Auther: A.G. Kanaris, A.A. Mouza and S.V. Paras
The need for designing process equipment that complies with the principles of economic and ecological sustainability acted as a driving force towards the evolution in the design of plate heat exchangers (PHE). Because of their compactness, close temperature approach and ease on inspection and cleaning (Shah & Wanniarachchi, 1991), PHEs are used in process and power industries for a wide range of temperatures. The plates of these PHE comprise some form of nearsinusoidal corrugations in a herringbone pattern (Fig. 1). When two of these plates are arranged and placed abutting, a channel with complicated passages is formed. As expected, the fluid flow inside a passage of this channel undergoes a series of periodic changes in flow direction, a kind of flow that augments heat transfer, while on the other hand it induces a significant resistance to the flow.
These conceptual PHEs have been numerically studied (in terms of heat transfer and fluid flow analysis), using a previously validated commercial CFD code (ANSYS CFX® 10) (Kanaris et al., 2006). To quest for the optimal design of the corrugated surface, an objective function is formulated, as a tradeoff between heat transfer and pressure drop, using a weighting factor to account for the relative significance of friction losses to heat recovery (i.e., electric energy vs. thermal energy). Five dimensionless groups are selected as design variables for the simulations, namely:
• the blockage ratio (BR=d/H) that expresses the percentage of the entrance of the channel 'blocked' with corrugations,
• the channel aspect ratio (ChanAR=H/W); a measure of how narrow the channel is,
• the corrugation aspect ratio (CorAR=d/z); a measure of the obtuseness of the corrugation,
• the sine of twice the angle of attack (sin2θ) and
• the Reynolds number, Re, defined as: = h uD Re ρ μ, where Dh the hydraulic diameter of the conduit and u is the mean entrance velocity.
Box-Behnken design was selected for the design variables in order to construct the response surface. The calculated values of the objective function are used to create a quadratic model to be optimized using response surface methodology (RSM) (Myers & Montgomery, 2002).
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