Geometrical Parameters Of Model Research Articles

Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images.

Optimization‐based pore network modeling approach for determination of hydraulic conductivity function of granular soils

AbstractA wide range of applications of unsaturated hydraulic conductivity is well known in geotechnical, hydrological, and agricultural engineering fields. The standard prediction models for hydraulic conductivity function overlook the complexity of soil pore structure and employ a simplistic approach based on the bundle of capillary tubes. This study proposes an alternative approach employing pore network models calibrated to match soil water retention data to predict the hysteretic hydraulic conductivity function of granular soils. A novel approach to constructing a multidirectional pore network built on an irregular lattice with variable coordination numbers is presented for the realistic representation of soil voids. The geometric and topological parameters of the pore network model are optimized using the genetic algorithm, and adequate pore‐scale processes (piston‐like advance and corner flow during drainage and piston‐like advance, pore body filling, and snap‐off during imbibition) are modeled to get reasonable predictions of hysteretic hydraulic conductivity functions over the entire suction range of granular soils. Comparisons between the pore network model results, standard physically based models, and measured data for a variety of granular soils show that the proposed pore network has a superior performance over other models and compares favorably to the experimental data.

Limited effect of the confluence angle and tributary gradient on Alpine confluence morphodynamics under intense sediment loads

Abstract. Confluences are dynamic morphological nodes that are found in all river networks. In mountain regions, they are influenced by hydraulic and sedimentary processes that occur in steep channels during extreme events in small watersheds. Sediment transport in the tributary channel and aggradation in the confluence can be massive, potentially causing overbank flooding and sedimentation into adjacent settlement areas. Previous works dealing with confluences have mainly focused on lowland regions, and those that have focused on mountain areas have used sediment concentrations and channel gradients that are largely under-representative of mountain river conditions. The presented work contributes to filling this research gap with 45 experiments that use a large-scale physical model. Geometric model parameters, the applied grain size distribution, and the considered discharges represent the conditions at 135 confluences in South Tyrol (Italy) and Tyrol (Austria). The experimental program allowed for a comprehensive analysis of the effects of (i) the confluence angle, (ii) the tributary gradient, (iii) the channel discharges, and (iv) the tributary sediment concentration. In contrast to most research dealing with confluences, results indicate that, in the presence of an intense tributary sediment supply and a small tributary-to-main-channel discharge ratio (0.1), the confluence angle does not have a decisive effect on confluence morphology. Adjustments to the tributary channel gradient yielded the same results. A reoccurring range of depositional geomorphic units was observed in which a deposition cone transitioned to a bank-attached bar. The confluence morphology and tributary channel gradient rapidly adjusted, tending towards an equilibrium state to accommodate both water discharges and the sediment load from the tributary. Statistical analyses demonstrated that the confluence morphology was controlled by the combined channel discharge and the depositional or erosional extent was controlled by the sediment concentration. Applying conclusions drawn from lowland confluence dynamics could misrepresent depositional and erosional patterns and the related flood hazard at mountain river confluences.

A miniaturized statically balanced compliant mechanism for on-chip ultralow wide-bandwidth vibrational energy harvesting

Abstract. This research demonstrates a miniaturized statically balanced compliant mechanism (SBCM) at the micro-electromechanical systems (MEMS) scale. The primary objective is to integrate the MEMS-scale SBCM on chip as the fundamental structure of vibrational energy harvesters for powering low-energy-cost sensors and circuits. The static and dynamic characteristics of the micro-scale SBCM are investigated based on a 2D finite element analysis (FEA) model in COMSOL Multiphysics®. Static balancing is achieved by finely tuning the geometric parameters of the FEA SBCM model. The analytical, numerical, and FEA results confirm that the MEMS-scale SBCM is sensitive to ultralow wide-bandwidth excitation frequencies with weak accelerations. This micro-scale SBCM structure provides a structural solution to effectively lower the working frequencies of MEMS vibrational energy harvesters to ultralow ranges within a wide bandwidth. It overcomes the working frequency limit imposed by the size effect. This would significantly improve the dynamic performance of vibrational energy harvesters at the MEMS scale. In addition, a conceptual structure of the MEMS-scale SBCM is preliminary proposed for the integration of piezoelectric materials by MEMS technologies for vibrational energy harvesting.

An automated optimization procedure for geometry parameters calibration of two-curvature conical heat source model

Welding is an important joining method in industrial manufacturing, and optimization techniques combined with advanced simulation methods in welding have greatly promoted the development of industries. Modeling the heat transfer phenomenon during the welding process can provide a basis for reliably calculating the temperature and residual stress fields of the weldments. Therefore, accurately describing the geometric parameters of the welding heat source model is crucial. An interactive computational procedure is proposed to achieve efficient automatic parameter calibration, which is combined numerical simulation with optimization algorithm. This procedure defines a novel objective function called the total fusion zone error, which measures the disparity in fusion zone shape. It calculates the fusion zone difference for each element near the weld line between the simulated value and the test value, and sums them up. The particle swarm algorithm (PSO) is then selected for the optimization procedure, with some improvement of continuously compressing the particle's search domain and replacing its un-computed fitness value by the neighboring particle's results to reduce the iterative computation time. Synthetically, the proposed procedure is implemented in ABAQUS software to automatically calculate the objective function during optimization iterations. In a case study, the two-curvature conical heat source model is deduced, its calibrated parameters conclude temperature field results with valuable accuracy and efficiency, through this proposed multi-software cooperation automated optimization process. The method proposed in this paper can achieve the requirement of accurate programmatic solution of heat source model parameters, reducing the dependence of simulation efficiency and accuracy on researchers' experience.

A supplementary processing method of residual material in impeller plunge milling

Plunge milling is highly suitable for processing deep groove type parts. In recent years, it has been gradually employed in the machining of integral impeller runners. However, when dealing with impeller runners that exhibit a significant blade twist, there tends to be a considerable amount of residual material left after the plunge milling process. The absence of an accurate computational model to depict these residual materials makes it challenging to plan the subsequent machining path, thereby adversely affecting processing efficiency. To address this issue, a geometric model of the material residual ridge is established. By solving the numerical equation governing the boundary characteristic curve of the residual material, a differential discretization method is employed to discretize the boundary feature curves. These curves are then connected to form a unit straight-line surface, thereby constructing an envelope model of the residual material. In order to carry out the supplementary processing of the residual material, a method is proposed based on the geometric parameters of the material residual envelope model. This method automatically divides the machining area of the residual material and plans plunge milling tool path. Simulation results demonstrate that the established residual material envelope model accurately represents the geometry of the residual material, with a volume error of less than 10 %. The supplementary processing tool path generated using the geometric parameters of the material residual material envelope model removes over 80 % of the material residual material volume. Remarkably, the supplementary processing time accounts for only 10 % of the total time required for the whole runner plunge milling process.

Observational constrained gravity cosmological model and the dynamical system analysis

In this paper, we have analyzed the geometrical and dynamical parameters of cosmological model, (R, being the Ricci scalar and Gauss–Bonnet invariant respectively), constraining the parameters through the cosmological data sets. It is exhibited that the model admits a viable radiation era, and early deceleration followed by late-time acceleration in the matter-dominated era. From the phase-space, portrait stability criterion has been analyzed, restricting the parameter β, different from . Additionally, we have explored the stability of the model from the behavior of critical points and obtained the present value of the density parameter for matter-dominated and dark energy components, which are identical to those obtained through cosmological data sets.

Novel geometrical model and design mechanical parameters for CMT-WAAM stainless steel

Wire and Arc Additive Manufacturing (WAAM) is one of the 3D printing techniques largely employed in the field of steel construction, with the ability to produce large-scale structural members with high speed and low cost. Cold Metal Transfer (CMT) is a more suitable welding technique for additive manufacturing with the advantages of high energy density, excellent surface forming, no splatter and fewer welding-related defect. In order to promote the use of the CMT-WAAM technique in the construction industry, fundamental research on the mechanical properties of CMT-WAAM materials is urgently required. To this end, a comprehensive study on geometrical and mechanical properties of 316 L stainless steel fabricated by CMT-WAAM technique has been carried out and the results are reported herein. 3D laser scanning was employed to capture the geometries of CMT-WAAM plates and a suggested geometrical model to describe the undulating surface of CMT-WAAM components was presented after a series of data acquisition and processing. A total of 162 tensile coupons were tested, covering different welding process parameters, surface types and extraction directions. As-built and machined coupons were tested to investigate the influence of the geometrical properties that are inherent in CMT-WAAM process on mechanical properties, while the coupons extracted from three orientations were tested to explore the anisotropy of the material. Finally, the design values of the main mechanical parameters for CMT-WAAM 316 L stainless steel were calibrated for three main orientations according to EN 1990-Eurocode 0 (EC0) to give design recommendations.

A New Workflow for Improved Resistivity-Based Water Saturation Assessment in Organic-Rich Mudrocks: Application to Haynesville, Eagle Ford, and Woodford Formations

Summary Reliable fluid saturation assessment in organic-rich mudrocks has been a challenge for the oil and gas industry. The composition and spatial distribution of rock components have a significant impact on electrical resistivity and, thus, on hydrocarbon reserves estimates. Clays are typically considered, in resistivity models, to be distributed in laminated or dispersed forms. Additionally, conventional resistivity models do not incorporate conductive components other than brine. Such assumptions can lead to uncertainty in fluid saturation assessment in organic-rich mudrocks. We introduce a well-log-based workflow that quantitatively assimilates the type and spatial distribution of all conductive components to improve reserves evaluation in organic-rich mudrocks and demonstrate its field application in the Eagle Ford, the Woodford, and the Haynesville formations. The introduced workflow consists of an inversion algorithm to estimate geometry-dependent parameters (depolarization factors or geometric model parameters) and water saturation. Inputs to the inversion algorithms include volume concentrations of minerals, estimated from the multimineral analysis. Other inputs are conductivity of rock components and porosity obtained from laboratory experiments and interpretation of well logs. The petrophysical model considers that brine forms the conductive background to which conductive (e.g., clay, pyrite, and kerogen) and nonconductive (e.g., grains and hydrocarbon) components are incorporated. The assumed/estimated petrophysical properties have an impact on the effective conductivity of the rock and thereby can impact the performance of the new resistivity-based method. We applied the new method to different organic-rich mudrock formations to test the universal nature of the method and its efficacy in organic-rich mudrock reservoirs with varying volumetric concentrations of minerals within the rock. We successfully applied the workflow to four wells in the Eagle Ford, the Woodford, and the Haynesville formations. The formation-by-formation inversion showed a variation in geometric model parameters in different petrophysical zones, resulting in improved water saturation estimates. A comparison of the results obtained from the new workflow against those from the Waxman-Smits and Archie models indicated a relative improvement in saturation estimates of 9.5 and 26.3% in the Eagle Ford formation. Similar improvements were noted in the Woodford and the Haynesville formations as well. The improvement can be enhanced in formations with larger fractions of conductive components. The results confirmed that the new workflow improves the reliability of water saturation estimates in organic-rich mudrocks, which has been a challenge for the oil and gas industry. In contrast to conventional techniques, the new method does not need water saturation obtained from core measurements for calibration efforts. All the parameters in the new workflow are geometry- or physics-based. We verified that formation-based geometric model parameters in the Eagle Ford formation were consistent in both wells, which is promising for calibration-free assessment of water/hydrocarbon saturation in the field-scale domain using electrical resistivity measurements. The new method minimizes the need for expensive and time-consuming core measurements of water saturation, which is a unique contribution of this work. Finally, the new workflow is physics-based and incorporates the volumetric concentration and electrical conductivity of all rock components. This enables the introduced workflow to be applied to different formations with ease for improved assessment of water saturation.

Density-guided Incremental Dominant Instance Exploration for Two-view Geometric Model Fitting.

Existing two-view multi-model fitting methods typically follow a two-step manner, i.e., model generation and selection, without considering their interaction. Therefore, in the first step, these methods have to generate a considerable number of instances in order to cover all desired ones, which not only offers no guarantees, but also introduces unnecessary expensive calculations. To address this challenge, this study presents a new algorithm, termed as D2Fitting, that incrementally explores dominant instances. Particularly, rather than viewing model generation and selection as two disjoint parts, D2Fitting fully considers their interaction, and thus performs these two subroutines alternatively under a simple yet effective optimization framework. This design can avoid generating too many redundant instances, thus reducing computational overhead and allowing the proposed D2Fitting being real-time. Meanwhile, we further design a novel density-guided sampler to sample high-quality minimal subsets during the model generation process, so as to fully exploit the spatial distribution of the input data. Also, to mitigate the influence of noise on the subsets sampled by the proposed sampler, a global-residual optimization strategy is investigated for the minimal subset refinement. With all the ingredients mentioned above, the proposed D2Fitting can accurately estimate the number and parameters of geometric models and efficiently segment the input data simultaneously. Extensive experiments on several public datasets demonstrate the significant superiority of D2Fitting over several state-of-the-arts.

Occurrence of Complex Behaviors in the Uncontrolled Passive Compass Biped Model

It is widely known that an appropriately built unpowered bipedal robot can walk down an inclined surface with a passive steady gait. The features of such gait are determined by the robot's geometry and inertial properties, as well as the slope angle. The energy needed to keep the biped moving steadily comes from the gravitational potential energy as it descends the inclined surface. The study of such passive natural motions could lead to ideas for managing active walking devices and a better understanding of the human locomotion. The major goal of this study is to further investigate order, chaos and bifurcations and then to demonstrate the complexity of the passive bipedal walk of the compass-gait biped robot by examining different bifurcation diagrams and also by studying the variation of the eigenvalues of the Poincaré map's Jacobian matrix and the variation of the Lyapunov exponents. We reveal also the exhibition of some additional results by changing the inertial and geometrical parameters of the bipedal robot model.

Automatic 3D mitral valve leaflet segmentation and validation of quantitative measurement

3D transesophageal echocardiography (TEE) is widely used in the diagnosis of mitral valve disease and is also well suited for guiding cardiac interventions. The aim of this work is to achieve patient-specific 3D TEE mitral valve leaflet segmentation without any user interaction and to assess the feasibility of 3D quantitative measurements on automatic segmentation model. We suggested a novel pre-training strategy to better implement automatic segmentation. The strategy refers to classify the diastolic and systolic states of the mitral valve through a 3D convolutional neural network architecture, and then use the pretrained weights obtained from the classification task to initialize the parameters of the 3D segmentation deep learning framework. To determine the accuracy of geometric parameters of segmentation model, the measurements of the segmentation model were compared with those obtained by the clinical software. Statistical analysis was performed by using Intraclass Correlation Coefficient and Bland–Altman method. Fourteen 3D volumes were used to evaluate the segmentation performance. The results show a Dice Similarity Coefficient (DSC) of 0.877±0.027 and an Average Surface Distance (ASD) of 0.925±0.392 mm. Twenty-eight 3D volumes were used for the quantitative measurement. The statistical results show that the mitral annular parameters have a good agreement between segmentation model and clinical software except for the annular height. We developed a fully automatic methodology to segment the mitral valve leaflet from 3D TEE and demonstrated the feasibility of improving segmentation performance with the proposed pre-training strategy. The automatic segmentation model was proved to be reliable for performing quantitative measurements of mitral valve annulus dimensions. The results indicate that the precision of the automatic segmentation methodology could pave the way for application in quantification, modeling and surgical planning tools.

On-Orbit Autonomous Geometric Calibration of Directional Polarimetric Camera

The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km).

Estimation of left ventricular parameters based on deep learning method.

Estimating material properties of personalized human left ventricular (LV) modelling is a central problem in biomechanical studies. In this work we use deep learning (DL) method to evaluating the passive myocardial mechanical properties inversely. In the first part of the paper, we establish a standardized geometric model of the LV. The geometric model parameters are optimized based on 27 different healthy volunteers. In the second part, we use statistical methods and Latin hypercube sampling (LHS) to obtain the geometric parameters data. The LV myocardium is described using a structure-based orthotropic Holzapfel-Ogden constitutive law. The LV diastolic pressure-volume (PV) curves are calculated by numerical simulation. Tn the third part, we establish the multiple neural networks to pblackict PV curve parameters. Then, instead of using constrained optimization problems to solve constitutive parameters, DL was used to establish the nonlinear mapping relationship of geometric parameters, PV curve parameters and constitutive parameters. The results show that the deep learning method can greatly improve the computational efficiency of numerical simulation and increase the possibility of its application in rapid feedback of clinical data.

Enhanced Assessment of Fluid Saturation in the Wolfcamp Formation of the Permian Basin

Conventional resistivity models often overestimate water saturation in organic-rich mudrocks and require extensive calibration efforts. Conventional resistivity-porosity-saturation models assume brine in the formation as the only conductive component contributing to resistivity measurements. They also do not reliably assimilate the spatial distribution of the clay network and pore structure. Moreover, they do not incorporate other conductive minerals and organic matter, impacting the resistivity measurements and leading to uncertainty in water saturation assessment. We recently introduced a resistivity-based model that quantitatively assimilates the type and spatial distribution of all rock constituents to improve reserves evaluation in organic-rich mudrocks using electrical resistivity measurements. This paper aims to expand the application of this model for well-log-based assessment of water/hydrocarbon saturation and to verify the reliability of the introduced method in the Wolfcamp Formation of the Permian Basin. Our recently introduced resistivity model uses pore combination modeling to incorporate conductive (clay, pyrite, kerogen, brine) and nonconductive (grains, hydrocarbon) components in estimating effective resistivity. The inputs to the model are volumetric concentrations of minerals, conductivity of rock components, and porosity obtained from laboratory measurements or interpretation of well logs. Geometric model parameters are also critical inputs to the model. To simultaneously estimate the geometric model parameters and water saturation, we developed an inversion algorithm with two objectives: (a) to estimate the geometric model parameters as inputs to the new resistivity model and (b) to estimate the water saturation. The geometric model parameters are determined for each rock type or formation by minimizing the difference between the measured resistivity and the resistivity estimated from pore combination modeling. We applied the new method to two wells drilled in the Wolfcamp Formation of the Permian Basin. The formation-based inversion showed variation in geometric model parameters, which improved the assessment of water saturation. Results demonstrated that the new method improved water saturation estimates by 24.1% and 32.4% compared to Archie’s and Waxman-Smits models, respectively, in the Wolfcamp Formation. The most considerable improvement was observed in the Middle and the Lower Wolfcamp Formations, where the average clay concentration was relatively higher than the other zones. There was an additional 70,000 bbl/acre of hydrocarbon reserve using the proposed method compared to when water saturation was quantified using Archie’s model in the Permian Basin, which is a 33% relative improvement. It should be highlighted that the new method did not require any calibration effort using core water saturation measurements, which is a unique contribution of this rock-physics-based workflow.

Melting of non-Newtonian phase change material in a finned triple-tube: Efficacy of non-uniform magnetic field

In this paper, a numerical study is presented on the melting process and heat transfer enhancement of a non-Newtonian phase change material (NPCM) in a double tube and a finned triple tube. The non-Newtonian power-law model and the enthalpy-porosity method were utilized to simulate the melting of non-Newtonian. The effects of geometrical parameters of storage enclosure, non-Newtonian model and non-uniform magnetic field on NPCM melting process were investigated. The results showed that the triple-tube could be more effective in enhancing the melting process than that of double-tube. By increasing the radius of the middle tube and decreasing the length of the fins attached to the middle tube, the melting process of non-Newtonian fluid decreased. In this study, increased consistency index and decreased non-Newtonian fluid index caused a decrease in the melting process. By using a non-uniform magnetic field and increasing its intensity, the melting performance of the triple-finned tube increased by 12–16%.

Combining data-and-model-driven 3D modelling (CDMD3DM) for small indoor scenes using RGB-D data

To solve problems in 3D modelling, including unclear 3D edges in point clouds, lack of geometric semantics, confusing topological relations between 3D models, and low degree of automation in traditional model-driven 3D modelling, a method for high-accuracy and automatic monomer 3D modelling must be developed. This paper is the first to propose a 3D modelling strategy, called combining data-and-model-driven 3D modelling (CDMD3DM), for small regular objects in indoor scenes using RGB-D data. The proposed method’s workflow is as follows: generation of initial 3D point cloud data using data-driven Kinect v2; segmentation of point cloud data, based on deep learning, that improves the accuracy and automation of geometric model recognition; definition of the initial model-driven parameters based on the instance segmentation results; optimization of geometric model parameters, based on generalized point photogrammetry theory, to generate monomer models in indoor scenes to overcome the shortcomings of confusing topological relationships and inaccurate 3D model edges; and finally, fusion of the results of data-driven and model-driven 3D modelling. The experimental results demonstrate that CDMD3DM is feasible, automatic, more accurate, more reliable, semantically richer and capable of producing clearer topological relationships than current 3D modelling results using indoor RGB-D data. These outcomes promote interdisciplinary integration between computer vision and photogrammetry.

Numerical Analysis and Experimental Verification of Optimum Heat Input in Additive Manufacturing of Aero Ultrathin Blade

Heat input is a crucial factor affecting the quality in blade additive manufacturing repairing. First, a moving heat source model was established, and through numerical analysis and experimental comparison, the optimal geometric parameters of the heat source model for ultrathin blade repair were obtained. Second, a heat transfer model is established based on the optimal heat source model. By analyzing the thermophysical properties of different alloys, the heat input range of the blade was calculated. By heat transfer calculation under different heat inputs, the heat transfer model of blade repair was optimized. Then, a mathematical model of additive height is established. The optimized heat transfer model is used to solve the temperature distribution of the additive section with time under different wire feeding speeds through numerical analysis, which further reduced the heat input range. Third, the experiments are carried out based on the results of numerical analysis. The evolution law of the microstructure and heat input rate of the additive manufacturing zone was revealed, and the optimal heat input parameters were obtained. Under the optimal parameters, the segregation zone disappeared; hence, the test data were close to the base metal, and the additive manufacturing zone achieved better quality. The results and methods are of great guiding significance for the optimization design in additive manufacturing repair of the aero blades. The study also contributes to carrying out a series of research on heat transfer of ultrathin nickel-based alloy welding.

Investigation of thermal and fluidic losses and their effect on the performances of a Stirling refrigerator

The design of a Stirling machine requires the understanding of the processes that govern their operation to predict the optimum operating parameters of the machine. The modified simple model using the geometrical parameters of the FEMTO-ST engine model is applied in this paper to investigate the trend of losses with respect to operating frequency and charging pressure. The variation of pressure drops across the regenerator and heat exchangers have been investigated as functions of operating frequency and charging pressure in order to predict the extent of fluid friction losses. The share of different power and heat losses as well as their effects on cooling performance have been evaluated at the different operating frequencies and charging pressures. The share of fluid friction loss and loss due to regenerator imperfection increase with rotational speed and charging pressure.

Geometric Model for Physical Simulation System of Coal and Gas Outburst

The physical simulation system is an important means to study the mechanism of coal and gas outburst. The small- and medium-sized coal and gas outbursts account for the largest proportion in China. So, the paper focuses on small- and medium-sized coal and gas outbursts based on key physical rules and their statistical characteristics. According to the geometric parameters statistical data of more than 100 caverns, the proportion of outburst caverns whose depth is less than 5 m is 80%, and whose depth is more than 6 m is 13%. The ratio of depth and cavern outlet’s diameter is 2.58–7.31. There are statistical relationship of 2 times between coal’s and cavern’s volume, and between gas emission volume per ton and gas content in seam. Then, the cavern of coal and gas outburst is simplified as an ellipsoid, and the shape of cavern outlet is simplified as a circle. The cavern’s and the prototype’s geometrical parameters of small and medium coal and gas outbursts were deduced. The depth of cavern is 5 m, width is 4 m, and diameter of cavern outlet is 0.68–1.94 m. The depth of coal and outburst prototype is 12.3 m, width is 6.5 m, and diameter of cavern outlet is 0.68 m. In order to guarantee the movement similarity, according to the relationships among statistical data of outburst coal particle’s size, cavern outlet’s size, and height of tunnel, the minimum cavern outlet’s size was calculated. Select 10 times the coal particle’s size (5 mm) as the smallest size. Then, maximum geometric similarity ratio of coal and gas outburst was deduced, and it is 13.6. The minimum geometrical parameters of coal and outburst similar model were obtained. Its depth is 0.9 m, width is 0.48 m, and diameter of cavern outlet is 50 mm. According to the results, the physical simulation system was developed. The results provide support to carry out physical simulation experiment of coal and gas outburst.