Airfoil shape optimization using improved Multiobjective Territorial Particle Swarm algorithm with the objective of improving stall characteristics

Nejat, Amir; Mirzabeygi, Pooya; Panahi, Masoud Shariat

doi:10.1007/s00158-013-1025-3

Cited by 22 publications

(6 citation statements)

References 39 publications

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“…Morris et al (2014), Nejat et al (2014), and Su et al (2015) have showed the shape of curves for different values of N 1 and N 2 . They also claimed that when describing an airfoil, the values we normally use are N 1 =0.5 and N 2 =1.…”

Section: Parameterization Methodsmentioning

confidence: 91%

“…Non-gradient-based numerical optimization methods, such as genetic algorithms, are generally not as efficient as gradientbased methods. However, gradient-based methods require a design space free from discontinuity as the derivatives have to be recalculated as the search progresses (Nejat et al, 2014). Further, when a problem is multimodal, gradient-based methods will fail to find solutions beyond the local best result close to the start point.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…It has been modified as class/shape refinement transformation (CSRT) by Straathof and van Tooren (2012) to improve local matching ability, but the number of variables increases for that method. A numerical method has been applied to obtain information about the flow field around airfoils by Nejat et al (2014), but the calculation program used, namely XFOIL, can deal with only flow with no viscosity, and they failed to discuss the precision that was calculated; the numerical results often failed to compare with the experimental data. In the optimization process, an increase in the number of design variables will lead to enormous computational costs; thus, a surrogate model and a reasonable sampling method are required to replace the numerical process.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A study of airfoil parameterization, modeling, and optimization based on the computational fluid dynamics method

Zhang

Huang

Wang

et al. 2016

J. Zhejiang Univ. Sci. A

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An excellent airfoil with a high lift-to-drag ratio may decrease oil consumption and enhance the voyage. Based on NACA 0012, an improved airfoil is explored in this paper. The class/shape function transformation has been proved to be a good method for airfoil parameterization, and in this paper it is modified to improve imitation accuracy. The computational fluid dynamics method is applied to obtain numerically the aerodynamic parameters of the parameterized airfoil, and the result is proved credible by comparison with available experimental data in the open literature. A polynomial-based response surface model and the uniform Latin hypercube sampling method are employed to decrease computational cost. Finally, the nonlinear programming by quadratic Lagrangian method is utilized to modify the multi-island genetic algorithm, which has an improved optimization effect than the method used on its own. The obtained result shows that the modified class/shape function transformation method produces a better imitation of an airfoil in the nose and tail regions than the original method, and that it will satisfy the tolerance zone of the model in a wind tunnel. The response surface model based on the uniform Latin hypercube sampling method gives an accurate prediction of the lift-to-drag ratio with changes in the design variables. The numerical result of the flow around the airfoil shows reasonable agreement with the experimental data graphically and quantitatively. Ultimately, an airfoil with better capacity than the original one is acquired using the multi-island genetic algorithm based nonlinear programming by quadratic Lagrangian optimization method. The pressure contours and lift-to-drag ratio along with the attack angle have been compared with those of the original airfoil, and the results demonstrate the strength of the optimized airfoil. The process for exploring an improved airfoil through parameterization to optimization is worth referencing in future work.

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Section: Parameterization Methodsmentioning

confidence: 91%

Section: Optimization Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A study of airfoil parameterization, modeling, and optimization based on the computational fluid dynamics method

Zhang

Huang

Wang

et al. 2016

J. Zhejiang Univ. Sci. A

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“…For instance, Wang et al [27] introduced a multi-objective particle swarm optimization algorithm for bi-directional functionally graded plates. In [22], a modified Particle Swarm Optimization (PSO) method was presented to tackle a shape optimization issue within an aerodynamics context. Ge et al [11] proposed a multiobjective genetic algorithm to address shape design problem in tube bank modeling.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy particle swarm optimization method with application to shape design problem

El Yazidi,

Ellabib

2023

RAIRO-Oper. Res.

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In this study, we focus on a specific class of bilateral free boundaries problems. We approach this problem by formulating it as a shape optimization problem using a defined cost functional. The existence of an optimal solution for the optimization problem is proved. To tackle this problem, we propose an iterative approach that combines the particle swarm optimization and fuzzy logic methods. Additionally, we employ the finite element method as a discretization technique for the state equation. To validate our approaches, we investigate various types of domains. Furthermore, we compare the performance of these approaches in two scenarios: one with exact measurements used for identification and another with noisy measurements.

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“…So far, many notable approaches and applications have been proposed regarding multiobjective particle swarm optimization (MOPSO). For example, MOPSO's application for solving the problems of multi-objective optimization (Panda and Pani, 2016;Zhang et al, 2014), using running proximity for particle swarm optimization (PSO)-based multi-objective optimization (Nasir et al, 2012;Sheikholeslami and Navimipour, 2017;Yuguang et al, 2016), automatic clustering using multi-objective immunized PSO (Nanda and Panda, 2013;Sheikholeslami and Navimipour, 2017), a multi-objective chaotic PSO for environmental/ financial transmission (Cai et al, 2010;Zhang et al, 2012), a developed territorial particle swarm optimization algorithm (Nejat et al, 2014;Sheikholeslami and Navimipour, 2017), using throng, transition and authority for improving PSO-based multi-objective optimization (Sierra and Coello, 2005), multi-objective produce optimization of compound manufacturing using quantum-behaved PSO (Fang et al, 2017;Li et al, 2017;Liu et al, 2017;Omkar et al, 2009) and using strength Pareto evolutionary Algorithm-2 based MOPSO for designing electrical dispensation systems joining sectionalizing tie-lines and substitutes (Davins-Valldaura et al, 2017;Sheikholeslami and Navimipour, 2017). Also, a new IJPCC 16,2 algorithm, named fuzzy-based MOPSO has been proposed in (Zhang and Xing, 2010) to solve timecostquality tradeoff problem.…”

Section: Introductionmentioning

confidence: 99%

Tasks mapping in the network on a chip using an improved optimization algorithm

Darbandi¹,

Ramtin

Sharafi

2020

IJPCC

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Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

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Airfoil shape optimization using improved Multiobjective Territorial Particle Swarm algorithm with the objective of improving stall characteristics

Cited by 22 publications

References 39 publications

A study of airfoil parameterization, modeling, and optimization based on the computational fluid dynamics method

A study of airfoil parameterization, modeling, and optimization based on the computational fluid dynamics method

A fuzzy particle swarm optimization method with application to shape design problem

Tasks mapping in the network on a chip using an improved optimization algorithm

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