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Current Research

Current research areas include computational fluid dynamics (CFD), aerodynamic shape optimization, multi-disciplinary design optimization, uncertainty quantification, and robust design.

Applications from talented students for graduate or undergraduate projects are always welcome.

We use Linux Operating systems, Fortran 90 and C computer languages, and MPI as well as OpenMP for inter-processor communication. Knowledge of these languages and environments is essential for these projects.



Selected Publications

Refereed Journal Papers

  1. M.P. Rumpfkeil and P. Beran "Multi-Fidelity Sparse Polynomial Chaos Surrogate Models Applied to Flutter Databases", AIAA Journal, Vol. 58, No. 3, pp. 1292-1303, 2020.

  2. M.P. Rumpfkeil and P. Beran "Multi-Fidelity Surrogate Models for Flutter Database Generation", Computer and Fluids, Vol. 197, 2020.

  3. R. Strong and M. P. Rumpfkeil "Lattice Boltzmann: A Better Mousetrap for Your Industry". In: Process Industry Informer 16.6 (2020).

  4. A. M. Briones, M.P. Rumpfkeil, N. R. Thomas, and B. A. Rankin "Effect of Deterministic and Continuous Design Space Resolution on Multiple-Objective Combustor Optimization", Journal of Engineering for Gas Turbines and Power, Vol 141, Issue 12, 2019.

  5. D.E. Bryson and M.P. Rumpfkeil "Aero-Structural Design Optimization using a Multifidelity Quasi-Newton Method" AIAA Journal of Aircraft, Vol. 56, No.5, pp. 2019-2031, 2019.

  6. D.E. Bryson and M.P. Rumpfkeil "A Multifidelity Quasi-Newton Method for Design Optimization", AIAA Journal, Vol. 56, No. 10, pp. 4074-4086, 2018.

  7. T. Khamlaj and M.P. Rumpfkeil "Analysis and Optimization of Ducted Wind Turbines", Energy, Volume 162, pp. 1234-1252, 2018.

  8. T. Vincent, M.P. Rumpfkeil and A. Chaudhary "Numerical Simulation of Molten Flow in Direct Energy Deposition using an Iterative Geometry Technique", Lasers in Manufacturing and Materials Processing, Volume 5, Issue 2, pp. 113-132, 2018.

  9. M.P. Rumpfkeil and P. Beran "Construction of Dynamic Multi-Fidelity Locally Optimized Surrogate Models", AIAA Journal, Vol. 55, No. 9, pp. 3169-3179, 2017.

  10. D.E. Bryson and M.P. Rumpfkeil "All-at-Once Approach to Multifidelity Polynomial Chaos Expansion Surrogate Modeling", Aerospace Science and Technology 70C, pp. 121-136, 2017.

  11. M. P. Rumpfkeil "Using Steady Flow Analysis for Noise Predictions", Computers and Fluids, 154C, pp. 347-357, 2017.

  12. J. Roland and M.P. Rumpfkeil "Wing Fuel Tank Heat Sink Calculation for Conceptual Aircraft Design", Journal of Aircraft, Vol. 54, No. 3, pp. 1172-1188, 2017.

  13. T. Khamlaj and M.P. Rumpfkeil "Theoretical Analysis of Shrouded Horizontal Axis Wind Turbines", Energies 10(1), 38, 2017.

  14. D.E. Bryson, R.M. Miller, C.R. Marks and M.P. Rumpfkeil "Multidisciplinary Design Optimization of Quiet, Hybrid Electric Small Unmanned Aerial Systems", Journal of Aircraft, Volume 53, No. 6, pp. 1959-1963, 2016.

  15. Mark G. Turner, Rory Roberts, M.P. Rumpfkeil, James T. Van Kuren, Jeffrey Bons, Tim Smith, Joseph K. Ausserer and Paul Litke "Thrust Vectoring Design Project at Six Universities", International Journal of Engineering Education, Volume 32(1), pp. 252-271, 2016.

  16. K. Boopathy and M.P. Rumpfkeil "A Unified Framework for Training Point Selection and Error Estimation for Surrogate Models", AIAA Journal, Volume 53, Number 1, pp. 215-234, 2015.

  17. J. D. Eldredge, I. Senocak, P. Dawson, J. Canino, W.W. Liou, R. LeBeau, D.L. Hitt, M.P. Rumpfkeil, and R.M. Cummings, "A Best Practices Guide to CFD Education in the Undergraduate Curriculum", International Journal of Aerodynamics, Volume 4, Number 3/4, pp. 200-236, 2014.

  18. J.A. Narvaez, M.P. Rumpfkeil, H. Thornburg and R.J. Wilkens "Computational Modeling of a Microchannel Cold Plate: Pressure, Velocity, and Temperature", International Journal of Heat and Mass Transfer, Volume 78, pp. 90-98, 2014.

  19. M.P. Rumpfkeil "Robust Design Under Mixed Aleatory/Epistemic Uncertainties Using Gradients and Surrogates", Journal of Uncertainty Analysis and Applications. Volume 1:7, October 2013.

  20. M.P. Rumpfkeil "Optimizations Under Uncertainty Using Gradients, Hessians, and Surrogate Models", AIAA Journal, Volume 51, Number 2, pp. 444-451, 2013.

  21. M.P. Rumpfkeil and D.J. Mavriplis "Efficient Hessian Calculations using Automatic Differentiation and the Adjoint Method with Applications", AIAA Journal, Volume 48, Number 10, pp. 2406-2417, 2010.

  22. M. P. Rumpfkeil and D.W. Zingg "A Hybrid Algorithm for Far-Field Noise Minimization", Computers and Fluids, doi:10.1016/j.compfluid.2010.05.006, Volume 39, Issue 9, pp. 1516-1528, 2010.

  23. M. P. Rumpfkeil and D.W. Zingg "The optimal control of unsteady flows with a discrete adjoint method", Journal of Optimization and Engineering, doi: 10.1007/s11081-008-9035-5, Volume 11, Issue 1, pp. 5-22, 2010.

Refereed Conference Papers

  1. M.P. Rumpfkeil, M. Lickenbrock, P. Beran and R. Kolonay "Multi-fidelity, Aeroelastic Analysis and Optimization with Control Surface Deflections of an Efficient Supersonic Air Vehicle", SciTech 2021, Nashville, Tennessee, January 2021. AIAA 2021-0732.

  2. A. Palmer, A. Pankonien, G.W. Reich, E. Rudnick-Cohen and M.P. Rumpfkeil "A Method for Capturing Structural Behavior Variations in the Realization of Optimized Graph-Based Topologies of a Morphing Airfoil", SciTech 2021, Nashville, Tennessee, January 2021. AIAA 2021-2027.

  3. M. Donovan, M.P. Rumpfkeil, C. Marks and N. Fletcher "Investigation of Elevated Turbulence on High-lift Low Pressure Turbine Endwall Flows", SciTech 2021, Nashville, Tennessee, January 2021. AIAA 2021-0389.

  4. A. Thelen, M.P. Rumpfkeil and P. Beran "Multi-fidelity Flutter Analysis of an Efficient Supersonic Air Vehicle", Aviation 2020, June 2020. AIAA 2020-3159.

  5. M.P. Rumpfkeil and P. Beran "Multi-Fidelity, Gradient-enhanced, and Locally Optimized Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Benchmark Problems", SciTech 2020, Orlando, Florida, January 2020. AIAA 2020-0677.

  6. M. Lickenbrock, M.P. Rumpfkeil, P. Beran and R. Kolonay "Multi-fidelity, Multidisciplinary Design Analysis of an Efficient Supersonic Air Vehicle", SciTech 2020, Orlando, Florida, January 2020. AIAA 2020-2223.

  7. A. M. Briones, M.P. Rumpfkeil, N. R. Thomas, and B. A. Rankin "Effect of Deterministic and Continuous Design Space Resolution on Multiple-Objective Combustor Optimization", Proceedings of the 2019 ASME Turbo Expo, Phoenix Arizona, June 2019. GT2019-91388.

  8. M.P. Rumpfkeil, D.E. Bryson, and P. Beran "Multi-Fidelity Sparse Polynomial Chaos Surrogate Models for Flutter Database Generation", SciTech 2019, San Diego, California, January 2019. AIAA 2019-1998.

  9. N. R. Thomas, M.P. Rumpfkeil, A. M. Briones, T. J. Erdmann, Jr. and B. A. Rankin "Multiple-objective Optimization of a Subsonic Small-scale Cavity-stabilized Combustor", SciTech 2019, San Diego, California, January 2019. AIAA 2019-0990.

  10. M.P. Rumpfkeil and P. Beran "Multi-Fidelity Surrogate Models for Flutter Database Generation", Tenth International Conference on Computational Fluid Dynamics (ICCFD10), Barcelona, Spain, July 2018.

  11. D.E. Bryson and M.P. Rumpfkeil "Aeroelastic Design Optimization using a Multifidelity Quasi-Newton Method", SciTech 2018, Kissimmee, Florida, January 2018. AIAA 2018-0102.

  12. T. Khamlaj and M.P. Rumpfkeil "Optimization Study of Shrouded Horizontal Axis Wind Turbine", SciTech 2018, Kissimmee, Florida, January 2018. AIAA 2018-0996.

  13. D.E. Bryson, M.P. Rumpfkeil and R.J. Durscher "Framework for Multifidelity Aeroelastic Vehicle Design Optimization", Aviation 2017, Denver, Colorado, June 2017. AIAA 2017-4322.

  14. D.G. Sagerman, M.P. Rumpfkeil, B. Hellman and N. Dasque "Comparisons of Measured and Modeled Aero-thermal Distributions for Complex Hypersonic Configurations", SciTech 2017, Grapevine, Texas, January 2017. AIAA paper 2017-0264

  15. M.P. Rumpfkeil, K. Hanazaki and P. Beran "Construction of Multi-Fidelity Locally Optimized Surrogate Models for Uncertainty Quantification", SciTech 2017, Grapevine, Texas, January 2017. AIAA paper 2017-1948.

  16. D.E. Bryson and M.P. Rumpfkeil "Comparison of Unified and Sequential-Approximate Approaches to Multifidelity Optimization", SciTech 2017, Grapevine, Texas, January 2017. AIAA paper 2017-0131.

  17. M.P. Rumpfkeil and P. Beran "Construction of Multi-Fidelity Surrogate Models for Aerodynamic Databases", Ninth International Conference on Computational Fluid Dynamics (ICCFD9), Istanbul, Turkey, July 2016.

  18. D.G. Sagerman, J. Tancred, M.P. Rumpfkeil and B. Hellman "Hypersonic Experimental Aero-thermal Capability Study through Multilevel Fidelity Computational Fluid Dynamics", Aviation 2016, Washington, D.C., June 2016. AIAA paper 2016-3577.

  19. D. Bryson and M.P. Rumpfkeil "Variable-Fidelity Surrogate Modeling of Lambda Wing Transonic Aerodynamic Performance", SciTech 2016, San Diego, California, January 2016. AIAA paper 2016-0294.

  20. S. Miller, M.P. Rumpfkeil and J.J. Joo "Fluid-Structure Interaction of a Variable Camber Compliant Wing", SciTech 2015, Kissimmee, Florida, January 2015. AIAA paper 2015-1235.

  21. K. Boopathy and M.P. Rumpfkeil "Robust Optimization of a Wing Under Structural and Material Uncertainties", SciTech 2015, Kissimmee, Florida, January 2015. AIAA paper 2015-0920.

  22. J. Tancred and M.P. Rumpfkeil "Aerodynamic Response Quantification of Complex Hypersonic Configurations using Variable Fidelity Surrogate Modeling", SciTech 2015, Kissimmee, Florida, January 2015. AIAA paper 2015-1703.

  23. M.P. Rumpfkeil "Using Steady Flow Analysis for Noise Predictions", Eighth International Conference on Computational Fluid Dynamics (ICCFD8), Chengdu, China, July 2014.

  24. K. Boopathy and M.P. Rumpfkeil "Robust Optimizations of Structural and Aerodynamic Designs", Aviation 2014, Atlanta, Georgia, June 2014. AIAA paper 2014-2595.

  25. C. Marks, M.P. Rumpfkeil and G. Reich "Predictions of the effect of wing camber and thickness on airfoil self-noise", Aviation 2014, Atlanta, Georgia, June 2014. AIAA paper 2014-3299.

  26. M.P. Rumpfkeil, D. K. Robertson, and M. R. Visbal "Comparison of Aerodynamic Noise Propagation Techniques", SciTech 2014, National Harbor, Maryland, January 2014. AIAA paper 2014-0021.

  27. T. L. Kudla and M.P. Rumpfkeil "Initial Validation of a Non-equilibrium Wilcox k-omega Turbulence Model in Subsonic and Transonic Flow Regimes", SciTech 2014, National Harbor, Maryland, January 2014. AIAA paper 2014-0585.

  28. K. Boopathy and M.P. Rumpfkeil "A Multivariate Interpolation and Regression Enhanced Kriging Surrogate Model", 21st AIAA Computational Fluid Dynamics Conference, San Diego, California, June 2013. AIAA paper 2013-2964.

  29. Mark G. Turner, M.P. Rumpfkeil, Rory Roberts, James T. Van Kuren, Tim Smith, Jeffrey Bons, "Thrust Vectoring Design Project at Six Universities (Part I): Project Description and Final Designs", ASME Turbo Expo, San Antonio, Texas, June 2013. (Best technical paper Education Committee).

  30. M.P. Rumpfkeil, Mark G. Turner, Rory Roberts, James T. Van Kuren, Tim Smith, Jeffrey Bons, "Thrust Vectoring Design Project at Six Universities (Part II): Impact on Student Learning and Lessons Learned", ASME Turbo Expo, San Antonio, Texas, June 2013.

  31. M.P. Rumpfkeil "Optimization Under Mixed Aleatory/Epistemic Uncertainty Using Derivatives", 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Boston, Massachusetts, USA, April 2013. AIAA paper 2013-1754.

  32. M.P. Rumpfkeil "Optimization Under Uncertainty Using Derivatives and Kriging Surrogate Models", Seventh International Conference on Computational Fluid Dynamics (ICCFD7), Hawaii, USA, July 2012.

  33. M.P. Rumpfkeil "Optimization Under Uncertainty Using Gradients, Hessians, and Surrogate Models", 50th AIAA Aerospace Meeting and Exhibit, Nashville, Tennessee, USA, January 2012. AIAA paper 2012-149.

  34. M.P. Rumpfkeil, W. Yamazaki, and D.J. Mavriplis "A Dynamic Sampling Method for Kriging and Cokriging Surrogate Models", 49th AIAA Aerospace Meeting and Exhibit, Orlando, Florida, USA, January 2011. AIAA paper 2011-883.

  35. B.A. Lockwood, M. P. Rumpfkeil, W. Yamazaki and D. J. Mavriplis "Uncertainty Quantification in Viscous Hypersonic Flows using Gradient Information and Surrogate Modeling", 49th AIAA Aerospace Meeting and Exhibit, Orlando, Florida, USA, January 2011. AIAA paper 2011-885.

  36. M.P. Rumpfkeil, W. Yamazaki, and D.J. Mavriplis "Uncertainty Analysis Utilizing Gradient and Hessian Information", Sixth International Conference on Computational Fluid Dynamics (ICCFD6), St. Petersburg, Russia, July 2010.

  37. W. Yamazaki, M.P. Rumpfkeil, and D.J. Mavriplis "Design Optimization Utilizing Gradient/Hessian Enhanced Surrogate Model", 28th AIAA Applied Aerodynamics Conference, Chicago, Illinois, USA, June 2010. AIAA paper 2010-4363.

  38. M. P. Rumpfkeil and D.J. Mavriplis "Efficient Hessian Calculations using Automatic Differentiation and the Adjoint Method", 48th AIAA Aerospace Meeting and Exhibit, Orlando, Florida, USA, January 2010. AIAA paper 2010-1268.

  39. M. P. Rumpfkeil and D.W. Zingg "Far-Field Noise Minimization Using an Adjoint Approach", Fifth International Conference on Computational Fluid Dynamics (ICCFD5), Seoul, Korea, July 2008.

  40. M. P. Rumpfkeil and D.W. Zingg "Unsteady Optimization Using a Discrete Adjoint Approach Applied to Aeroacoustic Shape Design", 46th AIAA Aerospace Meeting and Exhibit, Reno, Nevada, USA, January 2008. AIAA paper 2008-18.

  41. M. P. Rumpfkeil and D.W. Zingg "Optimal Aeroacoustic Shape Design Using a Discrete Adjoint Approach". In Proceedings of the 15th Annual Conference of the CFD Society of Canada, Toronto, Ontario, May 2007. Paper 1106, pp. 221-228.

  42. M. P. Rumpfkeil and D.W. Zingg "The Remote Inverse Shape Design of Airfoils in Unsteady Flows". In Proceedings of the 12th Annual CASI Aerodynamics Symposium, Toronto, Ontario, April 2007. Paper 318.

  43. M. P. Rumpfkeil and D.W. Zingg "A General Framework for the Optimal Control of Unsteady Flows with Applications", 45th AIAA Aerospace Meeting and Exhibit, Reno, Nevada, USA, January 2007. AIAA paper 2007-1128.




My Ph.D. Thesis

Airfoil Optimization for Unsteady Flows with Application to High-Lift Noise Reduction (4.4MB)

Summary: The use of steady-state aerodynamic shape optimization methods in the field of computational fluid dynamics (CFD) is fairly well established. In particular, the use of adjoint methods has proven to be very beneficial because their computational cost is independent of the number of shape design variables. However, the application of optimization methods to airframe-generated noise has not received as much attention even though airframe-generated noise competes nowadays with engine noise as modern engines have become significantly more quiet. This is especially true during aircraft approach and landing, when engines operate at reduced thrust, and airframe components such as high-lift devices are in a deployed state.

In this thesis, a general framework is developed to calculate the gradient of a cost function in a nonlinear unsteady flow environment using the adjoint method. The flow is governed by the unsteady two-dimensional compressible Navier-Stokes equations (URANS) in conjunction with a one-equation turbulence model. The unsteady optimization algorithm developed in this work utilizes a Newton-Krylov approach: Newton’s method is applied to solve the nonlinear flow problem; the gradient-based optimizer uses the quasi-Newton method BFGS; and the Krylov subspace method solvers GMRES and Bi-CGSTAB are used to solve the resulting linear forward and adjoint problems, respectively. The efficacy of the unsteady optimization algorithm is demonstrated by applying it to several problems of interest including shocktubes, pulses in converging-diverging nozzles, rotating cylinders, transonic buffeting, and an unsteady trailing-edge flow.

Finally, to calculate the radiated far-field noise, an acoustic wave propagation program based on the Ffowcs Williams and Hawkings (FW-H) formulation is implemented and validated. The general framework is then used to derive the adjoint equations for a novel hybrid URANS/FW-H optimization algorithm in order to optimize the shape of airfoils based on their calculated far-field noise. Validation and application results for this novel hybrid URANS/FW-H optimization algorithm show that it is possible to optimize the shape of an airfoil in an unsteady flow environment to minimize its radiated far-field noise while maintaining good aerodynamic performance.