Estimating the Aerodynamic Coefficients of a Savonius Rotor Blade Profile Developed Through the Simplex Search Method

The Savonius rotor, a type of vertical-axis wind turbine, seems to be promising for small-scale power generation. Most of the studies conducted so far have focused on the evaluation of torque and power coefficients (CT, CP) of the rotor. This paper aims at analyzing the aerodynamic drag and lift coefficients (CD, CL) of a Savonius rotor blade profile that is generated by the simplex search method to maximize its CP. The optimization is carried out by coupling the numerical simulations with the simplex search method. The optimized blade profile thus obtained is symmetric about its axis, where one half is created through a natural cubic spline curve using three points. Two-dimensional (2D) unsteady numerical simulations have been conducted by adopting ANSYS FLUENT solver to examine the CD and CL of the optimized blade profile at an inlet air velocity of 7.30 m/s. The shear stress transport (SST) k-ω turbulence model is used to solve the transient Reynolds-averaged Navier-Stokes (RANS) equations. The aerodynamic analysis is performed over a range of tip speed ratios (TSRs). The total pressure, velocity magnitudes, and the turbulent intensity contours of the optimized blade profile are generated and studied at different angles of rotation. The CD and CL of the blade profile are investigated for a complete rotation with an increment of 1°. At TSR = 0.8, the optimized profile shows a CDmax of 1.91 at an angle of rotation of 54°, while CDmin is found to be 0.45 at an angle 147°.

A Knowledge-Informed Simplex Search Method Based on Historical Quasi-Gradient Estimations and Its Application on Quality Control of Medium Voltage Insulators

Quality control is of great significance for the economical manufacturing and reliable application of medium voltage insulators. With the increasingly stringent quality control requirement, traditional quality control methods in this field face a growing challenge on their efficiency. Therefore, this study aims to achieve quality specifications by optimizing process conditions with the least costs. Thus, a knowledge-informed simplex search method was proposed based on an idea of knowledge-informed optimization to enhance the optimization efficiency. Firstly, a new mathematical quantity, quasi-gradient estimation, was generated following a reconstruction of the simplex search from the essence and the development history of the method. Based on this quantity, the gradient-free method possessed the same gradient property and unified form as the gradient-based methods. Secondly, an implementation of the knowledge-informed simplex search method based on historical quasi-gradient estimations (short for GK-SS) was constructed. The GK-SS-based quality control method utilized the historical quasi-gradient estimations for each simplex generated during the optimization process to improve the method’s search directions’ accuracy in a statistical sense. Finally, this method was applied to the weight control of a kind of post insulator. The experimental simulation results showed that the method is effective and efficient in the quality control of medium voltage insulators.

Economic Power Dispatch using Hybrid Particle Swarm Optimization with Simplex Search Method

This paper presents the solution of economic power dispatch (EPD) in thermal power plants using the hybridization of particle swarm optimization (PSO) and simplex search method (SSM). EPD is obtaining the best generating schedule to supply the power demand and covering the transmission losses with minimum overall fuel cost. Physical constraints like valve point loading effects, ramp rate limits and prohibited operating zones are also included with basic EPD problem to increase the practicability in the problem. As PSO performs well in finding the global best solution and SSM in finding the local best solution, thus their combination improves the overall minimum results obtained for the generation fuel cost objective function. The performance of the proposed methodology is tested on different test systems having categories of small-scale, medium-scale and large-scale power system problems. The results obtained are then compared with other reported methods to show the superiority of the proposed algorithm.

Parametric optimization of anisogrid shells of irregular structure

The paper focuses on an approach to solving the problem of parametric optimization of anisogrid mesh shells with an irregular structure. Mesh structures are widely used in building and engineering. This study deals with the optimal design of such structures used in aerospace industry. The problem of optimal design of mesh structures is relevant, as it makes it possible to increase the efficiency of their use, minimizing the weight, provided the strength and stability conditions are met. In our work we formulate the problem of optimal design of mesh structures in general form, and introduce an optimization algorithm based on the simplex search method in which we use a partial predicate of a feasible region to describe the non-convex smooth areas of boundaries. The results of solving the optimization problem for a particular structure with a violation of the regularity of the rib structure are given. Findings of research show that the considered algorithm can be used for optimal design of both regular and non-regular mesh structures.

An Efficient Hybrid Evolution Strategy Algorithm with Direct Search Method for Global Optimization

The main purpose of this article is to demonstrate how evolution strategy optimizers can be improved by incorporating an efficient hybridization scheme with restart strategy in order to jump out of local solution regions. The authors propose a hybrid (μ, λ)ES-NM algorithm based on the Nelder-Mead (NM) simplex search method and evolution strategy algorithm (ES) for unconstrained optimization. At first, a modified NM, called Adaptive Nelder-Mead (ANM) is used that exhibits better properties than standard NM and self-adaptive evolution strategy algorithm is applied for better performance, in addition to a new contraction criterion is proposed in this work. (μ, λ)ES-NM is balancing between the global exploration of the evolution strategy algorithm and the deep exploitation of the Nelder-Mead method. The experiment results show the efficiency of the new algorithm and its ability to solve optimization problems in the performance of accuracy, robustness, and adaptability.