Physics Master of Science Thesis Defense by Emma Klinkhamer Date: Thursday, July 29, 2021 Time: 9:00 AM Topic: Computational Methods in Electromagnetic Applications Zoom Conference Link: https://us05web.zoom.us/j/83126756712?pwd=TUtKNTRkRDc5QzZVN04xdW41czczdz09 Meeting ID: 831 2675 6712 Passcode: st7Nxr Abstract: Computational methods and algorithms are quickly becoming a necessity in science and engineering fields. These modeling techniques not only take the computational onus from the human brain, but can also provide visualizations for the problem at hand. This research delves into the facets of these computing processes, specifically at the intersection of electrostatic applications and iterative computational modeling. By first laying the groundwork for governing electrostatic equations, a general overview of surface charge distribution is discussed, and the need for accurate modeling techniques in this field is reviewed. Afterwards, some common algorithm types are analyzed, including finite element analysis and basic iterative computational structures. The heart of the thesis is devoted to merging a well-established surface charge distribution code with a Python based meshing algorithm to create a novel code meant for determining surface charge distribution and electric field in a realistic wire-like domain. Prior to going through this analysis, the Ruth Chabay and Bruce Sherwood surface charge distribution code is examined. Similarly, the Python based meshing package, called PyDistMesh, is explained. Finally, with these two software pillars thoroughly understood, the novelty and effectiveness of the combined code is assessed. ADVISOR(S): Dr. Jianyi Wang, Department of Physics (firstname.lastname@example.org 508.999.9136 COMMITTEE MEMBERS: Dr. J.P. Hsu, Department of Physics Rob Slimm, Electrical Engineering Manager - Sensata Technologies
Mechanical Engineering MS Thesis Defense by Mr. Maxwell Shangraw DATE: August 6, 2021 TIME: 9:00 a.m. 11:00 a.m. LOCATION: Zoom link: https://umassd.zoom.us/j/97527658501? pwd=dHptSGZlbFN0YlhVdHoxZkJKUlZrUT09 Meeting ID: 975 2765 8501 Passcode: 099110 TOPIC: Application and Improvement of Digital Holographic Microscopy to Study Bacterial Motion ABSTRACT: In this work, we apply Digital Holographic Microscopy (DHM) to track the three-dimensional (3D) motion of bacteria. Measuring bacterial movement and bacterial interaction with solid walls is critical to understand the mechanism of biofilm formation and develop efficient anti-biofouling strategies. However, accurately tracking bacteria by DHM remains a challenge since bacteria have a small size and a refractive index very similar to the surrounding medium. Here, we develop new approaches to overcome this challenge. First, we distinguish between real and virtual images in DHM by analyzing the axial intensity distributions of the objects. This allows the hologram plane to be placed within the sample volume and thus maximizes the signal-to-noise ratio. Second, we detect particle centers based on the local maximum or minimum intensities in the reconstructed field consisting of both scattering wave and incident wave. We find this approach improves the particle localization accuracy when compared to previous methods. We also examine the impacts of sample concentration, sample thickness, and the iterative phase retrieval method on the quality of reconstructed images. With these improvements, we measure approximately 300 trajectories of Shewanella, a type of bacterium isolated from marine biofilm. We find bacterium-wall interactions similar to these reported in the literature. Last, we fabricate micro-textured surfaces which are able to trap gas bubbles when submerged in water. Future study will examine the impact of the entrapped gas bubbles on bacterial motion, as well as the anti-biofouling properties of the textured surfaces. ADVISOR: Dr. Hangjian Ling, Assistant Professor Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBERS: -Dr. Banafsheh Seyed-Aghazadeh, Assistant Professor of Mechanical Engineering, UMass Dartmouth -Dr. Pia Moisander, Associate Professor of Biology, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Hangjian Ling (email@example.com, 508-999-8540). Thank you, Sue Cunha, Administrative Assistant Mechanical Engineering Department firstname.lastname@example.org 508-999-8492
Mechanical Engineering MS Thesis Defense by Mr. Enjamamul Hoq DATE: August 10, 2021 TIME: 10:00 a.m.-12:00 p.m. LOCATION: Zoom link: https://umassd.zoom.us/j/94248504993? Pwd: OG5DaEdqakVyQU42TERRNnZXUTN0QT09 Meeting ID: 942 4850 4993 Passcode: 829733 TOPIC: Deep-Learning based Stress Field Prediction of Heterogeneous Materials ABSTRACT: Rapid and accurate stress field prediction in heterogeneous material systems is critical for a variety of applications, including design optimization, uncertainty quantification, structural health monitoring, materials failure assessment, system control and decision-making. Physics-based simulations such as the finite element method (FEM) provide high-fidelity predictions but can be computationally expensive and time consuming. On the other hand, data-driven approaches have the promise to rapidly predicting reliable results to meet real-time application constraints. In this study, different deep learning frameworks are applied and evaluated in predicting the high-dimensional stress field responses of heterogeneous materials. Two material models are considered: the first one is based on linear elastic materials while the second one involves nonlinear elastic-plastic materials. In the linear elastic model, the first framework employs a combination of model order reduction and artificial neural networks (ANN). The stress fields are first projected to a low-dimensional representation using a model order reduction technique of proper orthogonal decomposition (POD). After that, ANN is used to predict full-field responses based on POD reduced modes. The second framework is based on a deep Resnet-based Convolutional Neural Network (CNN), while the third one is based on a conditional Generative Adversarial Network (GAN) (cGAN). Two numerical examples are analyzed to evaluate the above frameworks. The first is a panel containing a heterogeneous material inclusion varying in terms of position and size. The reconstructed POD fields using ANN provide accurate predictions overall but more deviations appear when the inclusion is small or close to the boundary. On the other hand, CNN and cGAN approaches give more accurate and robust predictions. In the second example, a plate with a hole that varies in position and size is considered. Both CNN and cGAN models accurately captured stress concentrations as well as the full stress fields, while cGAN give better predictions in validations of unseen datasets. In the elastic-plastic model, a plate with multiple holes is considered. Convolutional Long Short Term Memory (LSTM) based CNN framework is implemented to capture the time evolution of the stress field as well as the deformation of holes. The above deep learning frameworks show remarkable potentials in predicting full-field responses for a variety of heterogeneous materials. ADVISOR: Dr. Jun Li, Assistant Professor of Mechanical Engineering, College of Engineering, UMassD COMMITTEE MEMBERS: -Dr. Wenzhen Huang, Professor of Mechanical Engineering, College of Engineering, UMassD -Dr. Alfa Heryudono, Associate Professor of Department of Mathematics, UMassD Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Jun Li (email@example.com). Thank you, Sue Cunha, Administrative Assistant firstname.lastname@example.org 508-999-8492
Mechanical Engineering MS Thesis Defense by Mr. Aleks Bourgoun DATE: August 17, 2021 TIME: 1:30 p.m. -3:30 p.m. LOCATION: ZOOM: https://umassd.zoom.us/j/94371085657?pwd=Y3dVMkU1cXVreFBIcjRVenVZTUpIZz09 Meeting ID:943 7108 5657 Passcode:291208 TOPIC: A Numerical and Experimental Study of Gas Diffusion from Super-Hydrophobic Surfaces to Under-Saturated Liquid ABSTRACT: The micro/nano-textured Super-Hydrophobic Surface (SHS) can trap a layer of gas bubbles when submerged in liquid, and consequently provides various benefits, such as self-cleaning, anti-corrosion, anti-icing, anti-biofouling, and drag reduction. However, the entrapped gas bubbles can be slowly dissolved into the ambient liquid when the liquid is under-saturated, causing a loss of all the benefits from the SHS. In this work, we study this gas diffusion process for an SHS fully submerged in under-saturated liquid through a combination of computational and experimental approaches. First, we solve the dissolved gas concentration in the liquid by COMSOL Multiphysics simulations. We present the time evolutions of gas concentration profile, mass flux, diffusion length, and amount of gas remaining on the surface. We find that the results agree very well with a simple one-dimensional diffusion model. We also examine the impact of SHS texture (e.g., fraction of surface area covered by gas, texture sizes) and domain size on the rate of mass flux and the lifetime of the gas on a SHS. Second, we experimentally measure the rate of gas transfer from an SHS to liquid by bright-field microscopy, planar laser induced fluorescence, and reflection interference contrast microscopy. We discuss the experimental setups, SHS fabrication methods, and data analysis procedures. Unlike numerical simulations, the experiment results show a non-uniform gas flux across the surface, probably due to the initial disturbance by the filling of water into the experimental chamber. Overall, our research provides guidelines on designing long lifetime SHSs for various underwater engineering applications. ADVISOR: Dr. Hangjian Ling, Assistant Professor Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBERS: -Dr. Sankha Bhowmick, Professor of Mechanical Engineering, UMass Dartmouth -Dr. Mehdi Raessi, Associate Professor of Mechanical Engineering, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Hangjian Ling (email@example.com, 508-999-8540). Thank you, Sue Cunha firstname.lastname@example.org
Whether you're a commuter or residential first-year student, you'll take part in a dynamic four-day Orientation experience between move-in and the first day of classes. Check your UMassD e-mail for specifics & email us at email@example.com with questions. We're here to help!
Starting at UMassD in the Fall of 2020 was a unique challenge. As an in-coming sophomore, you may still need some help acclimating to campus and connecting with our campus community. Whether you're a commuter or residential student, you'll take part in a dynamic two-day Sophomore Rewind experience between move-in and the first day of classes. Check your umassd.edu email account for specific details, including an invitation, an RSVP and details about all of the events & experiences we have planned for you!
Affordable programs! Options for students in any field! Scholarship resources! Students interested in studying abroad are encouraged to attend the Study Abroad Fair. Representatives from UMass Dartmouth partner organizations will be there to talk about the details of individual programs. IPO advisors and students will be available to answer questions about applying and how to use financial aid. There will be a free shirt giveaway (while supplies last!) Location: Quad, near the Fire Pit (In the event of rain, the fair will be held in the Library Grand Reading Room.)
Marc St. Pierre: Black & White Location: University Art Gallery, Star Store Campus, Downtown New Bedford, MA Open through September 9, 2021 Reception: AHA! Night, Thursday, August 12, 6-8 pm Gallery hours: Mon-Fri 9 am - 6 pm and until 9 pm during AHA! Nights (second Thursday of every month) The University Art Gallery in New Bedford is proud to present Black & White, an exhibition by a beloved UMass Dartmouth professor, the late Marc St. Pierre (June 23, 1952 - December 2, 2019), featuring a selection of mixed media drawings and collage with encaustic, as well as black and white photography. Drawings created between 2008 and 2012, layered geometric elements, and abstract forms create a rich and poignant experience filled with wonder and exploration. They took inspiration from the 'man-made', architectural plans, diagrams, and maps of early explorers. In his artist statement from 2012, Marc said,As the drawing process evolves, organic shapes and gestural lines are introduced over the groundwork in transparent overlays. This provides a counterpoint to the precision associated with the geometry and acts as a multiple exposure in photography. This pictorial space becomes a dialog of random marks that combines the constructed with the unsystematic. My drawings intend to move the eye within an abstract and shallow picture plane in a gradual release of time. The illusion of a 'deep space' in Marc's work, invites visitors into a complex, hazy, and multi-dimensional world of collage and encaustics that are presented in smaller formats in the exhibition. Describing his process in 2017, Marc wrote, "First, I use a variety of printmaking processes as a departure for recording marks, surfaces, and layers. This becomes a collage groundwork that allows me to invent the abstract equivalent of land patterns such as the meeting point of landmasses and water, for example. Secondly, additional layers of tracings from actual maps and topographic patterns are introduced. These drawings become a physical overlay suspended in translucent wax." Curiosity and the connection between the creative and scientific, as well as an equally strong interest in observing the tangible are also very much a part of Marc's pinhole camera work shown at Crapo Gallery. After his retirement in 2017, Marc was often seen counting the time of his exposures while photographing various New Bedford locations. According to Gallery Director, Viera Levitt, "Marc would talk about taking his pinhole photographs, leaving for these expeditions from the Star Store Campus with a repurposed box readied with a small hole for capturing images directly on the photo paper. He enjoyed telling stories to his impromptu audiences including the building guard, about the random passers-by, observing his 'mysterious box' on the ground, ready to 'shoot'! It was indeed magic to see the one-of-a-kind monotypes he was able to capture with such simple, but skilled technique and his great patience for experimentation." The exhibition is open through September 9 during the gallery hours Mon-Fri 9 am-6 pm and until 9 pm during AHA! Nights (the second Thursday of every month). The special reception is planned for Thursday, AHA! Night, August 12, from 6 to 8 pm. More information about the exhibition, please visit www.umassd.edu/cvpa/universityartgallery For information about the Marc St. Pierre Memorial Scholarship, please visit giving.umassd.edu/stpierre. Please Note: Masks are encouraged, but not required for fully vaccinated individuals. Please maintain a 6-foot distance from others. Large bags, food, and beverages are not allowed in the gallery. If you or a member of your party has or has had cold or flu-like symptoms, do not visit the gallery until after everyone is well and has completed a medically recommended quarantine period. facebook.com/UMassDartmouthGalleries instagram.com/UMassDartmouthGalleries
Today is the last day to add/drop a class. Today is the last day to audit a class. Students taking online or continuing education courses please consult the University Extension withdrawal and refund policy. http://www.umassd.edu/extension/tuitionandfees/withdrawalandrefundpolicy/