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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CommAwareNet: Towards Communication‐Aware Smart Facilities: Designing an Energy‐Efficient High‐Data‐Rate and Reliable Hybrid THz/VLC Comm. Arch. Reinforced with Intelligent Surfaces for Future Network

This project will develop foundations for energy-efficient, high-data-rate, and reliable communication links for next-generation advanced wireless technologies. The Internet of Everything is emerging as a major technology that can establish a massively connected network among both human-type users and machine-type devices. Particularly, massive machine-type communication is becoming the dominant communication paradigm for numerous emerging areas, including data center networking and wireless backhaul, healthcare (e-health), manufacturing (industry 4.0), utilities, transportation (connected cars and public safety), and virtual or augmented reality for human-machine interaction applications. The rising demand for high data rates, especially in indoor scenarios and densely populated outdoor environments, will eventually overload conventional RF-based technologies. This project aims to conduct fundamental research to design, optimize, and validate a communication-aware, energy-efficient, and reliable network (CommAwareNet) architecture for future smart facilities and environments that require unprecedented high throughput needs for massive numbers of the machine- and human-type users. The scientific and technical merit of this work lies in the research required to bring the proposed transformative CommAwareNet solution into fully functional and practical architecture.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A Dynamic Risk Framework for the Optimization of Physical Security Posture of Nuclear Power Plants

This paper describes an ongoing work within the Light Water Reactor Sustainability pathway at Idaho National Laboratory (INL) to optimize security and cost of nuclear power plants. It introduces the dynamic risk assessment tool developed at INL, Event Modeling Risk Assessment using Linked Diagrams (EMRALD). EMRALD was leveraged to optimize the security posture of a nuclear power plant by integrating force-on-force (FOF) simulations and operator mitigation actions including the dynamic and flexible coping strategies (FLEX). To illustrate the methodology, four attack scenarios were modeled in a commercially available FOF simulation tool using a hypothetical nuclear power plant facility. The simulation results provide valuable insights into possible attack outcomes, as well as the probabilistic risk of core damage event given these outcomes. Safety mitigation procedures were modeled in EMRALD dependent on the attack outcomes by considering human operator uncertainties. The results demonstrate that the number of armed responders can be optimized, while still maintaining the same protection level as the initial security posture. The proposed modeling and simulation framework of integrating FLEX equipment with FOF models enables the nuclear power plants to credit FLEX portable equipment in the plant security posture, resulting in an efficient and optimized physical security system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Effect of modeled time horizon on quantifying the need for long-duration storage

Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are $\$$13 USD/kWh (or less) and $113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids.

42 ENGINEERING↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Smart Charging of Fleet and Personal Electric Vehicles through Joint Vehicle-to-Grid Optimization

As electric vehicle (EV) adoption accelerates, vehicle-to-grid (V2G) strategies offer advantages over unmanaged charging (V0G) by enhancing grid stability, reducing fleet operation costs, and supporting integration of variable generation resources. This research develops a day-ahead optimization framework linked with agent-based simulations to evaluate coordinated V2G participation by fleet and personal EVs under 5 energy-pricing settings in Austin, Texas. Three scenarios (V0G, fleet-only V2G, and joint-V2G) are examined, considering real-time price and grid profiles, health-damage costs, and operational constraints for both fleet and personal EVs. Results show how V2G scenarios shift fleet EV charging to mid-day while enabling strategic battery-discharge during evening peaks, mitigating grid stress and lowering EV energy costs. V2G delivers close to 80% energy-cost savings for a 2000-EV fleet in Austin on grid-stressed days, with 55% lower charging pollutant outputs. Joint-V2G amplifies system-level benefits by complementing fleet discharge, but smart-charging equipment costs can offset those benefits.

Electric vehicle↗
DOE OSTI2026

A Full-Induction Magnetohydrodynamics Solver for Liquid Metal Fusion Blankets in Vertex-CFD

Multiphysics modeling of liquid metal fusion blankets, which produce tritium and convert energy of neutrons created via fusion reactions into heat, is crucial for predicting performance, ensuring structural integrity, and optimizing energy production. While traditional blanket modeling of liquid metal flows during normal steady operating conditions commonly employs the inductionless approximation of the magnetohydrodynamics (MHD) equations, transient scenarios, when the plasma-confining magnetic field varies on millisecond time scales, require a full-induction MHD approach that dynamically evolves the magnetic field via the time-dependent induction equation. This paper presents the formulation, implementation, and initial verification of a full-induction MHD solver integrated within the open-source Vertex-CFD framework, which aims to achieve tight multiphysics coupling, a flexible software design enabling easy extension and addition of physics models, and performance portability across computing platforms. The solver utilizes finite element spatial discretization, implicit Runge–Kutta time integration, and an inexact Newton method to solve the resulting discrete nonlinear system, leveraging Trilinos packages for efficient computation. Verification against selected benchmark problems demonstrates accuracy and robustness of the solver. Furthermore, when the solver is applied to an idealized blanket model in 2.5D and full 3D, results obtained with Vertex-CFD are in good agreement with recently published quasi-2D simulations. These findings establish a computational foundation for future simulations of transient MHD phenomena in liquid metal blankets with Vertex-CFD, and open avenues for future extensions and performance optimizations.

Endeve, Eirik [ORNL] (ORCID:0000000312519507)↗
DOE OSTI2025

Getting brighter: Impacts of improved day-ahead solar forecasts in high-solar, high-storage electricity systems

This paper analyzes the impacts of improved day-ahead solar forecasts on costs and dispatch in the solar-rich Southeast U.S. It uses an optimized high-solar, high-storage resource portfolio in which solar generation capacity accounts for 45 % of total installed capacity (34 %–36 % of generation) and energy storage capacity (43 GW) is equivalent to 33 % of peak demand. In a base scenario, improved day-ahead solar forecasts reduce production costs by $\$87$ million per year ($\$0.13$ per MWh load, $2023$$). This level of savings is within the range or lower than earlier studies of solar forecast improvements at lower levels of solar generation (<25 % of total generation). In this study, solar expansion was accompanied by two important sources of flexibility for managing solar forecast error: energy storage and day-ahead solar curtailment. Furthermore, the analysis finds that regional coordination complements day-ahead solar forecast improvements while natural gas commitment flexibility is a substitute for forecast improvements, as the improved solar forecast leads to sub-optimal commitment of thermal units. Day-ahead solar forecast improvements reduce reserves required to manage forecast error by 30 %. Fewer reserves to manage large, infrequent solar forecast errors could be an important benefit of improved solar forecasts.

14 SOLAR ENERGY↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Design space exploration and decision‐making for a segmented ultralight morphing 50‐MW wind turbine

Abstract Wind turbine design encompasses many different aspects including aerodynamic, structural, electrical, and control system design. To achieve optimal plant performance, a system design approach is utilized in which the performance of the whole wind turbine is evaluated and quantified during operational scenarios with subsystem interactions. In this paper, the design for a Segmented Ultralight Morphing Rotor (SUMR) 50‐MW wind turbine is presented utilizing levelized cost of energy (LCOE) for design choices, with additional quantification of simulated performance shortcomings at the 50‐MW scale. The multi‐disciplinary design process results in a final ultra‐scale turbine configuration that outperforms other existing offshore wind farms regarding the LCOE.

Kianbakht, Sepideh↗
DOE OSTI2023

A net-zero emissions strategy for China’s power sector using carbon-capture utilization and storage

Decarbonized power systems are critical to mitigate climate change, yet methods to achieve a reliable and resilient near-zero power system are still under exploration. This study develops an hourly power system simulation model considering high-resolution geological constraints for carbon-capture-utilization-and-storage to explore the optimal solution for a reliable and resilient near-zero power system. This is applied to 31 provinces in China by simulating 10,450 scenarios combining different electricity storage durations and interprovincial transmission capacities, with various shares of abated fossil power with carbon-capture-utilization-and-storage. Here, we show that allowing up to 20% abated fossil fuel power generation in the power system could reduce the national total power shortage rate by up to 9.0 percentages in 2050 compared with a zero fossil fuel system. A lowest-cost scenario with 16% abated fossil fuel power generation in the system even causes 2.5% lower investment costs in the network (or $16.8 billion), and also increases system resilience by reducing power shortage during extreme climatic events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Valuation of Distributed Wind Turbines Providing Multiple Market Services

The role of wind turbines has traditionally been limited to providing energy capacity to the grid, but the availability of smart inverters and recent regulatory changes provide the technical and policy capability for wind turbines to also provide ancillary services. However, in contrast to the technical and policy aspects, the valuation of distributed wind turbines providing such services has not been thoroughly studied. This paper presents an optimal market-participation method for distributed wind turbines and valuates different strategies in California Independent System Operator’s balancing area. The services include energy capacity, regulation up and down, and reserves. An optimization problem is formulated to determine optimal power output for each service and demonstrated using historical data for one complete year. The revenues from multiple services are quantified, and a sensitivity analysis is performed to relate market prices with revenues. It is found that the optimal strategy generates 6% more revenue compared to the revenue from participating in the energy market only. Also, the reduced energy prices in future scenarios increase the relative importance of market participation in ancillary services.

Bhatti, Bilal Ahmad↗
DOE OSTI2020

FY19 FES Theory Performance Target (Final Report)

Understanding the relevant turbulent transport mechanisms at the edge of a high-performance tokamak is essential for predicting and optimizing the H-mode pedestal structure in future burning plasma devices. Global electromagnetic gyrokinetic simulations will be performed based on representative experimental pedestal scenarios in order to clarify which instabilities are most important for each of the particle and heat transport channels. Edge transport modeling will be performed in order to estimate and bound the particle and heat sources—e.g., the ionization density source and the atomic energy loss channels due to ionization, charge exchange, and radiation. Comparisons will be made with data from the DIII-D, JET, C-Mod and NSTX or MAST experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Investigation of a Hybrid Carbon Capture System at a NGCC Power Plant That Performs Direct Air Capture During Off-Peak Hours

The goal of this project was to investigate the feasibility of coupling NGCC power plants with membrane and solid sorbent carbon capture to achieve net negative carbon emissions. This novel hybrid system has a couple of potential benefits: 1) the carbon capture systems could help the NGCC plant by minimizing its carbon footprint and giving it a new revenue stream from direct air capture, and 2) the NGCC plant could help direct air capture scale up quickly and costeffectively by providing it with electricity, steam and compressed air streams. In order to investigate this hybrid design, we developed a complex, integrated system model consisting of: 1) a NGCC power plant model developed by Dr. Bhattacharyya’s team at WVU, 2) a membrane carbon capture model developed by Dr. Lipscomb’s team at U Toledo, and 3) a solid sorbent carbon capture model developed by Dr. Hornbostel’s team at U Pittsburgh. Mr. Matuszewski’s team at AristoSys LLC integrated these three models into a single, integrated model using FOQUS, an optimization tool developed by the NETL CCSI 2 program. Optimizations were then performed on this integrated model to determine the cost and carbon capture rates under a range of electricity and carbon price scenarios.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

A Forward-Looking Dataset of EV Managed Charging Resource and Costs

This presentation summarizes a high-resolution, forward-looking dataset of EV adoption, EV charging, and managed charging resource. Vehicle-level data are grounded in current adoption and charging patterns, and ~200,000 real-world vehicle-weeks of travel data covering all on-road segments (i.e., light-duty, transit and school buses, local, regional and long-haul medium- and heavy-duty). The data, which include multiple charging profiles per vehicle to bound flexibility, are then processed and aggregated to describe baseline charging and charge management resource by county, hour, year, scenario, and vehicle type. Coupled with one of four scenarios of how EV managed charging costs might evolve over time, the dataset enables a power sector capacity expansion model to select cost-optimal quantities of EV managed charging and supply-side resources to reliably satisfy demand. Five integration strategies: Baseline, Daytime and Flat (passive), Flex (active), and Stress (anti-strategy), illustrate how baseline charging and flexibility potential changes with EVSE build-out and charging preferences.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Development of Automated Atom Probe Tomography capability to study the influence of applied voltage and laser power on the final apparent composition of the analyzed specimen

This study presents the development and implementation of an autonomous Bayesian optimization (BO) framework for controlling and optimizing experimental parameters in Atom Probe Tomography (APT). Using commercial silicon needle samples as a benchmark system, we demonstrate that BO can efficiently navigate the complex parameter space of voltage and laser power to achieve target charge state ratios (specifically Si + /(Si + +Si 2+ )) with minimal experimental evaluations. Our implementation integrates Gaussian Process modeling with the CAMECA atom probe control framework, enabling autonomous adjustment of experimental conditions in real-time. Results show that the algorithm successfully converges to target ratios under different scenarios: maintaining a reference ratio, increasing the ratio (favoring Si 1+ ), and decreasing the ratio (favoring Si 2+ ). The system adapts to specimen evolution during analysis, compensating for changes in apex geometry while maintaining optimization targets. This work establishes a proof of concept for AI-driven optimization in APT, addressing the traditional challenges of manual parameter tuning and paving the way for applications to more complex materials where compositional accuracy is critical.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Simulation Modeling Approach to Optimizing Nuclear Waste Dispositioning

The dispositioning of nuclear waste generated at facilities across the country is an ongoing battle that affects us all. National laboratories and research centers dealing in medical research, clean energy, and other nuclear activities such as the Department of Energy (DOE) facilities face the need to properly manage and dispose of nuclear waste. A dynamic modeling solution would enable the DOE and others to make decisions on waste disposal and technological options. In doing so, this research explores modeling techniques using available data to address these situations. The focus being on developing an initial robust and adaptable discrete event model using the ExtendSim tool. This modeling effort will target the dispositioning of transuranic waste at the Savannah River National Laboratory (SRNL) which can be expanded to represent the current state of disposition process for waste generated at other DOE facilities. The model aims to assess resource allocation and waste processing options to stabilize productivity and cut the backlog of nuclear waste. By assessing the results of different scenarios, this research aims to provide actionable insights for the DOE. This approach has the potential to significantly improve the management of radioactive waste, offering the capability of evaluating options for optimizing the process for nuclear waste disposal. The findings of this study can serve as a valuable resource for decision-makers and other national laboratories, or research entities engaged in nuclear operations by enabling them to make more informed choices.

Andaverde, Alexis↗
DOE OSTI2022

Kinetics-Informed Neural Networks

Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions to solve ordinary differential equations (ODEs) constrained by differential algebraic equations (DAEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multi-objective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We analyze a set of scenarios to establish the extent to which kinetic parameters can be retrieved from transient kinetic data, and assess the robustness of the methodology with respect to statistical noise. Furthermore, this approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deployment Pathways for Long Duration Energy Storage

We apply a least-cost generation expansion model of the continental United States to assess how optimal investments in long-duration energy storage (LDES) technologies are impacted by changes in system generation portfolios and technology costs, assessing 369 capacity expansion scenarios in total. The expansion model considers 8,760 h of chronological operations for the entire target year, 2040. We find that low-cost LDES technologies can reduce generation investments and system costs. Specifically, once the costs for 24- and 100-h storage reach $38/kWh and $14/kWh, respectively, substantial deployments are observed. The distribution of storage investments across durations is strongly influenced by the system generation portfolio. We also demonstrate that a high-fidelity temporal representation is required to capture the value of LDES in generation expansion. Finally, we conduct a regression analysis of our capacity expansion results and find that LDES deployments are positively correlated with the combined wind and solar capacity share and negatively correlated with peaking and baseload shares.

Levin, Todd↗