Prediction On The Distribution Of Geotechnical Thermophysical Parameters In Specific Region

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.