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X-WR-CALDESC:Events for Department of Aerospace Engineering
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250404T110000
DTEND;TZID=Asia/Kolkata:20250404T130000
DTSTAMP:20260517T001826
CREATED:20250403T043429Z
LAST-MODIFIED:20250407T044613Z
UID:10000067-1743764400-1743771600@aero.iisc.ac.in
SUMMARY:Ph.D. (Engg): Control of Alternating Flow Phenomena in Transonic Shock Wave Boundary Layer Interactions Over Payload Region of a Generic Launch Vehicle Model
DESCRIPTION:The transonic Mach number regime is a critical phase in the atmospheric ascent of launch vehicles\, where aerodynamic loads peak due to the combined effects of high freestream dynamic pressure and angle of attack. Besides high steady loads\, launch vehicles experience very high levels of pressure fluctuations caused by interactions between the unsteady λ-shock system and the boundary layer – a phenomenon known as Shock Wave Boundary Layer Interaction (SWBLI). These interactions can induce buffet excitation over the payload region\, leading to structural failure as well as control issues. NASA recommends limiting the nose cone semi-angle to 15° to mitigate shock oscillations\, labelling such designs as “Buffet-Proof.” However\, practical constraints such as payload mass & volume\, rocket diameter\, launch-pad limitations\, etc. necessitate the use of larger nose cone angles which are buffet-prone. While SWBLI has been well understood for two-dimensional flows\, data for three-dimensional launch vehicle type configurations is sparse in the literature\, with regard to even the basic understanding of the phenomena. Hence\, there is a need to develop physics-based models to handle SWBLI in practical cases.\nWind tunnel experiments were conducted to evaluate the aerodynamic impact of increasing nose cone angles to 20° and 25° in the transonic Mach number range. These investigations revealed critical flow characteristics such as abrupt jumps in pitching moments at small angles of attack (±4°)\, very high levels of pressure fluctuations\, λ-shock system oscillations\, and the occurrence of destabilizing counter-rotating vortices\, intermittent supersonic and subsonic flows (termed alternating flow phenomena) at specific Mach numbers of 0.90 and 0.94. The present research explores two approaches towards controlling SWBLI. The first involves a passive device\, a front-mounted Aerodisc\, systematically evaluated for the effect of geometric parameters at critical Mach numbers of 0.9 and 0.94 in the range of angles of attack of ±4°. The optimized Aerodisc configuration achieved the maximum noise reduction of 22 dB (Overall Sound Pressure Level\, OASPL). The second approach involves an active flow control technique using a pneumatic counterflow jet. The jet parameters were varied during the tests. Jets with exit diameters of 3 mm and 4 mm operating at a pressure ratio of 3.2 achieved the greatest suppression by nearly 20 dB. Both the passive and active techniques demonstrated that by energizing the boundary layer\, the oscillating shock waves were stabilized\, the counter-rotating vortices removed\, and the upstream travelling Kutta waves associated with the alternating flow phenomena completely suppressed.\nThis research clearly brings out the basic physics of SWBLI and its control for 3-dimensional launch vehicle type configurations at transonic Mach numbers\, highlighting that energizing the boundary layer is the key to control the transonic flow over launch vehicles with large blunt nose-cones. \n  \nSpeaker: Dheerendra Bahadur Singh \n  \nResearch Supervisor: Prof. G. Jagadeesh
URL:https://aero.iisc.ac.in/event/ph-d-engg-control-of-alternating-flow-phenomena-in-transonic-shock-wave-boundary-layer-interactions-over-payload-region-of-a-generic-launch-vehicle-model-2/
LOCATION:CEH Conference Hall- Room No.239\, Second Floor\, Department of Aerospace Engineering
CATEGORIES:Thesis Colloquium / Defence
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/04/Dheerendra-.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250416T150000
DTEND;TZID=Asia/Kolkata:20250416T170000
DTSTAMP:20260517T001826
CREATED:20250407T063952Z
LAST-MODIFIED:20250407T101249Z
UID:10000068-1744815600-1744822800@aero.iisc.ac.in
SUMMARY:Ph.D. (Engg): Behaviour Modelling of Non-Cooperative Space Objects and Strategies for Decision Support in Space Situational Awareness
DESCRIPTION:In this modern era\, Space is vital for a Nation’s prosperity and without space\, many critical functions would simply stop working. The increasing number of satellite launches in recent times\, is congesting the space environment. Space is also becoming an increasingly contested environment from the perspective of non-civilian applications of satellites. The civilian and non-civilian space applications mandatorily require a complete awareness of the space environment before taking any operational decisions. Space Situational Awareness [SSA] is the comprehensive knowledge of Resident Space Objects [RSOs] which may include satellites\, rocket bodies\, debris\, and the ability to track and understand their behaviour. Space objects can be majorly categorized into two broad types\, cooperative space objects and non-cooperative space objects. A noncooperative space object is defined as a non-friendly object in space and can be perceived as a threat if it performs anomalous maneuvers in space. Modelling pattern-of-life of non-cooperative space objects is an essential requirement of SSA. Maneuvers of non-cooperative satellites is an important event of interest in their life pattern. In this thesis\, we investigate the behaviour of various classes of satellites through data driven modelling. We also study the threat perception from non-cooperative space objects to space assets of our interest. There are four key areas\, in which the thesis has significantly contributed. The first area deals with investigating\, exploring and modelling pattern-of-life of non-cooperative space objects. We have crafted data-driven solution methodologies from time series analysis\, machine learning\, deep learning to suit specific requirements. The second area pertains to the maneuvers of non-cooperative space objects. Identifying them\, helps in analyzing their behaviour. Since there may be numerous non-cooperative space objects and not all maneuvers of non-cooperative space objects may be threatening in nature\, it is essential to segregate routine maneuvers needed by a satellite to maintain its orbit from anomalous and abnormal maneuvers which may be perceived as threat. In this thesis\, we designed an approach to segregate benign and regular pattern-of-life maneuvers of non-cooperative space objects from their orbital data . The routine pattern-of-life maneuvers of satellites are events of interest\, but are infrequent and hence the non-maneuver class was observed to be far more numerous than the maneuver class label in the dataset. Through this thesis work\, we have applied Synthetic Minority Oversampling Techniques (SMOTE) and its variants to handle the imbalance in dataset available for classification. Different missions of cooperative civilian satellites in Low Earth Orbit (LEO) space regime were evaluated to prove the efficacy of the approach. The third area of contribution is in developing methodologies to estimate the threat perception for Geostationary Orbit (GEO) space regime. Modelling pattern-of-life of non-cooperative GEO satellites helps to identify anomalous behaviour and is essential for SSA. Additionally\, given a satellite of interest\, an assessment of the area of influence of neighbourhood satellite operations is critical for assessment of threat. Nearest neighbour search is a fundamental problem in computational geometry and we studied two major concepts of computational geometry \, the Voronoi diagram and the Delaunay triangulation in detail and crafted algorithms to assess threat in the GEO space regime. The last area of contribution is with scheduling the limited and costly ground based sensors to monitor the large number of space objects. There exists a problem of gaps in the available orbital data of noncooperative satellites. Moreover\, the satellite maneuver (event of interest) occurrence information of some samples may be lost\, due to noise in the ground sensor observations or due to observation window limits or losing tracks. Conventional machine learning regression methods are not suited to be able to include both the event and time aspects as the outcome. The conventional models are also are not equipped to handle censored examples (incomplete data due to non-observability). Therefore\, in this thesis\, we devised a solution methodology by applying Time-to-Event data analysis (survival analysis) techniques to assess whether a satellite maneuvered\, that is whether the event of interest occurred or not\, and also estimate when the next maneuver would occur. We have explored a variety of approaches including Cox proportional hazards model\, Weibull distribution model\, Kaplan-Meier model\, Nelson-Aalen model\, Random survival forest\, Survival Support Vector Machines\, Gradient boosted survival analysis and Deep learning based survival analysis. Detailed experimental results based on real life satellite orbital datasets are presented to bring out the effectiveness of the solution methodology. To summarize\, the thesis contributes by developing a space situational awareness system to achieve behavioural modelling\, classification and characterization of space objects of interest\, maneuver classification\, anomaly detection and threat assessment through data driven methodologies. \n  \nSpeaker: Shiv Shankar S  \n  \nResearch Supervisor: Debasish Ghose
URL:https://aero.iisc.ac.in/event/ph-d-engg-behaviour-modelling-of-non-cooperative-space-objects-and-strategies-for-decision-support-in-space-situational-awareness/
LOCATION:Online
CATEGORIES:Thesis Colloquium / Defence
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/04/SHIV-.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250416T153000
DTEND;TZID=Asia/Kolkata:20250416T170000
DTSTAMP:20260517T001826
CREATED:20250416T051419Z
LAST-MODIFIED:20250416T051419Z
UID:10000070-1744817400-1744822800@aero.iisc.ac.in
SUMMARY:Miniaturised technologies for potential applications in space research
DESCRIPTION:Miniaturised technologies\, due to their portability\, rapid responses\, low powers and ability of multi-component integration\, have received an ever-growing interest in areas like healthcare\, air quality\, and space research. This talk will provide an overview of my research in 3 domains of miniaturised technologies: a) microfluidics\, b) MEMS sensors and c) nanoaerosol instruments. I will also highlight areas of space research where this work is potentially relevant. \nI will begin my talk with my work in microfluidic particle enrichment and gene therapy devices. Enrichment devices\, when integrated with a downstream sensor for target particle detection\, can significantly improve the sensor sensitivity. I will cover my work in developing enrichment devices and mitigation of some undesirable effects that can limit their reliability. I will also introduce my work in commercial-scale microfluidic mixers for gene therapy. The work in this theme is highly relevant to healthcare in manned space missions and CubeSats to understand in-space behaviour of bio-species. \nI will next cover my work in thin film MEMS mass sensors\, which offer several advantages over conventional sensors like QCMs thanks to their portability\, high sensitivities and excellent compatibility with semiconductor technology. This talk will cover my work towards enhancing their capabilities in areas of biosensing and simultaneous detection of multiple parameters. This work has a promising applicability in controlling ambient conditions inside spacecrafts\, and healthcare in manned space missions. \nI will conclude with my work in 2 miniaturised nano-aerosol technologies\, namely a) an instrument that can produce a constant number concentration of charged nanoaerosols\, a need unmet in aerosol instrumentation until now\, and b) a sensor that can both count and map the global distribution of airborne ultrafine particles\, a requirement crucial for the upcoming WHO air quality guidelines. The work in this theme has enormous significance in simulating cosmic dust conditions and satellite-based remote sensing of particulate matter distribution near the earth’s surface. \n  \nSpeaker: Dr. Akshay Shridhar Kale \nBiography: \nDr. Akshay Shridhar Kale is a senior postdoctoral affiliate at Trinity College and a teaching assistant at the Department of Engineering at the University of Cambridge\, UK. He is also an Honorary Adjunct Professor at the Department of Mechanical Engineering at COEP Technological University in Pune. His research interests lie in the development of miniaturised technologies and possesses a track record in the areas of microfluidic devices\, MEMS / acoustic devices and nanoaerosol instrumentation. He is also highly active in industry-oriented research and has completed several industrial consultancy projects in his areas of interest. His recent work on integration of miniaturisation principles with nanoaerosol instruments has won him grant funding awards that have partially supported the early stages of commercialisation of a portable nanoaerosol counter in collaboration with a spin-out company from his research group. At COEP\, he is actively involved in developing microfluidics research programs and a proposed centre of excellence in micro- and nano- manufacturing. Along with research and development\, he regularly teaches thermal and fluid science courses at Trinity College\, and has co-guided several undergraduate and Masters students through his research projects across Cambridge and COEP. Dr. Kale earned his B.Tech. in Mechanical Engineering at COEP\, followed by an MS and a PhD in thermal and fluid systems from Clemson University\, USA
URL:https://aero.iisc.ac.in/event/miniaturised-technologies-for-potential-applications-in-space-research/
LOCATION:STC Seminar Hall\, Dept. of Aerospace Engineering
CATEGORIES:AE Seminar
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/04/Akshay-.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250423T100000
DTEND;TZID=Asia/Kolkata:20250423T130000
DTSTAMP:20260517T001826
CREATED:20250422T055303Z
LAST-MODIFIED:20250422T055303Z
UID:10000071-1745402400-1745413200@aero.iisc.ac.in
SUMMARY:Ph.D. (Engg): Navigation of Autonomous Vehicles using Event Cameras and Modified RRT Methods
DESCRIPTION:Autonomous vehicles\, such as unmanned aerial vehicles (UAVs) and autonomous mobile robots (AMRs)\, are at the forefront of technological innovation and are widely used across various applications. As these vehicles become more agile and operate primarily in unstructured environments\, the components of the navigation pipeline must function in real time while optimizing limited onboard computing and memory resources. The challenges faced by a fast-moving vehicle in indoor environments differ significantly from those encountered by outdoor systems. This thesis focuses on autonomous vehicles operating in indoor\, GPS-denied\, and unstructured environments. The algorithms presented address these specific challenges and contribute to the growing body of research on real-time navigation solutions for such scenarios. In this thesis\, we have investigated and addressed various aspects of the autonomous vehicle navigation pipeline. A key focus throughout the work is ensuring real-time performance on edge computing systems. Inspired by the emergence of bio-inspired event cameras\, which offer potential solutions to the limitations of current state-of-the-art algorithms\, the first part of the thesis explores the use of these sensors for perception tasks such as localization and obstacle avoidance. Event cameras provide several advantages\, including motion blur-free data output\, a high dynamic range\, and enhanced low-light sensitivity. These features make them particularly suitable for improving Visual-Inertial Odometry (VIO) systems over traditional frame-based cameras. However\, the sparse and asynchronous nature of event data poses challenges for conventional computer vision algorithms. Existing approaches often convert event streams into image-like representations\, limiting the full potential of event cameras. To overcome these challenges\, asynchronous (data-driven) methods are essential for event-camera-based VIO solutions. The work here introduces an end-to-end data-driven event camera-based Visual-Inertial Odometry (AeVIO) algorithm that updates the system state based on camera velocity. The algorithm performs event feature detection and tracking asynchronously from the event stream and integrates these measurements with IMU data using a structureless Extended Kalman Filter (EKF) to refine state estimates. Given that the data rate of event cameras depends on the scene texture and the relative motion between the object and the camera\, we also explore their application for high-speed obstacle avoidance. Time-to-contact (TTC) is a critical measure estimating the time before collision if the current motion remains unchanged. While event cameras excel at capturing small\, rapid changes\, they lack the detailed scene information that depth cameras provide. We present a novel approach to fuse the low temporal resolution data from a depth camera with the high-speed output of an event camera to compute TTC with obstacles. The proposed algorithm is integrated into the AirSim simulator and evaluated across various dynamic obstacle scenarios\, demonstrating its effectiveness in collision avoidance. The second part of this thesis focuses on the path planning component of the autonomous navigation pipeline. Effective navigation for AMRs and UAVs requires advanced path planning that accounts for kinematic constraints and enables smooth trajectory execution in complex\, cluttered environments. We investigate a probabilistic framework based on the Rapidly Exploring Random Tree (RRT) algorithm\, which incorporates vehicle kinematics to identify the most likely direction for the next node generation. This approach utilizes Gaussian Mixture Models (GMMs) to improve node generation efficiency while addressing optimization challenges in both 2D and 3D spaces. This acts as dynamic bias in the algorithm. Additionally\, we introduce a next-node selection heuristic that directs the search tree expansion toward the goal while avoiding obstacles. To enhance convergence\, we explore methods to discretize both the action and search spaces. Initially\, the method is applied to AMRs and is subsequently extended to the more complex task of 3D path planning for UAVs. In summary\, this thesis contributes to the navigation pipeline by developing simple\, computationally efficient algorithms that leverage event sensors and probabilistic methods. These algorithms are designed to operate in real-time on modern UAVs and AMRs while preserving their agility\, enabling operation in indoor GPS-denied environments\, and accommodating limited onboard computing resources. \n  \nSpeaker: Ankit Gupta \nResearch Supervisor: Debasish Ghose
URL:https://aero.iisc.ac.in/event/ph-d-engg-navigation-of-autonomous-vehicles-using-event-cameras-and-modified-rrt-methods/
LOCATION:Online
CATEGORIES:Thesis Colloquium / Defence
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/04/Ankit-.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250429T170000
DTEND;TZID=Asia/Kolkata:20250429T183000
DTSTAMP:20260517T001826
CREATED:20250424T044437Z
LAST-MODIFIED:20250424T044437Z
UID:10000072-1745946000-1745951400@aero.iisc.ac.in
SUMMARY:Eulerian-Lagrangian Modeling of Flash-boiling Injection Processes in Internal Combustion Engines
DESCRIPTION:Reducing greenhouse gas emissions from the transportation sector\, especially carbon dioxide\, is one of the main global challenges to achieve a more sustainable future. Developing internal combustion engines with advanced injection and combustion concepts that improve efficiency and decrease pollutant emissions are essential steps towards reducing their environmental impact. Over the past decades\, flash-boiling injection has become a promising alternative to generate a much finer spray compared to high-pressure injection. The rapid phase-change phenomenon during flash-boiling injection occurs due to the superheating of the liquid fuel upon entering the combustion chamber\, resulting in tiny droplets due to the abrupt disintegration of the liquid jet\, which in turn enhances the mixture homogeneity between air and fuel by increasing the vaporization rate\, widening the spray plume due to the increased radial expansion via bubble growth\, and reducing the droplet velocities\, thus leading to shorter penetrations. A detailed understanding of the underlying mechanisms of the flash-boiling process\, such as nucleation of vapor bubbles\, bubble growth\, and finally jet burst\, at a microscopic droplet level is necessary to accurately quantify its effect on the macroscopic spray structure. In this talk\, I will first discuss the modeling of single-droplet flash-boiling behavior using a Lagrangian particle tracking (LPT) technique. Following this\, a novel reduced-order Lagrangian model will be introduced to accurately capture the vapor bubble growth in superheated microdroplets\, accounting for interaction among multiple bubbles. Next\, a simplified nondimensional semi-analytical solution for bubble growth\, based on dimensional analysis of the modified Rayleigh-Plesset equation\, will be presented. This solution offers accurate predictions of bubble growth considering bubble interactions using larger time step sizes\, making it effective for simulating large-scale superheated sprays with numerous droplets under varied conditions. Finally\, a three-dimensional two-way coupled large-eddy simulation of superheated spray case will be discussed\, incorporating the newly developed bubble growth model within the LPT framework. \nSpeaker : Dr. Avijit Saha \nBiography: \nDr.-Ing. Avijit Saha is a postdoctoral researcher at the Center for Aeromechanics Research\, Department of Aerospace Engineering and Engineering Mechanics\, The University of Texas at Austin\, USA. His current research primarily focuses on terahertz time-domain spectroscopy (THz-TDS) for the characterization of plasma properties\, including electron density and collision frequency. In addition to his experimental work\, he is developing a novel Bayesian framework for quantifying uncertainties in measurement data\, with the goal of enhancing the reliability and interpretability of spectroscopic diagnostics. He obtained his Ph.D. in Mechanical Engineering from RWTH Aachen University in September 2023\, making him the youngest individual to receive the doctorate degree from ITV. His dissertation focused on the physics based reduced-order modeling of flash-boiling injection processes in internal combustion engines. Prior to this\, he completed his B.Tech. (Hons.) and M.Tech. in Aerospace Engineering from IIT Kharagpur. He was the first recipient of the distinguished ASME IGTI Student Scholarship in the Aerospace department. His research interests span experimental fluid dynamics\, optical diagnostics\, multiphase flow modeling (DNS\, LES\, reduced-order models)\, combustion instabilities\, high-performance computing\, and their applications in aerospace propulsion systems. He has authored numerous publications in leading international journals and conferences\, earning recognition through several prestigious awards. Among his accolades are the Jang Young Sil Post-doctoral Research Fellowship from Korea Advanced Institute of Science & Technology (KAIST) in 2024\, Post-doctoral fellowship from MIT in 2025\, and his role as Principal Investigator for a high-impact compute-time research project under National High-Performance Computing Center for Computational Engineering Science (NHR4CES)\, Germany. Dr. Saha also serves as a reviewer for several notable journals like Nuclear Technology\, Physics of Fluids\, Proceedings of Combustion Institute\, Atomization and Sprays\, and SAE International Journals.
URL:https://aero.iisc.ac.in/event/eulerian-lagrangian-modeling-of-flash-boiling-injection-processes-in-internal-combustion-engines/
LOCATION:Online
CATEGORIES:AE Seminar
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/04/Avijit-.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250430T110000
DTEND;TZID=Asia/Kolkata:20250430T123000
DTSTAMP:20260517T001826
CREATED:20250429T092155Z
LAST-MODIFIED:20250429T092155Z
UID:10000073-1746010800-1746016200@aero.iisc.ac.in
SUMMARY:Electrographic Seizure Detection and Forecasting for People with Epilepsy
DESCRIPTION:About fifty million people worldwide suffer from epilepsy\, a neurological disorder marked by sudden\, recurrent episodes of abnormal electrical activity in the brain\, potentially causing sensory disturbances\, convulsions and/or loss of consciousness. Seizure diaries that record the start and end times of each seizure\, along with associated information are important in the management of the disease. However\, video electroencephalogram (EEG) systems available in epilepsy monitoring units and at home ambulatory monitoring units are bulky and unwieldy for continuously monitoring patients during activities of their everyday life. In this talk\, I will describe ongoing efforts to address this issue by utilizing single channel\, wireless and wearable EEG sensors\, and a machine learning approach to continuously monitor persons with epilepsy to detect and characterize electrographic seizures. In addition to explaining the basic approach to automated seizure analysis\, I will discuss: (1) an approach to generalizing the method so that systems trained on one set of patients can be used to monitor other patients; (2) an approach to enhancing the training of the machine learning system when sufficient amount of data is not available; (3) a probabilistic method for determining the type of seizure; (4) our approaches to converting intermediate\, segment-level decisions to seizure event-level decisions; and (5) a personalized algorithm for seizure forecasting to warn patients of impending seizures. I will illustrate the viability of our algorithms using data collected in a multi-center study. \n  \nSpeaker : V John Mathews \nBiography :  \nV John Mathews is a professor in the School of Electrical Engineering and Computer Science at the Oregon State University and Prof. Satish Dhawan (IoE) Visiting Chair Professor at the Indian Institute of Science\, Bangalore. He received his Ph.D. and M.S. degrees in electrical and computer engineering from the University of Iowa\, Iowa City\, Iowa in 1984 and 1981\, respectively\, and the B.E. (Hons.) degree in electronics and communication engineering from the Regional Engineering College (now National Institute of Technology)\, Tiruchirappalli\, India in 1980. \nHis research interests are in nonlinear and adaptive signal processing and application of signal processing and machine learning techniques in neural engineering\, biomedicine\, and structural health management. Mathews is a Fellow of the IEEE. He has served in many leadership positions of the IEEE Signal Processing Society.
URL:https://aero.iisc.ac.in/event/electrographic-seizure-detection-and-forecasting-for-people-with-epilepsy/
LOCATION:Auditorium (AE 005)\, Department of Aerospace Engineering
ATTACH;FMTTYPE=image/jpeg:https://aero.iisc.ac.in/wp-content/uploads/2025/04/Poster-distinguished-lecture-1_page-0001.jpg
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