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TZID:Asia/Kolkata
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250404T110000
DTEND;TZID=Asia/Kolkata:20250404T130000
DTSTAMP:20260430T100157
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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DTSTART;TZID=Asia/Kolkata:20250416T150000
DTEND;TZID=Asia/Kolkata:20250416T170000
DTSTAMP:20260430T100157
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
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DTSTART;TZID=Asia/Kolkata:20250423T100000
DTEND;TZID=Asia/Kolkata:20250423T130000
DTSTAMP:20260430T100157
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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