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X-WR-CALDESC:Events for Department of Aerospace Engineering
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Kolkata:20240405T173000
DTEND;TZID=Asia/Kolkata:20240405T183000
DTSTAMP:20260617T165705
CREATED:20240405T112534Z
LAST-MODIFIED:20240802T165512Z
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SUMMARY:CyberSHM: A cyberphysical continuous monitoring technology for safety-critical structures
DESCRIPTION:Continuous monitoring is crucial for ensuring the proper functioning and longevity of operational structures in safety-critical applications. To address\, we are exploring the use of smart\, self-sensing structures that combine edge computing\, physics-informed and machine learning-enabled monitoring techniques. Of particular interest are thin-walled laminated composite structures with high-density cores and discontinuities that are especially challenging due to their complex waveguide behaviour. This talk will focus on the use of acousto-ultrasonic signals to monitor and interrogate such thin-walled structures for hidden barely visible damages. \nThe dispersion and wave scattering associated with these structures make traditional time-of-arrival (ToA) techniques ineffective for holistic damage identification. A singular focus on ToA results in under exploitation of several signal features needed for a multiclass representation of damages. Data-driven machine learning-based approaches are being explored which can map complex set of signal features to acoustic source characteristics. But rather than working as a black-box model for damage identification\, these must complement the physics-based model predictions to incorporate physical plausibility and thus establish a robust grey-box predictor. Physics-based dispersion characteristics is modelled with a semi-analytical approach which allows for interlaminar damage features to be incorporated into the model. The data-driven component focusses on training a high-dimensional Bayesian surrogate model which maps complex signal features in the time-frequency domain to the damage parameters such as location\, type and severity. Inverse identification is performed with a Bayesian approach which quantifies and incorporates measurement and model-form uncertainty into robust predictions of structural damage metrics and the associated confidence bounds. \nThe stated aim of continuous monitoring presents several challenges ranging from reducing the footprint of signal acquisition/processing hardware to combining cloud computing with edge computing to be deployed for conditioning and transmission of signals for real-time decision making. We conceptualise this as a Cyberphysical Structural Health Monitoring or a CyberSHM system which is an automated monitoring framework integrated with the internet and working collaboratively with human end-users. The study uses carbon-fibre composite panels with stiffeners as a test bench\, subjecting them to impact and fatigue loading and monitored with a CyberSHM system\, thus realising a generalized automated approach for online monitoring of thin-walled structures highlighting its effectiveness\, challenges and a futuristic vision of this technology. \n  \nSpeaker: Dr. Abhishek Kundu \nBiography: Dr Abhishek Kundu is a Senior Lecturer at the Computational Mechanics & Engineering AI research group at the Cardiff School of Engineering\, Cardiff University\, UK and an elected member of the Royal Aeronautical Society. His research interests span the fields of structural health monitoring (SHM)\, stochastic structural dynamics\, uncertainty quantification\, machine learning and Bayesian identification. His main contribution lies in efficient computational techniques for the study of stochastic structural dynamics systems and control and data-driven approaches for SHM. He completed his PhD from Swansea University as Zienkiewicz scholar in 2014. Dr Kundu has authored more than 50 scientific publications and was awarded the best paper at the European Workshop on Structural Health Monitoring (EWSHM 2018). Amongst his main research engagements\, he has been the recipient of Royal Academy of Engineering’s Industrial Fellowship with Airbus and currently serves as the principal investigator in the EPSRC funded project on CyberSHM.
URL:https://aero.iisc.ac.in/event/cybershm-a-cyberphysical-continuous-monitoring-technology-for-safety-critical-structures/
LOCATION:AE Auditorium
CATEGORIES:AE Seminar
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DTSTART;TZID=Asia/Kolkata:20240418T213000
DTEND;TZID=Asia/Kolkata:20240418T223000
DTSTAMP:20260617T165705
CREATED:20240418T112845Z
LAST-MODIFIED:20240803T053223Z
UID:10000005-1713475800-1713479400@aero.iisc.ac.in
SUMMARY:[PhD Colloquium] A class of vector fields for path following guidance
DESCRIPTION:With rapidly evolving application scenarios\, Unmanned Aerial Vehicles (UAVs) are often desired to autonomously follow predefined paths. Prospective path following guidance methods should cater to the dynamic capability of the UAV\, curvature variation along the path\, and provide accurate performance while utilizing\, preferably\, a computationally inexpensive guidance logic. This thesis presents a class of vector fields addressing a variety of path following guidance problems. \nThe first part of the thesis considers constant curvature paths\, namely\, straight lines and circular orbits. The key idea is to generate the commanded UAV course angle as a vector field based on the instantaneous UAV position. The vector field logic uses an arcsine shaping function based on the UAV position with respect to the desired path. A stability analysis guarantees asymptotic convergence of the UAV position error to zero. A detailed comparative study with a popular approach demonstrates that the proposed method significantly reduces the maximum curvature and the total control effort along the guided trajectory. Numerical simulation studies consider a second-order course hold autopilot\, first-order airspeed control and different UAV initial conditions to demonstrate the effectiveness of the proposed guidance method. \nThe second part of the thesis considers scenarios wherein the path exhibits variation in its curvature. First case considers an elliptic path following scenario\, and a course angle guidance command is proposed which encompasses path convergent and path tangential components. The path convergent term is deduced using an arcsine shaping function of the UAV position error with respect to the path\, while the tangential component is obtained using the slope information of the path. The second path following case considers paths described explicitly as y = f(x). Therein\, again the course angle guidance command comprises path-tracking and path-attracting elements. Subject to the proposed guidance methods\, asymptotically converging behaviour of the UAV position error is deduced using Lyapunov stability theory. Extensive simulation studies are carried out with several UAV initial positions for following elliptic\, sinusoidal\, and parabolic paths. \nNext\, the thesis introduces rectangular boundary surveillance guidance using Lamé curve paths. Geometric properties of the Lamé curve paths are analysed\, and an efficient Lamé curve path-based circumscription of a rectangular boundary is proposed. Considering a given UAV maximum turn rate\, a comparative analysis highlights that the proposed Lamé curve path offers significantly reduced path length in circumscribing a rectangular boundary as compared to widely used elliptic circumscription. Further\, a vector field guidance method is introduced to follow the Lamé curve path\, and its stability properties are established. Numerical simulations include sample scenarios with several UAV initial conditions and comparative studies with different rectangular dimensions. \nUsing an indoor motion capture facility and a quadrotor UAV platform\, the last part of the thesis presents experimental validation studies for the proposed guidance methods. Flight trials consider a variety of constant and variable curvature paths and demonstrate the effectiveness of the proposed guidance methods. \nOverall\, the proposed guidance methods present simple\, analytic\, and easily computable path-following logic that utilize only the UAV position information. Deterministic performance guarantees and extensive validation studies further add to the merit of the proposed guidance solutions. \n  \nSpeaker: Amit Shivam
URL:https://aero.iisc.ac.in/event/a-class-of-vector-fields-for-path-following-guidance/
LOCATION:Auditorium (AE 005)\, Department of Aerospace Engineering
CATEGORIES:Thesis Colloquium / Defence
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