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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 937 records · Page 52

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DOE OSTI2019

Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts

Despite recent progress in seasonal forecast systems, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Niño–Southern Oscillation (ENSO) forcing together with a seasonally modulated Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System version 2 and the Scale Interaction Experiment–Frontier system. The improvements mainly originate from better prediction of ENSO-dependent IOD events and from reducing false alarms. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should translate to more skillful IOD predictions.

58 GEOSCIENCES↗

DOE OSTI2022

Demonstration of a fault impact reduction control module for wind turbines

Abstract. Traditionally, wind turbines in distributed applications make control decisions as isolated systems. They generally provide maximum power output during operation and manage internal faults with little consideration of the rest of the power system. Although fault detection and tolerance schemes are widely researched and implemented, controls to ameliorate such faults are uncommon in research and industry. The rapid shutdown of a wind turbine in a large transmission-connected wind plant will have a minimal impact on a large power system, but in a microgrid or isolated grid context the abrupt loss of a single wind turbine may cause grid instability and high stress on the system. This paper demonstrates a fault impact reduction control (FIRC) module for a wind turbine, which implements wider warning thresholds around fault thresholds. When the turbine crosses a warning threshold, the controller sends its predicted action to the grid controller, which facilitates the grid operator’s response to a potential wind turbine fault and then takes appropriate action to ameliorate the fault. Various test cases demonstrate the controller action under a variety of faults, and various scenarios demonstrate the grid benefit of an FIRC in both microgrid- and grid-connected contexts. The FIRC maximizes wind turbine generation and eases generation transition under a variety of fault scenarios. The FIRC module is easy to integrate with most existing controllers, just requiring derate capability, and can be easily modified to include the various warnings and thresholds that the user desires. This analysis is mainly performed in a MATLAB-Simulink-based research wind turbine model and is also implemented in the existing LabVIEW-based controller of the same research turbine at NREL.

17 WIND ENERGY↗

NASA NTRS1967

Two dimensional manual control systems with separate displays

Two dimensional manual control systems with separate displays - prediction of human controller behavior in complex multivariable systems

Elkind, J. I.↗

NASA NTRS1994

A feasibility study for long-path multiple detection using a neural network

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Feuerbacher, G. A.↗

NASA NTRS1999

CEAS/AIAA/ICASE/NASA Langley International Forum on Aeroelasticity and Structural Dynamics 1999

These proceedings represent a collection of the latest advances in aeroelasticity and structural dynamics from the world community. Research in the areas of unsteady aerodynamics and aeroelasticity, structural modeling and optimization, active control and adaptive structures, landing dynamics, certification and qualification, and validation testing are highlighted in the collection of papers. The wide range of results will lead to advances in the prediction and control of the structural response of aircraft and spacecraft.

Woodrow Whitlow, Jr.↗

NASA NTRS1999

CEAS/AIAA/ICASE/NASA Langley International Forum on Aeroelasticity and Structural Dynamics 1999

The proceedings of a workshop sponsored by the Confederation of European Aerospace Societies (CEAS), the American Institute of Aeronautics and Astronautics (AIAA), the National Aeronautics and Space Administration (NASA), Washington, D.C., and the Institute for Computer Applications in Science and Engineering (ICASE), Hampton, Virginia, and held in Williamsburg, Virginia June 22-25, 1999 represent a collection of the latest advances in aeroelasticity and structural dynamics from the world community. Research in the areas of unsteady aerodynamics and aeroelasticity, structural modeling and optimization, active control and adaptive structures, landing dynamics, certification and qualification, and validation testing are highlighted in the collection of papers. The wide range of results will lead to advances in the prediction and control of the structural response of aircraft and spacecraft.

Woodrow Whitlow, Jr.↗

DOE OSTI2022

Nucleation Rate Theory for Coordination Number: Elucidating Water-Mediated Formation of a Zigzag Na 2 SO 4 Morphology

Predicting and controlling nanostructure formation during nucleation can pave the way to synthesizing novel energy materials via crystallization. However, such control over nucleation and crystallization remains challenging due to an inadequate understanding of critical factors that govern evolving atomistic structures and dynamics. Here, in this paper, we utilize coordination number as a reaction coordinate and rate theory to investigate how sodium sulfate, commonly known as a phase-change energy material, nucleates in a supersaturated aqueous solution. In conjunction with ab initio and force field-based molecular dynamics simulation, the rate theoretical analysis reveals that sodium sulfate from an initially dissolved metastable state transits to a heterogeneous mixture of prenucleated clusters and finally to a large cylindrical zigzag morphology. Measurements of Raman spectra and their ab initio modeling confirm that this nucleated morphology contains a few waters for every sulfate. Rate processes such as solvent exchange and desolvation exhibit high sensitivity to the evolving prenucleation/nucleation structures, providing a means to distinguish between critical nucleation precursors. Desolvation and forming the first-shell interionic coordination structure via monomer-by-monomer addition around sulfates are found to explain the formation of large nuclei. Thus, a detailed understanding of the step-by-step structure formation across scales has been achieved. This can be leveraged to predict nucleation-related structures and dynamics and potentially control the synthesis of novel phase-change materials for energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Advancing Platooning with ADAS (Advanced Driver-Assistance Systems) Control Integration and Assessment

Application of Cooperative Adaptive Cruise Control (CACC) to heavy duty trucks known as truck platooning has shown fuel economy improvements on the test track under ideal driving conditions. However, limited test data is available to assess the truck platooning under real-world driving conditions. Under this Cummins-led project that was funded by the U.S. Department of Energy, truck platooning with CACC has been tested on a real-world interstate highway and the results of the project are reviewed in this report. At first, the real-world driving conditions were characterized using National Renewable Energy Laboratory (NREL) Fleet DNA database to define test factors, including route, terrain, and highway traffic. Afterward, both test track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning under controlled and real-world driving conditions. On-highway testing was done on a highway route in Indiana, consisting of low, medium, and high road grade segments. The highway test results of 2-truck and 3-truck platooning showed considerably reduced fuel savings compared to the controlled test track data, which mainly stems from the traffic or high-grade portions of the route. However, integration of Cummins powertrain and vehicle eco-driving features such as predictive cruise control and neutral coasting called ADEPT™ on the lead truck showed an improvement of fuel saving for the trucks in CACC operation. Furthermore, the importance of tire connectivity in efficient and safe operation of the trucks in platooning is characterized.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1998

Overview of Active Flow Control at NASA Langley Research Center

The paper summarizes Active Flow Control projects currently underway at the NASA Langley Research Center. Technology development is being pursued within a multidisciplinary, cooperative approach, involving the classical disciplines of fluid mechanics, structural mechanics, material science, acoustics, and stability and control theory. Complementing the companion papers in this session, the present paper will focus on projects that have the goal of extending the state-of-the-art in the measurement, prediction, and control of unsteady, nonlinear aerodynamics. Toward this goal, innovative actuators, micro and macro sensors, and control strategies are considered for high payoff flow control applications. The target payoffs are outlined within each section below. Validation of the approaches range from bench-top experiments to wind-tunnel experiments to flight tests. Obtaining correlations for future actuator and sensor designs are implicit in the discussion. The products of the demonstration projects and design tool development from the fundamental NASA R&D level technology will then be transferred to the Applied Research components within NASA, DOD, and US Industry. Keywords: active flow control, separation control, MEMS, review

Pack, L. G.↗

NASA NTRS2008

Close Approach Prediction Analysis of the Earth Science Constellation with the Fengyun-1C Debris

Routine satellite operations for the Earth Science Constellation (ESC) include collision risk assessment between members of the constellation and other orbiting space objects. Each day, close approach predictions are generated by a U.S. Department of Defense Joint Space Operations Center Orbital Safety Analyst using the high accuracy Space Object Catalog maintained by the Air Force's 1" Space Control Squadron. Prediction results and other ancillary data such as state vector information are sent to NASAJGoddard Space Flight Center's (GSFC's) Collision Risk Assessment analysis team for review. Collision analysis is performed and the GSFC team works with the ESC member missions to develop risk reduction strategies as necessary. This paper presents various close approach statistics for the ESC. The ESC missions have been affected by debris from the recent anti-satellite test which destroyed the Chinese Fengyun- 1 C satellite. The paper also presents the percentage of close approach events induced by the Fengyun-1C debris, and presents analysis results which predict the future effects on the ESC caused by this event. Specifically, the Fengyun-1C debris is propagated for twenty years using high-performance computing technology and close approach predictions are generated for the ESC. The percent increase in the total number of conjunction events is considered to be an estimate of the collision risk due to the Fengyun-1C break- UP.

Duncan, Matthew↗

DOE OSTI2022

Acoustic Data Analytics For Predictive Technologies

The software utilizes frequency, amplitude, and phase features from sensor signals (either raw or processed) that are extracted using recursive frequency analysis, enabling (1) understanding of reactor operations under different operating conditions, (2) identifying of quiescent frequency ranges for sensor development, (3) system controls (4) predictive analytics, (5) structural health monitoring, and (6) loose part monitoring.

Smith, JamesA.↗

DOE OSTI2023

Understanding the Structure and Dynamics of Conjugated Polymers by Advancing Deuteration Chemistry and Neutron Scattering (Final Report)

The overarching goal of the proposed work was to set up a partnership between the University of Southern Mississippi (USM) and Oak Ridge National Laboratory (ORNL) to develop novel approaches to measure the backbone rigidity of conjugated polymers (CPs) and understand the critical role of sidechains on the backbone conformation and the materials macroscopic property. The backbone rigidity greatly influences the electronic properties of CPs, which ultimately determines the functionality and performance of these materials. Improvements in the electronic properties of CPs would allow for enhanced charge transport in semiconductor devices, improved photovoltaic performance, recycling of waste heat in thermoelectrics, and discovery of new phenomena that will enable the next generation of energy technologies. Although significant progress has been made to optimize the optical and electronic properties of CPs, largely through Edisonian methodologies, it remains a challenge to experimentally characterize conjugated backbone conformation (chain rigidity, torsion, planarity, and short-range order) and relate these to the fundamental optical and electronic properties (electronic coupling, charge transport, etc.). This has left fundamental gaps in our knowledge of the most basic structure/property relationships within these systems, precluded the study of fundamental physical phenomena, and constrained the design and realization of new electronic and device functionalities. Thus, the major goal of this work is to use novel deuteration methodologies via systematic synthetic approaches, and neutron scattering techniques to comprehensively characterize the structural and dynamic properties of CPs in contrast-matching solvents. Our work would, for the first time, elucidate the relationship between backbone rigidity and macroscopic properties. They will also allow a rational formulation of design principles for next-generation CPs that are resilient to disorder through precise control of the delocalized electrons along the polymer backbone. Overall, this project will advance our understanding of the structure, dynamics, and fundamental physics of these materials, which is crucial for enabling the prediction, design, control, and manipulation of current and emerging material electronic properties.

36 MATERIALS SCIENCE↗

NASA NTRS1992

Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

Kranbuehl, D.↗

NASA NTRS2017

Progress Towards a Microgravity CFD Validation Study Using the ISS SPHERES-SLOSH Experiment

CFD↗

NASA NTRS2017

Progress Towards a Microgravity CFD Validation Study Using the ISS SPHERES-SLOSH Experiment

microgravity↗

NASA NTRS2017

Progress Towards a Microgravity CFD Validation Study Using the ISS SPHERES-SLOSH Experiment

Storey, Jedediah M.↗

DOE OSTI2021

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2016

Experimental, Numerical, and Analytical Slosh Dynamics of Water and Liquid Nitrogen in a Spherical Tank

Understanding, predicting, and controlling fluid slosh dynamics is critical to safety and improving performance of space missions when a significant percentage of the spacecraft's mass is a liquid. Computational fluid dynamics simulations can be used to predict the dynamics of slosh, but these programs require extensive validation. Many experimental and numerical studies of water slosh have been conducted. However, slosh data for cryogenic liquids is lacking. Water and cryogenic liquid nitrogen are used in various ground-based tests with a spherical tank to characterize damping, slosh mode frequencies, and slosh forces. A single ring baffle is installed in the tank for some of the tests. Analytical models for slosh modes, slosh forces, and baffle damping are constructed based on prior work. Select experiments are simulated using a commercial CFD software, and the numerical results are compared to the analytical and experimental results for the purposes of validation and methodology-improvement.

slosh↗