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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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A simulator is described which generates and transmits a beam of audible sound energy mathematically similar to the SPS power beam. The simulator provides a laboratory means for analysis of ground based closed loop SPS phase control and of ionospheric effects on the SPS microwave power beam.
A simulator which generates and transmits a beam of audible sound energy mathematically similar to the SPS power beam is described. The simulator provides a laboratory means for analysis of ground based closed loop SPS phase control and of ionospheric effects on the SPS microwave power beam.
A simulator is described which generates and transmits a beam of audible sound energy mathematically similar to the solar power satellite (SPS) power beam. The simulator provides a laboratory means for analysis of ground based closed loop SPS phase control and of ionospheric effects on the SPS microwave power beam.
The methods and results of an analytical study comparing the effectiveness of four actuators in damping the vibrations of a planar clamped-free beam are presented. The actuators studied are two inertia-type actuators, the proof mass and reaction wheel, and two variable geometry trusses, the planar truss and the planar truss proof mass (a combination variable geometry truss/inertia-type actuator). Actuator parameters used in the models were chosen based on the results of a parametric study. A full-state, LQR optimal feedback control law was used for control in each system. Numerical simulations of each beam/actuator system were performed in response to initial condition inputs. These simulations provided information such as time response of the closed-loop system and damping provided to the beam. This information can be used to determine the 'best' actuator for a given purpose.
A document describes a sheet membrane spacesuit water membrane evaporator (SWME), which allows for the use of one common water tank that can supply cooling water to the astronaut and to the evaporator. Test data showed that heat rejection performance dropped only 6 percent after being subjected to highly contaminated water. It also exhibited robustness with respect to freezing and Martian atmospheric simulation testing. Water was allowed to freeze in the water channels during testing that simulated a water loop failure and vapor backpressure valve failure. Upon closing the backpressure valve and energizing the pump, the ice eventually thawed and water began to flow with no apparent damage to the sheet membrane. The membrane evaporator also serves to de-gas the water loop from entrained gases, thereby eliminating the need for special degassing equipment such as is needed by the current spacesuit system. As water flows through the three annular water channels, water evaporates with the vapor flowing across the hydrophobic, porous sheet membrane to the vacuum side of the membrane. The rate at which water evaporates, and therefore, the rate at which the flowing water is cooled, is a function of the difference between the water saturation pressure on the water side of the membrane, and the pressure on the vacuum side of the membrane. The primary theory is that the hydrophobic sheet membrane retains water, but permits vapor pass-through when the vapor side pressure is less than the water saturation pressure. This results in evaporative cooling of the remaining water.
This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of subcritical coal fired power plants.
This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of sub-critical coal fired power plants.
A study to measure and compare pilot time delay when using a space shuttle rotational hand controller and a more conventional control stick was conducted at NASA Ames Research Center's Dryden Flight Research Facility. The space shuttle controller has a palm pivot in the pitch axis. The more conventional controller used was a general-purpose engineering simulator stick that has a pivot length between that of a typical aircraft center stick and a sidestick. Measurements of the pilot's effective time delay were obtained through a first-order, closed-loop, compensatory tracking task in pitch. The tasks were implemented through a space shuttle cockpit simulator and a critical task tester device. The study consisted of 450 data runs with four test pilots and one nonpilot, and used three control stick configurations and two system delays. Results showed that the heavier conventional stick had the lowest pilot effective time delays associated with it, whereas the shuttle and light conventional sticks each had similar higher pilot time delay characteristics. It was also determined that each control stick showed an increase in pilot time delay when the total system delay was increased.
The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems.
Because of the low thrust characteristics of solid-propellant pulsed plasma thrusters and their operational requirement to operate in a vacuum environment, unique and sensitive test techniques are required. A technique evolved for testing and evaluating pulsed plasma thrusters in an open- or closed-loop system mode employs a unique air bearing platform as a single-axis simulator on which the thruster is mounted. The simulator described was developed to evaluate pulsed plasma thrusters in the low micropound range; however, the simulator can be extended to cover the operational range of currently developed millipound thrusters.
A joint experiment to investigate simulator validation and cue fidelity was conducted by the Dryden Flight Research Facility of NASA Ames Research Center (Ames-Dryden) and NASA Langley Research Center. The primary objective was to validate the use of a closed-loop pilot-vehicle mathematical model as an analytical tool for optimizing the tradeoff between simulator fidelity requirements and simulator cost. The validation process includes comparing model predictions with simulation and flight test results to evaluate various hypotheses for differences in motion and visual cues and information transfer. A group of five pilots flew air-to-air tracking maneuvers in the Langley differential maneuvering simulator and visual motion simulator and in an F-14 aircraft at Ames-Dryden. The simulators used motion and visual cueing devices including a g-seat, a helmet loader, wide field-of-view horizon, and a motion base platform.
During the third quarter of 1996, the Closed-Loop Systems Laboratory was established at the NASA Langley Research Center (LaRC) to study the effects of High Intensity Radiated Fields on complex avionic systems and control system components. This new facility provided a link and expanded upon the existing capabilities of the High Intensity Radiated Fields Laboratory at LaRC that were constructed and certified during 1995-96. The scope of the Closed-Loop Systems Laboratory is to place highly integrated avionics instrumentation into a high intensity radiated field environment, interface the avionics to a real-time flight simulation that incorporates aircraft dynamics, engines, sensors, actuators and atmospheric turbulence, and collect, analyze, and model aircraft performance. This paper describes the layout and functionality of the Closed-Loop Systems Laboratory, and the open-loop calibration experiments that led up to the commencement of closed-loop real-time flight experiments.
A real-time Sensor Failure Simulator (SFS) was designed and assembled for the Advanced Detection, Isolation, and Accommodation (ADIA) program. Various designs were considered. The design chosen features an IBM-PC/XT. The PC is used to drive analog circuitry for simulating sensor failures in real-time. A user defined scenario describes the failure simulation for each of the five incoming sensor signals. Capabilities exist for editing, saving, and retrieving the failure scenarios. The SFS has been tested closed-loop with the Controls Interface and Monitoring (CIM) unit, the ADIA control, and a real-time F100 hybrid simulation. From a productivity viewpoint, the menu driven user interface has proven to be efficient and easy to use. From a real-time viewpoint, the software controlling the simulation loop executes at greater than 100 cycles/sec.
Mars Science Laboratory (MSL) will use the Skycrane architecture to execute final descent and landing maneuvers. The Skycrane phase uses closed-loop feedback control throughout the entire phase, starting with rover separation, through mobility deploy, and through touchdown, ending only when the bridles have completely slacked. The integrated ADAMS simulation described in this paper couples complex dynamical models created by the mechanical subsystem with actual GNC flight software algorithms that have been compiled and linked into ADAMS. These integrated simulations provide the project with the best means to verify key Skycrane requirements which have a tightly coupled GNC-Mechanical aspect to them. It also provides the best opportunity to validate the design of the algorithm that determines when to cut the bridles. The results of the simulations show the excellent performance of the Skycrane system.
In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.
Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. Here, these two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.
Torque-vectoring technology demonstrates great potential in improving the safety and performance of ground vehicles. In this paper, a novel generalized forces and moment governor for torque vectoring is suggested. The proposed solution strategically combines prescribed performance control, noncertainty equivalent adaptive control design, and a smooth projection operator. The main advantage of the proposed control strategy lies in its capability to guarantee both the transient performance and prompt recovery of the desired deterministic behavior of the closed-loop adaptive system, even in the presence of parametric uncertainties. ASM simulation results are presented to validate the efficacy of the proposed generalized forces and moment governor and to demonstrate its superiority over a baseline solution.
An analysis was performed in order to find the best placement for proof-mass actuators and to determine the importance of placement, i.e., what is the sensitivity of beam flexure to actuator placement. The analysis was performed by using the NASTRAN finite element model for a flexible beam with 21 grid points on beam, by using the nonlinear DISCOS simulation of 20 deg slew and the use of a closed-loop linear quadratic regulator (lqr). Some conclusions reached are: (1) proof-mass actuators can reduce flexure amplitude and damp oscillations; (2) amplitude of deformations during slew is relatively insensitive to placement of actuators; (3) damping factor of oscillations is sensitive to actuator placement; and (4) the degree of controllability method indicates most effective placement for actuators.