Multiphysics Modeling and Design Optimization Laboratory
Welcome
Many engineering systems and structures are multidisciplinary in nature. Modeling and design optimization of these complex engineering systems is a challenging task, since it involves analyzing and optimizing a wide range of disciplines such as structures, fluids and controls, and their associated interactions and uncertainties.
A promising technique is multiphysics analysis and design optimization combined with uncertainty quantification and machine learning techniques to create advanced systems that are environmental friendly and economically viable. Physics-based multidisciplinary methods can be used to design and optimize such systems, but several challenges have to be addressed. The goal of multiphysics modeling and design optimization laboratory is to address important challenges in this area such as:
How to model and optimize the complex multiphysics interaction between the fluids, controls and structures?
How to quantify and mitigate different sources of uncertainty in engineering modeling and design?
How to make physics-based modeling and design optimization a routine computational tool for industry?
How to employ machine learning techniques to make an intelligent design?
Research in Multiphysics Modeling and Design Optimization Laboratory involves both theory and application. From theoretical perspective, the lab researchers develop numerical methods to model the physics of unsteady flow fields, nonlinear composite structures, optimal control algorithms, advanced optimization techniques, uncertainty quantification methods and machine learning algorithms. From applied perspective, the focus area is the analysis and optimization of complex engineering systems. This includes application areas such as soft robotics, unmanned aerial vehicles and energy systems for many industrial applications.
Prof. Ashuri founded the multiphysics modeling and design optimization laboratory in 2016 to advance the design of complex mechanical and aerospace structures and systems, quantification and mitigation of design uncertainties, and design using machine learning techniques.
We are continuously looking for collaboration with other researchers, and at moment the lab has collaboration with researchers from University of Michigan, Ann Arbor, University of Texas at Dallas, University of Arkansas for Medical Sciences, University of Arkansas at Little Rock and Delft University of Technology, the Netherlands.
Latest News
3/2022: Our joint proposal titled "Design and Fabrication of a Soft Parallel Robot for Transcatheter Interventions" is funded by the National Institute of Health (NIH). Dr. Amir Amiri Moghadam from the Department of Robotics and Mechatronics at Kennesaw State University is the PI on this award, with Dr. Ayse Tekes from the Department of Mechanical Engineering at Kennesaw State University as the Co-PI.
2/2022: Our paper entitled "A Machine Learning Approach for Simulating the Airborne Transmission of Infectious Diseases," is accepted for publication in the NCUR Conference, 2022. Noah Clark who is an Undergraduate Researcher working in the lab is the first author of the paper. Well done Noah!
1/2022: Our joint proposal titled "Raising Student Learning Success: A Process-focused Automated Feedback System" is funded by Bluenotes Community and Explorance, Inc. Dr. Amir Amiri Moghadam from the Department of Robotics and Mechatronics at Kennesaw State University is the Co-PI on this award.
Acknowledgment
We gratefully acknowledge the continued funding support from NASA, NIH and Bluenotes Explorance to support the lab's research and our graduate and undergraduate students.
