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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Testing- and Model- Based Optimization of Coal-fired Primary Heater Design for Indirect Supercritical CO 2 Power Cycles (Final Scientific and Technical Report)

The overall objective of this project was to perform the R&D necessary to mitigate the risk associated with the design of a primary heat exchanger for a solid-fired combustion system coupled with an indirect-fired closed-loop Brayton Cycle utilizing supercritical CO 2 . The key technological hurdle was the coupling of a solid-fuel firing system with the primary heater, which poses a singular challenge, which is the management of burner performance and operational conditions in a way to manage heat exchanger tube metal temperatures and temperature ramp rates in the absence of fluid phase change on the inside of the tubes. We designed and built the first ever pseudo power system employing a simple recuperated supercritical CO 2 closed-loop Brayton Cycle coupled to a solid-fuel fired system. Advanced coupled CFD and process modeling were used to design the primary heat exchanger (PHX), which consisted of both radiative and convective sections, to limit tube metal temperatures resulting from the heat release profile of the solid fuel flame near the radiative tubes. The heat exchanger was designed to produce finished CO 2 temperatures of 600 °C a pressure of 20.7 MPa and CO 2 flow of 5.5 kg/s. The constructed PHX was capable of 1.2 MWth heat uptake. During design of the PHX, the modeling showed that most variables influencing flame shape (burner stoichiometric ratio and register velocities and swirl) were not suitable to manage heat flux to the metal surfaces. This is because they substantially increased adiabatic flame temperature through the influence of localized stoichiometric ratio. Excess air and firing rate were the two most powerful variables that could be used to control tube surface temperatures. The coupled system was operated for a total of 407 hours, with the longest continuous run of 248 hours. For 62% of the operational time, the unit was unmanned and in automatic control. The fuels used for the testing included natural gas, two Utah Bituminous coals, woody biomass, and bagasse. During the testing we were able to verify the 1.2 MWth heat uptake and we operated at a finished CO 2 temperature of 607 °C and a pressure of 20.3 MPa simultaneously. The real-time corrosion rate of the Super 304H tube CO 2 surface in the region of the radiative section of the PHX were measured, at an approximate temperature of 550 °C. The two key variables related to corrosion rate are the pressure and flow rate of the CO 2 . A technoeconomic analysis was performed at a scale of 120 MWE. The updated analysis showed that the efficiency of an sCO 2 power producing plant will be related to the pressure drop of the PHX.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

A Dual-Mode Hybrid System Combining Solar Thermal With Pumped Thermal Energy Storage

A hybrid system that delivers renewable electricity generation and electricity storage capabilities is introduced. This dual-mode hybrid system is based on Pumped Thermal Energy Storage (PTES) which uses a heat pump to convert electricity into thermal energy that is trans-ferred to silica particles which are stored in concrete silos. The stored heat is later converted back to electricity in a heat engine. The heat pump and heat engine use a closed Joule-Brayton cycle and fluidized bed heat exchangers. If the PTES system is co-located with an array of concentrating solar mirrors and a particle receiver, then the silica particles may also be heated up by the concentrating solar power (CSP) system. The PTES heat engine may also be used to convert the stored solar heat into electricity, thereby sharing this system between the PTES and CSP systems and reducing costs. However, this requires careful management of the heat engine off-design parameters. A thermodynamic model is used to evaluate the performance of the dual mode PTES-CSP hybrid system. The model accounts for turbomachinery effi-ciency, and approach temperature and pressure loss in heat exchangers, as well as other sources of inefficiency, such as motor-generator losses, and air fan power. The hybrid system also requires the evaluation of the turbomachinery efficiency and pressure ratio at off-design conditions since the CSP system operates over different temperature ratios than the Carnot Battery. Several design variables and design modifications are investigated, such as pressure ratios and maximum temperatures.

14 SOLAR ENERGY↗
DOE OSTI2025

Surrogate models for linear response

Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field vacuum; however, its high computational cost limits model calibration and uncertainty quantification studies. Here, we present two complementary QRPA surrogate models and apply them to study response functions of finite nuclei. One is a reduced-order model that exploits the underlying QRPA structure, while the other utilizes the recently developed parametric matrix model algorithm to construct a map between the system’s Hamiltonian and observables. Our benchmark applications, the calculation of the electric dipole polarizability of 180 Yb and the 𝛽-decay half-life of 80 Ni, show that both emulators can achieve 0.1%–1% accuracy while offering a 6–7 orders of magnitude speedup compared to state-of-the-art QRPA solvers. These results demonstrate that the developed QRPA emulators are well positioned to enable Bayesian calibration and large-scale studies of computationally expensive physics models describing the properties of many-body systems.

Beta decay↗
DOE OSTI2026

Integrated Process Model Utilization and Development for Inertial Fusion Energy

This CRADA between LLNS and Longview Fusion Energy Systems utilized a modernized LLNL Integrated Process Model (IPM) to assess technoeconomic viability and power plant configurations for Inertial Fusion Energy (IFE). During the collaboration period, a streamlined IPM model was produced by LLNL, which consolidated a combination of 3 models under one file. Additionally, obsolete parameters were removed and parameters available for trade space analysis were organized in a user-friendly fashion on the front interface. Gain scaling curves were discussed and analyzed in the framework of the IPM. Additional updates were made to account for cost scaling to today's dollars using an average inflation rate. Heat transfer material costing was updated using a ground up approach with public vendor data. Further levelized cost of electricity (LCOE) methods and models were discussed and reviewed for applicability to the IPM. Prior published literature on optical scaling was also discussed. Moreover, as part of this CRADA, the Participant developed a fusion technology development roadmap and point designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Integrated Process Model Utilization and Development for Inertial Fusion Energy

This CRADA between LLNS and Longview Fusion Energy Systems utilized a modernized LLNL Integrated Process Model (IPM) to assess technoeconomic viability and power plant configurations for Inertial Fusion Energy (IFE). During the collaboration period, a streamlined IPM model was produced by LLNL, which consolidated a combination of 3 models under one file. Additionally, obsolete parameters were removed and parameters available for trade space analysis were organized in a user-friendly fashion on the front interface. Gain scaling curves were discussed and analyzed in the framework of the IPM. Additional updates were made to account for cost scaling to today's dollars using an average inflation rate. Heat transfer material costing was updated using a ground up approach with public vendor data. Further levelized cost of electricity (LCOE) methods and models were discussed and reviewed for applicability to the IPM. Prior published literature on optical scaling was also discussed. Moreover, as part of this CRADA, the Participant developed a fusion technology development roadmap and point designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Cost-Effective Wave Energy Harvesting System with Maximum Power Point Tracking Capability

In this paper, we propose a low-cost bidirectional four-switch inverter that transfers three-phase energy between a permanent magnet synchronous generator (PMSG) driven by a wave energy collector and the power grid or a local ac load. To achieve high control performance, the machine-side inverter uses a high-gain observer for rotor position estimation and a Lyapunov-based approach for torque control. For the gridside inverter control, we estimate the filter capacitor voltage with a model reference adaptive system and control the dc-link voltage with a Lyapunov-based energy function. We also track the maximum power point from the sea waves with an integral control law that actively matches the generator impedance with that of the wave collection device. The proposed system supports both grid-following/forming modes. We validate our control design with simulation results on a 3-kW hardware setup.

wave energy, power systems integration, control de↗
DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Wave

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wave energy conversion device. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen. While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the wave energy, NLR used a wave energy converter model from PacWave. These devices can be equipped with accumulators and pressure relief values to smooth the power output by storing and releasing hydraulic energy. Using a peak power output of 10 MW, the model created two 25-minute profiles: one with and one without the accumulators and pressure relief valves. To down select the profile data from the native resolution of 20 Hz to 1 Hz, NLR took the mean of every 20 data points. NLR experimented with two simulated wave energy power plants: one that peaks at 10 MW, and one that peaks at 5 MW. These profiles were scaled for the physical 1.25 MW electrolyzer by multiplying the original profiles by one eighth and one quarter, respectively. The first profile matches the capacity rating of eight of the 1.25 MW electrolyzers, while the second matches four electrolyzers. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wave electrolysis experiment and is formatted as follows: {technology}-{accumulator?}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “wavePacWave-Noacc_4-400.zip” represents the 25 minute-long experiment using the PacWave’s wave energy converter model, equipped with no accumulator, connected to four 1.25-MW electrolyzers with their power supplies set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. An experiment, labeled “characterization_200.zip”, demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all wave profiles combined into one dataset labeled "combined_wave_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis.

08 HYDROGEN↗
DOE OSTI2025

Capturing Historic Reliability Performance Through Graph Databases: A Model Based System Engineering Approach

With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the discovery of anomalous behaviors or degradation trends, the identification of the possible causes behind such behaviors and trends, and the prediction of their direct consequences. This paper directly targets the generation of knowledge from ER data by putting “data into context”. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by identifying first which elements of the developed MBSE elements they are referring to. This task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process “knowledge extraction” where our methods to extract knowledge from textual data. Lastly, once numeric and textual ER data elements have been processed and “understood”, we discover possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if there is a temporal relation among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Symmetry-projected spin-AGP methods applied to spin systems

Symmetry-projected wave function methods capture static correlation by breaking and restoring the symmetries of a system. In this article, we present the symmetry-projected spin antisymmetrized geminal power (spin-AGP) state projected onto space group symmetry as well as complex conjugation, spin-flip, and time-reversal symmetries. The method is benchmarked on the 1D XXZ model and the 2D J 1 − J 2 model with square and triangular lattices. Our results indicate that symmetry projection methods provide a powerful tool for frustrated spin systems.

Antisymmetrized geminal power↗
DOE OSTI2025

Modeling of Inductive Constant Power Load for Electromagnetic-Transient Simulations-Part II

This paper improves the dynamic constant power (CP) load model that was published in Part I, which is appropriate for electromagnetic-transient (EMT). The improved model conserves all features of its predecessor. For instance, it maintains a fixed power consumption (both active and reactive parts) and a fixed power factor for loads that are predominantly inductive. Furthermore, as the proposed model is a time-dependent system, it is applicable to both sinusoidal and non-sinusoidal case studies. However, the previous model cannot be easily integrated with numeric solvers because it simulated load data over one cycle all together, not sequentially in a time-step manner, due to the limitation involved with the power factor. The improved version, on the contrary, allows the load to be simulated at every time step, which would facilitate its integration with numeric solvers. The model's validity is confirmed by comparing its response with data that is synthesized from constant impedance load, and the result is satisfactory.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

High-Fidelity Multiphysics Assessment of the Molten Chloride Reactor Experiment (MCRE) Integrating Neutronics, Thermal-hydraulics and Thermochemistry

This work presents a MOOSE-based Multiphysics model of the LOTUS MSR reactor system. The model includes the coarse-mesh turbulent thermal-hydraulics including passive advection of delayed neutron precursors of the primary system through Pronghorn, the power density and neutronics calculation employing Griffin, and the chemical interactions through Gibbs energy minimization and redox potential with Thermochimica. Incorporating a plate-out model, the full multi-physics model is used to evaluate the effects of deposition and removal of solid compounds formed in the molten salt in the reactor surfaces, evaluating the concentrations and regions in which nickel, chromium and iron would be deposited. Results indicate an almost isothermal state of the fuel during operation at 25 kW that do not increase the plate-out effects due to low temperature gradients.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Joint Management and Optimization of Residential Natural Gas and Electricity Distribution Networks Coupled via Fuel Cells

The interesting properties of natural gas as well as the growing electric power demand worldwide have led to increasing attention to natural-gas-based distributed generation applications in electric distribution systems. This paper goes over the interdependency between a residential natural gas network and an electric distribution network that are coupled via fuel cells. The modeling of the gas network is introduced first, and then the algorithm for gas flow study is presented. The optimal placement and sizing of fuel cell based distributed generation systems are formulated to minimize the losses in both the gas and electric distribution networks, subject to their model constraints. In addition to this, in order to capture the probabilistic nature of the optimization problem under study, the K-means clustering algorithm is applied to the gas and electricity demands to determine hourly load states and their corresponding probabilities. Furthermore, simulation studies are carried out on an integrated system consisting of the IEEE 69-bus distribution feeder and a radial 27-node natural gas network to verify the developed optimization model and the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Modeling and Optimization of a Nuclear Integrated Energy System for the Remote Microgrid on El Hierro

Nuclear microreactors are a potential technology to provide heat and electricity for remote microgrids. There is potential for the microgrid on the island of El Hierro to use a microreactor, within an integrated energy system (IES), to generate electricity and provide desalinated water. This work proposes a workflow for optimizing and analyzing IESs for microgrids. In this study, an IES incorporating a microreactor, thermal energy storage (TES) system, combined heat and power plant, and a thermal desalination plant was designed, optimized, and analyzed using Idaho National Laboratory’s Framework for Optimization of Resources and Economics (FORCE) toolset. The optimization tool, Holistic Energy Resource Optimization Network (HERON), was used to determine the optimal capacity sizes and dispatch for the reactor and thermal energy storage systems to meet demand. The optimized reactor and TES sizes were found to be 11.61 MWth and 58.47 MWhth, respectively, when optimizing the IES to replace 95% of the island’s existing diesel generation needs. A dynamic model of the system was created in the Modelica language, using models from the HYBRID repository, to analyze and verify the dispatch from the optimizer. The dynamic model was able to meet the ramp rates while maintaining reactor power with minimal control adjustments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A bi-level advanced control framework for large-scale control of buildings with system-level impact

Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure. This work describes a method to improve the reliability of the grid through large-scale advanced building control. This paper develops a bi-level distributed control framework to shift the load of 153 buildings to achieve a system-level objective of tracking a power reference signal. This bi-level control is based on the previously-developed ANPV-MPC, a predictive controller that uses a Bayesian neural network to generate an accurate control model and adapt to changing conditions over time. By shifting the building electricity demand to better match the available power, the grid system supplying the buildings is more reliable as evidenced by the analysis of node voltages across an IEEE 13-bus distribution system. The proposed bi-level control framework tracks the system-level power reference with enough accuracy to regulate node voltages across the IEEE 13-bus distribution system within ANSI limits of ±5%. Additionally, the adaptive nature of ANPV-MPC allows each building across the system to adapt to changing conditions, further amplifying the system-level reliability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Integration of 5G and Time Sensitive Networks in Fossil Energy Generation Systems: A Case Study

Precise timing and data transmission within stringent time constraints are critical for numerous applications, such as robotics, virtual and augmented reality, industrial automation, energy and medical business, and various other sectors. Time-sensitive networks (TSN) and fifth-generation wireless communications (5G) are crucial for industrial communications, enabling convergent communication for various services using a common network core. Applications that are time-sensitive and require deterministic communications with low latency fall into this category, such as generation plant systems. This paper presents a simulated model of 5G-TSN for a fossil power plant based on the wired network parameters implemented in situ. Metrics analysis, comparison and future work are presented. © 2024 IEEE.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Multi-Timescale Optimal Operation Framework for Integrated Economic and Reliability Analysis of Hybrid Power Plants

This paper introduces a hierarchical modeling framework for hybrid power plants (HPP) to facilitate the operation of HPP in power systems similar to conventional generators (Congens) in the integrated multi-timescale optimal operation framework. To consider the uncertainties of HPP renewable power in the day-ahead scheduling, distributionally robust optimization (DRO) is used. To ensure that the state-of-charge (SOC) of energy storage systems in HPPs aligns closely with the planned value for long-term reliability, real-time SOC management is incorporated. In addition, an adjustable real-time control is designed for the robust delivery of HPP real-time services. Case studies performed on a revised IEEE 39-bus system demonstrate the effectiveness of the proposed framework for HPP operation. Simulation results highlight that the proposed framework not only can help operators schedule HPP similar to Congens in varying weather conditions but can also maintain the frequency reliability of the system.

frequency stability↗