Modeling of wavy water film by application of artificial neural network - a state of art study

Abstract

Nowadays, artificial intelligence (AI) tools have attained a state of maturity and have advanced at a quick rate of achievement in various fields of study. Thus, the predictive and classification of artificial intelligence AI capabilities in manufacturing, commercial, and scientific research applications have resulted in remarkable success. It is widely known that two-phase flow is such a complex problem, and it has a vital state of study due to its importance and wide range of applications. Such as water film phenomena are critical for various industries, including nuclear protection, energy generation, absorption processes, internal combustion engines, chemical reactors, and spray cooling. The present study presented a realistic model for a particular two-phase flow problem of water film thickness. Also, we used neurotic networks for prediction and classification, such as the conventional neural network (CNN) and Long Short-Term Memory (LSTM). The proposed model was evaluated using various raw data, including the input liquid Reynolds number, the Reynolds gas number, the angle of inclination, and the thickness of the water film. The water film thickness was determined using a planar laser-induced fluorescence (PLIF) technique on the various inclination platforms. The current experimental work's inclination angle varied between 0 and 50 degrees. However, when the Reynolds number of the input water changed from 175 to 435, the thickness of the water film was determined; Reynolds gas numbers range from 6950 to 34800.The current study findings indicate an effective alternative method for detecting and forecasting water films on a sloping plate. Nash-Sutcliffe Efficiency (NSE) affected 99 %, 97 %, and 94 % of forecasts, respectively. The findings show tremendous improvement when deep learning uses both LSTM and CNN. The gravitational pressure and air share tension push the water film.