Wei Hou

Wei Hou

Computational Math and Physics

Wei Hou

I am a PhD candidate at California Institute of Technology working with Prof. Tim Colonius. I am interested in computational methods and their applications in fluid mechanics.

High-fidelity and high-performance numerical algorithms

We derive and implement large-scale numerical simulation algorithms for incompressible external flows. Specifically, I work on applying lattice Green’s function, immersed boundary method, and adaptive mesh refinement to simulate high Reynolds number flows with accurate far-field outer flows.

  • Hou, W., & Colonius, T. (2024). Fast and robust method for screened Poisson lattice Green’s function using asymptotic expansion and Fast Fourier Transform. submitted, arXiv preprint arXiv:2403.03076.
  • Hou, W., & Colonius, T. (2024). An adaptive lattice Green’s function method for external flows with two unbounded and one homogeneous directions. accepted by Journal of Computational Physics.
  • Sader, J., Hou, W., Hinton, E., Pullin, D., & Colonius, T. (2023). The starting vortices generated by a translating and rotating flat plate in a viscous fluid. accepted by Journal of Fluid Mechanics

Flow analysis using eigenspectrum and pseudospectrum

We utilize large-scale computation to study the stability and sensitivity of flows via stability analysis and resolvent analysis.

  • Hou, W., & Colonius, T. (2023). Three-dimensional Stability and Resolvent Analysis of External Flows Over Spanwise-homogeneous Immersed Bodies. In AIAA AVIATION 2023 Forum (p. 3414).

Machine learning algorithms for gust detection

I worked on machine learning algorithms for gust detection from surface measurements. We created a new machine learning framework to predict the onset of gusts in the flow effectively. We also derived accompanying regularization techniques to prevent overfitting and regularization algorithms to find optimal sensor placement locations for gust detection.

  • Hou, W., Darakananda, D., & Eldredge, J. D. (2019). Machine-learning-based detection of aerodynamic disturbances using surface pressure measurements. AIAA Journal, 57(12), 5079-5093.
  • Le Provost, M., Hou, W., & Eldredge, J. D. (2020). Deep learning and data assimilation approaches to sensor reduction in estimation of disturbed separated flows. In AIAA Scitech 2020 Forum (p. 0799).