Robust airfoil optimization based on improved particle swarm optimization method

Wang, Yuan‐Yuan; Zhang, Binqian; Chen, Ying‐Chun

doi:10.1007/s10483-011-1497-x

Cited by 25 publications

(11 citation statements)

References 10 publications

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“…At first, the CVTs approach [17] is proposed to distribute original locations of the particles further dispersedly. Next, a second-order oscillating scheme [18] is exploited to update the locations and velocities of the particles. Further, an FDR item [17] is augmented to the velocity updating equation.…”

Section: Self-adaptive Pso Algorithmmentioning

confidence: 99%

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

2020

JNACS

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Generally, Multiple-Input Multiple-Output (MIMO) improves radio communication with enhanced reliability and capacity. The transmitter and receiver side, since there is an occurrence of multiple antennas, the appropriate Transmit Antenna Selection (TAS) for obtaining effectual performance is still a difficult point. In this work, a TAS method in the LTE system by exploiting Self-Adaptive Particle Swarm Optimization (SAPSO) is proposed for enhancing the system performance. Moreover, it develops self adaptiveness in the PSO by deciding the ability enhancement attained by each candidate solution for the TAS issue goes after by updating the particle solution on the basis of the enhancement. The experimentation model considers both Rician and Rayleigh channel, for four antenna configurations such as 2×2, 3×2, 4×2 and 4×4. To the next of the experimentation, it evaluates the performance of SAPSO-TAS with conventional Genetic Algorithm (GA)-TAS, Artificial Bee Colony (ABC)-TAS, Firefly (FF)-TAS, PSO-TAS, and Grey Wolf Optimization (GWO)-TAS models.

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Section: Self-adaptive Pso Algorithmmentioning

confidence: 99%

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

2020

JNACS

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“…The search process is initially employed in the integer domain using an improved particle swarm optimization (PSO) algorithm 17,18 to find an approximation of the initial guess, which would be used for subpixel registration. Since the radius of convergence of the inverse compositional Gauss-Newton algorithm (IC-GN) is within three pixels, 19 a swarm of particles is defined that are equally spaced within three pixels.…”

Section: Real-time Three-dimensional Digital Image Correlationmentioning

confidence: 99%

Real-time three-dimensional digital image correlation for biomedical applications

Wu¹,

Arola

et al. 2016

J. Biomed. Opt

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Digital image correlation (DIC) has been successfully applied for evaluating the mechanical behavior of biological tissues. A three-dimensional (3-D) DIC system has been developed and applied to examining the motion of bones in the human foot. To achieve accurate, real-time displacement measurements, an algorithm including matching between sequential images and image pairs has been developed. The system was used to monitor the movement of markers which were attached to a precisely motorized stage. The accuracy of the proposed technique for in-plane and out-of-plane measurements was found to be ?0.25% and 1.17%, respectively. Two biomedical applications were presented. In the experiment involving the foot arch, a human cadaver lower leg and foot specimen were subjected to vertical compressive loads up to 700 N at a rate of 10??N/s and the 3-D motions of bones in the foot were monitored in real time. In the experiment involving distal tibio fibular syndesmosis, a human cadaver lower leg and foot specimen were subjected to a monotonic rotational torque up to 5 Nm at a speed of 5 deg per min and the relative displacements of the tibia and fibula were monitored in real time. Results showed that the system could reach a frequency of up to 16 Hz with 6 points measured simultaneously. This technique sheds new lights on measuring 3-D motion of bones in biomechanical studies.

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“…23,24 To summarize, the design optimization of an airfoil needs to be improved by emphasizing on many uncertain factors. 12,25,26 An approach for probability-based robust design optimization of airfoils is presented in this article.…”

Section: Introductionmentioning

confidence: 99%

An effective approach for robust design optimization of wind turbine airfoils with random aerodynamic variables

Zhang

2019

Advances in Mechanical Engineering

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The robust design optimization of an airfoil needs to continuously realize the probability-based aerodynamic simulation for various combinations of geometry and wind climate parameters. The simulation time is lengthy when a full aerodynamic model is embedded for the numerical iteration. To this end, a second-order polynomial-based response surface model is first presented to relate the airfoil performance indicator with geometry and random aerodynamic variables. This allows to quickly evaluate the response moments and optimization constraints. Then, the robust design optimization is formulated to simultaneously maximize the mean aerodynamic performance and minimize the variance of design results due to the variation of geometry and aerodynamic parameters. The robust design optimization based on the NACA63418 and the DU93-W-210 airfoils with random Mach and Reynolds numbers is presented to demonstrate potential applications of this proposed model. Results have shown that the mean-value aerodynamic indicator is generally improved, whereas the variance is minimized to archive the robust design objective. The proposed approach is simple and accurate, suggesting an attractive tool for robust design optimization of airfoils with random aerodynamic variables.

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Robust airfoil optimization based on improved particle swarm optimization method

Cited by 25 publications

References 10 publications

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

Real-time three-dimensional digital image correlation for biomedical applications

An effective approach for robust design optimization of wind turbine airfoils with random aerodynamic variables

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