Dynamic Optimization of Hydrostatic Supported Worktable System with Circular Oil Recess

2013 ◽  
Vol 299 ◽  
pp. 8-11
Author(s):  
Hong Ling Ye ◽  
Pin Wen ◽  
Xiao Long Zheng
Keyword(s):  
Dynamic Optimization ◽  
Continuous Variables ◽  
Optimization Strategy ◽  
Optimal Parameters ◽  
Discrete Variable ◽  
Optimal Model ◽  
Two Phase ◽  
Design Variables ◽  
Continuous Components ◽  
Phase Optimization

In this paper, optimal parameters in hydrostatic supported worktable system with circular oil recess are calculated. The optimal model is established in which the objective is to make natural frequency deviate from speeds of the rotary work axis, and the design variables are recess number, initial oil film thickness and recess radius. As the design variable vector includes both discrete and continuous components, two-phase optimization strategy is developed. Furthermore Fibonacci method is applied to deal with the problem of discrete variable, and response surface methodology is used to form an explicit expression between the objective expression and continuous variables. Then the optimal model is solved by gradient projection method and simplex method. As a result, numerical calculation of dynamic optimization with hydrostatic worktable system with circular recess shows that two-strategy optimization algorithm is stable and effectively.

Mode Pursuing Sampling Method for Discrete Variable Optimization on Expensive Black-Box Functions

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Behnam Sharif ◽  
G. Gary Wang ◽  
Tarek Y. ElMekkawy
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Continuous Variable ◽  
Black Box ◽  
Test Problems ◽  
Continuous Variables ◽  
Optimization Strategy ◽  
Discrete Variable ◽  
Mps Method ◽  
Search Control

Based on previously developed Mode Pursuing Sampling (MPS) approach for continuous variables, a variation of MPS for discrete variable global optimization problems on expensive black-box functions is developed in this paper. The proposed method, namely, the discrete variable MPS (D-MPS) method, differs from its continuous variable version not only on sampling in a discrete space, but moreover, on a novel double-sphere strategy. The double-sphere strategy features two hyperspheres whose radii are dynamically enlarged or shrunk in control of, respectively, the degree of “exploration” and “exploitation” in the search of the optimum. Through testing and application to design problems, the proposed D-MPS method demonstrates excellent efficiency and accuracy as compared to the best results in literature on the test problems. The proposed method is believed a promising global optimization strategy for expensive black-box functions with discrete variables. The double-sphere strategy provides an original search control mechanism and has potential to be used in other search algorithms.

CAD and Optimization of Helical Torsion Springs

1994 ◽  
Author(s):  
Sayed M. Metwalli ◽  
M. Alaa Radwan ◽  
Abdel Aziz M. Elmeligy
Keyword(s):  
Iterative Process ◽  
Minimum Weight ◽  
Design Parameters ◽  
Optimal Parameters ◽  
Direct Evaluation ◽  
Torsion Spring ◽  
Performance Requirements ◽  
Design Variables ◽  
Method Of Lagrange Multipliers ◽  
Torsion Springs

Abstract The convensional procedure of helical torsion spring design is an iterative process because of large number of requirements and relations that are to be attained once at a time. The design parameters are varied at random until the spring design satisfies performance requirements. A CAD of the spring for minimum weight is formulated with and without the variation of the maximum normal stress with the wire diameter. The CAD program solves by employing the method of Lagrange-Multipliers. The optimal parameters, in a closed form are obtained, normalized and plotted. These explicit relations of design variables allow direct evaluation of optimal design objective and hence, an absolute optimum could be achieved. The comparison of optimum results with those previously published, shows a pronounced achievement in the reduction of torsion spring weight.

Based on ANSYS APDL Language Moving Blade Adjustable Axial Flow Fan Optimization Design

2013 ◽  
Vol 655-657 ◽  
pp. 435-444
Author(s):  
Dong Xia Niu ◽  
Xian Yi Meng ◽  
Ai Hua Zhu
Keyword(s):  
Optimization Design ◽  
Lift Coefficient ◽  
Axial Flow ◽  
Optimization Method ◽  
Structure Parameters ◽  
Continuous Variables ◽  
State Variables ◽  
Axial Flow Fan ◽  
Installation Angle ◽  
Design Variables

In the case of multiple loading conditions, a moving blade adjustable axial flow fan structure parameters are optimized by ANSYS. It is to achieve greater efficiency and less noise for the optimization goal. For different conditions, establish efficiency, noise comprehensive objective function using weighted coefficient method. Select impeller diameter, the wheel hub ratio, leaf number, lift coefficient, speed as design variables, Choose blade installation Angle, the wheel hub place dynamic load coefficient, cascade consistency, allowable safety coefficient as optimization of the state variables. Design variables contain continuous variables and discrete variable. Through the optimization method, we get the optimal structure parameters finally. And at the same time get the corresponding optimal blade installation Angle,under different working conditions.

Design of a Damper for a Suspension System via Evolutive Optimization

2013 ◽  
Author(s):  
Carlos A. Duchanoy ◽  
Marco A. Moreno-Armendáriz ◽  
Carlos A. Cruz-Villar
Keyword(s):  
Dynamic Optimization ◽  
Optimization Algorithm ◽  
Physical Characteristics ◽  
Suspension System ◽  
Dynamic Interactions ◽  
Multi Objective ◽  
Damping Coefficients ◽  
Design Variables ◽  
Parameterized Model ◽  
Stiffness And Damping

In this paper a dynamic optimization methodology for designing a passive automotive damper is proposed. The methodology proposes to state the design problem as a dynamic optimization one by considering the nonlinear dynamic interactions between the damper and the other elements of the suspension system, emphasizing geometry, dimensional and movement constraints. In order to obtain realistic simulations of the suspension, a link between a Computer-Aided Engineering Model (CAEM) and a multi-objective dynamic optimization algorithm is developed. As design objectives we consider the vehicle safety and the passenger comfort which are represented by the contact area of the tire and the vibrations of the cockpit respectively. The damper is optimized by stating a set of physical variables that determine the stiffness and damping coefficients as independent variables for the dynamic optimization problem, they include the spring helix diameter, the spring wire diameter, the oil physical characteristics and the bleed orifice diameters among others. The optimization algorithm that we use to solve the problem at hand is a multi-objective evolutive optimization algorithm. For this purpose we developed a parameterized model of the damper which is used to link the CAE tools and the optimization software, thus enabling fitness evaluations during the dynamic optimization process. By selecting the physical characteristics of the damper as design variables instead of the typical stiffness and damping coefficients, it is possible to consider important design constrains as the damper size, movement limitations and anchor points. As result of the proposed methodology a set of blueprints of non dominated Pareto configurations of the damper are provided to the decision maker.

scholarly journals Two-Phase Optimization Method for Determining Distribution Center Locations and Distribution Routes (Case Study: X Ltd.)

2020 ◽  
Vol 0 (5) ◽  
pp. 45
Author(s):  
Muhammad Rayhan Azzindani ◽  
Nabila Fajri Kusuma Ningrum ◽  
Mega Rizkah Sudiar ◽  
Anak Agung Ngurah Perwira Redi
Keyword(s):  
Optimization Method ◽  
Distribution Center ◽  
Two Phase ◽  
Phase Optimization

Two-phase optimization for the design of multiple coils

2005 ◽  
Vol 41 (10) ◽  
pp. 4093-4095 ◽  
Author(s):  
Woochul Kim ◽  
Jae Eun Kim ◽  
Yoon Young Kim
Keyword(s):  
Two Phase ◽  
Phase Optimization

scholarly journals Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

2014 ◽  
Vol 24 (3) ◽  
pp. 669-682 ◽  
Author(s):  
D. Thresh Kumar ◽  
Hamed Soleimani ◽  
Govindan Kannan
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Supply Chain Design ◽  
Second Phase ◽  
Planning Problem ◽  
Two Phase ◽  
Closed Loop Supply Chain ◽  
Inference System ◽  
Forecasting Method ◽  
Phase Optimization

Abstract Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network

scholarly journals A Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1324
Author(s):  
Cheng Gong ◽  
Chao Guo ◽  
Haitao Xu ◽  
Chengcheng Zhou ◽  
Xiaotao Yuan
Keyword(s):  
Sensor Networks ◽  
Communication Networks ◽  
Energy Supply ◽  
Large Scale ◽  
Joint Optimization ◽  
Second Phase ◽  
Optimization Strategy ◽  
Two Phase ◽  
Game Equilibrium ◽  
Wireless Rechargeable Sensor Networks

Wireless Sensor Networks (WSNs) have the characteristics of large-scale deployment, flexible networking, and many applications. They are important parts of wireless communication networks. However, due to limited energy supply, the development of WSNs is greatly restricted. Wireless rechargeable sensor networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a two-phase scheme is proposed to improve the energy management efficiency for WRSNs. In the first phase, we designed an annulus virtual force based particle swarm optimization (AVFPSO) algorithm for area coverage. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a queuing game-based energy supply (QGES) algorithm was designed. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy supply, and then extends the lifetime of WSN.

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