In cold and humid environments in high-latitude, high-altitude, and offshore regions, ice accretion sometimes occurs on airfoil blade surfaces, such as those of wind turbines. Therefore, a potential hazard exists for the equipment. For this reason, the aerodynamic characteristics of the airfoil blade degrade and power generation decreases. In the present study, the icing characteristics of airfoils in cold and foggy environments were investigated. A novel icing wind tunnel with a low LWC of 0.3 g/m(3) and a small MVD of 10 mu m was designed and built. An airfoil sample with the aerodynamic profile of NACA 0018 was selected, and the effects of the airfoil material and the temperature on the icing area, the thickness of ice, and the coverage scope of ice were tested and analyzed. The experimental results showed that the temperature had a more significant effect on the icing characteristics in comparison with the airfoil material. At the medium temperature, -7 degrees C in the present study, the icing area, the thickness of ice, and coverage scope all reached their maximum value. Specifically, the maximum cross-sectional icing areas (CIAs) on the aluminum airfoil at -4 degrees C, -7 degrees C, and -10 degrees C for 60 min were 35.088 mm(2), 66.357 mm(2,) and 51.538 mm(2), respectively, and those on the FRP airfoil were 36.204 mm(2), 70.352 mm(2), and 47.814 mm(2), respectively. The FRP airfoil had a larger icing area and thickness of ice. In contrast, the aluminum airfoil had a larger coverage scope of ice, which was -0.10 similar to 0.15. In addition, the aerodynamic performance of the iced airfoil, including C-d and C-m, was also obtained through CFD. The research findings provided a foundation for further exploring the atmospheric icing of wind turbines and other structures with airfoil profiles.
To cope with the advancement of energy demand and environmental challenges, and to solve the issues of unstable and limited efficiency of single energy supply, a thermophotovoltaic model complementary to solar energy and fuel energy is studied and constructed. To analyze its energy conversion process, the Brayton cycle is innovatively introduced by combining solar energy and fuel energy to study energy conversion. The findings denoted that when the solar irradiance increased from 200 to 1000 W/m2, the fuel compensation flow rate decreased from 1.1 m3/h to 0, the total input thermal energy increased from 32.5 to 43.1 kW, and the output electrical power increased synchronously from 3.9 to 7.1 kW. The total input thermal energy was positively correlated with the output electrical power, ensuring the coordinated change of system energy input and output. Under clear and cloudy conditions (700 W/m2), the model had the highest output power of 5.9 kW and a total system efficiency of 19.2
With the continuous development of intelligent manufacturing and precision machining technology, laser processing technology has been paid more and more attention and used in industrial production. The path planning and control strategy have a decisive effect on the processing efficiency and the quality of the workpiece. However, traditional path planning methods often use the path of single-sensor data or static process parameters. Nevertheless, those are not suited to complex and dynamic past processing environments and nonlinear process behavior, which limits the rapid development of intelligent thinking systems. In this paper, the intelligent control model driven by multi-sensor data for laser path planning is proposed to achieve real-time perception, dynamic optimization, and adaptive control of the entire process of processing. In this paper, this general problem is solved with the task. First, it constructs a data acquisition system consisting of a variety of sensors, including temperature, optical power signals, visual images, and vibration sensors. Low-level fusion algorithms are proposed to solve the problem of asynchronization, noise, and multiscale characteristics by layer-by-layer, obtaining a stable high-dimensional state description. Then, the data-driven model of the fusion state creates a path with a simple model. This algorithm combines reinforcement learning and heuristic algorithms, considering the uniformity of energy and smoothness of latitude to generate the optimal track. An intelligent regulatory mechanism and an adaptive feedback control mechanism are introduced, and laser power and scanning speeds are dynamically adjusted. The simulation and real experiments verify that the proposed system is better than traditional methods for path accuracy, surface quality, and energy consumption. Multi-sensor fusion and data-driven thinking strategies as the rapid development direction of laser processing system construction are feasible. This article provides new ideas and technical support for the construction and promotion of an intelligent laser processing system.
