CFD Analysis and Shape Optimization of Airfoils Using Class Shape Transformation and Genetic Algorithm—Part I

Abstract

This paper presents the parameterization and optimization of two well-known airfoils. The aerodynamic shape optimization investigation includes the subsonic (NREL S-821) and transonic airfoils (RAE-2822). The class shape transformation is employed for parametrization while the genetic algorithm is used for optimization purposes. The absolute scheme of the optimization process is carried out for the minimization of the drag coefficient and maximization of lift to drag ratio. In-house MATLAB code is incorporated with a genetic algorithm to calculate the drag coefficient and lift to drag ratio of the resulting optimized airfoil. The panel method is utilized in genetic algorithm optimization code to calculate pressure distribution, lift coefficient, and lift to drag ratio for optimized airfoil shapes and validates with XFOIL and NREL experimental data. Furthermore, CFD analysis is conducted for both the original (NREL S-821) and optimized airfoil obtained. The present method shows that the optimized airfoil achieved an improvement in lift to drag ratio by 7.4% and 15.9% of S-821 and RAE-2822 airfoil, respectively, by the panel technique method and provides high design desirable stability parameters. These features significantly improve the overall aerodynamic performance of the newly optimized airfoils. Finally, the improved aerodynamics results are reported for the design of turbulence modeling and NREL phase II, Phase III, and Phase VI HAWT blades.

Design and optimization of new energy storage systems for energy transformation

“…Firstly, for thermal networks containing internal combustion engines, waste heat boilers, gas boilers, and thermal loads, during the sampling period 0 t The initial measurement values of the output and heat load of internal combustion engines, waste heat boilers, and gas boilers are used as the initial state, and the power state vector is established as follows: (10) In the equation: T  Is the scheduling duration;  …”

Airfoil Optimization using Design-by-Morphing

“…For the reader's convenience, it is noted that a design variable vector ŵ is Pareto-dominated by another design variable vector w if 𝑓 𝑘 ( ŵ) ≤ 𝑓 𝑘 ( w) for all To obtain the Pareto-front, especially when objectives cannot be weighted or when a non-convex black-box function is considered, evolutionary or genetic algorithms are a natural choice [70,76]. In fact, they have been commonly implemented in many previous aerodynamic optimization studies due to their gradient-free nature and wide region of the search domain [31,[77][78][79]. On the other hand, when the cost functions are expensive to compute (e.g.…”

“…One typical way of determining the basis modes is through proper orthogonal decomposition (POD) of a set of airfoil data, and dimensionality can be reduced by using only the dominant modes [27,28]. Other methods include the Hicks-Henne's approach [29], which uses a linear combination of sine functions to deform the airfoil surface, and class/shape function transformation (CST) method proposed by Kulfan [30,31], which represents an airfoil shape as the product of a class function and a shape function formed by a linear combination of Bernstein polynomials. Similar to all the other methods on the spectrum, in order to resemble high-fidelity features, more basis modes have to be included, which again falls into the so-called the curse of dimensionality.…”

Optimization of Photovoltaic Panel Array Configurations to Reduce Lift Force Using Genetic Algorithm and CFD

“…Meerimatha et al [21] investigated the effect of partial shading on the power output by reconfiguring PV arrays using a Genetic algorithm. Rajan et al [22] studied PV array reconfiguration using the concept of standard deviation and GA. Akram et al [23] concluded that the GA and Class Shape Transformation techniques couldbe very efficient for optimizingairfoil shapes. Using these techniques, a reduction in drag coefficients of 10% and 12% was achieved, while the lift-to-drag ratios improved by 7.4% and 15.9% for two different airfoils.…”