Growing worldwide energy demands and concerns for environmental impacts from energy conversion (e.g., CO2 emissions, water availability, air quality, etc.) are driving a paradigm shift in how the world thinks about new energy technologies and infrastructures. For example, throughout the U.S., many states are mandating high renewable energy integration targets such as 50% by 2030 in New York and California. Meeting this and more aggressive goals necessitate a multiscale perspective of energy technologies, as shown in the figure below.
In the Dowling Lab, we seek to develop novel mathematical modeling and computational frameworks to optimize energy technologies across materials, devices, and systems length and timescales, as well as in the context of infrastructures. This multiscale perspective naturally facilitates both bottom-up and top-down thinking such as:
- Rapidly assess the potential of new materials to impact devices, systems, and infrastructures.
- Use infrastructure level goals (e.g., renewable adoption, emission reductions, limited water use, etc.) to set design priorities for material, device, and system-level metrics.
- Discover new materials, devices, and systems that can help mitigate uncertainty and lead to more resilient infrastructures.
We emphasize understanding the propagation of uncertainty through multiscale optimization problems. Our work is at the intersection of engineering, applied mathematics, and computational sciences.
We are looking for outstanding undergraduate researchers, graduate students, and post doctoral scholars.
Interested in mathematical modeling and computer programming? Contact Prof. Dowling (alex [at] dowlinglab.org) to learn more about research projects for ND undergraduate students (especially those majoring in computer science, mathematics, statistics, economics, or ANY engineering discipline).
Department of Chemical and Biomolecular Engineering