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Sara Focaccia - One of the best experts on this subject based on the ideXlab platform.
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Estimating Thermal Response Test Coefficients: Choosing Coordinate Space of The Random Function
Mathematical Geosciences, 2016Co-Authors: Roberto Bruno, Francesco Tinti, Sara FocacciaAbstract:In shallow geoThermal systems, the main equivalent underground Thermal properties are commonly calculated with a Thermal Response Test (TRT). This is a borehole heat exchanger production Test where the temperature of a heat transfer fluid is recorded over time at constant power heat injection/extraction. The equivalent Thermal parameters (Thermal conductivity, heat capacity) are simply deduced from temperature data regression analysis that theoretically is a logarithmic function in the time domain, or else a linear function in the log-time domain. By interpreting the recorded temperatures as a regionalized variable whose drift is the regression function, in both cases the formal problem is a linear estimation of the mean. If the autocorrelation function (variogram, covariance) of residuals is known, coefficient variance can be directly deduced. Coefficient estimates are independent of the drift form adopted, and the residuals are the same in the same points. The random function is different in the time domain, however, and in the log-time domain. In fact, residual variograms are different due to the transformation of the coordinate space. This paper uses a TRT case study to examine the consequences of coordinate space transformation for a random function, namely its variogram. The specific question addressed is the choice of coordinate space and variogram.
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Thermal Response Test for shallow geoThermal applications: a probabilistic analysis approach
Geothermal Energy, 2015Co-Authors: Francesco Tinti, Roberto Bruno, Sara FocacciaAbstract:Background Thermal Response Test (TRT) is an onsite Test used to characterize the Thermal properties of shallow underground, when used as heat storage volume for shallow geoThermal application. It is applied by injecting/extracting heat into geoThermal closed-loop circuits inserted into the ground. The most common types of closed loop are the borehole heat exchangers (BHE), horizontal ground collectors (HGC), and energy piles (EP). The interpretation method of TRT data is generally based on a regression technique and on the calculation of Thermal properties through different models, specific for each closed loop and Test conditions. Methods A typical TRT record is a graph joining a series of experimental temperatures of the Thermal carrier fluid. The proposed geostatistical approach considers the temperature as a random function non-stationary in time, with a given trend, therefore the record is considered as a ‘realization’, one of the possible results; the random nature of the Test results is transferred to the fluctuations and a variogram modeling can be applied, which may give many information on the TRT behavior. Results In this paper, a nested probabilistic approach for TRT output interpretation is proposed, which can be applied for interpreting TRT data, independently of the different methodologies and technologies adopted. In the paper, for the sake of simplicity, the probabilistic approach is applied to the ‘infinite line source’ (ILS) methodology, which is the most commonly used for BHE. Conclusions The probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t _0 and the final time t _f) inside which makes temperature regression analysis possible. Moreover, variograms are used into the analysis itself to increase estimation precision of Thermal parameter calculation (ground conductivity λ _g, ground capacity c _g, borehole resistance R _b). Finally, the probabilistic approach helps keep under control the effect of any cause of result variability. Typical behaviors of power, flows, and temperatures and of their interaction with the specific closed-loop circuit and geo-hydrological system are deepened by variogram analysis of fluctuations.
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Thermal Response Test for shallow geoThermal applications a probabilistic analysis approach
Geothermal Energy, 2015Co-Authors: Francesco Tinti, Sara Focaccia, Roberto BrunoAbstract:Thermal Response Test (TRT) is an onsite Test used to characterize the Thermal properties of shallow underground, when used as heat storage volume for shallow geoThermal application. It is applied by injecting/extracting heat into geoThermal closed-loop circuits inserted into the ground. The most common types of closed loop are the borehole heat exchangers (BHE), horizontal ground collectors (HGC), and energy piles (EP). The interpretation method of TRT data is generally based on a regression technique and on the calculation of Thermal properties through different models, specific for each closed loop and Test conditions. A typical TRT record is a graph joining a series of experimental temperatures of the Thermal carrier fluid. The proposed geostatistical approach considers the temperature as a random function non-stationary in time, with a given trend, therefore the record is considered as a ‘realization’, one of the possible results; the random nature of the Test results is transferred to the fluctuations and a variogram modeling can be applied, which may give many information on the TRT behavior. In this paper, a nested probabilistic approach for TRT output interpretation is proposed, which can be applied for interpreting TRT data, independently of the different methodologies and technologies adopted. In the paper, for the sake of simplicity, the probabilistic approach is applied to the ‘infinite line source’ (ILS) methodology, which is the most commonly used for BHE. The probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t 0 and the final time t f) inside which makes temperature regression analysis possible. Moreover, variograms are used into the analysis itself to increase estimation precision of Thermal parameter calculation (ground conductivity λ g, ground capacity c g, borehole resistance R b). Finally, the probabilistic approach helps keep under control the effect of any cause of result variability. Typical behaviors of power, flows, and temperatures and of their interaction with the specific closed-loop circuit and geo-hydrological system are deepened by variogram analysis of fluctuations.
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Thermal Response Test numerical modeling using a dynamic simulator
Geothermal Energy, 2013Co-Authors: Sara FocacciaAbstract:Background Borehole heat exchangers are a growing technology in the area of house/building air conditioning, most of all in northern Europe. Methods In order to have a good project, we need to have a reliable value of ground Thermal conductivity, which is normally obtained by interpreting the data retrieved by running a Thermal Response Test. Different are the ways of interpreting the data provided by the Test (e.g., infinite line source theory, finite line source theory, etc.), and in this paper. Results We will first simulate a Thermal Response Test using finite element subsurface flow system, a heat and flow dynamic simulator. Conclusions Then, a sensitivity analysis of the effect of the different grout properties on the results of a Thermal Response Test is shown.
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Thermal Response Test numerical modeling using a dynamic simulator
Geothermal Energy, 2013Co-Authors: Sara FocacciaAbstract:Borehole heat exchangers are a growing technology in the area of house/building air conditioning, most of all in northern Europe. In order to have a good project, we need to have a reliable value of ground Thermal conductivity, which is normally obtained by interpreting the data retrieved by running a Thermal Response Test. Different are the ways of interpreting the data provided by the Test (e.g., infinite line source theory, finite line source theory, etc.), and in this paper. We will first simulate a Thermal Response Test using finite element subsurface flow system, a heat and flow dynamic simulator. Then, a sensitivity analysis of the effect of the different grout properties on the results of a Thermal Response Test is shown.
Roberto Bruno - One of the best experts on this subject based on the ideXlab platform.
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Estimating Thermal Response Test Coefficients: Choosing Coordinate Space of The Random Function
Mathematical Geosciences, 2016Co-Authors: Roberto Bruno, Francesco Tinti, Sara FocacciaAbstract:In shallow geoThermal systems, the main equivalent underground Thermal properties are commonly calculated with a Thermal Response Test (TRT). This is a borehole heat exchanger production Test where the temperature of a heat transfer fluid is recorded over time at constant power heat injection/extraction. The equivalent Thermal parameters (Thermal conductivity, heat capacity) are simply deduced from temperature data regression analysis that theoretically is a logarithmic function in the time domain, or else a linear function in the log-time domain. By interpreting the recorded temperatures as a regionalized variable whose drift is the regression function, in both cases the formal problem is a linear estimation of the mean. If the autocorrelation function (variogram, covariance) of residuals is known, coefficient variance can be directly deduced. Coefficient estimates are independent of the drift form adopted, and the residuals are the same in the same points. The random function is different in the time domain, however, and in the log-time domain. In fact, residual variograms are different due to the transformation of the coordinate space. This paper uses a TRT case study to examine the consequences of coordinate space transformation for a random function, namely its variogram. The specific question addressed is the choice of coordinate space and variogram.
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Thermal Response Test for shallow geoThermal applications: a probabilistic analysis approach
Geothermal Energy, 2015Co-Authors: Francesco Tinti, Roberto Bruno, Sara FocacciaAbstract:Background Thermal Response Test (TRT) is an onsite Test used to characterize the Thermal properties of shallow underground, when used as heat storage volume for shallow geoThermal application. It is applied by injecting/extracting heat into geoThermal closed-loop circuits inserted into the ground. The most common types of closed loop are the borehole heat exchangers (BHE), horizontal ground collectors (HGC), and energy piles (EP). The interpretation method of TRT data is generally based on a regression technique and on the calculation of Thermal properties through different models, specific for each closed loop and Test conditions. Methods A typical TRT record is a graph joining a series of experimental temperatures of the Thermal carrier fluid. The proposed geostatistical approach considers the temperature as a random function non-stationary in time, with a given trend, therefore the record is considered as a ‘realization’, one of the possible results; the random nature of the Test results is transferred to the fluctuations and a variogram modeling can be applied, which may give many information on the TRT behavior. Results In this paper, a nested probabilistic approach for TRT output interpretation is proposed, which can be applied for interpreting TRT data, independently of the different methodologies and technologies adopted. In the paper, for the sake of simplicity, the probabilistic approach is applied to the ‘infinite line source’ (ILS) methodology, which is the most commonly used for BHE. Conclusions The probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t _0 and the final time t _f) inside which makes temperature regression analysis possible. Moreover, variograms are used into the analysis itself to increase estimation precision of Thermal parameter calculation (ground conductivity λ _g, ground capacity c _g, borehole resistance R _b). Finally, the probabilistic approach helps keep under control the effect of any cause of result variability. Typical behaviors of power, flows, and temperatures and of their interaction with the specific closed-loop circuit and geo-hydrological system are deepened by variogram analysis of fluctuations.
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Thermal Response Test for shallow geoThermal applications a probabilistic analysis approach
Geothermal Energy, 2015Co-Authors: Francesco Tinti, Sara Focaccia, Roberto BrunoAbstract:Thermal Response Test (TRT) is an onsite Test used to characterize the Thermal properties of shallow underground, when used as heat storage volume for shallow geoThermal application. It is applied by injecting/extracting heat into geoThermal closed-loop circuits inserted into the ground. The most common types of closed loop are the borehole heat exchangers (BHE), horizontal ground collectors (HGC), and energy piles (EP). The interpretation method of TRT data is generally based on a regression technique and on the calculation of Thermal properties through different models, specific for each closed loop and Test conditions. A typical TRT record is a graph joining a series of experimental temperatures of the Thermal carrier fluid. The proposed geostatistical approach considers the temperature as a random function non-stationary in time, with a given trend, therefore the record is considered as a ‘realization’, one of the possible results; the random nature of the Test results is transferred to the fluctuations and a variogram modeling can be applied, which may give many information on the TRT behavior. In this paper, a nested probabilistic approach for TRT output interpretation is proposed, which can be applied for interpreting TRT data, independently of the different methodologies and technologies adopted. In the paper, for the sake of simplicity, the probabilistic approach is applied to the ‘infinite line source’ (ILS) methodology, which is the most commonly used for BHE. The probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t 0 and the final time t f) inside which makes temperature regression analysis possible. Moreover, variograms are used into the analysis itself to increase estimation precision of Thermal parameter calculation (ground conductivity λ g, ground capacity c g, borehole resistance R b). Finally, the probabilistic approach helps keep under control the effect of any cause of result variability. Typical behaviors of power, flows, and temperatures and of their interaction with the specific closed-loop circuit and geo-hydrological system are deepened by variogram analysis of fluctuations.
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a software tool for geostatistical analysis of Thermal Response Test data ga trt
Computers & Geosciences, 2013Co-Authors: Sara Focaccia, Francesco Tinti, Roberto BrunoAbstract:In this paper we present a new method (DCE - Drift and Conditional Estimation), coupling Infinite Line Source (ILS) theory with geostatistics, to interpret Thermal Response Test (TRT) data and the relative implementing user-friendly software (GA-TRT). Many methods (analytical and numerical) currently exist to analyze TRT data. The innovation derives from the fact that we use a probabilistic approach, able to overcome, without excessively complicated calculations, many interpretation problems (choice of the guess value of ground volumetric heat capacity, identification of the fluctuations of recorded data, inability to provide a measure of the precision of the estimates obtained) that cannot be solved otherwise. The new procedure is based on a geostatistical drift analysis of temperature records which leads to a precise equivalent ground Thermal conductivity (@l"g) estimation, confirmed by the calculation of its estimation variance. Afterwards, based on @l"g, a monovariate regression on the original data allows for the identification of the theoretical relationship between ground volumetric heat capacity (c"g) and borehole Thermal resistance (R"b). By assuming the monovariate Probability Distribution Function (PDF) for each variable, the joint conditional PDF to the c"g-R"b relationship is found; finally, the conditional expectation allows for the identification of the correct and optimal couple of the c"g-R"b estimated values.
Xuedan Zhang - One of the best experts on this subject based on the ideXlab platform.
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A New Correcting Algorithm for Thermal Response Test Data Evaluation
Environmental Science and Engineering, 2020Co-Authors: Xuedan Zhang, Tiantian Zhang, Yiqiang JiangAbstract:Thermal Response Test (TRT) has become a very popular method of evaluating geoThermal properties for ground-coupled heat pump systems. However, a Test must be carried out under certain operating conditions of the project it serves, which is a strong restriction to its standardization. Few studies have previously focused on how to deal with TRT results in alternative conditions. Based on line source model, the influences of different factors on TRT results were analyzed, and a new algorithm for correcting TRT results was developed in this paper. The algorithm was applied to a case study, and the results calculated using the new algorithm show that Thermal conductivity difference before and after comparison changed from −0.97 W/(m K) to 0.10 W/(m K) at different Test conditions, and the relative error between the corrected values of ground Thermal conductivity was reduced to 4.63%. Therefore, the new proposed correcting algorithm provides reference to the standardization of TRT and the generalization of Test conditions, which can help save Test time and cost caused by repeating Tests.
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comparison of four methods for borehole heat exchanger sizing subject to Thermal Response Test parameter estimation
Energies, 2019Co-Authors: Xuedan Zhang, Tiantian Zhang, Yiqiang JiangAbstract:The impact of different parameter estimation results on the design length of a borehole heat exchanger has received very little attention. This paper provides an in-depth investigation of this problem, together with a full presentation of six data interpretation models and a comprehensive comparison of four representative sizing methods and their inter models. Six heat transfer models were employed to interpret the same Thermal Response Test data set. It was found that the estimated parameters varied with the data interpretation model. The relative difference in borehole Thermal resistance reached 34.4%, and this value was 11.9% for soil Thermal conductivity. The resulting parameter estimation results were used to simulate mean fluid temperature for a single borehole and then to determine the borehole length for a large bore field. The variations in these two correlated parameters caused about 15% and 5% relative difference in mean fluid temperature in the beginning and at the end of the simulation period, respectively. For computing the borehole design length, software-based methods were more sensitive to the influence of parameter estimation results than simple equation-based methods. It is expected that these comparisons will be beneficial to anyone involved in the design of ground-coupled heat pump systems.
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ground heat exchanger design subject to uncertainties arising from Thermal Response Test parameter estimation
Energy and Buildings, 2015Co-Authors: Xuedan Zhang, Yiqiang Jiang, Gongsheng Huang, Tiantian ZhangAbstract:Abstract This paper presents a new design paradigm of ground heat exchanger (GHE), which takes account of uncertainty in the estimation of geoThermal properties, including ground Thermal conductivity and borehole Thermal resistance from a Thermal Response Test (TRT). Some challenges during the TRT parameter estimation process are discussed: A sensitivity analysis to a more accurate solution of the infinite line source model is introduced to identify the parameters to be estimated; a nonlinear least-square method is employed to estimate the selected parameters; and then validated by a traversing method. Afterwards, a case study is conducted to illustrate how the proposed method can be employed to a practical engineering application. The proposed uncertainty design method that provides a quantified margin of design output can be an alternative to traditional deterministic methods that simply add safety factor to expected design value.
Jun Zhao - One of the best experts on this subject based on the ideXlab platform.
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a Thermal dissipation correction method for in situ soil Thermal Response Test experiment and simulation under multi operation conditions
Energy and Buildings, 2019Co-Authors: Junyao Wang, Jun Zhao, Mohamed E Zayed, Qi Shao, Mingyuan SunAbstract:Abstract The in-situ Thermal Response Test (TRT) is a key process to measure the soil Thermal properties and has been widely applied for precise design and optimal operation of borehole heat exchanger (BHE). However, it is difficult to accurately calculate and simulate Thermal dissipation from fluids to ambient air in Test processes. This paper experimentally investigated geotechnical indoor Testing and TRTs at 11 sites under multi-operation conditions. Furthermore, a Thermal-dissipation correction method (TDCM) is introduced and established in TRNSYS to simulate the Thermal dissipation. Infinite line source model (ILSM) and infinite cylindrical source model (ICSM) are adopted to comprehensively evaluate experimental and simulation results. The simulation results of TDCM are well verified and parallelly compared under multi-operation conditions. TDCM works well with ICSM on the correction of Thermal resistance with improved accuracy over 10% and also improves the accuracy of average fluid temperature, soil Thermal conductivities and heat flux per unit length of BHE. Besides, TDCM is more efficient to correct results processed with ICSM than that with ILSM and is valid for simulations with heating powers of 4–8 kW. This study proposes an efficient model to estimate Thermal dissipation of fluids in TRTs and select appropriate operating parameters of BHEs.
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numerical simulation of soil Thermal Response Test with Thermal dissipation corrected model
Energy Procedia, 2017Co-Authors: Zhiyou Gao, Yongzhen Wang, Yunchuan Sun, Jun Zhao, Ning FengAbstract:Abstract Based on the duct ground heat storage model on TRNSYS software, a Thermal-dissipation-corrected transient model which takes the heat dissipation from ground and Testing tube surfaces into consideration is established. An experimental platform is built for in-situ Thermal Response Test (in-situ TRT) in Shandong Province, China. The presented model is verified by in-situ TRT with similar inlet and outlet temperatures of borehole heat exchanger (BHE). Furthermore, the key parameters, such as injected heat power, circulation flowrate, etc. are analyzed to study the influences on identified soil Thermal conductivity, borehole Thermal resistance and heat flow per unit length of BHE. It is showed that Test duration has the largest impact on identified soil Thermal conductivity, followed by injected heat power, abandoned initial hours, the circulation flowrate and backfill material conductivity; injected heat power has the largest influence on heat flow per unit length of BHE.
Ning Feng - One of the best experts on this subject based on the ideXlab platform.
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numerical simulation of soil Thermal Response Test with Thermal dissipation corrected model
Energy Procedia, 2017Co-Authors: Zhiyou Gao, Yongzhen Wang, Yunchuan Sun, Jun Zhao, Ning FengAbstract:Abstract Based on the duct ground heat storage model on TRNSYS software, a Thermal-dissipation-corrected transient model which takes the heat dissipation from ground and Testing tube surfaces into consideration is established. An experimental platform is built for in-situ Thermal Response Test (in-situ TRT) in Shandong Province, China. The presented model is verified by in-situ TRT with similar inlet and outlet temperatures of borehole heat exchanger (BHE). Furthermore, the key parameters, such as injected heat power, circulation flowrate, etc. are analyzed to study the influences on identified soil Thermal conductivity, borehole Thermal resistance and heat flow per unit length of BHE. It is showed that Test duration has the largest impact on identified soil Thermal conductivity, followed by injected heat power, abandoned initial hours, the circulation flowrate and backfill material conductivity; injected heat power has the largest influence on heat flow per unit length of BHE.
