Research Article

Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis

¹ Research Scholar, ANU, School of Mechanical Engineering, R.G.M. College of Engineering & Technology, Nandyal, India
² Department of Mechanical Engineering, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India
³ Department of Mechanical Engineering, NIT Andhra, Tadepalligudem, Andhra Pradesh, India

^* e-mal: madhukumar325@gmail.com

Received: 15 July 2019
Accepted: 26 August 2020

Abstract

By setting two vortex tubes in hot cascade type Vortex tube manner, can achieve two cooling points for spot cooling applications with the single input. These cooling points play a vital role to cool tools in machining operations. The present work aims to optimize the output parameters such as outlet temperature, Coefficient of Performance (COP). Based on the literature, the performance of this vortex tube mainly depends on its input parameters such as air inlet pressure, length to diameter ratio, and the number of nozzles. In the present work, the above input parameters have been experimented on this vortex tube, based on the Taguchi L18 array. The optimal condition for both temperatures, COP at hot and cold outlets was calculated using grey relational analysis (GRA). The obtained experimental results were analyzed using the ANOVA approach. Also for multi responses, 1st and 2nd order predicted mathematical models developed by using Minitab 18 software and its accuracy checked. The achieved results are at first spot cooling point temperature 294.9 K, COP_c1 as 0.0203, second spot cooling point temperature 284.2 K, and COP_c2 as 0.1628. This work proved that for solving multi-attribute decision-making problems, grey relational analysis methodology was efficient.