Quantifying hail size distributions from the sky - Application of drone aerial photogrammetry

A new technique, named "HailPixel", is introduced for measuring the maximum dimension and intermediate dimension of hailstones from aerial imagery. The photogrammetry procedure applies a convolutional neural network for robust detection of hailstones against complex backgrounds and an edge detection method for measuring the shape of identified hailstones. This semi-automated technique is capable of measuring many thousands of hailstones within a single survey, which is several orders of magnitude larger (e.g. 10 000 or more hailstones) than population sizes from existing sensors (e.g. a hail pad). Comparison with a co-located hail pad for an Argentinian hailstorm event during the RELAMPAGO project demonstrates the larger population size of the HailPixel survey significantly improves the shape and tails of the observed hail size distribution. When hail fall is sparse, such as during large and giant hail events, the large survey area of this technique is especially advantageous for resolving the hail size distribution.Fil: Soderholm, Joshua S.. Universitat Bonn; AlemaniaFil: Kumjian, Matthew R.. State University of Pennsylvania; Estados UnidosFil: McCarthy, Nicholas. University of Queensland; AustraliaFil: Maldonado, Paula Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Wang, Minzheng. Northraine Pty. Ltd.; Australi

Experiments of vertical fracture propagation based on the digital speckle technology

The vertical crack propagation behavior for sandstone/mudstone layered specimen made by the similar material was analyzed. The stress and strain field was obtained using Digital Speckle Correlation Method to analyze the propagation characteristics of vertical cracks. The test shows that the higher the strength of the interface layer, the more susceptible the crack to deflect from the side of the low-strength than from the side of the high-strength of the material. It is easy for the interface to unglue when the interfacial adhesion strength is weak, which results in the dislocation propagation after the crack passing the interface whether crack extending from the side of high-strength material or the side of low-strength material. The longer the expansion path is before the crack passing the interface, the more likely it deflects after passing the interface. Similarly, the shorter the initial prefabricated crack is, the more susceptible to deflect after the crack passing the interface. The main factor of the deflection is the difference in the properties of the composite materials, resulting in a larger shear strain of the interfacial layer. The shear strain can induce type II shear fracture, so it is the main factor leading to crack deflection. The bigger the shear deformation is, the more intensive the crack deflection is. Key words: vertical crack, sand/mudstone interface, crack propagation, crack deflection, Digital Speckle Correlation Metho

Research on Magnetic Field Simulation of Cylindrical Linear Synchronous Motor

In order to obtain the influence of different structures and frequencies on the thrust of cylindrical permanent magnet linear synchronous motor, the transient simulation of magnetic field of cylindrical permanent magnet linear synchronous motor is carried out by using Ansoft software, and the correctness of the simulation is verified by experiments. Through simulation, the characteristics of the linear motor under different voltage frequency, air gap size, and permanent magnet width and its influence on the linear motor thrust are obtained

Main control factors affecting mechanical oil recovery efficiency in complex blocks identified using the improved k-means algorithm.

The system efficiency of pumping units in the middle and late stages of oil recovery is characterized by several factors, complex data and poor regulation. Further, the main control factors that affect system efficiency in different blocks vary greatly; therefore, it is necessary to obtain the block characteristics to effectively improve system efficiency. The k-means algorithm is simple and efficient, but it assumes that all factors have the same amount of influence on the output value. This cannot reflect the obvious difference in the influence of several factors in the block on the efficiency. Moreover, the algorithm is sensitive to the selection of the initial cluster centre point, so each calculation result that reflects the efficiency characteristics of the block system cannot be unified. To solve the aforementioned problems affecting the k-means algorithm, the correlation coefficient of all the factors was first calculated, followed by extracting the system efficiency of the positive and negative indicators of standardization. Next, the moisture value was calculated to obtain the weight of each factor used as a coefficient to calculate the Euclidean distance. Finally, the initial centre point selection of the k-means algorithm problem was solved by combining the dbscan and weighted k-means algorithm. Taking an oil production block in the Daqing Oilfield as the research object, the k-means and improved algorithm are used to analyse the main control factors influencing mechanical production efficiency. The clustering results of the two algorithms have the characteristics of overlapping blocks, but the improved algorithm's clustering findings are as follows: this block features motor utilization, pump efficiency and daily fluid production, which are positively correlated with system efficiency. Further, low-efficiency wells are characterized by the fact that the pump diameter, power consumption, water content, daily fluid production, oil pressure and casing pressure are significantly lower than the block average; high-efficiency wells are characterized by pump depths lower than the block average. For this block, it is possible to reduce the depth of the lower pump and increase the water-injection effect to increase the output under conditions of meeting the submergence degree, which can effectively improve the system efficiency

The solution steps of the fault diagnosis model.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.</div

The matrix representation of the digital image.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.</div

The LVQ neural network structure.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.</div

A schematic diagram of the rod pump injection and production system in the same well.

1. The conventional beam pumping unit. 2. The production pump. 3. The upper production pump valve. 4. The lower production pump valve. 5. The sealing piston. 6. The bridge packer. 7. The injection production pump. 8. The upper injection pump valve. 9. The lower injection pump valve. 10. The oil-water separator.</p

Injection pump failure.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.</div

A schematic diagram of the distance between the adjacent neurons.

A schematic diagram of the distance between the adjacent neurons.</p