Jaw Functional Orthopedics and Cranoficial Growth
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The analysis of the destabilizing motion of a hyperbolic cooling tower during demolition blasting
No full textThe destabilizing motion characteristics of the hyperbolic cooling tower in demolition blasting are thoroughly investigated through the establishment of a numerical simulation calculation model, leading to the following conclusions regarding its destabilizing motion. The tensile-compression elastic-plastic model, which possesses the characteristics of parameter modification function and independence from unit size, can more effectively capture the mechanical properties of concrete materials and find better application in the simulation and calculation research of reinforced concrete structures. The self-oscillation frequency check and collapse morphological analysis are employed to validate the accuracy of the simulation calculation model for hyperbolic cooling towers, as well as to assess the rationality of parameters in the tensile-compression elastic-plastic model. The collapse of a cooling tower induces flexural deformation in the lateral wall, tensile disturbance in the upper and middle sections of the cylinder, and compressive disturbance in the vertical cross-section. The cylinder body has incurred damage as a result of the tower wall’s front end striking the ground at the directional window position on the front side of the throat, leading to a significant extrusion deformation issue. The buckling deformation in the central and lower sections of the rear wall propagated towards the back side of the tower wall upon reaching the ground, ultimately resulting in an “inverted V-shaped” damage along the buckling deformation line. The research findings hold significant relevance for future endeavors
Perspectives of the offshore CCS development in the polish EEZ on a Baltic Sea: insights from ongoing preliminary research for pilot CO2 injection
No full textThe key to effectively combatting progressive climate change lies in promptly reducing greenhouse gas (GHG) emissions, particularly carbon dioxide (CO2), whose concentration continues to rise due to human activities. The European Union (EU) has established legally binding targets, including achieving climate neutrality by 2050, with an intermediate goal of reducing GHG emissions by 55 % by 2030 compared to 1990 levels. Poland, one of the major CO2 emitters in Europe, also possesses significant storage potential in terms of projected CO2 capacity within its sedimentary basins. Considering this, the implementation of Carbon Capture and Storage (CCS) technology could play a crucial role in Poland's efforts to decarbonize its economy. For several years, the Oil and Gas Institute – National Research Institute (INiG – PIB) has been at the forefront of domestic research activities concerning underground CO2 injection. The involvement dates back to 1996 by pioneering the development of a concept, design, and implementation of one of Europe's earliest industrial installations for reinjecting acid gas, consisting of approximately 80 % CO2 and 20 % H2S, into the reservoir water underlying a productive natural gas field. This facility in Borzęcin operated by PGNiG (now part of ORLEN GROUP), is a unique testing ground where the injection process has been running continuously for 27 years. The research conducted at INiG – PIB has played an important role in identifying appropriate geological structures in Poland with a total storage capacity of 10-15 Gt CO2. Around 90-93 % of the CO2 storage capacity is found in saline aquifers, with a significant portion of approximately 7-10 % identified within mature hydrocarbon fields. Despite recent progress in managing industrial CO2 emissions, the peace of CCS development falls short of meeting the objectives set by the Paris Agreement. The main hindrance lies in the absence of a suitable regulatory framework for CO2 transport and storage infrastructure. Recognizing this challenge, the national offshore operator, Lotos Petrobaltic (LPB), presented in 2021 a Green Paper on CCS development in Poland. This document outlines a set of recommendations for legislative alternations to facilitate the initiation of large-scale, commercial CCS projects within the country. Given the current national regulations and assumptions regarding low social barriers, the most expeditious approach to implementing a First-of-a-Kind (FOAK) large-scale CCS project in Poland seems to involve deploying depleted hydrocarbon reservoirs located at the Polish Exclusive Economic Zone (EEZ) within the Baltic Sea. The LPB, with research and scientific support from INiG – PIB, has launched a program aimed at conducting a preliminary assessment of CO2 injection in Middle Cambrian sandstones. The project is scheduled to begin with a pilot injection into the well-identified, depleted structure of the B3 oil reservoir. Subsequently, it may be expanded to include adjacent hydrocarbon reservoirs and could ultimately encompass the entire Cambrian aquifer. The Cambrian aquifer is characterized by complex tectonics, comprising several blocks separated by fault zones. These fault zones may act as barriers to the propagation of reservoir fluids, as demonstrated by the presence of hydrocarbon traps in the vicinity of some fault zones. The B3 oil field, covering an area of 36,2 km2, is an elongated SW-NE, asymmetric anticline, cut on the west side by an inverted fault zone. The reservoir interval, with an average depth of 1450 meters below sea level comprises of sandstones of the Paradoxides Paradoxissimus (Middle Cambrian Zone) horizon showing a monoclinal dip towards the south-east. The reservoir formation exhibits heterogeneity in both within the vertical profile and the horizontal direction, characterized by a wide range of petrophysical parameters values. According to preliminary assessments, the CO2 storage capacity of the B3 site is estimated to be around 7 Mt CO2. The expansion of CO2 storage to the remaining hydrocarbon fields and the overarching Cambrian aquifer megastructure could potentially increase the total storage capacity to more than 150 Mt. The envisioned CO2 sources include emitters from the chemical industry, where CO2 is a by-product of the fertilizer production process. During the project’s pilot phase, the predicted CO2 injection rate is expected to be between 25-50 kt CO2 per year. As the project transitions to the upscaled phase, the injection rate is projected to increase significantly, reaching approximately 2 Mt CO2 per year. The transportation of CO2 for full-scale injection is planned through multimodal means, considering potential synergies with the multimodal CO2 Terminal in Gdansk, which is being planned under the ECO2CEE project (formerly EU CCS Interconnector). A pipeline connection between the offshore storage area and the CO2 Terminal is under consideration. The main research problems addressed within the INiG – PIB and LPB joint initiative include determining the sequestration potential of the Cm2pp aquifer, assessing the feasibility of the CO2-EOR process, recognizing the effects of injected CO2/reservoir rock/caprock interactions on injectivity, geomechanical parameters, and sealing integrity, as well as investigating CO2-induced corrosion of steel components in the transport and injection plant
Exploring CO2 storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach
No full textThe increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO2 sequestration [1]. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations [2]. This study presents a comprehensive methodology for assessing CO2 storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes [3, 4]. These petrophysical properties were derived from core samples collected from these formations. Utilizing machine learning algorithms, porosity was estimated while the Lattice Boltzmann method (LBM) was applied to determine permeability [5]. The methodology employed for estimating these petrophysical parameters was initially validated using samples from formations analogous to Lithuanian formations. Subsequently, it was applied to rock samples specifically obtained from Lithuanian formations. The estimated petrophysical properties were compared with peer-reviewed data from published literature. When fluids such as CO2 or H2 are injected into sub-surface reservoirs, they can alter pore and grain characteristics. Therefore, it is crucial to extract representative element volumes (REVs) from segmented volumes to study the impact of fluids on porosity and their distribution [6]. These mini models, representing small portions of the larger formation, assist in predicting fluid flow within the formation, which is vital for assessing the efficiency and safety of carbon capture and storage (CCS) operations. Subsequently, numerical modelling was conducted using the petrophysical parameters as inputs to assess the storage capacity of the Lithuanian formations using tNavigator software [7]. This research contributes to an enhanced understanding of pore space distribution and its role in various aspects of long-term CO2 storage. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO2 storage. This study shall aid in enhanced understanding of pore space distribution and their contribution towards various aspects of long-term storage. The results can be extended to study the geochemical reactions and geo-mechanical behaviour of the rocks. Such studies shall further facilitate identification of reservoir(s) wherein sequestration potential can be reliably explored
Joint effects of thermal diffusion and diffusion thermo on MHD three dimensional nanofluid flow towards a stretching sheet
No full textThis communication reports the joint effects of Thermal Diffusion and Diffusion Thermo on viscous and incompressible three-dimensional nanofluid flow towards a stretching sheet in connection to the influence of a magnetic field. In this study, nanofluid model is employed for the effects of thermophoresis and Brownian motion. Following that, similarity variables are chosen to turn the dimensional nonlinear system into dimensionless expressions and the resultant transformed equations are solved numerically using Finite Element method. Special emphasis has been given to the parameters of physical interest. These findings are visually presented through graphical representations, providing a clear and insightful understanding involved in this flow scenario. In addition, the final results are examined in light of past research and it is determined that they meet the convergence standards to an exceedingly satisfactory degree. The study’s findings are beneficial for many technical and commercial endeavours
Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform
No full textAs the continuous growth of the machinery industry, the importance of rolling bearings as key connecting parts in machinery movement is also increasing. However, the extraction and diagnosis of rolling bearing fault signals are difficult, and how to use modern transform analysis methods to raise the extraction efficiency and diagnostic accuracy becomes the focus. For this, a rolling bearing fault signal extraction and diagnosis model is designed based on empirical wavelet transform. The diagnostic model is optimized by using support vector machine and quantum genetic algorithm to design a rolling bearing fault signal extraction and diagnosis model based on improved empirical wavelet transform-support vector machine. The test results show that the research method can obtain four component signals showing different anomalies when generating time domain diagrams. Only five component peaks are generated and one group is extracted as output when generating component peaks. The abnormal amplitude of envelope spectrum basically reaches 0.40×10-6 or above. The judgment accuracy of component diagnosis reaches 98.12%. The above results show that the research method has better fault signal extraction ability and better diagnostic accuracy when performing fault signal diagnosis, which can provide new technical support for rolling bearing fault signal extraction and diagnosis
Mechanism of time-delay feedback control of suspension damping with an annular vibration-absorbing structure
No full textWith the aim of enhancing both the ride comfort and the safety of the vehicle, we propose a new type of suspension with an annular vibration-absorbing structure, and establish a 3-DOF 1/4 vehicle model. The structure parameters and time-delay feedback control parameters are determined by particle swarm optimization algorithms, which take the root mean values of body acceleration, suspension dynamic deflection, and tire dynamic displacement as their optimization objectives. We analyze the stability of the suspension control system to ensure the stability of the time-delay control system through the Routh-Hurwitz stability criterion, characteristic root method, and stability switching method. Then, we compare and analyze the response characteristics of conventional suspension, new suspension without time-delay feedback control, and new suspension with time-delay feedback control under simple harmonic excitation and random excitation. The results show that the new suspension with time-delay feedback control has a significant damping effect on the body under the premise of ensuring the stability of the system
Automatic vibration control method for grasping end of flexible joint robot
No full textBecause flexible robots have flexible components such as reducers, there are problems of accuracy deviation and end vibration in the process of external interference and trajectory tracking. This leads to the proposal of a Sliding Mode Control Approach Based on RBF Neural Network (SMC-RBF) parameter optimization. This method is mainly applied to reduce the end vibration and running position error of flexible robot. Firstly, the Newton-Euler method is used to establish the dynamic model of robot considering joint flexibility. At the same time, the experiment optimizes the Sliding Mode Control (SMC) method through RBF neural network. The experiments verify the control methods of the two-joint flexible robot and the six-joint flexible robot respectively. In the control of two-joint robot, the maximum tracking curve error of SMC is only about 0.25 rad under the interference of pulse signal; And the recovery time is only about 1 s. In the control of 6-joint robot, the maximum error of RBF-sliding mode control method on XYZ axis is 0.7 mm, 0.25 mm and 1.25 mm respectively; The error on three axes is smaller than that of traditional PD control method. The results demonstrate that the tracking error of the improved mode control is small, the chattering phenomenon of the robot system is weakened as well
Research on lightweight pedestrian detection based on improved YOLOv5
No full textAiming at the problems of low detection accuracy and the large size of the pedestrian detection algorithm, to improve the edge intelligent recognition capability of the terminal, this paper proposes a lightweight pedestrian detection scheme based on the improved YOLOv5. In this paper, the algorithm first takes the original YOLOv5 as the basic framework and uses the Ghost Bottleneck module to replace the C3 module in the original YOLOv5 network to reduce the number of parameters, eliminate redundant features, and obtain a more lightweight model. Then the attention mechanism CBAM module is added to improve the feature extraction capability and detection accuracy of the algorithm. After experimental verification, the improved lightweight YOLOv5 algorithm significantly reduces the model size and computational cost while guaranteeing accuracy, which is suitable for deployment in edge devices
The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm
No full textThe reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry
Influence of wheel web structure on the tight-curve short-pitch corrugation of metro
No full textShort-pitch corrugation is a common phenomenon that occurs on tight-curve rails in metro systems. However, the contributing factors of this problem are still not fully understood, and effective control measures have yet to be developed. In this study, we investigated the contributing factors of short-pitch corrugation on tight-curve rails in metro systems using the complex eigenvalue analysis method according to the theory of friction-induced vibration. We also explored control measures for short-pitch corrugation from the perspective of optimizing the wheel web structure. Our results indicate that friction-induced vibration is the primary contributing factor to short-pitch corrugation in the wheel-rail system. The shape of the web structure significantly affects rail corrugation, and compared to the straight web structure, unstable vibrations are more pronounced in the S-shape web structure. In contrast, the bow web structure can significantly improve the system stability of the wheel-rail interaction, and the greater the web curvature, the better the inhibitory effect. The wheel deformation under contact force varies with the web curvature, and when the axial deformation of the wheel extends toward the inner rail, the wheel-rail system no longer exhibits unstable vibrations. Conversely, when the axial deformation of the wheel extends toward the outer rail, the greater the deformation, the greater the instability of the wheel-rail system