{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117778"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117778","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bio-Inspired Flow Control Using Fluid-Structure Interaction Modeling and Machine Learning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Nair, Nirmal Jayaprasad"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Goza, Andres","Ansell, Phillip","Bodony, Daniel","Tran, Huy","Wissa, Aimy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Fluid-structure Interaction","Strongly-coupled","Coverts","Flow Control","Machine Learning","Reinforcement Learning","Active","Passive"],"languages":["en","eng"],"rights":["Copyright 2022 Nirmal Jayaprasad Nair"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117778","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Goza, Andres","Ansell, Phillip","Bodony, Daniel","Tran, Huy","Wissa, Aimy"]},{"key":"dc:creator","label":"Author","values":["Nair, Nirmal Jayaprasad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-11-30"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Fluid-structure Interaction","Strongly-coupled","Coverts","Flow Control","Machine Learning","Reinforcement Learning","Active","Passive"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Nirmal Jayaprasad Nair"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117778"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Nirmal Jayaprasad Nair, accepted the attached license on 2022-11-23 at 19:31.","The student, Nirmal Jayaprasad Nair, submitted this Dissertation for approval on 2022-11-23 at 19:41.","This Dissertation was approved for publication on 2022-11-30 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18630 on 2023-04-12 at 07:32:08","Birds have a remarkable ability to perform complex maneuvers at post-stall angles of attack, partly due to the lift augmenting capabilities of self-actuating covert feathers. Covert-feathers-inspired flaps can be therefore leveraged to develop flow control strategies for next-generation unmanned- and micro-aerial vehicles that require high agility and maneuverability to operate in adverse environments in defence and commercial sectors. This thesis focuses on simulating and characterizing the aerodynamic benefits of such covert-inspired flow control methods via fluid-structure interaction (FSI) modeling and machine learning. Firstly, to computationally model covert-inspired flow control, we develop an efficient numerical algorithm for strongly-coupled FSI problems in an immersed boundary (IB) framework. Existing strongly-coupled IB methods are plagued by a severe computational bottleneck related to how the large-dimensional equations embedded withing a small-dimensional fluid-structure coupling matrix are evaluated. We present a remedy for this bottleneck wherein we precompute a modified matrix that encapsulates the large-dimensional operations to achieve significant computational savings. We also present a parallel implementation of the algorithm that scales favorably across multiple processors. Using the FSI solver, we simulate the flow past a covert-inspired passive flow control system. Most studies involving covert-inspired passive flow control model the feathers as a freely moving or a rigidly attached flap on a wing. A flap mounted via a torsional spring enables a configuration more emblematic of the finite stiffness associated with the covert-feather dynamics. The performance benefits and flow physics associated with this more general case remain largely unexplored. In this work, we model covert feathers as a passively deployable, torsionally hinged flap on the suction surface of a stationary airfoil. We numerically investigate this airfoil-flap system at a low Reynolds number of Re=1,000 and post-stall angle of attack of 20 deg. A parametric study is performed by varying the stiffness of the spring, mass of the flap and location of the hinge. The lift-enhancing FSI mechanisms are then analyzed in detail. Finally, we describe a covert-inspired hybrid active-passive flow control strategy as an extension of the passive counterpart to achieve even greater aerodynamic benefits. This method consists of actively varying the stiffness of the hinge in time to passively control the flap motion. The hinge stiffness is varied via a reinforcement learning (RL)-trained closed-loop feedback controller. The performance of the hybrid controller is analyzed in steady freestream conditions as well as in the presence of vortex gusts. In this hybrid method, to address the issue of practical unavailability of off-surface flow-field data, we also propose a state estimation framework that can estimate the full flow-field from limited sensor measurements located on the body surface using deep learning. We demonstrate the accuracy of this state estimation approach on a canonical problem of a flow past a flat plate, but emphasize its utility in the hybrid method."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bio-Inspired Flow Control Using Fluid-Structure Interaction Modeling and Machine Learning"]}]}],"canonical_facts":{"dc:contributor":["Goza, Andres","Ansell, Phillip","Bodony, Daniel","Tran, Huy","Wissa, Aimy"],"dc:creator":["Nair, Nirmal Jayaprasad"],"dc:date":["2022-12","2022-11-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Nirmal Jayaprasad Nair, accepted the attached license on 2022-11-23 at 19:31.","The student, Nirmal Jayaprasad Nair, submitted this Dissertation for approval on 2022-11-23 at 19:41.","This Dissertation was approved for publication on 2022-11-30 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18630 on 2023-04-12 at 07:32:08","Birds have a remarkable ability to perform complex maneuvers at post-stall angles of attack, partly due to the lift augmenting capabilities of self-actuating covert feathers. Covert-feathers-inspired flaps can be therefore leveraged to develop flow control strategies for next-generation unmanned- and micro-aerial vehicles that require high agility and maneuverability to operate in adverse environments in defence and commercial sectors. This thesis focuses on simulating and characterizing the aerodynamic benefits of such covert-inspired flow control methods via fluid-structure interaction (FSI) modeling and machine learning. Firstly, to computationally model covert-inspired flow control, we develop an efficient numerical algorithm for strongly-coupled FSI problems in an immersed boundary (IB) framework. Existing strongly-coupled IB methods are plagued by a severe computational bottleneck related to how the large-dimensional equations embedded withing a small-dimensional fluid-structure coupling matrix are evaluated. We present a remedy for this bottleneck wherein we precompute a modified matrix that encapsulates the large-dimensional operations to achieve significant computational savings. We also present a parallel implementation of the algorithm that scales favorably across multiple processors. Using the FSI solver, we simulate the flow past a covert-inspired passive flow control system. Most studies involving covert-inspired passive flow control model the feathers as a freely moving or a rigidly attached flap on a wing. A flap mounted via a torsional spring enables a configuration more emblematic of the finite stiffness associated with the covert-feather dynamics. The performance benefits and flow physics associated with this more general case remain largely unexplored. In this work, we model covert feathers as a passively deployable, torsionally hinged flap on the suction surface of a stationary airfoil. We numerically investigate this airfoil-flap system at a low Reynolds number of Re=1,000 and post-stall angle of attack of 20 deg. A parametric study is performed by varying the stiffness of the spring, mass of the flap and location of the hinge. The lift-enhancing FSI mechanisms are then analyzed in detail. Finally, we describe a covert-inspired hybrid active-passive flow control strategy as an extension of the passive counterpart to achieve even greater aerodynamic benefits. This method consists of actively varying the stiffness of the hinge in time to passively control the flap motion. The hinge stiffness is varied via a reinforcement learning (RL)-trained closed-loop feedback controller. The performance of the hybrid controller is analyzed in steady freestream conditions as well as in the presence of vortex gusts. In this hybrid method, to address the issue of practical unavailability of off-surface flow-field data, we also propose a state estimation framework that can estimate the full flow-field from limited sensor measurements located on the body surface using deep learning. We demonstrate the accuracy of this state estimation approach on a canonical problem of a flow past a flat plate, but emphasize its utility in the hybrid method."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117778"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Nirmal Jayaprasad Nair"],"dc:subject":["Fluid-structure Interaction","Strongly-coupled","Coverts","Flow Control","Machine Learning","Reinforcement Learning","Active","Passive"],"dc:title":["Bio-Inspired Flow Control Using Fluid-Structure Interaction Modeling and Machine Learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}