Optimized Design of the Horn of Ultrasonic Roll Welding

2012 ◽  
Vol 482-484 ◽  
pp. 2223-2226 ◽  
Author(s):  
Kuen Ming Shu ◽  
Yu Guang Li ◽  
Chun Chi Chan ◽  
Jonq Bor Kuan
Keyword(s):  
Random Search ◽  
Optimal Solution ◽  
Optimization Method ◽  
Resonant Mode ◽  
Mode Frequency ◽  
Optimized Design ◽  
Solar Panels ◽  
Design Variables ◽  
Local Optimal Solution ◽  
The Impact

Previous studies on the amplitude horn only calculated sizes in consistent with the axial resonant mode frequency and disc bending resonant mode frequency without considering the overall stress and the amplitude of the disc’s outer ring. The resonant frequency of the amplitude horn cannot occur around 35 kHz. Such a design results in the inability to weld and may damage solar panels or lead to poor welding quality. Using the optimization method to address these problems, the proposed design process in this study is to conduct sensitivity analysis by the gradient method to understand the impact of design variables on the objective function for the selection of design variables. Then, this study applied the random search method to find out the feasible design of arrays to optimize the structure of two arrays closest to the design objective by the full factorial experiment method to ensure to get the global optimal solution rather than the local optimal solution. Finally, by design examples, this study used the sub-problem approximation method to search the optimized solution and compared the differences of the two methods, in order to confirm whether the objective of optimized design of amplitude horn had been achieved.

Optimization Method Based on a Mix Strategy

2013 ◽  
Vol 816-817 ◽  
pp. 1154-1157
Author(s):  
Xu Yin ◽  
Ai Min Ji
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Solution ◽  
Collaborative Optimization ◽  
Design Variables ◽  
Gradient Optimization ◽  
Local Optimal Solution ◽  
Optimal Values ◽  
Low Efficiency

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

2013 ◽  
Vol 389 ◽  
pp. 849-853
Author(s):  
Fang Song Cui ◽  
Wei Feng ◽  
Da Zhi Pan ◽  
Guo Zhong Cheng ◽  
Shuang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Volatility Model ◽  
Local Optimal Solution ◽  
The Impact ◽  
Improved Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

The Spindle Structural Optimization Design of HTC3250µn NC Machine Tool Based on ANSYS

2012 ◽  
Vol 457-458 ◽  
pp. 60-64 ◽  
Author(s):  
Hua Long Xie ◽  
Hui Min Guo ◽  
Qing Bao Wang ◽  
Yong Xian Liu
Keyword(s):  
Machine Tool ◽  
Optimization Design ◽  
Optimization Method ◽  
Optimized Design ◽  
Nc Machine Tool ◽  
Mathematical Mode ◽  
Design Variables ◽  
Before And After ◽  
Nc Machine ◽  
Important Significance

The optimization of spindle has important significance. The optimization method based on ANSYS is introduced and spindle mathematical mode of HTC3250µn NC machine tool is given. By scanning of design variables, the main optimized design variables are determined. The single objective and multi-objective optimizations are done. In the end, the main size comparison of spindle before and after optimization is given.

Innovative Turbine Stator Well Design Using a Kriging-Assisted Optimization Method

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Julien Pohl ◽  
Harvey M. Thompson ◽  
Ralf C. Schlaps ◽  
Shahrokh Shahpar ◽  
Vincenzo Fico ◽  
...  
Keyword(s):  
Engine Performance ◽  
Internal Flow ◽  
Optimization Method ◽  
Optimized Design ◽  
Element Analysis ◽  
Baseline Design ◽  
Turbine Stator ◽  
Air Temperatures ◽  
Design Variables ◽  
Deflector Plate

At present, it is a common practice to expose engine components to main annulus air temperatures exceeding the thermal material limit in order to increase the overall engine performance and to minimize the engine specific fuel consumption. To prevent overheating of the materials and thus the reduction of component life, an internal flow system is required to cool and protect the critical engine parts. Previous studies have shown that the insertion of a deflector plate in turbine cavities leads to a more effective use of reduced cooling air, since the coolant is fed more effectively into the disk boundary layer. This paper describes a flexible design parameterization of an engine representative turbine stator well geometry with stationary deflector plate and its implementation within an automated design optimization process using automatic meshing and steady-state computational fluid dynamics (CFD). Special attention and effort is turned to the flexibility of the parameterization method in order to reduce the number of design variables to a minimum on the one hand, but increasing the design space flexibility and generality on the other. Finally, the optimized design is evaluated using a previously validated conjugate heat transfer method (by coupling a finite element analysis (FEA) to CFD) and compared against both the nonoptimized deflector design and a reference baseline design without a deflector plate.

Reduction in the aerodynamic drag around a generic vehicle by using a non-smooth surface

2016 ◽  
Vol 231 (1) ◽  
pp. 130-144 ◽  
Author(s):  
Yiping Wang ◽  
Cheng Wu ◽  
Gangfeng Tan ◽  
Yadong Deng
Keyword(s):  
Drag Reduction ◽  
Drag Coefficient ◽  
Surrogate Model ◽  
Smooth Surface ◽  
Aerodynamic Drag ◽  
Optimal Solution ◽  
Optimization Method ◽  
Aerodynamic Optimization ◽  
Design Variables ◽  
Aerodynamic Drag Coefficient

Numerical investigations are carried out to investigate the reduction in the aerodynamic drag of a vehicle by employing a dimpled non-smooth surface. The computational scheme was validated by the experimental data reported in literature. The mechanism and the effect of the dimpled non-smooth surface on the drag reduction were revealed by analysing the flow field structure of the wake. In order to maximize the drag reduction performance of the dimpled non-smooth surface, an aerodynamic optimization method based on a Kriging surrogate model was employed to design the dimpled non-smooth surface. Four structure parameters were selected as the design variables, and a 16-level design-of-experiments method based on orthogonal arrays was used to analyse the sensitivities and the influences of the variables on the drag coefficient; a surrogate model was constructed from these. Then a multi-island genetic algorithm was employed to obtain the optimal solution for the surrogate model. Finally, the surrogate model and the simulation results showed that the optimal combination of design variables can reduce the aerodynamic drag coefficient by 5.20%.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

scholarly journals Bi-Objective Optimization Method and Application of Mechanism Design Based on Pigs' Payoff Game Behavior

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
Lu Wang ◽  
Jian-gang Wang ◽  
Rui Meng ◽  
Neng-gang Xie
Keyword(s):  
Optimization Problems ◽  
Optimization Method ◽  
Payoff Function ◽  
Strategy Space ◽  
Design Variables ◽  
Game Player ◽  
Payoff Functions ◽  
Game Players ◽  
Behavior Characteristics ◽  
The Impact

It takes two design goals as different game players and design variables are divided into strategy spaces owned by corresponding game player by calculating the impact factor and fuzzy clustering. By the analysis of behavior characteristics of two kinds of intelligent pigs, the big pig's behavior is cooperative and collective, but the small pig's behavior is noncooperative, which are endowed with corresponding game player. Two game players establish the mapping relationship between game players payoff functions and objective functions. In their own strategy space, each game player takes their payoff function as monoobjective for optimization. It gives the best strategy upon other players. All the best strategies are combined to be a game strategy set. With convergence and multiround game, the final game solution is obtained. Taking bi-objective optimization of luffing mechanism of compensative shave block, for example, the results show that the method can effectively solve bi-objective optimization problems with preferred target and the efficiency and accuracy are also well.

scholarly journals Multi-Fidelity Local Surrogate Model for Computationally Efficient Microwave Component Design Optimization

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3023 ◽  
Author(s):  
Yiran Song ◽  
Qingsha S. Cheng ◽  
Slawomir Koziel
Keyword(s):  
Surrogate Model ◽  
Polynomial Interpolation ◽  
Computational Cost ◽  
Optimal Solution ◽  
Wide Band ◽  
Optimization Method ◽  
Local Region ◽  
Optimization Process ◽  
Optimized Design ◽  
Mimo Antenna

In order to minimize the number of evaluations of high-fidelity (“fine”) model in the optimization process, to increase the optimization speed, and to improve optimal solution accuracy, a robust and computational-efficient multi-fidelity local surrogate-model optimization method is proposed. Based on the principle of response surface approximation, the proposed method exploits the multi-fidelity coarse models and polynomial interpolation to construct a series of local surrogate models. In the optimization process, local region modeling and optimization are performed iteratively. A judgment factor is introduced to provide information for local region size update. The last local surrogate model is refined by space mapping techniques to obtain the optimal design with high accuracy. The operation and efficiency of the approach are demonstrated through design of a bandpass filter and a compact ultra-wide-band (UWB) multiple-in multiple-out (MIMO) antenna. The response of the optimized design of the fine model meet the design specification. The proposed method not only has better convergence compared to an existing local surrogate method, but also reduces the computational cost substantially.

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