Publications

Journal Papers

Conference Proceedings (Refereed)

Presentations

  • Blonigan, P., Ching, D., Arienti, M., Rizzi, F., and Fike, J., “Least-Squares Petrov—Galerkin Reduced-Order Models for Steady Hypersonic Aerodynamics”, Presented at the 16th US National Congress on Computational Mechanics, July 2021, Online.
  • Blonigan, P., Rizzi, F., Parish, E., and Tencer, J., “Pressio: A computational framework enabling projection-based model reduction for large-scale nonlinear dynamical systems”, Presented at the 2021 SIAM Conference on Computational Science and Engineering, Online.
  • Rizzi, F., Blonigan, P., Parish, E., and Carlberg, K., “Pressio: A computational framework enabling projection-based model reduction for large-scale nonlinear dynamical systems”, Presented at the 2020 SIAM Annual Meeting, Online. 
  • Blonigan, P.J, Carlberg, K.T., Rizzi, F., Howard, M., and Fike, J., “Least-Squares Petrov-Galerkin Reduced-Order Models for Hypersonic Flight Vehicles”, Presented at the 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, WA, 2019. Abstract ID: BAPS.2019.DFD.H41.11
  • Geraci, G., Blonigan, P.J., Rizzi, F., Gorodetsky, A.A., Carlberg, K.T., and Eldred, M.S., “An integrated and efficient framework for embedded Reduced Order Models for multifidelity uncertainty quantification”, Presented at the 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, WA, 2019. Abstract ID: BAPS.2019.DFD.H41.5
  • Blonigan, P., “Adjoint-Based Sensitivity Analysis and Error Estimation for Scale-Resolving Turbulent Flow Simulations”, Presented at the SIAM Conference on Computational Science and Engineering, Spokane, WA, 2019.
  • Blonigan, P., Ekelschot, D., Garai, A., Diosady, L., and Murman, S., “Adjoint-Based Error-Estimation and Mesh Adaptation for Turbulent Flows”, Presented at the International Conference on High Order and Spectral Methods, London, UK, 2018.
  • Blonigan, P., Diosady, L., Garai, A., and Murman, S., “Adjoint Sensitivity Analysis for Scale-Resolving Turbulent Flow Solvers”, Presented at the 70th Annual Meeting of the APS Division of Fluid Dynamics, Denver, CO, 2017. Abstract ID: BAPS.2017.DFD.D31.1
  • Blonigan, P., Diosady, L., Garai, A., and Murman, S., “Adjoint Sensitivity Analysis for Scale-Resolving Turbulent Flow Solvers”, Presented at the SIAM Conference on Computational Science and Engineering, Atlanta, GA, 2017.
  • Blonigan, P., Fernandez, P., Murman, S., and Wang, Q., “Lyapunov Exponents and Covariant Vectors for Turbulent Flow Simulations.” Presented at the 69th Annual Meeting of the APS Division of Fluid Dynamics, Portland, OR, 2016. Abstract ID: BAPS.2016.DFD.G38.1
  • Blonigan, P., Wang, Q., Nielsen, E., and Diskin, B., “Sensitivity Analysis of Chaotic Flow around a Two-Dimensional Airfoil”.  Presented at the 68th Annual Meeting of the APS Division of Fluid Dynamics, Boston, MA, 2015. Abstract ID: BAPS.2015.DFD.D8.7
  • Blonigan, P. and Wang, Q., “Least Squares Shadowing for Adjoint Calculation of Chaotic and Turbulent PDEs”, Presented at the SIAM Conference on Computational Science and Engineering, Salt Lake City, UT, 2015.
  • Blonigan, P., Talnikar, C., Bodart, J., Bose, S., Pierce, B., and Wang, Q., “Optimization of a Turbine Blade Trailing Edge using Large Eddy Simulations”.  Presented at the 67th Annual Meeting of the APS Division of Fluid Dynamics, San Francisco, CA, 2014. Abstract ID: BAPS.2014.DFD.R2.5
  • Blonigan, P., Gomez, S., and Wang, Q., “Least Squares Shadowing Sensitivity Analysis of Chaotic and Turbulent Fluid Flows”.  Presented at the 66th Annual Meeting of the APS Division of Fluid Dynamics, Pittsburgh, PA, 2013. Abstract ID: BAPS.2013.DFD.L21.1
  • Blonigan, P., Gomez, S., and Wang Q., “Towards Least Squares Sensitivity Analysis of Chaotic Fluid Flows”. Presented at the 2013 SIAM Annual Meeting, San Diego, CA.
  • Blonigan, P. and Wang, Q., “New Methods for Sensitivity Analysis in Chaotic, Turbulent Fluid Flows”.  Presented at the 65th Annual Meeting of the APS Division of Fluid Dynamics, San Diego, CA, 2012. Abstract ID: BAPS.2012.DFD.H21.7

Thesis