Browsing by Author "Srivastava, Ankur"
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Item A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements(Hindawi, 2015) Srivastava, Ankur; Meade, Andrew J.Use of probabilistic techniques has been demonstrated to learn air data parameters from surface pressure measurements. Integration of numerical models with wind tunnel data and sequential experiment design of wind tunnel runs has been demonstrated in the calibration of a flush air data sensing anemometer system. Development and implementation of a metamodeling method, Sequential Function Approximation (SFA), are presented which lies at the core of the discussed probabilistic framework. SFA is presented as a tool capable of nonlinear statistical inference, uncertainty reduction by fusion of data with physical models of variable fidelity, and sequential experiment design. This work presents the development and application of these tools in the calibration of FADS for a Runway Assisted Landing Site (RALS) control tower. However, the multidisciplinary nature of this work is general in nature and is potentially applicable to a variety of mechanical and aerospace engineering problems.Item A greedy algorithm for learning pilot ratings from helicopter shipboard dynamic interface tests(2007) Srivastava, Ankur; Meade, Andrew J., Jr.In a real world pattern recognition application a user cannot assess the performance of a classifier on an unlabeled data set. Classifiers cannot give their best performance because they require user-controlled parameters. As a Solution, a Sequential Function Approximation (SFA) method has been' developed for classification that determines the values of the control parameters during learning. In this dissertation, experiments were carried out on real world data sets where SFA, using only the training subset, had comparable performance to a number of other popular classification schemes whose user-defined parameters were optimized utilizing the entire data set. By the statistical significance of the results it was concluded at 95% confidence that the performance of SFA will be equivalent or significantly better than those of the other popular classification tools. After establishing SFA as a proper classification tool in this dissertation, it is applied to a US Navy flight test problem. The current problem at hand is to predict pilot ratings from HH-60H Sea-Hawk helicopters based on 369 at sea take-off and landing DI tests. Least significant inputs with respect to classification were pointed out with the potential of accelerating through the DI test matrix. And finally an effort was made to give the DI test pilots an estimate of how many tests were necessary to be conducted before generating enough data for the SFA classification tool to satisfactorily learn.Item Calibration of Flush Air Data Sensing Systems Using Surrogate Modeling Techniques(2011) Srivastava, Ankur; Meade, Andrew J., Jr.In this work the problem of calibrating Flush Air Data Sensing (FADS) has been addressed. The inverse problem of extracting freestream wind speed and angle of attack from pressure measurements has been solved. The aim of this work was to develop machine learning and statistical tools to optimize design and calibration of FADS systems. Experimental and Computational Fluid Dynamics (EFD and CFD) solve the forward problem of determining the pressure distribution given the wind velocity profile and bluff body geometry. In this work three ways are presented in which machine learning techniques can improve calibration of FADS systems. First, a scattered data approximation scheme, called Sequential Function Approximation (SFA) that successfully solved the current inverse problem was developed. The proposed scheme is a greedy and self-adaptive technique that constructs reliable and robust estimates without any user-interaction. Wind speed and direction prediction algorithms were developed for two FADS problems. One where pressure sensors are installed on a surface vessel and the other where sensors are installed on the Runway Assisted Landing Site (RALS) control tower. Second, a Tikhonov regularization based data-model fusion technique with SFA was developed to fuse low fidelity CFD solutions with noisy and sparse wind tunnel data. The purpose of this data model fusion approach was to obtain high fidelity, smooth and noiseless flow field solutions by using only a few discrete experimental measurements and a low fidelity numerical solution. This physics based regularization technique gave better flow field solutions compared to smoothness based solutions when wind tunnel data is sparse and incomplete. Third, a sequential design strategy was developed with SFA using Active Learning techniques from the machine learning theory and Optimal Design of Experiments from statistics for regression and classification problems. Uncertainty Sampling was used with SFA to demonstrate the effectiveness of active learning versus passive learning on a cavity flow classification problem. A sequential G-optimal design procedure was also developed with SFA for regression problems. The effectiveness of this approach was demonstrated on a simulated problem and the above mentioned FADS problem.Item Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements(Hindawi, 2014) Srivastava, Ankur; Meade, Andrew J.Wind tunnel tests to measure unsteady cavity flow pressure measurements can be expensive, lengthy, and tedious. In this work, the feasibility of an active machine learning technique to design wind tunnel runs using proxy data is tested. The proposed active learning scheme used scattered data approximation in conjunction with uncertainty sampling (US). We applied the proposed intelligent sampling strategy in characterizing cavity flow classes at subsonic and transonic speeds and demonstrated that the scheme has better classification accuracies, using fewer training points, than a passive Latin Hypercube Sampling (LHS) strategy.