Multi-objective Optimisation of Turbomachinery Blades Using Tabu Search

Kipouros, Timoleon; Jaeggi, Daniel; Dawes, Bill; Parks, GT; Savill, A. M.

doi:10.1007/978-3-540-31880-4_62

Cited by 10 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…O NE of the great advantages that computational fluid dynamics (CFD) has over experimental methods is the ease with which automated optimization exercises can be conducted [1]. In these exercises, target output functions are maximized while compatibility with a given set of input parameters is maintained.…”

Section: Introductionmentioning

confidence: 99%

Perfectly Parallel Optimization for Cutback Trailing Edges

Watson

Tucker

2016

AIAA Journal

View full text Add to dashboard Cite

Previous attempts have been made to optimize the performance of film-cooling slots for cutback trailing edges, but these involved the use of steady calculation methods, which have been shown to be inappropriate for accurately capturing the behavior of this class of flows. Here, an unsteady method (large-eddy simulation on a coarse grid, or very large-eddy simulation) is used to compute the flow. To take advantage of the enormous parallel capacity of modern supercomputers and distributed computing nets, as well as the relatively low cost of very large-eddy simulation, while at the same time mitigating its lower scope for significant parallelization, a perfectly parallel evolutionary optimization process was undertaken. A relatively crude optimization target of maximizing the adiabatic wall film-cooling effectiveness averaged over the entire exposed cutback surface was used as a proof of concept. The optimizing heuristic then used an evolutionary approach to design a turbulator planform, subject to some imposed design restrictions. Six hundred large-eddy simulation type simulations were carried out over 12 generations, and the best performing designs from the last generation are examined. The optimized design showed a considerable improvement in the target metric over the previous experimental geometries. The influence of various geometric parameters on several of the metrics of film cooling is also explored by mining data from the populations generated over the course of the optimization. In a targeted optimization exercise, it is likely that these data could be used to steer the course of the evolution down favorable paths more quickly. Nomenclature= mass flow rate Q = conserved variable p = static pressure p 0 = stagnation pressure t = time U = fluid speed u, v, w = components of velocity γ = ratio of specific heats Δ = grid spacing Δx , Δy , Δz = nondimensional wall distance Δx = finite length scale η aw = adiabatic wall film-cooling effectiveness ρ = density τ R ij = residual stress tensor ϕ = convected scalar

show abstract

Section: Introductionmentioning

confidence: 99%

Perfectly Parallel Optimization for Cutback Trailing Edges

Watson

Tucker

2016

AIAA Journal

View full text Add to dashboard Cite

show abstract

“…The pitchline analysis and the throughflow calculation techniques with the Davidon-Fletcher-Powell minimization method are employed in the formulation of the multivariable single objective function. 3,4 Another work 5 combines a geometry parameterization scheme with a CFD simulation and a multiobjective tabu search optimization algorithm, in order to improve the performance of turbomachinery blades design. In Benini 6 an approach was developed for transonic compressor multiobjective design optimization and applied to the three-dimensional NASA rotor 37 shape.…”

Section: Introductionmentioning

confidence: 99%

Coupling of inverse method and cuckoo search algorithm for multiobjective optimization design of an axial flow pump

Chikh

Belaidi

Khelladi

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part A:

View full text Add to dashboard Cite

This work describes the application of a multiobjective cuckoo search method for turbomachinery design optimization of an axial pump. Maximization of the total efficiency and minimization of the required net positive suction head of the pump are the two objective functions considered for the optimization problem. The optimization process is carried out on a range of imposed volumetric flow rates, with taking into account at each discretized radius between the hub and tip of the rotor: the profile camber, rotor wall thickness, angular deviation, and the solidity, regarded as geometrical constraints and nominal flow rate as mechanical constraint. Two strategies are proposed in order to solve the problem. In the first one, three forms of mono-objective model with two variables, total efficiency and net positive suction head, are considered. In the second one, a multiobjective model with nondominated sorting scheme is adopted. A comparative evaluation of results obtained from the proposed approach with those of a reference machine and genetic algorithm allowed us to validate the present work.

show abstract

“…objective optimization to facilitate engineering decisions in the PDP of internal flow fluid/thermal components Kipouros, T., Jaeggi, D., Dawes, B., Parks, G., Savill, M. (2005). present a multiobjective method for optimizing the overall performance of turbo machinery blades.…”

mentioning

confidence: 99%

Multi-objective optimization based engineering decision tool

Shuttleworth¹

View full text Add to dashboard Cite

Prior to the acceptance of computer aided engineering (CAE) software in the product development process (PDP), product development was characterized by a designtest-redesign-test cycle. This activity was time consuming and resource intensive. As CAE software tools have been integrated into the PDP, the PDP can be characterized by a design-simulate-redesign-test cycle. The addition of CAE tools to the PDP has reduced the time to market and resource consumption. In the last decade, CAE software has become easier to use and computer power has increased such that CAE software is more widely used in the PDP. In parallel, there has been a desire, in the last decade, to further reduce product development times and resource consumption. To achieve this next step in reduction of PDP time and resource consumption, the need for increased integration of CAE software earlier in the PDP is needed. This will provide the design engineer with increased design problem knowledge earlier in the PDP, which is when increased knowledge about the design problem is most valuable in the PDP timeline and can impact the product design the most. Design problems are characterized by having multiple solutions. The implication of this is that there are multiple acceptable solutions but there are few global optimum solutions. It is the design engineer's chief aim to find the most optimum solution to the design problem at hand.

show abstract

Multi-objective Optimisation of Turbomachinery Blades Using Tabu Search

Cited by 10 publications

References 11 publications

Perfectly Parallel Optimization for Cutback Trailing Edges

Perfectly Parallel Optimization for Cutback Trailing Edges

Coupling of inverse method and cuckoo search algorithm for multiobjective optimization design of an axial flow pump

Multi-objective optimization based engineering decision tool

Contact Info

Product

Resources

About