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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling and Power-Hardware-in-the-Loop Validation of Synchronous Wind: An Inverterless Grid-Forming Wind Power Plant

Grid-forming (GFM) control of Type-3 and Type-4 wind turbine generators (WTGs) has attracted substantial attention in power systems research; however, the limited overcurrent capability of power electronics converters continues to deteriorate the grid strength of the evolving power systems. Synchronous wind, also known as a Type-5 WTG, offers a unique GFM solution to address grid integration and grid strength issues by keeping the grid largely synchronous at very high integration levels of renewable generation. A Type-5 WTG interfaces with the electric grid via a synchronous generator driven by a variable-speed hydraulic torque converter; hence, the wind rotor operates in variable-speed mode for maximum power generation, and the generator shaft remains synchronous to the grid. This paper develops and tests a high-fidelity model of a Type-5 WTG in a power-hardware-in-the-loop (PHIL) testing environment. The PHIL demonstration shows that a Type-5 WTG inherently behaves as a GFM unit and can obtain similar performance in terms of power responses, wind rotor dynamics, and efficiency compared to a Type-3 WTG in high-wind conditions. The developed model provides further insight into how Type-5 WTGs can benefit the smooth transition to power systems with high integration levels of inverter-based resources.

grid strength↗
DOE OSTI2024

Hydrogen Energy Storage System at Borrego Springs Towards an H2 Enabled 100% Renewable Microgrid

San Diego Gas & Electric's Borrego Springs Microgrid is one of the largest microgrids in the USA, serving 2500 residential customers, 300 commercial and industrial customers, and has a peak demand of approximately 14 MW. This microgrid is located at the end of a long transmission line, and is subjected to extreme weather events like storms, wildfires, and flooding, which frequently cause grid outages. The microgrid currently relies on 3.65 MW of diesel-powered generators to provide grid-forming services during these outages, which results in greenhouse gas and criteria pollutant emissions. However, the microgrid has access to approximately 37 MW of installed solar generation and struggles with overgeneration and curtailment. 1.5 MW/4.5 MWh of grid-scale batteries have been installed to capture some the overgeneration and provide resiliency, but a longer-duration low-greenhouse gas emission energy storage solution is needed. In our project, the team will evaluate in the lab and demonstrate in the field a grid-forming fuel cell inverter that can provide grid-forming services while utilizing hydrogen's energy storage scalability. We will first utilize NREL's Renewable Energy Integration and Optimization (REopt) platform to perform analyses on future microgrid scenarios that use hydrogen assets to reduce or eliminate the need for diesel backup generators. We will then evaluate the grid-forming inverter through power hardware-in-the-loop and controller hardware-in-the-loop experiments at ARIES, and de-risk the field deployment and operation of hydrogen assets in the microgrid setting. Finally, this project will demonstrate the operation of the fuel cell inverter and updated microgrid controller by operating in grid-forming mode in the Borrego Springs microgrid.

blackstart with fuel cells↗
NASA NTRSDate not supplied

Comparison of Exploration Portable Life Support Subsystem (xPLSS) Thermal Modeling to Thermal Vacuum Testing

To support NASA’s goal to return to the Moon through the Artemis mission, the development of an exploration portable life support system (xPLSS) has been conducted at Johnson Space Center (JSC). As part of this development process, a large system level thermal/fluid model of the xPLSS was developed using Thermal Desktop and an in-house human model (METMAN). The xPLSS model was used throughout the design process to predict the performance and temperature of nearly all components within the xPLSS. In the fall of 2023, the design, verification, and testing (DVT) unit of the xPLSS was tested in a thermal vacuum (TVAC) chamber at JSC. This testing consisted of combining the xPLSS with an upper torso of the exploration pressure garment system (xPGS) and simulating five extravehicular activities (EVAs) in extreme thermal conditions (two cold EVAs and three hot EVAs). The data generated in this test series provided system level data of the xPLSS operating in vacuum and at flight-like environmental temperatures for the first time. This data was compared to results output by the xPLSS system model to assess the accuracy of previous analyses and improve the fidelity and accuracy of the xPLSS system model. The comparison between model and test hardware provides valuable insight that will help improve the design and fidelity of next generation space suits. In general, comparison between test data and model data generally showed slightly un-conservative values (model predicting colder temperatures than test in hot environments, and hotter temperatures than test in cold environments). Model assumptions and test assumptions were assessed to understand potential causes of some of these sources in error. Recommendations to improve the fidelity of the xPLSS model were also made based on the results presented in this paper.

xPLSS↗
NASA NTRSDate not supplied

Comparison of Exploration Portable Life Support Subsystem (xPLSS) Thermal Modeling to Thermal Vacuum Testing

To support NASA’s goal to return to the Moon through the Artemis Mission, the development of an Exploration Portable Life Support Subsystem (xPLSS) has been conducted at Johnson Space Center (JSC). As part of this development process, a large system level thermal/fluid model of the xPLSS was developed using Thermal Desktop and an in-house human model (METMAN). The xPLSS model was used throughout the design process to predict the performance and temperature of nearly all components within the xPLSS. In the fall of 2023, the Design, Verification, and Testing (DVT) unit of the xPLSS was tested in a thermal vacuum (TVAC) chamber at JSC. This testing consisted of combining the xPLSS with an upper torso of the Exploration Pressure Garment System (xPGS) and simulating five Extravehicular Activities (EVAs) in extreme thermal conditions (two cold EVAs and three hot EVAs). The data generated in this test series provided system level data of the xPLSS operating in vacuum and at flight-like environmental temperatures for the first time. These data were compared to results output by the xPLSS system model to assess the accuracy of previous analyses and improve the fidelity and accuracy of the xPLSS system model. The comparison between model and test hardware provides valuable insight that will help improve the design and fidelity of next generation space suits. In general, comparison between test data and model data generally showed slightly un-conservative values (model predicting colder temperatures than test in hot environments, and hotter temperatures than test in cold environments). Model assumptions and test assumptions were assessed to understand potential causes of some of these sources in error. Recommendations to improve the fidelity of the xPLSS model were also made based on the results presented in this paper.

xPLSS↗
DOE OSTI2022

Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation: Preprint

This paper presents the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., lost a large generation unit). The NLM engine is a central dispatch control system that provides high-speed cost-optimal coordination of a set of net loads (combination of generation and deferrable loads) connected to the same electrical network. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm. The experimental results indicate that the NLM engine can achieve the targeted system voltage and frequency and balance load and generation to serve the critical facility.

droop control↗
DOE OSTI2022

Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation

This paper presents the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability.

grid-following inverter↗
DOE OSTI2022

Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation

This presentation discusses the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability.

droop control↗
NASA NTRS2013

Dry wind tunnel system

This invention is a ground flutter testing system without a wind tunnel, called Dry Wind Tunnel (DWT) System. The DWT system consists of a Ground Vibration Test (GVT) hardware system, a multiple input multiple output (MIMO) force controller software, and a real-time unsteady aerodynamic force generation software, that is developed from an aerodynamic reduced order model (ROM). The ground flutter test using the DWT System operates on a real structural model, therefore no scaled-down structural model, which is required by the conventional wind tunnel flutter test, is involved. Furthermore, the impact of the structural nonlinearities on the aeroelastic stability can be included automatically. Moreover, the aeroservoelastic characteristics of the aircraft can be easily measured by simply including the flight control system in-the-loop. In addition, the unsteady aerodynamics generated computationally is interference-free from the wind tunnel walls. Finally, the DWT System can be conveniently and inexpensively carried out as a post GVT test with the same hardware, only with some possible rearrangement of the shakers and the inclusion of additional sensors.

Chen, Ping-Chih↗
NASA NTRS2018

Advanced Stirling Radioisotope Generator Engineering Unit 2 Anomaly Investigation

The Advanced Stirling Radioisotope Generator (ASRG) Engineering Unit 2 (EU2) is the highest fidelity electrically heated Stirling radioisotope generator built to date. NASA Glenn Research Center completed the assembly of the ASRG EU2 in September 2014 using hardware from the now cancelled ASRG flight development project. The ASRG EU2 integrated the first pair of Sunpower's Advanced Stirling Convertors (ASC−E3 #1 and #2) in an aluminum generator housing with Lockheed Martin's (LM's) Engineering Development Unit (EDU) 4 controller. After just 179 hr of EU2 generator operation, the first power fluctuation occurred on ASC−E3 #1. The first power fluctuation occurred 175 hr later on ASC−E3 #2. Over time, the power fluctuations became more frequent on both convertors and larger in magnitude. Eventually the EU2 was shut down in January 2015. An anomaly investigation was chartered to determine root cause of the power fluctuations and other anomalous observations. A team with members from Glenn, Sunpower, and LM conducted a thorough investigation of the EU2 anomalies. Findings from the EU2 disassembly identified proximate causes of the anomalous observations. Discussion of the team's assessment of the primary possible failure theories, root cause, and conclusions is provided. Recommendations are made for future Stirling generator development to address the findings from the anomaly investigation. Additional findings from the investigation are also discussed.

Stirling convertors↗
NASA NTRS2015

Green Propellant Demonstration with Hydrazine Catalyst of F-16 Emergency Power Unit

Some space vehicle and aircraft Auxiliary Power Units (APUs) use hydrazine propellant for generating power. Hydrazine is a toxic, hazardous fuel which requires special safety equipment and processes for handling and loading. In recent years, there has been development of two green propellants that could enable their use in APU's: the Swedish LMP-103S and the Air Force Research Laboratory (AFRL) AF-M315E. While there has been work on development of these propellants for thruster applications (Prisma and Green Propulsion Infusion Mission, respectively), there has been less focus on the application to power units. Beginning in 2012, an effort was started by the Marshall Space Flight Center (MSFC) on the APU application. The MSFC plan was to demonstrate green propellants with residual Space Shuttle hardware. The principal investigator was able to acquire a Solid Rocket Booster gas generator and an Orbiter APU. Since these test assets were limited in number, an Air Force equivalent asset was identified: the F-16 Emergency Power Unit (EPU). In June 2013, two EPU's were acquired from retired aircraft located at Davis Monthan Air Force Base. A gas generator from one of these EPU's was taken out of an assembly and configured for testing with a version of the USAF propellant with a higher water content (AF-M315EM) to reduce decomposition temperatures. Testing in November 2014 has shown that this green propellant is reactive with the Hydrazine catalyst (Shell 405) generating 300 psi of pressure with the existing F-16 EPU configuration. This paper will highlight the results of MSFC testing in collaboration with AFRL.

Robinson, Joel W.↗
NASA NTRS1995

Telescience operations with the solar array module plasma interaction experiment

The Solar Array Module Plasma Interactions Experiment (SAMPIE) is a flight experiment that flew on the Space Shuttle Columbia (STS-62) in March 1994, as part of the OAST-2 mission. The overall objective of SAMPIE was to determine the adverse environmental interactions within the space plasma of low earth orbit (LEO) on modern solar cells and space power system materials which are artificially biased to high positive and negative direct current (DC) voltages. The two environmental interactions of interest included high voltage arcing from the samples to the space plasma and parasitic current losses. High voltage arcing can cause physical damage to power system materials and shorten expected hardware life. parasitic current losses can reduce power system efficiency because electric currents generated in a power system drain into the surrounding plasma via parasitic resistance. The flight electronics included two programmable high voltage DC power supplies to bias the experiment samples, instruments to measure the surrounding plasma environment in the STS cargo bay, and the on-board data acquisition system (DAS). The DAS provided in-flight experiment control, data storage, and communications through the Goddard Space Flight Center (GSFC) Hitchhiker flight avionics to the GSFC Payload Operations Control Center (POCC). The DAS and the SAMPIE POCC computer systems were designed for telescience operations; this paper will focus on the experiences of the SAMPIE team regarding telescience development and operations from the GSFC POCC during STS-62. The SAMPIE conceptual development, hardware design, and system verification testing were accomplished at the NASA Lewis Research Center (LeRC). SAMPIE was developed under the In-Space Technology Experiment Program (IN-STEP), which sponsors NASA, industry, and university flight experiments designed to enable and enhance space flight technology. The IN-STEP Program is sponsored by the Office of Space Access and Technology (OSAT).

Wald, Lawrence W.↗
DOE OSTI2023

Posiform planting: generating QUBO instances for benchmarking

We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. Such problems are NP-hard in general, implying that the exact minima of randomly generated instances are hard to find and thus typically unknown. While brute forcing smaller instances is possible, such instances are typically not interesting due to being too easy for both quantum and classical algorithms. In this contribution, we propose a novel method, called posiform planting , for generating random QUBO instances of arbitrary size with known optimal solutions, and use those instances to benchmark the sampling quality of four D-Wave quantum annealers utilizing different interconnection structures (Chimera, Pegasus, and Zephyr hardware graphs) and the simulated annealing algorithm. Posiform planting differs from many existing methods in two key ways. It ensures the uniqueness of the planted optimal solution, thus avoiding groundstate degeneracy, and it enables the generation of QUBOs that are tailored to a given hardware connectivity structure, provided that the connectivity is not too sparse. Posiform planted QUBOs are a type of 2-SAT boolean satisfiability combinatorial optimization problems. Our experiments demonstrate the capability of the D-Wave quantum annealers to sample the optimal planted solution of combinatorial optimization problems with up to 5, 627 qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Design and Closed‐Loop Motion Planning of an Untethered Swimming Soft Robot Using 2D Discrete Elastic Rods Simulations

Despite tremendous progress in the development of untethered soft robots in recent years, existing systems lack the mobility, model‐based control, and motion planning capabilities of their piecewise rigid counterparts. As in conventional robotic systems, the development of versatile locomotion of soft robots is aided by the integration of hardware design and control with modeling tools that account for their unique mechanics and environmental interactions. Here, a framework for physics‐based modeling, motion planning, and control of a fully untethered swimming soft robot is introduced. This framework enables offline co‐design in the simulation of robot parameters and gaits to produce effective open‐loop behaviors and enables closed‐loop planning over motion primitives for feedback control of a frog‐inspired soft robot testbed. This pipeline uses a discrete elastic rods (DERs) physics engine that discretizes the soft robot as many stretchable and bendable rods. On hardware, an untethered aquatic soft robot that performs frog‐like rowing behaviors is engineered. Hardware validation verifies that the simulation has sufficient accuracy to find the best candidates for sets of parameters offline. The simulator is then used to generate a trajectory library of the robot's motion in simulation that is used in real‐time closed‐loop path following experiments on hardware.

Huang, Xiaonan↗
DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Increasing the hardness of posiform planting using random QUBOs for programmable quantum annealer benchmarking

Posiform planting is a method for constructing QUBO instances with a unique planted solution that can be tailored to arbitrary connectivity graphs. In this study we investigate making posiform planted QUBOs computationally harder by fusing many smaller random Ising models, whose global minimum is computed classically, with posiform planted QUBOs. The unique ground state of the resulting QUBO is the concatenation of (exactly one of) the ground states of each smaller problem. Our method generates QUBO instances that have a unique solution, are native to the hardware graph, and have tunable computational hardness. We use our QUBOs to benchmark three D-Wave quantum annealing processors (with 563–5627 qubits), and compare them against simulated annealing and Gurobi. Surprisingly, we find that the D-Wave ground state sampling success rate is not dependent on the glued random QUBO size, and that some QUBO classes are solved at high success rates at short annealing times on the Zephyr processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A Survey: Handling Irregularities in Neural Network Acceleration with FPGAs

In the last decade, Artificial Intelligence (AI) through Deep Neural Networks (DNNs) has penetrated virtually every aspect of science, technology, and business. Many types of DNNs have been and continue to be developed, including Convolutional Neural Networks (CNNs), Recurrent Neural Net- works (RNNs), and Graph Neural Networks (GNNs). The overall problem for all of these Neural Networks (NNs) is that their target applications generally pose stringent constraints on latency and throughput, while also having strict accuracy requirements. There have been many previous efforts in creating hardware to accelerate NNs. The problem designers face is that optimal NN models typically have significant irregularities, making them hardware-unfriendly. In this paper, we first define the problems in NN acceleration by characterizing common irregularities in NN processing into 4 types; then we summarize the existing works that handle the four types of irregularities efficiently using hardware, especially FPGAs; finally, we provide a new vision of next-generation FPGA-based NN acceleration: that the emerging heterogeneity in the next-generation FPGAs is the key to achieving higher performance.

Geng, Tong↗
DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.

Ahammed, K.↗
DOE OSTI2021

Using MLIR Framework for Codesign of ML Architectures Algorithms and Simulation Tools

MLIR (Multi-Level Intermediate Representation), is an extensible compiler framework that supports high-level data structures and operation constructs. These higher-level code representations are particularly applicable to the artificial intelligence and machine learning (AI/ML) domain, allowing developers to more easily support upcoming heterogeneous AI/ML accelerators and develop flexible domain specific compilers/frameworks with higher-level intermediate representations (IRs) and advanced compiler optimizations. The result of using MLIR within the LLVM compiler framework is expected to yield significant improvement in the quality of generated machine code, which in turn will result in improved performance and hardware efficiency

97 MATHEMATICS AND COMPUTING↗