塔式太阳能吸热器特性研究

发布时间：2018-04-13 20:35

本文选题：塔式太阳能吸热器 + 温度分布预测　；参考：《华北电力大学(北京)》2017年博士论文

【摘要】：作为新能源产业的重要组成部分,塔式太阳能发电系统被认为是一种具有广阔发展前景的发电技术。随着国家对光热发电产业政策的陆续出台和不断完善,塔式太阳能技术将在清洁能源领域扮演举足轻重的角色。吸热器将太阳辐射能转化为热能,是塔式电站的核心部分,直接影响到整个发电系统的效率和经济性。为理解吸热器中所涉及的复杂物理机制,确保塔式太阳能电站安全、高效运行,本文对吸热器的热特性进行深入研究。主要工作如下:(1)建立了吸热器流动-传热-力学效应耦合的复杂多物理场模型,数值研究了管壁的温度分布和热应力分布,熔盐的温度分布和流速分布,揭示了在不同风速条件下熔盐出口平均温度和最高温度、管壁最高温度随熔盐进口温度和进口流速的变化规律,以及管壁热应力和位移分布规律。(2)建立了基于BP神经网络(Back-propagation neural network,BPNN)的吸热器熔盐温度和管壁温度预测模型,克服了传统数值计算方法需要求解复杂的控制方程,计算复杂性高,计算时间长,难以准确给定初始条件、边界条件、几何条件、物性参数等弊端。数值研究结果证实了该方法具有预测精度高、鲁棒性好、泛化能力强等优点。(3)基于热传递和?传递理论,建立了吸热器获得最大有用能的最优化模型,利用万有引力(Gravitational search,GS)算法和模拟退火(Simulated annealing,SA)算法分别求解单根吸热管以及整个吸热器获得最大能源利用效率的最优化问题,获得最优运行工况。揭示了在管内熔盐最优进口温度和流速下沿熔盐流动方向能量传递的数量和质量的细节,为高效利用太阳能提供了科学依据。(4)提出了基于传热反问题的利用有限温度测量数据反演吸热管外热流分布的方法,克服难以直接测量管外热流分布难题。通过建立目标泛函将反问题转化为一个最优化问题的求解,采用Broyden-Fletcher-Goldfarb-Shanno(BFGS)算法有效地求解该模型。数值实验表明,该方法能够利用有限的温度测量数据准确反演管外热流分布,为在实际应用中获取吸热器表面热流分布提供一种有效方法。(5)提出一种利用最小二乘支持向量机(Least square support vector machine,LSSVM)和高斯过程回归(Gaussian process regression,GPR)快速预测吸热管中两相对流换热系数的方法,克服了实验方法周期长、成本高,数值计算方法时间成本高、计算复杂性高,尤其是经验公式(Empirical correlation,EC)法需要预先确定函数关系式等弊端。利用群搜索(Group search optimizer,GSO)算法优化最小二乘支持向量机模型的超参数,改善预测质量。该方法有利于减少实验次数和实验成本,缩短设计周期,为研究吸热器中气液两相流的换热特性提供一种有效方法。研究发现对确保吸热器安全高效运行、促进太阳能光热发电技术大规模应用提供了科学依据,为我国的节能减排战略做出贡献。
[Abstract]:As an important part of the new energy industry, the tower solar power generation system is considered to be a kind of power generation technology with broad development prospects.With the introduction and improvement of national policies on photothermal power generation industry, tower solar energy technology will play an important role in the field of clean energy.Heat absorber converts solar radiation energy into heat energy, which is the core part of tower power station, which directly affects the efficiency and economy of the whole power generation system.In order to understand the complex physical mechanism involved in the heat absorber and ensure the safe and efficient operation of the tower solar power station, the thermal characteristics of the absorber are studied in this paper.The main work is as follows: (1) A complex multi-physical field model coupled with flow-heat transfer and mechanical effects of heat exchanger is established. The temperature distribution and thermal stress distribution of tube wall, the temperature distribution and velocity distribution of molten salt are numerically studied.The variation of the average and maximum temperature at the outlet of molten salt and the maximum temperature of pipe wall with the inlet temperature and inlet velocity of molten salt under different wind speeds are revealed.Based on BP neural network Back-propagation neural Network (BPNN), the prediction model of molten salt temperature and pipe wall temperature of heat absorber is established. It overcomes the need of solving complex control equation by traditional numerical calculation method, and the complexity of calculation is high.It is difficult to accurately set initial condition, boundary condition, geometry condition, physical parameter and so on.The numerical results show that the proposed method has the advantages of high prediction accuracy, good robustness and strong generalization ability.Based on the transfer theory, an optimization model for the maximum useful energy of the heat absorber is established. Using the Gravitational search (GS) algorithm and the simulated annealing simulated annealing (SA) algorithm, the optimization problems of the single endothermic tube and the whole heat absorber to obtain the maximum energy efficiency are solved, respectively.The optimal operating conditions are obtained.The details of the quantity and quality of energy transfer along the flow direction of molten salt under the optimum inlet temperature and velocity of molten salt in the tube are revealed.This paper provides a scientific basis for the efficient use of solar energy. (4) A method for retrieving the heat flux distribution outside the endothermic tube using finite temperature measurement data based on the inverse heat transfer problem is proposed to overcome the difficulty of directly measuring the heat flux distribution outside the tube.The inverse problem is transformed into an optimization problem by establishing the objective functional, and the Broyden-Fletcher-Goldfarb-ShannoBFGSalgorithm is used to solve the model effectively.Numerical experiments show that the proposed method can accurately retrieve the heat flux distribution outside the tube using the limited temperature measurement data.In order to provide an effective method for obtaining heat flux distribution on the surface of heat absorber in practical application, a fast prediction method of two-phase convection heat transfer coefficient in endothermic pipes using least square support vector machine (LSSVM) and Gao Si process regression Gaussian process regression (GPRs) is presented.It overcomes the disadvantages of long period, high cost, high time cost and high computational complexity of the experimental method, especially the empirical formula empirical correlation (ECC) method needs to determine the function relation in advance.The group search group search optimizer (GSO) algorithm is used to optimize the super-parameters of the least squares support vector machine (LS-SVM) model to improve the prediction quality.This method is helpful to reduce the number and cost of experiments, shorten the design period, and provide an effective method for studying the heat transfer characteristics of gas-liquid two-phase flow in an absorber.It is found that it provides a scientific basis for ensuring the safe and efficient operation of the absorber and promotes the large-scale application of solar photothermal power generation technology and contributes to the strategy of energy saving and emission reduction in China.
【学位授予单位】：华北电力大学(北京)
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TM615

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