Optimization of the Thermal Efficiency of a Two-Phase Closed Thermosyphon using Active Learning on the Human Algorithm Interaction

Abstract

Here, pristine and functionalized multi-walled carbon nanotubes with silver/water nanofluids were first synthesized. To investigate thermal performance of two-phase closed thermosyphon, thermal efficiency was experimentally obtained as a key parameter. To obtain optimal points in operational condition, the active learning method was employed in concentration ranges of 0–1 wt% as well as input power of 30–150 W which cannot be accessed. The active learning method is based on the fuzzy logic rules; here query synthesis and the measure human algorithm interaction (HAI) were used for learning. As the primary data obtained from experiments is small, this method was used to suggest the most optimal conditions. First, primary data obtained from experiments are given to the algorithm and then algorithm proposed some suggestions based on the maximum uncertainty. Subsequently, thermal efficiency is estimated based on fuzzy inference. Here, two mechanisms are employed as combined ones. The mentioned suggestions will be tested in the offered operational conditions. If the accuracy of the suggestions was confirmed by the obtained data, these data would be added to the primary ones. In fact, the used method can be considered in the area of HAI performing with the aid of experienced human simultaneously with intelligent algorithms. Meanwhile, the best working concentration to obtain the most optimum thermal efficiency obtained in the range 0.90 to 0.95 for both nanofluids.

Notes

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