Performance of a Propeller Coated with Hydrophobic Material

Computational and experimental methods were used to study a propeller coated with hydrophobic material and a propeller with a conventional surface for comparison. In CFD simulations, the blade surface mesh was arranged in a way to set non-slip or free slip wall boundary conditions with different proportions to define the level of surface slip. The conventional and the hydrophobic material propellers defined by different surface slip rates were simulated under different advance speed coefficients and different rotational speeds. Propeller performance results, blade pressure, and the Liutex vorticity distribution were studied. An experimental platform was established to study the velocity field around the propeller using a Particle Image Velocimetry (PIV) device. The CFD calculation results were compared with the PIV results. It was found that the calculation results using a 75% surface slip rate were closer to the experimental results. The calculation results show that the propeller coated with hydrophobic material has improved thrust and efficiency compared with the propeller with conventional material. The hydrophobic material can significantly reduce the low-speed region downstream of the propeller hub. The hub and the tip vortices shown by the Liutex are also significantly reduced. Those changes help to improve the propulsion efficiency

Water Quality Predictions Based on Grey Relation Analysis Enhanced LSTM Algorithms

With the growth of industrialization in recent years, the quality of drinking water has been a great concern due to increasing water pollution from industries and industrial farming. Many monitoring stations are constructed near drinking water sources for the purpose of fast reactions to water pollution. Due to the relatively low sampling frequencies in practice, mathematic prediction models are clearly needed for such monitoring stations to reduce the delay between the time points of pollution occurrences and water quality assessments. In this work, 2190 sets of monitoring data from automatic water quality monitoring stations in the Qiandao Lake, China from 2019 to 2020 were collected, and served as training samples for prediction models. A grey relation analysis-enhanced long short-term memory (GRA-LSTM) algorithm was used to predict the key parameters of drinking water quality. In comparison with conventional LSTM models, the mean absolute errors (MAEs) to predict the four parameters of water quality, i.e., dissolved oxygen (DO), permanganate index (COD), total phosphorus (TP), and potential of hydrogen (pH), were reduced by 23.03%, 10.71%, 7.54%, and 43.06%, respectively, using our GRA-LSTM algorithm, while the corresponding root mean square errors (RMSEs) were reduced by 24.47%, 5.28%, 6.92%, and 35.89%, respectively. Such an algorithm applies to predictions of events with small amounts of data, but with high parametric dimensions. The GRA-LSTM algorithm offers data support for subsequent water quality monitoring and early warnings of polluting water sources, making significant contributions to real-time water management in basins.publishedVersio