Events for the 2nd week of September
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AME Seminar
Wed, Sep 11, 2019 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Eckart Meiburg, UC Santa Barbara
Talk Title: Settling of Cohesive Sediment: Particle-resolved Simulations
Abstract: We develop a physical and computational model for performing fully coupled, grain resolving Direct Numerical Simulations of cohesive sediment, based on the Immersed Boundary Method. The model distributes the cohesive forces over a thin shell surrounding each particle, thereby allowing for the spatial and temporal resolution of the cohesive forces during particle particle interactions.
We test and validate the cohesive force model for binary particle interactions in the Drafting Kissing Tumbling (DKT) configuration. Cohesive sediment grains can remain attached to each other during the tumbling phase following the initial collision, thereby giving rise to the formation of flocs. The DKT simulations demonstrate that cohesive particle pairs settle in a preferred orientation, with particles of very different sizes preferentially aligning themselves in the vertical direction, so that the smaller particle is drafted in the wake of the larger one. This preferred orientation of cohesive particle pairs is found to remain influential for much larger simulations of 1,261 polydisperse particles released from rest. These simulations reproduce several earlier experimental observations by other authors, such as the accelerated settling of sand and silt particles due to particle bonding, the stratification of cohesive sediment deposits, and the consolidation process of the deposit. This final phase also shows the build-up of cohesive and direct contact intergranular stresses. The simulations demonstrate that cohesive forces accelerate the overall settling process primarily because smaller grains attach to larger ones and settle in their wakes. An investigation of the energy budget shows that the work of the collision forces substantially modifies the relevant energy conversion processes.
Bio
Eckart Meiburg received his Ph.D. from the University of Karlsruhe. After a postdoc at Stanford, he became an assistant professor in applied mathematics at Brown. He then moved to USC as associate then full professor. He later moved to UC Santa Barbara.
His research interests are fluid dynamics and transport phenomena, primarily computational fluid dynamics. He uses highly resolved direct numerical simulations to investigate physical mechanisms governing the spatio temporal evolution of a wide variety of geophysical, porous media, and multiphase flow fields. Some of his current interests are gravity and turbidity currents, Hele Shaw displacements, double diffusive phenomena in particle laden flows, and internal bores.
Meiburg has received a Presidential Young Investigator Award, a Humboldt Senior Research Award, and a Senior Gledden Fellowship (Institute of Advanced Studies, University of Western Australia). He is fellow of the American Physical Society and the ASME, was the 2012 Lorenz G. Straub Award Keynote Speaker (Univ. Minn.), gave the Ronald F. Probstein Lecture at MIT in 2018, and was Shimizu Visiting Professor at Stanford University.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Location: John Stauffer Science Lecture Hall (SLH) - 102
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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Ph.D. Dissertation Defense
Fri, Sep 13, 2019 @ 01:00 PM - 02:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Pradeep Rajendran, AME Ph.D. candidate
Talk Title: Speeding Up Trajectory Planning for Autonomous Robots Operating in Complex Environments
Abstract: Advances in sensing and computing hardware have physically equipped robots to operate in complex environments. In many real-world settings, we desire robots to operate at a high-level of autonomy to reduce operating costs and manpower requirements. A high-level of autonomy can be achieved only when robots are able to plan missions and tasks themselves. Trajectory planning is a fundamental building block required to support high-level decision making in robots.
Trajectory planning for autonomous robots operating in complex environments is a challenging problem. The complexity of trajectory planning problems stems from the dimensionality of robot's state space, the complexity of the robot kinematic and dynamic model, the nature of environmental constraints (e.g., obstacles), task constraints (e.g., rules), the optimization objective function, and the planning-time requirements needed for deployment in the real world. Depending on the complexity, these problems can be solved by existing methods to produce feasible trajectories. But, in many practical applications (e.g., automated package delivery), computing a feasible trajectory alone is not enough. The quality of the computed trajectory is also important. However, in many cases, computing truly optimal trajectories is computationally intensive and thus, very time-consuming. As a result, existing methods do not satisfy planning-time constraints required by the application while maintaining optimality. We need a method that produces high-quality trajectories and at the same time produce those trajectories quickly. Anytime methods handle exactly this problem. However, these methods produce high-quality trajectories quickly only when good heuristics are used.
This work focuses on techniques for anytime algorithms that speed up trajectory planning for autonomous robots in complex environments. It is anticipated that the methodology presented in this work will be applicable to mobile robots operating in an outdoor setting such as uneven terrain, water bodies. In such settings, the speed-up techniques will allow the robot to quickly react to the environment and perform tasks safely. Depending on the application domain, this will also serve as an enabling technology for more advanced services. Many industrial processes are currently use high degree-of-freedom manipulators that are manually programmed by a human operator. Methods presented in this work can greatly simplify workflows related to manipulators and improve manufacturing throughput.
Host: SK Gupta
Location: Robert Glen Rapp Engineering Research Building (RRB) - Laufer Library
Audiences: Everyone Is Invited
Contact: SK Gupta