Faculty in the Department of Mechanical Engineering are creating computer-aided design tools for process simulations and novel algorithms for the biomodeling of molecules using computational methods.
Computational geometry, CAD, and CAE
Our faculty investigate computational approaches and computer-aided design to create tools for visualization, modeling, simulation, sketched-based user interfaces, and human-computer problem solving. These can impact areas ranging from product design to medical imaging.
Computational tools for manufacturing
Our experts create and apply computational design tools and computer-aided manufacturing tools to advance manufacturing processes and technologies. The work includes design optimization for manufacturability and analysis software for process simulations.
Computation in energy
From improving energy conversion and storage efficiency to reducing energy consumption, our experts explore micro/nano-scale phenomena through atomic- and meso-scale simulations and machine learning.
Computational biology and medicine
Our researchers seek to improve human health by exploring the biomechanics of cells and molecules, developing better medical imaging technologies, and developing novel algorithms to biomodel molecules, cells, tissue, and organs.
They are combining molecular dynamic simulations, machine learning, and statistical learning to understand and predict the properties and interactions of bio-molecules such as DNA and proteins.
Computational fluid dynamics (CFD) and multiphysics simulations
Our faculty are developing the computational tools to simulate engineering applications and the phenomena that impact them in their full-scale complexity.
They perform multiphase flow simulations, direct numerical simulations (DNS), large-eddy simulations (LES), porous media flow simulations, electrokinetics, and multiphysics simulations.
Modeling neuron traffic jams in the brain Opens in new window
MechE’s Jessica Zhang and Angran Li have developed a new way to model material transport regulation in neurons using cutting-edge parametrization technology and isogeometric analysis. This new, much more detailed modeling will help provide insights on “neural traffic jams,” which contribute to neurodegenerative diseases such as Alzheimer's, Huntington’s, and Parkinson’s disease.
Simulating fluid flow to study disease
New MechE faculty member Noelia Grande Gutiérrez uses computational modeling to study the fluid mechanics of blood flow to design personalized therapies for thrombosis, earlier diagnosis of coronary artery disease, and precise, targeted drug delivery for cancer.
Using deep learning to research material transport in the brain
Understanding the causes of degenerative diseases like Alzheimer's, Huntington's, and Parkinson's will require the meticulous investigation into the complex, branch-like neurite networks of the brain. Machine learning can be an efficient, highly accurate part of this process.
Center for Atmospheric Particle Studies