Mixed Integer Optimization With NURBs HyPerModels

2007 ◽  
Author(s):  
Cameron J. Turner ◽  
Richard H. Crawford ◽  
Matthew I. Campbell
Keyword(s):  
Design Space ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Underlying Structure ◽  
Optimization Strategy ◽  
Integer Optimization ◽  
Design Variables ◽  
Complementary Approach ◽  
Integer Programming Problems ◽  
Optimum Solution

The challenge of determining the best design in a multimodal design space with multiple local optimal solutions often challenges the best available optimization techniques. By casting the objective function of the optimization problem in the form of a Non-Uniform Rational B-spline (NURBs) metamodel, known as a HyPerModel, significant optimization advantages can be achieved, including the ability to efficiently find the global metamodel optimum solution with less computational expense than traditional approaches. This optimization strategy, defined by the HyPerOp algorithm, uses the underlying structure of a HyPerModel to intelligently select starting points for optimization runs and to identify regions of the design space that do not contain locations for the global metamodel optimum location. This paper describes the application of the HyPerOp algorithm to mixed integer programming problems and demonstrates its use with two example applications. The algorithm works with design spaces composed of continuous and integer design variables and provides a complementary approach for improved optimization capabilities.

CFD Optimization Application on Airside Plate Fins of Condenser Coil of Gravity-Assisted Heat Pipe

2005 ◽  
Author(s):  
Jing Wang ◽  
Jianbing Wang ◽  
Liuyang Guo ◽  
Suili Wei ◽  
Dayong Hu
Keyword(s):  
Heat Pipe ◽  
Design Space ◽  
Flow Characteristics ◽  
Optimization Procedure ◽  
Mixed Integer ◽  
Optimal Size ◽  
Integer Optimization ◽  
Geometry Design ◽  
Design Variables ◽  
Fluid Flow Characteristics

The heat transfer and fluid flow characteristics of airside plate fins of the condenser coil of a gravity-assisted heat pipe are numerically predicted. Based on these CFD computations, the optimization of the airside structure of the heat pipe is carried out by studying the influence of different geometry design variables on a combined heat exchanger evaluation function K, which can be changed by an engineer according to his application experience. In this work, one hundred of DOE’s points are sampled across the design space of the heat pipe based on the Latin Hypercube method. The Least Squares Fitting method is used to fit these sampled points to the RSM of the design space. The Mixed Integer Optimization Algorithm is used to explore the maximum specially-defined K on the Response Surface; and the corresponding optimal size and shape of the heat pipe are finally obtained. The optimization procedure is performed automatically by employing the optimizer-Optimus, grid generator-Gambit, and CFD solver-Fluent.

L-System-Generated Topology Optimization of Compliant Mechanisms Using Graph-Based Interpretation

2018 ◽  
Author(s):  
Brent R. Bielefeldt ◽  
Darren J. Hartl ◽  
Ergun Akleman
Keyword(s):  
Topology Optimization ◽  
Design Space ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Level Set Methods ◽  
Optimization Techniques ◽  
L System ◽  
Design Variables ◽  
System L ◽  
Reconfigurable Structures

Traditional topology optimization techniques, such as density-based and level set methods, have proven successful in identifying potential design configurations but suffer from rapidly increasing design space dimensionality and convergence to local minima. A heuristic alternative to these approaches couples a genetic algorithm with a Lindenmayer System (L-System), which encodes design variables and governs the development of the structure when coupled with some sort of interpreter. This work discusses the development of a graph-based interpretation scheme referred to as Spatial Interpretation for the Development of Reconfigurable Structures (SPIDRS). This framework allows for the effective exploration of the design space using a limited number of design variables. The theory and implementation of this method are detailed, and a compliant mechanism case study is presented to demonstrate the ability of SPIDRS to generate structures capable of achieving multiple design goals.

L-System-Generated Mechanism Topology Optimization Using Graph-Based Interpretation

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Brent R. Bielefeldt ◽  
Ergun Akleman ◽  
Gregory W. Reich ◽  
Philip S. Beran ◽  
Darren J. Hartl
Keyword(s):  
Topology Optimization ◽  
Design Space ◽  
Level Set Methods ◽  
Optimization Techniques ◽  
L System ◽  
Design Variables ◽  
System L ◽  
Multiple Case ◽  
Multiple Case Studies ◽  
Reconfigurable Structures

Traditional topology optimization techniques, such as density-based and level set methods, have proven successful in identifying potential design configurations for structures and mechanisms but suffer from rapidly increasing design space dimensionality and the possibility of converging to local minima. A heuristic alternative to these approaches couples a genetic algorithm with a Lindenmayer system (L-system), which encodes design variables and governs the development of the structure when coupled with an interpreter to translate genomic information into structural topologies. This work discusses the development of a graph-based interpretation scheme referred to as spatial interpretation for the development of reconfigurable structures (SPIDRS). This framework allows for the effective exploration of mechanism design spaces using a limited number of design variables. The theory and implementation of this method are detailed, and multiple case studies are presented to demonstrate the ability of SPIDRS to generate adaptive structures capable of achieving multiple design goals.

Hybrid ANNs-GAs Strategy Based Constrained Optimization and Application in Sheet Metal Flanging Forming

2010 ◽  
Vol 20-23 ◽  
pp. 1522-1527
Author(s):  
Jin Jun Rao ◽  
Tong Yue Gao ◽  
Zhen Jiang ◽  
Zhen Bang Gong
Keyword(s):  
Sheet Metal ◽  
Optimal Solution ◽  
General Purpose ◽  
Strong Nonlinearity ◽  
Optimization Strategy ◽  
Function Generator ◽  
Design Variables ◽  
Comparison Approach ◽  
Searching Ability ◽  
Optimum Solution

In order to provide a general purpose method to search optimum solution for complex constrained engineering problems without explicit system model, a hybrid optimization strategy based on artificial neural networks (ANNs) and genetic algorithms (GAs) is proposed in this paper. This strategy combines the strong nonlinearity mapping abilities of ANNs and effective and robust evolutionary searching ability of GAs. Firstly, ANNs are utilized to model the un-known system using inputs and outputs of system. Then the direct comparison approach based improved GAs are employed to search optimal solution in the constrained design space, using the trained ANNs as the function generator of system outputs. This strategy is implemented in optimization of design variables for sheet metal flanging process. The verification results of numerical simulation and the experiments demonstrate the feasibility and effectiveness of the strategy.

An analytical approach for fantasy football draft and lineup management

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Adrian Becker ◽  
Xu Andy Sun
Keyword(s):  
National Football League ◽  
Mixed Integer ◽  
Trade Association ◽  
Optimization Strategy ◽  
Integer Optimization ◽  
Fantasy Sports ◽  
Fantasy Football ◽  
The Rich ◽  
Set Up ◽  
Mixed Integer Optimization Model

AbstractIn this paper, we consider fantasy football, an increasingly-popular online game based on the actual, on-the-field performances of players in the National Football League. It is estimated by the Fantasy Sports Trade Association that in 2011 there were 35 million people in the US and Canada playing fantasy sports online. About 85 percent of all fantasy sports participants play fantasy football, most of whom have their games set up in major media websites such as Yahoo!, ESPN, MSN, and NFL. Numerous websites specialize in reporting NFL games, providing preseason rankings, fantasy points projections, team and player statistics, and expert draft opinions. However, despite the vast popularity of the game, the intensive analysis by experts, and various online tools that offer prediction for the values of players, to the best of our knowledge, there is no method that provides a comprehensive optimization strategy for the entire Fantasy Football season. We set out to develop such a methodology that predicts team and player performance based on the rich historical data, and builds a mixed-integer optimization model using such predictions for the draft selection as well as weekly line-up management that incorporates the entire objective of winning a fantasy football season. Numerical tests of our model show promising performance.

Sign in / Sign up

Export Citation Format

Share Document