LAUSR

Abstract A wickless heat pipe (WHP) comprises of an evacuated-close tube filled with an appropriate amount of working fluid. In this study, the effect of Al2O3/water nanofluid as the working media on thermal performance of WHP investigated and compared with pure water by designing an optimized Artificial Neural Network (ANN). ANN trained with the collected test data obtained from experimental setup and validated. Multilayer Perceptron configuration (MLP) adopted for the ANN. The MLP architecture consists of four input nodes representing the parameters; input power, volume concentration of nanofluid, filling ratio and mass rate in condenser section, and a single output node representing the thermal efficiency of WHP. According to sensitivity analysis results, volume concentration is the most significant parameter which affects the WHP performance. Also, since the ANN test output data are sufficiently close to experimental one, it can be inferred that the ANN model can be applied to accurately model WHP thermal performance.