A dynamic multi-objective optimization procedure for water cooling of a photovoltaic module

2021 ◽  
Vol 45 ◽  
pp. 101111
Author(s):  
Mohammad Hassan Shahverdian ◽  
Ali Sohani ◽  
Hoseyn Sayyaadi ◽  
Saman Samiezadeh ◽  
Mohammad Hossein Doranehgard ◽  
...  
Keyword(s):  
Optimization Procedure ◽  
Photovoltaic Module ◽  
Water Cooling ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

Multi-Objective Optimization Analysis of Motor Cooling System in Articulated Dump Truck

2011 ◽  
Vol 383-390 ◽  
pp. 4715-4720
Author(s):  
Yan Zhang ◽  
Yan Hua Shen ◽  
Wen Ming Zhang
Keyword(s):  
Field Distribution ◽  
Cooling System ◽  
Dump Truck ◽  
Water Cooling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Temperature And Pressure ◽  
Water Cooling System ◽  
Optimal Set

In order to ensure the reliable and safe operation of the electric driving motor of the articulated dump truck, water cooling system is installed for each motor. For the best performance of the water cooling system, not only the heat transfer should be enhanced to maintain the motor in relatively low temperature, but also the pressure drop in the water cooling system should be reduced to save energy by reducing the power consumption of the pump. In this paper, the numerical simulation of the cooling progress is completed and the temperature and pressure field distribution are obtained. The multi-objective optimization model is established which involves the cooling system structure, temperature field distribution and pressure field distribution. To improve the computational efficiency, the surrogate model of the simulation about the cooling process is established based on the Response Surface Methodology (RSM). After the multi-objective optimization, the Pareto optimal set is obtained. The proper design point, which could make the average temperature and pressure drop of the cooling system relative desirable, is chosen from the Pareto optimal set.

Interactive Multiobjective Optimization Design Strategy for Decision Based Design

1999 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Procedure ◽  
Design Strategy ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Original Algorithm ◽  
Multi Objective ◽  
Local Preference ◽  
Preference Functions

Abstract This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An Interactive Multi-Objective Optimization Procedure (IMOOP) developed in [12] has been modified in this research to include the DM’s local preference functions in an Iterative Decision Making Strategy (IDMS). This enhanced multiobjective optimization procedure called the interactive MultiObjective Optimization Design Strategy (iMOODS) provides the DM with a formal means for efficient design exploration around a given Pareto point. The use of local preference functions allows the original algorithm [12] to be modified such that the second order Pareto surface approximation is more accurate in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objectives and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.

Application of Constraint Multi-Objective Optimization to the Design of Offshore Structure Hulls

2007 ◽  
Author(s):  
Lothar Birk
Keyword(s):  
Computer Aided Design ◽  
Parametric Design ◽  
Pareto Frontier ◽  
Optimization Procedure ◽  
Design System ◽  
Multi Objective Optimization ◽  
Offshore Structure ◽  
Multi Objective ◽  
Heave Motion ◽  
Automated Optimization

The paper reports on the continuous development of an automated optimization procedure for the design of offshore structure hulls. Advanced parametric design algorithms, numerical analysis of wave-body interaction and formal multi-objective optimization are integrated into a computer aided design system that produces hull shapes with superior seakeeping qualities. By allowing multiple objectives in the procedure naval architects may pursue concurrent design objectives, e.g. minimizing heave motion while simultaneously maximizing deck load. The system develops a Pareto frontier of the best design alternatives for the user to choose from. Constraints are directly considered within the optimization algorithm thus eliminating infeasible or unfit designs. The paper summarizes the new developments in the shape generation, illustrates the optimization procedure and presents results of the multi-objective hull shape optimization.

Interactive evolutionary multi-objective optimization and decision-making on life-cycle seismic design of bridge

2018 ◽  
Vol 21 (15) ◽  
pp. 2227-2240 ◽  
Author(s):  
Yu-Jing Li ◽  
Hong-Nan Li
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Seismic Design ◽  
Life Cycle Cost ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Multi Objective Optimization ◽  
Seismic Capacity ◽  
Preference Information ◽  
Multi Objective

Considering future seismic risk and life-cycle cost, the life-cycle seismic design of bridge is formulated as a preference-based multi-objective optimization and decision-making problem, in which the conflicting design criteria that minimize life-cycle cost and maximize seismic capacity are treated simultaneously. Specifically, the preference information based on theoretical analysis and engineering judgment is embedded in the optimization procedure. Based on reasonable displacement ductility, the cost preference and safety preference information are used to progressively construct value function, directing the evolutionary multi-objective optimization algorithm’s search to more preferred solutions. The seismic design of a reinforced concrete pier is presented as an application example using the proposed procedure for the global Pareto front corresponding with engineering designers’ preference. The results indicate that the proposed model is available to find the global Pareto front satisfying the corresponding preference and overcoming the difficulties of the traditional multi-objective optimization algorithm in obtaining a full approximation of the entire Pareto optimal front for large-dimensional problems as well as cognitive difficulty in selecting one preferred solution from all these solutions.

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