Prediction of Layered Thermal Conductivity Using Artificial Neural Network in Order to Have Better Design of Ground Source Heat Pump System

Abstract

Ground source heat pumps (GSHPs) have been widely applied worldwide in recent years because of their high efficiency and environmental friendliness. An accurate estimation of the thermal conductivity of rock and soil layers is important in the design of GSHP systems. The distributed thermal response test (DTRT) method incorporates the standard test with a pair of fiber optic-distributed temperature sensors in the U-tube to accurately calculate the layered thermal conductivity of the rock/soil. In this work, in situ layered thermal conductivity was initially obtained by DTRT for four boreholes in the study region. A series of laboratory tests was also conducted on the rock samples obtained from drilling. Then, an artificial neural network (ANN) model was developed to predict the layered thermal conductivity on the basis of the DTRT results. The primary modeling factors were water content, density, and porosity. The results showed that the ANN models can predict the layered thermal conductivity with an absolute error of less than 0.1 W/(m·K). Finally, the trained ANN models were used to predict the layered thermal conductivity for another study region, in which only the effective thermal conductivity was measured with the thermal response test (TRT). To verify the accuracy of the prediction, the product of pipe depth and layered thermal conductivity was suggested to represent heat transfer capacity. The results showed that the discrepancies between the TRT and ANN models were 5.43% and 6.37% for two boreholes, respectively. The results prove that the proposed method can be used to determine layered thermal conductivity.Energies 2018, 11, 1896 2 of 25 influenced by many factors, with the most important one being the ground thermal conductivity around GHE [10,11] (thermal conductance is the quantity of heat that passes in unit time through a plate of unit area and thickness when its opposite faces differ in temperature by one kelvin, and defines the units as W/(m·K)). Two methods are available to measure GHE performance: laboratory experiments and thermal response test (TRT) methods [12][13][14][15]. Laboratory experiments test the thermal parameters of rock and soil samples collected at the test site using a steady-or non-steady-state heat flow method. This method can test the thermal conductivity of each layer of different soil and rock types. However, considering that the disturbance of soil or rock mass during the sampling process causes larger measurement errors, the resulting parameters cannot be directly applied to the GSHP design. The TRT method for GHE was first proposed by Mogensen [16], and is referred to as "the standard TRT" in the current study. The standard TRT simulates the actual operation of the project by cooling or heating a cycle medium at a constant power, and records the inlet and outlet fluid temperature variations during the test period. The standard TRT obtains comprehensive in situ borehole thermal parameters by analyzing the temperature data based on the line heat source model [17]. It is ...

Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network

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