3D printing high-viscosity muck stirring and mixing uniformity analysis method

Abstract

The invention provides a 3D printing high-viscosity muck stirring and mixing uniformity analysis method which comprises the following steps: 1) respectively measuring geometric structure data and motion parameter data of a stirrer, constructing a virtual stirrer model through modeling software, importing the virtual stirrer model into simulation software, and performing a discrete element method simulation experiment to obtain a simulation model; obtaining particle simulation data; 2) setting an initial simulation parameter through the particle simulation data to construct a particle contact model, and performing a trial-and-error simulation experiment on the particle contact model to obtain an optimized simulation parameter which enables a simulation result to accord with an actual result, and simulating particle distribution and motion characteristics of muck particles in different stirring processes through a particle contact model with optimized simulation parameters, and calculating the mixing uniformity of muck stirring by adopting an in-plane RGB mixing index. The analysis method provided by the invention promotes sustainable development of green building materials, and has environmental and economic benefits.

Description

Technical Field

[0001] The present invention belongs to the technical field of 3D printing engineering and relates to a method for analyzing the mixing uniformity of 3D printed high-viscosity engineering slag stirring, and specifically relates to a method for analyzing the mixing uniformity of the 3D printed high-viscosity engineering slag stirring process based on a particle discrete element model (DEM). Background Art

[0002] In recent years, with the rise of Industry 4.0, the application of 3D printing technology in the construction sector has made significant progress, effectively accelerating the construction process, promoting ecological transformation, and upgrading to intelligent technologies. At the same time, the sustainability of the built environment has received increasing attention from all sectors of society over the past few decades. Therefore, integrating sustainable development concepts into digital 3D printing technology to ensure the effective recycling of construction waste while maintaining ecological safety has become a critical topic that requires in-depth research.

[0003] In recent years, with rising environmental awareness and the growing popularity of resource recycling, people have actively explored various renewable and sustainable building materials. Against this backdrop, 3D printing using construction waste has become a highly anticipated new technology. Waste generated during construction, once considered an unmanageable burden, has been given new life and value through the innovative application of 3D printing technology. First, recycling waste effectively addresses the issue of construction waste disposal, reduces environmental pressure, and aligns with the concept of green, low-carbon, and sustainable development. Second, after scientifically formulated and specially treated, waste possesses physical and mechanical properties that meet the requirements of civil engineering, providing an economical and practical new material option for the construction industry.

[0004] However, slag comes from a wide range of sources, and the properties of slag in different soil layers are also different. High-viscosity slag has strong adhesion and poor fluidity. Fine particles in the slag easily adhere to each other and agglomerate together to form larger particle clumps, making it difficult for the slag to be fully dispersed and evenly mixed during the mixing process. In addition, high-viscosity slag usually contains a high amount of water. The presence of water molecules further enhances the adhesion between slag particles. The high water content also further reduces the fluidity of the slag, increasing the difficulty of mixing. Therefore, it is more difficult to evaluate the mixing uniformity of high-viscosity slag. The mixing uniformity of slag has an important influence on the final printing quality and structural performance. However, most of the existing slag mixing methods rely on manual experience or simple mechanical models, and lack accurate simulation and quantitative evaluation tools. Therefore, a new method is urgently needed to improve the accuracy and efficiency of the slag mixing process.

[0005] In summary, the mixing process and the evaluation of uniformity after mixing are very important for 3D printing. However, the mixing effect and uniformity evaluation of engineering waste soil, which comes from a wide range of sources, have not received much attention. In order to further effectively evaluate the mixing uniformity of 3D printed engineering waste soil and promote the effective reuse of engineering waste soil, a simple, efficient and reliable method for evaluating the mixing uniformity of 3D printed engineering waste soil is urgently needed. Summary of the Invention

[0006] In view of the characteristics of the prior art described above, the purpose of the present invention is to provide a 3D printing high-viscosity slag mixing uniformity analysis method, which is suitable for accurately evaluating the mixing uniformity during the treatment and modification of engineering slag, and solving the problems of uneven mixing or insufficient simulation accuracy during the reuse of high-viscosity slag.

[0007] To achieve the above-mentioned and other related purposes, the present invention provides a method for analyzing the uniformity of mixing of 3D printed high-viscosity slag, comprising the following steps:

[0008] 1) The geometric structure data and motion parameter data of the mixer are measured respectively, a virtual mixer model is constructed using modeling software, and then the virtual mixer model is imported into simulation software to perform discrete element method simulation experiments to obtain particle simulation data;

[0009] 2) The particle contact model is constructed by setting the initial simulation parameters through the particle simulation data. The particle contact model is subjected to a trial-and-error simulation experiment to obtain the optimized simulation parameters so that the simulation results are consistent with the actual results. The particle contact model with the optimized simulation parameters is then used to simulate the particle distribution and movement characteristics of the slag particles under different mixing processes, and the in-plane RGB mixing index is used to calculate the mixing uniformity of the slag mixing.

[0010] As described above, the present invention provides a method for analyzing the uniformity of mixing and stirring high-viscosity slag in 3D printing, which organically combines engineering slag recycling technology, discrete element simulation technology, and 3D printing technology. This method can not only improve the material uniformity and strength of 3D printed high-viscosity slag, laying a material foundation for its promotion and application, but also fully utilize the huge amount of engineering slag in stock as building materials, providing a new approach to the commonly used engineering slag landfill treatment in the industry, thereby bringing considerable benefits in terms of economy and ecology. Compared with the existing technology, the present invention has the following significant advantages and effects:

[0011] (1) DEM-based mixing uniformity evaluation method: This invention applies the particle discrete element model (DEM) to the mixing process of 3D printing engineering slag for the first time, accurately simulating the motion trajectory and stress conditions of slag particles. Based on the particle discrete element model (DEM), the present invention can accurately simulate the motion trajectory and stress conditions of slag particles, provide quantitative mixing uniformity evaluation indicators (such as RGB mixing index M value), and can quickly and efficiently evaluate the uniformity of 3D printed high-viscosity slag after mixing. Compared with the existing technology that relies on manual experience or simple mechanical models, this method has higher accuracy and reliability.

[0012] (2) Refined simulation and parameter optimization: Through the refined setting and optimization of simulation parameters, the accuracy of mixing uniformity evaluation is improved, and the complexity and resource consumption of real experiments are reduced.

[0013] (3) Real-time dynamic adjustment mechanism: The present invention can make dynamic adjustments during the mixing process by simulating the mixing process in real time, thereby optimizing the mixing efficiency and the mixing uniformity of the slag in a timely manner.

[0014] (4) Innovative RGB mixing index uniformity evaluation method: The present invention combines the DEM discrete element method and proposes a red, green and blue coloring method for slag particles in different directions. The RGB values of slag particles with different color distributions in different cross-sections are used to quantitatively evaluate the distribution uniformity of the mixing process. The RGB mixing index uniformity evaluation method is fast and efficient, and can achieve rapid evaluation of the mixing uniformity of slag particles.

[0015] (5) Reuse of large quantities of highly viscous construction waste: Construction waste comes from a wide range of sources, and the soil composition varies across different strata. High-water-content viscous soil has poor fluidity and is difficult to mix, making it challenging to assess mixing uniformity. Mixing and assessing mixing uniformity present certain technical bottlenecks. This invention promotes the large-scale application of highly viscous construction waste and improves printing quality.

[0016] (6) This method simulates the mixing process in detail, accurately calculates key factors such as the motion trajectory, velocity distribution, and shear force distribution of various particles, and combines the particle contact model to quantitatively evaluate the mixing uniformity during the mixing process of high-viscosity engineering slag. By simulating and analyzing the particle distribution and motion characteristics under different mixing conditions, it can provide a scientific basis for the mixing design and optimization of high-viscosity engineering slag. Compared with traditional mixing methods, the present invention can significantly improve the mixing efficiency and mixing uniformity, thereby improving the working performance and printing quality of 3D printed high-viscosity engineering slag.

[0017] (7) This method combines the construction waste recycling technology, discrete element simulation technology and 3D printing technology, which can quickly and efficiently evaluate the uniformity of 3D printed construction waste after mixing. It can not only promote the sustainable development of green building materials and meet the requirements of modern construction industry for environmental protection and energy conservation, but also provide effective support for the promotion, application and industrialization of 3D printing technology in my country's construction industry, and has high environmental and economic benefits.

[0018] (8) This method is highly operational and practical, and can provide a new technical path for the recycling of construction waste, and promote the sustainable development of green building materials. This method not only meets the requirements of modern construction industry for environmental protection and energy conservation, but also provides effective support for the promotion, application and industrialization of 3D printing technology in my country's construction industry, with high environmental and economic benefits.

[0019] (9) This method reduces experimental costs and resource consumption: By using simulation software to conduct virtual stirring experiments, the material, equipment, and time costs required for a large number of real experiments are avoided. Compared with traditional experimental methods, this method significantly reduces resource consumption and improves R&D efficiency.

[0020] (10) The present invention can monitor the motion state of particles in real time during the simulation process, dynamically adjust and optimize it in real time, and dynamically adjust stirring parameters (such as rotation speed and stirring time) based on the simulation results to optimize stirring efficiency and mixing uniformity. Existing technologies are generally unable to achieve real-time adjustment, resulting in a long optimization cycle for the stirring process.

[0021] (11) This method is applicable to complex working conditions. The present invention can simulate the particle distribution and motion characteristics under different mixing conditions and is suitable for complex working conditions (such as high-viscosity slag, high-water content slag, etc.). Existing technologies often have difficulty in accurately assessing mixing uniformity when dealing with complex working conditions.

[0022] (12) The present invention promotes the sustainable development of green building materials. By accurately assessing the mixing uniformity of construction waste, the present invention can effectively promote the reuse of construction waste, reduce the discharge of construction waste, and conform to the concept of green, low-carbon sustainable development. The application of existing technologies in the reuse of construction waste is relatively limited, making it difficult to achieve large-scale promotion.

[0023] (13) The present invention improves 3D printing quality and structural performance. By optimizing the mixing process, the present invention improves the uniformity and strength of the slag, providing high-quality building materials for 3D printing and significantly improving printing quality and structural performance. The shortcomings of existing technologies in assessing the uniformity of slag mixing often lead to unstable printing quality.

[0024] (14) This method also has wide applicability and scalability. The analysis method of the present invention is not only applicable to highly viscous soil, but can also be extended to the assessment of mixing uniformity of other types of building materials (such as concrete, mortar, etc.). Existing technologies are usually targeted at specific materials and have limited applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram showing the workflow for analyzing the uniformity of mixing of highly cohesive soil.

[0026] Figure 2 The diagram shows the viscoelastic particle-liquid bridge model of soil, where i is the first particle, j is the second particle, and R i is the effective radius of the first particle, R j is the effective radius of the second particle, β is the liquid bridge coefficient, θ is the contact angle of the liquid-gas interface, a is the shortest distance between the two particles, d sp / sp is the immersion distance between two particles.

[0027] Figure 3 Displayed are DEM modeling and initial particle state diagram based on a real stirring model.

[0028] Figure 4 Displayed as a velocity distribution diagram of soil particles.

[0029] Figure 5 Displayed is the force distribution diagram of the airfoil-shaped spiral stirring shaft.

[0030] Figure 6 Displayed is the velocity distribution diagram of particle 1 trajectory.

[0031] Figure 7 Displayed is the coloring by height direction at the initial moment and the particle color distribution after 30 seconds.

[0032] Figure 8 Displayed are the horizontal coloring at the initial moment and the particle color distribution after 30 seconds.

[0033] Figure 9 Displayed is the color distribution of particles colored in the radial direction at the initial moment and 30s later. DETAILED DESCRIPTION

[0034] The following detailed description specifically discloses an implementation method of the present application's method for analyzing the uniformity of mixing of 3D printed high-viscosity slag. However, there may be cases where unnecessary detailed descriptions are omitted. For example, there are cases where detailed descriptions of well-known matters and repeated descriptions of actually the same structure are omitted. This is to avoid the following description from becoming unnecessarily lengthy and to facilitate the understanding of those skilled in the art. In addition, the following description is provided for those skilled in the art to fully understand the present application and is not intended to limit the subject matter described in the claims.

[0035] " scope " disclosed in the present application is limited in the form of lower limit and upper limit, and given range is limited by selecting a lower limit and an upper limit, and selected lower limit and upper limit define the boundary of special scope.The scope that this mode limits can be to include end value or not include end value, and can be combined arbitrarily, and promptly any lower limit can form a scope with any upper limit combination.For example, if listed the scope of 60-120 and 80-110 for specific parameter, be interpreted as the scope of 60-110 and 80-120 also is expected.In addition, if listed minimum range value 1 and 2, and if listed maximum range value 3,4 and 5, then following scope can all be expected: 1-3,1-4,1-5,2-3,2-4 and 2-5.

[0036] Unless otherwise specified, all embodiments and optional embodiments of the present application can be combined with each other to form a new technical solution.

[0037] Unless otherwise specified, all technical features and optional technical features of this application can be combined with each other to form a new technical solution.

[0038] Unless otherwise specified, all steps of the present application may be performed sequentially or randomly, preferably sequentially. For example, the method includes steps (1) and (2), which means that the method may include steps (1) and (2) performed sequentially, or may include steps (2) and (1) performed sequentially. For example, the method may further include step (3), which means that step (3) may be added to the method in any order, for example, the method may include steps (1), (2) and (3), or may include steps (1), (3) and (2), or may include steps (3), (2) and (1), etc.

[0039] Unless otherwise specified, the terms "include" and "comprising" used in this application may be open-ended or closed-ended. For example, "include" and "comprising" may mean that other components not listed may also be included or that only the listed components are included.

[0040] Unless otherwise specified, the term "or" is used in this application to be inclusive. For example, the phrase "A or B" means "A, B, or both A and B." More specifically, the condition "A or B" is satisfied if any of the following conditions are met: A is true (or exists) and B is false (or does not exist); A is false (or does not exist) and B is true (or exists); or both A and B are true (or exist).

[0041] The present invention provides a method for analyzing the uniformity of mixing of 3D printed high-viscosity slag. Figure 1 As shown, the following steps are included:

[0042] 1) The geometric structure data and motion parameter data of the mixer are measured respectively, a virtual mixer model is constructed using modeling software, and then the virtual mixer model is imported into simulation software to perform discrete element method simulation experiments to obtain particle simulation data;

[0043] 2) The particle contact model is constructed by setting the initial simulation parameters through the particle simulation data. The particle contact model is subjected to a trial-and-error simulation experiment to obtain the optimized simulation parameters so that the simulation results are consistent with the actual results. The particle contact model with the optimized simulation parameters is then used to simulate the particle distribution and movement characteristics of the slag particles under different mixing processes, and the in-plane RGB mixing index is used to calculate the mixing uniformity of the slag mixing.

[0044] In the above analysis method, the high-viscosity slag is slag with a plasticity index PI ≥ 20, a clay content ≥ 30% and a particle size less than 0.002 mm.

[0045] In the above analysis method, in step 1), the mixer is a conventional soil mixer, specifically a single-shaft vertical mixer or a double-shaft vertical mixer.

[0046] In the above analysis method, in step 1), the measurement is performed using a measuring tool, which is a laser rangefinder.

[0047] In the above analysis method, in step 1), the geometric structure data of the mixer is selected from one or more combinations of the diameter of the mixer, the height of the mixer, the diameter of the mixing barrel, the length of a single blade, the width of a single blade, the thickness of a single blade, the distribution shape of multiple blades, the size of the feed port or the size of the discharge port.

[0048] In one embodiment, the diameter of the stirrer is 100-1000 mm, such as 100-300 mm, 300-600 mm, 600-1000 mm, specifically 100 mm, 200 mm, 300 mm, 400 mm, 500 mm, 600 mm, 700 mm, 800 mm, 900 mm, 1000 mm, preferably 500 mm.

[0049] In one embodiment, the height of the mixer is 500-1000 mm, such as 500-700 mm, 700-900 mm, 900-1000 mm, specifically 500 mm, 600 mm, 700 mm, 800 mm, 900 mm, 1000 mm, preferably 800 mm.

[0050] In one embodiment, the diameter of the mixing barrel is 500-1000 mm, such as 500-600 mm, 600-800 mm, 800-1000 mm, specifically 500 mm, 600 mm, 700 mm, 800 mm, 900 mm, 1000 mm, preferably 700 mm.

[0051] In one embodiment, the length of the single blade is 100-500 mm, such as 100-300 mm, 300-500 mm, specifically 100 mm, 200 mm, 300 mm, 400 mm, 500 mm, preferably 200 mm.

[0052] In one embodiment, the width of the single blade is 10-100 mm, such as 10-30 mm, 30-60 mm, 60-100 mm, specifically 10 mm, 20 mm, 30 mm, 40 mm, 50 mm, 60 mm, 70 mm, 80 mm, 90 mm, 100 mm, preferably 50 mm.

[0053] In one embodiment, the thickness of the single blade is 1-10 mm, such as 1-3 mm, 3-6 mm, 6-10 mm, specifically 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, 7 mm, 8 mm, 9 mm, 10 mm, preferably 5 mm.

[0054] In one embodiment, the distribution shapes of the plurality of blades include but are not limited to elliptical, palm-shaped, fan-shaped, diamond-shaped, circular, needle-shaped, triangular, spiral, etc., preferably spiral.

[0055] In one embodiment, the size (length×width) of the feed port is 50-200 mm×50-200 mm, for example, 50 mm×50 mm, 100 mm×100 mm, 150 mm×150 mm, 200 mm×200 mm, preferably 100 mm×100 mm.

[0056] In one embodiment, the size (length×width) of the discharge port is 50-200mm×50-200mm, for example, 50mm×50mm, 100mm×100mm, 150mm×150mm, 200mm×200mm, preferably 100mm×100mm.

[0057] In the above analysis method, in step 1), the motion parameter data of the stirrer is selected from one or more combinations of the rotation speed of the stirring shaft, the stirring speed, the stirring direction or the stirring period.

[0058] In one embodiment, the rotation speed of the stirring shaft is 10-100 rpm, such as 10-30 rpm, 30-60 rpm, 60-100 rpm, specifically 10 rpm, 20 rpm, 30 rpm, 40 rpm, 50 rpm, 60 rpm, 70 rpm, 80 rpm, 90 rpm, 100 rpm, preferably 60 rpm.

[0059] In one embodiment, the stirring speed is 0.1-1m / s, for example 0.1-0.4m / s, 0.4-0.6m / s, 0.6-1m / s, specifically 0.1m / s, 0.2m / s, 0.3m / s, 0.4m / s, 0.5m / s, 0.6m / s, 0.7m / s, 0.8m / s, 0.9m / s, 1m / s, preferably 0.5m / s.

[0060] In one embodiment, the stirring direction is clockwise or counterclockwise, preferably clockwise.

[0061] In one embodiment, the stirring period is 10-60 seconds, such as 10-20 seconds, 20-40 seconds, 40-60 seconds, specifically 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, 60 seconds, preferably 30 seconds.

[0062] In the above analysis method, in step 1), the modeling software is selected from one of Blender, SolidWorks or AutoCAD, preferably SolidWorks.

[0063] SolidWorks, with its powerful physics engine, is well-suited for complex pre-physics simulation modeling. Based on the measured geometry, the individual components of the blender were drawn in the software. Using the assembly function, these components were combined to create a complete blender model, creating a 3D model of the blender. During the modeling process, care was taken to maintain accuracy and proportionality, ensuring that all components were accurately proportioned, particularly the curvature and angle of the blender blades.

[0064] In the above analysis method, in step 1), the virtual mixer model is a particle discrete element simulation model (DEM). Specifically, the model features particle discretization, a contact model, geometric structure accuracy, dynamic motion simulation, boundary condition setting, initial particle distribution, adjustable simulation parameters, data output and analysis, real-time dynamic adjustment, and high-precision simulation. These features ensure that the model can accurately simulate the particle movement and mixing behavior of highly viscous soil during the mixing process, providing reliable technical support for the assessment of mixing uniformity of 3D-printed engineering soil.

[0065] In the above analysis method, in step 1), the virtual mixer model is exported from the modeling software, and the format of the virtual mixer model is STL format.

[0066] In the above analysis method, in step 1), the simulation software is a commonly used simulation software suitable for DEM, preferably YADE simulation software.

[0067] In the above analysis method, in step 1), the measurement conditions of the discrete element method simulation experiment are: gravity acceleration is 9.5-10m / s 2 , preferably 9.8m / s 2 ; Direction along the -Z axis; Time step is 5-10e -5 s, preferably 8e -5 s; the main rotating shaft speed is π-3πrad / s, preferably 1.5π-2.5πrad / s, more preferably 2πrad / s; the airfoil spiral stirring shaft speed is 3π-5πrad / s, preferably 3.5π-4.5πrad / s, more preferably 4πrad / s; the dispersing blade speed is 16π-20πrad / s, preferably 17π-19πrad / s, more preferably 18πrad / s; the simulation time is 30-50 seconds, preferably 40 seconds.

[0068] The above measurement conditions are achieved through multiple discrete element method simulations, with adjustments to parameters such as contact stiffness, damping coefficient, and time step size between particles to simulate real-world interactions. For example, contact stiffness determines the response speed of the contact force between particles, while the damping coefficient influences the energy dissipation of the interparticle interaction. These parameters are continuously adjusted until the simulation results (such as particle force, displacement, and motion trajectory) match the actual results, thus obtaining particle simulation data.

[0069] In the above analysis method, in step 1), the particle simulation data is selected from one or more combinations of particle force data, particle displacement data, or particle motion trajectory data. The particle simulation data is obtained by automatic calculation using discrete element method using simulation software.

[0070] The particle simulation data is used to derive key data such as the force, displacement, velocity of the particles during the stirring process, and the contact force between the particles through simulation software. These data are the basis for subsequent analysis.

[0071] In one embodiment, the force data of the particles is force data recorded on each particle during the stirring process, such as normal force data or tangential force data.

[0072] In one embodiment, the particle displacement data is data recording the displacement change of each particle during the stirring process.

[0073] In one embodiment, the particle motion trajectory data records the motion trajectory of each particle during the stirring process, such as the length and complexity of the trajectory.

[0074] In the above analysis method, in step 2), the initial simulation parameters and the optimized simulation parameters are both high-viscosity soil simulation parameters, which can be set and optimized in terms of particle size, contact parameters, stirring speed, time step, etc.

[0075] In the above analysis method, in step 2), the initial simulation parameters are selected from at least one of the radius of the slag particles, the shape of the slag particles, the density of the slag particles, the friction coefficient of the slag particles, the gravitational acceleration, the boundary conditions, the initial particle filling method, the liquid bridge volume, the surface tension of the slag particles, the friction angle of the slag particles, the contact time of the slag particles, the normal restitution coefficient, and the tangential restitution coefficient.

[0076] Using professional data visualization tools (such as ParaView), the complex particle simulation data is converted into intuitive charts and animations to more clearly demonstrate the particle motion and the changes in various parameters during the mixing process. Simulation animations can also be viewed using YADE's built-in visualization tools or external software (such as ParaView) to intuitively understand the particle motion. Based on these charts and animations, and according to the actual characteristics of the soil particles, key factors affecting particle mixing uniformity, such as the shape and speed of the mixing shaft and blades, are compared and analyzed to determine the initial simulation parameters. These initial simulation parameters directly affect the accuracy and reliability of the simulation results.

[0077] In one embodiment, the radius of the soil particles is 10-20 mm, such as 10-14 mm, 14-16 mm, 16-20 mm, and preferably 15 mm.

[0078] In one embodiment, the shape of the soil particles is spherical. The simulation software allows users to customize the geometric shape of the particles to approximate the shape of real soil particles.

[0079] In one embodiment, the density of the soil particles is 2700-2800 kg / m 3 , for example 2700-2740kg / m 3 、2740-2760kg / m 3 、2760-2800kg / m 3 , preferably 2750kg / m 3 .

[0080] In one embodiment, the friction coefficient of the slag particles is 0.4-0.6, preferably 0.5.

[0081] In one embodiment, the acceleration due to gravity is 9.8 m / s 2 .

[0082] In one embodiment, the boundary condition requires the mixing tank wall to serve as a fixed boundary.

[0083] In one embodiment, the initial particles are filled in a random or uniform manner.

[0084] In one embodiment, the liquid bridge volume is 0.5e -9 -2e -9 m 3 , preferably 1e -9 m 3 .

[0085] In one embodiment, the surface tension of the soil particles is 0.1-0.3 N / m, preferably 0.2 N / m.

[0086] In one embodiment, the friction angle of the soil particles is 25-35°, preferably 30°.

[0087] In one embodiment, the contact time of the soil particles is 0.004-0.006 s, preferably 0.005 s.

[0088] In one embodiment, the normal restitution coefficient is 0.1.

[0089] In one embodiment, the tangential restitution coefficient is 0.1.

[0090] In the above analysis method, in step 2), the particle contact model is a viscoelastic particle-liquid bridge model. Specifically, the viscoelastic particle-liquid bridge model is based on the physical properties and rheological behavior of the slag material, namely, the physical properties of high-moisture viscous slag particles (such as shape, size, and surface roughness) and the characteristics of the liquid bridge (such as liquid type, viscosity, and surface tension).

[0091] The above-mentioned viscoelastic particle liquid bridge model is suitable for scenarios considering the liquid bridge force between particles of high-moisture-content viscous soil, and can simulate the liquid bridge force between particles formed by moisture.

[0092] In the above analysis method, in step 2), the test conditions of the trial-and-error simulation experiment are: the contact stiffness is 0.5-2e 5 N / m, preferably 1e 5 N / m; damping coefficient is 0.05-0.2, preferably 0.1; time step is 5-10e -5 s, preferably 8e -5 s; the simulation time is 30-50 seconds, preferably 40 seconds.

[0093] The trial-and-error simulation experiment employed a conventional trial-and-error approach. Specifically, it involved setting preliminary simulation parameters, running the initial simulation experiment, analyzing and evaluating the results, adjusting and optimizing the preliminary simulation parameters, performing sensitivity analysis, performing multiple simulation verifications, and ultimately determining the optimized simulation parameters. The results were then outputted as consistent with the actual results. These steps ensured the accuracy and reliability of the optimized simulation parameters and effectively simulated the particle movement and mixing behavior of highly viscous soil during the mixing process, providing a scientific basis for evaluating the mixing uniformity of soil used in 3D printing projects.

[0094] In one embodiment, the sensitivity analysis is performed by systematically changing the initial simulation parameters and observing the degree of influence of these parameter changes on the establishment of the particle contact model, thereby determining which initial simulation parameters have a significant impact on the simulation results. Through sensitivity analysis, the initial simulation parameters that have a greater impact on the simulation results can be adjusted preferentially to obtain optimized simulation parameters with good accuracy and reliability, thereby ensuring that the simulation results are highly consistent with the actual results. Specifically, a sensitivity analysis is performed on the key parameters in the initial simulation parameters to determine which parameters have a significant impact on the simulation results, and these parameters are adjusted preferentially to obtain optimized simulation parameters. Sensitivity analysis is intended to determine which initial simulation parameters have the greatest impact on the simulation results, provide a basis for subsequent parameter optimization, and thus guide subsequent experimental design and model optimization.

[0095] In the above analysis method, in step 2), the actual results are either existing experimental results or theoretically expected results. To ensure consistency between the simulation results and the actual results, the accuracy and reliability of the particle contact model are evaluated by comparing the simulation results with the actual results. If there is a significant deviation, it is necessary to adjust the initial simulation parameters to achieve the optimal simulation parameters.

[0096] In the above analysis method, in step 2), the optimization simulation parameter is selected from at least one of an elastic parameter, a viscosity parameter, and a friction coefficient.

[0097] Optimized simulation parameters refer to a set of parameters closer to reality, adjusted based on initial simulation parameters through comparison with experimental data, error analysis, or mathematical inversion. Initial parameters are typically derived from literature or empirical estimates, while optimized parameters are modified through calculation formulas, data fitting, or constraints (such as friction angle and liquid bridge force). Common optimization methods include error minimization, parameter inversion, and regression mapping, with the core goal of improving simulation accuracy and experimental consistency.

[0098] Specifically, the optimized simulation parameters are constructed using initial simulation parameters such as the radius, shape, density, and friction coefficient of the soil particles. These parameters directly affect the accuracy of the simulation results. Furthermore, the optimized simulation parameters can be obtained by further setting the simulation time and time step, and running trial-and-error simulation experiments to observe the basic motion behavior of the particles in the mixer.

[0099] For example, the model's agitator shaft speed can be simulated at multiple speeds based on the speed range of an actual mixer to analyze its impact on mixing uniformity. The time step size can be less than one-tenth of the particle collision timescale. An appropriate time step size can be selected for step size optimization to ensure both computational efficiency and accuracy of simulation results.

[0100] For example, during the simulation experiment, the motion state of the particles (such as speed, displacement), energy dissipation, contact force distribution, etc. can be continuously monitored to ensure that the simulation experiment process is stable and conforms to physical laws.

[0101] In one embodiment, the elastic parameter is selected from at least one of the elastic modulus of the particle or the Poisson's ratio of the particle to reflect the stiffness of the material.

[0102] In a preferred embodiment, the elastic modulus of the particles is a commonly used elastic modulus, which can be obtained through material testing such as compression testing.

[0103] In a preferred embodiment, the Poisson's ratio of the particles is a commonly used Poisson's ratio, which can be obtained through material tests such as compression tests.

[0104] In one embodiment, the viscosity parameter is selected from at least one of the breaking strength of the liquid bridge and the volume of the liquid bridge, and is used to evaluate the effect of cohesion between particles.

[0105] In a preferred embodiment, the breaking strength of the liquid bridge is calculated according to formula (1),

[0106] The formula (1) is:

[0107] Where, F cis the fracture strength of the liquid bridge, N; R is the particle radius, mm; γ is the surface tension of the liquid, N / m, such as 0.072N / m for water; θ is the contact angle; V is the volume of the liquid bridge, mm 3 ; s is the distance between two particles (fracture distance at critical fracture), mm.

[0108] The breaking strength of the liquid bridge is the maximum tensile force that the liquid bridge can withstand before breaking.

[0109] In a preferred embodiment, the liquid bridge volume is a conventional liquid bridge volume, which can be calculated by combining the total amount of liquid with the wetting conditions and the particles.

[0110] In one embodiment, the friction coefficient is calculated based on the internal friction angle of the soil. This can affect the sliding resistance between particles. Specifically, the internal friction angle of the soil is a macroscopic mechanical parameter of the particle system. Typically, a shear stress-normal stress curve is obtained through simulated shear testing, and then the internal friction angle is obtained through Mohr-Coulomb criterion fitting.

[0111] In the above analysis method, in step 2), the particle distribution and motion characteristics are selected from at least one of the trajectory morphology of each particle, the shear force between particles, the number of contacts of each particle, the contact frequency of each particle, or the contact duration of each particle.

[0112] The above-mentioned particle distribution and motion characteristics, as simulation results, can be analyzed and optimized in terms of velocity distribution, trajectory morphology, and uniformity. These data are generated by setting optimized simulation parameters to drive the particle system under different mixing conditions. This data is then output by the simulation software and post-processed for quantitative evaluation of the uniformity and dynamic behavior of the mixing process.

[0113] Collect relevant data on particle distribution and motion characteristics under different stirring processes, such as trajectory morphology, shear force distribution, etc. Specifically, the DEM (discrete element method) simulation software is used to derive the velocity vectors of the particles during the stirring process. Based on these velocity data, the overall and local average velocities and the standard deviation of the velocities are calculated to measure the degree of velocity discreteness, so as to evaluate the activity and uniformity of the particle movement. At the same time, the motion trajectory of each particle is recorded, and the length and complexity of the trajectory (such as the degree of tortuosity of the trajectory) and the degree of trajectory overlap between particles are calculated. This information helps to understand the spatial distribution and interaction of particles in the stirring vessel.

[0114] In one embodiment, the trajectory morphology is to record the motion trajectory of each particle during the entire stirring period, and calculate the trajectory length, trajectory complexity (such as quantified by the tortuosity of the trajectory), and the degree of overlap of the trajectories between particles.

[0115] In one embodiment, the shear force between the particles is an important indicator for evaluating mixing efficiency. Based on the contact force data between the particles, the distribution of the shear force can be obtained by calculating the tangential components of the contact force between adjacent particles and summing them.

[0116] In one embodiment, the contact number of each particle, the contact frequency of each particle, or the contact duration of each particle are all automatically calculated by simulation software using the discrete element method. These parameters can reflect the interaction strength between particles.

[0117] In the above analysis method, in step 2), the in-plane RGB mixing index is calculated according to formula (2):

[0118] The formula (2) is:

[0119] Where,

[0120] M is the RGB mixing index, which is used to measure the mixing uniformity; c i Represents the RGB value (such as mass or volume) of the i-th particle in a plane; The standard RGB values of red, green and blue represent the particles, which is 255 here; N is the total number of particles; i is the number of particles.

[0121] In one embodiment, the RGB mixing index M is between 0 and 1, with a larger M value indicating more uniform mixing. For optimization of 3D printing materials, a target M value range can be set to determine ideal mixing time and conditions.

[0122] The above-mentioned in-plane RGB mixing index is used to assess the uniformity of mixing of highly viscous soil. Based on the RGB distribution of particle color at different times and planes, the uniformity of particle color distribution within different cross-sections is compared. The RGB mixing index (M) reflects the degree of particle dispersion and the uniformity of the mixing process. This index takes into account the positional changes of particles during mixing. A higher M value indicates a more uniform particle distribution and a better mixing effect.

[0123] The in-plane RGB mixing index assesses the uniformity of particle distribution during stirring. A larger M value indicates more uniform particle mixing. This index takes into account the positional changes of particles during stirring and can reflect the mixing efficiency. Specific evaluation indicators also include particle distribution uniformity, synchronization of particle movement, and energy efficiency during stirring.

[0124] All collected simulation data, such as particle trajectory morphology, shear force distribution, RGB mixing index M value, etc., are sorted and summarized to form comparative pictures for analysis.

[0125] In the above analysis method, in step 2), optimization suggestions are made based on the mixing uniformity of the soil mixing, and the geometric structure data and motion parameter data of the mixer are adjusted to improve the uniformity of the soil and the final printing effect.

[0126] In one embodiment, when adjusting the geometric structure data, the diameter of the mixer, the height of the mixer, the diameter of the mixing barrel, the length of a single blade, the width of a single blade, the thickness of a single blade, the distribution shape of multiple blades, the size of the feed port or the size of the discharge port, etc. can be adjusted.

[0127] Specifically, the blade design can be optimized. Based on the particle motion trajectory and force distribution in the simulation results, the shape of the mixing blade can be adjusted (for example, by increasing the blade curvature or changing the blade tilt angle) to enhance the axial and radial flow of particles and avoid the formation of dead zones in the mixing barrel. The number of blades on the mixing shaft can also be increased, especially at the edges of the mixing barrel, to enhance particle dispersion and improve mixing uniformity.

[0128] Specifically, when the diameter of the mixing barrel is adjusted, the geometric shape of the mixing barrel can be optimized (such as using a conical bottom) according to the flow pattern of the particles in the simulation results to promote the natural flow and mixing of the particles.

[0129] In one embodiment, when adjusting the motion parameter data, the rotation speed, stirring speed, stirring direction or stirring period of the stirring shaft may be adjusted.

[0130] Specifically, the vertical position of the agitator shaft in the mixing drum can be adjusted based on the particle distribution in the simulation results to ensure that particles are fully mixed in the upper, middle, and lower parts of the drum. The agitator shaft can also be designed with an eccentric position to increase the complexity of the particle movement path, promote cross-mixing of particles, and prevent particle accumulation in the center of the drum.

[0131] For example, when adjusting the agitator shaft speed, the speed can be adjusted based on the particle velocity and shear force distributions from the simulation results to find the optimal speed range. Higher speeds can enhance particle fluidity, but excessively high speeds can lead to particle breakage or increased energy consumption. A multi-speed stirring strategy can also be employed, dynamically adjusting the speed during the stirring process. For example, initially using a higher speed to quickly disperse the particles, then reducing the speed to maintain uniform mixing. Based on the simulation results, the mixing efficiency of the particles at different speeds can be analyzed. The speed can be gradually adjusted to find the optimal speed range to achieve optimal mixing uniformity and energy efficiency.

[0132] For another example, when adjusting the stirring period, the minimum stirring time required to achieve a predetermined mixing uniformity can be determined based on the particle mixing uniformity index (such as the RGB mixing index M value) in the simulation results to avoid increased energy consumption and particle breakage caused by excessive stirring. By determining the minimum stirring time required to achieve a predetermined mixing uniformity, the stirring time can be optimized to avoid increased energy consumption and particle breakage caused by excessive stirring.

[0133] Adjusting the aforementioned motion parameter data can be used to adjust the particle size distribution of the soil particles based on the mixing effects of particles of different sizes as shown in the simulation results, thereby promoting inter-particle interactions and mixing efficiency. For example, increasing the proportion of small particles can fill the gaps between larger particles and improve mixing uniformity. By adjusting the particle size distribution based on the impact of different particle sizes on mixing, optimizing inter-particle interactions and promoting more efficient mixing can be achieved.

[0134] Based on the particle flow pattern and mixing efficiency in the simulation, analyze the impact of the agitator shaft and blade shape on particle mixing. Consider adjusting the angle, width, and number of blades to promote more uniform particle distribution.

[0135] When the above-mentioned stirring parameters such as stirring speed, stirring direction or stirring period are dynamically adjusted, the movement state of the particles (such as speed, displacement, shear force distribution, etc.) is monitored in real time during the stirring process, and the stirring parameters (such as speed, stirring time, etc.) are dynamically adjusted according to the monitoring results to optimize the stirring efficiency and mixing uniformity.

[0136] The proposed optimization solution was re-entered into the DEM model for a new round of simulation verification. Mixing uniformity indicators (such as the RGB mixing index M) before and after optimization were compared to ensure the effectiveness of the optimization measures for the 3D printing high-viscosity soil mixing process.

[0137] The following describes the embodiments of the present invention through specific examples. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention.

[0138] In the following examples, unless otherwise specified, raw materials or processing techniques are conventional commercially available raw materials or conventional processing techniques in the art.

[0139] Example 1

[0140] like Figure 2-3As shown in the figure, a virtual mixer model containing highly viscous soil particles was constructed. Measuring tools (such as a laser rangefinder) were used to measure the mixer's geometric structure in detail. The data included a mixer diameter of 500 mm, a mixer height of 800 mm, a mixing drum diameter of 700 mm, a single blade length of 200 mm, a single blade width of 50 mm, a single blade thickness of 5 mm, a spiral blade pattern, a feed port size of 100 mm × 100 mm, and a discharge port size of 100 mm × 100 mm. The mixer components were drawn in SolidWorks modeling software and assembled into a complete mixer model using the assembly function. The mixer's motion parameters were also measured, including a stirring shaft speed of 60 rpm, a stirring velocity of 0.5 m / s, a clockwise stirring direction, and a stirring period of 30 seconds.

[0141] The three-dimensional virtual mixer model created in SolidWorks modeling software was exported to STL format and then imported into YADE simulation software for discrete element method simulation experiments to obtain particle simulation data. The particle simulation data includes particle force data, particle displacement data, and particle motion trajectory data. The force data is normal force data and tangential force data, and the particle motion trajectory data is the length and complexity of the trajectory. The measurement conditions of the discrete element method simulation experiment are: gravitational acceleration is 9.8m / s 2 ; Direction along the -Z axis; Time step is 8e -5 s; the main rotating shaft speed is 2πrad / s; the airfoil spiral stirring shaft speed is 4πrad / s; the dispersing blade speed is 18πrad / s; and the simulation time is 40 seconds.

[0142] like Figure 4-6 As shown in Figure 2, the velocity vectors of the particles during the stirring process were derived using YADE simulation software. Based on these velocity data, the global and local average velocities and the standard deviation of the velocities were calculated. Figure 4 It can be seen that the particles are mainly affected by the wing-shaped spiral stirring shaft. In the area where the stirring shaft is close to the cylinder wall, the force on the particles is the largest and the speed change is more obvious.

[0143] Extract the force distribution of the wing-shaped spiral stirring shaft to provide a basis for the size correction of the customized stirring screw. Figure 5 It can be seen from the force distribution of the wing-shaped spiral stirring shaft that the reaction force on the stirring shaft close to the cylinder wall is greater than that on the side close to the center, which is consistent with the force characteristics of the outer edge.

[0144] Extract the motion trajectories of representative particles, calculate the length and complexity (such as the degree of tortuosity of the trajectory) of the trajectory, and the degree of trajectory overlap between particles, so as to understand the spatial distribution and interaction of particles in the mixing vessel and provide a basis for subsequent optimization of the mixing process. Figure 6 It can be seen that at the beginning, the particles are affected by the wing-shaped spiral stirring shaft, which drives the particles to move in a circular motion around the drum. As the simulation progresses, the particles approach the center area of the cylinder and, under the action of the stirring shaft, reciprocate up and down in the center of the cylinder. As time goes by, the particles leave the center area of the mixing drum and move radially toward the cylinder wall, indicating that the rotational motion of the spiral stirring shaft can make the particles move rapidly in the axial and radial directions in the cylinder. The external disturbance of the spiral shaft can well disperse the particles in the cylinder quickly, so that the slurry is quickly homogenized and mixed.

[0145] In the YADE simulation software, the particle contact model was constructed by setting initial simulation parameters based on the particle simulation data. The particle contact model is a viscoelastic particle liquid bridge model. The initial simulation parameters were set based on the characteristics of liquid bridging between high-water-content viscous soil particles. The initial simulation parameters included a soil particle radius of 15 mm, a spherical shape, and a soil particle density of 2750 kg / m 3 The friction coefficient of the slag particles is 0.5, and the acceleration due to gravity is 9.8 m / s. 2 The boundary conditions require that the wall of the mixing tank be a fixed boundary and the initial particle filling method be randomly distributed. See Table 1 below for details.

[0146] Table 1

[0147] Material parameters Value Particle radius 15mm Liquid bridge force model Lambert model Liquid bridge volume <![CDATA[1e -9 m 3 ]]> surface tension 0.2N / m density <![CDATA[2750kg / m 3 ]]> Friction angle 30° Contact time 0.005s Normal restitution coefficient 0.1 Tangential restitution coefficient 0.1

[0148] The particle contact model was subjected to a trial-and-error simulation experiment to obtain the optimized simulation parameters so that the simulation results are consistent with the actual results. The measurement conditions of the trial-and-error simulation experiment are: the contact stiffness is 1e 5 N / m; damping coefficient is 0.1; time step is 8e -5 s; the simulation time is 40 seconds. The optimized simulation parameters include elastic parameters, viscosity parameters, and friction coefficient. Among them, the elastic parameter is the elastic modulus of the particles, the viscosity parameter is the fracture strength of the liquid bridge, and the friction coefficient is calculated based on the internal friction angle of the slag. The shear stress-normal stress relationship curve is obtained through simulated shear testing, and the internal friction angle is then obtained through Mohr-Coulomb criterion fitting. The fracture strength of the liquid bridge is calculated according to the formula: Where, F c is the fracture strength of the liquid bridge, N; R is the particle radius, mm; γ is the surface tension of the liquid, N / m, such as 0.072N / m for water; θ is the contact angle; V is the volume of the liquid bridge, mm 3 ; s is the distance between two particles (fracture distance at critical fracture), mm.

[0149] The particle contact model with optimized simulation parameters is used to simulate the particle distribution and motion characteristics of soil particles in different mixing processes, including the trajectory of each particle, the shear force between particles, the number of contacts of each particle, the contact frequency of each particle, and the contact duration of each particle. Specifically, Figure 7-9 As shown in the figure, the initial moments of the slag particles are colored red, green and blue according to the height, horizontal and radial directions, and based on the above particle contact model, the mixing time of each working condition is controlled to be 30 seconds. The professional data visualization tool Paraview is used to convert the data into intuitive animation. Figure 7 , Figure 8 and Figure 9 It can be seen that within a very short mixing time, the various colored particles are evenly distributed in all axial and radial sections, achieving macroscopic uniformity in a short period of time. Thereafter, microscopic uniformity is achieved primarily through diffusion and shearing. The nearly uniform distribution of various colored particles indicates intense interaction between large and small particle sizes.

[0150] The color distribution of the slices within the plane under each working condition was extracted, and the mixing uniformity of the high-viscosity slag was calculated according to the RGB mixing index within the plane. Based on the RGB distribution of the particle colors on different planes at different times, the uniformity of the particle color distribution within different section planes was compared. The RGB mixing index (M) reflects the degree of particle dispersion and the uniformity during the mixing process.

[0151]

[0152] Where M is the RGB mixing index, which is used to measure the mixing uniformity; c i Indicates the RGB value quality of the i-th particle in a certain plane; The standard RGB values for the red, green, and blue colors of a particle are represented, here taken as 255; N is the total number of particles; i is the number of particles. This index takes into account the positional changes of particles during mixing. A higher M value indicates a more uniform particle distribution and better mixing. Calculations show that the RGB mixing indices for highly viscous soil in the vertical, horizontal, and radial distributions are 0.91, 0.74, and 0.98, respectively. Mixing equipment can generate stronger axial and radial flows in the mixing drum, resulting in more uniform vertical and radial distribution. This enhances the circulation of highly viscous soil within the drum and avoids the formation of mixing isolation zones above and below the agitator.

[0153] Based on the experimental results of Example 1, the proposed method for evaluating the mixing uniformity of 3D-printed high-viscosity soil using a particle discrete element model can effectively assess the material uniformity of 3D-printed high-viscosity soil. Combining construction soil reuse technology, discrete element simulation, and 3D printing technology not only promotes the sustainable development of green building materials but also effectively supports the promotion, application, and industrialization of 3D printing technology in my country's construction industry, generating substantial economic and ecological benefits.

[0154] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form or substance. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the method of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention. Any equivalent changes, modifications and evolutions made by technicians familiar with this profession without departing from the spirit and scope of the present invention by using the technical content disclosed above are all equivalent embodiments of the present invention; at the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for analyzing the uniformity of mixing of 3D printed high-viscosity soil, comprising the following steps: 1) The geometric structure data and motion parameter data of the mixer are measured respectively, a virtual mixer model is constructed using modeling software, and then the virtual mixer model is imported into simulation software to perform discrete element method simulation experiments to obtain particle simulation data; 2) The particle contact model is constructed by setting the initial simulation parameters through the particle simulation data. The particle contact model is subjected to a trial-and-error simulation experiment to obtain the optimized simulation parameters so that the simulation results are consistent with the actual results. The particle contact model with the optimized simulation parameters is then used to simulate the particle distribution and movement characteristics of the slag particles under different mixing processes, and the in-plane RGB mixing index is used to calculate the mixing uniformity of the slag mixing.

2. The 3D printing high viscosity slag mixing uniformity analysis method according to claim 1, characterized in that: In step 1), any one or more of the following conditions are included: A1) geometric data of the mixer are selected from one or more combinations of the mixer diameter, the mixer height, the mixer barrel diameter, the length of a single blade, the width of a single blade, the thickness of a single blade, the distribution of multiple blades, the size of the feed port, or the size of the discharge port; A2) the motion parameter data of the stirrer is selected from one or more combinations of the rotational speed of the stirring shaft, the stirring speed, the stirring direction or the stirring period; A3) the modeling software is selected from one of Blender, SolidWorks or AutoCAD; A4) the virtual mixer model is a particle discrete element simulation model; A5) The simulation software is YADE simulation software.

3. The 3D printing high viscosity slag mixing uniformity analysis method according to claim 2, characterized in that: A1) includes any one or more of the following conditions: A101) the diameter of the stirrer is 100-1000 mm; A102) the height of the mixer is 500-1000 mm; A103) the diameter of the mixing barrel is 500-1000 mm; A104) the length of the single blade is 100-500 mm; A105) the width of the single blade is 10-100 mm; A106) the thickness of the single blade is 1-10 mm; A107) the distribution shape of the plurality of blades is selected from one of elliptical, palm-shaped, fan-shaped, diamond-shaped, circular, needle-shaped, triangular, and spiral; A108) the feed port has a length × width dimension of 50-200 mm × 50-200 mm; A109) The length × width dimensions of the discharge port are 50-200 mm × 50-200 mm.

4. The method for analyzing the uniformity of mixing of 3D printed high-viscosity slag according to claim 2, wherein: A2) includes any one or more of the following conditions: A201) the rotational speed of the stirring shaft is 10-100 rpm; A202) the stirring speed is 0.1-1 m / s; A203) the stirring direction is clockwise or counterclockwise; A204) The stirring period is 10-60 seconds.

5. The method for analyzing the uniformity of mixing of 3D printed high-viscosity slag according to claim 1, wherein: In step 1), the measurement conditions of the discrete element method simulation experiment are: gravity acceleration is 9.5-10m / s 2 ; Direction along the -Z axis; Time step is 5-10e -5 s; the main rotating shaft speed is π-3πrad / s; the airfoil spiral stirring shaft speed is 3π-5πrad / s; The speed of the disperser disc is 16π-20πrad / s; the simulation time is 30-50 seconds.

6. The method for analyzing the uniformity of mixing of 3D printed high-viscosity slag according to claim 1, characterized in that: In step 2), any one or more of the following conditions are included: B1) the initial simulation parameters are selected from at least one of the radius of the slag particles, the shape of the slag particles, the density of the slag particles, the friction coefficient of the slag particles, the gravitational acceleration, the boundary conditions, the initial particle filling method, the liquid bridge volume, the surface tension of the slag particles, the friction angle of the slag particles, the contact time of the slag particles, the normal restitution coefficient, and the tangential restitution coefficient; B2) the particle contact model is a viscoelastic particle liquid bridge model; B3) The optimization simulation parameter is selected from at least one of an elastic parameter, a viscosity parameter, and a friction coefficient.

7. The method for analyzing the uniformity of mixing of 3D printed high-viscosity soil according to claim 6, wherein: B1) includes any one or more of the following conditions: B101) The radius of the slag particles is 10-20 mm; B102) The shape of the slag particles is spherical; B103) The density of the slag particles is 2700-2800 kg / m 3 ; B104) The friction coefficient of the slag particles is 0.4-0.6; B105) The acceleration due to gravity is 9.8 m / s 2 ; B106) The boundary condition requires the mixing tank wall to be a fixed boundary; B107) The initial particles are randomly distributed or uniformly distributed; B108) The liquid bridge volume is 0.5e -9 -2e -9 m 3 ; B109) The surface tension of the soil particles is 0.1-0.3 N / m; B110) The friction angle of the soil particles is 25-35°; B111) the contact time of the slag particles is 0.004-0.006s; B112) the normal restitution coefficient is 0.1; B113) The tangential restitution coefficient is 0.

1.

8. The method for analyzing the uniformity of mixing of 3D printed high-viscosity soil according to claim 6, wherein: B2) includes any one or more of the following conditions: B301) the elastic parameter is selected from at least one of the elastic modulus of the particle or the Poisson's ratio of the particle; B302) the viscosity parameter is selected from at least one of the fracture strength of the liquid bridge and the volume of the liquid bridge; Preferably, the breaking strength of the liquid bridge is calculated according to formula (1), The formula (1) is: Where, F c is the fracture strength of the liquid bridge, N; R is the particle radius, mm; γ is the surface tension of the liquid, N / m, such as 0.072N / m for water; θ is the contact angle; V is the volume of the liquid bridge, mm 3 ; s is the distance between two particles, mm.

9. The method for analyzing the uniformity of mixing of 3D printed high-viscosity soil according to claim 1, wherein: In step 2), the test conditions of the trial-and-error simulation experiment are: the contact stiffness is 0.5-2e 5 N / m; The damping coefficient is 0.05-0.2; The time step is 5-10e -5 s; simulation time is 30-50 seconds.

10. The method for analyzing the uniformity of mixing of 3D printed high-viscosity soil according to claim 1, characterized in that: In step 2), the in-plane RGB mixing index is calculated according to formula (2), The formula (2) is: Where M is the RGB mixing index, which is used to measure the mixing uniformity; c i Represents the RGB value (such as mass or volume) of the i-th particle in a plane; The standard RGB values of red, green and blue represent the particles, which is 255 here; N is the total number of particles; i is the number of particles.

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