Shape Optimization of Busemann-Type Biplane Airfoil for Drag Reduction Under Non-Lifting and Lifting Conditions Using Genetic Algorithms

The focus of this chapter is on the shape optimization of the Busemann-type biplane airfoil for drag reduction under both non-lifting and lifting conditions using genetic algorithms. The concept of biplane airfoil was first introduced by Adolf Busemann in 1935. Under design conditions at a specific supersonic flow speed, the Busemann biplane airfoil eliminates all wave drag due to its symmetrical biplane configuration; however, it produces zero lift. Previous research has shown that the original Busemann biplane airfoil shows a poor performance under off-design conditions. In order to address this problem of zero lift and to improve the off-design-condition performance, shape optimization of an asymmetric biplane airfoil is performed. The commercially available computational fluid dynamics (CFD) solver ANSYS FLUENT is employed for computing the inviscid supersonic flow past the biplane airfoil. A single-objective genetic algorithm (SOGA) is employed for shape optimization under the non-lifting condition to minimize the drag, and a multi-objective genetic algorithm (MOGA) is used for shape optimization under the lifting condition to maximize both the lift and the lift-to-drag ratio. The results obtained from both SOGA and MOGA show a significant improvement in the design and off-design-condition performance of the optimized Busemann biplane airfoil compared to the original airfoil

Shape Optimization of Airfoils Without and With Ground Effect Using a Multi-Objective Genetic Algorithm

The focus of this thesis is on shape optimization using a genetic algorithm. A multi-objective genetic algorithm (MOGA) is employed to optimize the shape of an airfoil to improve its lift and drag characteristics, in particular to achieve two objectives simultaneously that is to increase its lift as well as its lift to drag ratio. The commercially available software FLUENT is employed to calculate the flow field on an adaptive structured mesh, which is generated by the commercial mesh generating software ICEM. The flow field is calculated using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a two equation k-ω SST turbulence model. Bezier Curves are employed to generate airfoil shapes for a particular generation of the genetic algorithm; these shapes are tested by MOGA in conjunction with FLUENT to evaluate their fitness by calculating their lift and lift to drag ratio. The process is continued for a number of generations until the lift and lift to drag ratios converge to their optimal values. MOGA optimization method is used to optimize a well-known wind turbine airfoil S809 and NACA 4412 airfoil in ground effect. The results show significant improvement in both the lift coefficient and lift-to-drag ratio of the optimized airfoil compared to the original airfoil

Shape Optimization of Busemann-Type Biplane Airfoil for Drag Reduction under Nonlifting and Lifting Conditions Using Genetic Algorithms

The focus of this thesis is on the shape optimization of the Busemann-type biplane airfoil for drag reduction under both nonlifting and lifting conditions using genetic algorithms. The concept of the Busemann-type biplane airfoil was first introduced by Adolf Busemann in 1935. Under its design condition at a specific supersonic flow speed, the Busemann biplane airfoil eliminates all wave drag due to its symmetrical biplane configuration; however it produces zero lift. Previous research has shown that the original Busemann biplane airfoil design has a poor performance under off-design conditions as well. In order to solve this problem of zero lift and to improve the off-design-condition performance of the biplane airfoil, shape optimization of the asymmetric biplane airfoil is performed to minimize the drag while maximizing the lift. In this thesis, the commercially available CFD solver ANSYS FLUENT is employed for computing the inviscid flow past the biplane airfoil. An unstructured mesh is generated using ICEM software. A second-order accurate steady density-based solver is employed to compute the supersonic flow field. A single-objective genetic algorithm (SOGA) is employed to optimize the Busemann biplane airfoil shape under nonlifting condition to minimize the drag coefficient and a multi-objective genetic algorithm (MOGA) is employed to optimize the Busemann biplane airfoil shape under lifting condition to maximize both the lift coefficient and the lift to drag ratio simultaneously. Both results obtained by using SOGA and MOGA show significant improvement in the design and off-design-condition performance of the optimized Busemann biplane airfoil compared to the original one

Influence of selected turbulence model on the optimization of a class-shape transformation parameterized airfoil

An airfoil was parameterized using the class-shape transformation technique and then optimized via genetic algorithm. The aerodynamic characteristics of the airfoil were obtained with the use of a CFD software. The automated numerical technique was validated using available experimental data and then the optimization procedure was repeated for few different turbulence models. The obtained optimized airfoils were then compared in order to gain some insight on the influence of the different turbulence models on the optimization result

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

Appliction of nontraditional optimization techniques for airfoil shape optimization

The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer

VAWT optimization using genetic algorithm and CST airfoil parameterization

Vetroturbina sa vertikalnom osom Dareiusovog tipa optimizovana je primenom genetskih algoritama (GA). Oblik aeroprofila parametrizovan je pomoću Klasa-Oblik transformacionog (CST) metoda. Metod dvojne višestrujne cevi sa Gormont modifikacijom za dinamički slom uzgona je kori-ćen za određivanje performansi vetroturbine sa vertikalnom osom. Kad su numerički kodovi validirani sa dostupnim eksperimentalnim rezultatima, parametri aeroprofila su varirani kako bi se postigla optimalna vrednost funkcije cilja genetskog algoritma. PR Projekat Ministarstva nauke Republike Srbije, br. 35035.Darrieus type vertical axis wind turbine (VAWT) is optimized using the genetic algorithm (GA). The airfoil shape is parameterized using the Class-Shape Transformation (CST) method. The double multiple stream tube (DMST) method with the Gormont dynamic stall modification is used for the calculation of the VAWT performance parameters. Once the numerical codes are validated using available experimental results, the airfoil parameters are varied as to achieve the optimum value of the genetic algorithm fitness function

Study of the development and verification of an integrated code for UAV design

L'objectiu d'aquest estudi és desenvolupar una eina de disseny d'aeronaus utilitzant algoritmes d'optimització per a facilitar el procés. Es pretén incorporar el codi d'estudi i simulació de les actuacions d'un UAV desenvolupat per l'equip Trencalòs Team en un software de disseny aerodinàmic ja existent, ja sigui XFLR5 o AVL. Les funcions objectiu incorporades seran les que l'equip considera per a la participació en el concurs internacional Air Cargo Challenge, amb la intenció de desenvolupar una eina de treball per a Trencalòs que permeti fer un disseny òptim dins del marc de la competició. El treball es dividirà en tres etapes: 1. Incorporació del codi desenvolupat per Trencalòs al software de disseny aerodinàmic2. Fer ús dels algoritmes d'optimització de funcions objectiu per a facilitar el procés de disseny3. Verificació els resultats obtinguts.