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

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1896 ◽  
Author(s):  
Yanjun Zhang ◽  
Ling Zhou ◽  
Zhongjun Hu ◽  
Ziwang Yu ◽  
Shuren Hao ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Thermal Response ◽  
Thermal Response Test ◽  
Study Region ◽  
Response Test ◽  
Ground Source ◽  
Ann Models ◽  
Artificial Neural

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.

Study on the influence factors of short-time thermal response test based on artificial neural network

Geothermics ◽  
2021 ◽  
Vol 95 ◽  
pp. 102171
Author(s):  
Zongwei Han ◽  
Xueping Zhang ◽  
Xinwei Meng ◽  
Qiang Ji ◽  
Gui Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influence Factors ◽  
Thermal Response ◽  
Thermal Response Test ◽  
Response Test ◽  
Artificial Neural ◽  
Short Time

In-Situ Thermal Response Test for Ground Source Heat Pump Application

2011 ◽  
Author(s):  
Wenzhi Cui ◽  
Quan Liao ◽  
Guiqin Chang ◽  
Qingyuan Peng ◽  
Tien-Chien Jen
Keyword(s):  
Thermal Conductivity ◽  
Thermal Resistance ◽  
Heat Pump ◽  
Thermal Response ◽  
Ground Source Heat Pump ◽  
Thermal Response Test ◽  
Response Test ◽  
Ground Source ◽  
Borehole Thermal Resistance

The design and performance optimization of ground source heat pump (GSHP) systems need the exact thermal properties of the soil, such as ground thermal conductivity and capacity, and the borehole thermal resistance of borehole heat exchanger (BHE). In-situ thermal response test (TRT) is the most widely used method to determine the overall thermal physical properties of the geological structure around the borehole. A TRT experimental apparatus has been developed and thermal response test was performed in Chongqing, southwest China. Both single-U and double-U borehole heat exchangers are studied in this work. The test duration is about 70 hours. Data direct fitting and parameter estimate method are both used to determine the soil thermal conductivity and the borehole thermal resistance. The results showed that the average ground thermal conductivity of the test region for single U and double U BHE conditions are 2.55 and 2.51 Wm−1K−1, and borehole thermal resistance are 0.116 and 0.066 mKW−1, respectively.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

An Investigation of Hydrological Effects on the Thermal Performance of Standing Column Well Using the in-situ Thermal Response Test

2019 ◽  
Vol 27 (02) ◽  
pp. 1950015 ◽  
Author(s):  
Keun Sun Chang ◽  
Young Jae Kim ◽  
Min Jun Kim
Keyword(s):  
Thermal Conductivity ◽  
Thermal Resistance ◽  
Flow Rate ◽  
Large Scale ◽  
Thermal Response ◽  
Thermal Response Test ◽  
Response Test ◽  
Geological Conditions ◽  
Wide Range ◽  
Borehole Thermal Resistance

The standing column well (SCW) for ground source heat pump (GSHP) systems is a highly promising technology with its high heat capacity and efficiency. In this study, a large-scale thermal response tester has been built, which is capable of imposing a wide range of heat on the SCW ground heat exchangers and measuring time responses of their thermal parameters. Two standing column wells in one site but with different well hydrological and geological conditions are tested to study their effects on the thermal performances. Borehole thermal resistance ([Formula: see text]) and the effective thermal conductivity ([Formula: see text]) are derived from data obtained from the thermal response test (TRT) by using a line source method. Results show that the influence of groundwater movement on the thermal conductivity of the SCW is not very significant (3.6% difference between two different geological conditions). This indicates that results of one TRT measurement can be applied to other SCWs in the same site, with which considerable time and cost are saved. The increase of circulation flow rate enhances the ground thermal conductivity moderately (4.5% increase with flow rate increase of 45%), but the borehole thermal resistance is substantially lowered (about 25.9%).

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