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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

On the Effect of Feedback Control on Benard Convection in a Boussinesq Fluid

The effect of nonlinear feedback control strategies on the platform of convection in a Boussinesq fluid heated from below is investigated. In the absence of the control, given that non-Boussinesq effects may be neglected, it is well known that convection begins in the form of a supercritical bifurcation to rolls. Non-Boussinesq behaviour destroys the symmetry of the basic state, and through a subcritical bifurcation leads to the formation of hexagonal cells. Here we discuss the influence of regulation of the lower surface temperature by means of a control mechanism, made up of a combination of a proportional linear and nonlinear controller, on the stability of the hexagonal cell pattern.

Shortis, Trudi A.↗
NASA NTRS2000

Predictive Multiple Model Switching Control with the Self-Organizing Map

A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framework. Multiple SOMs collectively model the global response of a nonautonomous system to a finite set of representative prototype controls. Each SOM provides a codebook representation of the dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the global minimization of a similarity metric. The SOM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme that selects the best available model for the applied control. SOM based linear models are used to predict the response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

Motter, Mark A.↗
NASA NTRS2005

Robust Load-Sharing Control of Spacecraft Formations

This paper focuses on the design of autonomous and collaborative control strategies to govern the relative distances among multiple spacecraft in formation with no ground intervention. A coordinate load-sharing control structure for formation flying and a methodology to control their dynamic models with slow time-varying and uncertain parameters are the main objectives of this work. The method is applied to a deep space formation example, where the uncertainty in spacecraft fuel masses is also considered.

multi spacecraft↗
NASA NTRS2010

Minnowbrook VI: 2009 Workshop on Flow Physics and Control for Internal and External Aerodynamics

Topics covered include: Flow Physics and control for Internal and External Aerodynamics (not in TOC...starts on pg13); Breaking CFD Bottlenecks in Gas-Turbine Flow-Path Design; Streamwise Vortices on the Convex Surfaces of Circular Cylinders and Turbomachinery Blading; DNS and Embedded DNS as Tools for Investigating Unsteady Heat Transfer Phenomena in Turbines; Cavitation, Flow Structure and Turbulence in the Tip Region of a Rotor Blade; Development and Application of Plasma Actuators for Active Control of High-Speed and High Reynolds Number Flows; Active Flow Control of Lifting Surface With Flap-Current Activities and Future Directions; Closed-Loop Control of Vortex Formation in Separated Flows; Global Instability on Laminar Separation Bubbles-Revisited; Very Large-Scale Motions in Smooth and Rough Wall Boundary Layers; Instability of a Supersonic Boundary-Layer With Localized Roughness; Active Control of Open Cavities; Amplitude Scaling of Active Separation Control; U.S. Air Force Research Laboratory's Need for Flow Physics and Control With Applications Involving Aero-Optics and Weapon Bay Cavities; Some Issues Related to Integrating Active Flow Control With Flight Control; Active Flow Control Strategies Using Surface Pressure Measurements; Reduction of Unsteady Forcing in a Vaned, Contra-Rotating Transonic Turbine Configuration; Active Flow Control Stator With Coanda Surface; Controlling Separation in Turbomachines; Flow Control on Low-Pressure Turbine Airfoils Using Vortex Generator Jets; Reduced Order Modeling Incompressible Flows; Study and Control of Flow Past Disk, and Circular and Rectangular Cylinders Aligned in the Flow; Periodic Forcing of a Turbulent Axisymmetric Wake; Control of Vortex Breakdown in Critical Swirl Regime Using Azimuthal Forcing; External and Turbomachinery Flow Control Working Group; Boundary Layers, Transitions and Separation; Efficiency Considerations in Low Pressure Turbines; Summary of Conference; and Final Plenary Session Transcript.

LaGraff, John E.↗
NASA NTRS1991

Station keeping strategy for multiple spacecraft interferometry

The feasibility of multiple spacecraft stationkeeping for submillimeter and optical interferometry is examined. A condition for interferometry is that two or more spacecraft must control their relative positions with better than 1 mn accuracy indefinitely in both radial and transverse directions although separated by as much as 1 Km in LEO and 100 Km in GEO. They must also maneuver through a useful area of the U-V plane of an arbitrary astronomical source. The problem is first outlined and a solution which utilizes gravity gradient forces to do most of the work and ion thrusters for additional maneuvering is proposed. All the perturbing forces are shown to be small compared to the ion thruster requirements. An inertial position and attitude control strategy is suggested which utilizes existing or soon to be available sensors and actuators. Finally, the fuel and power system mass requirements are estimated and found to be within reason for a 10 year mission.

Decou, Anthony B.↗
DOE OSTI2023

Model Based Validation of Intelligent Powertrain Strategies for Connected and Automated Vehicles

Systems incorporating Vehicle to Everything (V2X) and conventional cellular based communication in vehicles can significantly help improve energy consumption via a combination of intelligent powertrain control strategies, smarter routing algorithms and driving in such a way as to minimize fuel economy and the emission of carbon dioxide, known as "eco-driving." In projects led by the Southwest Research Institute (SwRI), large-scale traffic simulations are created to model real-world scenarios with dynamic behavior that is reactive to imposed changes. Coupled with high fidelity powertrain models, the closed loop framework enables research and development of such Connected and Automated Vehicle (CAV) enabled technologies at scale. This paper will discuss a traffic system simulation environment that was built based on the High Street urban corridor in Columbus, Ohio. Eco-driving strategies were tested at scale on a variety of powertrain platforms – internal combustion engines, hybrid electric and fully electric vehicles. Furthermore, the paper will focus on hybrid electric powertrain modeling along with details on how the powertrain model was leveraged to develop a sophisticated clustering scheme to help down-select speed traces from large scale simulation studies for validation on vehicle dynamometer. Nominal energy consumption improvement around 12% was observed with good match between simulation studies and vehicle testing.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗
DOE OSTI2021

I.1 Heavy-Duty Diesel Combustion (Sandia National Laboratories)

Regulatory drivers and market demands for lower pollutant emissions, lower carbon dioxide emissions, and lower fuel consumption motivate the development of cleaner and more fuel-efficient engine operating strategies. Most current production heavy-duty diesel engines use a combination of both in-cylinder and exhaust emissions-control strategies to achieve these goals. The emissions and efficiency performance of in-cylinder strategies depend strongly on flow and mixing processes that can be influenced by using multiple fuel injections. Past work performed under this project showed that adding a second injection can reduce soot to levels below what would have been produced by an unchanged first injection, thereby increasing load while decreasing soot and potentially reducing brake specific fuel consumption. Information characterizing the important in-cylinder processes with multiple injections has been gleaned from ensemble-averaged planar laser-induced incandescence (PLII) imaging visualizing the soot cloud and planar induced fluorescence (PLIF) of OH characterizing the soot oxidation regions. PLII showed a consistent disruption of the first injection soot cloud by the second injection. In conjunction with OH-PLIF, differences in soot oxidation patterns for multiple injections compared to single injections were observed. This understanding was further enhanced in FY20, when high-speed imaging resolving the above-mentioned effects in a single cycle were combined with direct numerical simulations investigating the multiple-injection ignition process on the microscopic level of turbulence and chemistry interaction. In FY21, these findings in conjunction with findings from other researchers published in the scientific literature were composed into a preliminary multiple-injection conceptual model of fuel-mixing, injection and ignition processes. Remaining key research questions were also highlighted. In addition, wall heat flux was investigated experimentally and with numerical simulations to understand the potential of multiple injections to reduce the engine heat losses and further enhance the efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Analysis of Real-World Preignition Data Using Neural Networks

Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure. Further, this indicates that the alternating pattern of preignition cycles within clusters is influenced by the thermodynamic state as reflected in the pressure, but that the trigger for the initial preignition cycle is not thermodynamic in nature, but more likely tied to a critical threshold in the chemistry of the fuel/lubricant mixture in the upper crevice or other factors related to the presence of an ignition source.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1990

Intelligent processing of intermetallic composite consolidation

Intermetallic composites based upon titanium aluminide matrices and silicon carbide of aluminum oxide fibers are emerging candidates for future hypersonic vehicle airframes and engines. To tailor the properties of these 'engineered' materials for specific application, to increase process yield, and to reduce costs, 'intelligent processing of materials' (IPM) control strategies are being explored for their processing. Recent progress is evaluated in the development of predictive process models, advanced sensors and control methodologies (the components of the IPM approach) for the critical near net-shape consolidation process step of continuous fiber reinforced intermetallic composite manufacture.

Wadley, H. N. G.↗
NASA NTRS1990

Decentralized control experiments on the JPL flexible spacecraft

Decentralized control experiments were successfully demonstrated for the JPL/AFAL Flexible Structure. A simulation package using MATRIXx showed strong correlation between the simulations and experimental result, while providing a means for test and debug of the various control strategies. Implementation was simplified by a modular software design that was easily transported from the simulation environment to the experimental environment. Control designs worked well for suppression of the dominant modes of the structure. Static decentralized output feedback dampened the excited modes of the structure, but sometimes excited higher order modes upon startup of the controller. A second-order frequency shaping controller helped to eliminate excitation of the higher order modes by attenuating high frequencies in the control effort. However, it also resulted in slightly longer settling times.

Ozguner, U.↗
NASA NTRS1992

Using qualitative maps to direct reactive robots

The principal advantage of mobile robots is that they are able to go to specific locations to perform useful tasks rather than have the tasks brought to them. It is important therefore that the robot be used to reach desired locations efficiently and reliably. A mobile robot whose environment extends significantly beyond its sensory horizon must maintain a representation of the environment, a map, in order to attain these efficiency and reliability requirements. We believe that qualitative mapping methods provide useful and robust representation schemes and that such maps may be used to direct the actions of a reactively controlled robot. In this paper we describe our experience in employing qualitative maps to direct, through the selection of desired control strategies, a reactive-behavior based robot. This mapping capability represents the development of one aspect of a successful deliberative/reactive hybrid control architecture.

Bertin, Randolph↗
DOE OSTI2026

VECTOR Phase 1 Dataset: CAV Trajectory and Energy Consumption Records

This dataset contains benchmark experimental data from Phase 1 of the VECTOR project, focusing on the energy impact of CAV hardware components. The dataset includes vehicle trajectory data (speed and position) and corresponding energy consumption records collected from a CAV platform equipped with lidar, cameras, onboard computation units, and communication modules. The primary objective is to quantify the baseline energy consumption attributable to sensing and computing systems, independent of any advanced cooperative control strategies. During experiments, the leading vehicle followed a predetermined velocity profile, and the following CAV mirrored this trajectory using a basic car-following control to ensure consistent driving behavior. This setup enables a reliable benchmark for assessing the energy cost introduced by onboard CDA hardware (e.g., lidar and GPU-based processing). The dataset is essential for evaluating energy baselines and supports future comparative studies involving additional cooperative strategies. ![system img](system.png) ![vector img](vector.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Rattlesnake Vibration Controller v.2.0

SAND2021-7300 O The Rattlesnake Vibration Controller is a combined-environments, multiple input/multiple output control system for dynamic excitation of structures under test. It provides capabilities to control multiple responses on the part using multiple exciters and control strategies. Rattlesnake is written in the Python programming language to facilitate multiple input/multiple output vibration research by allowing users to prescribe custom control laws to the controller. Rattlesnake can target multiple hardware devices, or even perform synthetic control to simulate a test virtually. Rattlesnake has been used to execute control problems with up to 200 response channels and 12 drives. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rohe, Daniel↗
DOE OSTI2021

Rattlesnake User's Manual

Rattlesnake is a combined-environments, multiple input/multiple output control system for dynamic excitation of structures under test. It provides capabilities to control multiple responses on the part using multiple exciters using various control strategies. Rattlesnake is written in the Python programming language to facilitate multiple input/multiple output vibration research by allowing users to prescribe custom control laws to the controller. Rattlesnake can target multiple hardware devices, or even perform synthetic control to simulate a test virtually. Rattlesnake has been used to execute control problems with up to 200 response channels and 12 drives. This document describes the functionality, architecture, and usage of the Rattlesnake controller to perform combined environments testing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Rattlesnake User's Manual (V.3)

Rattlesnake is a combined-environments, multiple input/multiple output control system for dynamic excitation of structures under test. It provides capabilities to control multiple responses on the part using multiple exciters using various control strategies. Rattlesnake is written in the Python programming language to facilitate multiple input/multiple output vibration research by allowing users to prescribe custom control laws to the controller. Rattlesnake can target multiple hardware devices, or even perform synthetic control to simulate a test virtually. Rattlesnake has been used to execute control problems with up to 200 response channels and 24 shaker drives. This document describes the functionality, architecture, and usage of the Rattlesnake controller to perform combined environments testing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗