Freeform Deformation vs. B-Spline Representation in Inverse Airfoil Design

2006 ◽  
Author(s):  
Eleftherios I. Amoiralis ◽  
Ioannis K. Nikolos
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Flow Conditions ◽  
B Spline ◽  
De Algorithm ◽  
Spline Curves ◽  
Design Variables ◽  
Spline Representation ◽  
Freeform Deformation ◽  
Reference Pressure

In this work FFD technique is compared to the classical parameterization technique using B-Spline curves by performing inverse airfoil design tests, with a Differential Evolution (DE) algorithm to serve as the optimizer. The criteria of the comparison between the two techniques are the achieved accuracy in the approximation of the reference pressure distribution and the convergence behavior of the optimization algorithm. Experiments are presented, comparing FFD to B-Spline techniques under the same flow conditions, for various numbers of design variables.

Freeform Deformation Versus B-Spline Representation in Inverse Airfoil Design

2008 ◽  
Vol 8 (2) ◽  
Author(s):  
Eleftherios I. Amoiralis ◽  
Ioannis K. Nikolos
Keyword(s):  
Three Dimensional ◽  
Differential Evolution Algorithm ◽  
3D Animation ◽  
B Spline ◽  
Spline Curves ◽  
Design Variables ◽  
Evolution Algorithm ◽  
Freeform Deformation ◽  
Reference Pressure ◽  
Insight Into

Freeform deformation (FFD) is a well established technique for 3D animation applications, used to deform two—or three-dimensional geometrical entities. Over the past few years, FFD technique has aroused growing interest in several scientific communities. In this work, an extensive bibliographic survey of the FFD technique is initially introduced, in order to explore its capabilities in shape parametrization. Moreover, FFD technique is compared to the classical parametrization technique using B-spline curves, in the context of the airfoil design optimization problem, by performing inverse airfoil design tests, with a differential evolution algorithm to serve as the optimizer. The criterion of the comparison between the two techniques is the achieved accuracy in the approximation of the reference pressure distribution. Experiments are presented, comparing FFD to B-spline techniques under the same flow conditions, for various numbers of design variables. Sensitivity analysis is applied for providing further insight into the differences in the performance of the two techniques.

scholarly journals A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Lijin Wang ◽  
Yiwen Zhong ◽  
Yilong Yin ◽  
Wenting Zhao ◽  
Binqing Wang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Iteration Process ◽  
Global Optimum ◽  
Backtracking Search ◽  
De Algorithm ◽  
Search Optimization ◽  
Backtracking Search Optimization Algorithm ◽  
Mutation Strategies ◽  
Multimodal Problems

The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

Optimum design of rolling element bearings using a genetic algorithm—differential evolution (GA—DE) hybrid algorithm

2010 ◽  
Vol 225 (3) ◽  
pp. 714-721 ◽  
Author(s):  
W Y Lin
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimum Design ◽  
Dynamic Loads ◽  
Representative Case ◽  
De Algorithm ◽  
Design Variables ◽  
Deep Groove ◽  
Rolling Element

Binary-code genetic algorithms (BGA) have been used to obtain the optimum design for deep groove ball bearings, based on maximum fatigue life as an objective function. The problem has ten design variables and 20 constraint conditions. This method can find better basic dynamic loads rating than those listed in standard catalogues. However, the BGA algorithm requires a tremendous number of evaluations of the objective function per case to achieve convergence (e.g. about 5 200 000 for a representative case). To overcome this difficulty, a hybrid evolutionary algorithm by combining real-valued genetic algorithm (GA) with differential evolution (DE) is used together with the proper handling of constraints for this optimum design task. Findings show that the GA—DE algorithm can successfully find the better dynamic loads rating, about 1.3—11.1 per cent higher than those obtained using the traditional BGA. Moreover, the mean number of evaluations of the objective function required to achieve convergence is about 3011, using the GA—DE algorithm, as opposed to about 5 200 000 for a representative case using the BGA. Comparison shows the GA—DE algorithm to be much more effective and efficient than the BGA.

Parametric Design and Optimization of Sailing Yachts

1999 ◽  
Author(s):  
Stefan Harries ◽  
Claus Abt
Keyword(s):  
Geometric Modeling ◽  
Parametric Design ◽  
Generation Process ◽  
B Spline ◽  
Design And Optimization ◽  
Spline Curves ◽  
Design Variables ◽  
Hull Forms ◽  
High Level ◽  
Important Form

A new and flexible method for the geometric modeling of ship hull forms is presented. The underlying methodology is the parametric design of B-spline curves and surfaces. Important form parameters like displacement, center of buoyancy, waterplane area, center of flotation etc. are utilized as high-level descriptors of the intended shapes. Instead of interactively manipulating B-spline vertices, the generation process is viewed as a constrained optimization problem where fairness measures are applied as objective functions, vertices are treated as design variables and form parameters are preserved as equality constraints - making the approach a novelty in B­spline modeling. The new design methodology is discussed and mathematical principles are outlined. Examples are given to demonstrate the applicability of the parametric approach. They include the design of a 33ft IMS yacht with focus on the bare hull without rudder and keel.

scholarly journals Solving optimal reactive power problem by improved variable mesh optimization algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 279
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Iteration Process ◽  
Test System ◽  
Mesh Optimization ◽  
Local Optima ◽  
De Algorithm ◽  
Variable Mesh ◽  
Power Problem

<p>In this work Improved Variable Mesh Optimization Algorithm (IVM) has been applied to solve the optimal reactive power problem. Projected Improved VMO algorithm has been modeled by hybridization of Variable mesh optimization algorithm with Clearing-Based Niche Formation Technique, Differential Evolution (DE) algorithm. Mesh formation and exploration has been enhanced by the hybridization. Amongst of niche development process, clearing is a renowned method in which general denominator is the formation of steady subpopulations (niches) at all local optima (peaks) in the exploration space. In Differential Evolution (DE) population is formed by common sampling within the stipulated smallest amount and maximum bounds. Subsequently DE travel into the iteration process where the progressions like, mutation, crossover, and selection, are followed. Proposed Improved Variable Mesh Optimization Algorithm (IVM) has been tested in standard IEEE 14,300 bus test system and simulation<br />results show the projected algorithm reduced the real power loss extensively.</p>

scholarly journals A Novel Differential Evolution Invasive Weed Optimization Algorithm for Solving Nonlinear Equations Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Yongquan Zhou ◽  
Qifang Luo ◽  
Huan Chen
Keyword(s):  
Differential Evolution ◽  
Nonlinear Equations ◽  
Optimization Algorithm ◽  
Circle Packing ◽  
Differential Evolution Algorithm ◽  
Packing Problem ◽  
Population Diversity ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
De Algorithm

In view of the traditional numerical method to solve the nonlinear equations exist is sensitive to initial value and the higher accuracy of defects. This paper presents an invasive weed optimization (IWO) algorithm which has population diversity with the heuristic global search of differential evolution (DE) algorithm. In the iterative process, the global exploration ability of invasive weed optimization algorithm provides effective search area for differential evolution; at the same time, the heuristic search ability of differential evolution algorithm provides a reliable guide for invasive weed optimization. Based on the test of several typical nonlinear equations and a circle packing problem, the results show that the differential evolution invasive weed optimization (DEIWO) algorithm has a higher accuracy and speed of convergence, which is an efficient and feasible algorithm for solving nonlinear systems of equations.

scholarly journals Optimization of steel roof trusses using machine learning-assisted differential evolution

2021 ◽  
Vol 15 (4) ◽  
pp. 99-110
Author(s):  
Nguyen Tran Hieu ◽  
Nguyen Quoc Cuong ◽  
Vu Anh Tuan
Keyword(s):  
Differential Evolution ◽  
Computational Cost ◽  
Differential Evolution Algorithm ◽  
Truss Structures ◽  
De Algorithm ◽  
Design Variables ◽  
Evolution Algorithm ◽  
Structural Engineers ◽  
Steel Trusses ◽  
Steel Roof

A steel truss is a preferred solution in large-span roof structures due to its good attributes such as lightweight, durability. However, designing steel trusses is a challenging task for engineers due to a large number of design variables. Recently, optimization-based design approaches have demonstrated the great potential to effectively support structural engineers in finding the optimal designs of truss structures. This paper aims to use the AdaBoost-DE algorithm for optimizing steel roof trusses. The AdaBoost-DE employed in this study is a hybrid algorithm in which the AdaBoost classification technique is used to enhance the performance of the Differential Evolution algorithm by skipping unnecessary fitness evaluations during the optimization process. An example of a duo-pitch steel roof truss with a span of 24 meters is carried out. The result shows that the AdaBoost-DE achieves the same optimal design as the original DE algorithm, but reduces the computational cost by approximately 36%.

scholarly journals A Hybrid Global Optimization Algorithm Based on Wind Driven Optimization and Differential Evolution

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Zongfan Bao ◽  
Yongquan Zhou ◽  
Liangliang Li ◽  
Mingzhi Ma
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Global Optimization Algorithm ◽  
Population Evolution ◽  
De Algorithm ◽  
Information Sharing Mechanism ◽  
Difference Operation ◽  
Low Dimensional ◽  
Double Population

This paper presents a new hybrid global optimization algorithm, which is based on the wind driven optimization (WDO) and differential evolution (DE), named WDO-DE algorithm. The WDO-DE algorithm is based on a double population evolution strategy, the individuals in a population evolved by wind driven optimization algorithm, and a population of individuals evolved from difference operation. The populations of individuals both in WDO and DE employ an information sharing mechanism to implement coevolution. This paper chose fifteen benchmark functions to have a test. The experimental results show that the proposed algorithm can be feasible in both low-dimensional and high-dimensional cases. Compared to GA-PSO, WDO, DE, PSO, and BA algorithm, the convergence speed and precision of WDO-DE are higher. This hybridization showed a better optimization performance and robustness and significantly improves the original WDO algorithm.

Shape Optimization of Cam Profiles Using a B-Spline Representation

1989 ◽  
Vol 111 (2) ◽  
pp. 195-201 ◽  
Author(s):  
E. Sandgren ◽  
R. L. West
Keyword(s):  
Combustion Engine ◽  
Control Point ◽  
Radius Of Curvature ◽  
Flow Area ◽  
B Spline ◽  
Design Variables ◽  
Spline Representation ◽  
Cam Follower ◽  
Gradient Based ◽  
Acceleration Profile

An arbitrary acceleration profile for the cam follower acceleration is generated using a B-spline representation. The control point locations for the B-spline become the design variables in the nonlinear programming problem. The B-spline representation provides for local control of the acceleration profile which is required in order to generate reliable optimization results. Constraints are imposed in order to place appropriate limits on the contact stress, lift, duration, acceleration, jerk, radius of curvature, manufacturing requirements, and to avoid cam-follower separation. The objective function may take on a number of forms depending upon the design requirements. The optimization is carried out with a gradient based penalty function algorithm. A specific example is presented in which the flow area is maximized for an internal combustion engine.

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