Events

Examinations end today.

Mechanical Engineering / Industrial Systems Engineering MS Project Presentation by Mr. Thomas J. McEleney

Mechanical Engineering (MNE) / Industrial Systems Engineering (ISE) MS Project Presentation by Mr. Thomas J. McEleney DATE: December 18, 2019 TIME: 10:00 a.m. - 11:00 a.m. LOCATION: Science & Engineering Building (SENG), Room 114 TOPIC: Optimization of the Rheolube 374A Formulation and Production Process for Bearing Corrosion Using Design of Experiments Methodology ABSTRACT: To ensure safe design and consistent performance, every component of an aircraft is critical, including the type of lubricating grease. Aircraft landing gear experiences high loads and extreme environments that require a grease capable of withstanding harsh conditions to protect components. Nye Lubricants, Inc. is the formulator and manufacturer of Rheolube 374A, a fully synthetic landing gear grease that passes the stringent MIL-PRF-32014 specification and is used on large military and wide-body Boeing aircraft. To meet the MIL-PRF-32014 specification requires a grease formulation that contains many additives. This creates a complex system that is sensitive to even small changes in the production process or raw materials. Over the last couple years, several production batches of 374A have not passed the bearing corrosion test ASTM D-5969. The focus of this project is to better understand the 374A formulation and production process and to improve anti-corrosion consistency between batches. Design of Experiments methodologies were applied to analyze the impact of various formulation and production factors. A model was created to help understand and predict future changes to the process and formulation. Response optimization was used to refine a procedure that maximizes the products anti-corrosion potential. Understanding the impact of these factors has allowed Nye to create a more robust 374A formulation and process that translates to a more consistent product for the end customer. ADVISOR: -Dr. Wenzhen Huang, Professor and Department Chair, Mechanical Engineering Department, UMass Dartmouth COMMITTEE MEMBERS: -Dr. Bharatendra Rai, Associate Professor and Chair, Decision & Information Sciences / Charlton College of Business, UMass Dartmouth -Dr. Jason Galary, Director of Technology, Nye Lubricants, New Bedford, MA Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Wenzhen Huang (whuang@umassd.edu, 508-910-6568). Thank you, Sue Cunha, Administrative Assistant Mechanical Engineering Department scunha@umassd.edu 508-999-8492

Dec18

EAS Doctoral Defense by Jared M. Buckley

TITLE : A New Model of Satellite-derived Near-surface Currents, the Impact of Lateral Advection, and a Fuzzy Logic Approach to Observing Cold Pools in the Bay of Bengal DATE: Wednesday, December 18, 2019 TIME: 10 AM LOCATION: Textiles - 105A (CSCVR Room) Abstract: Estimating near-surface currents is important for understanding the influence of advection on observed changes in the upper-ocean. Simultaneous in-situ mesoscale observations of the ocean sufficient to cover entire ocean basins are logistically and practically impossible with current technologies. In order to circumvent these limitations, previous studies have developed algorithms to estimate near-surface currents in the global oceans using satellite observations. We present a modification to these global algorithms designed specifically for the Bay of Bengal: The Bay of Bengal surface current and advection estimation (BoBcat). The Bay of Bengal is one of the freshest parts of the world ocean, and this algorithm accounts for the influence of strong salinity stratification in setting the magnitude of wind-driven currents. This modification results in improvements in both qualitative and quantitative assessments of near-surface current errors in the Bay of Bengal. The Bay of Bengal is subject to strong lateral advection of low-salinity water. This advection leads to the development of strong upper-ocean stratification, which can have a significant impact on the evolution of SST and SSS by modifying mixing near the surface. We use a 1-dimensional (1-D) ocean mixing model forced with in-situ airâ€“sea fluxes from a surface mooring, along with estimates of horizontal surface freshwater and heat advection from a satellite ocean current product. We develop an ensemble of 1-D simulations by varying estimates of the advection used to force the model and analyze how the advection of temperature and salinity influences the evolution of SST and SSS. The use of an ensemble of solutions, rather than any single solution, compensates for uncertainty in our satellite estimates and significantly improves confidence in our results. Our results indicate that airâ€“sea fluxes are not sufficient to properly simulate the evolution of SST in the northern Bay of Bengal, and that accounting for freshwater advection is required in order to reduce error in SST. Atmospheric cold pools occur when anomalous dense air descends to the surface after being cooled by the evaporation of precipitation. Cold pools have been implicated in the aggregation of convective systems, leading to a need for more observations of their size, strength, frequency, and duration over the tropical oceans. Using observations from a WHOI mooring and two OMNI (Ocean Moored Buoy Network for northern Indian Ocean) moorings in the northern Bay of Bengal, we use a new fuzzy logic framework to objectively identify cold pools, analyze their properties, and determine their size distribution. Designed to compensate for the vagueness in defining thresholds for cold pools in air temperature time series, our framework moves beyond the strict binary cold-pool-or-not-cold-pool viewpoint by permitting the inclusion of partial cold pools. Our results reveal that cold pools occur frequently in the northern Bay of Bengal, with their size and duration following a lognormal distribution. Advisor: Dr. Amit Tandon, Department of Mechanical Engineering, UMassD Committee members: Dr. Daniel MacDonald, Civil & Environmental Engineering, UMassD; Dr. Robert Weller, Woods Hole Oceanographic Institution; and Dr. Gaurav Khanna, Physics, UMassD For further information, please contact Dr. Amit Tandon by email atandon@umassd.edu.

Dec18

ECE Doctor Of Philosophy Dissertation Defense By: Paul C. Proffitt

Topic: High Clutter, Close Range Wi-Fi Imaging And Probabilistic, Learning Classifier Location: College of Engineering Conference Room, Textiles Building (TXT), Room 101E Abstract: In the past few years, researchers have worked on using Wi-Fi for identifying actions, but this overlooked using Wi-Fi on static (non-moving) or relatively non-moving objects due to its inherent problems. These problems involve many challenges such as very close range, high clutter, running real time, rough blob-like images, and many other issues encountered. Another area where both have had little research is in classifying the images created with Wi-Fi. Machine learning is a growing topic due to fast GPUs, but no research has been conducted in applying Artificial Intelligence (AI) to classifying non-moving or relatively non-moving objects imaged with Wi-Fi signals. This research used Wi-Fi signal processing and neural networks to identify static objects. The images created needed to be classified (identified). Classifying the resultant images were a challenge and required some form of AI. AI can identify objects by seeing persistent characteristics in rough images. Here is where Wi-Fi imaging and AI worked hand in hand. There were two major portions to this research. The first was the signal processing portion, where images were created, and the second was the image classification portion, which used AI neural networks to identify the rough images. This work brought the researcher's theoretical ideas to life in real-world experiments, which provided the proof of concept. In the signal processing portion, images were created by using Wi-Fi signals transmitted and received on Ettus Universal Software Radio Peripheral (USRP) hardware and directional antennas. Different directional antennas were designed and manufactured to form 1x2, 2x2, and 3x2 antenna arrays for beamforming and scanning objects. Upon creating Wi-Fi images, region-based convolutional neural networks (RCNNs) were employed and trained to result in identifying these objects. The results showed that such a Wi-Fi imaging and classification system is possible for static objects. This research solved problems of identifying static objects not well seen by visible light cameras, night vision, thermal vision, or millimeter wavelength processing where cost or environment factors interfered. Problem areas that were shown to benefit from this research were fire and rescue robotics, automation of security in bus and subway systems, and many other applications. NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. Advisor: Dr. Honggang Wang Committee Members: Dr. Dayalan P. Kasilingam and Dr. Liudong Xing, Department of Electrical & Computer Engineering, UMass Dartmouth; Dr. Shaoen Wu, Department of Computer Science, Ball State University *For further information, please contact Dr. Honggang Wang at 508.999.8469, or via email at hwang1@umassd.edu.

Dec20

Final grades due

Dec26

First day of Intersession classes

Intersession classes begin today.

Jan1

New Year's Day: No classes

New Year's Day, no classes today.

Jan15

Last day/final exams of Winter Session classes

Jan21

First Day of Classes