Parametric Analysis and Thermal Optimization of Triple Concentric Tube Heat Exchanger Using RSM

Numerical simulation of triple concentric tube heat exchanger and thermal hydraulic performance using CFD and optimization using RSM and Taguchi method: After the numerical approach was validated, optimization was carried out to build a functional relationship between design elements and response variables, as well as to determine the best values for a variety of design variables. Reynolds number (Re: 2500–10,000) and length to hydraulic diameter ratio (L/Dh: 100–220) are the design parameters evaluated.             The response variables have been chosen as Nusselt number (Nu) and friction factor (f). The basic conclusions of this work have been summarized as follows-           i.          Based on the results of numerical simulation and multi-objective optimization. From Figure, that the first Level provides maximum value of Nusselt number. (a) Reynolds number A1 2500 (b) Length to hydraulic diameter ratio B2 180         ii.          From Figure, that the first Level provides minimum value of pressure drop (friction factor) (a) Reynolds number A1 2500 (b) Length to hydraulic diameter ratio B2 180.       iii.          Table shows the ANOVA result for friction factor. It is observed that the Reynolds number (P=0.000) (54.08 %) is most influences the friction factor followed by hydraulic diameter ratio (P= 0.000) (40.33%)        iv.          The F-test determines whether the parameters are significantly different statistically. The greater the impact on the friction factor performance characteristics, the higher the F value [15]. For Reynolds number (P=0.000), larger F values are found (58.57 %)          v.          Table 4.3 shows the ANOVA result for Nusselt number. It is observed that the Reynolds number (P=0.000) (54.08 %) is most influences the friction factor followed by hydraulic diameter ratio (P= 0.000) (37.53%)        vi.          The predicted value of the response variables is precisely closer to the numerical results and hence, it has helped in reducing the size of the required data as the RSM provides useful interaction between the various parameters of the system.

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