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X-WR-CALDESC:Events for Department of Aerospace Engineering
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TZID:Asia/Kolkata
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251201T110000
DTEND;TZID=Asia/Kolkata:20251201T130000
DTSTAMP:20260430T125613
CREATED:20251201T040004Z
LAST-MODIFIED:20251201T040004Z
UID:10000097-1764586800-1764594000@aero.iisc.ac.in
SUMMARY:Ph.D. (Engg) : Autorotation of Single-Winged Spinning Samaras
DESCRIPTION:Nature has consistently served as a powerful source of innovation\, offering elegant and sustainable solutions to complex engineering problems. Among these\, the spinning samara seed stands out as a biologically efficient system for passive aerial transport. Samaras\, such as those from mahogany and Buddha coconut trees\, exhibit stable autorotative descent\, making them strong candidates for biomimicry in aerial delivery systems. Understanding and replicating the flight mechanics of samaras requires accurate analytical modelling. The dynamics of a single-winged spinning samara can be described using Newton’s laws and Euler’s rigid‐body equations\, while the aerodynamic forces acting on the wing can be derived from the Navier–Stokes equations or using Blade Element Momentum Theory (BEMT). Together\, these frameworks provide a foundation for predicting its motion\, thrust generation\, and stability in samara-inspired designs. Building on this theoretical basis\, the present thesis delivers a comprehensive experimental study on the bioinspired engineering of single-winged spinning samaras\, focusing on their aerodynamic behavior\, kinematic characteristics\, structural morphology\, and wake dynamics. To investigate the kinematics\, a custom-designed drop rig was developed to capture high-resolution visual data of the steady-state descent. Parameters such as descent velocity\, coning angle\, wingtip trajectory\, and precession were extracted and analyzed. The results revealed a complex motion involving coupled coning and precession\, challenging simplified theoretical models that typically assume a steady\, non-precessing descent. Parallel morphological studies using high-resolution 3D scanning of natural samaras highlighted spanwise variations in chord length\, camber\, and sweep\, which contribute significantly to aerodynamic performance. Five 3D printed models incorporating geometric variations were fabricated to evaluate their aerodynamic efficiency. Experimental observations showed that models featuring variable chord\, sweep\, and anhedral/dihedral configurations achieved the lowest descent velocities\, underscoring the importance of structural morphology in enhancing autorotative performance. To examine local flow physics in detail\, a custom low-Reynolds-number vertical wind tunnel was developed and characterized. Flat-plate airfoils were studied using Particle Image Velocimetry (PIV) across a wide range of angles of attack and Reynolds numbers\, revealing flow regimes ranging from steady attached flow to unsteady vortex shedding. Wake flow physics of samaras were further captured within a transparent glass chamber using seeded PIV\, revealing stable wingtip vortices extending several diameters downstream and confirming the presence of a windmill-brake state analogous to helicopter autorotation. Induced velocities computed using Momentum Theory showed close agreement with theoretical predictions. To evaluate practical feasibility\, a bioinspired delivery system was designed and tested through drone-based release experiments. Six models (FR01 to FR06) were fabricated and deployed under varying payloads and wind conditions. All models demonstrated successful autorotation and stable descent\, confirming the viability of samara-inspired mechanisms for passive aerial delivery. This research advances the understanding of samara aerodynamics and opens pathways for bioinspired applications in unmanned aerial systems. Future work should explore 3D motion capture\, high-fidelity simulations\, and optimization of geometries and materials. The insights from this thesis provide a strong foundation for future innovations in samara-inspired flight technologies. \nSpeaker :  G.YOGESHWARAN \n  \nResearch Supervisor: Gopalan Jagadeesh \nCo-Research Supervisor: Srisha Rao M V
URL:https://aero.iisc.ac.in/event/ph-d-engg-autorotation-of-single-winged-spinning-samaras/
LOCATION:STC Seminar Hall\, Dept. of Aerospace Engineering
CATEGORIES:Thesis Colloquium / Defence
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/12/YOGESHWARAN.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251219T110000
DTEND;TZID=Asia/Kolkata:20251219T130000
DTSTAMP:20260430T125613
CREATED:20251212T053059Z
LAST-MODIFIED:20251218T103035Z
UID:10000101-1766142000-1766149200@aero.iisc.ac.in
SUMMARY:Ph.D. (Engg) : Investigations on Elastic Deformation of Ferromagnetic Rods and Ribbons
DESCRIPTION:Bulk ferromagnetic materials exhibit Joule magnetostriction with characteristic strain magnitudes on the order of (10^{-6}) to (10^{-4})\, which is often insufficient for the displacement requirements of modern soft robotic and adaptive structural systems. Ferromagnetic elastic slender structures provide a promising alternative\, offering the potential for large actuation displacements under small external magnetic fields. This enhanced response results from a rich coupling between magnetic and elastic phenomena in slender structures\, where external magnetic fields can induce significant displacements with relatively small field strengths. This thesis develops a novel unified theoretical framework to describe the coupled magnetoelastic behavior of ferromagnetic elastic rods and ribbons. The framework formulates the total energy functional\, incorporating elastic and magnetic energy components for both soft and hard ferromagnetic materials. The elastic energy is derived from Kirchhoff and Wunderlich models for rods and ribbons. The magnetic energy is formulated using the micromagnetic energy functional composed of exchange\, anisotropy\, magnetostriction\, demagnetization\, and Zeeman energies. Central to the framework is the interplay between elastic energy and the competing magnetic effects: demagnetization energy in soft ferromagnets and Zeeman energy in hard ferromagnets. \nIn the first part of this thesis\, we construct the total energy formulation for ferromagnetic rods undergoing planar deformation and utilize Kirchhoff kinetic analogy for our investigation. A detailed bifurcation analysis distinguishes the Hamiltonian phase portraits of elastic rods and soft ferromagnetic rods in longitudinal and transverse magnetic fields\, revealing distinct subcritical and supercritical pitchfork bifurcations. The extension of Kirchhoff’s kinetic analogy to ferromagnetic rods enables the prediction of equilibrium shapes under various boundary conditions and applied fields. However\, the kinetic analogy framework does not directly address the stability of these equilibrium states. \n\n                                                                                                                                            Motivated by this limitation\, the second part presents a comprehensive one-dimensional model for ferromagnetic elastic rods/ribbons systematically incorporating micromagnetic energy for curved geometries. The resulting equilibrium equations are derived for both soft and hard magnetic cases\, which are then solved numerically to trace load–deflection responses. Stability analysis via a Sturm–Liouville eigenvalue approach reveals tensile critical buckling loads for soft ribbons and uncovers novel stable post-buckling configurations\, especially for fixed-fixed boundary conditions under transverse magnetic fields. For hard ferromagnetic ribbons\, the buckling loads are shifted\, but the corresponding equilibrium shapes approach those of purely elastic ribbons. The restriction to planar deformations\, however\, leaves open the question of whether these configurations remain stable with respect to fully three-dimensional perturbations.\nTo address this issue\, the third part extends the study to spatially deforming ferromagnetic elastic rods subjected to combined magnetic and terminal mechanical loading. The Hamiltonian derived from the total energy is analyzed\, revealing a significant difference: the purely elastic and hard ferromagnetic rods exhibit subcritical pitchfork bifurcations\, whereas the soft ferromagnetic rod shows no bifurcation under similar conditions. Furthermore\, localized buckling deformation of soft ferromagnetic rod shows non-collinear straight segments.\nThis work is\, to the best of our knowledge\, the first to demonstrate the role of demagnetization energy in the large deformation of ferromagnetic slender structures. As one of the dominant contributions to the micromagnetic energy functional\, the demagnetization energy is inherently geometry dependent and therefore crucial to structural deformation. As the slender structure deforms and its geometry changes and the demagnetization energy is correspondingly modified. Our results provide an understanding of the interplay of magnetic and elastic forces\, paving the way for the design of advanced smart materials and potential applications in magnetically-actuated soft robots\, adaptive medical devices\, and remote actuation.\n\nSpeaker:  Mr. G R Krishna Chand Avatar\n\nResearch Supervisor : Dr. Vivekanand Dabade
URL:https://aero.iisc.ac.in/event/ph-d-engg-investigations-on-elastic-deformation-of-ferromagnetic-rods-and-ribbons/
LOCATION:STC Seminar Hall\, Dept. of Aerospace Engineering
CATEGORIES:Thesis Colloquium / Defence
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/12/Krishna.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251222T150000
DTEND;TZID=Asia/Kolkata:20251222T170000
DTSTAMP:20260430T125613
CREATED:20251218T100932Z
LAST-MODIFIED:20251218T100932Z
UID:10000104-1766415600-1766422800@aero.iisc.ac.in
SUMMARY:Ph.D. (Engg) : Characterization of time-frequency behavior of flow intermittency in transitional boundary layers
DESCRIPTION:The importance of transitional flow studies can be realized from the fact that it acts as a bridge between the laminar and turbulent flows. The present work deals with the investigation of time-frequency characteristics of transitional flows and their modelling\, which is presented in three parts.\nFirst\, we propose a wavelet-transform based smooth detector function which is used to detect the presence of turbulent spots in a signal. We also propose a novel wavelet transform based algorithm to calculate the intermittency for various transitional and turbulent boundary layers with the primary objective to remove the subjectivity of current methods. The method is also used to calculate the intermittencies for temporal and spatial distributions of velocities of a computational dataset. The algorithm involves calculation of a sensitive detector and then obtaining an indicator based on Monte-Carlo like iterations. A cut-off on the number of iterations ​is obtained based on RMS of the laminar part of the signal. The method is also able detect the turbulent/non-turbulent interface in wall-normal and wall-parallel planes. This wide spectrum of results prove the generality of the scheme\, which to our knowledge has been demonstrated for the first time.\nSecondly\, the substructures of stream-wise velocity fluctuations within a turbulent spot are investigated using time-spectral and probability density function(PDF) based analyses. The pre-multiplied Fourier spectrum shows that the turbulent spots appear rather  “suddenly” at the onset of transition. The high frequency structures inside a near-wall turbulent spot at the onset of transition are found to be highly time localized. The presence of large amplitude events near the onset location hints towards a near “singular” structure within a nascent spot that has high frequencies\, amplitudes and time localization. It is also seen that the transition process only modifies the structures at higher frequencies and the lower frequencies remain almost unchanged. For lower frequencies\, the structure throughout the transition zone\, for all the transition as well as the turbulent boundary layer cases show a universal nature.\nIn the third part\, Cellular Automaton (CA) is used to model the growth\, propagation and merging of turbulent spots. CA simulations are shown to handle a variety of different practical/theoretical spot generation scenarios. These simulations are computationally inexpensive and easily parallelizable. They represent a promising avenue for modelling the kinematics of turbulent spots.\n\nSpeaker : Satyajit De\n\nResearch Supervisor : Sourabh S. Diwan
URL:https://aero.iisc.ac.in/event/ph-d-engg-characterization-of-time-frequency-behavior-of-flow-intermittency-in-transitional-boundary-layers/
LOCATION:STC Seminar Hall\, Dept. of Aerospace Engineering
CATEGORIES:Thesis Colloquium / Defence
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/12/Satyajit.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251230T103000
DTEND;TZID=Asia/Kolkata:20251230T130000
DTSTAMP:20260430T125613
CREATED:20251229T130027Z
LAST-MODIFIED:20251231T083130Z
UID:10000107-1767090600-1767099600@aero.iisc.ac.in
SUMMARY:Ph.D. (Engg) : Enhancing Precise Label Prediction and Imbalance Robustness in Multi-Label Learning
DESCRIPTION:Multi-label learning (MLL) addresses learning problems in which a single data instance may simultaneously belong to multiple semantic categories. This formulation arises naturally in many real-world applications\, including image and video understanding\, medical diagnosis\, text categorization\, and bioinformatics. In many of these settings\, it is not sufficient to merely rank relevant labels higher than irrelevant ones; instead\, models must accurately identify the exact set of labels associated with each instance. Such exact label prediction is critical when each label carries direct semantic or operational meaning\, for example when detecting disease conditions in medical data or identifying pedestrians and traffic signs in autonomous driving scenes. In addition\, real-world deployments frequently expose multi-label models to out-of-distribution (OOD) inputs caused by domain shifts\, novel concepts\, or evolving environments\, making reliable OOD detection an important requirement. Many practical applications are also sequential in nature\, where data and label spaces evolve over time\, leading to the continual multi-label learning (CMLL) problem in which models must acquire new knowledge while mitigating catastrophic forgetting. Together\, these considerations motivate the need to study multi-label learning with emphasis on exact label prediction\, robustness to data imbalance\, improved OOD detection\, and learning under continual data arrival. \nThe first contribution of this thesis introduces Bipolar Networks\, a novel architectural formulation for multi-label classification designed to improve exact label prediction. Unlike conventional single-output architectures that produce continuous confidence scores per label\, Bipolar Networks represent each label using two complementary outputs that encode positive and negative evidence. The final label decision is derived from the relative difference between these outputs\, enabling exact label predictions. To support effective training of this architecture\, the thesis develops a family of bipolar loss functions by reformulating standard objectives such as Binary Cross-Entropy and Focal Loss\, along with margin-based variants. Extensive experiments on benchmark datasets demonstrate that Bipolar Networks consistently improve F1 scores while maintaining competitive mean average precisions. \nBuilding on the improved discriminative behaviour of Bipolar Networks\, the second contribution addresses out-of-distribution detection in multi-label learning. While most existing OOD detection methods are designed for single-label classification or rely on computationally intensive mechanisms\, this thesis proposes a bipolar joint energy score tailored to the bipolar architecture. By leveraging the improved exact label prediction capability of Bipolar Networks\, the proposed scoring function enables more effective separation between in-distribution and out-of-distribution samples in multi-label settings\, demonstrating that stronger multi-label classification performance on in-distribution data can naturally translate into improved OOD detection. \nThe third contribution presents Learn What Matters\, a generalizable training framework that enhances exact label prediction without modifying model architectures or loss formulations. Learn What Matters operates at the optimization level by selectively masking parameter updates based on the ratio of gradient to parameter magnitudes\, suppressing low-information updates while rescaling the remaining gradients to preserve learning dynamics. This approach acts as a form of dropout during backpropagation and directs learning toward informative regions of the parameter space. Applied to standard single-output multi-label networks trained with ranking-based losses\, Learn What Matters yields substantial improvements in F1 score with only marginal impact on mAP scores\, providing a model-agnostic alternative to architectural modifications. \nThe fourth contribution explores biologically inspired learning approaches for multi-label classification. Drawing inspiration from neural computation in the human brain\, the thesis develops several bio-inspired models\, including Bipolar Spiking Neural Networks\, Adaptive Margin Spiking Neural Networks\, and NIMBLE\, a neuro-inspired multi-label learning framework. These methods leverage spike-based computation\, selective update mechanisms\, and adaptive stability–plasticity behavior to naturally support exact label prediction and efficient learning. Experimental results demonstrate that these biologically motivated designs improve exact label prediction and imbalance robustness in multi-label settings. \nFinally\, the thesis extends the proposed architectures and learning algorithms to the continual multi-label learning setting\, where data arrives sequentially without access to past samples or task identities. The resulting methods are task-agnostic and memory-free\, and empirical evaluations across multiple benchmarks show consistent improvements in exact label prediction and reduced catastrophic forgetting. Overall\, this thesis presents a unified framework spanning architectural design\, optimization strategies\, and biologically inspired learning for advancing exact label prediction\, OOD detection\, and continual learning in multi-label learning models. \n  \nSpeaker :  Sourav Mishra \n  \nResearch Supervisor: Prof. Suresh Sundaram
URL:https://aero.iisc.ac.in/event/ph-d-engg-enhancing-precise-label-prediction-and-imbalance-robustness-in-multi-label-learning/
LOCATION:STC Seminar Hall\, Dept. of Aerospace Engineering
CATEGORIES:Thesis Colloquium / Defence
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/12/Sourav.jpg
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