【作者】 李坤；

【导师】 蔡姗姗；

【作者基本信息】 华中科技大学 ， 动力工程， 2020， 硕士

【摘要】 地源热泵技术在建筑中的应用是解决当今能源耗费过快问题的有效方法之一。浅层岩土的热物性参数作为地源热泵系统设计的核心,其测试结果的准确性对浅层地热能的开发利用具有重要意义。本文基于地质统计学中的两种空间插值法——克里金法和序贯高斯法对武汉市区浅层岩土的主要热物性参数(有效导热系数和体积比热容)分布进行模拟,并提出序贯高斯法的优化方案,改进对该区域热物性参数分布的预测精度,为地源热泵系统的优化设计提供一定的参考依据。本文首先对武汉某区域内30个钻孔的热响应测试数据进行收集与整理,得到含有钻孔热物性参数以及坐标信息的研究区域图。考虑到分区处理下地质因素对空间模拟的影响,对该区域地质图进行了简绘,将所得的地质信息与研究区域图进行了结合,并分别对整体区域和局部区域的变异函数进行了构建,最终得到用于模拟的参数信息。在预处理基础上,依次运用克里金模型和单、多次实现下的序贯高斯模型对研究区域有效导热系数进行预测并与实测数据进行对比分析。分析结果发现,克里金法在描述参数分布规律方面较差,具有平滑效应,但其预测精度较高。单次实现下的序贯高斯模拟结果恰好相反,而多次实现取平均可解决精度较差的问题但又克服不了平滑效应。通过制图效果、制图精度等方面的对比,选取序贯高斯模型进行优化。在序贯高斯模型中应用常规分区处理方法可更准确地描述参数的分布规律,但在预测精度上却有所降低。因此,结合影响分区处理精度的两个因素——变异函数和分布函数,可对序贯高斯模型进一步进行优化。优化结果表明,当各区域内的整体变异函数用高斯结构的区域变异函数替换,并选用三角分布函数描述其内部数据的统计规律时,分区处理后的热导图在预测精度上相较于普通序贯高斯模型结果有所提升,同时在描述参数分布规律方面呈现出更多细节,基于优化方案预测出的热导图与实测数据的相对误差可控制在3.54%～12.55%。参考热导图的模拟方法,对区域热容图进行构建,预测出的热容图与实测数据的相对误差为10.43%～15.11%,预测精度相较热导图有所降低。可能需获取更多数据点来增强其空间自相关性以及选取适合的分布函数模型,从而提升预测精度。更多还原

【Abstract】 The application of GSHP(Ground Source Heat Pump)in the architecture is one of the effective ways to solve the problem of excessive energy consumption.As the core of the design of GSHP systems,the accuracy of the measurement of the thermophysical parameters of the shallow geotechnical has great significance for the development and utilization of the geotemperature energy.In this paper,two types of spatial interpolation methods in geostatistics——Kriging and SGS(Sequential Gaussian Simulation)are used to simulate the distribution of the thermophysical parameters(the effective thermal conductivity and the volumetric specific heat capacity)of the shallow geotechnical in Wuhan city.In addition,an optimization approach of SGS is proposed to improve the prediction accuracy of the distribution of the thermophysical parameters in this area,which provides theoretical basis for the optimal design of GSHP systems.In this paper,the thermal response test data of 30 boreholes in Wuhan city are collected and pretreated.The data map are prepared by including information related to the thermophysical parameters and the local coordinates of boreholes.Considering the influence of geological factors on the spatial simulation during partition processing,the complex geological map is simplified and combined with the data map.Based on the data distribution,the variogram of the entire and the local regions are constructed,respectively.By using the regional parameters and variograms derived during the pretreatment,the Kriging model and the SGS model(single realization and multiple realizations)are developed to predict the effective thermal conductivity in the study area.The simulated results are compared to the data derived from local thermal response tests.The comparative results show that the Kriging method is weak in describing the distribution of parameters and has smoothing effect,but the average prediction accuracy is high.The SGS method with single realization behaves contrary to the previous characteristic of Kriging method.Averaging the results in multiple realizations can solve the poor accuracy existed in single realization,but cannot overcome the smoothing effect.Although the application of conventional partition processing in SGS model can describe the distribution of parameters more accurately,the prediction accuracy is reduced.Therefore,the SGS model can be further optimized by combining the two factors that affect the precision of partition processing——the variogram and the distribution function.With the improved partition processing method,which is substituting the global variogram with the regional variogram of Gaussian structure,and selecting the triangular distribution function to describe the statistical law of its internal data,the prediction accuracy of the thermal conductivity map is improved and more details are presented in describing the regularities of distribution of parameters.The relative error between the thermal conductivity map predicted based on the optimization scheme and the thermal response data can be controlled within 3.54% ～ 12.55%.By applying the same method used in the construction of thermal conductivity map,the heat capacity map is built and discussed.The relative error between the predicted heat capacity map and the thermal response data is 10.43% ～ 15.11%.The prediction accuracy is lower than that of the thermal conductivity map.In order to improve the accuracy,it’s necessary to acquire more data to enhance their spatial autocorrelation and rebuilt the distribution function model by considering the collected data of heat capacity.更多还原