Simulation of the effect of stand-off parameter on collapse behaviours of a single cavitation bubble in jet drilling

Cavitation jet drilling has been extensively employed for the exploitation of geo-energy resources. The dynamics of cavitation bubbles in close proximity to the solid boundary have been a subject of great interest during jet drilling, as they play a crucial role in determining the cavitation performance. In present work, the dynamics of a single cavitation bubble near a solid surface is numerically investigated by using the axisymmetric Navier-Stokes equations and the volume of fluid method with considering the surface tension of gas-liquid interface, liquid viscosity and compressibility of gas in bubble. The simulated profiles are qualitatively and quantitatively consistent with the experimental images, which proves the reliability of employed numerical model. The effects of stand-off distance on the bubble profiles, bubble volume and collapse time have been analysed. Moreover, the cavitation erosion patterns towards the solid wall are also revealed for different dimensionless standoff distances. The simulation results reveal two distinct collapse patterns for the bubble profiles. The solid wall significantly impedes the shrinkage rate of the bubble, resulting in the longest collapse time when the dimensionless stand-off distance is 1.0. Three erosion patterns of cavitation bubbles towards the solid wall are observed, with the shock wave and micro-jet both contributing significantly to the damage caused by cavitation erosion. The shock wave sweeps the wall resulting in circular corrosion pits with a severely eroded centre, while the micro jet penetrates the wall leading to small spot corrosion pits.Document Type: Original articleCited as: Wu, X., Zhang, Y., Huang, H., Hui, C., Hu, Z., Li, G. Simulation of the effect of stand-off parameter on collapse behaviours of a single cavitation bubble in jet drilling. Advances in Geo-Energy Research, 2023, 8(3): 181-192. https://doi.org/10.46690/ager.2023.06.0

Information Recovery Algorithm for Ground Objects in Thin Cloud Images by Fusing Guide Filter and Transfer Learning

Ground object information of remote sensing images covered with thin clouds is obscure. An information recovery algorithm for ground objects in thin cloud images is proposed by fusing guide filter and transfer learning. Firstly, multi-resolution decomposition of thin cloud target images and cloud-free guidance images is performed by using multi-directional nonsubsampled dual-tree complex wavelet transform. Then the decomposed low frequency subbands are processed by using support vector guided filter and transfer learning respectively. The decomposed high frequency subbands are enhanced by using modified Laine enhancement function. The low frequency subbands output by guided filter and those predicted by transfer learning model are fused by the method of selection and weighting based on regional energy. Finally, the enhanced high frequency subbands and the fused low frequency subbands are reconstructed by using inverse multi-directional nonsubsampled dual-tree complex wavelet transform to obtain the ground object information recovery images. Experimental results of Landsat-8 OLI multispectral images show that, support vector guided filter can effectively preserve the detail information of the target images, domain adaptive transfer learning can effectively extend the range of available multi-source and multi-temporal remote sensing images, and good effects for ground object information recover are obtained by fusing guide filter and transfer learning to remove thin cloud on the remote sensing images

Innovations in Shiplift Navigation Concepts

Proceedings Online: https://doi.org/10.1007/978-981-19-6138-

How to Account for Changes in Carbon Storage from Coal Mining and Reclamation in Eastern China? Taking Yanzhou Coalfield as an Example to Simulate and Estimate

Carbon sequestration in terrestrial ecosystems plays an essential role in coping with global climate change and achieving regional carbon neutrality. In mining areas with high groundwater levels in eastern China, underground coal mining has caused severe damage to surface ecology. It is of practical significance to evaluate and predict the positive and negative effects of coal mining and land reclamation on carbon pools. This study set up three scenarios for the development of the Yanzhou coalfield (YZC) in 2030, including: (1) no mining activities (NMA); (2) no reclamation after mining (NRM); (3) mining and reclamation (MR). The probability integral model (PIM) was used to predict the subsidence caused by mining in YZC in 2030, and land use and land cover (LULC) of 2010 and 2020 were interpreted by remote sensing images. Based on the classification of land damage, the LULC of different scenarios in the future was simulated by integrating various social and natural factors. Under different scenarios, the InVEST model evaluated carbon storage and its temporal and spatial distribution characteristics. The results indicated that: (1) By 2030, YZC would have 4341.13 ha of land disturbed by coal mining activities. (2) Carbon storage in the NRM scenario would be 37,647.11 Mg lower than that in the NMA scenario, while carbon storage in the MR scenario would be 18,151.03 Mg higher than that in the NRM scenario. Significantly, the Nantun mine would reduce carbon sequestration loss by 72.29% due to reclamation measures. (3) Carbon storage has a significant positive spatial correlation, and coal mining would lead to the fragmentation of the carbon sink. The method of accounting for and predicting carbon storage proposed in this study can provide data support for mining and reclamation planning of coal mine enterprises and carbon-neutral planning of government departments

Solar Parks and Wind Farms Along Inland Waterways: Mitigating Measures Concerning Hindrance for Vessel Traffic

In the search for space for producing renewable energy, possible negative effects of solar parks and wind farms along inland waterways can easily be overseen. This paper provides an exploratory description of effects for navigation like blinding of helmsmen, disturbance of radio communication and exaggeration of vessel’s radar images and concludes with a chapter on mitigating measures

How to Account for Changes in Carbon Storage from Coal Mining and Reclamation in Eastern China? Taking Yanzhou Coalfield as an Example to Simulate and Estimate

Carbon sequestration in terrestrial ecosystems plays an essential role in coping with global climate change and achieving regional carbon neutrality. In mining areas with high groundwater levels in eastern China, underground coal mining has caused severe damage to surface ecology. It is of practical significance to evaluate and predict the positive and negative effects of coal mining and land reclamation on carbon pools. This study set up three scenarios for the development of the Yanzhou coalfield (YZC) in 2030, including: (1) no mining activities (NMA); (2) no reclamation after mining (NRM); (3) mining and reclamation (MR). The probability integral model (PIM) was used to predict the subsidence caused by mining in YZC in 2030, and land use and land cover (LULC) of 2010 and 2020 were interpreted by remote sensing images. Based on the classification of land damage, the LULC of different scenarios in the future was simulated by integrating various social and natural factors. Under different scenarios, the InVEST model evaluated carbon storage and its temporal and spatial distribution characteristics. The results indicated that: (1) By 2030, YZC would have 4341.13 ha of land disturbed by coal mining activities. (2) Carbon storage in the NRM scenario would be 37,647.11 Mg lower than that in the NMA scenario, while carbon storage in the MR scenario would be 18,151.03 Mg higher than that in the NRM scenario. Significantly, the Nantun mine would reduce carbon sequestration loss by 72.29% due to reclamation measures. (3) Carbon storage has a significant positive spatial correlation, and coal mining would lead to the fragmentation of the carbon sink. The method of accounting for and predicting carbon storage proposed in this study can provide data support for mining and reclamation planning of coal mine enterprises and carbon-neutral planning of government departments

Automated Extraction of Ground Fissures Due to Coal Mining Subsidence Based on UAV Photogrammetry

Widespread ground fissures caused by coal mining subsidence are a main cause of ecological destruction in coal mining areas, and the rapid monitoring of ground fissures is essential for ecological restoration. Traditional fissure monitoring technologies are time consuming and laborious. Therefore, we developed a method to automatically extract ground fissures from high-resolution UAV images. First, a multiscale Hessian-based enhancement filter was utilized to enhance the ground fissures in grayscale images. Then, a simple single-thresholding operation was applied to segment the enhanced image to generate a binary ground fissure map. Finally, incomplete path opening was performed to eliminate the noises in the fissure extraction results. We selected the N1212 working face of the Ningtiaota Coal Mine in Shenmu County, China, as the study area. The results indicated that the ranges of correctness, completeness, and the kappa coefficient of the extracted results were 66.23–79.00%, 69.03–73.22%, and 67.91–75.88%, respectively. Image resolution is the key factor for successful fissure detection; the method proposed in this paper can extract ground fissures with a width greater than one pixel (2.64 cm), and the detection ratio for fissures with a width greater than two pixels was over 87%. Our research has solved the problem of the rapid monitoring of ground fissures to a certain extent and can act as a valuable tool for ecological restoration in mining areas

Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks

This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training samples is expanded due to the insufficient number of available UAV images. Experimental results show that the proposed method is better than traditional machine learning approaches, such as support vector machine and AdaBoost, and methods of DCNN, such as Alexnet, Inception and Faster R-CNN. Through sample expansion and background removal, the proposed method achieves effective detection of diseased pine trees in UAV images by using deep learning technology