Numerical optimization of drying energy consumption from multiple jets impinging on a moving curved surface

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Chitsazan, A.; Klepp, G.; Glasmacher, B.; Pour, K.M.: Numerical optimization of drying energy consumption from multiple jets impinging on a moving curved surface. In: International Journal of Heat and Technology 39 (2021), Nr. 3, S. 755-762. DOI: https://doi.org/10.18280/ijht.390309

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/16710

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Abstract: 
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%.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2021
Appears in Collections:Fakultät für Maschinenbau

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1 image of flag of Germany Germany 2 50.00%
2 image of flag of United States United States 1 25.00%
3 image of flag of China China 1 25.00%

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