Use of Cuckoo Search Algorithm for Performance Evaluation of Split Elliptic Shaped Fins for Enhanced Rate of Heat Transfer

2021 ◽  
Vol 143 (6) ◽  
Author(s):  
Alok Ranjan ◽  
Ranjan Das ◽  
Sagnik Pal ◽  
Arindam Majumder ◽  
Madhujit Deb

Abstract Proper dissipation of thermal energy has always been a need for desirable efficiency of a system. Extended surface aids in releasing the heat to the immediate surrounding by inducing an extra area. This particular work assesses thermal and fluid flow behavior of extended surfaces with circular and elliptic shaped cross section. Extended surfaces of unvaried cross section are mounted over a square plate arrayed in a staggered manner. With the aid of different thermofluidic parameters, the elliptic shaped pin fin is established to provide a higher thermal performance enhancement of nearly 15% over cylindrical pin fin at inlet flow velocity of 2.35 m/s. Further, for elevating the interaction between the surface of the fin and the fluid, elliptic fins are reoriented to form a split. In contrast to cylindrical shaped fin, modification using split shows better result with the highest heat transfer increment of nearly 25%. Further, in order to maximize Nusselt number (Nu), a single objective cuckoo search optimization analysis is done by adopting the response surface method. After analyzing the optimization, it is found that the maximum value of Nu is obtained at dimensionless transverse offset (TO*) = 0.125 and dimensionless longitudinal offset (LO*) = 0, which has been further validated with the numerical result within 0.97% accuracy. Further, for the cylindrical fin, the present simulations agree with the available empirical correlation within 6.22% accuracy.

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.


Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao

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.


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