Aerospace Computational Engineering Lab

Assistant Professor M. Yano
University of Toronto
Institute for Aerospace Studies
4925 Dufferin St., Ontario, Canada M3H 5T6

Phone: +1-416-667-7705
Fax: +1-416-667-7799
Email: myano (at) utias.utoronto.ca
Web http://arrow.utias.utoronto.ca/~myano/

Education

  • Ph.D. – Massachusetts Institute of Technology
  • S.M. – Massachusetts Institute of Technology
  • B.S. – Georgia Institute of Technology

Research Summary

Professor Yano’s research focuses on the development of computational methods for problems in aerospace sciences and engineering. Specifically, his research interests lie in numerical methods, scientific computation, and numerical analysis for partial differential equations (PDEs) with applications in aerodynamics, continuum mechanics, acoustics, and transport.

The goal of Prof. Yano’s group is to improve the reliability and autonomy of numerical simulations. Here, reliability refers to the ability to estimate and control various sources of error in numerical predictions. Autonomy refers to the ability to complete the analysis with minimal user intervention.

Reliable and automated simulations play important roles in engineering design and analysis. A reliable solver accurately characterizes complex flow phenomena, in which user instincts may fail to identify relevant features. A reliable solver permits exploration of radically different designs, for which little prior knowledge exists. A reliable solver enables modern engineering challenges, such as robust optimization and real-time control. The group aims to provide reliable and automated computational tools that maximize their predictive potential and utility in understanding physical phenomena and ultimately making engineering decisions.

Much of the emphasis of the group is advanced numerical methods for PDEs. Example of current research topics include:

  1. error estimation and adaptation techniques to provide high-fidelity prediction of complex aerodynamic flows in automated manner;
  2. model reduction techniques to provide rapid solution of parametrized PDEs in many-query or real-time scenarios arising in optimization, uncertainty quantification, and in-situ computation;
  3. model reduction techniques for large-scale and high-dimensional engineering systems;
  4. data assimilation techniques that incorporate experimental data and simulation within a single mathematical framework to address model errors.

The group works on both the fundamental development and analysis of numerical methods as well as their application to aerospace engineering problems. On one hand, rigorous mathematical analyses are essential to the development of robust computational methods. On the other hand, the application to industrial problems is essential to assess the effectiveness of the methods in real engineering scenarios.