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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
DOE OSTI2024

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

random forest↗
NASA NTRS1981

Acoustic power dissipation on radiation through duct terminations - Experiments

An acoustic impulse technique is used to study the acoustic transmission characteristics of ducts, nozzles, orifices, and perforated plates. High intensity pulses were generated by discharging a capacitor across a spark gap for both heated and unheated flows, with and without simulated flights. The effect of a termination geometry on power absorption for high acoustic intensities was observed, showing the existence of power absorption at low frequencies for the duct, nozzles, orifice plates, and perforated plates at a no flow condition; the amount of power absorption was controlled primarily by the open area at the exit. Power conservation with flow was similar to that for no flow: Power absorption initially increased for the duct and conical nozzles and then decreased with increasing Mach number. Power absorption results for the suppressor nozzle were similar to those for the conical nozzle, and the nonlinear effect was found to be insignificant in the presence of flow. A power imbalance was noted for heated jets, with a greater power loss at low frequencies than for unheated jets. Low frequency power absorption increased with the simulated flight, and a small amount of low frequency power absorption was found even for low intensity sound. A typical schlieren photograph of vortex formation (Whiffen and Ahuja, 1981) helped to confirm that sound energy can be converted into vortical energy and may account for low frequency power losses.

Salikuddin, M.↗
DOE OSTI2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (ders). to address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines taylor series expansion knowledge with a data-driven regression technique. the proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified taylor expansion. to mitigate the approximation loss that surges due to the der integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. comparative analysis in the 13-bus and 123-bus ieee unbalanced feeders shows that the proposed hybrid algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

data-driven↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1973

H2-O2 auxiliary power unit for Space Shuttle vehicles - A progress report.

Description of a program to establish technology readiness of hydrogen-oxygen (H2-O2) auxiliary power units for use on board the Space Shuttle orbiter vehicle. Fundamental objectives include experimentally establishing an acceptable propellant flow control method, verification of combustor stability, and adequate thermal management. An initial auxiliary power unit (APU) configuration with recycled hydrogen flow has been studied and revised toward greater simplicity and scaling ease. The selected APU is a recuperated open-cycle, turbine-driven unit. Series flow of cryogenic hydrogen removes internally-generated heat and heat from the hydraulic system. The revised configuration schematic and its calculated performance are reviewed. A weight comparison is made between the shuttle baseline hydrazine and H2-O2 APU systems, showing that hydrogen-oxygen APUs have the potential of increasing the payload of the Space Shuttle.

Joyce, J. P.↗
NASA NTRS1974

Digital computer study of nuclear reactor thermal transients during startup of 60-kWe Brayton power conversion system

A digital computer study was made of reactor thermal transients during startup of the Brayton power conversion loop of a 60-kWe reactor Brayton power system. A startup procedure requiring the least Brayton system complication was tried first; this procedure caused violations of design limits on key reactor variables. Several modifications of this procedure were then found which caused no design limit violations. These modifications involved: (1) using a slower rate of increase in gas flow; (2) increasing the initial reactor power level to make the reactor respond faster; and (3) appropriate reactor control drum manipulation during the startup transient.

Jefferies, K. S.↗
NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗
NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗
NASA NTRS2016

Performance of a High-Fidelity 4kW-Class Engineering Model PPU and Integration with HiVHAc System

The High Voltage Hall Accelerator (HiVHAc) propulsion system consists of a thruster,power processing unit (PPU), and propellant feed system. An engineering model PPU was developed by Colorado Power Electronics, Inc. funded by NASA's Small Business Innovative Research Program. This PPU uses an innovative 3-phase resonant converter to deliver 4 kW of discharge power over a wide range of input and output voltage conditions.The PPU includes a digital control interface unit that automatically controls the PPU and a xenon flow control module (XFCM). It interfaces with a control computer to receive high level commands and relay telemetry through a MIL-STD-1553B interface. The EM PPU was thoroughly tested at GRC for functionality and performance at temperature extremes and demonstrated total efficiencies a high as 95 percent. It was integrated with the HiVHAc thruster and the XFCM to demonstrate closed-loop control of discharge current with anode flow. Initiation of the main discharge and power throttling were also successfully demonstrated and discharge oscillations were characterized.

Electric Propulsion↗
NASA NTRS2016

Performance of a High-Fidelity 4kW-Class Engineering Model PPU and Integration with HiVHAc System

The High Voltage Hall Accelerator (HiVHAc) propulsion system consists of a thruster, power processing unit (PPU), and propellant feed system. An engineering model PPU was developed by Colorado Power Electronics, Inc. funded by NASA's Small Business Innovative Research Program. This PPU uses an innovative 3-phase resonant converter to deliver 4 kW of discharge power over a wide range of input and output voltage conditions. The PPU includes a digital control interface unit that automatically controls the PPU and a xenon flow control module (XFCM). It interfaces with a control computer to receive highlevel commands and relay telemetry through a MIL-STD-1553B interface. The EM PPU was thoroughly tested at GRC for functionality and performance at temperature limits and demonstrated total efficiencies a high as 95 percent. Integrated testing of the unit was performed with the HiVHAc thruster and the XFCM to demonstrate closed-loop control of discharge current with anode flow. Initiation of the main discharge and power throttling were also successfully demonstrated and discharge oscillations were characterized.

Power Processing Unit↗
NASA NTRS1984

Engineering model system study for a regenerative fuel cell: Study report

Key design issues of the regenerative fuel cell system concept were studied and a design definition of an alkaline electrolyte based engineering model system or low Earth orbit missions was completed. Definition of key design issues for a regenerative fuel cell system include gaseous reactant storage, shared heat exchangers and high pressure pumps. A power flow diagram for the 75 kW initial space station and the impact of different regenerative fuel cell modular sizes on the total 5 year to orbit weight and volume are determined. System characteristics, an isometric drawing, component sizes and mass and energy balances are determined for the 10 kW engineering model system. An open loop regenerative fuel cell concept is considered for integration of the energy storage system with the life support system of the space station. Technical problems and their solutions, pacing technologies and required developments and demonstrations for the regenerative fuel cell system are defined.

Chang, B. J.↗
NASA NTRS1986

Growth of high-quality thin-film Ge single crystals by plasma-enhanced chemical vapor deposition

Thin-film Ge single crystals (approx. 10 microns) have been epitaxially grown on polished NaCl(100) substrates at 450C by using plasma-enhanced chemical vapor deposition. Films on approximately 1 sq cm and larger were separated from the NaCl by either melting the salt or by differential shear stress upon cooling to room temperature. The ordered growth of the Ge was found to be most sensitive to the initial plasma power and to the continuum flow dynamics within the carbon susceptor. The films were visually specular and exhibited a high degree of crysalline order when examined by X-ray diffraction. The films were found to be p-type with a carrier concentration of approximately 3 x 10 to the 16th power/cu cm, a resistivity of 0.11 ohm-cm, and a Hall hole mobility of 1820 sq cm/v/s at room temperature. Vacuum firing minimized the primary contaminant, Na, and corresponding lowered the carrier concentration to 4 x 10 to the 14th power/cu cm.

Outlaw, R. A.↗
NASA NTRS1988

Electron-cyclotron-resonance (ECR) plasma thruster research

An experimental apparatus to scientifically investigate the process of Electron-Cyclotron-Resonance (ECR) plasma acceleration has been tested. The apparatus consists of a vacuum facility, a 20 kW microwave power supply, and an argon ECR plasma thruster. Present instrumentation allows measurement of microwave input power, reflected power, propellant flow rate, and static pressure in the vacuum tank. In initial tests, an argon ECR plasma has been initiated repeatably at power levels ranging from 0.3 to 7.0 kW. The ECR plasma appears to diverge and separate from the applied magnetic field as predicted by theory. These tests suggest that the ECR plasma acceleration concept can be studied scientifically in the new apparatus.

Sercel, Joel C.↗
NASA NTRSDate not supplied

Towards Real-time Global Assessment of Post-fire Debris Flow Hazard

As the risk of wildfires increases worldwide, burned steeplands are vulnerable to the secondary hazard of widespread sediment mobilization through debris flows. Following an initial burn, sediment and soil previously restrained by vegetation are no longer consolidated, allowing for easy mobilization into channels and along steep hillslopes through runoff. Sufficiently powerful rainfall incorporates entrained material into turbulent flows and serves as the primary trigger for debris flow initiation. There is thus an ongoing need to establish the relationship between rainfall and debris flow initiation based on a variety of spatiotemporal preconditions. Previous work establishes regional and local thresholds to constrain the effect of rainfall in recently burned areas, but no empirical or numerical solution has worldwide application. Building from regionally-based efforts in the U.S., this work considers how remote sensing data can be applied to better approximate the post-fire debris flow hazards worldwide using freely available global datasets and software. Our work assesses the utility of remote sensing resources for analyzing burn characteristics, topography, rainfall intensity/duration, and, thus, debris flow initiation. Early results show that global observations are sufficient to delineate background rainfall rates from storms likely to cause debris flows across a variety of burn severity and topographic conditions. However, the dearth of publicly-available post-fire debris flow inventories globally limit the ability to test how the model framework performs within different climatologic and morphologic areas. This work will present preliminary analysis over the Western United States and demonstrate the feasibility of a global, near-real time model to provide situational awareness of potential hazards within recently burned areas worldwide. Future work will also consider how global or regional precipitation forecasts may increase the lead time for improved early warning of these hazards.

Elijah Orland↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗