Optimization of convective drying performance of multiple porous moist objects in a 3D channel

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dc.contributor.authorSelimefendigil, F
dc.contributor.authorCoban, SO
dc.contributor.authorÖztop, HF
dc.date.accessioned2024-07-18T11:53:22Z
dc.date.available2024-07-18T11:53:22Z
dc.description.abstractIn this study, a procedure for optimizing the convective drying performance of multi porous moist objects in a three dimensional channel is proposed. The numerical simulation is performed by using the finite element method and COBYLA optimization algorithm is used to find the optimum spacing between the objects without mass transfer in the first stage. Then, heat and mass transfer equations for the porous moist objects are coupled with the channel flow equations at the optimum spacing which delivers the best convective drying performance. It is observed that the flow recirculation and flow reversal in the inter-spacing with various distances between the objects resulted in thermal gradient variations along the multi object surfaces. The average Nusselt number rises for second block while it shows non-monotonic behavior for the first block when the distance between the first and second group objects are varied. Distance between the second and third objects also affected the average Nu variation for all of the objects. The lateral distance between first and second group objects resulted in up to 50% variation in the average Nu for the second block. The optimum spacing between the objects for the maximum Nusselt number of the objects are obtained as d(1)=5.93h(c), d(2)=7h(c). and d(3)=0.584h(c). The moisture reduction amounts for each of the object at the optimums spacing are found higher as compared to parametric variation of unsteady simulation results. The computational cost for the parametric unsteady coupled heat and mass transport equations in the channel and in the porous moist objects is 75 h 12 min while the optimization assisted simulation results reduced the computational cost to 2 h 33 minutes. Also, artificial neural networks are utilized to obtain the dynamic feature of convective drying at the optimum spacing considering various values of hot dry air temperature which delivers fast and accurate prediction results when compared to high fidelity computational fluid dynamics simulation results.
dc.identifier.issn1290-0729
dc.identifier.other1778-4166
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/5565
dc.language.isoEnglish
dc.publisherELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
dc.subjectARTIFICIAL NEURAL-NETWORKS
dc.subjectHEAT-TRANSFER ENHANCEMENT
dc.subjectFORCED-CONVECTION
dc.subjectMASS-TRANSFER
dc.subjectNATURAL-CONVECTION
dc.subject3-DIMENSIONAL LAMINAR
dc.subjectMIXED CONVECTION
dc.subjectFOOD PROCESSES
dc.subjectFLOW
dc.subjectBLOCKS
dc.titleOptimization of convective drying performance of multiple porous moist objects in a 3D channel
dc.typeArticle

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