Comparison of influence factors on horizontal ground heat exchanger performance through numerical simulation and gray correlation analysis

Shi, Yu; Xu, Fei; Li, Xiaojiang; Lei, Zhihong; Cui, Qiliang

doi:10.1016/j.applthermaleng.2022.118756

Cited by 19 publications

(5 citation statements)

References 38 publications

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“…To quantitatively study the influence of various parameters on Young's modulus of glutenites, this study adopted the Sobol index method [43], which is more suitable for conducting detailed global sensitivity analysis in low-dimensional parameter spaces compared to OAT [44] and the Morris method [45]. And multiple linear regression [46], gray correlation analysis [47], Pearson correlation analysis [48], and random forest [49] were used to investigate the effects of various parameters on Young's modulus. Specific impact results are shown in Table 1 and Figures 22-25.…”

Section: Parameter Sensitivity and Correlation Analysismentioning

confidence: 99%

Simulation Study on the Prediction of Macroscale Young’s Modulus Based on the Mesoscale Characteristics of Tight Glutenite Reservoirs

Xiao,

Zhang,

Yan

et al. 2024

Processes

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To obtain the macroscale Young’s modulus of glutenite under gravel inclusions, a numerical simulation of macroscale Young’s modulus prediction based on the mesoscale characteristics of glutenite was carried out. Firstly, the micron indentation test was used to obtain the meso-mechanical parameters of gravel and matrix in glutenite to ensure the reasonableness of the numerical simulation parameter settings; secondly, a two-dimensional glutenite physical model generation method based on the secondary development of Python was put forward; and then, the macroscale Young’s modulus variation rule of glutenite under different gravel sizes, particle size ratios, and content characteristics were analyzed using the finite element method (FEM). The results show that Young’s modulus of gravel is larger than Young’s modulus of the matrix, and Young’s modulus of different gravel and matrix has some differences. The gravel content is the main controlling factor affecting the macroscale Young’s modulus of glutenite; the gravel content and Young’s modulus of glutenite show a strong positive correlation, and the gravel size and particle size ratio have less influence on the macroscale Young’s modulus of glutenite. The difference in Young’s modulus between gravel and matrix causes the formation of local stress concentrations during loading and compression of glutenite. The smaller the gravel grain size, the higher the degree of non-uniformity of the grain size, the more likely it is to form higher local stresses. The results of the study provide a new prediction method for the prediction of the macroscale Young’s modulus of a glutenite reservoir.

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Section: Parameter Sensitivity and Correlation Analysismentioning

confidence: 99%

Simulation Study on the Prediction of Macroscale Young’s Modulus Based on the Mesoscale Characteristics of Tight Glutenite Reservoirs

Xiao,

Zhang,

Yan

et al. 2024

Processes

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“…Then, K-means is shown 9 . Definition 2.1 Grey correlation analysis 10 calculates the similarity of attributes to give the results of their relationship. Attribute set is…”

Section: Backgroudmentioning

confidence: 99%

Part heuristic k-means based on improved grey correlation analysis for infectious analysis

Liu

2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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It is necessary to build effectiveness infectious disease analysis methodology to avoid a large spreading of the disease. The factors playing role in epidemic come from different domain; moreover, their relationship is complex. Thus, it is very hard to mine the rule by single analysis. In this work, a total review is done to analyze the infected in the unit of years, which can provide a foundation to conclude the infectious rule. In order to finish this goal, Part Heuristic K-means based on Improved Grey Correlation Analysis is proposed. It uses improved grey correlation analysis to recognize the relevance among different diseases which has ability to guide the weight. Then, the year is partitioned into clusters based on distance function. It is found that the proportion of three degrees is respectively 21.4%, 28.6%, and 50%; the maximum of relevance is 0.888.

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“…bentonite, silica sand and coarse/fine sand) and their effects on the performance of buried pipe heat exchangers. Shi et al [ 15 ] comprehensively analyzed the impact of many key factors on GHE performance by comparing numerical simulation and grey correlation analysis. The results show that the most significant factor affecting the performance of GHE is groundwater flow rate, circulating flow rate, precipitation and air temperature.…”

Section: Introductionmentioning

confidence: 99%

Heat exchange characteristics of underground and pavement buried pipes for bridge deck heating conditions

Zheng,

Song,

Ding

2024

PLoS ONE

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Geothermal energy is increasingly employed across diverse applications, with bridge deck snow melting emerging as a notable utilization scenario. In Jinan city, China, a project is underway to utilize ground source heat pumps (GSHPS) for heating bridges. However, essential operational parameters, including fluid medium, temperature, and heat exchange details, are currently lacking. This study addresses the thermal design challenges associated with ground heat exchangers (GHE) for bridge heating through a combination of numerical modeling and field experiments. Utilizing software Fluent, a refined three-dimensional multi-condition heat transfer numerical analysis was carried out. Field tests based on actual operating conditions were also conducted and the design parameters were verified. The results indicate that an inlet temperature of 5°C and an aqueous solution of ethylene glycol with a mass concentration of 35% as the heat exchange medium are suitable for the GSHPS in Jinan; Moreover, the influence of backfill material and operation time on the heat transfer efficiency was revealed and the suitable material with 10% bentonite and 90% SiO2 was suggested; Finally, based on the influence of the pipe spacing on the heating characteristics of bridge deck, the transition spacing of 0.2 m is given for the temperature response of the bridge deck. This comprehensive study contributes valuable insights through simulation and experimental analysis of the thermal environment variation, aiming to advance the development of GSHPS for bridge deck heating in Jinan, China.

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Comparison of influence factors on horizontal ground heat exchanger performance through numerical simulation and gray correlation analysis

Cited by 19 publications

References 38 publications

Simulation Study on the Prediction of Macroscale Young’s Modulus Based on the Mesoscale Characteristics of Tight Glutenite Reservoirs

Simulation Study on the Prediction of Macroscale Young’s Modulus Based on the Mesoscale Characteristics of Tight Glutenite Reservoirs

Part heuristic k-means based on improved grey correlation analysis for infectious analysis

Heat exchange characteristics of underground and pavement buried pipes for bridge deck heating conditions

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