arbitrage. We examine the valuation of electricity storage through the lens of commodity markets to assess the impacts of large-scale storage deployment.

energy storage↗
DOE OSTI2022

Analytic error analysis of cross section interpolation methods in nodal diffusion codes - I : Theory

This paper discusses two cross section interpolation methods commonly found in popular nodal codes; the partial derivatives and multiple tables models. The motivation for choosing a model, and thus a case matrix structure, is a trade off between accuracy and computational cost. Due to decades of experience, there are default structures that are sufficient for current light water reactor analysis. However, this is not necessarily the case for advanced reactor designs. Therefore, it is advantageous to understand the sources of error in cross section interpolation models so that the quality of a case matrix may be improved. A mathematical framework for these models is presented in this work that provides a more rigorous connection between the nuclear engineering field's cross section interpolation methods and the broader mathematical field of function approximation. The two cross section models examined in this paper were found to utilize Lagrange interpolation and are a subset of Lagrange tensor products. Classical results of Lagrange polynomial error analysis were then applied to the partial derivative and multiple tables models to derive expressions for the total point-wise error. The analytical results classify the total error into two parts: the model form error and interpolation error. Finally, based on our observations, a better foundation for improving the quality of a case matrix is proposed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

LCA-TEA of Sorbent-Based Direct Air Capture with Temperature-Vacuum Swing Adsorption Model

This poster presentation discusses different aspects of Life Cycle Assessment (LCA) and Techno Economic Analysis (TEA) of sorbent-based case study updates and methodologies utilized for carbon dioxide removal technologies. The combined TEA and LCA approach adopted herein provides insight into trade-offs between environmental performance and cost efficiency under various energy supply scenarios.

carbon dioxide removal↗
DOE OSTI2026

Techno-Economic Comparison of Molten-Salt Electrolysis and Carbothermic Reduction for the Production of Metallurgical-Grade Silicon

Metallurgical-grade silicon (MG-Si) is an important source material for many industrial applications, including the manufacture of alloys, solar photovoltaics, and electronics. The process to refine raw materials into MG-Si is energy-intensive, with the predominant method of submerged-arc furnaces requiring energy consumption of approximately 11–13 kWh/kg Si. Recent research has discussed promising methods for reducing the energy required for the silicon production process, including the use of molten-salt electrolysis (MSE), a technique that offers potential savings in energy consumption without requiring carbon inputs for the process. This paper presents a techno-economic study of a potential industrial-scale MSE plant for MG-Si production to evaluate the trade-offs between capital and operating costs of the system. Capital costs are sourced from recent MG-Si plants and an existing cost model developed for MSE processes that includes the size of the plant and the operating temperature among its inputs. The results show that MSE technology has the potential to be an economically cost-competitive option for MG-Si production if the technology successfully scales to industrial production and matures enough to allow for financing costs similar to that of a comparably sized submerged-arc furnace plant.

14 SOLAR ENERGY↗
DOE OSTI2020

ROOT I/O compression improvements for HEP analysis

We overview recent changes in the ROOT I/O system, increasing performance and enhancing it and improving its interaction with other data analysis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment's software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically increased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are significant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space.

Shadura, Oksana↗
DOE OSTI2024

Model-Based Energy and Cost Analysis of Direct Air Capture Using ePTFE-Based Laminate-Structured Gas–Solid Contactors

Carbon dioxide removal (CDR) technologies will play a significant role in limiting global warming if implemented on a large scale. Direct air capture (DAC) is a scalable approach for removing atmospheric carbon, yet the true scope of its scalability remains unclear due to the early stage of technology development and high first plant costs. This study provides groundwork for understanding the technoeconomic trade-offs in developing DAC systems using laminate-structured gas–solid contactors, encompassing the analysis of both contactor and process design spaces. The robust mass transfer and process models outlined in this study provide tools for evaluating DAC processes and designing DAC plants based on cost and energy analysis. First, the key contactor geometrical parameters are identified to understand the CO 2 productivity–energy demand trade-offs, where geometries yielding higher mass transfer rates can achieve higher CO 2 productivities at the expense of energy consumption by fans and steam use. Next, a detailed process parametric study is conducted for DAC systems coupled with steam-assisted temperature-vacuum swing adsorption (S-TVSA) to visualize the trade-offs in the multidimensional design space. The main cost driver dramatically changes over different process conditions, but the operating cost prevailed on the Pareto front, with potential to operate as low as 150 $/tonne-CO 2 (within the cost range of 148–504 $/tonne-CO 2 in this study where the DAC system is coupled with industrial facilities for steam production).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗