Dan Stoecklein
Assistant Professor
Department of Mechanical Engineering
Rose-Hulman Institute of Technology
I am an Assistant Professor of Mechanical Engineering at Rose-Hulman Institute of Technology, with a focus on thermo-fluid science, numerical methods, computational design, and optimization. You can read more about my current projects on my Research page, but broadly, I place my research interests into two different categories:
- Fluid-Structure Interaction
Fluid-structure interaction is often studied from the perspective of the structure, i.e., analyzing bridges, buildings, airplanes, or boats in the presence of fluid flow to optimize their structure or performance. While these are important concerns, I am also interested in how the fluid is affected by the structure, and how we can manipulate fluid flow to achieve some other objectives, for example:
- Shaping polymer precursors for custom fiber and particle shapes
- Improving fluid mixing for enhanced heat transfer or chemical reactions
- Manipulating fluid flow to achieve fluid-particle separation
- Machine Learning for Computational Design
Machine learning has offered new ways to analyze and generate data, make predictions, and accelerate simulations. I am interested in how machine learning can be used to supplement or entirely replace costly numerical methods, particularly computational fluid dynamics (CFD) simulations. I am also interested in how machine learning can be leveraged to efficiently explore a design space and quantify uncertainty. I have been working to apply deep learning for the following problems:
- Rapidly predicting the shape of extruded flows
- Using GANs with Bayesian inference for the early detection of breast cancer