Visual simulation method for whole oil inlet and return process of moving part of aviation gear box

Abstract

The invention provides a visual simulation method for the whole oil inlet and return process of a moving part of an aviation gear box, and the method comprises the steps: carrying out the pretreatment of an aviation gear box model, maintaining necessary features, and removing features which have no influence on the oil inlet and return; a fluid effective area and solid and fluid physical attributes are set, so that simulation particle attributes fit reality, and simulation errors are reduced; the whole process of oil inlet, lubrication and oil return of visual simulation is realized by establishing a lubricating oil jet flow and oil return boundary, setting moving part motion attributes and setting simulation calculation parameters; the pressure, the speed and the internal fitting lubricating oil form of the lubricating oil particles in the aviation gear box are visualized through a post-processing calculation result, and the amount of the lubricating oil particles in a meshing area is visualized; through key error prediction, parameters which have great influence on the simulation result in the simulation process are set, and the accuracy of the simulation result is improved. According to the invention, the oil inlet and return conditions of the real aviation gear box can be simulated, and the optimization direction is given, so that the working performance of the aviation gear box is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aero-engine simulation calculation, and particularly relates to a kind of visualization simulation method of oil inlet and return of aero gear box moving parts whole process. BACKGROUND

[0002] In the field of aviation, as a key component of aero-engine, the performance of aero gear box is directly related to the flight safety and efficiency of the aircraft. In the aero gear box, the oil inlet and return system not only ensures sufficient lubrication and cooling for components such as gears and bearings, but also timely recycles used oil to the oil tank to realize the recycling of lubricating oil, which plays a crucial role in the overall function realization of the aero gear box. With the continuous development of aero-engine technology towards high speed and high load, the rotational speed of the gear transmission system continues to increase. In this process, the high-speed rotation of the gear will vigorously stir the surrounding air, forming a complex and variable gas pressure field, which not only affects the splashing and spraying state of the oil in the box, leading to uneven oil distribution, but also the power loss caused by wind resistance accounts for an increasing proportion in the total power loss of the gear system. Therefore, in the high-speed gear lubrication design, the wind factor has become a key factor that cannot be ignored.

[0003] At the same time, the lubrication process in the aero gear box is very complex, involving oil flow, injection, splashing, atomization and interaction with gas, etc. If the oil inlet and return system is not reasonably designed, it may lead to insufficient lubrication of gears and bearings, causing excessive wear, fatigue and even failure, which seriously threatens the reliable operation of the aero-engine. The traditional design of the oil inlet and return system of the aero gear box mainly relies on empirical formula and test. However, the empirical formula is usually derived based on limited test data and specific working conditions, and has poor adaptability to complex actual working conditions, making it difficult to accurately describe the complex fluid flow phenomena in the aero gear box. Although test can obtain real performance data, it has problems such as high cost, long cycle and limited test conditions.

[0004] In order to overcome the limitations of traditional design methods and improve the design level of the oil inlet and return system of the aero gear box, numerical simulation technology has become one of the current mainstream choices. By establishing an accurate mathematical model and using computational fluid dynamics (CFD) and other methods to simulate and analyze the fluid flow in the aero gear box, various physical phenomena in the oil inlet and return process can be understood in depth, and the system performance can be predicted to provide a strong basis for optimization design. At present, the specific phenomena of lubricating oil inlet and return in the aero gear box cannot be observed by simulation through the conventional Fluent software, and there will always be some errors between simulation and actual test working conditions, some of which have a greater impact on the accuracy of the calculation results. SUMMARY

[0005] Purpose of the invention: The purpose of this invention is to provide a visual simulation method for the entire process of oil intake and return of moving parts in an aerospace gearbox that is more intuitive and accurate.

[0006] Technical solution: A visualization simulation method for the entire process of oil inlet and outlet of moving parts in an aircraft gearbox, comprising the following steps:

[0007] S1. Establish an aircraft gearbox model in 3D modeling software, perform preprocessing on the aircraft gearbox model, set the effective fluid area on the preprocessed aircraft gearbox model, confirm the correct spatial position relationship of the moving parts in the aircraft gearbox, set the physical parameters of the moving parts and lubricating oil, and obtain the aircraft gearbox inlet and return oil model.

[0008] S2. Import the aviation gearbox inlet and outlet oil model into the computational fluid dynamics simulation software, establish the jet boundary and return oil boundary of the lubricating oil, set the motion parameters of the moving parts in the aviation gearbox, and after confirming that the motion state of the moving parts is correct, set the simulation calculation parameters and perform simulation calculation to obtain the simulation calculation results of lubricating oil particles.

[0009] S3. Post-process the simulation results of lubricating oil particles to create pressure distribution cloud maps and velocity distribution cloud maps of lubricating oil particles; set the detection area for lubricating oil particles and monitor the number of lubricating oil particles; perform mesh fitting processing on the surface of lubricating oil particles.

[0010] S4. Perform error prediction on the simulation calculation results of lubricating oil particles in the post-processing, and perform corresponding error tuning to obtain the predicted results of lubrication performance.

[0011] Specifically, in step S1, the preprocessing of the aircraft gearbox model includes: omitting or merging features in the aircraft gearbox model that are unrelated to the lubricating oil flow field; and the setting of the effective fluid region for the preprocessed aircraft gearbox model includes: setting the internal space of the aircraft gearbox, the inlet of the lubricating oil jet boundary, and the outlet of the lubricating oil return boundary as the effective fluid region.

[0012] Specifically, in step S2, the boundary conditions of the jet boundary include the spatial geometric position of the jet, the shape of the jet boundary, the jet mode, the jet velocity, the upper limit of the jet volume, the injection angle, and the randomization of particles in the jet; the boundary conditions of the return oil boundary include the geometry of the return oil port, the spatial geometric position of the return oil port, and the return oil mode.

[0013] Specifically, in step S2, the motion parameters of the moving parts in the aerospace gearbox include motion mode, motion speed unit, motion speed vector direction, and motion speed magnitude; the simulation calculation parameters include simulation particle size, gravity vector, simulation method, pressure type, viscosity type, surface tension parameters, and turbulence parameters.

[0014] Preferably, the size of the simulated particles is set as follows: the diameter is 1 / 10 of the minimum clearance width in the oil inlet and outlet model of the aerospace gearbox; the simulation method adopts the semi-implicit moving particle method.

[0015] Specifically, in step S3, the surface mesh fitting process for lubricating oil particles includes: first, surface particle identification; then, implicit field construction or point cloud reconstruction; and finally, isosurface extraction to obtain the surface mesh fitting result for lubricating oil particles.

[0016] Specifically, in step S4, the error prediction includes: density estimation error prediction caused by insufficient particle resolution, slip error prediction caused by boundary condition approximation, and pressure error prediction caused by simplification of the two-phase flow model.

[0017] Specifically, the formula for calculating the density estimation error caused by insufficient particle resolution is as follows:

[0018]

[0019] In the formula: ∈ ρ For density estimation error; C is a constant related to kernel function and dimension, h 2 The square of the particle resolution. The second derivative of the density field is... It is a higher-order infinitesimal with particle resolution.

[0020] Specifically, the formula for predicting the slip error caused by boundary condition approximation is as follows:

[0021]

[0022] In the formula: ∈ slip For the slip error, δr j For virtual particle position deviation, For the virtual particle velocity gradient, W(|r i -r virtual,j |,h) represents the kernel function weights.

[0023] Specifically, the pressure error prediction caused by the simplification of the two-phase flow model is calculated using the following formula:

[0024]

[0025] In the formula: ∈ P For pressure error, Δt 2 The square of the time step. This represents the rate of change of pressure over time.

[0026] Beneficial effects: Compared with the prior art, the significant effects of the present invention are: (1) The present invention simplifies the aircraft gearbox model and optimizes the real-time visualization of splash lubrication, lubricating oil injection, and internal flow channel lubricating oil phenomena in the oil inlet and outlet process of each moving part of the aircraft gearbox through lubricating oil particleization, which provides more intuitive guidance for analyzing the oil inlet and outlet performance of the aircraft gearbox; (2) Through lubricating oil particleization, the lubrication effect of the meshing area of ​​the moving parts can be visualized and observed, and the lubricating oil flow and high pressure area in the area can be monitored at the same time, which can more accurately simulate the lubrication effect of the moving parts and provide guidance for optimizing the lubrication performance; (3) Predict the key errors generated in the simulation process and further improve the accuracy of the simulation calculation results. Attached Figure Description

[0027] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention.

[0028] Figure 2 This is a simplified schematic diagram of the aviation gearbox in Embodiment 1 of the present invention.

[0029] Figure 3 This is a schematic diagram of the jet inlet and oil return inlet of the aviation gearbox in Embodiment 1 of the present invention.

[0030] Figure 4 This is a schematic diagram of the spatial geometric relationship of the moving parts of the aircraft gearbox in Embodiment 1 of the present invention.

[0031] Figure 5 This is a velocity distribution cloud map of the lubricating oil inside an aircraft gearbox obtained through simulation calculation in Embodiment 1 of the present invention.

[0032] Figure 6 This is a pressure distribution cloud map of the lubricating oil inside an aircraft gearbox obtained through simulation calculation in Embodiment 1 of the present invention.

[0033] Figure 7 This is a schematic diagram of the lubricating oil morphology formed by surface mesh fitting obtained from simulation calculation in Embodiment 1 of the present invention.

[0034] Figure 8 This is a schematic diagram of the particle detection region created in Embodiment 1 of the present invention. Detailed Implementation

[0035] A preferred embodiment of the present invention will be further described below with reference to the accompanying drawings.

[0036] Example 1

[0037] Please see Figure 1 As shown, this embodiment provides a visualization simulation method for the entire process of oil inlet and outlet of moving parts in an aircraft gearbox, including the following steps:

[0038] S1. Model preprocessing:

[0039] S101. Create an aircraft gearbox model in 3D modeling software (Spaceclaim software), and perform preprocessing on the aircraft gearbox model.

[0040] S102. Set the effective fluid region for the pre-processed aerospace gearbox model.

[0041] S103. Confirm whether the spatial relationship of the moving parts in the aircraft gearbox is correct;

[0042] S104. If the spatial relationship of the moving parts in the aircraft gearbox is correct, set the physical parameters of the moving parts and the lubricating oil to obtain the aircraft gearbox oil inlet and return model.

[0043] The moving parts of an aerospace gearbox comprise multiple high-precision gear pairs and several high-performance bearing systems. The gear pairs are responsible for power transmission and speed change, encompassing various forms such as helical / spur gears, planetary gears, and spiral / straight bevel gears. The bearing systems provide reliable support and precise positioning for the rotating components (gears), and typically employ high-speed angular contact ball bearings (often in pairs), high radial load-bearing cylindrical roller bearings, and tapered roller bearings capable of withstanding large combined loads. The gear pairs and bearing systems work together to ensure the high efficiency, reliability, and durability of power transmission in aerospace gearboxes.

[0044] S2. Model simulation process:

[0045] S201. Import the aviation gearbox inlet and outlet oil model into the computational fluid dynamics simulation software (Particleworks platform) to establish the jet boundary and outlet oil boundary of the lubricating oil.

[0046] S202. Set the motion parameters of the moving parts in the aircraft gearbox;

[0047] S203. Confirm whether the movement state of the moving parts in the aircraft gearbox is correct;

[0048] S204. If the motion state of the moving parts in the aircraft gearbox is correct, set the simulation calculation parameters and perform simulation calculations to obtain the simulation calculation results of lubricating oil particles.

[0049] S3. Post-processing of simulation results for lubricating oil particles:

[0050] S301. Create pressure distribution cloud maps and velocity distribution cloud maps for lubricating oil particles;

[0051] S302. Set the lubricating oil particle detection area and monitor the number of lubricating oil particles;

[0052] S303, Perform surface mesh fitting processing on lubricating oil particles.

[0053] S4. Prediction of key model errors:

[0054] Error prediction is performed on the simulation calculation results of lubricating oil particles in the post-processing, and corresponding error tuning is carried out to obtain the predicted results of lubrication performance.

[0055] The steps described above will be explained in detail below.

[0056] Please refer to Figure 2 As shown, in this embodiment, the outer parts of the aircraft gearbox model are merged, and the reserved countersunk holes and boss features are simplified. The small fillets on the moving parts, the spaces reserved for processing convenience, and the gear mounting slots are all filled, thus completing the simplification of features in the aircraft gearbox model that are unrelated to the lubricating oil flow field. The simplified aircraft gearbox model fully retains the functional structure of the first moving part 7, the second moving part 8, the third moving part 9, and the aircraft gearbox model shell 10.

[0057] In this embodiment, the effective fluid region is set on the pre-processed aerospace gearbox model using the Particleworks platform. The internal space of the aerospace gearbox, the inlet of the lubricating oil jet boundary, and the outlet of the lubricating oil return boundary are set as the effective fluid region. Specifically, the effective fluid region set in the platform is a regular polygon that includes all structures of the aerospace gearbox, with each side offset by 200mm. This offset is only used to observe whether the return port is returning oil normally. The specific parameter settings are as follows. The specific parameter values ​​are only for explaining this embodiment and do not constitute a limitation on the scope of application of the simulation method of the present invention. The fluid density is 920kg / m³. 3 The kinematic viscosity is 0.0074 m. 2 / s, surface tension coefficient is 0.05, non-Newtonian fluid is set to not be non-Newtonian fluid, and since fluid-structure interaction is not considered, the parameters of the polygonal solid are set to the software default parameters. After setting the physical properties of the fluid and solid, they are assigned to the lubricating oil and various components in the aerospace gearbox.

[0058] Please refer to Figure 3 As shown, Figure 3 Sub-figure (a) is a three-dimensional view of the aircraft gearbox model from a certain angle. Figure 3 Sub-figure (b) is the front view of the aircraft gearbox model. Figure 3 Sub-figure (c) is a three-dimensional view of the aircraft gearbox model from another angle. In this embodiment, the boundary conditions of different oil return ports are set differently. Among them, the first oil return port 1 is negative pressure oil extraction, and the second oil return port 2 is normal atmospheric pressure oil discharge. The boundary conditions of different jet ports are also not completely the same. Among them, the first jet port 3 and the second jet port 4 are medium speed jets, and the second jet port 4 and the third jet port 5 are low speed jets.

[0059] In this embodiment, the specific parameters of the jet orifice and return oil port are set as follows: both the jet orifice and the return oil port are circular, wherein the radius of the first jet orifice 3 and the second jet orifice 4 is 5mm, the radius of the third jet orifice 5 is 10mm, the radius of the fourth jet orifice 6 is 8mm, the radius of the first return oil port 1 and the second return oil port 2 is 8mm, and the geometric coordinates of the first jet orifice 3 are (x: -60.5mm, y: 243.5mm, z: 0mm). The rotation angle is (x: 53.5°, y: 90°, z: 0°). The geometric coordinates of the second jet port 4 are (x: 88.5mm, y: 358.5mm, z: 0mm), and the rotation angle is (x: 53.5°, y: 90°, z: 0°). The geometric coordinates of the third jet port 5 are (x: 253mm, y: 333mm, z: 0mm), and the rotation angle is (x: -53.5°, y: -90°). The geometric coordinates of the fourth jet port 6 are (x: 158.5mm, y: -222mm, z: 30mm), and the rotation angle is (x: -90°, y: 0°, z: 0°). The geometric coordinates of the first return port 1 are (x: 279mm, y: 0mm, z: -197mm), and the rotation angle is (x: 0°, y: 0°, z: 0°). The geometric coordinates of the second return port 2 are (x: 279mm, y: 0mm, z: -197mm), and the rotation angle is (x: 0°, y: 0°, z: 0°). 9mm (y: 0mm, z: 197mm), rotation angle (x: 0°, y: 0°, z: 0°), jet mode selected as jet velocity mode, jet velocity at first jet port 3 is 1.315m / s, jet velocity at second jet port 4 is 0.303m / s, jet velocity at third jet port 5 is 0.303m / s, jet velocity at fourth jet port 6 is 3.608m / s, upper limit of jet volume is 1000m³. 3 The injection angle is 0°, the randomization of particles in the jet is in the open mode, the pressure of the first return port 1 is -50Kpa, and the pressure of the second return port 2 is 0Kpa.

[0060] Please refer to Figure 4As shown, in this embodiment, the first moving part 7 consists of a first bearing 71 and a first spiral bevel gear 72 and a second spiral bevel gear 73 fixedly connected to the first bearing 71; the second moving part 8 consists of a second bearing 81 and a third spiral bevel gear 82 and a first spur gear 83 fixedly connected to the second bearing 81; the third moving part 9 consists of a third bearing 91 and a second spur gear 92 fixedly connected to the third bearing 91; the second spiral bevel gear 73 meshes with the third spiral bevel gear 82; the first spur gear 83 meshes with the second spur gear 92. The outer shell 10 of the aircraft gearbox model is set as a fixed constraint, and the motion mode is rotation. The red arrow in the figure indicates the rotation direction of the moving parts. The unit of motion speed is rad / min. The velocity vector direction of the first moving part 7 is (x: 1, y: 0, z: 0), and the magnitude of the motion speed is 5741.75 rad / min. The velocity vector direction of the second moving part 8 is (x: -0.593542, y: 0.804803, z: 0), and the magnitude of the motion speed is -11806 rad / min. The velocity vector direction of the third moving part 9 is (x: -0.593542, y: 0.804803, z: 0), and the magnitude of the motion speed is 7186 rad / min. In the above parameters, positive and negative represent clockwise rotation and counterclockwise rotation, respectively.

[0061] In this embodiment, the simulated particle size is set to 0.01 mm, and the gravity vector is (x: 0 m / s²). 2 y: 0m / s 2 z: 9.8m / s 2 The simulation was performed using a semi-implicit moving particle method. The pressure type was implicitly stable, the viscosity type was implicit, the surface tension parameter was set to potential, the dynamic contact angle was not set, and the turbulence parameters were set to default.

[0062] Using the above parameters and model, simulation calculations are performed to obtain the simulation calculation results of lubricating oil particles. Step S3 is then executed to perform post-processing on the simulation calculation results of lubricating oil particles.

[0063] Please refer to Figure 5 and Figure 6 As shown, velocity and pressure cloud maps are created and assigned to each particle generated by the calculation, and rainbow color zones are used. By analyzing the particle velocity cloud maps, the lubricating oil motion state of each region inside the aircraft gearbox is obtained. Extra attention is paid to the high-pressure part in the pressure cloud map, especially the high-pressure particles inside the meshing area of ​​moving parts. If the high-pressure area coincides with the critical structure, the calculation results are used for optimization, thereby avoiding potentially dangerous conditions during the experiment.

[0064] Please refer to Figure 7As shown, surface mesh fitting is performed on the particles in the splash lubrication part. The overlapping particle surfaces and non-overlapping particle surfaces are removed to better fit the lubricating oil morphology, so that the gaps between particles are filled and the lubricating oil distribution at this moment is displayed in a more realistic lubricating oil morphology. At the same time, the mesh after particle fitting is desharpened and smoothed to enhance the accuracy of the mesh.

[0065] Please refer to Figure 8 As shown, first, a probe is created as the particle detection area, and then the probe's geometry is adjusted to the gear meshing area. Figure 8 (The area within the yellow box in the middle) Note that the probe size should be slightly larger than the visible meshing area. Finally, the particle density inside the monitoring area is output as a .csv file and plotted as a curve to evaluate the lubrication effect within the monitoring area.

[0066] In this embodiment, due to the use of a semi-implicit moving particle method for simulation, discrete particles are introduced to replace the continuous mesh, boundary conditions are approximated, and the two-phase flow model is simplified. Therefore, related errors exist in the simulation results, and it is necessary to quantitatively analyze the key errors to optimize the simulation results. In this embodiment, the key errors include: density estimation error caused by insufficient particle resolution, slip error caused by boundary condition approximation, and pressure error caused by the simplification of the two-phase flow model. The above errors are analyzed and calculated below.

[0067] To address the density estimation error caused by insufficient particle resolution, since the density is obtained by weighted summation of surrounding particles using a kernel function, we should start with the density estimation formula:

[0068] ρ i =∑ j m j W(|r i -r j |,h)

[0069] In the formula: ρ i Let m be the estimated density of particle i. j Let W(|r) be the mass of particle j. i -r j |,h) is the kernel function, r i -r j Let be the Euclidean distance between particles i and j, and h be the interparticle distance.

[0070] Assuming all particles have the same mass, then:

[0071] ρ i =m∑ j W(|r i -r j |,h)

[0072] In the formula: m is the particle mass;

[0073] The Taylor expansion of the real density field is:

[0074]

[0075] In the formula: ρ(r) j Let ρ(r) be the true density of the particle at position j. i (i) represents the actual density of the particle at position i. The density gradient vector, It is a higher-order infinitesimal of the Euclidean distance difference;

[0076] Ideally, the density estimate should satisfy:

[0077]

[0078] In the formula: The ideal density value for particle i;

[0079] However, in actual discrete calculations, it is as follows:

[0080]

[0081] In the formula: Calculate the density value for particle i.

[0082] Substituting the Taylor expansion into the numerical estimation formula and separating the error term, we obtain:

[0083]

[0084] In the formula: ∈ ρ For density estimation error; C is a constant related to kernel function and dimension, h 2 The square of the particle resolution. The second derivative of the density field is... It is a higher-order infinitesimal with particle resolution.

[0085] If the particle resolution is too low, the interactions between particles may not accurately cover the continuous medium properties of the fluid, resulting in an excessively large error. This is because the density estimation error is proportional to the square of the particle resolution, h. 2 and the second derivative of the density field Proportional to the error coefficient C, higher-order kernel functions can reduce the error coefficient C, but they will also increase the computational cost.

[0086] To address the slip error caused by boundary condition approximation, a no-slip condition is applied in the simulation using virtual particles. However, due to the discretization of the boundary conditions, the position and velocity of the virtual particles are discrete approximations, while the actual boundary is a continuous surface. This introduces slip error at the fluid boundary during simulation, affecting the subsequent mesh fitting and thus the lubricating oil morphology. In a continuous medium, the actual boundary conditions are:

[0087] u(r wall )=u wall

[0088] In the formula: u(r) wall ) represents the fluid velocity at the wall, u wall The wall velocity;

[0089] The boundary conditions for the discrete virtual particle method are:

[0090]

[0091] In the formula: r virtual,j For the virtual particle position, u i Let i be the velocity vector of particle i;

[0092] Assuming the true velocity field near the wall can be Taylorically expanded as follows:

[0093]

[0094] In the formula: Let u(r) be a higher-order infinitesimal of the position difference between the particle and the wall, and let u(r) be the actual velocity field. For velocity gradient;

[0095] Since virtual particles use interpolation, the velocity interpolation value is:

[0096]

[0097] In the formula: It is a higher-order infinitesimal of the interparticle distance;

[0098] The positional error of the virtual particle leads to the slip error as follows:

[0099]

[0100] In the formula: ∈ slip For the slip error, δr j For virtual particle position deviation, For the virtual particle velocity gradient, W(|r i -r virtual,j |,h) represents the kernel function weights, where h is the particle spacing, controlling the geometric resolution.

[0101] Through error magnitude analysis, we can conclude that:

[0102]

[0103] In the formula: The velocity gradient modulus reflects the flow shear strength.

[0104] Based on the above error tuning, the slip error can be reduced by densifying the boundary layer, optimizing the virtual particle generation method, or using higher-order interpolation.

[0105] The pressure error caused by the simplification of the two-phase flow model is due to the weak compressibility of the air phase, which is ignored in the simulation calculation. This simplification inevitably leads to errors in the calculated pressure field. The complete governing equations include:

[0106] mass conservation equation:

[0107] Momentum conservation equation:

[0108] Linearized compression model equation: ρ air =ρ0(1+β(P-P0))

[0109] In the formula: ρ air ν is the air density (varying with time and pressure); u is the fluid velocity field; P is the pressure field; ν is the kinematic viscosity; g is the gravitational acceleration; ρ0 is the air density at the reference pressure P0; β is the air compressibility coefficient.

[0110] Ignoring the compressibility of air, the continuity equation simplifies to:

[0111]

[0112] In the formula: For the Laplace operator;

[0113] Combining the compressible continuity equation and the momentum equation, neglecting viscous terms and gravity, and substituting the derivative with respect to time into the equation of state:

[0114]

[0115] In the formula: c 2 It is the square of the speed of sound in air. Convection acceleration term

[0116] In the incompressible model, pressure is solved using the Poisson equation (ignoring higher-order derivatives of pressure with time):

[0117]

[0118] In the formula: P incompThe pressure calculated for the incompressible model;

[0119] Comparing the complete equation and the simplified equation, we get:

[0120]

[0121] In the formula: ∈ P =P true -P incomp For pressure error;

[0122] Since the time step is small, it can be approximated as:

[0123]

[0124] In the formula: ∈ P For pressure error, Δt 2 The square of the time step. This represents the rate of change of pressure over time.

[0125] In this embodiment, the pressure error varies with Δt 2 As the pressure increases and the dynamic process (gear meshing impact, rapid oil retraction) progresses, the rate of pressure change has a greater impact on the error. After assessing the magnitude of the error, it is necessary to introduce compressibility correction (adding a compression term to the pressure Poisson equation) or higher-order time integrals if necessary.

Claims

1. A visualization simulation method for the entire process of oil inlet and outlet of moving parts in an aircraft gearbox, characterized in that, Includes the following steps: S1. Establish an aircraft gearbox model in 3D modeling software, perform preprocessing on the aircraft gearbox model, set the effective fluid area on the preprocessed aircraft gearbox model, confirm the correct spatial position relationship of the moving parts in the aircraft gearbox, set the physical parameters of the moving parts and lubricating oil, and obtain the aircraft gearbox inlet and return oil model. S2. Import the aviation gearbox inlet and outlet oil model into the computational fluid dynamics simulation software, establish the jet boundary and return oil boundary of the lubricating oil, set the motion parameters of the moving parts in the aviation gearbox, and after confirming that the motion state of the moving parts is correct, set the simulation calculation parameters and perform simulation calculation to obtain the simulation calculation results of lubricating oil particles. S3. Post-process the simulation results of lubricating oil particles to create pressure distribution cloud maps and velocity distribution cloud maps of lubricating oil particles; set the detection area for lubricating oil particles and monitor the number of lubricating oil particles; perform mesh fitting processing on the surface of lubricating oil particles. S4. Perform error prediction on the simulation calculation results of lubricating oil particles in the post-processing, and perform corresponding error tuning to obtain the predicted results of lubrication performance.

2. The visualization simulation method for the entire process of oil inlet and outlet of the moving parts of an aircraft gearbox according to claim 1, characterized in that, In step S1, the preprocessing of the aircraft gearbox model includes: omitting or merging features in the aircraft gearbox model that are unrelated to the lubricating oil flow field; the setting of the effective fluid region for the preprocessed aircraft gearbox model includes: setting the internal space of the aircraft gearbox, the inlet of the lubricating oil jet boundary, and the outlet of the lubricating oil return boundary as the effective fluid region.

3. The visualization simulation method for the entire process of oil inlet and outlet of the moving parts of an aircraft gearbox according to claim 1, characterized in that, In step S2, the boundary conditions of the jet boundary include the spatial geometric position of the jet, the shape of the jet boundary, the jet mode, the jet velocity, the upper limit of the jet volume, the injection angle, and the randomization of particles in the jet; the boundary conditions of the return oil boundary include the geometry of the return oil port, the spatial geometric position of the return oil port, and the return oil mode.

4. The visualization simulation method for the entire process of oil inlet and outlet of the moving parts of an aircraft gearbox according to claim 1, characterized in that, In step S2, the motion parameters of the moving parts in the aerospace gearbox include motion mode, motion speed unit, motion speed vector direction, and motion speed magnitude; the simulation calculation parameters include simulation particle size, gravity vector, simulation method, pressure type, viscosity type, surface tension parameter, and turbulence parameter.

5. The visualization simulation method for the entire oil inlet and outlet process of the moving parts of an aircraft gearbox according to claim 4, characterized in that: The size of the simulated particles is set as follows: the diameter is 1 / 10 of the minimum clearance width in the oil inlet and outlet model of an aviation gearbox; the simulation method adopts the semi-implicit moving particle method.

6. The visualization simulation method for the entire process of oil inlet and outlet of the moving parts of an aircraft gearbox according to claim 1, characterized in that, In step S3, the process of fitting the lubricating oil particle surface mesh includes: first, identifying the surface particles; then, constructing the implicit field or reconstructing the point cloud; and finally, extracting the isosurface to obtain the lubricating oil particle surface mesh fitting result.

7. The visualization simulation method for the entire process of oil inlet and outlet of the moving parts of an aircraft gearbox according to claim 1, characterized in that, In step S4, the error prediction includes: density estimation error prediction caused by insufficient particle resolution, slip error prediction caused by boundary condition approximation, and pressure error prediction caused by simplification of the two-phase flow model.

8. The visualization simulation method for the entire oil inlet and outlet process of the moving parts of an aircraft gearbox according to claim 7, characterized in that: The formula for predicting the density estimation error caused by insufficient particle resolution is as follows: In the formula: ∈ ρ For density estimation error; C is a constant related to kernel function and dimension, h 2 The square of the particle resolution. The second derivative of the density field is... It is a higher-order infinitesimal with particle resolution.

9. The visualization simulation method for the entire oil inlet and outlet process of the moving parts of an aircraft gearbox according to claim 7, characterized in that: The formula for predicting the slip error caused by the boundary condition approximation is as follows: In the formula: ∈ slip For the slip error, δr j For virtual particle position deviation, For the virtual particle velocity gradient, W(|r i -r virtual,j |,h) represents the kernel function weights.

10. The visualization simulation method for the entire process of oil inlet and outlet of the moving parts of an aircraft gearbox according to claim 7, characterized in that: The pressure error prediction caused by the simplification of the two-phase flow model is calculated using the following formula: In the formula: ∈ P For pressure error, Δt 2 The square of the time step. This represents the rate of change of pressure over time.

Images