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Oil and gas reservoir modeling integrates multidisciplinary information from logging， geology， and seismic data through geostatistics and other methods， and it is a powerful tool for oil and gas field development. The geological model of reservoirs can quantitatively characterize the variation and distribution of various geological features of the reservoir in three-dimensional space and is a high generalization of the type， geometry， scale， internal structure of the oil and gas reservoir， reservoir parameters， and fluid distribution. The geological model of reservoirs is the core of the geological model of oil and gas reservoirs， which can comprehensively characterize the sedimentary characteristics， heterogeneity， physical properties， and fluid characteristics. However， it is difficult to characterize reservoir distribution under the condition of a large-scale sedimentary system and sparse well pattern in the exploration stage， covering ① quantitative representation of geological knowledge， including how to represent the experience of geological experts digitally； ② it is impossible to accurately and quantitatively describe the development scale， distribution direction， and structural characteristics of geological bodies directly with logging data and construct geological models under the condition of sparse well pattern， and complex sedimentary systems in large-scale space cannot be characterized by simple mathematical functions； ③ traditional geostatistics and other methods can not realize the fusion of seismic， logging， geological， reservoir， and other multi-dimensional data in the construction of exploration model. Therefore， the theory and technology of reservoir modeling based on deterministic modeling and stochastic modeling， such as traditional geostatistics， have met great challenges. On the basis of systematic analysis of traditional reservoir modeling technology processes and methods， the authors constructed a multidisciplinary big data knowledge base of geoscience covering geology， logging， seismic， analytical， and laboratory information and carried out the characterization of the sedimentary facies pattern library by hierarchical clustering through multi-dimensional data condensation and the intelligent modeling based on the generation network. In addition， a multidisciplinary collaborative modeling technology strategy and system are proposed for oil and gas reservoir exploration， which quantitatively characterized the matching relationship among structure， sediment， and reservoir. This technology system has been systematically applied in the exploration and deployment of steep slope and subsag belts in the north of Dongying Sag， and the geological models of lithofacies， physical properties， and oil and gas migration and accumulation has been constructed， which integrates multiple information such as paleogeomorphology， paleoprovenance， transport channels， logging， and seismic attributes. Based on the new paradigm of the model， the deployment of well locations has been guided， and the exploration practice of complex glutenites and shale oil in continental faulted basins has been supported. The authors further discuss the development trend and application prospect of oil and gas reservoir exploration and modeling technology in the future by deeply analyzing the practical difficulties and accuracy of exploration and modeling in the north belt of Dongying Sag.

The Cassegrain telescope is widely used in aerospace exploration equipment, characterized by compact structure, complex optical path, and high imaging quality. However, due to the difficulty in establishing an accurate correspondence between the relative pose and imaging quality of a multi mirror group with real machining errors, the current Cassegrain telescope assembly process is very difficult, with blind operation, time-consuming, and low accuracy. This article proposes a high-precision adaptive alignment method for Cassegrain dual mirror optical systems based on machine learning algorisms, and an experimental adaptive alignment system with uncoupled degrees of freedom is developed. Firstly, the overall architecture of the adaptive alignment method is proposed consists of detection, calculation and alignment modules. In the detection module, the Zernike polynomial coefficient of wavefront aberration of the optical system is detected online through the interferometer, meanwhile the pose coordinates of the secondary mirror is accurately fed back through a 6-DOF nanoscale micro mechanism. In the calculation module, machine learning algorithm is applied to build a nonlinear mapping model between the Zernike coefficient and the pose coordinates of the secondary mirror. In the alignment module, the pose coordinates of the secondary mirror can be forced to adjust to the target position. Then, during the real alignment process, the Zernike coefficient test data of the optical system alignment process is monitored in real time, and the nonlinear mapping model is employed to calculate the pose coordinates and then the misalignment of the secondary mirror. Finally, the alignment module is driven to execute the pose correction according to the calculated misalignment value, realizing a high-precision adjustment of the Cassegrain dual mirror optical system. Experimental results shows that the average alignment time cost can be dramatically reduced from around 7 days using the current manual alignment method to just 30 minutes using the proposed adaptive alignment method for achieving a current standard alignment accuracy of wavefront aberration root mean square (RMS) less than 0.1λ, which greatly improves the assembly efficiency. This study proposes a new method for high-precision and efficient alignment of optical systems based on artificial intelligence and contributes to the efficiency improvement for optical assembly. [ABSTRACT FROM AUTHOR]

With the continuous development of manufacturing technology, precision instruments are increasingly moving towards miniaturization, precision, and integration. The assembly accuracy of the product has become a critical factor that limits the performance of the product. Assembly alignment is a significant part of the assembly process, and the accuracy of alignment detection has a significant effect on the final assembly accuracy of the product. Currently, the mainstream technology is to recognize and detect the final image of two parts using microscopic visual inspection technology. However, this method struggles to achieve sub-micron level alignment detection accuracy. In this paper, we presented a sub-micron alignment detection system based on optical diffraction, and the factors influencing the detection accuracy are analysed. Based on this, an experimental assembly alignment detection system was designed based on piezoelectric ceramic drive, laser confocal sensor monitoring feedback, and the mapping relationship between the nanoscale moving position of the diffraction aperture and the diffraction result to achieve sub-micron displacement detection. In addition, experimental tests have been carried out to verify the feasibility of this program. The experimental results indicate that the developed alignment detection method and system can achieve sub-micron alignment detection accuracy, providing a novel device and development concept for high-precision assembly alignment detection. [ABSTRACT FROM AUTHOR]

In order to adapt to the rapid development of deep space exploration, pulsar navigation and space astronomical observation technology, the development of high-efficiency and high-performance X-ray space telescopes has become one of the hotspots for research in the fields of autonomous spacecraft navigation and astronomical observation. Since the X-ray multilayer nested mirror shells are thin-walled and deformable cylindrical structures with high requirements for surface shape accuracy, a slight change in the position or surface shape will lead to a reduction in the focusing imaging quality, so it is necessary to develop a high-precision assembly and adjustment system based on closed-loop real-time monitoring. In this paper, a multi-layer nested space ray telescope assembly and adjustment system with online monitoring feedback is developed, and a method for precision assembly and adjustment of multi-layer nested mirror shells based on online real-time detection and feedback is proposed, which specifically includes key innovative assembly technologies such as three-dimensional face shape measurement, flexible vacuum adsorption, precision adjustment that have been broken through in the process, and the imaging quality of the focused spot of the mirror shells is evaluated and analyzed in the end. The results of the experiment indicate that the assembly and adjustment of the multilayer nested mirror shells can effectively complete the high-precision assembly, and the relevant results can provide a certain reference significance for the further enhancement of the assembly and adjustment performance of the multilayer nested mirror shells. [ABSTRACT FROM AUTHOR]

The precise control of thread clamping force is the main means to achieve the assembly performance of electromechanical systems. The end face perpendicularity error generated in thread machining causes changes in the meshing contact stress and end face stress after thread fitting, which makes the variations of the relationship between tightening torque and clamping force during the thread tightening process. As a result, the dispersion of the final clamping force is increased after the thread assembly, and the fit of the mounting edge becomes worse, which affects the overall performance of the electromechanical systems. In terms of the above issues, this paper takes a M5 bolt as an example to accurately represent and simulate the end face perpendicularity error and connection performance of the bolting using a computer. A digital twin model for thread clamping is established and the study of thread clamping is conducted. Firstly, the 3D point cloud data considering the perpendicularity error of the end face is obtained, and the NURBS surface numerical model is applied to developing the surface models of thread and end face. Then, a geometric digital twin model of the bolt with perpendicularity error is established using CAD. Furthermore, a physical digital twin model of the tightening process of bolting is established using finite element method. Based on the numerical simulation results, the effect of different perpendicularity errors on the stresses of the thread pair and end face are analyzed, and the variation law of tightening accuracy is determined. The influence mechanism of end face perpendicularity error on the clamping force of bolt assembly is revealed. Finally, by comparing the tightening process with M5 standard bolts, the effect of perpendicularity error on the error in the clamping force prediction is investigated, which provides a theoretical basis for achieving precise control of thread clamping force. [ABSTRACT FROM AUTHOR]

Precision microstructures are the core devices of electromechanical products, and their accuracy directly affects the product's overall performance. Laterally thin-walled structures such as precision tooth plates and silicon arms are the most common types of precision microstructures. Due to the characteristics of their tiny size and complex shape, the laterally thin-walled surface is challenging to measure and model, which in turn makes it impossible to realize the accurate prediction and quantitative control of its assembly accuracy. To address this problem, this paper proposes a geometric digital twin modeling method considering the laterally distributed error of thin-walled microstructures. A geometric digital twin model that accurately represents the geometric distributed error of the structure's lateral surfaces is built by applying an image measuring instrument to measure the point clouds of the upper and lower surface profiles, followed by point cloud filtering and merging based on a common datum, and finally surface reconstruction and integration with an ideal 3D model. The method is effectively validated on the silicon arm, a vital component of the thermal mechanical package. Its geometric modeling error is less than 1/10 of its tolerance, and the relative error between the model-based stiffness simulation calculation results and the experimental data is less than 10%. The method can effectively analyze the influence of the manufacturing errors of thin-walled lateral surfaces on the assembly accuracy and provides an adequate model basis for the control of the geometrical and physical performances of precision microstructures. [ABSTRACT FROM AUTHOR]

The reflective optical mirror group is the core component of precision optical instruments, and their assembly accuracy will directly impact the imaging of optical instruments. In recent years, with the rapid development of digital twin technology in the field of assembly, the application of digital twin to assembly process optimization is currently a research hotspot. This paper proposes an assembly process optimization method for a reflective optical mirror group based on an accurate digital twin model. By accurately simulating the geometrically distributed errors generated by machining and the physical properties of the assembled parts of the reflective optical mirror group during assembly and use, an accurate digital twin model is established to accurately characterize the geometrical and physical properties of the reflective optical mirror group. Using the accurate digital twin modeling method, the mirror deformation and stress distribution of a reflective optical mirror group are accurately predicted, and the main process parameters in the assembly process are optimized according to the prediction results, so that the target value of the imaging phase of the mirror group after assembly is improved by 24%, and the encircled energy is improved by 15.1%. The optimization calculation method proposed in this paper can obtain the optimized assembly process parameters more accurately, and the research results provide effective support for improving the assembly accuracy and performance of optical mirrors and precision digital twin modeling research. [ABSTRACT FROM AUTHOR]

Precision assembly process planning technology is one of the major challenges in the field of assembly. Compared with general assembly process planning, precision assembly process planning needs to consider a more complex assembly process, higher precision requirements, and more difficult process control. For this reason, it is of great significance to implement an intelligent assembly process planning and decision-making method for the development of precision assembly technology. This paper realizes the intelligent planning of precision assembly processes through the decision-making route of knowledge modeling and knowledge reasoning. Firstly, a theoretical system of a knowledge-driven precision assembly process decision-making framework is proposed. Secondly, a key precision assembly process decision-making technology is described in detail, which introduces a new approach for knowledge updates based on matching rules. Finally, the effectiveness of the knowledge update method is demonstrated through experimental cases. The knowledge-driven decision-making method combines the accuracy of domain experts' empirical knowledge with the efficiency of intelligent reasoning algorithms. The knowledge update method ensures the currency of knowledge used for decision making. Therefore, the proposed precision assembly decision-making system framework and knowledge updating techniques in this paper are of great significance for knowledge-driven precision assembly process planning. [ABSTRACT FROM AUTHOR]

To understand the pipeline leakage characteristics of the large-scale main gas drainage system in coal mines and the multi-physical fields of the gas flow in pipeline, a three-dimensional numerical model is developed based on Ansys CFX and the process of gas drainage pipeline leakage is investigated.The influence of different leakage positions, different leakage intensities, and different negative pressures of the pumping station on the gas flow in the gas drainage pipeline is studied.The results show that the leakage intensity and the negative pressure are the important factors affecting the internal gas flow field in the gas drainage pipeline, and the leakage occurring near the pumping station of main gas drainage system is prone to be captured easily.The results of this paper are expected to provide technical support for the safety prevention and control of pipeline leakage accidents in the main gas drainage system and also suggestions for the optimization of the allocation of safety prevention and control resources.