Aerodynamic simulation analysis method and system based on thermodynamic coupling

Abstract

The invention discloses an aerodynamic simulation analysis method and system based on thermodynamic coupling, and relates to the technical field of simulation analysis, and the method comprises the steps: calculating a sensitive index and an uncertain index of each reference parameter, and fully considering the characteristics of the reference parameters to accurately enhance the robustness for the description of a simulation model. According to the method, the geometric features of each region, the reference parameter features and the sensitive indexes of the reference parameters are counted to determine the corresponding required grid density of the region, and the grid densities of different regions of the geometric three-dimensional model are specifically set according to different features of the regions and the reference parameter features, so that the analysis and calculation requirements of different regions are met. According to the uncertain indexes of the reference parameters, the reference parameters in the simulation model are endowed with credibility, ship turbine equipment is subjected to simulation analysis through the simulation model, the calculation precision of different areas is reasonably distributed, and the complex and changeable working conditions of the equipment are met. The multi-physics coupling condition is effectively captured, and the accuracy and adaptability of simulation analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation analysis, and particularly to a gas dynamic simulation analysis method and system based on thermodynamics coupling. Background Art

[0002] In the field of marine engine equipment, with the increasing requirements for energy efficiency and environmental protection in the shipping industry, the demand for equipment performance optimization and fault prediction is becoming more urgent. When marine engine equipment operates, it involves complex thermodynamic processes and gas dynamic phenomena, and there are close thermodynamic coupling relationships among various components. For example, the combustion of gas in the engine combustion chamber generates high-temperature and high-pressure gas, and its expansion work process is closely related to the change of thermodynamic state, and will affect the temperature field of surrounding components through heat conduction, convection, etc., and then change the gas flow characteristics. Traditional simulation analysis methods often view thermodynamic and gas dynamic processes in isolation and are difficult to accurately simulate actual working conditions.

[0003] In the prior art, on the simulation model of marine engine equipment established, the analysis and calculation accuracies at different positions are the same, and the distribution of calculation accuracies is unreasonable, resulting in the inability to effectively and accurately simulate marine engine equipment, and the inability to effectively capture the coupling situation of thermodynamics, gas dynamics and other physical fields, so that the effect of the simulation analysis of marine engine equipment is poor and the adaptability is low.

[0004] Therefore, how to improve the rationality of the distribution of the calculation accuracy of the simulation model is a technical problem to be solved at present. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the prior art that the marine engine equipment cannot be effectively and accurately simulated and the rationality of the distribution of the calculation accuracy of the simulation model is poor, and to propose a gas dynamic simulation analysis method based on thermodynamics coupling, and the method includes,

[0006] Collect all reference parameters involved in the marine engine equipment, determine the functional relationship and coupling relationship between the reference parameters, and analyze the functional relationship and coupling relationship between the reference parameters to calculate the sensitivity index and uncertainty index of each reference parameter;

[0007] Establish a three-dimensional geometric model of the marine engine equipment, and divide the three-dimensional geometric model into multiple regions according to the structure and function of the marine engine equipment;

[0008] Statistically determine the grid density required for the corresponding region based on the geometric characteristics, reference parameter characteristics and sensitivity index of the reference parameters of each region, perform grid processing on the three-dimensional geometric model based on the grid density, and establish a simulation model of the marine engine equipment on the basis of the functional relationship and coupling relationship between the reference parameters and the three-dimensional geometric model;

[0009] Assign credibility to the reference parameters in the simulation model according to the uncertainty indicators of the reference parameters, and perform simulation analysis on the ship's marine engine equipment through the simulation model to achieve fault identification and prediction;

[0010] Among them, the reference parameters include one or more of equipment design parameters, mechanical parameters, thermodynamics parameters, fluid mechanics parameters, and electrical parameters.

[0011] In some embodiments of the present application, determine the functional relationship and coupling relationship between the reference parameters, including,

[0012] Collect data samples of the reference parameters, distinguish simple functional relationships and complex functional relationships. For simple functional relationships, describe the simple functional relationships through physical laws or relevant theoretical laws. For complex functional relationships, determine the initialization of the complex functional relationships through mathematical modeling, and optimize the initialization of the complex functional relationships through the data samples of the reference parameters;

[0013] Analyze the multi-physical field mechanism, energy and mass transfer mechanism involved in the ship's marine engine equipment, establish a coupling model, the coupling model is physical model coupling or numerical simulation coupling, and optimize the coupling model according to the data samples of the reference parameters.

[0014] In some embodiments of the present application, analyze the functional relationship and coupling relationship between the reference parameters, calculate the sensitivity index and uncertainty index of each reference parameter, including,

[0015] Screen out the effect parameters that describe the performance or operating state of the ship's marine engine equipment among the reference parameters, and establish multiple objective functions between other parameters and the effect parameters in the reference parameters;

[0016] Calculate the correlation between each reference parameter and the corresponding effect parameter in each objective function, obtain the normal value range of each reference parameter, and determine the perturbation amount based on the normal value range and correlation of the reference parameters;

[0017] In the simulation software, perform small perturbations on the reference parameters that bidirectionally perturb each objective function through the perturbation amount. The bidirectional perturbation includes the positive perturbation and negative perturbation of the reference parameters, and statistically calculate the change rates of the effect parameters and reference parameters in the objective function to obtain the central difference as the local sensitivity index;

[0018] For the same objective function, calculate the correlation between different reference parameters, screen out the corresponding relationships of strongly correlated reference parameters, and draw the change of the correlation with different value ranges under the corresponding relationship of the reference parameters, so as to determine the standard value range of the reference parameters;

[0019] Random sampling is performed using Latin hypercube sampling or Monte Carlo sampling based on the standard value range of the reference parameters to obtain multiple combinations of reference parameters. Each combination of reference parameters is input into the simulation software to obtain the magnitudes of the effect parameters on the objective function.

[0020] Evaluate the complexity of each objective function, and select the Sobol index order by combining the complexity of the objective function and the correlation between different reference parameters under the objective function to determine the global sensitivity index.

[0021] Determine the sensitivity index based on the local sensitivity index and the global sensitivity index of each reference parameter.

[0022] Describe the uncertainty index of each reference parameter through probability distribution.

[0023] In some embodiments of the present application, the uncertainty index of each reference parameter is described through probability distribution, including

[0024] Draw a histogram of each reference parameter, count the statistics of each reference parameter on the histogram, perform a goodness-of-fit test on each reference parameter to determine the distribution type, establish the original probability distribution of each parameter, and establish the joint probability distribution under the corresponding relationship of strongly correlated reference parameters. Determine the uncertainty index of each reference parameter according to the original probability distribution and the joint probability distribution.

[0025] In some embodiments of the present application, the three-dimensional geometric model is divided into multiple regions according to the structure and function of the ship's engine equipment, including

[0026] The three-dimensional geometric model is divided into multiple structural regions according to the different structural components of the ship's engine equipment, and the regions are further divided according to the functional differences within each structural region.

[0027] In some embodiments of the present application, the geometric characteristics, reference parameter characteristics, and sensitivity index of the reference parameters of each region are statistically analyzed to determine the grid density required for the region, including

[0028] Evaluate the geometric complexity of each type of geometric feature to generate evaluation indicators.

[0029] Confirm the categories of reference parameters involved in each region according to the functions of the ship's engine equipment and multiple physical processes, construct a set of reference parameters, each region corresponds to a set of reference parameters, and statistically analyze the physical field characteristics of the reference parameters. Define the calculation accuracy requirement level based on the evaluation indicators of the geometric characteristics, the physical field characteristics of the reference parameters, and the sensitivity index of the reference parameters, and map a grid density according to the calculation accuracy requirement level.

[0030] In some embodiments of the present application, credibility is assigned to the reference parameters in the simulation model according to the uncertainty index of the reference parameters, including

[0031] Statistically calculate the maximum value, minimum value, mode value, and median value of the uncertainty indicators of all reference parameters, respectively calculate the distances between the mode value and the maximum value and the minimum value, and take the minimum distance among the two distances as the proximity distance. Adjust the median value towards the maximum value or the minimum value through the proximity distance to obtain a standard threshold.

[0032] Use the standard threshold to distinguish between strongly credible parameters and weakly credible parameters among the reference parameters, and use two mapping relationships to determine the credibility of the strongly credible parameters and the weakly credible parameters respectively.

[0033] In some embodiments of the present application, a simulation analysis of the ship's turbine equipment is carried out through a simulation model, including,

[0034] Set boundary conditions and initial conditions on the simulation model, and fuse the credibility of strongly credible parameters and weakly credible parameters to identify and predict faults.

[0035] Correspondingly, the present application also provides a gas dynamic simulation analysis system based on thermodynamic coupling, including,

[0036] The first module is used to collect all reference parameters involved in the ship's turbine equipment, determine the functional relationship and coupling relationship between the reference parameters, and analyze the functional relationship and coupling relationship between the reference parameters to calculate the sensitivity index and uncertainty index of each reference parameter;

[0037] The second module is used to establish a three-dimensional geometric model of the ship's turbine equipment and divide the three-dimensional geometric model into multiple regions according to the structure and function of the ship's turbine equipment;

[0038] The third module is used to statistically calculate the geometric characteristics, reference parameter characteristics, and sensitivity index of the reference parameters of each region to determine the required grid density for the region, perform grid processing on the three-dimensional geometric model based on the grid density, and establish a simulation model of the ship's turbine equipment on the basis of the functional relationship and coupling relationship between the reference parameters and the three-dimensional geometric model;

[0039] The fourth module is used to assign credibility to the reference parameters in the simulation model according to the uncertainty index of the reference parameters, and carry out simulation analysis of the ship's turbine equipment through the simulation model to achieve fault identification and prediction;

[0040] Among them, the reference parameters include one or more of equipment design parameters, mechanical parameters, thermodynamic parameters, fluid mechanics parameters, and electrical parameters.

[0041] The present invention also has the following beneficial effects:

[0042] 1. Determine the functional relationships and coupling relationships among the reference parameters, providing a reliable basis for the subsequent analysis of the characteristics of the reference parameters and the construction of the simulation model. Calculate the sensitivity index and uncertainty index of each reference parameter, and fully consider the characteristics of the reference parameters to enhance the robustness of the accurate description of the simulation model. Statistically analyze the geometric characteristics, reference parameter characteristics, and sensitivity indices of the reference parameters in each region to determine the required grid density for the corresponding region. Set the grid density on different regions of the geometric three-dimensional model according to the different characteristics of the regions and the characteristics of the reference parameters to meet the analysis and calculation requirements of different regions.

[0043] 2. Based on the functional relationships, coupling relationships among the reference parameters, and the three-dimensional geometric model, establish a simulation model of the ship's marine engine equipment. Assign credibility to the reference parameters in the simulation model according to the uncertainty index of the reference parameters. Conduct simulation analysis on the ship's marine engine equipment through the simulation model, reasonably allocate the calculation accuracy of different regions, and meet the complex and changeable working conditions of the equipment. Effectively capture the coupling situation of multiple physics, improve the accuracy and adaptability of the simulation analysis, timely identify and predict the fault conditions of the ship's marine engine equipment, and ensure the normal operation of the equipment. Description of the Drawings

[0044] Figure 1 It is a schematic flow chart of a gas dynamic simulation analysis method based on thermodynamic coupling proposed by the present invention;

[0045] Figure 2 It is a schematic structural diagram of a gas dynamic simulation analysis system based on thermodynamic coupling proposed by the present invention. Detailed Embodiments

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0047] Refer to Figure 1 , a gas dynamic simulation analysis method based on thermodynamic coupling, including the following steps:

[0048] Step S101: Collect all the reference parameters involved in the ship's marine engine equipment, determine the functional relationships and coupling relationships among the reference parameters, and analyze the functional relationships and coupling relationships among the reference parameters to calculate the sensitivity index and uncertainty index of each reference parameter.

[0049] Among them, the reference parameters include one or more of equipment design parameters, mechanical parameters, thermodynamic parameters, fluid mechanics parameters, and electrical parameters.

[0050] In this embodiment, the equipment design parameters (such as cylinder diameter, stroke, etc.), mechanical parameters (such as rotational speed, torque, etc.), thermodynamic parameters (such as temperature, pressure, etc.), fluid mechanics parameters (such as flow rate, flow velocity, etc.), and electrical parameters (such as voltage, current, etc.) involved in the marine engine equipment are collected. To analyze the characteristics of the reference parameters, it is necessary to first determine the functional relationships and mutual coupling relationships involved in the reference parameters. The functional relationship refers to a clear and deterministic correspondence between two or more variables, that is, the value of one variable can be uniquely determined by the values of other variables through a definite mathematical expression or rule. In marine engine equipment, the functional relationship describes the direct and precisely calculable associations between various parameters. For example, the relationships between power and rotational speed, torque, and the relationship between fuel consumption rate and power. The coupling relationship refers to the relationship of mutual influence and interaction between two or more systems, parameters, or physical fields. This relationship is often complex and non-linear. A change in one parameter will not only affect itself but also affect other parameters or systems through a certain mechanism, and at the same time, the changes in other parameters or systems will also affect this parameter in turn. In marine engine equipment, the coupling relationship reflects the mutual association and restriction between various parts inside the equipment and different physical processes. Thermal-mechanical coupling, in a marine engine, the high temperature generated by combustion will cause the engine components to expand thermally, thereby changing the geometric dimensions and mechanical properties of the components, which will affect the mechanical motion and stress distribution of the engine. Conversely, the changes in mechanical motion and stress distribution may also affect the combustion process, such as affecting the gas flow and mixture formation in the cylinder, and further affecting the combustion efficiency and temperature distribution. This mutual influence between heat and force is the thermal-mechanical coupling relationship.

[0051] In some embodiments of the present application, determining the functional relationships and coupling relationships between the reference parameters includes,

[0052] Collecting data samples of the reference parameters, distinguishing simple functional relationships and complex functional relationships. For simple functional relationships, describe the simple functional relationships through physical laws or relevant theoretical laws. For complex functional relationships, determine the initialization of the complex functional relationships through mathematical modeling, and optimize the initialization of the complex functional relationships through the data samples of the reference parameters;

[0053] Analyzing the multi-physical field mechanisms and energy and mass transfer mechanisms involved in the marine engine equipment, establishing a coupling model. The coupling model is a physical model coupling or a numerical simulation coupling, and optimizing the coupling model according to the data samples of the reference parameters.

[0054] In this embodiment, for simple functional relationships, such as the relationship between power P, rotational speed n, and torque T, physical laws can be directly used for description. For complex functional relationships, such as the relationship between the fuel consumption rate of an engine and multiple parameters, first initialize through mathematical modeling, such as using a multiple linear regression model, and then optimize the model parameters using data samples. Coupled model establishment: Analyze multi-physical field mechanisms (such as thermal-mechanical coupling, fluid-structure coupling, etc.) and energy and mass transfer mechanisms, and establish a physical model coupling or numerical simulation coupling model. For example, during the combustion process of an engine, the heat transfer and gas flow are coupled with each other, and a coupled model can be established through finite element software and optimized using data samples.

[0055] In some embodiments of the present application, analyze the functional relationships and coupling relationships between reference parameters to calculate the sensitivity index and uncertainty index of each reference parameter, including

[0056] Screen out the effect parameters that describe the performance or operating status of ship's marine engine equipment among the reference parameters, and establish multiple objective functions between other parameters in the reference parameters and the effect parameters;

[0057] Calculate the correlation between each reference parameter and the corresponding effect parameter in each objective function, obtain the normal value range of each reference parameter, and determine the perturbation amount based on the normal value range and correlation of the reference parameters;

[0058] In the simulation software, perform small perturbations on the reference parameters that bidirectionally perturb each objective function through the perturbation amount. The bidirectional perturbation includes the positive perturbation and negative perturbation of the reference parameters, and statistically calculate the change rates of the effect parameters and reference parameters in the objective function to obtain the central difference as the local sensitivity index;

[0059] For the same objective function, calculate the correlation between different reference parameters, screen out the corresponding relationships of strongly correlated reference parameters, and plot the change of correlation with different value ranges under the corresponding relationship of the reference parameters, so as to determine the standard value range of the reference parameters;

[0060] Use Latin hypercube sampling or Monte Carlo sampling to perform random sampling based on the standard value range of the reference parameters to obtain multiple combinations of reference parameters, and input each combination of reference parameters into the simulation software to obtain the magnitude of the effect parameters on the objective function;

[0061] Evaluate the complexity of each objective function, and select the Sobol index order in combination with the complexity of the objective function and the correlation between different reference parameters under the objective function to determine the global sensitivity index;

[0062] Determine the sensitivity index based on the local sensitivity index and global sensitivity index of each reference parameter;

[0063] Describe the uncertainty index of each reference parameter through probability distribution.

[0064] In this embodiment, the effect parameters describing the performance or operating state of the ship's marine engine equipment are screened out, such as the power and efficiency of the engine. Establish the objective function of other parameters and the effect parameters. For example, taking power as the objective function, establish the objective function of parameters such as rotational speed and torque with power. For the sensitivity case, comprehensively determine the sensitivity index by combining local sensitivity and global sensitivity. Calculate the correlation between the reference parameter and the effect parameter in each objective function, and determine the normal value range of the reference parameter. For example, the rotational speed is generally in the range of 1000 - 3000 rpm. Determine the perturbation amount based on the normal value range and the correlation. The stronger the correlation, the larger the proportion. For example, take 5% of the normal range as the perturbation amount. In the simulation software, perform two-way perturbation on each objective function (such as a 5% positive perturbation and a 5% negative perturbation of the rotational speed), and statistically obtain the change rate of the effect parameter and the reference parameter, and obtain the central difference as the local sensitivity index. The reason for performing a small perturbation is that local sensitivity analysis is based on the local linear assumption, that is, within a small neighborhood of the parameter, the relationship between the objective function and the parameter can be approximated as a linear relationship. If a large-range perturbation is adopted, the relationship between the parameter and the objective function may no longer satisfy the linear assumption, resulting in inaccurate calculation results of local sensitivity. A small perturbation can make the change of the objective function caused by the parameter change in the linear or approximately linear region, thus making the calculation process more stable. For example, when calculating the sensitivity of the engine fuel efficiency to the fuel injection amount, a small change in the fuel injection amount will not cause a drastic non-linear change in the working state of the engine, and can more accurately reflect the influence of the parameter near a specific operating point. A large-range perturbation may cause the system to enter the non-linear working region, making the relationship between the objective function and the parameter complex and unstable. For example, if a large-range perturbation is performed on the fuel injection amount of the engine, it may cause significant changes in the combustion process, heat transfer process, etc. of the engine, resulting in the change of the objective function (such as fuel efficiency) no longer following a simple linear relationship, thus making the calculation of local sensitivity meaningless.

[0065] It can be understood that when calculating the change rate of the objective function, an algorithm with good numerical stability is selected. For example, when calculating the difference, use the central difference formula instead of the unilateral difference formula because the central difference formula has higher numerical accuracy and stability.

[0066] In global sensitivity analysis, the value range of sampled parameters should generally be determined based on the physical meaning of the parameters and the actual working conditions. However, it can also be adjusted to a certain extent according to the correlation between different reference parameters in the objective function. Calculate the correlation between different reference parameters, screen out the corresponding relationships of strongly correlated reference parameters. For example, rotational speed and torque are strongly correlated under certain conditions. Plot the curve of the correlation changing with different value ranges to determine the standard value range of the reference parameters. If there is a strong correlation between some parameters (such as fuel injection quantity and intake pressure may be positively correlated), the value range can be appropriately adjusted during sampling to avoid sampling unreasonable parameter combinations. This correlation can be maintained during sampling to improve the rationality of sampling. For strongly correlated parameters, a conditional sampling method can be used. That is, after the value of a certain parameter is determined, the value range of another parameter is determined according to its correlation. The Sobol index method calculates the sensitivity of parameters by decomposing the variance of the objective function. Its order is usually determined by the complexity of the objective function and the interaction between parameters. The complexity of the objective function is calculated by first-order quadratic, first-order second-order, etc. Combine the complexity of the objective function and the correlation between different reference parameters under the objective function to select the Sobol index order. Select the Sobol index order: According to the complexity of the objective function and the interaction between parameters, select a reasonable Sobol index order. Usually start from the first order and gradually increase the order. Determine the sensitivity index based on the local sensitivity index and global sensitivity index of each reference parameter, and comprehensively consider the calculation of the sensitivity index in both directions.

[0067] In some embodiments of the present application, the uncertainty index of each reference parameter is described by a probability distribution, including

[0068] Draw a histogram of each reference parameter, count the statistics of each reference parameter on the histogram, perform a goodness-of-fit test on each reference parameter to determine the distribution type, establish the original probability distribution of each parameter, and establish the joint probability distribution under the corresponding relationship of strongly correlated reference parameters. Determine the uncertainty index of each reference parameter according to the original probability distribution and the joint probability distribution.

[0069] In this embodiment, a data analysis tool (such as the Matplotlib and Seaborn libraries in Python or the ggplot2 package in R language) is used to draw the histogram. Statistics include kurtosis, skewness, etc. that reflect the distribution. According to the shape of the histogram and domain knowledge, initially guess the possible distribution types, such as normal distribution, uniform distribution, exponential distribution, etc. Goodness-of-fit test method:

[0070] Kolmogorov-Smirnov test (K-S test): Used to test whether the data follows a certain specific distribution.

[0071] Shapiro-Wilk test: Applicable to the normality test of small sample data.

[0072] Chi-square test: Used to test whether the distribution of data is consistent with the theoretical distribution.

[0073] Original probability distribution: According to the results of the goodness-of-fit test, determine the most suitable distribution type, such as the normal distribution N.

[0074] Joint probability distribution: For strongly correlated reference parameters, such as rotational speed and torque, establish a joint probability distribution. Methods such as Copula functions or multivariate normal distributions can be used.

[0075] Uncertainty index: Based on the original probability distribution and the joint probability distribution, calculate the uncertainty of the parameters. Commonly used uncertainty indices include:

[0076] Confidence interval: Such as the 95% confidence interval, indicating that there is a 95% probability that the parameter falls within this interval.

[0077] Coefficient of Variation (CV): The ratio of the standard deviation to the mean, reflecting the relative dispersion degree of the data.

[0078] Step S102, establish a three-dimensional geometric model of the marine engine equipment, and divide the three-dimensional geometric model into multiple regions according to the structure and function of the marine engine equipment.

[0079] In this embodiment, the three-dimensional geometric model is divided into multiple structural regions according to the structural components of the marine engine equipment (such as the cylinders, crankshafts of the engine, etc.). Within each structural region, the region is further divided according to functional differences (such as the combustion chamber, cooling channels of the cylinder, etc.).

[0080] In some embodiments of the present application, the three-dimensional geometric model is divided into multiple regions according to the structure and function of the marine engine equipment, including,

[0081] The three-dimensional geometric model is divided into multiple structural regions according to the differences of the structural components of the marine engine equipment, and within each structural region, the region is further divided according to functional differences.

[0082] Step S103, count the geometric features, reference parameter features and sensitive indices of the reference parameters of each region to determine the required grid density corresponding to the region, perform grid processing on the three-dimensional geometric model based on the grid density, and establish a simulation model of the marine engine equipment on the basis of the functional relationship and coupling relationship between the reference parameters and the three-dimensional geometric model.

[0083] In some embodiments of the present application, the grid density required for each region is determined by statistically analyzing the geometric features, reference parameter features, and sensitivity indices of reference parameters in each region, including:

[0084] Evaluating the geometric complexity of each type of geometric feature to generate evaluation indices;

[0085] Based on the functions of marine engine equipment and multiple physical processes, determine the categories of reference parameters involved in each region, construct a set of reference parameters, where each region corresponds to a set of reference parameters, and statistically analyze the physical field characteristics of the reference parameters. Define the calculation accuracy requirement level based on the evaluation indices of geometric features, the physical field characteristics of reference parameters, and the sensitivity indices of reference parameters, and map a grid density according to the calculation accuracy requirement level.

[0086] In this embodiment, the geometric complexity of each type of geometric feature is evaluated. For example, the shape complexity of a cylinder can be evaluated by calculating indices such as the ratio of its surface area to volume. Based on the equipment functions and physical processes, determine the categories of reference parameters involved in each region and construct a set of reference parameters. Statistically analyze the physical field characteristics of the reference parameters, such as the magnitude of the temperature field gradient. Define the calculation accuracy requirement level based on the evaluation indices of geometric features, physical field characteristics, and sensitivity indices, and map the grid density. For example, regions with a large temperature gradient require a higher grid density. The formula for the calculation accuracy requirement level is as follows: ;

[0087] where, is the calculation accuracy requirement level of the th region. Normalize the geometric features and reference parameter features, , are the conversion coefficients of geometric features and reference parameters respectively, , are the number of geometric features and the number of reference parameters of the th region respectively, is the combination weight of the th geometric feature, is the evaluation index of the th region's th geometric feature, is the combination weight of the th reference parameter, is the evaluation index (describing the complexity level) of the th region's th reference parameter that comprehensively considers all reference parameter features, is the sensitivity index of the th region's th reference parameter, is the The first constant of a reference parameter, is the second constant for the th region, [] is the rounding symbol, indicating the correction of the sensitive indicator to the evaluation indicator of the reference parameter. The first constant is used to balance the magnitude of the correction function, and the second constant is used to balance the magnitude of the calculation accuracy requirement level. The three-dimensional geometric model is meshed according to the determined grid density, and software such as ANSYS Meshing can be used.

[0088] Step S104, assign credibility to the reference parameters in the simulation model according to the uncertainty indicators of the reference parameters, and perform simulation analysis on the ship's turbine equipment through the simulation model to achieve fault identification and prediction;

[0089] In some embodiments of the present application, assigning credibility to the reference parameters in the simulation model according to the uncertainty indicators of the reference parameters includes,

[0090] Statistically calculate the maximum value, minimum value, mode value, and median value of the uncertainty indicators of all reference parameters, calculate the distances between the mode value and the maximum value and the minimum value respectively, and take the minimum distance of the two distances as the proximity distance. Adjust the median value towards the maximum value or the minimum value through the proximity distance to obtain a standard threshold;

[0091] Distinguish strong credible parameters and weak credible parameters among the reference parameters through the standard threshold, and use two mapping relationships to determine the credibility of strong credible parameters and weak credible parameters respectively.

[0092] In this embodiment, statistically calculating the maximum value, minimum value, mode value, and median value of the uncertainty indicators of all reference parameters can comprehensively understand the distribution of the uncertainty indicators. The maximum value and the minimum value can reflect the extreme range of the uncertainty indicators, the mode value reflects the most common value of the uncertainty indicators, and the median value is in the middle position of the data and is not affected by extreme values. By comprehensively considering these statistics, the overall characteristics of the uncertainty indicators can be grasped more accurately. Calculating the distances between the mode value and the maximum value and the minimum value, and taking the minimum distance as the proximity distance, this step is to find a reasonable boundary for subsequent adjustment of the median value. The proximity distance reflects the relative proximity between the mode value and the extreme values, and different proximity distances correspond to different adjustment coefficients, which are corrected in the form of the product of the adjustment coefficient and the median value. The mapping relationships corresponding to strong credible parameters and weak credible parameters are different. By distinguishing strong credible parameters and weak credible parameters through the standard threshold, the reference parameters of the ship's turbine equipment can be classified and managed. Strong credible parameters mean that their uncertainty indicators are low and the data reliability is high; weak credible parameters are the opposite, with high uncertainty indicators and low data reliability.

[0093] In some embodiments of the present application, performing simulation analysis on the ship's turbine equipment through the simulation model includes,

[0094] Set boundary conditions and initial conditions on the simulation model, and fuse the credibility of strongly credible parameters and weakly credible parameters to identify and predict faults.

[0095] In this embodiment, boundary conditions (such as intake pressure, exhaust pressure, etc.) and initial conditions (such as initial temperature, rotational speed, etc.) are set on the simulation model. The credibility of strongly credible parameters and weakly credible parameters is fused, for example, different weights are assigned to parameters with different credibility levels in the simulation calculation. The simulation model is run, and the simulation results are analyzed, such as abnormal changes in parameters such as temperature and pressure. Combining sensitive indicators and uncertainty indicators to identify and predict faults. For example, when the cylinder temperature exceeds the normal range, the sensitive indicator is high, and the uncertainty indicator is low, it may indicate a cylinder fault.

[0096] Correspondingly, the present application also provides a gas dynamic simulation analysis system based on thermodynamic coupling, as Figure 2 shown, including,

[0097] The first module is used to collect all reference parameters involved in the ship's marine engine equipment, determine the functional relationship and coupling relationship between the reference parameters, analyze the functional relationship and coupling relationship between the reference parameters, and calculate the sensitive indicator and uncertainty indicator of each reference parameter;

[0098] The second module is used to establish a three-dimensional geometric model of the ship's marine engine equipment and divide the three-dimensional geometric model into multiple regions according to the structure and function of the ship's marine engine equipment;

[0099] The third module is used to count the geometric characteristics, reference parameter characteristics, and sensitive indicators of the reference parameters of each region to determine the required grid density corresponding to the region, perform grid processing on the three-dimensional geometric model based on the grid density, and establish a simulation model of the ship's marine engine equipment on the basis of the functional relationship and coupling relationship between the reference parameters and the three-dimensional geometric model;

[0100] The fourth module is used to assign credibility to the reference parameters in the simulation model according to the uncertainty indicators of the reference parameters, and perform simulation analysis on the ship's marine engine equipment through the simulation model to achieve fault identification and prediction;

[0101] Among them, the reference parameters include one or more of equipment design parameters, mechanical parameters, thermodynamic parameters, fluid mechanics parameters, and electrical parameters.

[0102] The present invention also has the following beneficial effects:

[0103] 1. Determine the functional relationships and coupling relationships among the reference parameters, providing a reliable basis for the subsequent characteristic analysis of the reference parameters and the construction of the simulation model. Calculate the sensitivity index and uncertainty index of each reference parameter, and fully consider the characteristics of the reference parameters to enhance the robustness of the accurate description of the simulation model. Statistically analyze the geometric characteristics, reference parameter characteristics, and sensitivity indices of the reference parameters in each region to determine the required grid density for the corresponding region. Set the grid density on different regions of the geometric 3D model according to the different characteristics of the regions and the characteristics of the reference parameters, so as to meet the analysis and calculation requirements of different regions.

[0104] 2. Based on the functional relationships and coupling relationships among the reference parameters and the 3D geometric model, establish a simulation model of the marine engine equipment. Assign credibility to the reference parameters in the simulation model according to the uncertainty index of the reference parameters. Conduct simulation analysis on the marine engine equipment through the simulation model, reasonably allocate the calculation accuracy of different regions, and meet the complex and changeable working conditions of the equipment. Effectively capture the coupling situation of multiple physics, improve the accuracy and adaptability of the simulation analysis, timely identify and predict the fault situation of the marine engine equipment, and ensure the normal operation of the equipment.

[0105] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard disk, etc.), including several instructions for causing a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0106] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the drawings are not necessarily essential for implementing the present invention.

[0107] Those skilled in the art can understand that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more systems different from the present implementation scenario. The modules in the above implementation scenario can be combined into one module, or further split into multiple sub-modules.

[0108] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A gas dynamic simulation analysis method based on thermodynamic coupling, characterized in that including collecting all reference parameters involved in marine engine equipment, determining the functional relationships and coupling relationships between the reference parameters, and analyzing the functional relationships and coupling relationships between the reference parameters to calculate the sensitivity index and uncertainty index of each reference parameter; establishing a three-dimensional geometric model of the marine engine equipment and dividing the three-dimensional geometric model into multiple regions according to the structure and function of the marine engine equipment; statistically analyzing the geometric characteristics, reference parameter characteristics, and sensitivity index of reference parameters in each region to determine the required grid density for the corresponding region, performing grid processing on the three-dimensional geometric model based on the grid density, and establishing a simulation model of the marine engine equipment based on the functional relationships and coupling relationships between the reference parameters and the three-dimensional geometric model; assigning credibility to the reference parameters in the simulation model according to the uncertainty index of the reference parameters, and performing simulation analysis on the marine engine equipment through the simulation model to achieve fault identification and prediction; wherein, the reference parameters include one or more of equipment design parameters, mechanical parameters, thermodynamic parameters, fluid mechanics parameters, and electrical parameters.

2. The gas dynamic simulation analysis method based on thermodynamic coupling according to claim 1, characterized in that Determining the functional relationships and coupling relationships between the reference parameters, including collecting data samples of the reference parameters, distinguishing simple functional relationships and complex functional relationships. For simple functional relationships, describe the simple functional relationships through physical laws or relevant theoretical laws. For complex functional relationships, determine the initialization of the complex functional relationships through mathematical modeling, and optimize the initialization of the complex functional relationships through the data samples of the reference parameters; analyzing the multi-physical field mechanism, energy and mass transfer mechanism involved in the marine engine equipment, establishing a coupling model, the coupling model being physical model coupling or numerical simulation coupling, and optimizing the coupling model according to the data samples of the reference parameters.

3. The gas dynamic simulation analysis method based on thermodynamic coupling according to claim 1, characterized in that Analyzing the functional relationships and coupling relationships between the reference parameters to calculate the sensitivity index and uncertainty index of each reference parameter, including screening out the effect parameters that describe the performance or operating state of the marine engine equipment among the reference parameters, and establishing multiple objective functions between other parameters and the effect parameters in the reference parameters; calculating the correlation between each reference parameter and the corresponding effect parameter in each objective function, obtaining the normal value range of each reference parameter, and determining the perturbation amount based on the normal value range and correlation of the reference parameters; in the simulation software, performing small perturbations on the reference parameters of each objective function through the perturbation amount for two-way perturbation, the two-way perturbation including positive perturbation and negative perturbation of the reference parameters, and statistically analyzing the change rates of the effect parameters and reference parameters in the objective function to obtain the central difference as the local sensitivity index; for the same objective function, calculating the correlation between different reference parameters, screening out the corresponding relationships of strongly correlated reference parameters, and plotting the change of the correlation with different value ranges under the corresponding relationship of the reference parameters to determine the standard value range of the reference parameters; performing random sampling based on the standard value range of the reference parameters by using Latin hypercube sampling or Monte Carlo sampling to obtain multiple combinations of reference parameters, and inputting each combination of reference parameters into the simulation software to obtain the magnitude of the effect parameters on the objective function; Evaluate the complexity of each objective function, select the Sobol index order by combining the complexity of the objective function and the correlation between different reference parameters under the objective function, and determine the global sensitivity index; Determine the sensitivity index based on the local sensitivity index and the global sensitivity index of each reference parameter; Describe the uncertainty index of each reference parameter through probability distribution.

4. The gas dynamic simulation analysis method based on thermodynamic coupling according to claim 3, characterized in that Describe the uncertainty index of each reference parameter through probability distribution, including Draw the histogram of each reference parameter, count the statistics of each reference parameter on the histogram, conduct a goodness-of-fit test on each reference parameter to determine the distribution type, establish the original probability distribution of each parameter, and establish the joint probability distribution under the corresponding relationship of strongly correlated reference parameters. Determine the uncertainty index of each reference parameter according to the original probability distribution and the joint probability distribution.

5. The gas dynamic simulation analysis method based on thermodynamic coupling according to claim 1, characterized in that And divide the three-dimensional geometric model into multiple regions according to the structure and function of the ship's engine equipment, including Divide the three-dimensional geometric model into multiple structural regions according to the different structural components of the ship's engine equipment, and re-divide the regions according to the functional differences within each structural region.

6. The gas dynamic simulation analysis method based on thermodynamic coupling according to claim 1, characterized in that Statistically analyze the geometric characteristics, reference parameter characteristics, and sensitivity indicators of reference parameters in each region to determine the mesh density required for the corresponding region, including Evaluate the geometric complexity of each type of geometric feature and generate evaluation indicators; Confirm the category of reference parameters involved in each region according to the functions of the ship's engine equipment and multiple physical processes, construct a set of reference parameters, each region corresponds to a set of reference parameters, and statistically analyze the physical field characteristics of the reference parameters. Define the calculation accuracy requirement level based on the evaluation indicators of geometric characteristics, the physical field characteristics of reference parameters, and the sensitivity indicators of reference parameters, and map a mesh density according to the calculation accuracy requirement level.

7. The gas dynamic simulation analysis method based on thermodynamic coupling according to claim 1, characterized in that Assign credibility to the reference parameters in the simulation model according to the uncertainty index of the reference parameters including Statistically analyze the maximum value, minimum value, mode value, and median value of the uncertainty indicators of all reference parameters, calculate the distances between the mode value and the maximum value and the minimum value respectively, and take the minimum distance of the two distances as the proximity distance. Adjust the median value towards the maximum value or the minimum value through the proximity distance to obtain a standard threshold; Distinguish the reference parameters as strongly credible parameters and weakly credible parameters through the standard threshold, and use two mapping relationships to determine the credibility of the strongly credible parameters and the weakly credible parameters respectively.

8. The gas dynamic simulation analysis method based on thermodynamic coupling according to claim 7, characterized in that Conduct simulation analysis on the ship's engine equipment through the simulation model including Set boundary conditions and initial conditions on the simulation model, and fuse the credibility of strongly credible parameters and weakly credible parameters to identify and predict faults.

9. A gas dynamic simulation analysis system based on thermodynamic coupling, characterized in that, including The first module is used to collect all reference parameters involved in the ship's engine equipment, determine the functional relationship and coupling relationship between the reference parameters, and analyze the functional relationship and coupling relationship between the reference parameters to calculate the sensitivity index and uncertainty index of each reference parameter; The second module is used to establish a three-dimensional geometric model of the ship's engine equipment and divide the three-dimensional geometric model into multiple regions according to the structure and function of the ship's engine equipment; The third module is used to count the geometric features, reference parameter features, and sensitivity indicators of reference parameters for each region to determine the required grid density corresponding to the region, perform meshing on the three-dimensional geometric model based on the grid density, and establish a simulation model of the marine engine equipment on the basis of the functional relationship and coupling relationship between reference parameters and the three-dimensional geometric model; The fourth module is used to assign credibility to the reference parameters in the simulation model according to the uncertainty indicators of the reference parameters, and perform simulation analysis on the marine engine equipment through the simulation model, so as to achieve fault identification and prediction; Among them, the reference parameters include one or more of equipment design parameters, mechanical parameters, thermodynamics parameters, fluid mechanics parameters, and electrical parameters.

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