Utilizing parallel optimization in computational fluid dynamics
| dc.contributor.advisor | Meade, Andrew J., Jr. | |
| dc.creator | Kokkolaras, Michael | |
| dc.date.accessioned | 2009-06-04T08:22:35Z | |
| dc.date.available | 2009-06-04T08:22:35Z | |
| dc.date.issued | 1998 | |
| dc.description.abstract | General problems of interest in computational fluid dynamics are investigated by means of optimization. Specifically, in the first part of the dissertation, a method of optimal incremental function approximation is developed for the adaptive solution of differential equations. Various concepts and ideas utilized by numerical techniques employed in computational mechanics and artificial neural networks (e.g. function approximation and error minimization, variational principles and weighted residuals, and adaptive grid optimization) are combined to formulate the proposed method. The basis functions and associated coefficients of a series expansion, representing the solution, are optimally selected by a parallel direct search technique at each step of the algorithm according to appropriate criteria; the solution is built sequentially. In this manner, the proposed method is adaptive in nature, although a grid is neither built nor adapted in the traditional sense using a-posteriori error estimates. Variational principles are utilized for the definition of the objective function to be extremized in the associated optimization problems, ensuring that the problem is well-posed. Complicated data structures and expensive remeshing algorithms and systems solvers are avoided. Computational efficiency is increased by using low-order basis functions and concurrent computing. Numerical results and convergence rates are reported for a range of steady-state problems, including linear and nonlinear differential equations associated with general boundary conditions, and illustrate the potential of the proposed method. Fluid dynamics applications are emphasized. Conclusions are drawn by discussing the method's limitations, advantages, and possible extensions. The second part of the dissertation is concerned with the optimization of the viscous-inviscid-interaction (VII) mechanism in an airfoil flow analysis code. The VII mechanism is based on the concept of a transpiration velocity boundary condition, whose convergence to steady state is accelerated. The number of variables in the associated optimization problem is reduced by means of function approximation concepts to ensure high number of parallel processors to number of necessary function evaluations ratio. Numerical results are presented for the NACA-0012 and the supercritical RAE-2822 airfoils subject to transonic flow conditions using a parallel direct search technique. They exhibit a satisfactory level of accuracy. Speed-up depends on the number of available computational units and increases for more challenging flow conditions and airfoil geometries. The enhanced code constitutes a useful tool for airfoil flow analysis and design and an acceptable alternative to computationally expensive high fidelity codes. | |
| dc.format.extent | 156 p. | en_US |
| dc.format.mimetype | application/pdf | |
| dc.identifier.callno | THESIS M.E. 1998 KOKKOLARAS | |
| dc.identifier.citation | Kokkolaras, Michael. "Utilizing parallel optimization in computational fluid dynamics." (1998) Diss., Rice University. <a href="https://hdl.handle.net/1911/19276">https://hdl.handle.net/1911/19276</a>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/19276 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | Aerospace engineering | |
| dc.subject | Mechanical engineering | |
| dc.title | Utilizing parallel optimization in computational fluid dynamics | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Mechanical Engineering | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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