Design and Optimization of Multiple Microchannel Heat Transfer Systems Based on Multiple Prioritized Preferences

2012 ◽  
Author(s):  
Po Ting Lin ◽  
Jingru Zhang ◽  
Yogesh Jaluria ◽  
Hae Chang Gea
Keyword(s):  
Heat Transfer ◽  
Objective Function ◽  
Optimal Solution ◽  
Optimization Strategy ◽  
Pressure Drops ◽  
Design And Optimization ◽  
Heat Flows ◽  
First Case ◽  
Efficiency Measures ◽  
Objective Function Values

Multiple microchannel heat transfer systems have been developed for the urge of rapid and effective cooling of the electronic devices, which have become smaller and more powerful but also produced more heat. Two different types of single-phase liquid cooling, including the straight and U-shaped microchannel heat sinks, have been utilized to reduce the temperature of the electronic chips. The cooling performances however depend on the preferences of different factors such as the thermal resistances, the pressure drops, and the heat flows at the solid-fluid interfaces. Lower thermal resistance represents higher temperature reduction; lower pressure drop means lower usage of the pumping power; and higher heat flows indicates more effective cooling between the heat spreader and the liquid. In this paper, an optimization strategy based on the prioritized performances has been developed to find the optimal design variables for multiple objectives: minimal thermal resistances, minimal pressure drops and maximal heat flows. The fuzzy and correlated preferences are modeled by the Gaussian membership functions with respect to different levels of the objective function values. The overall performances are formulated based on the prioritized preferences and maximized on the Pareto-optimal solution set to find the solutions for various preference conditions. Two case studies have been discussed. The first case considered the prioritized preferences based on uni-objective function values while the second one focused on the preferences of the thermal resistances and the efficiency measures, correlatively evaluated by the flow rates, pressure drops, and heat flows.

A Closeness Degree Method for Multi-Objective Optimization of Belt Transmission

1992 ◽  
Author(s):  
Lin Qun ◽  
Wu Meijuan
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Optimization Design ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Objective Function Values ◽  
Belt Transmission ◽  
Fuzzy Subsets ◽  
The Ideal

Abstract A mathematical model for multi objective optimization design of belt transmission is proposed in this paper. The normal fuzzy distribution is used to convert the ideal and non-inferior solutions into fuzzy subsets over the space of objective function values. The optimal solution which is closest to the ideal one could then be found on the basis of closeness degree method.

scholarly journals Locating abrupt disaster emergency logistics centres using improved artificial bee colony (IABC) algorithm

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110162
Author(s):  
Qiang Sun ◽  
Shupei Liu
Keyword(s):  
Objective Function ◽  
Optimal Solution ◽  
Global Search ◽  
Emergency Relief ◽  
Multi Objective ◽  
Bee Colony ◽  
Emergency Logistics ◽  
Objective Function Values ◽  
The Stability ◽  
Better Than

Emergency management is conceptualized as a complex, multi-objective optimization problem related to facility location. However, little research has been performed on the horizontal transportation of emergency logistics centres. This study makes contributions to the multi-objective locating abrupt disaster emergency logistics centres model with the smallest total cost and the largest customer satisfaction. The IABC algorithm is proposed in this paper to solve the multi-objective emergency logistics centres locating problem. IABC algorithm can effectively calculate the optimal location of abrupt disaster emergency logistics centres and the demand for relief materials, and it can solve the rescue time satisfaction for different rescue sites. (1) IABC has better global search capabilities to avoid premature convergence and provide a faster convergence speed, and it has optimal solution accuracy, solution diversity and robustness. (2) From the three optimal objective function values obtained, the optimal objective function values obtained by IABC algorithm are obviously better than ABC and GABC algorithms. (3) From the convergence curves of three objective functions the global search ability and the stability of IABC algorithm are better than those of ABC and GABC algorithm. The improved ABC algorithm has proven to be effective and feasible. However, emergency relief logistics systems are very complex and involve many factors, the proposed model needs to be refined further in the future.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun-Yung Chen ◽  
Te-Wen Tu
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Hollow Cylinder ◽  
General Type ◽  
Particle Swarm ◽  
Time History ◽  
Time Varying ◽  
Swarm Optimization ◽  
Self Learning

Abstract An inverse methodology is proposed to estimate a time-varying heat transfer coefficient (HTC) for a hollow cylinder with time-dependent boundary conditions of different kinds on inner and outer surfaces. The temperatures at both the inner surface and the interior domain are measured for the hollow cylinder, while the time history of HTC of the outer surface will be inversely determined. This work first expressed the unknown function of HTC in a general form with unknown coefficients, and then regarded these unknown coefficients as the estimated parameters which can be randomly searched and found by the self-learning particle swarm optimization (SLPSO) method. The objective function which wants to be minimized was found with the absolute errors between the measured and estimated temperatures at several measurement times. If the objective function converges toward the null, the inverse solution of the estimated HTC will be found eventually. From numerical experiments, when the function of HTC with exponential type is performed, the unknown coefficients of the HTC function can be accurately estimated. On the contrary, when the function of HTC with a general type is conducted, the unknown coefficients of HTC are poorly estimated. However, the estimated coefficients of an HTC function with the general type can be regarded as the equivalent coefficients for the real function of HTC.

Multivariate design and analysis of aircraft HEX under multiple working conditions within flight envelope

2021 ◽  
pp. 1-18
Author(s):  
Qihang Liu ◽  
G.Q. Xu ◽  
Jie Wen ◽  
Yanchen Fu ◽  
Laihe Zhuang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Working Conditions ◽  
Design Method ◽  
Structural Parameters ◽  
Optimal Structure ◽  
Clear Correlation ◽  
Pressure Drops ◽  
Design Variables ◽  
Wide Range ◽  
Common Weight

Abstract This paper presents a multi-condition design method for the aircraft heat exchanger (HEX), marking with light weight, compactness and wide range of working conditions. The quasi-traversal genetic algorithm (QT-GA) method is introduced to obtain the optimal values of five structural parameters including the height, the tube diameter, the tube pitch, and the tube rows. The QT-GA method solves the deficiency of the conventional GA in the convergence, and gives a clear correlation between design variables and outputs. Pressure drops, heat transfer and the weight of the HEX are combined in a single objective function of GA in the HEX design, thus the optimal structure of the HEX suitable for all the working conditions can be directly obtained. After optimization, the weight of the HEX is reduced to 2.250 kg, more than 20% lower than a common weight of around 3 kg. Based on the optimal structure, the off-design performance of the HEX is further analyzed. Results show that the extreme working conditions for the heat transfer and the pressure drops are not consistent. It proves the advance of the multi-condition design method over traditional single-condition design method. In general, the proposed QT-GA design method is an efficient way to solve the multi-condition problems related to the aircraft HEX or other energy systems.

Numerical Simulation of Heat Transfer in Laminar Natural Convection of Mixed Newtonian-Non-Newtonian and Pure Non-Newtonian Nanofluids in a Square Enclosure

2021 ◽  
pp. 1-44
Author(s):  
Ajay Vallabh ◽  
P.S. Ghoshdastidar
Keyword(s):  
Heat Transfer ◽  
Natural Convection ◽  
Base Fluid ◽  
Volume Fraction ◽  
Numerical Study ◽  
Transfer Model ◽  
Nanoparticle Concentration ◽  
Left Wall ◽  
Square Enclosure ◽  
First Case

Abstract This paper presents a steady-state heat transfer model for the natural convection of mixed Newtonian-Non-Newtonian (Alumina-Water) and pure Non-Newtonian (Alumina-0.5 wt% Carboxymethyl Cellulose (CMC)/Water) nanofluids in a square enclosure with adiabatic horizontal walls and isothermal vertical walls, the left wall being hot and the right wall cold. In the first case the nanofluid changes its Newtonian character to Non-Newtonian past 2.78% volume fraction of the nanoparticles. In the second case the base fluid itself is Non-Newtonian and the nanofluid behaves as a pure Non-Newtonian fluid. The power-law viscosity model has been adopted for the non-Newtonian nanofluids. A finite-difference based numerical study with the Stream function-Vorticity-Temperature formulation has been carried out. The homogeneous flow model has been used for modelling the nanofluids. The present results have been extensively validated with earlier works. In Case I the results indicate that Alumina-Water nanofluid shows 4% enhancement in heat transfer at 2.78% nanoparticle concentration. Following that there is a sharp decline in heat transfer with respect to that in base fluid for nanoparticle volume fractions equal to and greater than 3%. In Case II Alumina-CMC/Water nanofluid shows 17% deterioration in heat transfer with respect to that in base fluid at 1.5% nanoparticle concentration. An enhancement in heat transfer is observed for increase in hot wall temperature at a fixed volume fraction of nanoparticles, for both types of nanofluid.

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