A performance prediction method for aviation fuel gear pump based on lumped parameters

Abstract

The present invention discloses a method for predicting the performance of an aviation fuel gear pump based on concentrated parameters, which relates to the field of aviation hydraulic simulation technology. The method comprises dividing the fluid working chamber according to the geometric structure of the fuel gear pump, defining the flow exchange relationship and pressure change relationship between each fluid working chamber, and establishing a coupled calculation path to form a basic prediction model; extracting the biofuel blending ratio and water content parameters based on real-time input fuel component parameters, triggering corresponding correction rules by comparing with a preset blending ratio threshold, generating a compensation coefficient, and combining the water content parameter and the compensation coefficient to generate an elastic modulus parameter; inputting preset operating condition parameters and the elastic modulus parameter into the basic prediction model, calculating mass flow exchange data through the flow exchange relationship, and driving the pressure change relationship to solve and output an initial pressure distribution vector. The present invention solves the problem of static correction distortion of physical property parameters under variable component operating conditions through the dynamic generation step of the elastic modulus parameter.

Description

Technical Field

[0001] The invention relates to the technical field of aviation hydraulic simulation, in particular to a method for predicting the performance of an aviation fuel gear pump based on concentrated parameters. Background Art

[0002] The current approach to predicting aviation fuel gear pump performance primarily relies on a parallel approach using computational fluid dynamics simulation and lumped parameter models. Computational fluid dynamics methods capture microscopic flow characteristics through three-dimensional flow field analysis; lumped parameter models, on the other hand, construct a real-time computational framework based on the fluid network topology, enabling rapid performance evaluation through the mass-momentum conservation equation. In recent years, multiscale coupled modeling has become a research hotspot, integrating localized, refined simulation results into system-level models to balance accuracy and efficiency.

[0003] Existing prediction methods have some shortcomings. The real-time mapping mechanism of dynamic physical parameters is missing. Changes in the biofuel blending ratio and water content cause the fuel elastic modulus to exhibit nonlinear attenuation characteristics, resulting in deviations in volumetric efficiency prediction and distortion of pressure boundary conditions. In addition, the leakage-pressure transient coupling mechanism is broken, and the nonlinear relationship between leakage flow and pressure difference under high-pressure conditions is simplified to a constant gain coefficient, which leads to an increase in the volumetric efficiency prediction error in the high-pressure area, causing flow pulsation phase lag and distortion in the calculation of the unevenness coefficient. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an aviation fuel gear pump performance prediction method based on lumped parameters to solve the problems of physical parameter mapping distortion and insufficient coupling accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a performance prediction method for an aviation fuel gear pump based on lumped parameters, comprising: dividing the fluid working chamber according to the geometric structure of the fuel gear pump, defining the flow exchange relationship and pressure change relationship between each fluid working chamber, and establishing a coupled calculation path to form a basic prediction model; extracting the biofuel blending ratio and water content parameters based on the real-time input fuel component parameters, triggering the corresponding correction rule by comparing with a preset blending ratio threshold, generating a compensation coefficient, and generating an elastic modulus parameter by combining the water content parameter and the compensation coefficient; inputting the preset operating condition parameters and the elastic modulus parameters into the basic prediction model, calculating the mass flow exchange data through the flow exchange relationship, driving the pressure change relationship to be solved, and outputting an initial pressure distribution vector; inputting the real-time pressure difference between the initial pressure distribution vector and the measured pressure value into a nonlinear saturation function to generate a leakage flow compensation amount, inputting the leakage flow compensation amount into a constructed leakage-pressure response model, and outputting updated mass flow exchange data and a final pressure distribution vector; and calculating the volumetric efficiency value, flow unevenness coefficient, and pressure pulsation peak-to-peak value in real time based on the updated mass flow exchange data and the final pressure distribution vector to generate a performance evaluation report.

[0008] As a preferred solution of the aviation fuel gear pump performance prediction method based on lumped parameters of the present invention, the forming of the basic prediction model comprises the following specific steps:

[0009] Based on the geometric structure of the fuel gear pump, the working chamber for oil suction fluid, the working chamber for oil discharge fluid and the working chamber for inter-tooth fluid are divided;

[0010] Define the fuel suction mass transfer rule from the oil suction fluid working chamber to the inter-tooth fluid working chamber; define the high-pressure discharge mass transfer rule from the inter-tooth fluid working chamber to the oil discharge fluid working chamber and the leakage mass transfer rule between adjacent inter-tooth fluid working chambers;

[0011] The fuel intake mass transfer rule, high-pressure discharge mass transfer rule, and leakage mass transfer rule are defined as flow exchange relationships; the pressure response rule in each fluid working chamber is defined as a pressure change relationship;

[0012] A one-way data-driven chain of flow exchange relationship and pressure transformation relationship is defined to form a coupled calculation path. The flow exchange relationship, pressure change relationship and coupled calculation path are integrated to build a basic prediction model.

[0013] As a preferred solution of the aviation fuel gear pump performance prediction method based on concentrated parameters of the present invention, the triggering corresponding correction rule comprises the following specific steps:

[0014] Obtain fuel component parameters and separate biofuel blending ratio and water content parameters;

[0015] If the biofuel blending ratio exceeds the preset blending ratio threshold, the high blending ratio correction rule is triggered; if the biofuel blending ratio does not exceed the preset blending ratio threshold, the low blending ratio correction rule is triggered.

[0016] As a preferred solution of the aviation fuel gear pump performance prediction method based on lumped parameters of the present invention, the specific steps of generating the elastic modulus parameter are as follows:

[0017] Generate corresponding compensation coefficients according to different correction rules;

[0018] The moisture attenuation factor is calculated based on the moisture content parameter, and the elastic modulus parameter is generated by combining the moisture attenuation factor and the compensation coefficient with a preset reference elastic modulus.

[0019] As a preferred solution of the aviation fuel gear pump performance prediction method based on lumped parameters of the present invention, the output of the initial pressure distribution vector is specifically performed as follows:

[0020] Extract the inlet and outlet pressures and gear speed from the preset working condition parameters and input them into the basic prediction model in combination with the elastic modulus parameters;

[0021] Calculate the fuel intake mass flow, high-pressure discharge mass flow and leakage mass flow through the flow exchange relationship, and generate mass flow exchange data;

[0022] The mass flow exchange data is input into the pressure change relationship, the pressure differential equation of each fluid working chamber is solved based on the pressure change relationship, and the initial pressure distribution vector is output.

[0023] As a preferred solution of the aviation fuel gear pump performance prediction method based on lumped parameters of the present invention, the specific steps of generating the leakage flow compensation amount are as follows:

[0024] The measured pressure value of the outlet pipeline of the oil discharge fluid working chamber is monitored in real time by the pressure sensor, and the real-time pressure difference value is calculated based on the initial pressure distribution vector;

[0025] Substitute the real-time pressure difference into the nonlinear saturation function to generate the leakage flow compensation value.

[0026] As a preferred embodiment of the aviation fuel gear pump performance prediction method based on lumped parameters of the present invention, the output of updated mass flow exchange data and final pressure distribution vector is carried out in the following steps:

[0027] The input layer is constructed based on the leakage flow compensation and mass flow exchange data, the processing layer is constructed based on the orifice flow formula, and the output layer is constructed based on the pressure distribution rule;

[0028] A leakage-pressure response model is constructed based on the input layer, processing layer and output layer. The leakage flow compensation amount and mass flow exchange data are input into the leakage-pressure response model. The updated mass flow exchange data and the final pressure distribution vector are generated through static calculation of the orifice pressure drop.

[0029] As a preferred solution of the aviation fuel gear pump performance prediction method based on concentrated parameters of the present invention, the specific steps of generating the performance evaluation report are as follows:

[0030] Extracting actual output flow, theoretical displacement and high-pressure discharge mass flow time series data from the updated mass flow exchange data;

[0031] The volumetric efficiency value is calculated based on the actual output flow and theoretical displacement, and the flow unevenness coefficient is calculated through the high-pressure discharge mass flow time series data;

[0032] The peak-to-peak value of pressure pulsation is calculated using the final pressure distribution vector, and the volumetric efficiency value, flow unevenness coefficient, and peak-to-peak value of pressure pulsation are integrated into a performance evaluation report.

[0033] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the aviation fuel gear pump performance prediction method based on centralized parameters as described in the first aspect of the present invention is implemented.

[0034] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the aviation fuel gear pump performance prediction method based on concentrated parameters as described in the first aspect of the present invention is implemented.

[0035] The beneficial effects of the present invention are as follows: through the dynamic generation step of elastic modulus parameters, accurate mapping of the nonlinear attenuation characteristics of the biofuel blending ratio and water content on the fuel compression characteristics is achieved, solving the problem of static correction distortion of physical parameters under variable component conditions; through the leakage-pressure transient coupling step, combined with the nonlinear saturation function to suppress and compensate overshoot, real-time phase synchronization of high-frequency pressure pulsation and leakage flow is achieved, solving the problem of insufficient leakage compensation accuracy of traditional linear models in high-pressure areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 The flowchart of the aviation fuel gear pump performance prediction method based on lumped parameters.

[0038] Figure 2 Flowchart for building a basic prediction model.

[0039] Figure 3 Flowchart for outputting the initial pressure distribution vector.

[0040] Figure 4 Flowchart for generating performance evaluation reports. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0044] Reference Figure 1 , is an embodiment of the present invention, which provides an aviation fuel gear pump performance prediction method based on concentrated parameters, comprising the following steps:

[0045] S1, divide the fluid working chamber according to the geometric structure of the fuel gear pump, define the flow exchange relationship and pressure change relationship between each fluid working chamber, and establish a coupling calculation path to form a basic prediction model. For details, please refer to Figure 2 :

[0046] S1.1, based on the geometric structure of the fuel gear pump, the working chambers for suction fluid, discharge fluid, and inter-gear fluid are divided;

[0047] It should be noted that, based on the internal contour of the fuel gear pump housing and the meshing position of the gear pair, the inlet flange connection area is divided into an oil suction fluid working chamber, and the boundary of the oil suction fluid working chamber is jointly defined by the inlet end face of the fuel gear pump body, the gear tooth top circle and the end cover sealing surface; the outlet flange connection area is defined as an oil discharge fluid working chamber, and the boundary of the oil discharge fluid working chamber is determined by the outlet end face of the fuel gear pump body, the gear tooth top circle and the high-pressure sealing groove; through the analysis of the meshing motion trajectory of the gear pair, the periodic change area of ​​the inter-tooth volume of the driving gear and the driven gear is divided into an inter-tooth fluid working chamber, and the boundary of the inter-tooth fluid working chamber is dynamically constrained by the tooth profile surface, the tooth top clearance size and the end face clearance size; thus, the geometric space division result of the three types of chambers is formed.

[0048] S1.2, define the fuel intake mass transfer rules from the oil suction fluid working chamber to the inter-tooth fluid working chamber; define the high-pressure discharge mass transfer rules from the inter-tooth fluid working chamber to the oil discharge fluid working chamber, and define the leakage mass transfer rules between adjacent inter-tooth fluid working chambers;

[0049] It should be noted that the fuel intake mass transfer rule is defined as follows: the fuel mass transfer amount from the suction fluid working chamber to the inter-tooth fluid working chamber is driven by the gear meshing line moving speed and the inlet pressure difference, the transfer rate is proportional to the volume expansion rate of the inter-tooth fluid working chamber, and the instantaneous suction mass flow rate is defined by the gear speed and tooth profile geometry parameters; the gear meshing line moving speed is obtained by calibrating the gear base circle radius and the real-time speed; the inlet pressure difference refers to the real-time difference between the suction fluid working chamber pressure and the inter-tooth fluid working chamber pressure; the volume expansion rate of the inter-tooth fluid working chamber is generated by calibrating the gear pair's tooth profile geometry parameters and the real-time speed; the flow coefficient is a constant determined by calibration experiments, which is used to correct the geometric efficiency loss of the suction flow area, and its value is determined by the shape and surface roughness of the suction orifice;

[0050] The high-pressure discharge mass transfer rule is defined as follows: the fuel mass transfer from the inter-tooth fluid working chamber to the oil discharge fluid working chamber is driven by the gear rotation displacement effect, and the transfer rate is defined by the inter-tooth fluid working chamber volume contraction rate and the outlet pressure gradient. The gear rotation displacement effect refers to the phenomenon in which the active gear teeth push the fuel to produce forced displacement, and the gear rotation displacement force is defined by the gear base circle radius and rotational speed. The inter-tooth fluid working chamber volume contraction rate refers to the decrease in the inter-tooth fluid working chamber volume per unit time, and is obtained by the time derivative of the inter-tooth fluid working chamber volume function. The outlet pressure gradient refers to the rate of change of pressure between the outlet flange and the downstream pipeline, and is defined by the real-time difference between the oil discharge fluid working chamber pressure and the external pipeline pressure, and the flow path length.

[0051] The leakage mass transfer rule is defined as follows: the lateral leakage mass transfer amount of the fluid working chamber between adjacent teeth is determined by the tooth top clearance pressure difference and the end face clearance flow resistance. The transfer rate follows the laminar leakage equation, and the leakage mass flow rate is generated by the tooth top clearance size, the end face clearance size and the pressure difference between adjacent chambers; the tooth top clearance pressure difference refers to the real-time pressure difference between the fluid working chambers between adjacent teeth; the end face clearance flow resistance refers to the flow resistance characteristics of the fuel when it flows through the gap between the gear end face and the fuel gear pump housing, which is jointly determined by the end face clearance size, gap length and fuel dynamic viscosity.

[0052] S1.3, define the fuel intake mass transfer rule, high-pressure discharge mass transfer rule, and leakage mass transfer rule as flow exchange relationships; define the pressure response rule within each fluid working chamber as a pressure change relationship;

[0053] It should be noted that based on the fuel intake mass transfer rule, high-pressure discharge mass transfer rule, and leakage mass transfer rule, the three types of transfer rules are integrated into a flow exchange relationship to describe the dynamic calculation logic of the intake mass flow from the oil suction fluid working chamber to the inter-tooth fluid working chamber, the discharge mass flow from the inter-tooth fluid working chamber to the oil discharge fluid working chamber, and the leakage mass flow of the adjacent inter-tooth fluid working chambers;

[0054] The pressure response rules in each fluid working chamber include the fluid compression effect. When the net inflow mass flow enters the fluid working chamber, the fuel is compressed, causing the pressure to rise. The pressure change rate is proportional to the net inflow flow and inversely proportional to the chamber volume; the volume dilution effect. When the volume of the fluid working chamber expands, the fuel expands, causing the pressure to drop. The pressure change rate is inversely proportional to the volume expansion rate and inversely proportional to the chamber volume. The pressure response rule is defined as the pressure change relationship.

[0055] S1.4, define the one-way data-driven chain of flow exchange relationship and pressure transformation relationship, form a coupled calculation path, integrate and divide the fluid working chamber, flow exchange relationship, pressure change relationship and coupled calculation path, and build a basic prediction model.

[0056] It should be noted that based on the flow exchange relationship and the pressure change relationship, a one-way data-driven chain of the flow exchange relationship and the pressure change relationship is established, and the suction mass flow, discharge mass flow, and leakage mass flow output by the flow exchange relationship are clearly defined as input parameters of the pressure change relationship. The solution of the pressure change relationship is prohibited from being reversely transferred to the flow exchange relationship, forming an irreversible coupling calculation path.

[0057] The mathematical framework of the basic prediction model is constructed by integrating the geometric constraints of the fluid working chamber division, the mass flow calculation capability of the flow exchange relationship, the pressure response solution capability of the pressure change relationship, and the calculation flow control of the one-way data drive chain. The fluid working chamber division provides volume expansion rate and volume contraction rate, the flow exchange relationship generates three types of mass flow in real time, and the pressure change relationship solves the pressure differential equation based on the input mass flow. The one-way data drive chain ensures the strict progression of the calculation sequence, forming a basic prediction model with executable centralized parameters.

[0058] Training of the basic prediction model: Based on the geometric structural parameters of the fuel gear pump (number of teeth, module, tooth width and center distance), the gear meshing kinematic formula is used to obtain the inter-tooth fluid working chamber volume change function, and a chamber volume data sequence is generated through the inter-tooth fluid working chamber volume change function; a high-fidelity data set is generated through the chamber volume data sequence using a fluid mechanics simulation method, and the high-fidelity data set is used to calibrate the flow coefficient in the flow exchange relationship and the fuel dynamic viscosity in the leakage equation; the calibration parameters are imported into the flow exchange relationship to generate three types of mass flow and input the pressure change relationship to solve the simulated pressure value; by comparing the deviation between the simulated pressure value and the high-fidelity data, the geometric parameters in the leakage mass transfer rule (such as the tooth top clearance size and the end face clearance size) are optimized, and iterative updates are performed until the deviation between the simulated pressure value and the high-fidelity data does not exceed the target deviation threshold (defined based on the aviation fuel pump performance standard); completing the training and parameter calibration of the basic prediction model.

[0059] S2, based on the real-time input fuel component parameters, extracts the biofuel blending ratio and water content parameters, triggers the corresponding correction rule by comparing with the preset blending ratio threshold, generates the compensation coefficient, and combines the water content parameter and the compensation coefficient to generate the elastic modulus parameter. For details, please refer to Figure 3 :

[0060] S2.1, obtaining the real-time input fuel component parameters and separating the biofuel blending ratio and water content parameters;

[0061] It should be noted that the data stream of fuel component parameters input in real time is received, the hydrocarbon components in the fuel are separated through near-infrared spectroscopy analysis, the intensity value of the characteristic absorption peak of biofuel is extracted and converted into the biofuel blending ratio: the blending ratio-absorbance standard curve is generated by fitting the spectral data of standard samples with known biofuel blending ratios of 0%-100%, the slope and intercept of the blending ratio-absorbance standard curve are extracted, and the characteristic absorption peak intensity value, slope and intercept of the biofuel are substituted into the linear regression equation to calculate the biofuel blending ratio. The expression is:

[0062] ;

[0063] in, represents the biofuel blending ratio; represents the slope; Indicates the intensity value of the characteristic absorption peak of biofuel; represents the intercept;

[0064] Fuel samples with a gradient of water content (0%, 1%, 2%, 3%, 4%, and 5%) are prepared at a constant temperature (typically 20°C, representing the global average ambient temperature and a typical operating condition) and the dielectric constant of each fuel sample at each water content is measured using a dielectric constant sensor. This yields a set of dielectric constant-water content data points. A nonlinear least squares method is then used to fit the dielectric constant-water content data points, solving for the proportional coefficient, exponential coefficient, and offset in the exponential equation.

[0065] A dielectric constant sensor is used to detect the fuel dielectric constant in the current fuel component parameter data stream, and the exponential equation is applied to calculate the water content percentage to generate the water content parameter. The expression is:

[0066] ;

[0067] in, Indicates the water content parameter; represents the proportionality coefficient; represents the exponential coefficient; Indicates the dielectric constant of fuel; Indicates the offset.

[0068] S2.2, if the biofuel blending ratio exceeds a preset blending ratio threshold, a high blending ratio correction rule is triggered; if the biofuel blending ratio does not exceed the preset blending ratio threshold, a low blending ratio correction rule is triggered; corresponding compensation coefficients are generated according to different correction rules;

[0069] It should be noted that the blending ratio threshold is calibrated at 10% according to the civil aviation fuel pump airworthiness certification standard; the biofuel blending ratio is compared with the blending ratio threshold (10%) in real time. If the biofuel blending ratio exceeds the blending ratio threshold, the high blending ratio correction rule is triggered; if the biofuel blending ratio does not exceed the blending ratio threshold, the low blending ratio correction rule is triggered;

[0070] The high blending ratio correction rule refers to a mechanism that dynamically increases the compensation coefficient based on the proportion by which the biofuel blending ratio exceeds the blending ratio threshold. Specifically, fuel samples with increasing biofuel blending ratios (e.g., 12%, 15%, and 18%) are configured in the experiment. The volumetric efficiency loss rate of each fuel sample is measured using a flow sensor. The ratio of the incremental volumetric efficiency loss rate of adjacent fuel samples to the incremental biofuel blending ratio is calculated as the flow gain coefficient. The volumetric efficiency of the current biofuel blending ratio is measured using a flow sensor and marked as the measured volumetric efficiency. The theoretical volumetric efficiency is 100% (corresponding to the ideal displacement under zero-leakage conditions). The difference between the theoretical and measured volumetric efficiencies is the base flow compensation. The product of the difference between the biofuel blending ratio and the blending ratio threshold and the flow gain coefficient is added to the base flow compensation to generate the compensation coefficient. The expression is:

[0071] ;

[0072] ;

[0073] ;

[0074] in, represents the compensation coefficient; Indicates the basic flow compensation amount; represents the flow gain coefficient; represents the biofuel blending ratio; Indicates the mixing ratio threshold; represents the measured volumetric efficiency; Indicates the Volumetric efficiency loss rate of each fuel sample; Indicates the Volumetric efficiency loss rate of each fuel sample; Indicates the index of the fuel sample; Indicates the Biofuel blending ratio of each fuel sample; Indicates the Biofuel blending ratio of each fuel sample;

[0075] The low-blending ratio correction rule refers to a mechanism that uses a fixed value determined through calibration experiments as a compensation coefficient when the biofuel blending ratio does not exceed the blending ratio threshold. Specifically, fuel samples with blending ratio gradients of 0%, 5%, and 10% are prepared in the laboratory. The volumetric efficiency loss rate of each fuel sample under standard operating conditions is measured using a flow sensor. The arithmetic mean of the loss rates of all fuel samples in the 0%-10% biofuel blending ratio range is calculated as a constant compensation value, which is the compensation coefficient.

[0076] It should also be noted that standard operating conditions refer to the mandatory specification conditions for aviation fuel pump performance testing, including temperature of 20±0.5℃, inlet pressure of 0.1MPa and outlet pressure of 5.0MPa.

[0077] S2.3, calculating a moisture attenuation factor based on the moisture content parameter, and combining the moisture attenuation factor and the compensation coefficient with a preset reference elastic modulus to generate an elastic modulus parameter.

[0078] It should be noted that the water content attenuation factor is generated based on the water content parameter using the attenuation factor calculation formula. The bulk elastic modulus value measured by the resonant cavity method under standard operating conditions for pure mineral jet fuel (no bio-blends and 0% water content) is the baseline elastic modulus, which characterizes the compressive strength of pure mineral jet fuel in the absence of impurities. The intermediate elastic modulus value is generated using the baseline elastic modulus and the compensation coefficient, and the product of the intermediate elastic modulus value and the water content attenuation factor is used as the elastic modulus parameter, expressed as follows:

[0079] ;

[0080] ;

[0081] ;

[0082] in, represents the elastic modulus parameter; Indicates the middle value of elastic modulus; represents the moisture content attenuation factor; represents the base elastic modulus; represents the water attenuation rate constant, which is defined based on the calibration experiment of gradient water samples.

[0083] S3, inputting the preset working condition parameters and elastic modulus parameters into the basic prediction model, calculating the mass flow exchange data through the flow exchange relationship, and driving the pressure change relationship to solve, and outputting the initial pressure distribution vector;

[0084] S3.1, extracting the inlet and outlet pressures and gear speed from the preset operating parameters, and inputting them into the basic prediction model in combination with the elastic modulus parameters;

[0085] It should be noted that the operating parameters include inlet pressure, outlet pressure and gear speed;

[0086] The inlet pressure refers to the pressure setting value at the inlet flange of the fuel gear pump (e.g. 0.5 MPa), which is defined in accordance with the fuel gear pump pressure pulsation characteristic assessment specification and serves as the initial pressure boundary of the suction fluid working chamber.

[0087] Outlet pressure refers to the rated pressure of the outlet pipeline (e.g. 5.0 MPa), which is defined according to the high-pressure fuel gear pump outlet rated pressure setting specification and serves as the pressure output boundary of the working chamber of the discharge fluid;

[0088] Gear speed refers to the rated rotational speed of the driving gear (e.g. 20,000 rpm), which is defined according to the fuel gear pump nameplate parameters;

[0089] The inlet pressure, outlet pressure, gear speed and elastic modulus parameters are synchronously injected into the basic prediction model, where the inlet pressure and outlet pressure serve as pressure boundary conditions to drive the initial pressure assignment of the oil suction fluid working chamber and the oil discharge fluid working chamber of the basic prediction model. The volume change rate of the inter-tooth fluid working chamber is generated by the gear speed, and the elastic modulus parameters are input into the pressure change relationship as material parameters to form a complete initial parameter set of the basic prediction model.

[0090] S3.2, calculate the fuel intake mass flow, high-pressure discharge mass flow, and leakage mass flow using the flow exchange relationship, and summarize the calculations to generate mass flow exchange data;

[0091] It should be noted that the back pressure reference value is generated by the sealing efficiency coefficient (defined by the high-pressure seal groove size and material) and the outlet pressure, and is assigned to initialize the pressure of the inter-tooth fluid working chamber. The fuel suction mass flow rate from the oil suction fluid working chamber to the inter-tooth fluid working chamber is calculated by the operating parameters and the volume change rate of the inter-tooth fluid working chamber. The expression is:

[0092] ;

[0093] in, Indicates the suction mass flow rate; It represents the flow coefficient; Indicates the suction flow area, which refers to the effective area of ​​the gear meshing line opening, generated by the distance between the tooth top circle and the end cover sealing surface; Indicates inlet pressure; Indicates the initialization of the inter-tooth fluid working chamber pressure, which is the back pressure reference value; Indicates fuel density; Indicates the rate of change of volume of the inter-tooth fluid working chamber; Indicates the volume of the inter-tooth fluid working chamber; Indicates a moment;

[0094] The high-pressure discharge mass flow rate from the inter-tooth fluid working chamber to the oil discharge fluid working chamber is calculated by the outlet pressure gradient and the outlet pressure. The expression is:

[0095] ;

[0096] in, Indicates high-pressure discharge mass flow rate; represents the outlet pressure gradient; Indicates outlet pressure;

[0097] The leakage mass flow rate of the fluid working chamber between adjacent teeth is calculated by the pressure difference between the tooth top gap and the flow resistance of the end face gap. The expression is:

[0098] ;

[0099] in, Indicates leakage mass flow rate; Indicates the size of the tooth top clearance; Indicates the pressure difference in the fluid working chamber between adjacent teeth; Indicates fuel dynamic viscosity; Indicates tooth width; Indicates the end face gap size; Indicates the tooth root circle radius; Indicates the radius of the fuel gear pump housing.

[0100] The suction mass flow, discharge mass flow and leakage mass flow are summarized into mass flow exchange data as a triplet data structure.

[0101] S3.3, input the mass flow exchange data into the pressure change relationship, solve the pressure differential equation of each fluid working chamber based on the pressure change relationship, and output the initial pressure distribution vector.

[0102] It should be noted that the mass flow exchange data is combined with the elastic modulus parameters and the working condition parameters (gear speed) to construct the pressure differential equations of the oil suction fluid working chamber, the oil discharge fluid working chamber, and the inter-tooth fluid working chamber based on the pressure change relationship defined in the basic prediction model;

[0103] The pressure differential equation of the oil suction fluid working chamber is expressed as:

[0104] ;

[0105] in, Indicates the real-time pressure of the oil suction fluid working chamber; Indicates the volume of the oil suction fluid working chamber;

[0106] The pressure differential equation of the inter-tooth fluid working chamber is expressed as:

[0107] ;

[0108] in, Indicates the real-time pressure of the inter-tooth fluid working chamber; Indicates that at a certain moment The volume of the fluid working chamber between the teeth;

[0109] The differential equation of the pressure in the working chamber of the oil discharge fluid is expressed as:

[0110] ;

[0111] in, Indicates the real-time pressure of the working chamber of the discharged fluid; Indicates the volume of the working chamber of the discharged fluid; It represents the mass flow rate of the outlet pipeline, which is defined based on the operating parameters and the high-pressure discharge mass transfer rule;

[0112] The pressure differential equations of the three types of fluid working chambers are solved synchronously by using the fourth-order Runge-Kutta numerical integration method. -5 The calculation is iterated every 10 seconds (based on the gear speed and real-time monitoring requirements) until the pressure change rate is lower than the convergence threshold (such as 0.1%, defined based on the aviation fuel pump safety standard), and the real-time pressure of each fluid working chamber is output as the initial pressure distribution vector.

[0113] S4, inputs the real-time pressure difference between the initial pressure distribution vector and the measured pressure value into the nonlinear saturation function to generate the leakage flow compensation value, inputs the leakage flow compensation value into the constructed leakage-pressure response model, and outputs the updated mass flow exchange data and the final pressure distribution vector. For details, please refer to Figure 4 :

[0114] S4.1, using a pressure sensor to monitor the measured pressure value of the outlet pipeline of the oil discharge fluid working chamber in real time, and calculating and generating a real-time pressure difference value based on the initial pressure distribution vector; substituting the real-time pressure difference value into a nonlinear saturation function to generate a leakage flow compensation value;

[0115] It should be noted that the measured pressure value of the outlet pipeline of the oil discharge fluid working chamber is collected in real time by the pressure sensor (sampling frequency is 50 kHz, based on the sampling frequency defined in the general specification for dynamic monitoring of fluid pressure). According to the real-time pressure of the oil discharge fluid working chamber in the initial pressure distribution vector, the real-time pressure difference between the measured pressure value of the outlet pipeline and the real-time pressure of the oil discharge fluid working chamber is calculated; the real-time pressure difference is input into the nonlinear saturation function, and the leakage flow compensation amount is generated through instantaneous operation of the function mapping relationship. The expression is:

[0116] ;

[0117] in, Indicates the leakage flow compensation amount; Indicates the real-time pressure difference; It represents the pressure gain coefficient, which is calibrated based on the gradient pressure difference experiment and has a fixed value of 0.5; It represents the reference pressure, which is defined based on the fluid mechanics principle of the laminar-turbulent transition critical point and is 20% of the outlet pressure.

[0118] S4.2, constructing an input layer based on leakage flow compensation and mass flow exchange data, constructing a processing layer based on the orifice flow formula, constructing an output layer based on the pressure distribution rule, and constructing a leakage-pressure response model based on the input layer, processing layer, and output layer;

[0119] It should be noted that the leakage flow compensation input port and the mass flow exchange data input port are created, and the distribution node is constructed: the chamber flow distribution matrix is ​​deployed inside the input layer, and the chamber flow distribution matrix contains three dedicated channels: the oil suction chamber-inter-tooth chamber compensation channel, the inter-tooth chamber-inter-tooth chamber leakage channel, and the inter-tooth chamber-oil discharge chamber compensation channel; the chamber flow distribution matrix topology maps the oil suction fluid working chamber, the inter-tooth fluid working chamber, and the oil discharge fluid working chamber; a corrected flow matrix is ​​set at the end of the input layer and output in a three-dimensional tensor format to complete the construction of the input layer;

[0120] A pressure drop conversion black box is set up to completely encapsulate the internal logical calculation process. Only two standardized interfaces are exposed on the outside of the shell: the input interface directly connects to the corrected flow matrix of the input layer; the output interface generates a pressure drop data vector port in the pressure gradient sequence format. The algorithmic basis of the orifice flow formula is declared by embedding an algorithm identifier in the data packet header, and a dynamic elastic modulus pointer is set in the parameter call area to realize on-demand loading of fuel attribute parameters, completing the construction of the processing layer.

[0121] A fluid network topology is constructed based on the spatial relationship between the three fluid working chambers. It includes an oil suction fluid working chamber node (1), an array of inter-tooth fluid working chamber nodes (the number is twice the number of gear pair teeth), and an oil discharge fluid working chamber node (1). Each fluid working chamber node is connected according to unidirectional and bidirectional rules: the oil suction fluid working chamber node → the first inter-tooth fluid working chamber node (unidirectional flow), the inter-tooth fluid working chamber nodes are bidirectionally interconnected, and the last inter-tooth fluid working chamber node → the oil discharge fluid working chamber node (unidirectional flow). A pressure field fusion gateway is deployed to orthogonally mix the pressure drop data vector output by the processing layer with the initial pressure distribution vector through a cross-coupling architecture. A dual-channel separate output port is set (updating the mass flow exchange data interface and the final pressure distribution vector interface).

[0122] The leakage-pressure response model is constructed by connecting the input layer, processing layer and output layer in series through a standardized data interface;

[0123] The no-load working condition test bench was used to measure the leakage mass flow of the tooth top clearance and the end face clearance under no-load working conditions (outlet pressure 5.0MPa and speed 20000rpm). Combined with the gradient pressure difference experiment, the gradient pressure difference sequence of the fluid working chamber between adjacent teeth was synchronously collected in the pressure difference range of 0.1MPa~5.0MPa. Based on the fuel sample with biofuel blending ratio of 0%~20%, the gear speed was changed from 5000~30000. rpm) and variable water content (0%-5%) operating conditions, the dynamic mapping relationship between leakage mass flow and pressure difference is obtained using a positive displacement flowmeter and a dynamic pressure sensor. The leakage mass flow under no-load conditions, the gradient pressure difference sequence, and the leakage mass flow under variable conditions are input into the leakage-pressure response model processing layer. The gain coefficient of the orifice flow formula is fitted using the least squares method, and the weight factor of the pressure balance node is iteratively optimized using the golden section method until the deviation between the updated mass flow exchange data output by the leakage-pressure response model and the measured leakage mass flow is lower than the convergence threshold (e.g., 0.1%, defined based on the aviation fuel pump safety standard), completing the training and parameter solidification of the leakage-pressure response model.

[0124] S4.3, construct a leakage-pressure response model based on the input layer, the processing layer, and the output layer, input the leakage flow compensation amount and the mass flow exchange data into the leakage-pressure response model, and generate updated mass flow exchange data and a final pressure distribution vector through static calculation of the orifice pressure drop.

[0125] It should be noted that the leakage flow compensation amount is allocated to the leakage mass flow item in the mass flow exchange data according to the chamber flow direction to generate a compensated and corrected flow matrix; the corrected flow matrix is ​​input into the input layer of the leakage-pressure response model, and the static pressure drop is calculated through the orifice flow formula encapsulated by the processing layer, and the pressure drop data vector of the adjacent inter-tooth cavity is output; in the output layer, the pressure drop data vector and the initial pressure distribution vector are connected to the fluid network topology structure together, and the updated mass flow exchange data and the final pressure distribution vector are generated through the static transmission of the pressure balance node group.

[0126] S5, through the updated mass flow exchange data and the final pressure distribution vector, calculates the volumetric efficiency value, flow unevenness coefficient and pressure pulsation peak-to-peak value in real time and generates a performance evaluation report.

[0127] S5.1, extracting the actual output flow, theoretical displacement, and high-pressure discharge mass flow time series data from the updated mass flow exchange data;

[0128] It should be noted that the high-pressure discharge mass flow rate is extracted from the updated mass flow exchange data and directly used as the actual output flow rate; the theoretical displacement is generated by combining the volume of the inter-tooth fluid working chamber, the number of teeth, the gear speed, and the fuel density; the high-pressure discharge mass flow rate is selected from the high-pressure discharge mass flow rate to generate the high-pressure discharge mass flow time series data, and the expression for calculating the theoretical displacement is:

[0129] ;

[0130] in, Indicates theoretical displacement; Indicates the number of teeth in the gear pair; Indicates the gear speed; Indicates the unit conversion factor, which converts the speed unit rpm (revolutions per minute) to rps (revolutions per second).

[0131] S5.2, calculate the volumetric efficiency value based on the actual output flow and the theoretical displacement, and calculate the flow non-uniformity coefficient using the high-pressure discharge mass flow time series data;

[0132] It should be noted that the percentage ratio of the actual output flow rate to the theoretical displacement is used as the volumetric efficiency value; based on the high-pressure discharge mass flow time series data, the flow sampling sequence is extracted within the time window of three consecutive meshing cycles, and all sampling points within the time window are scanned in real time by a parallel comparator to obtain the maximum and minimum values ​​of the high-pressure discharge mass flow rate, and the arithmetic mean of the high-pressure discharge mass flow rate within the time window is calculated; the maximum, minimum and arithmetic mean values ​​of the high-pressure discharge mass flow rate are input into the flow unevenness coefficient calculation formula to generate the flow unevenness coefficient, which is expressed as follows:

[0133] ;

[0134] in, It represents the flow unevenness coefficient; Indicates the maximum value of high-pressure discharge mass flow rate; Indicates the minimum value of high-pressure discharge mass flow rate; Indicates the arithmetic mean of the high-pressure discharge mass flow rate.

[0135] S5.3, calculate the peak-to-peak value of pressure pulsation using the final pressure distribution vector, and integrate the volumetric efficiency value, flow non-uniformity coefficient, and peak-to-peak value of pressure pulsation into a performance evaluation report.

[0136] It should be noted that based on the final pressure distribution vector, the entire final pressure distribution vector is scanned by a parallel comparator, the maximum pressure value and the minimum pressure value are extracted, and the difference between the maximum pressure value and the minimum pressure value is used as the peak-to-peak value of the pressure pulsation; the volumetric efficiency value, flow unevenness coefficient and peak-to-peak value of the pressure pulsation are input into the report compilation program, and integrated into a three-axis performance evaluation report including the volumetric efficiency value, flow pulsation intensity and pressure pulsation intensity according to the standard report template.

[0137] This embodiment also provides a computer device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the aviation fuel gear pump performance prediction method based on centralized parameters as proposed in the above embodiment.

[0138] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0139] This embodiment also provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the program implements the aviation fuel gear pump performance prediction method based on centralized parameters proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0140] In summary, the present invention achieves a precise mapping of the nonlinear attenuation characteristics of the biofuel blending ratio and water content on the fuel compression characteristics through the dynamic generation step of the elastic modulus parameters, thus solving the problem of static correction distortion of physical parameters under variable component conditions. Through the leakage-pressure transient coupling step, combined with the nonlinear saturation function to suppress and compensate overshoot, real-time phase synchronization of high-frequency pressure pulsations and leakage flow is achieved, thus solving the problem of insufficient leakage compensation accuracy of traditional linear models in the high-pressure area.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for predicting the performance of an aviation fuel gear pump based on lumped parameters, characterized by: include: Divide the fluid working chambers according to the geometric structure of the fuel gear pump, define the flow exchange relationship and pressure change relationship between each fluid working chamber, and establish a coupling calculation path to form a basic prediction model; Based on the real-time input fuel component parameters, the biofuel blending ratio and water content parameters are extracted. By comparing with the preset blending ratio threshold, the corresponding correction rule is triggered to generate a compensation coefficient. The elastic modulus parameter is generated by combining the water content parameter and the compensation coefficient; Input the preset working condition parameters and elastic modulus parameters into the basic prediction model, calculate the mass flow exchange data through the flow exchange relationship, and drive the pressure change relationship to solve and output the initial pressure distribution vector; Input the real-time pressure difference between the initial pressure distribution vector and the measured pressure value into a nonlinear saturation function to generate a leakage flow compensation value, input the leakage flow compensation value into the constructed leakage-pressure response model, and output updated mass flow exchange data and a final pressure distribution vector; The updated mass flow exchange data and final pressure distribution vector are used to calculate the volumetric efficiency value, flow unevenness coefficient and pressure pulsation peak-to-peak value in real time, and generate a performance evaluation report.

2. The aviation fuel gear pump performance prediction method based on lumped parameters according to claim 1, characterized in that: The specific steps of forming the basic prediction model are as follows: Based on the geometric structure of the fuel gear pump, the working chamber for oil suction fluid, the working chamber for oil discharge fluid and the working chamber for inter-tooth fluid are divided; Define the fuel suction mass transfer rule from the oil suction fluid working chamber to the inter-tooth fluid working chamber; define the high-pressure discharge mass transfer rule from the inter-tooth fluid working chamber to the oil discharge fluid working chamber and the leakage mass transfer rule between adjacent inter-tooth fluid working chambers; The fuel intake mass transfer rule, high-pressure discharge mass transfer rule, and leakage mass transfer rule are defined as flow exchange relationships; the pressure response rule in each fluid working chamber is defined as a pressure change relationship; A one-way data-driven chain of flow exchange relationship and pressure transformation relationship is defined to form a coupled calculation path. The flow exchange relationship, pressure change relationship and coupled calculation path are integrated to build a basic prediction model.

3. The aviation fuel gear pump performance prediction method based on lumped parameters according to claim 2, characterized in that: The triggering corresponding correction rule, the specific steps are as follows: Obtain fuel component parameters and separate biofuel blending ratio and water content parameters; If the biofuel blending ratio exceeds the preset blending ratio threshold, the high blending ratio correction rule is triggered; if the biofuel blending ratio does not exceed the preset blending ratio threshold, the low blending ratio correction rule is triggered.

4. The aviation fuel gear pump performance prediction method based on lumped parameters according to claim 3, characterized in that: The specific steps of generating elastic modulus parameters are as follows: Generate corresponding compensation coefficients according to different correction rules; The moisture attenuation factor is calculated based on the moisture content parameter, and the elastic modulus parameter is generated by combining the moisture attenuation factor and the compensation coefficient with a preset reference elastic modulus.

5. The aviation fuel gear pump performance prediction method based on lumped parameters according to claim 4, characterized in that: The specific steps of outputting the initial pressure distribution vector are as follows: Extract the inlet and outlet pressures and gear speed from the preset working condition parameters and input them into the basic prediction model in combination with the elastic modulus parameters; Calculate the fuel intake mass flow, high-pressure discharge mass flow and leakage mass flow through the flow exchange relationship, and generate mass flow exchange data; The mass flow exchange data is input into the pressure change relationship, the pressure differential equation of each fluid working chamber is solved based on the pressure change relationship, and the initial pressure distribution vector is output.

6. The aviation fuel gear pump performance prediction method based on lumped parameters according to claim 5, characterized in that: The specific steps of generating the leakage flow compensation amount are as follows: The measured pressure value of the outlet pipeline of the oil discharge fluid working chamber is monitored in real time by the pressure sensor, and the real-time pressure difference value is calculated based on the initial pressure distribution vector; Substitute the real-time pressure difference into the nonlinear saturation function to generate the leakage flow compensation value.

7. The aviation fuel gear pump performance prediction method based on lumped parameters according to claim 6, characterized in that: The steps of outputting the updated mass flow exchange data and the final pressure distribution vector are as follows: The input layer is constructed based on the leakage flow compensation and mass flow exchange data, the processing layer is constructed based on the orifice flow formula, and the output layer is constructed based on the pressure distribution rule; A leakage-pressure response model is constructed based on the input layer, processing layer and output layer. The leakage flow compensation amount and mass flow exchange data are input into the leakage-pressure response model. The updated mass flow exchange data and the final pressure distribution vector are generated through static calculation of the orifice pressure drop.

8. The aviation fuel gear pump performance prediction method based on lumped parameters according to claim 7, characterized in that: The specific steps for generating a performance evaluation report are as follows: Extracting actual output flow, theoretical displacement and high-pressure discharge mass flow time series data from the updated mass flow exchange data; The volumetric efficiency value is calculated based on the actual output flow and theoretical displacement, and the flow unevenness coefficient is calculated through the high-pressure discharge mass flow time series data; The peak-to-peak value of pressure pulsation is calculated using the final pressure distribution vector, and the volumetric efficiency value, flow unevenness coefficient, and peak-to-peak value of pressure pulsation are integrated into a performance evaluation report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the aviation fuel gear pump performance prediction method based on lumped parameters according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the aviation fuel gear pump performance prediction method based on lumped parameters according to any one of claims 1 to 8 are implemented.

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