Estimation and optimization of heat transfer and overall pressure drop for a shell and tube heat exchanger
作者： Bala Bhaskara Rao , V. Ramachandra Raju , B. B. V. L. Deepak
DOI: 10.1007/S12206-016-1239-6
关键词:
摘要: Most thermal/chemical industries are equipped with heat exchangers to enhance thermal efficiency. The performance of highly depends on design modifications in the tube side, such as cross-sectional area, orientation, and baffle cut tube. However, these parameters do not exhibit a specific relation determining optimum condition for shell maximum transfer rate reduced pressure drops. Accordingly, experimental numerical simulations performed exchanger varying geometries. considered this investigation is single-shell, multiple-pass device. A Generalized regression neural network (GRNN) applied generate among input output process data sets. Then, an Artificial immune system (AIS) used GRNN obtain optimized parameters. Lastly, results presented developed hybrid GRNN-AIS approach.
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