Calibration of Flush Air Data Sensing Systems Using Surrogate Modeling Techniques

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dc.contributor.advisorMeade, Andrew J., Jr.en_US
dc.creatorSrivastava, Ankuren_US
dc.date.accessioned2013-03-08T00:39:12Zen_US
dc.date.available2013-03-08T00:39:12Zen_US
dc.date.issued2011en_US
dc.description.abstractIn 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.en_US
dc.format.extent170 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS M.E. 2011 SRIVASTAVAen_US
dc.identifier.citationSrivastava, Ankur. "Calibration of Flush Air Data Sensing Systems Using Surrogate Modeling Techniques." (2011) Diss., Rice University. <a href="https://hdl.handle.net/1911/70450">https://hdl.handle.net/1911/70450</a>.en_US
dc.identifier.digitalSrivastavaAen_US
dc.identifier.urihttps://hdl.handle.net/1911/70450en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectApplied sciencesen_US
dc.subjectFlush air data sensingen_US
dc.subjectSurrogate modelingen_US
dc.subjectRadial basis functionsen_US
dc.subjectScattered data approximationen_US
dc.subjectMechanical engineeringen_US
dc.titleCalibration of Flush Air Data Sensing Systems Using Surrogate Modeling Techniquesen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentMechanical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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