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- Dec 5 Dec 5 2:00PMMechanical Engineering Seminar by Mohammed Shonar and Ali Nosrati
Mechanical Engineering (MNE) SEMINAR DATE: December 5, 2023 TIME: 2pm-3pm LOCATION: Science and Engineering (SENG), Room 110 and on Zoom https://umassd.zoom.us/j/91640406955?pwd=eklBZWVDOXVDa2VwUFMra1kwNWhjdz09 (Contact firstname.lastname@example.org or email@example.com for the Passcode) ------------------------------------------------------------ SPEAKER #1: Mohammed Shonar, MS in Mechanical Engineering (Advisor: Dr. Vijaya Chalivendra) TOPIC: Mode-I Fracture Characterization of Polyacrylamide-Amylopectin Single Network Hydrogel with Chitosan Interfacial Reinforcement ABSTRACT: Hydrogels are soft and water-rich polymer networks with tunable adhesive properties, that are extensively utilized in the biomedical field. Due to their bonding characteristics, certain hydrogel networks can adhere to a variety of surfaces, including skin tissue. In this study, single network hydrogels composed of Polyacrylamide and Amylopectin were photocured into adherents of T-shaped cross-sections and then bonded together to form 50mm long specimens. The interface of the adherents is reinforced with 200 L chitosan solution consisting of average molecular weights of 1.5 kDa, 15 kDa, 250 kDa, 343 kDa, and a control group with no chitosan solution. The study investigates the effect of different chitosan molecular weights and pH levels (ranging from 2.5 to 4.5) on the mode-I fracture toughness. The mode-I fracture initiation toughness is evaluated using nonlinear J-integral fracture mechanics. It was observed that the chitosan with the highest molecular weight and pH level resulted in a 200% increase in fracture toughness compared to no chitosan reinforcement, which is attributed to crack tip blunting phenomena. These results are supplemented with analysis by Digital Image Correlation (DIC), which is utilized to compare the strain distribution at fracture initiation. ------------------------------------------------------------ SPEAKER #2: Ali Nosrati, MS in Mechanical Engineering (Advisors: Dr. Hangjian Ling, Dr. Mehdi Raessi) TOPIC: Impact of Undersaturation Level on the Longevity of Super-Hydrophobic Surfaces in Stationary Liquids ABSTRACT: Although the longevity of super-hydrophobic surface (SHS) induced by diffusive gas transfer has been extensively studied, the scaling relation between SHS longevity and undersaturation level of the liquid is still an open question. In this study, we address this question by performing experiments where the plastron decay is visualized by a non-intrusive optical technique based on light reflection, the gas diffusion is introduced by using liquid with low dissolved gas concentrations, and the SHS longevity is measured based on the status of gas on the entire surface. We find that the SHS longevity (tf) follows a scaling relation: tf ~ (1s)2, where s is the ratio of gas concentration in liquid to that in the plastron. This scaling relation implies that as the gas is dissolving into the liquid, mass flux J reduces with time as: J~t0.5. Furthermore, we find that the diffusion length LD reduces as the undersaturation level increases, following the scaling relation of LD ~ (1s)1. Lastly, we show that a SHS with a greater texture depth has a longer longevity and a larger LD. Our results provide a better understanding of SHS longevity in undersaturated liquid. For more information please contact Dr. Hangjian Ling, MNE Seminar Coordinator (firstname.lastname@example.org). All are welcome. Students taking MNE-500 are ENCOURAGED to attend! All other MNE students are invited to attend. EAS students are invited to attend.
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- Dec 8 Dec 8 2:00PMMechanical Engineering Seminar by Dr. Tiantian Li
Mechanical Engineering (MNE) SEMINAR DATE: December 8, 2023 TIME: 2:00 p.m. - 3:00 p.m. Location: Science & Engineering (SENG), Room 110 and on Zoom:https://umassd.zoom.us/j/91640406955?pwd=eklBZWVDOXVDa2VwUFMra1kwNWhjdz09 (contact email@example.com or firstname.lastname@example.org for Passcode) SPEAKER: Dr. Tiantian Li, Postdoctoral Research Associate Mechanical and Industrial Engineering, Northeastern University TOPIC: 3D Keyed Octahedron: A New Family of Auxetic Metamaterials with Enhanced Mechanical Properties ABSTRACT: Mechanical metamaterials are engineering materials with artificial structures, targeting a specific set of mechanical properties. These properties often conflict with each other. (i) For example, engineering materials often achieve high energy dissipation by sacrificing resilience, as limits their performance and applications under cyclic loading. Designing material with both high resilience and high energy dissipation capability is challenging. (ii) Moreover, most existing 2D and 3D mechanical metamaterial with cellular designs can only achieve orthotropy instead of isotropy. Few of them break this trend as small deformation, while most of them show highly anisotropic under large deformation due to the distortion of micro ligaments/cell walls. Therefore, it is another challenge to design 3D mechanical metamaterial with certain isotropic preserved mechanical properties under large deformation. (iii) Furthermore, most existing mechanical metamaterials have a cellular structure which cannot achieve high energy dissipation under dynamic loading, as limits their performance and applications. Therefore, designing 3D mechanical metamaterial with high impact resistance is still challenging. To meet the aforementioned challenges, we propose a new design strategy to create a family of mechanical metamaterials: 3D keyed octahedron metamaterial. This metamaterial shows high resilience while achieving large mechanical hysteresis synergistically under large compressive strain. Especially, this metamaterial exhibits ideal isotropy approaching the theoretical limit of isotropic Poissons ratio, -1, as rarely seen in existing mechanical metamaterials. In addition, the new class of metamaterial provides wide tunability on mechanical properties and behaviors, including an unusual coupled auxeticity and twisting under normal compression. Interestingly, one two-phase design in this family of metamaterial shows a significantly enhanced impact resistance compared with its conventional cellular counterpart. In summary, these remarkable and unusual mechanical behaviors of this new family of 3D mechanical metamaterial can broad many applications in soft robotics, mechanical actuators and dampers, and engineering materials/systems for energy absorption/impact and vibration mitigation. BIO: Dr. Tiantian Li is a postdoctoral researcher in the department of Mechanical and Industrial Engineering at Northeastern University. Previously, he was a postdoctoral associate at the University of Cambridge. He received his B.E (2010) from Tsinghua University and M.E (2012) from Tsukuba University, both majoring in Material Science. After that, Tiantian Li received his Ph.D. in 2018 from New York State University at Stony Brook, majoring in solid mechanics. The research interests of Dr. Tiantian Li include design of novel architected materials; additive manufacturing; mechanical behavior of materials; micromechanics of deformation and fracture, bio-inspired materials, and mechanical metamaterials. For more information please contact Dr. Hangjian Ling, MNE Seminar Coordinator (email@example.com). All are welcome. Students taking MNE-500 are REQUIRED to attend this seminar! All other MNE students are encouraged to attend.
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- Dec 11 Dec 11 9:30AMEAS PhD Dissertation Defense by Shayan Razi
EAS PhD Dissertation Defense by Shayan Razi Date: December 11, 2023 Time: 9:30am Topic: An Energy-Based Lattice Element Approach to Modeling Linear and Nonlinear Response of Complex Building Systems Location: CSCDR TXT 105 Abstract: Extreme loading conditions due to natural hazards such as windstorms, earthquakes, and floods can lead to the failure of both structural and non-structural elements, and subsequently, damage to the entire structural system. The significant economic repercussions of such events call for the revisiting of engineering approaches to resilience assessment towards the examination of the functional integrity of civil infrastructure. This assessment requires the development of accurate yet computationally efficient frameworks to model the failure of systems comprising structural and non-structural components. To this end, we leverage a Potential-of-Mean-Force (PMF) approach to Lattice Element Method (LEM), a class of discrete methods demonstrated to be particularly advantageous for simulating failure and fracture, to capture the mechanical response of structural systems in both linear and nonlinear regimes. The premise of the proposed framework is to discretize the system into a set of particles that interact with each other through prescribed potential functions, which represent the mechanical properties of different types of members. Lending itself to damage assessment due to its discrete nature, our PMF-based LEM transcends the limitations of continuum mechanics approaches and enables the incorporation of a range of effective interaction potentials, to simulate the linear and nonlinear behavior of structural components. Since the determination of elastic behavior is a precursor to the failure analysis of structural components, harmonic potentials are initially adopted to model the linear response of various structural members, e.g., beams, columns, roofs, and walls. The calibration procedure for such potentials is thus carried out via a handshake with continuum mechanics theories, e.g., the Timoshenko beam theory and Kirchhoff-Love plate theory. This calibration is then carried out for non-harmonic potentials by adopting section properties that encapsulate the nonlinear stress-strain responses of the materials, e.g., nonlinear moment-curvature relations. Upon the calibration of non-harmonic potentials, ductile failure of the structural members is modeled by breaking bonds between particles according to an energy-based failure criterion. Finally, the utility and accuracy of the proposed framework is demonstrated through its application in (i) both quasi-static linear and nonlinear simulations of large-scale buildings under different loading conditions, (ii) the simulation of progressive structural failure due to the propagation of local structural damage. ADVISOR(S): Dr. Mazdak Tootkaboni, Dept of Civil & Environmental Engineering (firstname.lastname@example.org) COMMITTEE MEMBERS: Dr. Arghavan Louhghalam, Dept of Civil & Environmental Engineering Dr. Yanlai Cheng, Department of Mathematics Dr. Alfa Heryudono, Department of Mathematics NOTE: All EAS Students are ENCOURAGED to attend.
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- Dec 12 Dec 12 10:30AMELEC Doctor of Philosophy Dissertation Defense by Christian C. Ellis-ECE
Topic: Terrain Aware Autonomous Ground Navigation in Unstructured Environments Informed by Human Demonstrations Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/3753158123 Meeting ID: 375 315 8123 Passcode: 495427 Abstract: Mobile robots equipped with the capability to perform autonomous waypoint navigation can replace humans for applications such as humanitarian assistance, nuclear cleanup, and reconnaissance. In such tasks, the robot must be able to accurately and reliably perform complex behaviors such as the ability to navigate over unstructured terrain and respond to unseen situations similar to how a human would. However, to implement complex behaviors beyond obstacle avoidance, many current approaches employ machine learning methods requiring large amounts of labeled data. While simulations can quickly generate large amounts of labeled data, the same cannot be said for real world environments, limiting adoption of mobile robots to complete the aforementioned applications. Moreover, solutions are often brittle, exhibiting poor performance when operating outside of environments beyond where they were designed or trained. Therefore, there is a need for methods which can quickly learn navigation behaviors from limited data while being able to adapt to dynamic scenarios. To communicate both positive and negative environmental scenarios, roboticists assign rewards (or inversely, costs) to all the relevant environmental features expected. For robots that need to quickly transition between varying unstructured environments, defining rewards prior to understanding all future features the robot will encounter is often unachievable as either: (i) the robot is unable to perceive the new feature, and (ii) the numerical reward for each feature is unknown. In such scenarios, it is more effective for a human supervisor to provide examples of desired behavior than for an engineer to explicitly define it. Therefore, this dissertation focuses on a ground robot's ability to incorporate human demonstrations as a weak supervisory signal to solve each respective preceding problem by (i) obtaining a semantic perception model capable of classifying terrains present in the current environment given a sequence of unlabeled (unsupervised) images and (ii) using Bayesian inverse reinforcement learning to learn rewards associated with the terrains identified to build cost-maps online for autonomous waypoint traversal. Advisor(s): Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Jiawei Yuan, Associate Professor, Department of Computer Information Science, UMASS Dartmouth; Dr. Craig T. Lennon and Dr. Maggie B. Wigness, United States Army Research Laboratory NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Lance Fiondella at 508.999.8596 or via email at email@example.com.
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- Dec 12 Dec 12 11:00AMEAS Doctoral Proposal Defense by David Gillcrest
EAS Doctoral Proposal Defense by David Gillcrest Date: Tuesday, December 12, 2023 Time: 11:00 a.m. Topic: Inverse Analysis and Calibration of Physical Models: An Approached Based on Greedy Kaczmarz Multi-Fidelity Surrogates and Aggregated Directional Statistics Location: CSCDR - TXT 105 Abstract: Advances in surrogate modeling have allowed for highly accurate Polynomial Chaos (PC) expansions to be constructed for physical models ranging from mild to major complexity in their respective parameter spaces. Multi-fidelity techniques in the field of uncertainty analysis have been employed to efficiently capture the variations in the outputs of models given reasonable perturbations in these parameters. Recently, a greedy Kaczmarz algorithm (GKA) has been used to cheaply construct surrogate models in a way that greatly outperforms the previous least square approaches that demand samples about 2-3 times the number of coefficients in the PC expansion. In this research we look at the utility of GKA-produced surrogate models in solving parameter estimation problems - inverse problems typically solved using statistical least squares methods - for partial differential equations (PDEs). We present a generalized approach to solving two-dimensional parameter estimations for PDEs and demonstrate its potential using two Advection-Diffusion-Reaction equations: one for the source localization of a river contaminant, and the other for the estimations of both the area source contamination magnitude as well as ambient pollution concentration in the context of an urban heat island under the influence of mesoscale wind. The physical domain of each problem is discretized in such a way as to accommodate the hypothetical usage of detection apparatuses. An array of surrogate models is constructed to capture the variation in outputs at these detectors with respect to the PDEs' parameter spaces. Using directional statistical techniques we examine the optimal combination of the surrogate models, specifically by looking at the local interactions of each model's slope field. The quality of the combination of surrogates can then be quantified and aggregated in order to achieve a cost value for a grouping of surrogates. ADVISOR(S): Dr. Mazdak Tootkaboni, Dept of Civil and Environmental Engineering (firstname.lastname@example.org) Dr. Yanlai Chen, Dept. of Mathematics (email@example.com) COMMITTEE MEMBERS: Dr. Sigal Gottlieb, Department of Mathematics Dr. Zheng Chen, Department of Mathematics NOTE: All EAS Students are ENCOURAGED to attend.
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Fall 2023 Hours
Business Center, LARTS 010
Monday - Friday, 9am - 5pm
STEM Learning Lab, SENG 217
Monday - Friday, 9am - 5pm
Nights and Weekends