People

Neil Wu

Research Interests

• Multifidelity methods in MDO
• Gradient-based optimizers
• Large-scale aerostructural optimizations for industrial applications
• High-performance computing
• Geometry parameterization
• DevOps and code maintenance in scientific computing

Education

• Ph.D., Aerospace Engineering, University of Michigan, 2018 - Present
• M.S.E., Aerospace Engineering, University of Michigan, 2017 - 2018
• B.A.Sc., Engineering Science, University of Toronto, 2012-2017

Publications

1. Sensitivity-based Geometric Parameterization for Aerodynamic Shape Optimization

   N. Wu, C. Mader, and J. R. R. A. Martins

   AIAA AVIATION 2022 Forum, 2022

   doi:10.2514/6.2022-3931

   Details

2. A Gradient-based Sequential Multifidelity Approach to Multidisciplinary Design Optimization

   N. Wu, C. A. Mader, and J. R. R. A. Martins

   Structural and Multidisciplinary Optimization, 65131–151, 2022

   doi:10.1007/s00158-022-03204-1

   Details

3. Large-scale Multifidelity Aerostructural Optimization of a Transport Aircraft

   N. Wu, C. Mader, and J. R. R. A. Martins

   33rd Congress of the International Council of the Aeronautical Sciences, 2022

   Details

4. pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems

   N. Wu, G. Kenway, C. A. Mader, J. Jasa, and J. R. R. A. Martins

   Journal of Open Source Software, 5(54):2564, 2020

   doi:10.21105/joss.02564

   Details

Awards

• First place, MDO Student Paper Competition at AVIATION 2022 for the paper Sensitivity-based Geometric Parameterization for Aerodynamic Shape Optimization

Activities

I worked for two years on the Blue Sky Solar Racing team at the University of Toronto, where I was the aerodynamics lead and chief engineer. I also worked on the AeroVelo Eta, a bicycle which still holds the world record for the fastest human-powered vehicle at 89 mph.