Stanford UniversityDepartment of Aeronautics & AstronauticsAerospace Computing Laboratory

Kaveh Hosseini

149 Commonwealth Drive
Menlo Park, CA 94025
Phone: (650) 688-6909
Fax: (650) 328-2990
Mobile: (650) 353-1015
E-mail: K a v i a t o r [a t] g m a i l [d o t] c o m


Department of Aeronautics and Astronautics, Stanford University (1998-2005)

Master of Science
Department of Aeronautics and Astronautics, Stanford University (1997-1998)

Business Administration Degree
Institut d'Administration des Entreprises (IAE), Toulouse, France (1996-1997)

Mechanical and Aeronautical Engineer Degree
Ecole Nationale Supérieure de Mécanique et d'Aérotechnique (ENSMA), Poitiers, France (1993-1996)

Research Interests

Local matrix preconditioning of the Euler and Navier-Stokes equations

Local matrix preconditioning can help the convergence acceleration of time-marching methods in solving the systems of Euler and Navier-Stokes equations by improving the condition number of the continuous system of equations and by clustering the eigenvalues of the discretized system in regions of high damping and propagative efficiency. Preconditioning can also preserve the accuracy of the solution for low Mach numbers. Various preconditioners such as the Block-Jacobi, the van Leer-Lee-Roe or the Turkel preconditionner are being investigated in order to combine their advantages and to extend the benefits of Euler preconditionners to the Navier-Stokes case.

Optimization of multistage coefficients for explicit multigrid flow solvers

Explicit Euler and Navier-Stokes flow solvers based on multigrid schemes combined with modified Runge-Kutta multistage methods have become very popular due to their efficiency and ease of implementation. An appropriate choice of multistage coefficients allows for high damping and propagative efficiencies in order to accelerate convergence to the steady-state solution. It is possible to optimize the damping and propagative efficiencies of modified Runge-Kutta multistage methods by expressing the problem within the framework of constrained non-linear optimization. The optimization process must be done in a systematic way for a variety of objective functions and constraints in order to assess the roles of both propagation and damping and thus find the coefficients that maximize convergence. Such coefficients being different for a given set of Mach number, flow angle and aspect ratio, it is possible to do an adaptive multistaging, i.e. to use a different set of optimized coefficients for each computational cell.


K. Hosseini, "Practical Implementation of Robust Preconditioners for Optimized Multistage Flow Solvers", Ph.D. thesis, Stanford University, June 2005. (The first link has margins for one-sided printing. Here is the version with margins suitable for two-sided printing).

S. Kim, K. Hosseini, K. Leoviriyakit, and A. Jameson, "Enhancement of Adjoint Design Methods via Optimization of Adjoint Parameters", 43rd AIAA Aerospace Sciences Meeting & Exhibit, AIAA Paper 2005-0448, Reno, NV, January 10-13, 2005.

K. Hosseini and J. Alonso, "Practical Implementation and Improvement of Preconditioning Methods for Explicit Multistage Flow Solvers", 42nd AIAA Aerospace Sciences Meeting & Exhibit, AIAA Paper 2004-0763, Reno, NV, January 5-8, 2004.

K. Hosseini and J. Alonso, "Optimization of Multistage Coefficients for Preconditioned Explicit Multigrid Flow Solvers", 16th AIAA Computational Fluid Dynamics Conference, AIAA Paper 2003-3705, Orlando, FL, June 23-26, 2003.


Presentation Slides

Squared Preconditioning and Optimized Multistaging for Explicit Multistage Flow Solvers, California Institute of Technology, September 2004

Last Modified:  March 10, 2005