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U-bend deep borehole heat exchanger (DBHE) is a new promising kind of ground heat exchanger, but nowadays there are lack of accurate efficient models to simulate U-bend DBHE. This paper has developed a semi-analytical model of U-bend DBHE, which considers the unsteady heat transfer of borehole, heterogenous soil thermal properties and geothermal gradient. A 2D numerical model and an analytical model based on infinite line-source theory are also developed, and the three models are compared with experimental data for different boundary conditions. Based on the soil thermal properties estimated by logging data and rock identification, the three models have large errors when compared with experimental data. Based on a soil thermal conductivity λ s estimated by matching experimental data, the three models match better with experimental data, and it is inferred that λ s should be measured by thermal response test instead of rock identification. The semi-analytical model and 2D numerical model match very well for all the time, but the analytical model has large differences with 2D numerical model, which is because the analytical model ignores borehole heat capacity and time-varying heat flows at the borehole wall. This paper could provide a cost-effective semi-analytical model and some advices for U-bend DBHE. [ABSTRACT FROM AUTHOR]

U-bend deep borehole heat exchanger (DBHE) is a new promising kind of ground heat exchanger, but nowadays there are lack of accurate efficient models to simulate U-bend DBHE. This paper has developed a semi-analytical model of U-bend DBHE, which considers the unsteady heat transfer of borehole, heterogenous soil thermal properties and geothermal gradient. A 2D numerical model and an analytical model based on infinite line-source theory are also developed, and the three models are compared with experimental data for different boundary conditions. Based on the soil thermal properties estimated by logging data and rock identification, the three models have large errors when compared with experimental data. Based on a soil thermal conductivity λ s estimated by matching experimental data, the three models match better with experimental data, and it is inferred that λ s should be measured by thermal response test instead of rock identification. The semi-analytical model and 2D numerical model match very well for all the time, but the analytical model has large differences with 2D numerical model, which is because the analytical model ignores borehole heat capacity and time-varying heat flows at the borehole wall. This paper could provide a cost-effective semi-analytical model and some advices for U-bend DBHE. [ABSTRACT FROM AUTHOR]

Accurately estimating ground thermal properties from thermal response tests (TRTs) is critical to design ground source heat pump system (GSHPS). The traditional method may lead to large errors due to the difference between the heat transfer process described by identification models and actual situation. To avoid it, this paper proposes a high-precision identification method based on artificial neural network (ANN), which can directly establish the nonlinear mapping relationship between thermal response parameters (TRPs) and ground thermal properties. Through the inversed orthogonal method, the training and validation samples are obtained from a large number of TRTs on a full-scale simulation platform that is verified by experiments. The estimation accuracy of traditional method and ANN under different ground thermal properties is studied. The results indicate that the estimation accuracy of traditional method varies greatly under different ground thermal properties, and the relative errors of identifying thermal conductivity and volumetric heat capacity vary from −3.61% to 60.14% and −52.06%–110.20% respectively. The estimation accuracy of ANN is almost not affected by the ground thermal properties, and the corresponding errors range from −7.78% to 0.28% and −1.75%–15.6% respectively. This paper provides a new perspective to reduce error caused by identification model. Image 1 • A high-precision identification method for the ground parameters identification is proposed. • The new concept enables deriving ground thermal properties without using identification model. • A three-layer neural network based on back propagation algorithm is constructed. • Ground thermal conductivity and volumetric specific heat are evaluated by conventional method and neural network. • The estimation accuracy of the constructed neural network is almost not affected by the true thermal properties of ground. [ABSTRACT FROM AUTHOR]

The ground thermal properties and borehole thermal resistance are the essential parameters for the design of borehole heat exchanger (BHE) field, and they are usually estimated using the experimental inlet/outlet fluid temperatures of BHE in thermal response test (TRT). This paper proposes the coupled two-step parameter estimation procedure (TSPEP) for estimating ground thermal conductivity and borehole thermal resistance of BHE by evaluating the actual averaged–over-the –depth mean fluid temperature (MFT) using the quasi-three-dimensional model inside the borehole. The simulated annealing algorithm (SAA) is used to iteratively find the minimum values of the two objective functions to obtain the optimal estimated results. In TSPEP, the estimated ground volumetric heat capacity and weighted factor f in the 1st step are transferred to calculate MFT using the experimental data in the 2nd step, which guarantees the direct approach based on the infinite line source model (ILSM)applied to improve the accuracy of the estimated borehole thermal resistance. For 50 m depth BHE, the estimated borehole thermal resistance is increased by 12.1% using TSPEP than the effective borehole thermal resistance evaluated by the arithmetic average fluid temperature (AFT). The estimated ground thermal conductivity in TSPEP is almost same with that from the direct approach based on ILSM, and the maximum relative error between them is only 0.91% even though borehole depth of BHE changes from 50 m to 200 m. • Averaged-over-the-depth mean fluid temperature calculates borehole thermal resistance R b. • A two-step parameter estimation procedure (TSPEP) is proposed to estimate R b in TRT. • The feasibility and accuracy of TSPEP are validated and discussed. [ABSTRACT FROM AUTHOR]

Ground thermal properties (thermal conductivity and heat capacity) are the key parameters for design of borehole heat exchanger (BHE) in ground source heat pump (GSHP). This study presented a novel sequential estimation for ground thermal properties of coaxial BHE by using distributed thermal response test (DTRT) data. Firstly, a comprehensive heat transfer model for coaxial BHE was built to obtain the vertical temperature files. Then a new sensitivity analysis using Spearman correlation method was performed to obtain the correlation between the thermal properties and fluid vertical temperature, determining the estimation sequence. Lastly, according to the sequence, Monte Carlo stochastic method was used to estimate the thermal properties by applying the DTRT data. In addition, the effect of borehole depth and random samples on estimations was investigated. The simulations revealed that the heat input into BHE was mainly used to heat the circulating fluid during the earlier time. It was found that the estimation steps following the thermal conductivity first, then heat capacity got higher precision than other estimation steps with 0.6% and 4% respectively for the two parameters. In addition, the effect of borehole depth on estimation error could be eliminated by using the DTRT data. • DTRT data was interpreted for ground thermal properties estimation. • Sequential parameters estimation was presented for DTRT analysis. • The estimation precision for ground heat capacity was improved to 4%. • Effect of borehole depth on estimate error was eliminated by using DTRT data. [ABSTRACT FROM AUTHOR]

Thermal properties are very important for the design of ground heat exchangers (GHE). The line source model is typically used to identify these parameters, based on the inlet and outlet water temperature of an in-situ thermal response test (TRT). Since the model is based on thermal conduction, convection of groundwater can affect identification results. In this paper, a three-dimensional numerical simulation model was established to simulate the dynamic heat exchange for GHEs. A series of TRTs were conducted to analyse the effect of groundwater seepage on identification of thermal properties of soils. The equivalent thermal conductivity (ETC) was found to be increased gradually, while the equivalent volume heat capacity (EVHC) and the change rates of both parameters decreased with increases in groundwater velocity. The seepage direction was vital for identification, especially when the velocity was fast. The ETC was larger and the EVHC was smaller when the seepage direction was perpendicular as opposed to parallel to the buried pipe plane. The height of the seepage layer significantly affected identification results, and the greater the seepage velocity was, the more significant the effect was. In addition, the higher height led to the larger ETC and the smaller EVHC. The location of the seepage layer had little influence on results, but the ETC was larger and the EVHC was smaller if the location was closer to the ground surface. • An accurate 3-D numerical simulation model was established and verified. • The effect of seepage velocity and direction on the identification results of TRT was analysed. • The effect of height and location of the aquifer was also studied. [ABSTRACT FROM AUTHOR]