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Reduced Order Modeling of Fluid Dynamics in Large Scale Urban Domains

Project

Project Details

Program
Applied Mathematics and Computer Science
Field of Study
Computational Science and Scientific Machine Learning
Division
Computer, Electrical and Mathematical Sciences and Engineering

Project Description

The main objective of this project is to investigate the feasibility of implementing a reduced-order model for weather simulations in urban areas. The effectiveness of reduced order models is strictly dependent on the application. Therefore, a proper campaign of simulations is required to evaluate its feasibility in the context of large-scale weather simulations in the urban scale. This project bridges the fields of computational mathematics and aerospace engineering to evaluate the potential of reduced-order modeling in large-scale urban simulations.

About the Researcher

Matteo Parsani
Associate Professor, Applied Mathematics and Computational Science

Desired Project Deliverables

To achieve this objective, several steps must be taken: - Literature review on state-of-the-art results and data of atmospheric boundary layer for wind and temperature (in urban areas). - Generate an appropriate 3D mesh of the computational domains representing urban areas. - Formulate realistic boundary conditions based on available data. - Set up the computational fluid dynamics (CFD) pipeline for large scale simulations of incompressible flows (coupled with temperature) in a supercomputer. - Postprocessing of the CFD solutions - Explore the solution vector space to construct a reduced-order model. - Estimate the associated errors.

Recommended Student Background

Good knowledge in numerical linear algebra, numerical methods and CFD.
Basic programming knowledge, ideally in Python, C/C++ .
CAD modeling experience.