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Results for “Distributed generation”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Torque command steering law for double-gimbaled control moment gyros applied to rotor energy storage

A steering law is presented which has all the features required for space applications, assuming the CMG outer gimbal freedom is unlimited. The reason is the idea of mounting all the outer gimbal axes of the CMGs parallel to each other. This allows the decomposition of the steering law problem into a linear one for the inner gimbal angle rates and a planar one for the outer gimbal angle rates. The inner gimbal angle rates are calculated first, since they are not affected by the outer gimbal angle rates. For the calculation of the outer rates, the inner rates are then known quantities. An outer gimbal angle distribution function (to avoid singularities internal to the total angular momentum envelope) generates distribution rates next, and finally the pseudoinverse method is used to insure that the desired total torque is delivered.

Kennel, H. F.↗
DOE OSTI2024

Calibrating Bayesian generative machine learning for Bayesiamplification

Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

A modified Monte Carlo model for the ionospheric heating rates

A Monte Carlo method is adopted as a basis for the derivation of the photoelectron heat input into the ionospheric plasma. This approach is modified in an attempt to minimize the computation time. The heat input distributions are computed for arbitrarily small source elements that are spaced at distances apart corresponding to the photoelectron dissipation range. By means of a nonlinear interpolation procedure their individual heating rate distributions are utilized to produce synthetic ones that fill the gaps between the Monte Carlo generated distributions. By varying these gaps and the corresponding number of Monte Carlo runs the accuracy of the results is tested to verify the validity of this procedure. It is concluded that this model can reduce the computation time by more than a factor of three, thus improving the feasibility of including Monte Carlo calculations in self-consistent ionosphere models.

Mayr, H. G.↗
NASA NTRS1973

A modified Monte Carlo model for the ionospheric heating rates.

A Monte Carlo method is adopted as a basis for the derivation of the photoelectron-heat input into the ionospheric plasma. Since a great number of Monte Carlo runs are required normally for the computation of the heating rates, this approach is modified in an attempt to minimize the computation time. The heat-input distributions are computed for arbitrarily small source elements that are spaced apart at distances corresponding to the photoelectron dissipation range. By means of a nonlinear interpolation procedure their individual heating-rate distributions are utilized to produce synthetic ones that fill the gaps between the Monte Carlo generated distributions. By varying these gaps and the corresponding number of Monte Carlo runs the accuracy of the results is tested to verify the validity of this procedure. It is concluded that this model can reduce the computation time by as much as an order of magnitude, thus improving the feasibility of including Monte Carlo calculations in self-consistent ionosphere models.

Mayr, H. G.↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Prototype Installation and Testing Awardee: Accelerate Wind

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Accelerate Wind for Prototype Installation and Testing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Prototype Installation and Testing Awardee: Pecos Wind Power

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Pecos Wind Power for Prototype Installation and Testing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2026

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Inverter Listing Awardee: Eocycle America Corporation

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Eocycle America Corporation for Inverter Listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NLR on behalf of DOE, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2026

U.S. Department of Energy Competitiveness Improvement Project 2024 Technology Commercialization Awardee: Intelligent Energy Systems

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Intelligent Energy Systems for technology commercialization. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NLR on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed-generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1983

Computer programs for smoothing and scaling airfoil coordinates

Detailed descriptions are given of the theoretical methods and associated computer codes of a program to smooth and a program to scale arbitrary airfoil coordinates. The smoothing program utilizes both least-squares polynomial and least-squares cubic spline techniques to smooth interatively the second derivatives of the y-axis airfoil coordinates with respect to a transformed x-axis system which unwraps the airfoil and stretches the nose and trailing-edge regions. The corresponding smooth airfoil coordinates are then determined by solving a tridiagonal matrix of simultaneous cubic-spline equations relating the y-axis coordinates and their corresponding second derivatives. A technique for computing the camber and thickness distribution of the smoothed airfoil is also discussed. The scaling program can then be used to scale the thickness distribution generated by the smoothing program to a specific maximum thickness which is then combined with the camber distribution to obtain the final scaled airfoil contour. Computer listings of the smoothing and scaling programs are included.

Morgan, H. L., Jr.↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Small Turbine Certification and/or Listing Awardee: Sonsight Wind

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Sonsight Wind for Small Turbine Certification and/or Listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Technology Commercialization Awardee: Siva Powers America Inc.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Siva Powers America Inc. for a Technology Commercialization Award. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Manufacturing Process Innovation Awardee: Bergey Windpower Co.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Bergey Windpower Co. for manufacturing process innovation. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Component Innovation Awardee: Windurance LLC

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Windurance LLC for component innovation. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Small Turbine Certification and/or Listing Awardee: NPS Solutions LLC

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by NPS Solutions LLC for Small Turbine Certification and/or Listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Small Turbine Certification and Listing Awardee: Uprise Energy

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Uprise Energy for small turbine certification and listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Technology Commercialization Awardee: EWT Americas Inc.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by EWT Americas, Inc., for technology commercialization. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
NASA NTRS1984

Comparison of theoretical and flight-measured local flow aerodynamics for a low-aspect-ratio fin

Flight test and theoretical aerodynamic data were obtained for a flight test fixture mounted on the underside of an F-104G aircraft. The theoretical data were generated using two codes, a two dimensional transonic code called Code H, and a three dimensional subsonic and supersonic code call wing-body. Pressure distributions generated by the codes for the flight test fixture as well as boundary layer displacement thickness generated by the two dimensional code were compared to the flight test data. The two dimensional code pressure distributions compared well except at the minimum pressure point and trailing edge. Shock locations compared well except at high transonic speeds. The three dimensional code pressure distributions compared well except at the trailing edge of the flight test fixture. The two dimensional code does not predict displacement thickness of the flight test fixture well.

Johnson, J. B.↗