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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Acceleration of the Fast Solar Wind by Solitary Waves in Coronal Holes

The purpose of this investigation is to develop a new model for the acceleration of the fast solar wind by nonlinear, time-dependent multidimensional MHD simulations of waves in solar coronal holes. Preliminary computational studies indicate that solitary-like waves are generated in coronal holes nonlinearly by torsional Alfven waves. These waves in addition to thermal conduction may contribute considerably to the accelerate the solar wind. Specific goals of this proposal are to investigate the generation of nonlinear solitary-like waves and their effect on solar wind acceleration by numerical 2.5D MHD simulation of coronal holes with a broad range of plasma and wave parameters; to study the effect of random disturbances at the base of a solar coronal hole on the fast solar wind acceleration with a more advanced 2.5D MHD model and to compare the results with the available observations; to extend the study to a full 3D MHD simulation of fast solar wind acceleration with a more realistic model of a coronal hole and solar boundary conditions. The ultimate goal of the three year study is to model the fast solar wind in a coronal hole, based on realistic boundary conditions in a coronal hole near the Sun, and the coronal hole structure (i.e., density, temperature, and magnetic field geometry) that will become available from the recently launched SOHO spacecraft.

Ofman, Leon↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2013

FUN3D Airload Predictions for the Full-Scale UH-60A Airloads Rotor in a Wind Tunnel

An unsteady Reynolds-Averaged Navier-Stokes solver for unstructured grids, FUN3D, is used to compute the rotor performance and airloads of the UH-60A Airloads Rotor in the National Full-Scale Aerodynamic Complex (NFAC) 40- by 80-foot Wind Tunnel. The flow solver is loosely coupled to a rotorcraft comprehensive code, CAMRAD-II, to account for trim and aeroelastic deflections. Computations are made for the 1-g level flight speed-sweep test conditions with the airloads rotor installed on the NFAC Large Rotor Test Apparatus (LRTA) and in the 40- by 80-ft wind tunnel to determine the influence of the test stand and wind-tunnel walls on the rotor performance and airloads. Detailed comparisons are made between the results of the CFD/CSD simulations and the wind tunnel measurements. The computed trends in solidity-weighted propulsive force and power coefficient match the experimental trends over the range of advance ratios and are comparable to previously published results. Rotor performance and sectional airloads show little sensitivity to the modeling of the wind-tunnel walls, which indicates that the rotor shaft-angle correction adequately compensates for the wall influence up to an advance ratio of 0.37. Sensitivity of the rotor performance and sectional airloads to the modeling of the rotor with the LRTA body/hub increases with advance ratio. The inclusion of the LRTA in the simulation slightly improves the comparison of rotor propulsive force between the computation and wind tunnel data but does not resolve the difference in the rotor power predictions at mu = 0.37. Despite a more precise knowledge of the rotor trim loads and flight condition, the level of comparison between the computed and measured sectional airloads/pressures at an advance ratio of 0.37 is comparable to the results previously published for the high-speed flight test condition.

Lee-Rausch, Elizabeth M.↗
DOE OSTI2023

Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization (Final Technical Report)

Stochastic optimization and simulation models ubiquitously arise in designing and operating complex service/engineering systems. They can be extreme in scale due to high-dimensional data and decisions, and can also involve decisions made sequentially in response to newly revealed data, both causing significant computational challenge. The objective of this research is to explore a unified framework that integrates machine learning with discrete optimization and risk-averse modeling, to improve the efficiency of decomposition paradigms for stochastic optimization and simulations at extreme scale. The models we consider represent a broad class of complex decision-making problems, where 0-1 or continuous decisions are made before and/or after knowing multiple sources of uncertainties that could be correlated. We will employ machine learning methods to dynamically decide and prioritize computational procedures, including cut generation, branching, and bounding of the optimal objective. Furthermore, the research will shed new lights on the traditional decomposition algorithms for extreme-scale computing. Deliverables of the research include new modeling and computational methods for advancing the state-of-the-art research in optimization and simulation, bringing many relevant risk-averse, data-driven optimization problems in practice within the range of tractability. Examples include distributed computing server scheduling and sensor deployment for monitoring critical infrastructures. Success in this effort will enable progress in solving multiple extreme-scale problems in the complex system design and operations arising from DoE missions in energy, environment, and national security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Design, development and testing of an ambient lighting simulator for external illumination of a transport simulator cockpit

Researchers at the NASA-Langley Research Center several years ago began to examine concepts for a facility which could simulate the full range of lighting conditions encountered in flight. The purpose of this facility was to evaluate advanced technology display devices in a cockpit environment. The Aircraft Cockpit Ambient Lighting Simulation System (ACALSS) has been developed to meet that need. The ACALSS surrounds a wide-body part-task simulator cockpit, the Advanced Display Evaluation Cockpit, and interfaces with a VAX 11/780 computer, which is used to host both a math model of a modern transport aircraft and a solar motion model (which animates a servoed sun simulator). Several concepts were evaluated and an efficient design using an ellipsoid reflector and Fresnel luminaires was selected for implementation.

Batson, Vernon M.↗
DOE OSTI2020

Assessment of Precipitating Marine Stratocumulus Clouds in the E3SMv1 Atmosphere Model: A Case Study from the ARM MAGIC Field Campaign

This paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine boundary layer and its low clouds and precipitation are compared to observations including subcloud drizzle retrievals from a combination of Doppler radar and lidar backscatter measurements. Additionally, the SCM is also compared to a large-eddy simulation (LES) of the same case. The combination of advanced remote sensing observations and LES is a powerful framework to evaluate the physical parameterizations of large-scale models. Given the observed large-scale environment, the E3SMv1 SCM realistically represents the evolution of clouds and boundary layer structure during the stratocumulus-to-cumulus transition. The model well simulates the liquid water path and its diurnal cycle in the stratocumulus period as well as the two-layer vertical thermodynamic structure and lower cloud fraction in the transition period. E3SMv1’s success in simulating the cloud in the stratocumulus period permitted examination of its precipitation processes. Here problems were identified with E3SMv1 producing an unrealistically small subcloud precipitation fraction, an unrealistic double peak in the vertical profiles of precipitation mass, and drizzle that evaporates too close to the surface. Further model diagnostics determined that these unrealistic characteristics resulted from an overly long microphysics time step and an unrealistic parameterization of the precipitation fraction. These results imply that careful consideration of these issues is needed in order to better simulate precipitation processes in marine stratocumulus.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems

This document contains the proceedings of the Training Workshop on Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems held at NASA Langley Research Center, Hampton, Virginia, March 5 - 6, 2002. The workshop was jointly sponsored by Old Dominion University's Center for Advanced Engineering Environments and NASA. Workshop attendees were from NASA, other government agencies, industry, and universities. The objectives of the workshop were to give overviews of the diverse activities in hierarchical approach to material modeling from continuum to atomistics; applications of multiscale modeling to advanced and improved material synthesis; defects, dislocations, and material deformation; fracture and friction; thin-film growth; characterization at nano and micro scales; and, verification and validation of numerical simulations, and to identify their potential for future aerospace systems.

Noor, Ahmed K.↗
DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗
DOE OSTI2020

A Moist Physics Parameterization Based on Deep Learning

Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Taking Climate Model Evaluation to the Next Level

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

Eyring, Veronika↗
NASA NTRS1986

Bearing tester data compilation, analysis, and reporting and bearing math modeling

A test condition data base was developed for the Bearing and Seal Materials Tester (BSMT) program which permits rapid retrieval of test data for trend analysis and evaluation. A model was developed for the Space shuttle Main Engine (SSME) Liquid Oxygen (LOX) turbopump shaft/bearing system. The model was used to perform parametric analyses to determine the sensitivity of bearing operating characteristics and temperatures to variations in: axial preload, contact friction, coolant flow and subcooling, heat transfer coefficients, outer race misalignments, and outer race to isolator clearances. The bearing program ADORE (Advanced Dynamics of Rolling Elements) was installed on the UNIVAC 1100/80 computer system and is operational. ADORE is an advanced FORTRAN computer program for the real time simulation of the dynamic performance of rolling bearings. A model of the 57 mm turbine-end bearing is currently being checked out. Analyses were conducted to estimate flow work energy for several flow diverter configurations and coolant flow rates for the LOX BSMT.

Source record↗
DOE OSTI2025

Effect of pore fluid chemistry on the mechanical behavior of a divalent compacted bentonite, an experimental and constitutive study

Ongoing research in isolating high-level nuclear waste and spent fuel has highlighted compacted bentonite as a suitable material for engineered barrier systems in deep geological repositories due to its extraordinary swelling and retention properties. This research focuses on the chemo-mechanical behavior of compacted bentonite exposed to different pore fluids with different concentrations and loading conditions. The study involves swelling pressure and compressibility experiments along with mineralogy analysis employing X-ray diffraction (XRD) and Cation exchange. The tests were conducted on BCV (a Mg/Ca- bentonite) compacted at a dry density of 1.48 ± .02 Mg/m 3 . An advanced chemical-mechanical constitutive model for unsaturated highly expansive clays was adopted to simulate the material response and better understand its behavior. The model is able to account for the main phenomena at both macro and microstructural levels and the interactions between them. The model successfully replicated experimental observations. The XRD analyses support the macroscopic observation, indicating that salinity impacts crystalline swelling as demonstrated by the reduction of basal spacing from 19.27 Å to 15.68 Å when the osmotic suction increases from 0 MPa to 33 MPa. The results suggested that the osmotic pressure generated by the concentration in the pore fluids promotes a reduction in swelling pressures, swelling strains, and crystalline swelling of clay minerals. Also, it affects the pre-consolidation stress and the compressibility of the compacted samples. In conclusion, it was also observed that both solution type and solution concentration impact the clay swelling pressure.

Chemo-mechanical constitutive model↗
DOE OSTI2024

Device and material investigations of GaN enhancement-mode transistors for Venus and harsh environments

This Letter reports the device and material investigations of enhancement-mode p-GaN-gate AlGaN/GaN high electron mobility transistors (HEMTs) for Venus exploration and other harsh environment applications. The GaN transistor in this work was subjected to prolonged exposure (11 days) in a simulated Venus environment (460 °C, 94 bar, complete chemical environment including CO2/N2/SO2). The mechanisms affecting the transistor performance and structural integrity in harsh environment were analyzed using a variety of experimental, simulation, and modeling techniques, including in situ electrical measurement (e.g., burn-in) and advanced microscopy (e.g., structural deformation). Through transistor, Transmission Line Method (TLM), and Hall-effect measurements vs temperature, it is revealed that the mobility decrease is the primary cause of reduction of on-state performance of this GaN transistor at high temperature. Material analysis of the device under test (DUT) confirmed the absence of foreign elements from the Venus atmosphere. No inter-diffusion of the elements (including the gate metal) was observed. The insights of this work are broadly applicable to the future design, fabrication, and deployment of robust III-N devices for harsh environment operation.

Physics↗
DOE OSTI2024

Efficient ECCD non-inductive plasma current start-up, ramp-up, and sustainment for an ST fusion reactor

The elimination of the need for an Ohmic heating solenoid may be the most impactful design driver for the realization of economical compact fusion tokamak reactor systems. However, this would require fully non-inductive start-up and current ramp-up from zero plasma current and low electron temperature of sub-keV to the full plasma current of ~10–15 MA at 20–30 keV electron temperature. To address this challenge, an efficient solenoid-free start-up and ramp-up scenario utilizing a low-field-side-launched extraordinary mode at the fundamental electron cyclotron harmonic frequency (X–I) is proposed, which has more than two orders of magnitude higher electron cyclotron current drive (ECCD) efficiency than the conventional ECCD for the sub-keV start-up regime. A time dependent model was developed to simulate the start-up scenarios. For the Spherical Tokamak Advanced Reactor (STAR) (Menard et al 2023 Next-Step Low-Aspect-Ratio Tokamak Design Studies (IAEA)), it was found that to fully non-inductively ramp-up to 15 MA, it would take about 25 MW of EC power at 170 GHz. Because of the relatively large plasma volume of STAR, radiation losses must be considered. It is important to make sure that high Z impurities are kept sufficiently low during the early current start-up phase where the temperature is sub-keV range. Since the initial current ramp up takes place at a factor of ten lower density compared to the sustained regimes, it is important to transition into a higher bootstrap fraction discharge at lower density to minimize the ECCD power requirement during the densification. For the sustainment phase an array of eight gyrotron launchers with a total of about 60 MW of fundamental O-mode was found to be sufficient to provide the required axis-peaked external current drive. High efficiencies between 19–57 kA MW –1 were found with optimal aiming, and these were resilient to small changes in aiming angles and density and temperature profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems

The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, has the potential to fundamentally transform how we manage power systems, especially in scenarios with a higher proportion of renewable energy sources. One critical aspect is solving linear systems of equations, crucial for power system applications like power flow analysis, for which the Harrow-Hassidim-Lloyd (HHL) algorithm is a well-known quantum solution. However, HHL quantum circuits often exhibit excessive depth, making them impractical for current Noisy-Intermediate-Scale-Quantum (NISQ) devices. In this paper, we introduce a versatile framework, powered by NWQSim, that bridges the gap between power system applications and quantum linear solvers available in Qiskit. This framework empowers researchers to efficiently explore power system applications using quantum linear solvers. Through innovative gate fusion strategies, reduced circuit depth, and GPU acceleration, our simulator significantly enhances resource efficiency. Power flow case studies have demonstrated up to a eight-fold speedup compared to Qiskit Aer, all while maintaining comparable levels of accuracy.

quantum computing, Harrow-Hassidim-Lloyd, high-per↗
DOE OSTI2021

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) (Final Report)

This report details the accomplishments from the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows” under the award numbers FWP-66406 and DE-SC0012630, with a focus on the UC San Diego (Award No. DE-SC0012630) part of the accomplishments. We refer to the project as IPPD. The main activities of IPPD were centered on the development and integration of provenance information to capture empirically workflow information and identify the sources of bottlenecks as well as variability, and modeling and simulation to provide insights and predictively explore multiple scenarios for development of advanced techniques for optimization of resources and workflow execution. After a Phase I of the project, a Phase II effort focused on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. The project leveraged and extended our existing tools with new research and demonstrated our work on the Belle II workflow suite as well as on workflows from NSLS-II.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

FY 2021 Isolated Grids and Grid-Connected Turbine Reference Systems; Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL)

For individuals, businesses, and communities focused on building resilient electrical grid infrastructure, wind energy can provide an affordable, accessible, and compatible distributed energy resource option that also enhances the capabilities of local grid operations. The Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project is a multi-year distributed wind research effort, driven through a partnership between four Department of Energy National Laboratories and industry to develop and improve the planning, design, and operation of wind-centered microgrids to complement solar, energy storage, and other distributed energy resources for grid-tied and isolated operation (U.S. Department of Energy, 2021). This report documents the application of methods developed through the initial three years of the MIRACL project to two real-world distributed wind reference systems. Specifically, the methods demonstrated in this report include 1) a market valuation framework to comprehensively value the services distributed wind can provide and 2) a resilience framework that enables stakeholders to characterize distribution system resilience and compare grid investment decisions from a resilience perspective. Additional methods mentioned in this report include distributed hybrid system design methods for grid resilience, advanced control co-simulation platforms, and power hardware-in-the-loop (PHIL) models. Preliminary results from these additional methods are presented in this report and will be demonstrated and/or applied to the reference systems in the coming year. The purpose of applying these methods to reference systems is to drive technology transfer of the theories, methodologies, and technologies developed under the MIRACL project and increase the number of referenceable case studies available to stakeholders interested in additional value-added capabilities of wind systems beyond bulk energy supply (i.e. kilowatt-hours).

17 WIND ENERGY↗
DOE OSTI2021

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗