基于模型优化预测与流场分析的温室能耗控制方法

发布时间：2018-04-15 07:08

本文选题：农业温室 + 自适应速遗传粒子群算法　；参考：《浙江大学》2016年博士论文

【摘要】：现代温室是一种高能耗的抗逆性生产设施,降温和加温都需要消耗大量的能源,节约温室能耗已成为设施农业发展的突出问题。因此,为降低能耗和提高温室热性能,开展了温室能耗建模、降温与加温条件下的温室内计算流体(CFD)建模、基于流场分析的温室环境优化设计、基于CFD流场与能耗预测模型(EPM)的温室能耗控制等方面的研究。第1章:综述温室能耗模型、室内CFD建模和温室能耗控制方法的研究现状,分析了课题的研究背景和意义,给出了本课题的研究内容和总体框架。第2章:针对温室能量传递过程的非线性、多因素耦合等特点,提出了一种温室降温能耗的模型优化预测(MOP)方法。建立了温室能耗预测物理模型,结合Sobol'灵敏度分析方法对物理模型中的待定参数进行简化,有效提高了模型参数辨识过程的收敛速度;提出了一种面向温室能耗预测的自适应粒子群遗传算法(APSO-GA),实现温室降温能耗的快速和准确预测;通过在Venlo型温室进行降温实验,验证了模型及算法的有效性。第3章:针对半封闭式温室加温物理过程的特征,建立了温室的加热能耗预测模型,并结合自加速粒子群遗传算法(SPSO-GA)优化模型的待定参数,实现半封闭式温室加温能耗的准确预测;通过上海地区半封闭式温室的加温实验,证明了温室能耗的MOP方法具有较广泛的适用性。第4章:分析温室内空气流体动力学属性,基于Darcy-Forchheimer方程推导出作物对室内环境影响的质量、动力和能量源项方程;结合标准k-ε模型,构建并求解温室湿帘-风机降温条件下的三维非稳态模型;通过仿真分析温室的降温性能,揭示了湿帘面积和风机速度参数对室内降温环境的影响规律;提出了一种基于CFD流场分析的温室降温系统设计方法,实现不同温室条件下的降温湿帘面积和风机参数的合理选配。第5章:利用温室能耗预测模型分析了温室的热负荷,设计了浅表水源热泵加热系统(SWSHPS);采用Lam-Bremhorst低雷诺数模型构建温室加热系统CFD模型,实现了室内的风机盘管位置与分布的优化设计。提出了一种结合能耗预测和流场分析的温室加热系统的设计方法,并通过实验验证了设计方法的有效性。第6章:采用CFD离线预测方法确定风机盘管组的优先级,结合EPM在线预测和优化温室的能耗需求,提出了一种基于CFD-EPM的温室加热控制方法。构建实验平台,编写相应的控制软件,分析和评估CFD-EPM控制方法的效能,验证了控制方法具有能耗低、控制精度高和响应速度快等优势。第7章:总结了本文的主要研究内容和创新点,对未来的研究工作进行了展望。
[Abstract]:Modern greenhouse is a kind of production facility with high energy consumption. Cooling and heating need to consume a lot of energy. Saving energy consumption in greenhouse has become a prominent problem in the development of facility agriculture.Therefore, in order to reduce the energy consumption and improve the thermal performance of greenhouse, the modeling of greenhouse energy consumption, the modeling of CFD under the condition of cooling and heating, and the optimal design of greenhouse environment based on flow field analysis are carried out.The study of greenhouse energy consumption control based on CFD flow field and energy consumption prediction model.Chapter 1: review the research status of greenhouse energy consumption model, indoor CFD modeling and greenhouse energy consumption control methods, analyze the research background and significance of the subject, and give the research content and overall framework.Chapter 2: according to the characteristics of nonlinear and multi-factor coupling of greenhouse energy transfer process, a model optimization prediction method for energy consumption in greenhouse is proposed.The physical model of greenhouse energy consumption prediction is established, and the undetermined parameters in the physical model are simplified with the Sobol' sensitivity analysis method. The convergence rate of the model parameter identification process is improved effectively.An adaptive particle swarm optimization genetic algorithm (APSO-GAA) for energy consumption prediction in greenhouse is proposed to predict the energy consumption of greenhouse quickly and accurately, and the validity of the model and algorithm is verified by the cooling experiment in Venlo greenhouse.Chapter 3: according to the characteristics of physical process of semi-closed greenhouse heating energy consumption prediction model is established and the undetermined parameters of the model are optimized by using self-accelerating particle swarm optimization algorithm (SPSO-GA).The energy consumption of semi-closed greenhouse is predicted accurately, and the MOP method of greenhouse energy consumption is proved to be widely applicable by the experiment of semi-closed greenhouse in Shanghai.Chapter 4: analyze aerodynamics properties in greenhouse, derive mass, dynamic and energy source equation of crop effect on indoor environment based on Darcy-Forchheimer equation, combine with standard k- 蔚 model,The three-dimensional unsteady model under the condition of wet curtain and fan cooling in greenhouse is constructed and solved, and the influence of the wet curtain area and fan speed parameters on indoor cooling environment is revealed through the simulation analysis of the cooling performance of greenhouse.A design method of greenhouse cooling system based on CFD flow field analysis is proposed to realize the reasonable selection of cooling wet curtain area and fan parameters under different greenhouse conditions.Chapter 5: the heat load of greenhouse is analyzed by using the energy consumption prediction model of greenhouse, the shallow water source heat pump heating system is designed, and the CFD model of greenhouse heating system is constructed by using Lam-Bremhorst low Reynolds number model.The optimum design of the position and distribution of the fan-coil unit is realized.A design method of greenhouse heating system combining energy consumption prediction and flow field analysis is proposed and the effectiveness of the design method is verified by experiments.Chapter 6: the CFD off-line prediction method is used to determine the priority of the fan coil unit. Combined with the EPM on-line prediction and optimization of greenhouse energy consumption requirements, a greenhouse heating control method based on CFD-EPM is proposed.The experimental platform is constructed, the corresponding control software is compiled, and the effectiveness of CFD-EPM control method is analyzed and evaluated. It is proved that the control method has the advantages of low energy consumption, high control precision and fast response speed.Chapter 7: the main contents and innovations of this paper are summarized, and the future research work is prospected.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S625;O35

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