Omar M. Knio

Omar Mohamad Knio

Edmund T. Pratt Jr. School Professor of Mechanical Engineering and Materials Science

My research interests encompass computational fluid mechanics, oceanic and atmospheric flows, turbulent flow, physical acoustics, chemically-reactive flow, energetic materials, microfluidic devices, dynamical systems, numerical methods, and asymptotic and stochastic techniques.

Appointments and Affiliations

  • Edmund T. Pratt Jr. School Professor of Mechanical Engineering and Materials Science
  • Professor in the Department of Mechanical Engineering and Materials Science
  • Professor in the Department of Civil and Environmental Engineering

Contact Information

  • Office Location: 144 Hudson Hall, Box 90300, Durham, NC 27708
  • Office Phone: (919) 660-5344
  • Email Address: omar.knio@duke.edu

Education

  • Ph.D. Massachusetts Institute of Technology, 1990

Specialties

Computational Mechanics
Fluid Mechanics
Acoustics

Representative Publications

  • Langodan, S; Viswanadhapalli, Y; Dasari, HP; Knio, O; Hoteit, I, A high-resolution assessment of wind and wave energy potentials in the Red Sea, Applied Energy, vol 181 (2016), pp. 244-255 [10.1016/j.apenergy.2016.08.076] [abs].
  • Bisetti, F; Kim, D; Knio, O; Long, Q; Tempone, R, Optimal Bayesian Experimental Design for Priors of Compact Support with Application to Shock-Tube Experiments for Combustion Kinetics, International Journal for Numerical Methods in Engineering, vol 108 no. 2 (2016), pp. 136-155 [10.1002/nme.5211] [abs].
  • Wang, T; Le Maître, OP; Hoteit, I; Knio, OM, Path planning in uncertain flow fields using ensemble method, Ocean Dynamics, vol 66 no. 10 (2016), pp. 1231-1251 [10.1007/s10236-016-0979-2] [abs].
  • Contreras, AA; Le Maître, OP; Aquino, W; Knio, OM, Multi-model polynomial chaos surrogate dictionary for Bayesian inference in elasticity problems, Probabilistic Engineering Mechanics, vol 46 (2016), pp. 107-119 [10.1016/j.probengmech.2016.08.004] [abs].
  • Winokur, J; Kim, D; Bisetti, F; Le Maître, OP; Knio, OM, Sparse Pseudo Spectral Projection Methods with Directional Adaptation for Uncertainty Quantification, Journal of Scientific Computing, vol 68 no. 2 (2016), pp. 596-623 [10.1007/s10915-015-0153-x] [abs].