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An unmanned underwater vehicle (UUV) powered by a compressed air power system is proposed to address challenges for battery/motor-powered vehicles under high-speed navigation, long endurance, and high mobility. These vehicles actively utilize supercavitation drag reduction by the exhausted gas from the compressed air power system. MATLAB/Simulink and FLUENT are used to establish theoretical models of the compressed air power system and ventilation supercavitation. The relationship between system power and navigation resistance is examined with different air flows, along with a comparison of endurance of different power vehicles at various speeds. The issue of the endurance-enhancing effect of supercavitation at high speed is investigated. The results demonstrate that increasing the air flow leads to higher power and reduced navigation resistance, and there is a balance between them. Furthermore, compared to the battery-powered vehicles with equal energy storage capacity, the compressed air power system shows 210.08% to 458.20% longer endurance times at speeds of 30 kn to 60 kn. Similarly, considering equal energy storage mass, it achieves 42.02% to 148.96% longer endurance times at high speeds (30 kn to 60 kn). The integration of supercavitation and air-powered systems can greatly enhance the endurance and maneuverability of the vehicle at high speeds while ensuring a compact system structure. The investigations could offer valuable ideas for the development and application of compressed air power systems for UUV at 30 kn to 60 kn or higher maneuvering. [ABSTRACT FROM AUTHOR]

Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%. [ABSTRACT FROM AUTHOR]