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
  • Adjunct 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

Research Interests

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.

Specialties

Computational Mechanics
Fluid Mechanics
Acoustics

Representative Publications

  • Kim, D; El Gharamti, I; Hantouche, M; Elwardany, AE; Farooq, A; Bisetti, F; Knio, O, A hierarchical method for Bayesian inference of rate parameters from shock tube data: Application to the study of the reaction of hydroxyl with 2-methylfuran, Combustion and Flame, vol 184 (2017), pp. 55-67 [10.1016/j.combustflame.2017.06.002] [abs].
  • Siripatana, A; Mayo, T; Sraj, I; Knio, O; Dawson, C; Le Maitre, O; Hoteit, I, Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC, Ocean Dynamics, vol 67 no. 8 (2017), pp. 1067-1094 [10.1007/s10236-017-1074-z] [abs].
  • Giraldi, L; Le Maître, OP; Mandli, KT; Dawson, CN; Hoteit, I; Knio, OM, Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate, Computational Geosciences, vol 21 no. 4 (2017), pp. 683-699 [10.1007/s10596-017-9646-z] [abs].
  • Mycek, P; Contreras, A; Le Maître, O; Sargsyan, K; Rizzi, F; Morris, K; Safta, C; Debusschere, B; Knio, O, A resilient domain decomposition polynomial chaos solver for uncertain elliptic PDEs, Computer Physics Communications, vol 216 (2017), pp. 18-34 [10.1016/j.cpc.2017.02.015] [abs].
  • Toye, H; Zhan, P; Gopalakrishnan, G; Kartadikaria, AR; Huang, H; Knio, O; Hoteit, I, Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing, Ocean Dynamics, vol 67 no. 7 (2017), pp. 915-933 [10.1007/s10236-017-1064-1] [abs].