Modeling and optimization method of an indirectly irradiated solar receiver

Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been developed for solving many-objective truss optimization issues, it is important to assess modern algorithms performance on these issues to develop more effective techniques in the future. Therefore, this study contributes by investigating the comparative performance of eighteen well-established algorithms, in various dimensions, using four metrics for solving challenging truss problems with many objectives. The statistical analysis is performed based on the objective function best mean and standard deviation outcomes, and Friedman's rank test. MMIPDE is the best algorithm as per the overall comparison, while SHAMODE with whale optimisation approach and SHAMODE are the runners-up.•A comparative test to measure the efficiency of eighteen state-of-the-practice methods is performed.•Small to large-scale truss design challenges are proposed for the validation.•The performance is measured using four metrics and Friedman's rank test.

Demand factor definition—A dimensionless parameter for Vertical Axis Wind Turbines

The use of dimensionless numbers like Reynolds Number, Froude Number and Webber Number has historically simplified the process of comparison of phenomena irrespective of their scales and in their classification into different categories. This paper deals with the derivational aspects of a dimensionless parameter named “Demand Factor” for optimization of Vertical Axis Wind Turbine (VAWT).

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The input parameters considered in this derivation are power, wind velocity, the aspect ratio of the turbine, density of air and viscosity of air and the output parameters are length of the blade, number of blades, chord length, aerofoil shape, radius of the turbine and angular velocity at rated speed.

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Four rounds of variable definition trials are carried out through the arrangement of the input parameters on the numerator and denominator positions. With the filtering out of unsuitable combinations at different stages of elimination, out of 32 combinations the expression that holds the potential to represent demand factor was identified. The process of carrying out single point optimization based on Demand factor expression is discussed along with the steps involved in numerically calculating output parameters.

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The expression of Demand factor developed provides a different perspective on the process of design and optimization of VAWTs.