Aviation fuel pump bearing oil pollution simulation method based on Monte Carlo method
By simulating lubrication contamination in sliding bearings using the Monte Carlo method, the shortcomings of traditional simulation methods in simulating lubrication contamination are overcome, enabling more efficient and accurate prediction of lubrication performance and improving the design and maintenance guidance capabilities of gear pumps.
Patent Information
- Application Number
- CN202511989342.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-02-10
AI Technical Summary
Existing technologies have shortcomings in simulating oil contamination in sliding bearings. Traditional simulation methods are unable to accurately reflect the randomness and complexity of oil contamination, resulting in inaccurate simulation of lubrication performance.
A simulation method based on the Monte Carlo method is adopted. By randomly generating solid particles and dividing the region into a particle-containing region and a particle-free region, the oil film pressure distribution is updated separately. The Monte Carlo method is used to handle the uncertainty of contaminant particles, avoiding the complexity and high computational cost of the traditional Lagrange method.
It improves the accuracy and reliability of lubrication wear prediction, significantly enhances simulation efficiency, and enables more accurate assessment of gear pump service life and maintenance requirements.
Smart Images
Figure CN121503336A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of simulation of oil pollution of sliding bearings of aeronautical fuel gear pumps, and in particular to a simulation method of oil pollution of aeronautical fuel pump bearings based on the Monte Carlo method. BACKGROUND
[0002] In the fuel system of an aero-engine, the gear pump is a key component, and its performance directly affects the reliability and efficiency of the engine. As one of the core components of the gear pump, the lubrication performance and wear problem of the sliding bearing have always been the focus of research. In the prior art, there are studies on the modeling method of the degradation of the lubrication performance of the sliding bearing, such as analyzing the influence of load and speed on the friction and wear performance of copper alloy materials, and discussing the wear mechanism of different materials under different working conditions. In addition, there are also studies on the use of CFD simulation to analyze the internal flow characteristics of the gear pump to optimize the design of the unloading groove and relieve the oil retention problem.
[0003] However, the prior art still has deficiencies in simulating the oil pollution of the sliding bearing. For example, the traditional simulation method is mostly based on a deterministic model, which is difficult to accurately reflect the randomness and complexity of the oil pollution. SUMMARY
[0004] In order to overcome the above technical problems, the purpose of the present application is to provide a simulation method of oil pollution of aeronautical fuel pump bearings based on the Monte Carlo method, which can effectively handle the uncertainty of the pollution particles and more realistically simulate the influence of oil pollution on the lubrication performance of the sliding bearing, thereby providing more reliable technical support for the design and optimization of the gear pump.
[0005] The technical solution adopted by the present application is: A simulation method of oil pollution of aeronautical fuel pump bearings based on the Monte Carlo method, comprising the following steps: Step 1: input the basic physical and structural parameters into the simulation model, and initialize the oil film thickness and pressure distribution of the gear pump sliding bearing, and generate solid particles randomly by the Monte Carlo method; Step 2: according to the spatial position of the solid particles generated in step 1, divide the simulation area into particle-containing regions and particle-free regions; Step 3: according to the pressure boundary conditions of the aeronautical fuel pump, update and calculate the oil film pressure distribution in the particle-free region and the particle-containing region, respectively; Step 4: determine whether the pressure distribution has converged, if the pressure distribution has not converged, return to step 3 to continue updating and calculating; if the pressure distribution has converged, proceed to the next step; Step 5: output the oil film pressure distribution calculation result after the pressure distribution converges.
[0006] The specific step 1 is: (1.3) Calculate the oil film thickness distribution based on the given geometric parameters, physical property parameters, and operating condition parameters:
[0007] in, c This refers to the bearing clearance. epsilon This refers to the bearing eccentricity. W This represents the amount of wear, initially set to 0. delta e This refers to the increase in oil film thickness caused by elastic deformation; (1.4) Solid particles are randomly generated according to the Monte Carlo method, specifically including generating random particle size, random axial position, and random circumferential position: Based on the particle size of contaminants in the oil measured experimentally d p Follows distribution F ( d p Assuming the particle positions follow a uniform distribution, then:
[0008] in, Particle size distribution function F ( d p The inverse function of ). is a random number uniformly distributed in the interval [0,1].
[0009] Step two specifically involves: A judgment mechanism is set up so that for each point of the grid in each computational domain, it is first determined whether there are particles at the geometric location of this grid point. If there are particles, this is a particle-containing region; otherwise, it is a particle-free region. Based on this division, the process proceeds to step three and uses different governing equations for calculation.
[0010] Step three specifically involves: For the particle-free region, the governing equation is:
[0011] in, These are the circumferential and axial coordinates, respectively. Fuel dynamic viscosity; R The inner diameter of the bearing bush; u 1 represents the journal rotational speed and linear velocity; p Pressure distribution in the fluid domain; h Oil film thickness distribution; For the region with particles, the governing equation is: in, These are the circumferential and axial coordinates, respectively. Fuel dynamic viscosity; R The inner diameter of the bearing bush; u 1 represents the journal rotational speed and linear velocity; p Pressure distribution in the fluid domain; h Oil film thickness distribution; This is a positional parameter of the particles in the oil film; the closer it is to 1, the closer it is to the bearing. d p This represents the particle size currently being calculated.
[0012] Step four specifically involves: To determine whether the pressure has converged, relative error is used here. The calculation method is as follows: in, The three steps are respectively the first step. k Second and third k- The pressure field obtained from one iteration; if the relative error If the value is less than the threshold given by the user, it is considered to have converged and proceeds to the next step.
[0013] The beneficial effects of this invention are: This invention enables the simulation process to handle the uncertainty of pollutant particles. Specifically, the Monte Carlo method in step one transforms the uncertainty of particles into random sampling results, quantifying the uncertainty. At the same time, it avoids the complexity and high computational cost of traditional Lagrange particle tracking, greatly saving computational resources and time, and significantly improving simulation efficiency.
[0014] Furthermore, by comparing the pressure distribution with and without contaminant particles using the simulation results of the oil film pressure distribution in step five, this method obtains the impact of oil contamination on the lubrication performance of sliding bearings. This makes the simulation results closer to actual working conditions, significantly improving the accuracy and reliability of lubrication wear prediction. It also helps to more accurately assess the service life and maintenance requirements of gear pumps, demonstrating significant practical value and broad application prospects. Attached Figure Description
[0015] Figure 1 Simulation flowchart of oil film lubrication of sliding bearing in aviation fuel gear pump considering oil contamination.
[0016] Figure 2 A schematic diagram of the particle distribution results generated by the Monte Carlo method.
[0017] Figure 3 This is a schematic diagram of the pressure distribution simulation results under oil contamination conditions.
[0018] Figure 4This diagram illustrates the pressure distribution difference under particle-free conditions. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] The main technology employed in this invention is a simulation method for oil film lubrication of sliding bearings in the presence of particulate contamination in the oil. Its basic principle is summarized as follows: To achieve the above objectives, the present invention provides the following technical approach: Step 1: Input basic parameters and initialize oil film thickness and pressure distribution, then randomly generate solid particles using the Monte Carlo method; The first step is as follows: (1.1) Calculate the oil film thickness distribution based on the given geometric parameters, physical property parameters, and operating condition parameters:
[0021] in, c This refers to the bearing clearance. epsilon This refers to the bearing eccentricity. W This represents the amount of wear, initially set to 0. delta e This represents the increase in oil film thickness caused by elastic deformation.
[0022] (1.2) Solid particles are randomly generated according to the Monte Carlo method, specifically including generating random particle size, random axial position, and random circumferential position: Based on the particle size of contaminants in the oil measured experimentally d p Follows distribution F ( d p Assuming the particle positions follow a uniform distribution, then:
[0023] in, Particle size distribution function F ( d p The inverse function of ). is a random number uniformly distributed in the interval [0,1].
[0024] Step 2: Divide the simulation area into particulate and non-particulate regions. This step is to distinguish between areas of the oil film affected by particles and areas unaffected by particles, in order to perform more accurate simulation calculations; Step 3: Update the oil film pressure distribution in both the particle-free and particle-containing regions. This step uses iterative calculations to simulate the pressure changes of the oil film in different regions, reflecting the influence of particles on the oil film pressure distribution.
[0025] For the particle-free region, the governing equation is:
[0026] in, These are the circumferential and axial coordinates, respectively. Fuel dynamic viscosity; R The inner diameter of the bearing bush; u 1 represents the journal rotational speed and linear velocity; p Pressure distribution in the fluid domain; h This represents the oil film thickness distribution.
[0027] For the region with particles, the governing equation is: in, These are the circumferential and axial coordinates, respectively. Fuel dynamic viscosity; R The inner diameter of the bearing bush; u 1 represents the journal rotational speed and linear velocity; p Pressure distribution in the fluid domain; h Oil film thickness distribution; This is a positional parameter of the particles in the oil film; the closer it is to 1, the closer it is to the bearing. d p This represents the particle size currently being calculated.
[0028] Step 4: Determine if the pressure distribution has converged. If the pressure distribution has not converged, return to Step 3 to continue updating the calculation; if the pressure distribution has converged, proceed to the next step. Step 5: Once the pressure distribution converges, output the calculated oil film pressure distribution results. This step outputs the final simulation results for further analysis and application.
[0029] Experimental Example: The advantages of this invention can be further illustrated by the following simulation experiments: 1. Simulation parameters To ensure the accuracy and reliability of the simulation results of the oil-contaminated sliding bearing lubrication simulation model proposed in this invention, a set of experimental results from a fuel gear pump under a specific load spectrum were selected for comparison. The basic parameters of a certain type of bearing targeted by this invention are shown in Table 1, and the working medium is RP-3 aviation fuel.
[0030] Table 1 Main parameters of sliding bearings for aviation fuel gear pumps Based on this, 30 contaminant particles with normal particle size distribution were randomly generated using the above method, and the lubrication of the sliding bearing of the aviation fuel gear pump was simulated.
[0031] 2. Simulation Results Substitute the parameters from Table 1 into... Figure 1 The calculation process in the example yields the following simulation results: Figure 2 , 3 As shown in Figure 4. From Appendix Figure 2 It can be seen that the particle distribution in the computational domain has uniform randomness, which is consistent with the objective of applying the Monte Carlo method in step one.
[0032] 3. Results Analysis Conclusion 1: Through Figure 3 The simulation results were compared with the actual experimental results, and it was found that the oil film pressure distribution obtained by the method of the present invention is consistent with the experimental results. The accuracy of the lubrication model of the sliding bearing of the aviation fuel gear pump that takes into account oil contamination was verified.
[0033] Conclusion 2: Figure 4 The pressure distribution of the sliding bearing in an aviation fuel gear pump was compared under particulate and non-particulate conditions. The results show that the oil film pressure changes significantly under particulate contamination conditions, especially at the thinnest point of the oil film (0.03 m). The pressure changes under particulate conditions significantly affect bearing wear, and the impact of particles cannot be ignored. This means that compared to traditional bearing lubrication simulation methods that do not consider oil contamination, the method used in this study can provide more reasonable wear predictions, facilitating condition-based maintenance of aviation fuel gear pumps.
[0034] Conclusion 3: The Monte Carlo method used in this invention to generate particle distribution has strong randomness while conforming to the experimentally measured particle statistical characteristics. In practical applications, it avoids the complex and time-consuming Lagrange particle tracking and calculation, greatly saving computational and time costs.
Claims
1. A simulation method for oil contamination in aviation fuel pump bearings based on the Monte Carlo method, characterized in that, Includes the following steps; Step 1: Input basic physical and structural parameters into the simulation model, initialize the oil film thickness and pressure distribution of the gear pump sliding bearing, and randomly generate solid particles using the Monte Carlo method; Step 2: Based on the spatial location of the solid particles generated in Step 1, divide the simulation area into a particle-containing area and a particle-free area; Step 3: Based on the pressure boundary conditions of the aviation fuel pump, update the oil film pressure distribution in the particle-free region and the particulate region respectively; Step 4: Determine if the pressure distribution has converged. If the pressure distribution has not converged, return to Step 3 to continue updating the calculation; if the pressure distribution has converged, proceed to the next step. Step 5: Once the pressure distribution converges, output the calculated oil film pressure distribution results, and the simulation is complete.
2. The simulation method for aviation fuel pump bearing oil contamination based on the Monte Carlo method according to claim 1, characterized in that, The first step is as follows: Calculate the oil film thickness distribution based on the given geometric parameters, physical property parameters, and operating condition parameters: in, c This refers to the bearing clearance. ε This refers to the bearing eccentricity. W This represents the amount of wear, initially set to 0. δ e This refers to the increase in oil film thickness caused by elastic deformation; Solid particles are randomly generated using the Monte Carlo method, specifically including generating random particle size, random axial position, and random circumferential position: Based on the particle size of contaminants in the oil measured experimentally d p Follows distribution F ( d p Assuming the particle positions follow a uniform distribution, then: in, Particle size distribution function F ( d p The inverse function of ). is a random number uniformly distributed in the interval [0,1].
3. The simulation method for aviation fuel pump bearing oil contamination based on the Monte Carlo method according to claim 1, characterized in that, Step two specifically involves: A judgment mechanism is set up so that for each point of the grid in each computational domain, it is first determined whether there are particles at the geometric location of this grid point. If there are particles, this is a particle-containing region; otherwise, it is a particle-free region. Based on this division, the process proceeds to step three and uses different governing equations for calculation.
4. The simulation method for aviation fuel pump bearing oil contamination based on the Monte Carlo method according to claim 1, characterized in that, Step three specifically involves: For the particle-free region, the governing equation is: Among them, among them, These are the circumferential and axial coordinates, respectively. Fuel dynamic viscosity; R The inner diameter of the bearing bush; u 1 represents the journal rotational speed and linear velocity; p Pressure distribution in the fluid domain; h Oil film thickness distribution; For the region with particles, the governing equation is: in, These are the circumferential and axial coordinates, respectively. Fuel dynamic viscosity; R The inner diameter of the bearing bush; u 1 represents the journal rotational speed and linear velocity; p Pressure distribution in the fluid domain; h Oil film thickness distribution; This is a positional parameter of the particles in the oil film; the closer it is to 1, the closer it is to the bearing. d p This represents the particle size currently being calculated.
5. The simulation method for aviation fuel pump bearing oil contamination based on the Monte Carlo method according to claim 1, characterized in that, Step four specifically involves: To determine whether the pressure has converged, relative error is used here. The calculation method is as follows: in, The three steps are respectively the first step. k Second and third k- The pressure field obtained from one iteration; if the relative error If the value is less than the threshold given by the user, it is considered to have converged and proceeds to the next step.