RETRACTED ARTICLE: Numerical simulation of temperature field and temperature stress of thermal jet for water measurement

Statement of Retraction We, the Editor and Publisher of the journal European Journal of Remote Sensing, have retracted the following articles that were published in the Special Issue titled “Remote Sensing in Water Management and Hydrology”: Marimuthu Karuppiah, Xiong Li & Shehzad Ashraf Chaudhry (2021) Guest editorial of the special issue “remote sensing in water management and hydrology”, European Journal of Remote Sensing, 54:sup2, 1-5, DOI: 10.1080/22797254.2021.1892335 Jian Sheng, Shiyi Jiang, Cunzhu Li, Quanfeng Liu & Hongyan Zhang (2021) Fluid-induced high seismicity in Songliao Basin of China, European Journal of Remote Sensing, 54:sup2, 6-10, DOI: 10.1080/22797254.2020.1720525 Guohua Wang, Jun Tan & Lingui Wang (2021) Numerical simulation of temperature field and temperature stress of thermal jet for water measurement, European Journal of Remote Sensing, 54:sup2, 11-20, DOI: 10.1080/22797254.2020.1743956 Le Wang, Guancheng Jiang & Xianmin Zhang (2021) Modeling and molecular simulation of natural gas hydrate stabilizers, European Journal of Remote Sensing, 54:sup2, 21-32, DOI: 10.1080/22797254.2020.1738901 Tianyi Chen, Lu Bao, Liu Bao Zhu, Yu Tian, Qing Xu & Yuandong Hu (2021) The diversity of birds in typical urban lake-wetlands and its response to the landscape heterogeneity in the buffer zone based on GIS and field investigation in Daqing, China, European Journal of Remote Sensing, 54:sup2, 33-41, DOI: 10.1080/22797254.2020.1738902 Zhiyong Wang (2021) Research on desert water management and desert control, European Journal of Remote Sensing, 54:sup2, 42-54, DOI: 10.1080/22797254.2020.1736953 Ji-Tao Li & Yong-Quan Liang (2021) Research on mesoscale eddy-tracking algorithm of Kalman filtering under density clustering on time scale, European Journal of Remote Sensing, 54:sup2, 55-64, DOI: 10.1080/22797254.2020.1740894 Wei Wang, R. Dinesh Jackson Samuel & Ching-Hsien Hsu (2021) Prediction architecture of deep learning assisted short long term neural network for advanced traffic critical prediction system using remote sensing data, European Journal of Remote Sensing, 54:sup2, 65-76, DOI: 10.1080/22797254.2020.1755998 Yan Chen, Ming Tan, Jiahua Wan, Thomas Weise & Zhize Wu (2021) Effectiveness evaluation of the coupled LIDs from the watershed scale based on remote sensing image processing and SWMM simulation, European Journal of Remote Sensing, 54:sup2, 77-91, DOI: 10.1080/22797254.2020.1758962 Ke Deng & Ming Chen (2021) Blasting excavation and stability control technology for ultra-high steep rock slope of hydropower engineering in China: a review, European Journal of Remote Sensing, 54:sup2, 92-106, DOI: 10.1080/22797254.2020.1752811 Yufa He, Xiaoqiang Guo, Jun Liu, Hongliang Zhao, Guorong Wang & Zhao Shu (2021) Dynamic boundary of floating platform and its influence on the deepwater testing tube, European Journal of Remote Sensing, 54:sup2, 107-116, DOI: 10.1080/22797254.2020.1762246 Kai Peng, Yunfeng Zhang, Wenfeng Gao & Zhen Lu (2021) Evaluation of human activity intensity in geological environment problems of Ji’nan City, European Journal of Remote Sensing, 54:sup2, 117-121, DOI: 10.1080/22797254.2020.1771214 Wei Zhu, XiaoSi Su & Qiang Liu (2021) Analysis of the relationships between the thermophysical properties of rocks in the Dandong Area of China, European Journal of Remote Sensing, 54:sup2, 122-131, DOI: 10.1080/22797254.2020.1763205 Yu Liu, Wen Hu, Shanwei Wang & Lingyun Sun (2021) Eco-environmental effects of urban expansion in Xinjiang and the corresponding mechanisms, European Journal of Remote Sensing, 54:sup2, 132-144, DOI: 10.1080/22797254.2020.1803768 Peng Qin & Zhihui Zhang (2021) Evolution of wetland landscape disturbance in Jiaozhou Gulf between 1973 and 2018 based on remote sensing, European Journal of Remote Sensing, 54:sup2, 145-154, DOI: 10.1080/22797254.2020.1758963 Mingyi Jin & Hongyan Zhang (2021) Investigating urban land dynamic change and its spatial determinants in Harbin city, China, European Journal of Remote Sensing, 54:sup2, 155-166, DOI: 10.1080/22797254.2020.1758964 Balaji L. & Muthukannan M. (2021) Investigation into valuation of land using remote sensing and GIS in Madurai, Tamilnadu, India, European Journal of Remote Sensing, 54:sup2, 167-175, DOI: 10.1080/22797254.2020.1772118 Xiaoyan Shi, Jianghui Song, Haijiang Wang & Xin Lv (2021) Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group, European Journal of Remote Sensing, 54:sup2, 176-188, DOI: 10.1080/22797254.2020.1762247 GN Vivekananda, R Swathi & AVLN Sujith (2021) Multi-temporal image analysis for LULC classification and change detection, European Journal of Remote Sensing, 54:sup2, 189-199, DOI: 10.1080/22797254.2020.1771215 Yiting Wang, Xianghui Liu & Weijie Hu (2021) The research on landscape restoration design of watercourse in mountainous city based on comprehensive management of water environment, European Journal of Remote Sensing, 54:sup2, 200-210, DOI: 10.1080/22797254.2020.1763206 Bao Qian, Cong Tang, Yu Yang & Xiao Xiao (2021) Pollution characteristics and risk assessment of heavy metals in the surface sediments of Dongting Lake water system during normal water period, European Journal of Remote Sensing, 54:sup2, 211-221, DOI: 10.1080/22797254.2020.1763207 Jin Zuo, Lei Meng, Chen Li, Heng Zhang, Yun Zeng & Jing Dong (2021) Construction of community life circle database based on high-resolution remote sensing technology and multi-source data fusion, European Journal of Remote Sensing, 54:sup2, 222-237, DOI: 10.1080/22797254.2020.1763208 Zilong Wang, Lu Yang, Ping Cheng, Youyi Yu, Zhigang Zhang & Hong Li (2021) Adsorption, degradation and leaching migration characteristics of chlorothalonil in different soils, European Journal of Remote Sensing, 54:sup2, 238-247, DOI: 10.1080/22797254.2020.1771216 R. Vijaya Geetha & S. Kalaivani (2021) A feature based change detection approach using multi-scale orientation for multi-temporal SAR images, European Journal of Remote Sensing, 54:sup2, 248-264, DOI: 10.1080/22797254.2020.1759457 LianJun Chen, BalaAnand Muthu & Sivaparthipan cb (2021) Estimating snow depth Inversion Model Assisted Vector Analysis based on temperature brightness for North Xinjiang region of China, European Journal of Remote Sensing, 54:sup2, 265-274, DOI: 10.1080/22797254.2020.1771217 Yajuan Zhang, Cuixia Li & Shuai Yao (2021) Spatiotemporal evolution characteristics of China’s cold chain logistics resources and agricultural product using remote sensing perspective, European Journal of Remote Sensing, 54:sup2, 275-283, DOI: 10.1080/22797254.2020.1765202 Guangping Liu, Jingmei Wei, BalaAnand Muthu & R. Dinesh Jackson Samuel (2021) Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling, European Journal of Remote Sensing, 54:sup2, 284-295, DOI: 10.1080/22797254.2020.1771774 Yishu Qiu, Zhenmin Zhu, Heping Huang & Zhenhua Bing (2021) Study on the evolution of B&Bs spatial distribution based on exploratory spatial data analysis (ESDA) and its influencing factors—with Yangtze River Delta as an example, European Journal of Remote Sensing, 54:sup2, 296-308, DOI: 10.1080/22797254.2020.1785950 Liang Li & Kangning Xiong (2021) Study on peak cluster-depression rocky desertification landscape evolution and human activity-influence in South of China, European Journal of Remote Sensing, 54:sup2, 309-317, DOI: 10.1080/22797254.2020.1777588 Juan Xu, Mengsheng Yang, Chaoping Hou, Ziliang Lu & Dan Liu (2021) Distribution of rural tourism development in geographical space: a case study of 323 traditional villages in Shaanxi, China, European Journal of Remote Sensing, 54:sup2, 318-333, DOI: 10.1080/22797254.2020.1788993 Lin Guo, Xiaojing Guo, Binghua Wu, Po Yang, Yafei Kou, Na Li & Hui Tang (2021) Geo-environmental suitability assessment for tunnel in sub-deep layer in Zhengzhou, European Journal of Remote Sensing, 54:sup2, 334-340, DOI: 10.1080/22797254.2020.1788994 Hui Zhou, Cheng Zhu, Li Wu, Chaogui Zheng, Xiaoling Sun, Qingchun Guo & Shuguang Lu (2021) Organic carbon isotope record since the Late Glacial period from peat in the North Bank of the Yangtze River, China, European Journal of Remote Sensing, 54:sup2, 341-347, DOI: 10.1080/22797254.2020.1795728 Chengyuan Hao, Linlin Song & Wei Zhao (2021) HYSPLIT-based demarcation of regions affected by water vapors from the South China Sea and the Bay of Bengal, European Journal of Remote Sensing, 54:sup2, 348-355, DOI: 10.1080/22797254.2020.1795730 Wei Chong, Zhang Lin-Jing, Wu Qing, Cao Lian-Hai, Zhang Lu, Yao Lun-Guang, Zhu Yun-Xian & Yang Feng (2021) Estimation of landscape pattern change on stream flow using SWAT-VRR, European Journal of Remote Sensing, 54:sup2, 356-362, DOI: 10.1080/22797254.2020.1790994 Kepeng Feng & Juncang Tian (2021) Forecasting reference evapotranspiration using data mining and limited climatic data, European Journal of Remote Sensing, 54:sup2, 363-371, DOI: 10.1080/22797254.2020.1801355 Kepeng Feng, Yang Hong, Juncang Tian, Xiangyu Luo, Guoqiang Tang & Guangyuan Kan (2021) Evaluating applicability of multi-source precipitation datasets for runoff simulation of small watersheds: a case study in the United States, European Journal of Remote Sensing, 54:sup2, 372-382, DOI: 10.1080/22797254.2020.1819169 Xiaowei Xu, Yinrong Chen, Junfeng Zhang, Yu Chen, Prathik Anandhan & Adhiyaman Manickam (2021) A novel approach for scene classification from remote sensing images using deep learning methods, European Journal of Remote Sensing, 54:sup2, 383-395, DOI: 10.1080/22797254.2020.1790995 Shanshan Hu, Zhaogang Fu, R. Dinesh Jackson Samuel & Prathik Anandhan (2021) Application of active remote sensing in confirmation rights and identification of mortgage supply-demand subjects of rural land in Guangdong Province, European Journal of Remote Sensing, 54:sup2, 396-404, DOI: 10.1080/22797254.2020.1790996 Chen Qiwei, Xiong Kangning & Zhao Rong (2021) Assessment on erosion risk based on GIS in typical Karst region of Southwest China, European Journal of Remote Sensing, 54:sup2, 405-416, DOI: 10.1080/22797254.2020.1793688 Zhengping Zhu, Bole Gao, Renfang Pan, Rong Li, Yang Li & Tianjun Huang (2021) A research on seismic forward modeling of hydrothermal dolomite:An example from Maokou formation in Wolonghe structure, eastern Sichuan Basin, SW China, European Journal of Remote Sensing, 54:sup2, 417-428, DOI: 10.1080/22797254.2020.1811160 Shaofeng Guo, Jianmin Zheng, Guohua Qiao & Xudong Wang (2021) A preliminary study on the Earth’s evolution and condensation, European Journal of Remote Sensing, 54:sup2, 429-437, DOI: 10.1080/22797254.2020.1830309 Yu Gao, Ying Zhang & Hedjar Alsulaiman (2021) Spatial structure system of land use along urban rail transit based on GIS spatial clustering, European Journal of Remote Sensing, 54:sup2, 438-445, DOI: 10.1080/22797254.2020.1801356 Xia Mu, Sihai Li, Haiyang Zhan & Zhuoran Yao (2021) On-orbit calibration of sun sensor’s central point error for triad, European Journal of Remote Sensing, 54:sup2, 446-457, DOI: 10.1080/22797254.2020.1814164 Following publication, the publisher identified concerns regarding the editorial handling of the special issue and the peer review process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.


Introduction
In modern mining engineering, it has been more than a century since the method of heating was used to assist the breaking of rocks (Maurer, 1968;Soles & Geller, 1963).In hard formations, drilling with thermal cracking method can achieve a mechanical drilling rate 5-10 times that of traditional rotary drilling method.(Potter & Tester, 1998;Rauenzahn & Tester, 1989;Rauenzahnf & Tester, 1991).In addition, when drilling with the method of thermal-jet, the drilling tools will not contact with the rock surface, so there will be no problems such as the wear and failure of drilling tools in the process of traditional mechanical drilling (Potter & Tester, 1998;Rauenzahn, 1986;Wilkinson & Tester, 1993).After the impact of thermal-jet, a large amount of thermal energy is transferred to the rock by the high-temperature fluid (Thirumalai & Demou, 1970;Walsh et al., 2011).Since the rock itself is composed of various types of minerals, and the thermal conductivity of various mineral components varies greatly, so it is equivalent to the violent local heating behavior of the rock surface.During this process, large temperature gradients form on the rock surface.The locally heated part of the interior will expand nonuniformly.The resulting compression of the temperature stress will not only cause the original cracks in the rock to expand but also lead to new cracks between the mineral particles inside the rock.When its length reaches a critical point, the heated part of the rock surface buckles and falls off the whole rock (Preston & White, 1934;Thirumalai & Cheung, 1972).The response process of rocks under thermal shock is shown in Figure 1 (Augustine, 2009).But there is no study on temperature field and local thermal stress in the process of drilling, which has a great influence on rock breaking efficiency.So it is significant to study the distribution of the temperature and the thermal stress in thermaljet drilling.

Establishment and solution of thermal cracking drilling model
At present, high-temperature jet rock breaking is mainly based on continuous pipe technology, and its structure is shown in Figure 2 (Dreesen & Bretz, 2005).Because the time of thermal cracking is very short, the supercritical high-temperature fluid ejected from the nozzle is taken as a known heat source, and the contact part between the high-temperature medium and the rock surface is regarded as forced convection heat transfer, ignoring the heat loss caused by radiation and other reasons, which is simplified into the model shown in Figure 3.
In the calculation domain, the radius of the hightemperature jet nozzle and the wellbore radius are set as 5 mm and 25.4 mm, respectively (Song et al., 2016).The area outside the wellbore is the formation, which is treated as an infinite far-field, namely it can be treated as an unbounded area in the calculation process.The upper surface of the wellbore part in the model in contact with the high-temperature jet is set

R E T R A C T E D
as Robin boundary.That is the third kind of boundary condition.The remaining boundaries are set as adiabatic boundary conditions, because the rock matrix has a low thermal conductivity, and no heat diffuses to the outer surface of the rock within the calculated time interval (Meier et al., 2016).The initial temperature is set to the formation temperature of the rock.
Assuming that the physical properties of the rock do not change with the temperature, the governing equation of heat conduction can be obtained as shown in Equation (1). In Within the diameter of the thermal-jet nozzle, the third type of boundary condition, namely Robin boundary condition, is adopted to set the heat transfer coefficient between the high-temperature fluid and the rock surface and the formation temperature where the rock is located (which can be expressed by the initial temperature), as follows: In the equation: hconvection heat transfer coefficient of high-temperature fluid and rock surface, 10 kW/(m 2 •°C) (Meier et al., 2016); T inincident temperature of high-temperature fluid, °C; T 0the formation temperature of the rock, 20°C (Meier et al., 2016); n iunit outward normal vector.
Crank-Nicolson difference format (Ames, 2014) is used to discretize Equation (1), and the following equations are obtained: (5) For Robin boundary conditions, as is shown in Figure 4. T (m, n) is taken as the control node.In the control volume, the heat input from the incident fluid equals to the heat absorbed by the heat matrix plus the heat transferred from the surrounding nodes through heat conduction.According to the law of energy conservation, it is written as a difference scheme in the form of Equation ( 7).
Set Δ X = Δ Y, Equation ( 7) can be rewritten as follows: The rock types are set as sandstone, shale and granite, which are commonly encountered in the drilling process.The thermophysical properties and mechanical parameters are shown in Table 1 (Li et al., 2017).
When the temperature of an object increases from T1 to T2, its thermal strain can be calculated by the following equation: The strain is expressed as stress and temperature gradient, and the following equation can be obtained: Displacement boundary condition is adopted as the boundary condition at the shaft wall, and it is assumed to be in the formation at an infinite distance because the formation is treated as an infinite far-field, so the displacement boundary can be written as By solving Equation ( 10) according to the displacement, the stress is expressed as strain and temperature difference as follows: In the equation: σ -Normal stress, MPa; γ -Shear stress, MPa; τ -Shear strain; T υ -Temperature difference.

Temperature field analysis
The above formula was solved programmatically to obtain the change of borehole center temperature with time, as shown in Figure 5.As can be seen from Figure 5, when the jet medium just touches the rock surface, because there is a huge temperature gradient between the surrounding environment and the rock surface, the temperature in the center of the borehole increases sharply within 0 ~0.1 s, rising more than 300°C, and the slope of the curve is almost 90°.Within 1 s, the maximum temperature of the rock surface will reach 90% of the temperature of the high-temperature fluid.
Within 10 s ~20 s, the slope of the curve gradually becomes gentle, and the temperature only increases by about 30°C.This shows that the temperature in the borehole center increases with the time of jet flow, but

R E T R A C T E D
the increasing rate of temperature decreases.This is due to the poor thermal conductivity of the rock matrix.
With the continuous accumulation of incoming heat, the heat exchange efficiency between the incoming high-temperature fluid and the rock surface is reduced, resulting in the slower and slower growth rate of the rock surface temperature.
The XY plane is taken to calculate the radial and axial temperature distribution in the process of thermal cracking drilling, and the results are shown in Figures 6 and 7.As can be seen from the figure, as the heating time on the rock surface increases, the temperature in the nozzle diameter area increases rapidly, and the temperature starts to transfer from the borehole center to the surrounding area.Because the rock itself has poor thermal conductivity, the rate of temperature propagation is very low.As can be seen from Figure 6, in the radial direction, the center of the borehole has the highest temperature, and the farther away from the center of the borehole, the lower the temperature gradually.With the increasing of heating time, the temperature gradient along the radial direction gradually increases, that is, the range of temperature influence is limited.Therefore, a large temperature gradient and temperature stress can be formed inside the rock, resulting in rock cracking

R E T R A C T E D
and fragmentation.As can be seen from Figure 7, the further away from the borehole surface in the axial direction, the lower the temperature.As the heating time on the rock surface increases, the temperature spreads further, and the temperature can generate a larger temperature gradient in the axial direction than in the radial direction.Rock type is the biggest influence factor on drilling efficiency of high-temperature pyrolysis (Williams et al., 1996).As can be seen from Figure 8, rock types have a great influence on the temperature field.The larger the specific heat of the rock, the stronger its ability to absorb heat, and the higher the surface temperature in the same heating time.The larger the thermal conductivity of rock is, the stronger its heat transmission capacity is.Therefore, the spread range of temperature is larger, while the smaller the thermal conductivity of rock is, the slower the temperature transmission speed is, and the higher the slope of the curve is, the larger the temperature gradient can be formed.In general, the thermophysical properties of the three types of rocks shown in the figure are relatively close, but the temperature field is still quite different, so the influence of rock types on the thermal cracking effect cannot be ignored.

R E T R A C T E D
Temperature stress analysis In the process of thermal cracking drilling, the surface layer of rock will buckle under the influence of thermal shock and eventually peel off the rock surface.In the condition of cylindrical coordinates, the stress of rock is studied.It can be seen from Figure 9 that in the process of thermal cracking drilling, the rock matrix is subjected to compressive stress in the radial direction, and the compressive stress is the largest at the center of the borehole.This is mainly due to the large temperature gradient caused by the poor thermal conductivity of the rock after being heated, and the rapid expansion of the volume of the hightemperature area, which is caused by the compression of the surrounding rocks.The higher the temperature is, the larger the volume expansion degree is, and the more serious the extrusion is.Therefore, the greater the compressive stress is, and the farther away from the heated area, the faster the compressive stress decreases.With the increase of heating time, the compressive stress at the center of borehole increases rapidly.The compressive stress can reach the maximum value of 142.5Mpa within 1 s.Thereafter, the peak compressive stress hardly increases, only with the transfer of heat, the area of maximum compressive stress expands.Due to the symmetry of the model, it can be seen from Figure 10 that the annular stress along the borehole radius changes with time and the radial stress have the same characteristics.
It can be seen from Figure 11 that in the process of thermal cracking drilling, the shear force on the rock matrix is symmetrically distributed along the borehole radius in the borehole plane, but the absolute value of shear force is very small, less than 0.4Mpa.This is because in the process of rock heating, assuming that the physical properties of the rock matrix are uniform and isotropic, the volumetric strain of the rock in all directions along the radial direction is equal, so it is almost not affected by shear stress in the borehole plane.However, as shown in Figure 12, it is obvious that the rock is subjected to a great shear action in the axial direction.This is because in the process of rock heating, the rock matrix in the high-temperature area at the center will buckle after being expanded by heat and squeezed, resulting in the strain along the z-axis, which is perpendicular to the borehole plane, as shown in Figure 13.By comparing the stress state with the material strength and taking the average shear strength (9 Mpa) of granite as its strength limit (Augustine, 2009), it can be seen that the rock will be damaged within 0.1 s.It can be seen from Figure 13 that, along the radial direction, buckling occurs almost only on the surface of the heated area and its vicinity, while almost no displacement occurs far away from the heated area.The results obtained by numerical simulation are in good consistency with the stress and buckling response of the rock in the literature (Hassani et al., 2011).

R E T R A C T E D
(1) In the process of thermal-jet drilling, due to the poor thermal conductivity of the rock matrix, an uneven temperature field is formed, resulting in a temperature gradient in the radial and axial directions, thus forming a temperature stress.
(2) In the process of thermal-jet drilling, the rock matrix is subjected to compressive stress in the radial direction (without considering confining pressure), and the maximum value is 142.5Mpa, and the compressive stress in the center of the borehole is the maximum.

R E T R A C T E D
(3) Rock center heating parts can cause buckling under high temperature, produce the direction perpendicular to the borehole surface strain, by nearly 20 MPa and the direction of shear stress (regardless of the confining pressure), more than the shear strength of granite, rock surface under the action of high temperature from the rock matrix stripping off, this is consistent with experimental results in literatures.

Figure 2 .
Figure 2. Rock breaking by high-temperature jet impact based on coiled tube.

Figure 5 .
Figure 5. Time-dependent variation of central temperature in thermal drilling.

Figure 6 .
Figure 6.The change of radial temperature with time in thermal drilling.

Figure 7 .
Figure 7.The change of axial temperature with time in thermal drilling.

Figure 8 .
Figure 8.The variation of temperature of different rocks (t = 1 s).

Figure 9 .
Figure 9.The radial stress of borehole versus time in thermal drilling.

Figure 10 .
Figure 10.The hoop stress of borehole versus time in thermal drilling.

Figure 11 .
Figure 11.The shear stress along the borehole radius in thermal drilling.

Figure 12 .
Figure 12.The shear stress along the axial of the borehole in thermal drilling.

Figure 13 .
Figure 13.The buckling displacement versus time in thermal drilling.

Table 1 .
Thermophysical properties and mechanical parameters of three kinds of rocks.