Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2020-01-01
|
| Series: | International Journal for Simulation and Multidisciplinary Design Optimization |
| Subjects: | |
| Online Access: | https://www.ijsmdo.org/articles/smdo/full_html/2020/01/smdo190020/smdo190020.html |
| _version_ | 1818969479312310272 |
|---|---|
| author | Madhu Kumar Racharla Sudheer NVVS Babu Katam Ganesh |
| author_facet | Madhu Kumar Racharla Sudheer NVVS Babu Katam Ganesh |
| author_sort | Madhu Kumar Racharla |
| collection | DOAJ |
| description | 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, COPc1 as 0.0203, second spot cooling point temperature 284.2 K, and COPc2 as 0.1628. This work proved that for solving multi-attribute decision-making problems, grey relational analysis methodology was efficient. |
| first_indexed | 2024-12-20T14:21:14Z |
| format | Article |
| id | doaj.art-5d11874f1ac14957a328a7f3d0373094 |
| institution | Directory Open Access Journal |
| issn | 1779-6288 |
| language | English |
| last_indexed | 2024-12-20T14:21:14Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | International Journal for Simulation and Multidisciplinary Design Optimization |
| spelling | doaj.art-5d11874f1ac14957a328a7f3d03730942022-12-21T19:37:55ZengEDP SciencesInternational Journal for Simulation and Multidisciplinary Design Optimization1779-62882020-01-01112110.1051/smdo/2020015smdo190020Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysisMadhu Kumar Racharla0Sudheer NVVS1Babu Katam Ganesh2https://orcid.org/0000-0001-6998-5002Research Scholar, ANU, School of Mechanical Engineering, R.G.M. College of Engineering & TechnologyDepartment of Mechanical Engineering, RVR & JC College of EngineeringDepartment of Mechanical Engineering, NIT AndhraBy 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, COPc1 as 0.0203, second spot cooling point temperature 284.2 K, and COPc2 as 0.1628. This work proved that for solving multi-attribute decision-making problems, grey relational analysis methodology was efficient.https://www.ijsmdo.org/articles/smdo/full_html/2020/01/smdo190020/smdo190020.htmlcoefficient of performanceexperimentgrey relational analysistaguchitemperature differencevortex tube |
| spellingShingle | Madhu Kumar Racharla Sudheer NVVS Babu Katam Ganesh Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis International Journal for Simulation and Multidisciplinary Design Optimization coefficient of performance experiment grey relational analysis taguchi temperature difference vortex tube |
| title | Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis |
| title_full | Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis |
| title_fullStr | Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis |
| title_full_unstemmed | Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis |
| title_short | Multi-attribute decision making parametric optimization in two-stage hot cascade vortex tube through grey relational analysis |
| title_sort | multi attribute decision making parametric optimization in two stage hot cascade vortex tube through grey relational analysis |
| topic | coefficient of performance experiment grey relational analysis taguchi temperature difference vortex tube |
| url | https://www.ijsmdo.org/articles/smdo/full_html/2020/01/smdo190020/smdo190020.html |
| work_keys_str_mv | AT madhukumarracharla multiattributedecisionmakingparametricoptimizationintwostagehotcascadevortextubethroughgreyrelationalanalysis AT sudheernvvs multiattributedecisionmakingparametricoptimizationintwostagehotcascadevortextubethroughgreyrelationalanalysis AT babukatamganesh multiattributedecisionmakingparametricoptimizationintwostagehotcascadevortextubethroughgreyrelationalanalysis |