Several concentrated solar power demonstration plants are being constructed, and a few commercial plants have been announced in northwestern China. However, the mutual impacts between the concentrated solar power plants and their surrounding environments have not yet been addressed comprehensively in literature by the parties involved in these projects. In China, these projects are especially important as an increasing amount of low carbon electricity needs to be generated in order to maintain the current economic growth while simultaneously lessening pollution. In this study, the authors assess the potential environmental impacts of large-scale concentrated solar power plants. Specifically, the water use intensity, soil erosion and soil temperature are quantitatively examined. It was found that some of the impacts are favorable, while some impacts are negative in relation to traditional power generation techniques and some need further research before they can be reasonably appraised. In quantitative terms, concentrated solar power plants consume about 4000 L MW(-1) h(-1) of water if wet cooling technology is used, and the collectors lead to the soil temperature changes of between 0.5 and 4 °C; however, it was found that the soil erosion is dramatically alleviated. The results of this study are helpful to decision-makers in concentrated solar power site selection and regional planning. Some conclusions of this study are also valid for large-scale photovoltaic plants.
A time domain numerical approach is carried out to enhance the understanding of three dimensional blade row aeroelastic characteristics under the parallel computation. The vibration energy of unsteady aerodynamic force on the entire blade row is investigated using numerical solution of 3-D Navier-Stokes equations, coupled with structure finite element models for the blades to identify modal shapes and the structural deformations simultaneously. Interactions between fluid and structure are dealt with in a coupled manner, based on the interface information exchange until convergence in each time step. With this approach good agreement between the numerical results and the experimental data is observed. The flutter mechanism is analyzed according to deformation of the blades. The effect of inter-blade phase angle (IBPA) is included in the analysis by releasing the hypothesis of constant phase angle between adjacent blades in the traveling wave model. The results illustrate fully three dimensional unsteady nonlinear behaviors, such as limit-cycle oscillation. It is shown that all blades flutter at the same mode and frequency, but not at the same amplitude and IBPA. The analysis of the influence of different tip clearance gaps on the flutter characteristics of the blade row is also performed.
Focusing on the identification of dynamic system stability, a hybrid neural network model is proposed in this research for the rotating stall phenomenon in an axial compressor. Based on the data fusion of the amplitude of the spatial mode, the nonlinear property is well characterized in the feature extraction of the rotating stall. This method of data processing can effectively avoid the inaccurate recognition of single or multiple measuring sensors only depending on pressure. With the analysis on the spatial mode, a chaotic characteristic was shown in the development of the amplitude with the first-order spatial mode. With the prerequisite of revealing the essence of this dynamic system, a hybrid radial basis function (RBF) neural network was adopted to represent the properties of the system by artificial intelligence learning. Combining the advantages of the methods of K-means and Gradient Descent (GD), the Chaos–K-means–GD–RBF fusion model was established based on the phase space reconstruction of the chaotic sequence. Compared with the two methods mentioned above, the calculation accuracy was significantly improved in the hybrid neural network model. By taking the strategy of global sample entropy and difference quotient criterion identification, a warning of inception can be suggested in advance of 12.3 revolutions (296 ms) with a multi-step prediction before the stall arrival.
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