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Christian Perron

Research Engineer II
Advanced Configuration Division

Dr. Christian Perron is a Research Engineer with the Advanced Configuration division of the Aerospace System Design Laboratory (ASDL). His research focuses on developing advanced methods for the design and optimization of new aircraft configurations. Specifically, his work considers the use of machine learning methods combined with high-fidelity simulation for problems such as aerodynamic design, multi-disciplinary analysis, and uncertainty quantification. Dr. Perron’s work have been applied to various aerospace applications such as an over-the-wing nacelle configuration, a transonic truss-braced wing aircraft, a comercial supersonic vehicle, and an open-rotor propulsion system.

  • Doctor of Philosophy, Aerospace Engineering, 2020, Georgia Institute of Technology, Atlanta, GA - USA
  • Master of Science, Aerospace Engineering, 2017, Georgia Institute of Technology, Atlanta, GA - USA
  • Master of Science, Mechanical Engineering, 2015, Université Laval, Quebec City, QC - Canada
  • Bachelor of Engineering, Mechanical Engineering, 2012, Université Laval, Quebec City, QC - Canada

  • Computational Fluid Dynamics
  • Aerodynamic Design
  • Surrogate Modeling
  • Dimensionality Reduction
  • Numerical Optimization
  • Multi-Disciplinary Analysis and Optimization
  • Uncertainty Quantification

Journal Articles

  1. Decker, K., Iyengar, N., Rajaram, D., Perron, C., & Mavris, D. (2022) Manifold Alignment-based Non-Intrusive and Non-Linear Multi-Fidelity Reduced Order Modeling. AIAA Journal. (in-press)

  2. Perron, C., Sarojini, D., Rajaram, D., Corman, J., & Mavris, D. (2022). Manifold Alignment-Based Multi-Fidelity Reduced-Order Modeling Applied to Structural Analysis. Structural and Multidisciplinary Optimization, 1. Link

  3. Perron, C., Rajaram, D., & Mavris, D. N. (2021). Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 477(2253), 20210495. Link

  4. Rajaram, D., Perron, C., Puranik, T. G., & Mavris, D. N. (2020). Randomized Algorithms for Non-Intrusive Parametric Reduced Order Modeling. AIAA Journal, 1–19. Link

Conference Proceedings

  1. Baltman, E., Tai, J. C., Ahuja, J., Stewart, B., Perron, C., De Azevedo, J., Vlady, T. R., & Mavris, D. N. (2022). A Methodology for Determining the Interdependence of Fuel Burn and LTO Noise of a Commercial Supersonic Transport. AIAA AVIATION 2022 Forum, 1–16. Link

  2. Mufti, B., Chen, M., Perron, C., & Mavris, D. N. (2022). A Multi-Fidelity Approximation of the Active Subspace Method for Surrogate Models with High-Dimensional Inputs. AIAA AVIATION 2022 Forum, 1–26. Link

  3. Iyengar, N., Rajaram, D., Decker, K., Perron, C., & Mavris, D. (2022). Nonlinear Reduced Order Modeling using Domain Decomposition. AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022, 1–25. Link

  4. Decker, K., Iyengar, N., Perron, C., Rajaram, D., & Mavris, D. (2021). Nonlinear Multi-Fidelity Reduced Order Modeling Method using Manifold Alignment. AIAA AVIATION 2021 FORUM, 1–25. Link

  5. Perron, C., Rajaram, D., & Mavris, D. (2020). Development of a Multi-Fidelity Reduced-Order Model Based on Manifold Alignment. AIAA AVIATION 2020 FORUM, 1–20. Link

  6. Rajaram, D., Gautier, R. H., Perron, C., Pinon-Fischer, O. J., & Mavris, D. (2020, June 15). Non-Intrusive Parametric Reduced Order Models with High-Dimensional Inputs via Gradient-Free Active Subspace. AIAA AVIATION 2020 FORUM. Link

  7. Rajaram, D., Puranik, T. G., Perron, C., & Mavris, D. N. (2020). Non-Intrusive Parametric Reduced Order Modeling using Randomized Algorithms. AIAA Scitech 2020 Forum, January, 1–23. Link

  8. Rajaram, D., Perron, C., Puranik, T. G., & Mavris, D. N. (2020). Randomized Algorithms for Non-Intrusive Parametric Reduced Order Modeling. AIAA Journal, 1–19. Link

  9. Harish, A., Perron, C., Bavaro, D., Ahuja, J., Ozcan, M., Justin, C. Y., Briceno, S. I., German, B. J., & Mavris, D. (2016). Economics of Advanced Thin-Haul Concepts and Operations. 16th AIAA Aviation Technology, Integration, and Operations Conference, June, 1–16. Link

Technical Reports

  1. Berguin, S. H., Renganathan, S. A., Ahuja, J., Chen, M., Perron, C., Tai, J., & Mavris, D. N. (2018). CFD Study of an Over-Wing Nacelle Configuration. Link

Theses

  1. Perron, C. (2020). Multi-Fidelity Reduced-Order Modeling Applied to Fields with Inconsistent Representations. Georgia Institute of Technology. Link

  2. Perron, C. (2015). Étude CFD des effets du désalignement et du cisaillement sur les performances et le chargement des hydroliennes. Université Laval. Link