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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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Description of a one-stage approximation to the color-conversion model of Richards and Parks (1971). The modified model proposes three channels for color vision, each with different center-surround sensitivities. In its strongest form, the model predicts that the gain-setting control that alters the sensitivities of each channel is solely a function of achromatic contrast.
The effect of visual information in combination with basic display information on the approach performance. A pre-experimental model analysis was performed in terms of the optimal control model. The resulting aircraft approach performance predictions were compared with the results of a moving base simulator program. The results illustrate that the model provides a meaningful description of the visual (scene) perception process involved in the complex (multi-variable, time varying) manual approach task with a useful predictive capability. The theoretical framework was shown to allow a straight-forward investigation of the complex interaction of a variety of task variables.
A study is presented to determine the limits on the predictability of the coupled ocean-atmosphere system. Following the classical methods developed for atmospheric predictability studies, the model used is one of the simplest that realistically reproduces many of the important features of the observed interannual variability of sea surface temperature in the tropical Pacific Ocean when forced by observed wind stresses. As no reasonable analysis is available for all the fields, initial conditions for these prediction experiments were taken from a model control run in which the ocean model was forced by the observed surface winds. The atmospheric component of the coupled model is not capable of accurately simulating the large-scale components of the observed wind stress.
Generalized predictive control describes an algorithm for the control of dynamic systems in which a control input is generated that minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. A design technique is discussed for applying the single-input/single-output generalized predictive control algorithm to a problem of longitudinal/vertical terrain-following flight of a rotorcraft. By using the generalized predictive control technique to provide inputs to a classically designed stability and control augmentation system, it is demonstrated that a robust flight-path control system can be created that exhibits excellent tracking performance.
We describe a rather general model used to predict the dynamics and control performance of formations of spacecraft connected by tethers in heliocentric orbit and in low Earth orbit.
Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.
The Mars Atmosphere and Volatile Evolution mission (MAVEN) is the first mission devoted to studying the Martian atmosphere. From a Navigation perspective it is unique in that science is performed at near aerobraking altitudes. This results in the requirements on Navigation trajectory accuracy requirements which are an order of magnitude tighter than those of aerobraking phases on previous missions. Navigation experiences with the Mars atmosphere are described as they pertain to Navigation models, trajectory reconstructions, trajectory predictions, density corridor control, and collision avoidance of other bodies around Mars.
Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.
Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.
Abstract Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R 2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.
Cluster external contamination control evaluation was made throughout the Skylab Mission. This evaluation indicated that contamination control measures instigated during the design, development, and operational phases of this program were adequate to reduce the general contamination environment external to the Cluster below the threshold senstivity levels for experiments and affected subsystems. Launch and orbit contamination control features included eliminating certain vents, rerouting vents for minimum contamination impact, establishing filters, incorporating materials with minimum outgassing characteristics and developing operational constraints and mission rules to minimize contamination effects. Prior to the launch of Skylab, contamination control math models were developed which were used to predict Cluster surface deposition and background brightness levels throughout the mission. The report summarizes the Skylab system and experiment contamination control evaluation. The Cluster systems and experiments evaluated include Induced Atmosphere, Corollary and ATM Experiments, Thermal Control Surfaces, Solar Array Systems, Windows and Star Tracker.
Bioregenerative systems--life-support systems to regenerate oxygen, food, and water--are the key to establishing man's permanent presence in space. NASA is investigating the use of plant-growth chambers (PGC's) for space missions and for bases on the moon and Mars. PGC's serve several important purposes, including the following: (1) oxygen and food production; (2) carbon-dioxide removal; and (3) water purification and reuse. The key to the successful development of PGC's is a system to recover and reuse the water vapor that is transpired by the leaves of the growing plants. In this program we propose to develop a simple, reliable, membrane-based system that allows the recovery, purification, and reuse of the transpired water vapor through control of temperature and humidity levels in the PGC. This system has characteristics that make it ideally suited to use in space: (1) minimal power requirements; (2) small volume and mass; (3) simplicity; (4) reliability; and (5) versatility. In Phase 1 we will do the following: (1) develop an accurate, predictive model of our temperature- and humidity-control system, based on parametric tests of membrane modules; and (2) use this model to design systems for selected PGC's. In Phase 2, we will seek to design, fabricate, test, and deliver a breadboard unit to NASA for testing on a PGC.
This paper was directed at active control of discrete frequency noise generated by subsonic blade rows through cancellation of the blade row interaction generated propagating acoustic waves. First discrete frequency noise generated by a rotor and stator in a duct was analyzed to determine the propagating acoustic pressure waves. Then a mathematical model was developed to analyze and predict the active control of discrete frequency noise generated by subsonic blade rows through cancellation of the propagating acoustic waves, accomplished by utilizing oscillating airfoil surfaces to generate additional control propagating pressure waves. These control waves interact with the propagating acoustic waves, thereby, in principle, canceling the acoustic waves and thus, the far field discrete frequency tones. This model was then applied to a fan exit guide vane to investigate active airfoil surface techniques for control of the propagating acoustic waves, and thus the far field discrete frequency tones, generated by blade row interactions.
This paper presents an overview of the radio-controlled drop-model technique used for low-speed flight dynamics research at the NASA Langley Research Center. The overall test methodology is reviewed and the major elements of the system are discussed including model construction and equipment, the closed-loop control system, and the flight test facility. To illustrate the capabilities of the technique, selected results from an ongoing program to study the high-angle-of-attack flight dynamics of the X-29A configuration are reviewed. Three research areas are highlighted: motion prediction, control system effects, and motion analysis and modeling. Finally, several current upgrades to the overall technique are discussed along with the enhanced test capability that they will provide.
The Altitude Control Experiment (ALICE) is a balloon system that consists of a helium lifting balloon and a buoyancy-controlling freon R114 balloon. Condensation of freon gas in the cooler upper atmosphere and vaporization of liquid freon in the warmer lower atmosphere can be effectively used to adjust the net buoyant force so that the balloon system may float between desirable ranges of altitudes.This report summarizes efforts to produce an analytical model to predict the performance of this type of altitude control balloon in the earth's atmosphere. Various interactive parameters that are influential to the dynamics of the system were identified and treated. The analytical model developed herein correlated well with three ALICE day flight trajectories and one ALICE night flight trajectory in the earth's atmosphere. A similar approach can be applied to the design of a Venusian balloon system that could repeatedly travel to the lower, hot altitudes or surface of Venus for imaging or science experiments and then float higher in order to cool the electronics.
An analysis and a numerical lifting surface method are developed for predicting the unsteady airloads on two-dimensional circulation control airfoils in incompressible flow. The analysis and the computer program are validated by correlating the computed unsteady airloads with test data and also with other theoretical solutions. Additionally, a mathematical model for predicting the bending-torsion flutter of a two-dimensional airfoil (a reference section of a wing or rotor blade) and a computer program using an iterative scheme are developed. The flutter program has a provision for using the CC airfoil airloads program or the Theodorsen hard flap solution to compute the unsteady lift and moment used in the flutter equations. The adopted mathematical model and the iterative scheme are used to perform a flutter analysis of a typical CC rotor blade reference section. The program seems to work well within the basic assumption of the incompressible flow.
The cybersecurity threats of power system gradually grow due to the increased sophisticated interactions between Information Technology (IT) and Operational Technology (OT) networks. False data injection attack (FDIA) that aims to compromise the Supervisory Control and Data Acquisition (SCADA) measurement and disturb the system operation is one of such cyber threats. Such attacks can potentially lead to significant operational issues at the control centers and substations, and hence, result in severe physical consequences. To avoid catastrophic failure across the power grid resulting from these attacks, it is essential to arm the OT network with real-time vulnerability assessment tools. To this end, this paper outlines various drawbacks of the Purdue architecture model to defend against cyberattacks in the OT network. Furthermore, a novel ensemble-based state prediction model is proposed to detect cybersecurity anomalies in SCADA assisted OT networks. The proposed model uses control center level generation and load forecasts, scheduled, and forced outages, power flow solutions, and the substation level historical data. The hypothesis of the proposed scheme relies on the fact that additional control center and substation data can hardly be accessed and compromised by attackers. One of the vital features of the proposed scheme is an hour-ahead prediction of the operational feasibility of the SCADA measurement range at the control center and substation in real time helps in detecting anomalies in measurements across both substation and the control center.
The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot induced oscillations is formulated. Finally, a model based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.