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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Investigating the Impact of Power-Take-Off System Parameters and Control Law on a Rotational Wave Energy Converter’s Peak-to-Average Power Ratio Reduction

Due to the irregular nature of real waves, the power captured in a wave energy converter (WEC) system is highly variable. This is an important barrier to the effective use of WECs. To address this challenge, this study focuses on a rotational WEC power-take-off system in which high-speed and high-efficiency generators along with a torque/power smoothing inertia element can be effectively utilized. In the first phase of this study, the U.S. Department of Energy’s reference model 3 (WEC-Sim RM3; two-body point absorber), along with a slider-crank WEC, were integrated for linear to rotational conversion. Relative motion between the float and spar in RM3 was the driving force for this slider-crank WEC, which is connected to a motor/generator set through a gearbox. RM3 geometry was scaled down by 25 times to work within the limits of the physical motor/generator set used in the experimentation. Once the integration in a hardware-in-the-loop simulation environment was successfully completed, data on the peak-to-average power ratio was collected for various wave conditions including regular and irregular waves. The control algorithm designed to keep the system in resonance with waves was able to maintain relatively high speed depending on the specific gear ratio and wave period. Initial results with hardware-in-the-loop simulations reveal that gear ratio and crank radius have a strong impact on the peak-to-average power ratio. In addition, it was found that output power from the generator was maximized at a larger gear ratio, as the crank radius was increased.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

A bilevel multistage stochastic self-scheduling model with indivisibilities for trading in the continuous intraday electricity market

In this paper, we study the profit maximization problem of a virtual power plant trading in the continuous intraday electricity market. Our virtual power plant model is compatible with renewable, and thermal assets, covering a range of virtual power plants currently participating in energy markets. We model the trading problem as a bilevel multistage stochastic program. The upper level of the problem accounts for the profit maximization of the virtual power plant with explicit modeling of the technical constraints of the operational status of the thermal power plant including minimum start-up and shut-down times, ramp-up and ramp-down rates, and minimum generation level. The upper level also decides which continuous and indivisible (fill-or-kill) orders are submitted to the market. The lower-level problem accounts for the clearing of the continuous intraday market, i.e., matching of buy and sell orders. Because of the presence of fill-or-kill orders, the lower-level problem is mixed-integer, which prevents its direct conversion to a single-level problem using duality. In order to solve this challenging problem, we develop a convex-hull extended formulation for the lower-level problem, apply duality theory to obtain a single-level stochastic equivalent formulation, and employ McCormick envelopes to turn the problem into a multistage stochastic mixed-integer linear problem, which we solve using the stochastic dual dynamic integer programming algorithm. We conduct numerical experiments and analyze the optimal trading behavior of a virtual power plant trading in an ideal continuous market without arbitrage.

Bilevel multistage stochastic programming problem↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗
DOE OSTI2022

A Q-Learning-Based Approach for Distributed Beam Scheduling in mmWave Networks

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no centralized control or explicit coordination among them. Our goal is to design efficient distributed beam scheduling and power allocation schemes such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference conditions by learning from experience and thus can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

First evidence of dominant influence of E × B drifts on plasma cooling in an advanced slot divertor for tokamak power exhaust

Addressing power exhaust in tokamaks is presently recognized as one of the major remaining open issues for the development of fusion reactors. At the forefront of this endeavor is the effort to develop an advanced divertor by maximizing dissipation of plasma power and momentum inside the divertor. Here, we demonstrate, for the first time, that the electromagnetic (E × B) drifts exert a key influence on plasma and gas dynamics in a new advanced slot divertor in the DIII-D tokamak, named the small angle slot (SAS). SAS leverages the effect of drifts to achieve a highly dissipative divertor with electron temperature T e ≲ 10 eV over a wide range of plasma densities, for ion B × ∇B away from the divertor, as used for advanced tokamak operation on DIII-D. Modeling with the SOLPS-ITER code shows that for this drift direction, the E × B flow carries particles toward the outer common flux region (CFR) via the private flux region (PFR), reinforcing neutral recycling and enhancing divertor dissipation. In contrast, for the opposite field direction, the E × B flow carries particles away from the outer CFR into the PFR, offsetting the anticipated SAS geometric effects. Furthermore, this finding is an important step in the understanding of the behavior of advanced divertors for achieving a power exhaust solution for fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Subsonic flight test evaluation of a performance seeking control algorithm on an F-15 airplane

The subsonic flight test evaluation phase of the NASA F-15 (powered by F 100 engines) performance seeking control program was completed for single-engine operation at part- and military-power settings. The subsonic performance seeking control algorithm optimizes the quasi-steady-state performance of the propulsion system for three modes of operation. The minimum fuel flow mode minimizes fuel consumption. The minimum thrust mode maximizes thrust at military power. Decreases in thrust-specific fuel consumption of 1 to 2 percent were measured in the minimum fuel flow mode; these fuel savings are significant, especially for supersonic cruise aircraft. Decreases of up to approximately 100 degree R in fan turbine inlet temperature were measured in the minimum temperature mode. Temperature reductions of this magnitude would more than double turbine life if inlet temperature was the only life factor. Measured thrust increases of up to approximately 15 percent in the maximum thrust mode cause substantial increases in aircraft acceleration. The system dynamics of the closed-loop algorithm operation were good. The subsonic flight phase has validated the performance seeking control technology, which can significantly benefit the next generation of fighter and transport aircraft.

Gilyard, Glenn B.↗
DOE OSTI2023

Rapidly Viable Sustained Grid

Rapid recovery of power flow, possibly after a blackout, is a crucial need arising in scenarios that are increasingly becoming more frequent; here, solutions for rapid viability of power while the grid is being restored are urgently needed to keep critical infrastructure (CI) online. Increasingly, after the initial recovery phase, sustenance of reliable power requires assistive services to the grid for long periods of time. Even though the need is urgent, there is only sparse effort present toward a comprehensive framework/strategy for making power rapidly viable with an emphasis on sustained grid ancillary services; which is the focus of this proposal. The proposed concept envisions four phases. In the first phase, when a large portion of power is disrupted (see Figure 1(a)), emphasis is on bringing CI online with the objective of maximizing the time horizon of power viability using resources available at the CI. In the second phase (Figure 1(b)), neighborhood resources are tightly coordinated that forms the CI’s central-core (CC) to provide guaranteed viability of CI over a longer horizon. In the third phase, self-organizing power networks are expanded in a distributed layer supporting the central-core (Figure 1(c)). In the fourth phase, separate CI-networks coalesce and are controlled in a coordinated fashion to provide grid ancillary services (AS), such as primary frequency control and enhancing grid resiliency. With time, system efficiencies and penetration of renewables increase, while the time-horizon of guaranteed sustenance of CI is maximized. Under proposed work, the concept will be instantiated with a focus on medical centers as CI. Comprehensive power hardware in the loop strategies and emulated field tests will guide and validate devised solutions. A strong T2M effort to commercialize resulting technology is outlined. The proposed technology will be transformative for the grid. It will fundamentally change the way large contingencies are managed where power systems and critical infrastructure transition from being fragile to being robust using intelligent, self-organizing control for coordinating resources, enhanced resiliency and use of sustainable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Design and Study of Inductively Coupled Plasma Chamber Components Using the SupRISE Test Device at DIII-D

The DIII-D National Fusion Facility aims to increase the auxiliary heating power for the tokamak by upgrading the Neutral Beam Injection (NBI) system. In collaboration with North Carolina State University, the conventional ‘arc and filament’ NBI ion sources will be converted to inductively coupled plasma (ICP) sources which utilize radio frequency (RF) coupling to maximize reliability for high power operation. In support of this initiative, a full scale test device (SupRISE, Superior Radiofrequency Ion Source Experiment) is currently under construction at the DIII-D Facility. In preparation for the construction of a full scale prototype that can be installed on the DIII-D NBI system, experiments on SupRISE will be conducted to determine the optimal RF frequency for high power coupling, Faraday shield slit configuration, and ICP chamber length. SupRISE is comprised of an approximately 30 x 70 cm quartz dielectric vessel with an internal Faraday shield enclosed in a secondary vacuum chamber to ensure structural stability of the dielectric. Actively cooled front and back plates are designed to reduce the thermal stresses on the plasma facing components and mate with the existing accelerator used by the NBI system at DIII-D. 50 kW of RF power will be coupled to the plasma through the quartz over a variable frequency range of 4-8 MHz to sustain a plasma density of ∼ 10 18 m −3 for a 10 s ON, 210 s OFF duty cycle. Various modelling efforts have been employed to simulate the thermal and stress profiles over the primary components of the SupRISE device as well as the inductance behavior of the RF antenna. These simulation results and the final design for SupRISE will be presented. An additional reduced-scale predecessor ICP source (called RISE) has been used to develop a predictive match model that will be applied to frequency optimization studies on SupRISE. Furthermore, the outcomes of this research and complementary efforts at North Carolina State University are essential for the incorporation of ICP NBI positive ion sources at the DIII-D facility.

DIII-D↗
NASA NTRSDate not supplied

Advancements in the Camera Setup for Unsteady Pressure Sensitive Paint at NASA Ames Research Center

A test in the Unitary Plan Wind Tunnel at NASA Ames Research Center is scheduled for early 2024 with the intent of showcasing improvements made to unsteady Pressure Sensitive Paint technology. Analysis was performed to investigate how to maximize the system resolving power by optimizing the camera parameters, specifically focal length and focusing distance, while still subject to other system requirements such as field of view and relatively low illumination from the paint. Additionally, the system is upgrading to an 8-camera system from a 4-camera system, so analysis was performed to determine the best way to extend that system to maximize camera coverage and how to most effective reduce the system uncertainty by averaging multiple measurement using camera overlap. These analyses have uncovered new opportunities to improve the unsteady Pressure Sensitive Paint technology without the need for new or expensive hardware and will allow the system to collect better data with simple changes to the design process.

Camera Calibration↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Technoeconomic optimization of superalloy supercritical CO 2 microtube shell-and-tube-heat exchangers

Heat exchangers are critical components of aerospace systems and improve efficiency of operation by providing waste heat recovery. Supercritical CO has emerged as a promising candidate working fluid due to its potential in increasing the power density of heat exchangers. In this paper, a generalized costing model is developed to estimate the capital costs incurred to manufacture microtube shell-and-tube heat exchangers. This model is utilized in conjunction with an accurate and efficient 2D numerical shell-and-tube heat exchanger performance prediction model to conduct optimization studies with two key objectives – minimization of cost and maximization of heat exchanger power density – on supercritical CO microtube heat exchangers utilizing superalloy Haynes 282 as the solid material. A methodology is then demonstrated to optimize these heat exchangers using Particle Swarm Optimization for aerospace applications and highly compact and cost-effective optimal designs with power density around 20 and cost per conductance less than 5 $ ∙ K/W are obtained.

42 ENGINEERING↗
NASA NTRS1999

Design and Testing of a Breadboard Electrical Power Control Unit for the Fluid Combustion Facility Experiment

The Fluid Combustion Facility (FCF) Project and the Power Technology Division at the NASA Glenn Research Center (GRC) at Lewis Field in Cleveland, OH along with the Sundstrand Corporation in Rockford, IL are jointly developing an Electrical Power Converter Unit (EPCU) for the Fluid Combustion Facility to be flown on the International Space Station (ISS). The FCF facility experiment contains three racks: A core rack, a combustion rack, and a fluids rack. The EPCU will be used as the power interface to the ISS 120V(sub dc) power distribution system by each FCF experiment rack which requires 28V(sub dc). The EPCU is a modular design which contains three 120V(sub dc)-to-28V(sub dc) full-bridge, power converters rated at 1 kW(sub e) each bus transferring input relays and solid-state, current-limiting input switches, 48 current-limiting, solid-state, output switches; and control and telemetry hardware. The EPCU has all controls required to autonomously share load demand between the power feeds and--if absolutely necessary--shed loads. The EPCU, which maximizes the usage of allocated ISS power and minimizes loss of power to loads, can be paralleled with other EPCUs. This paper overviews the electrical design and operating characteristics of the EPCU and presents test data from the breadboard design.

Kimnach, Greg L.↗
NASA NTRS2018

Next-Generation Radioisotope Thermoelectric Generator Study

Radioisotope Thermoelectric Generators (RTGs) have been used to power NASA missions of various types throughout the past five decades. The most recent RTG iteration, used for NASA’s Mars Science Laboratory, is the Multi-Mission RTG (MMRTG), which is currently the only spaceflight-qualified system available. The U.S. planetary science community has expressed a desire for more power system options to be available to accommodate a range of ambitious future mission concepts across the solar system. Recent advancements in thermoelectric (TE) materials technology have raised a potential for significantly increased efficiency in future RTGs, which helped spur a recent in-depth NASA study of options for future systems. A “next-generation” RTG study was conducted to develop new RTG concepts that could meet the needs of planetary science missions through the 2030s and beyond. A Next-Generation RTG would aim to extend the types of potential NASA missions able to be supported, while fulfilling requirements related to technical risk and schedule. In this study, 21 potential thermoelectric couple configurations were analyzed by considering various high-performance, high-temperature TE materials and segmentation techniques that maximize convertor efficiency and power density. System modularity was explored, and found to be a promising means to offer improved flexibility for NASA mission concepts with varying scope and power requirements. This paper presents the results of the study, demonstrating the viability of developing an updated RTG system design, and defining conceptual system approaches for a new, potentially revolutionary RTG.

Matthes, Christopher S. R.↗