Research On Characteristics Of Receiver In Solar Power Tower System

The solar power tower system is considered to be a promising power generation technology.With the introduction and improvement of the policies in China,the solar power tower plant plays an important role in the low-carbon revolution,which is an important part in the new energy industry.The receiver servered as a key part of the solar thermal power plant converts solar energy into heat energy,which plays a crucial role in the efficiency and economy of the whole plant.In order to understand the complex physical mechanisms to ensure the safe and efficient operation of the solar power plant,the thermal performances of the receiver are researched in this thesis.The main contributions can be summarized as follows.(1)The complicated multi-physical models coupling flow-heat transfer-mechanics in a receiver are established.The temperature distributions and the thermal stress distributions of the tube wall,as well as the temperature and velocity distributions of the molten salt are numerically studied.The highest temperature of the tube wall,the highest temperature and the mean temperature of the molten salt at the outlet of the collector tube under different wind velocities are obtained.The characteristics of the above parameters with the variations of the inlet temperature and inlet velocity of the molten salt are revealed.The thermal stress and displacement distribution laws of the tube wall are discussed.(2)The back-propagation neural network(BPNN)method is developed to model the temperature prediction task of the molten salt and the tube wall.The proposed method can overcome the disadvantages in the common numerical methods,e.g.,the solution of complicated governing equations,high computational complexity,long computation period,and inaccurate initial conditions,boundary conditions,geometric conditions and thermal physical parameters of materials.The numerical results validate that the proposed method has encouraging numerical performances,such as the high prediction precision,excellent robustness and satisfactory generalization ability.(3)With the assistant of the heat transfer and exergy transfer theory,an optimization model and constraint conditions are created to obtain the maximum available energy in a receiver.The gravitational search(GS)algorithm and the simulated annealing(SA)algorithm are used to solve the proposed optimal problems,and the optimal working condition is obtained.The results reveal the quantity and quality of energy transfer along the flow path of the molten salt in the collector tube under the optimal inlet temperature and inlet velocity of the molten salt.The research findings provide a beneficial reference for the efficient utilization of the solar energy.(4)An inversion model is proposed to estimate the heat flux distribution outside the collector tube from the given limit number of the temperature measurement data,which overcome the difficulty of directly measuring the heat flux profile.A loss function is proposed to convert the inverse problem into an optimization problem,and the Broyden-Fletcher-Goldfarb-Shanno(BFGS)algorithm is employed to seek for the optimal solution of the proposed model.The numerical results indicate that the proposed method can accurately estimate the heat flux distribution in accordance with the limited number of the temperature measurement data.As a result,a promising method is introduced to estimate the heat flux distribution focused on the surface of the receiver in real-world applications.(5)With the assistance of the least square support vector machine(LSSVM)method and the Gaussian process regression(GPR)technique,a new method is proposed to predict the two-phase convective heat transfer coefficient in the collector tube,which overcomes the drawbacks of the experimental method,i.e.,long period,high cost,time-consuming and high computational complexity.Moreover,different from common empirical correlation methods,the propose method does not predetermine the function expression of the variables.The group search optimizer(GSO)algorithm is employed to solve the optimal hyper-parameters in the LSSVM method to improve the prediction accuracy.The proposed method can reduce the experiment period and testing cost,reduce the period of design,which provides a new insight for studying the heat transfer characteristics of the gas-liquid two-phase flow in the receiver.The research findings provide the scientific evidences for the safe and efficient operation of the receiver and the large-scale applications of solar thermal power generation technologies,which make contributions for the energy saving and emission reduction strategy in China.