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Fuzzy logic, introduced by Lotfi Zadeh in 1965, is a powerful method for modelling complex experiments. This study utilizes fuzzy logic to simulate and predict heat transfer in a double-pipe heat exchanger equipped with wavy inserts. The inserts, in the form of twisted tapes, have varying twist ratios (TR=9, 7, 6). The study investigates a range of Reynolds numbers (Re) from 6000 to 18000, with friction factors ranging from 0.03620 to 0.08231, and Nusselt numbers (Nu) between 66.13 and 253.28. The results for different twist ratios are compared to the ideal case. The experimental results indicate that the highest heat transfer occurs with a twist ratio of 6, leading to a significant increase of 162% in the Nusselt number and a 36.21% rise in the friction factor compared to the ideal scenario. In the fuzzy logic framework, the input variables are the twist ratio (Tr), temperature, and Reynolds number (Re), while the output variables are the friction factor (f) and Nusselt number (Nu). The study demonstrates that the Mamdani fuzzy inference system is an exceptionally effective tool for predicting experimental outcomes, given its low error rate. Upon analysing the data, it is observed that the graphs plotting the Nusselt number versus Reynolds number and friction factor versus Reynolds number, derived from both experimental data and the fuzzy logic model, exhibit nearly identical trends with a margin of error of just 3%. This high level of accuracy underscores the reliability of the fuzzy logic model in replicating the experimental results.
Kurhade et al. (Tue,) studied this question.