Open AccessNumerical Optimization of Drying Energy Consumption from Multiple Jets Impinging on a Moving Curved Surface
Author(s) -
Ali Chitsazan,
Georg Klepp,
Birgit Glasmacher,
Kamyar Mohammad Pour
Publication year - 2021
Publication title -
heat and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.283
H-Index - 29
ISSN - 0392-8764
DOI - 10.18280/ijht.390309
Subject(s) - jet (fluid) , nozzle , mechanics , turbulence , energy consumption , inlet , energy (signal processing) , turbulence kinetic energy , computational fluid dynamics , surface (topology) , materials science , mechanical engineering , mathematics , physics , geometry , engineering , statistics , electrical engineering
Due to the increasing energy cost, the efficiency of the industrial dryer as the energy-intensive processes should be improved. The designer should optimize the design parameters of industrial drying equipment to achieve the minimum drying energy consumption. SST k-ω turbulence model is used to simulate a real geometry for industrial drying applications. For the optimization of the impinging round jet, the specific drying energy consumption is set as the objective function to be minimized. The jet to surface distance, jet to jet spacing, jet inlet velocity, jet angle, and surface velocity are chosen as the design parameters. The SHERPA search algorithm is used to search for the optimal point from the weighted sum of all objectives method. One correlation is developed and validated for the specific drying energy consumption. It is found that the SST k-ω turbulence model succeeded with reasonable accuracy in reproducing the experimental results. The minimum specific energy consumption correlates with high values of the jet to jet spacing, jet angle, and surface velocity and low values of the nozzle to surface distance and jet inlet velocity. The agreement in the prediction of the specific drying energy consumption between the numerical simulation and correlation is found to be reasonable and all the data points deviate from the correlation by less than 7%.