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Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for FAFSA Help Labs in Foster 105 Jan 10, 2025, 3-4pm Contact Mark Yanni myanni@umassd.edu

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Topic: Experimental Study on Fluid-Structure Interactions of Highly Flexible High Aspect Ratio Wings Location: SENG 110 (Materials Science Lab) Abstract: High aspect-ratio wings have garnered substantial interest due to their aerodynamic benefits, particularly their ability to reduce undesirable tip vortex effects, thereby achieving a superior lift-to-drag ratio compared to lower aspect-ratio wings. However, this structural configuration inherently results in increased flexibility, which can lead to aeroelastic instabilities such as flutter, divergence, and control reversal. Flutter instability, in particular, poses a critical design challenge due to its potential to cause catastrophic failure. While extensive research has addressed both linear and nonlinear dynamics related to the onset of flutter in high aspect-ratio wings, few studies have systematically investigated the post-flutter behavior. Understanding this post-flutter response is essential for predicting and managing complex flutter phenomena, thereby enhancing design safety and resilience. The objective of this study is to fill the gap by conducting a thorough experimental study of the interaction between fluid and structure in highly flexible wings during the post-critical phase. When studying the performance of high aspect-ratio wings, it's crucial to recognize the Fluid-Structure Interaction (FSI) at play. This involves a full coupling between the fluid dynamics and structural mechanics. Therefore, a comprehensive understanding demands a simultaneous exploration of both the structural and flow aspects, allowing for a full understanding of the interaction dynamics. The majority of studies focusing on flow visualization around airfoils have been conducted on either stationary airfoils or those with limited degrees of freedom. While the study of flow around rigid airfoils has contributed to our fundamental understanding, the dynamics of flow around rigid wings differ significantly from the complex, three-dimensional dynamics seen in flexible wings. Recent efforts have offered insights into how wing flexibility influences surrounding flow; however, the complexity of 3-D flow physics and its interaction with flexible wing structures remains unexplored, and this gap is further compounded by a shortage of integrated studies that concurrently examine both structural and fluid dynamics. Additionally, the lack of comprehensive experimental data limits the ability of numerical models to accurately capture the three-dimensional flow behavior around flexible wings. This research presents a detailed experimental investigation of the flow-induced vibration characteristics of a highly flexible wing, focusing on parameters such as vibration amplitude, dominant frequencies, mode shapes, and mean deflection, with special attention to the post-flutter phase. A modal analysis-based method, along with digital image correlation (DIC) technique, was employed to measure the wings structural response. Concurrently, flow behavior around the wing was analyzed quantitatively using time-resolved volumetric particle tracking velocimetry (TR-PTV) and two-dimensional particle image velocimetry (TR-2D-PIV) techniques. The study examines a wide range of angles of attack and flow velocities to provide a comprehensive view of fluid-structure interactions in high aspect-ratio wings under varied operational conditions. Our preliminary results show that changes in the angle of attack significantly affect the onset of limit cycle oscillations, as well as the dominant oscillation frequencies and mode shapes. At higher flow velocities and angles of attack, a significant increase in tip deflection was observed, while minimal deflection occurred at lower or zero angles of attack. By employing the Q-criterion, we identified and visualized the coherent structure of vortices, uncovering the substantial influence of angle of attack and flow velocity on their behavior. At lower angles of attack, the leading edge and trailing edge vortices were almost vertical, with minimal interaction with the tip vortex. As the angle of attack increased, these vortices tilted to follow the wing's curvature and became larger and stronger, interacting more with the tip vortex. Our results show that at low-amplitude oscillations, the vortices dissipated quickly, whereas at high-amplitude oscillations, they were able to sustain their coherence for a longer duration, influencing the downstream flow pattern. ADVISOR(S): Dr. Banafsheh Seyed-aghazadeh, Department of Mechanical Engineering (b.aghazadeh@umassd.edu) COMMITTEE MEMBERS: Dr. Mehdi Raessi, Department of Mechanical Engineering and Dr. Hangjian Ling, Department of Mechanical Engineering and Dr. Geoffrey Cowles, SMAST Department of Fisheries Oceanography All EAS Students are ENCOURAGED to attend.

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Location: ROOM 374, LARTS, College of Arts & Sciences If you prefer to attend via zoom: Join Zoom Meeting https://umassd.zoom.us/j/93469055850?pwd=woZj412TF2VP7RiH7QfCsskPCnOtTH.1 Meeting ID: 934 6905 5850 Passcode: 834205 Title: Anger-Related Cognitive Processes and Affect: Considering Context Misperceptions to Understand Aggression Abstract: Use of aggression among undergraduates has demonstrated associations with negative mental and physical health outcomes. Evaluating factors which may influence aggressive tendencies is a promising approach to understand how individuals may be primed to utilize aggression. With this, the present study examined cognitive and affective factors which may impact the perception of situational context and ultimately predispose individuals to act aggressively. As such, it was expected that the relationship between anger rumination and aggression would be mediated serially by hostile attribution bias (HAB) and context-incongruent (CI) anger. Furthermore, as unique types of aggression can be utilized to exploit different outcomes, it was hypothesized that for those who ruminate, becoming angry in particular contexts (i.e., in threatening contexts [CIT anger] or in positive contexts [CIP anger]) may relate more strongly to one function of aggression over another. This study included a sample of 137 undergraduate students who completed assessments examining anger rumination, HAB, CI anger, context-congruent (CC) anger, and reactive and proactive aggression. Mediation analyses were completed to test the proposed hypotheses. As expected, HAB and CI anger individually mediated the relationship between anger rumination and aggression; this was not true for the serial mediation including both HAB and CI anger as mediators. Notably, substituting CC anger into this model did not result in significant findings. An alternative serial mediation model (i.e., anger rumination and CI anger mediating between HAB and aggression) did yield a significant serial mediation. As hypothesized, CIT anger, but not CIP anger, mediated between anger rumination and reactive aggression. Unexpectedly, CIP anger did not mediate between anger rumination and proactive aggression, but CIT anger did. These findings accentuate that together, anger-related cognitions and affect (particularly that which is seemingly incongruent with contextual cues) are associated with aggression among young adults. Results warrant further exploration and may be useful particularly in clinical settings. Advisor: Dr. Robin Arkerson Committee Members: Dr. Judith Sims-Knight, Dr. Ted Powers For additional information, please contact Verna Drayton at vdrayton@umassd.edu or 508-999-8380

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Financial Aid FAFSA Help Labs Cancelled today contact Mark Yanni myanni@umassd.edu

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Title: Secure and Robust UAV Tracking Systems Location: Zoom https://umassd.zoom.us/j/98095026817?pwd=3pHJ1Yt2arNEGoy5ilG45wH9uLFTNH.1 Meeting ID: 980 9502 6817 Passcode: 881612 Advisor: Dr. Jiawei Yuan, Department of Computer and Information Science Committee Members: Dr. Gokhan Kul, Department of Computer and Information Science Dr. Long Jiao, Department of Computer and Information Science Dr. Ruolin Zhou, Department of Electrical & Computer Engineering Abstract Visual object tracking is a fundamental research area in computer vision, which plays a critical role in robotic applications such as autonomous navigation and surveillance. Deep learning-based visual object trackers have significantly enhanced UAV tracking systems, but they also introduce security concerns due to the vulnerabilities of deep learning models to adversarial attacks. Traditional template-based trackers rely on annotated templates for initialization, limiting their applicability in real-time scenarios like UAV surveillance, where object descriptions are often provided in natural language. Template-free trackers address this limitation by leveraging multi-modal inputs and combining visual and semantic guidance. However, both methods remain susceptible to adversarial attacks. Template-based methods are vulnerable to visual perturbations on input frames, and the adoption of multi-modal inputs introduces new vulnerabilities to template-free methods, such as misaligned embeddings between visual and textual inputs. This research investigates the vulnerabilities of DL-based UAV tracking systems and proposes defense mechanisms to enhance their robustness. The research will be conducted in three directions: (1) enhance the robustness of template-based tracking methods to adversarial perturbations through input reconstruction (2) conduct in-depth security analysis and exploration of template-free object tracking methods, focusing on multi-modal vulnerabilities (3) design robust template-free tracking systems by integrating semantic references from textual descriptions and leveraging motion features across consecutive frames. For further information please contact Dr. Jiawei Yuan at jyuan@umassd.edu

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Title: Optimizing Datasets for Lyme Disease Detection Advisor: Iren Valova PhD, Associate Dean - College of Engineering - Professor, Computer & Information Science - University of Massachusetts Dartmouth Committee: Gokhan Kul PhD, Computer & Information Science - University of Massachusetts Dartmouth Firas Khatib PhD, Computer & Information Science - University of Massachusetts Dartmouth Date: Jan 17, 2025 Time: 1pm Location: Zoom https://umassd.zoom.us/j/98403102776?pwd=VKmd3RikQZbqdTkhOaIhoJdyXQE91k.1 Abstract: This thesis focuses on optimizing image datasets through augmentation methods for the detection of Lyme disease. Lyme disease often is accompanied by an erythema migrans rash, but other sorts of rashes could look similar to it. Using a public crowdsourced dataset, the object is to improve the accuracy of YoloV7 through image enhancements and augmentations. The study utilizes a combination of data preprocessing techniques, including CLAHE, photometric deformation, elastic deformation, and mixup to improve image quality and address dataset imbalances. YoloV7, an object detection model was trained on the enhanced dataset to accurately differentiate Lyme-related rashes from other dermatological conditions. The results favored the CLAHE result over the others. This work contributes to the development of more reliable, automated diagnostic tools for individual user. For further information contact Dr. Iren Valova at ivalova@umassd.edu

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