Evolutionary optimization techniques as versatile solvers for hard-to-converge problems in computational fluid dynamics

2006 ◽  
Vol 52 (3) ◽  
pp. 321-354 ◽  
Author(s):  
Raed I. Bourisli ◽  
Deborah A. Kaminski
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Evolutionary Optimization ◽  
Optimization Techniques ◽  
Evolutionary Optimization Techniques

scholarly journals Using Splitters to Control Secondary Flow

2017 ◽  
Vol 139 (09) ◽  
pp. 58-59
Author(s):  
C. Clark ◽  
G. Pullan
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Secondary Flow ◽  
Boundary Layers ◽  
Experimental Tests ◽  
Optimization Techniques ◽  
Automated Optimization

This article elaborates the concept of splitter vanes in controlling secondary flow. Secondary flow vortices are formed by the rotation of vorticity filaments, located in the endwall boundary layers, as the filaments move through the passage. The connection between the number of stators and the secondary kinetic energy suggests that the only way to significantly reduce the mixing loss is to increase the number of blades in the row. The designs evaluated were produced with fast turn-around computational fluid dynamics (10 minutes per solution) and automated optimization techniques. Experimental tests showed that the theory was correct, and that by increasing vane count, the secondary kinetic energy was reduced by up to 80%.

scholarly journals Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques

2019 ◽  
Vol 39 (2) ◽  
pp. 393-415
Author(s):  
Olurotimi A Dahunsi ◽  
Muhammed Dangor ◽  
Jimoh O Pedro ◽  
M Montaz Ali
Keyword(s):  
Particle Swarm Optimization ◽  
Random Search ◽  
Particle Swarm ◽  
Evolutionary Optimization ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Controlled Random Search ◽  
Evolutionary Optimization Techniques ◽  
Vehicle Suspension System

Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swarm optimization, modified particle swarm optimization and modified controlled random search. The most improved performance was achieved with the differential evolution algorithm. The modified particle swarm optimization and modified controlled random search algorithms performed better than their predecessors, with modified controlled random search performing better than modified particle swarm optimization in all aspects of performance investigated both in time and frequency domain analyses.

New Evolutionary Techniques for Optimization of Energy Systems Utilizing Computational Fluid Dynamics

2003 ◽  
Author(s):  
Douglas S. McCorkle ◽  
Kenneth M. Bryden
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Evolutionary Algorithms ◽  
Computational Cost ◽  
Solution Space ◽  
Optimization Techniques ◽  
Computational Time ◽  
Feed Forward Neural Network ◽  
Thermal Fluids ◽  
Fluid Systems

Optimization techniques that search a solution space without designer intervention are becoming important tools in the engineering design of many thermal fluid systems. Evolutionary algorithms are among the most robust of these optimization methods because the ability to optimize many designs simultaneously makes evolutionary algorithms less susceptible to premature convergence. However application of evolutionary algorithms to thermal and fluid systems described by high fidelity models (e.g. computational fluid dynamics) has been limited due to the high computational cost of the fitness evaluation. This paper presents a novel technique that combines two technologies used in the optimization of thermal fluids systems. The first is graph based evolutionary algorithms that are implemented to help increase the diversity of the evolving population of designs. The second is an algorithm utilizing a feed forward neural network that develops a stopping criterion for computational fluid dynamics solutions. This reduces the time required for each future evaluation in the evolutionary process and allows for more complex thermal fluids systems to be optimized. In the system examined here the overall reduction in computational time is approximately 8 times.

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