scholarly journals Shape Optimization of a Wooden Baseball Bat Using Parametric Modeling and Genetic Algorithms

AI ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 381-393
Author(s):  
Mohammad Sadegh Mazloomi ◽  
Philip D. Evans
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Parametric Modeling ◽  
Performance Criteria ◽  
Optimization Approach ◽  
Modeling And Optimization ◽  
Nodal Points ◽  
Subtractive Manufacturing ◽  
Baseball Bats

Baseball is a popular and very lucrative bat-and-ball sport that uses a wooden bat to score runs. We hypothesize that new design features for baseball bats will emerge from their shape optimization using parametric modeling and genetic algorithms. We converge the location of two points on bats made from maple (Acer sp.) and ash (Fraxinus sp.) wood that are associated with increased velocity of a ball rebounding off a bat: vibrational nodal points and the center of percussion (COP). Our modeling and optimization approach was able to reduce the distance between the nodal points and COP from 166.0 mm to 52.1 mm. This change was similar in both wood species and resulted from changes to the geometry of the bat, specifically shifting of the mass of the bat toward the center of the barrel and removing mass from the very end of the barrel. We conclude that the combination of parametric finite element modeling and optimization using genetic algorithms is a powerful tool for exploring virtual designs for baseball bats that are based on performance criteria and suggest that our designs could be realized in practice using subtractive manufacturing technology.

scholarly journals Reliable Shape Optimization of Structures Subjected to Transient Dynamic Loading Using Genetic Algorithms

2005 ◽  
Vol 12 (6) ◽  
pp. 407-424 ◽  
Author(s):  
Sabyasachi Chand ◽  
Anjan Dutta
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Analysis Tool ◽  
Time Step ◽  
Transient Dynamic ◽  
Boundary Definition ◽  
Automatic Mesh ◽  
Shape Changes

This paper presents a reliable method of solution of two dimensional shape optimization problems subjected to transient dynamic loads using Genetic Algorithms. Boundary curves undergoing shape changes have been represented by B-splines. Automatic mesh generation and adaptive finite element analysis modules are integrated with Genetic algorithm code to carry out the shape optimization. Both space and time discretization errors are evaluated and appropriate finite element mesh and time step values as obtained iteratively are adopted for accurate dynamic response. Two demonstration problems have been solved, which show convergence to the optimal solution with number of generations. The boundary curve undergoing shape optimization shows smooth shape changes. The combinations of automatic mesh generator with proper boundary definition capabilities, analysis tool with error estimation and Genetic algorithm as optimization engine have been observed to behave as a satisfactory shape optimization environment to deal with real engineering problems.

New approach to constrained shape optimization using genetic algorithms

AIAA Journal ◽  
1998 ◽  
Vol 36 ◽  
pp. 51-61 ◽  
Author(s):  
M. C. Sharatchandra ◽  
Mihir Sen ◽  
Mohamed Gad-el-Hak
Keyword(s):  
Genetic Algorithms ◽  
Shape Optimization ◽  
New Approach

scholarly journals μ-Synthesis for Fractional-Order Robust Controllers

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 911
Author(s):  
Vlad Mihaly ◽  
Mircea Şuşcă ◽  
Dora Morar ◽  
Mihai Stănese ◽  
Petru Dobra
Keyword(s):  
Control Problem ◽  
Fractional Order ◽  
Design Procedure ◽  
High Order ◽  
Numerical Control ◽  
Performance Criteria ◽  
Iteration Algorithm ◽  
Optimization Approach ◽  
Cnc Machine ◽  
Bee Colony

The current article presents a design procedure for obtaining robust multiple-input and multiple-output (MIMO) fractional-order controllers using a μ-synthesis design procedure with D–K iteration. μ-synthesis uses the generalized Robust Control framework in order to find a controller which meets the stability and performance criteria for a family of plants. Because this control problem is NP-hard, it is usually solved using an approximation, the most common being the D–K iteration algorithm, but, this approximation leads to high-order controllers, which are not practically feasible. If a desired structure is imposed to the controller, the corresponding K step is a non-convex problem. The novelty of the paper consists in an artificial bee colony swarm optimization approach to compute the nearly optimal controller parameters. Further, a mixed-sensitivity μ-synthesis control problem is solved with the proposed approach for a two-axis Computer Numerical Control (CNC) machine benchmark problem. The resulting controller using the described algorithm manages to ensure, with mathematical guarantee, both robust stability and robust performance, while the high-order controller obtained with the classical μ-synthesis approach in MATLAB does not offer this.

Shape and parameter reconstruction for the Robin transmission inverse problem

2016 ◽  
Vol 24 (6) ◽  
Author(s):  
Antoine Laurain ◽  
Houcine Meftahi
Keyword(s):  
Inverse Problem ◽  
Shape Optimization ◽  
Saddle Point Problem ◽  
Point Problem ◽  
Optimization Approach ◽  
Reconstruction Method ◽  
Piecewise Constant ◽  
Piecewise Constant Conductivity ◽  
Level Set Approach ◽  
Parameter Reconstruction

AbstractIn this paper we consider the inverse problem of simultaneously reconstructing the interface where the jump of the conductivity occurs and the Robin parameter for a transmission problem with piecewise constant conductivity and Robin-type transmission conditions on the interface. We propose a reconstruction method based on a shape optimization approach and compare the results obtained using two different types of shape functionals. The reformulation of the shape optimization problem as a suitable saddle point problem allows us to obtain the optimality conditions by using differentiability properties of the min-sup combined with a function space parameterization technique. The reconstruction is then performed by means of an iterative algorithm based on a conjugate shape gradient method combined with a level set approach. To conclude we give and discuss several numerical examples.

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