Intake-engine integrated rapid performance prediction method considering circumferential total pressure intake distortion

Abstract

The invention relates to the technical field of aero-engines, in particular to an intake-engine integrated rapid performance prediction method considering circumferential total pressure intake distortion, comprising: acquiring compressor performance diagram data under a uniform intake condition, and based on the compressor performance diagram data, acquiring slope data of compressor total pressure rise and an axial speed curve; acquiring initial velocity distribution data based on the inlet flow field distribution data; generating a fan inlet Mach number based on the initial speed distribution data; determining a volume force coefficient based on the fan inlet Mach number and the Mach number predicted by the pre-constructed fan inlet flow field prediction model; based on the volume force coefficient and the control equation, performance prediction data is generated. According to the method, the fan inlet flow field prediction model under the circumferential total pressure air inlet distortion is deduced and combined with the streamline volume force model, the relation between the Mach number and the volume force coefficient is reconstructed, and then the air inlet-engine integrated performance prediction method considering the circumferential total pressure air inlet distortion is constructed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of aero-engines, and in particular to an integrated intake-engine rapid performance prediction method considering circumferential total pressure intake distortion. Background Art

[0002] Circumferential total pressure intake distortion refers to the uneven circumferential distribution of total airflow pressure at the engine's air inlet. This distortion is often caused by factors such as aircraft maneuvers, air inlet design, or external airflow disturbances. It results in uneven circumferential pressure distribution of the airflow entering the engine, which in turn affects engine performance and stability. Therefore, it is necessary to develop a performance prediction method that accounts for circumferential total pressure intake distortion to quickly and accurately assess its impact on engine performance.

[0003] In related technologies, a volume force model is usually used to predict the impact of intake distortion on the fan. The volume force model effectively simulates the effect of blades on the airflow by simplifying the three-dimensional model of the compressor, thereby improving the accuracy of numerical calculations and reducing the consumption of computing resources. However, since the core of the volume force model is to determine the volume force coefficient, and in the early stages of engine design, the blade geometry has not yet been determined, the volume force coefficient is difficult to obtain accurately, which limits the applicability of the model. Secondly, although the volume force model can predict the flow field redistribution phenomenon under distorted intake conditions, its accuracy still depends on the accurate description of the intake distortion characteristics. If the distribution or intensity of the intake distortion changes, the model needs to be recalibrated, which increases the computational complexity and uncertainty, and ultimately leads to deviations between the predicted results and the actual situation. Summary of the Invention

[0004] In view of this, an exemplary embodiment of the present disclosure provides an intake-engine integrated rapid performance prediction method considering circumferential total pressure intake distortion to solve the problems existing in the related art.

[0005] One aspect of an exemplary embodiment of the present disclosure provides a method for rapid performance prediction of an engine-inlet integration taking into account circumferential total pressure intake distortion, the method comprising:

[0006] Obtaining compressor performance map data under uniform intake conditions, and obtaining slope data of a compressor total pressure rise versus axial velocity curve based on the compressor performance map data;

[0007] Based on the inlet flow field distribution data, the initial velocity distribution data at the fan inlet position is obtained;

[0008] generating a fan inlet Mach number based on the initial velocity distribution data;

[0009] Determining a volume force coefficient based on the fan inlet Mach number and the Mach number predicted by a pre-constructed fan inlet flow field prediction model;

[0010] Based on the body force coefficients and governing equations, performance prediction data is generated.

[0011] Another aspect of the exemplary embodiments of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in the exemplary embodiments of the present disclosure.

[0012] According to another aspect of the exemplary embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0013] According to another aspect of the exemplary embodiments of the present disclosure, a computer program product is provided, including a computer program / instruction. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0014] As will be described in detail below, according to an embodiment of the present disclosure, an integrated engine-engine rapid performance prediction method that considers circumferential total pressure intake distortion is provided. The method obtains compressor performance diagram data under uniform intake conditions, and based on the compressor performance diagram data, obtains the slope data of the compressor total pressure rise and axial velocity curve; based on the inlet flow field distribution data, obtains the initial velocity distribution data at the fan inlet position; based on the initial velocity distribution data, generates the fan inlet Mach number; based on the fan inlet Mach number and the Mach number predicted by the pre-constructed fan inlet flow field prediction model, determines the volume force coefficient; based on the volume force coefficient and the control equation, generates performance prediction data. The present disclosure derives a fan inlet flow field prediction model under circumferential total pressure intake distortion based on the small disturbance theory, and combines it with the streamline volume force model to reconstruct the correlation between the fan inlet Mach number and the volume force coefficient, thereby constructing an integrated engine-engine rapid performance prediction method that considers circumferential total pressure intake distortion. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other purposes, features, and advantages of the present disclosure will become more apparent through a more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and are not intended to limit the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 A schematic diagram of an intake distortion map provided by an embodiment of the present disclosure;

[0017] Figure 2A flow chart of a method for rapid performance prediction of an integrated engine and intake considering circumferential total pressure intake distortion provided by an embodiment of the present disclosure;

[0018] Figure 3 A flow chart showing a method for rapid performance prediction of an engine-inlet integration taking into account circumferential total pressure intake distortion according to an embodiment of the present disclosure;

[0019] Figure 4a A comparison chart of the total pressure distribution between the fan inlet flow field prediction model prediction results and the CFD simulation results provided by the embodiment of the present disclosure;

[0020] Figure 4b A comparison diagram of the velocity distribution between the fan inlet flow field prediction model prediction results and the CFD simulation results provided by the embodiment of the present disclosure;

[0021] Figure 5a A comparison chart of thermal insulation efficiency provided by an embodiment of the present disclosure;

[0022] Figure 5b A comparison chart of total pressure ratios provided in an embodiment of the present disclosure;

[0023] Figure 6 A schematic block diagram of the functional modules of the project design cycle evaluation device provided in an embodiment of the present disclosure;

[0024] Figure 7 A structural block diagram of an electronic device provided in an embodiment of the present disclosure;

[0025] Figure 8 A schematic diagram of a computer program product provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0029] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0030] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0031] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0032] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operations of the disclosed technical solution based on the prompt message.

[0033] As an optional but non-limiting implementation method, in response to receiving the user's active request, the method of sending a prompt message to the user can be, for example, a pop-up window, and the prompt message can be presented in the form of text in the pop-up window. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation method of the present disclosure. Other methods that meet relevant laws and regulations can also be applied to the implementation method of the present disclosure.

[0034] Before introducing the embodiments of the present disclosure, the following definitions are given for the relevant terms involved in the embodiments of the present disclosure:

[0035] Circumferential total pressure inlet distortion: Circumferential total pressure inlet distortion refers to the uneven distribution of total pressure along the circumference (i.e., around the engine axis) of the airflow as it enters the engine. Total pressure is the sum of the static and dynamic pressures of the airflow, reflecting the energy of the airflow.

[0036] Intake-engine integration: Intake-engine integration refers to the design and optimization of the intake duct and engine as a whole to improve overall performance.

[0037] Rotor 67: The Rotor 67 is a test rotor specifically designed for studying the performance of aircraft engine fans and compressors. It is typically used to evaluate the fan's aerodynamic performance under various operating conditions, including efficiency, flow rate, and pressure distribution.

[0038] Circumferential total pressure intake distortion refers to the uneven circumferential distribution of total airflow pressure at the engine's air inlet. This distortion is often caused by factors such as aircraft maneuvers, air inlet design, or external airflow disturbances. It results in uneven circumferential pressure distribution of the airflow entering the engine, which in turn affects engine performance and stability. Therefore, it is necessary to develop a performance prediction method that accounts for circumferential total pressure intake distortion to quickly and accurately assess its impact on engine performance.

[0039] In related technologies, a volume force model is usually used to predict the impact of intake distortion on the fan. The volume force model effectively simulates the effect of blades on the airflow by simplifying the three-dimensional model of the compressor, thereby improving the accuracy of numerical calculations and reducing the consumption of computing resources. However, since the core of the volume force model is to determine the volume force coefficient, and in the early stages of engine design, the blade geometry has not yet been determined, the volume force coefficient is difficult to obtain accurately, which limits the applicability of the model. Secondly, although the volume force model can predict the flow field redistribution phenomenon under distorted intake conditions, its accuracy still depends on the accurate description of the intake distortion characteristics. If the distribution or intensity of the intake distortion changes, the model needs to be recalibrated, which increases the computational complexity and uncertainty, and ultimately leads to deviations between the predicted results and the actual situation.

[0040] Therefore, to address the aforementioned issues, an exemplary embodiment of the present disclosure provides an integrated engine-engine rapid performance prediction method that considers circumferential total pressure inlet distortion. First, a fan inlet flow field prediction model (Upstream Influence Model, UIM) that considers circumferential total pressure inlet distortion is constructed based on small disturbance theory. Furthermore, the fan inlet flow field prediction model is combined with a streamwise body force model (SBFM), reconstructing the relationship between the fan inlet Mach number and the body force coefficient to form a new streamwise body force model. This method enables integrated engine-engine rapid performance prediction under circumferential total pressure inlet distortion.

[0041] For example, Figure 1 A schematic diagram of the intake distortion spectrum provided by the embodiment of the present disclosure is shown as follows: Figure 1 As shown in the figure, the coordinate axis represents the circumferential angle (θ) and the color represents the total pressure. The map shows the distribution of total pressure in the circumferential direction, reflecting the different intensities of total pressure distortion. The map may contain curves or areas of different colors, representing different intensities of total pressure distortion.

[0042] For example, Figure 2 A flow chart of a method for rapid performance prediction of an engine-inlet integration considering circumferential total pressure intake distortion provided by an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the following steps may be specifically included:

[0043] Step S210: constructing a streamline body force model.

[0044] In this embodiment, the streamlined body force model is a novel method for rapidly simulating three-dimensional fan calculations based on the body force model. This model effectively simulates the effects of blades on airflow by simplifying the three-dimensional compressor model, thereby improving numerical calculation accuracy and reducing computational cost. Furthermore, the body force coefficient is used to add source terms for momentum and energy to the governing equations, enabling rapid prediction of fan characteristics.

[0045] Step S220: constructing a fan inlet flow field prediction model based on the small disturbance theory.

[0046] For example, a fan inlet flow field prediction model can be derived based on small perturbation theory to predict the impact of fan potential interference on fan inlet flow field parameters under circumferential total pressure inlet distortion. This model derives the velocity and pressure perturbations of the fan inlet flow field through linearized equations of motion, stream function definitions, and Fourier series expansions.

[0047] The specific derivation process is as follows:

[0048] First, the intake distortion area is divided into three regions: upstream, sector, and downstream. The velocity distortion caused by the total pressure distortion is decomposed into the average amount and the disturbance amount:

[0049]

[0050] u y =u y (2)

[0051] in, represents the axial velocity of uniform flow; u′ x and u′ y represents the disturbance component.

[0052] Furthermore, assuming u' y / u'x<<1, then the linearized motion equation can be expressed as:

[0053]

[0054]

[0055]

[0056] where p' represents the static pressure disturbance.

[0057] Based on the above linearized equation of motion, the stream function ψ is defined u It can be expressed as:

[0058]

[0059]

[0060] Since the velocity disturbance caused by the total pressure distortion is periodic in distribution with respect to y at infinity, it can be analyzed using the Fourier series:

[0061]

[0062] Among them, A k represents the Fourier coefficient; w represents the angular frequency; k represents the order.

[0063] Then, the stream function and the disturbance are solved. Combining the above formulas (6)-(8), the stream function ψ can be solved. u The expression is:

[0064]

[0065] Among them, C k represents an undetermined constant.

[0066] Substituting formulas (8) and (9) into formulas (3)-(5), we can obtain the expressions of axial velocity disturbance and pressure disturbance:

[0067]

[0068]

[0069] The disturbance of the compressor potential disturbance on the upstream axial velocity and pressure is closely related to the compressor characteristics and can be expressed as:

[0070]

[0071] Where θ represents the circumferential angle; The slope of the curve representing the total pressure rise at a uniform inlet versus axial velocity can be obtained from the compressor characteristics.

[0072] Furthermore, the relationship between total pressure, static pressure and Mach number is obtained according to the aerodynamic function, which can be expressed as:

[0073]

[0074] Here, γ represents the gas adiabatic index.

[0075] According to the binomial theorem, we can expand equation (13) to obtain:

[0076]

[0077] According to Bernoulli's equation, the total pressure can be expressed as:

[0078]

[0079] Where ρ represents density; U represents absolute velocity; and m represents the airflow compressibility correction coefficient, which can be expressed as:

[0080]

[0081] Similar to the velocity decomposition, the total pressure can also be decomposed into the average amount and the disturbance amount, that is:

[0082]

[0083] Substituting formula (15) into formula (17) we can get the expression of total pressure disturbance:

[0084]

[0085] Combining formulas (10)-(12) and (18), we can determine the unknown constant C k for:

[0086]

[0087] Among them, A k represents the Fourier coefficient; w represents the angular frequency; k represents the order; ρ represents the density; m represents the airflow compressibility correction coefficient; represents the slope parameter; represents the axial velocity of uniform flow.

[0088] Substituting the expression of the undetermined constant into formula (10), we can obtain:

[0089]

[0090] Among them, A krepresents the Fourier coefficient; w represents the angular frequency; k represents the order; ρ represents the density; m represents the airflow compressibility correction coefficient; represents the slope parameter; represents the axial velocity of uniform flow.

[0091] Finally, a fan inlet flow field prediction model that considers the fan potential interference effect is obtained. This model can predict the velocity and pressure distribution at the fan inlet, providing a basis for subsequent inlet-exit integrated calculations.

[0092] Based on this, through the steps of velocity decomposition, linearization of motion equations, stream function definition and Fourier series expansion, the velocity disturbance and pressure disturbance of the fan inlet flow field can be derived, and then the changes of the fan inlet flow field can be predicted.

[0093] Step S230: Combining the streamline body force model and the fan inlet flow field prediction model, the relationship between the fan inlet Mach number and the body force coefficient is reconstructed to form an integrated engine-engine rapid performance prediction method that takes into account the circumferential total pressure inlet distortion.

[0094] In the embodiment, the streamline body force model and the fan inlet flow field prediction model are combined, and the body force coefficient is adjusted by using the fan inlet flow field parameters obtained by the prediction model so that the flow field is consistent with the prediction model results.

[0095] For example, Figure 3 The schematic diagram of the process of the fast performance prediction method of the engine-inlet integration considering the circumferential total pressure intake distortion provided by the embodiment of the present disclosure is as follows: Figure 3 As shown, the following steps may be specifically included:

[0096] Step S310: Obtain compressor performance map data under uniform intake conditions, and based on the compressor performance map data, obtain slope data of a compressor total pressure rise versus axial velocity curve.

[0097] In the embodiment, based on the compressor performance diagram data under uniform intake conditions, the slope data of the compressor total pressure rise and axial velocity curve are calculated by the above formula (12): The slope data represents the sensitivity of the compressor total pressure to the change of axial velocity and is used to correct the relationship between the disturbance velocity and total pressure in the fan inlet flow field prediction model.

[0098] Step S320: Based on the inlet flow field distribution data, initial velocity distribution data at the fan inlet position is obtained.

[0099] In the embodiment, based on the inlet flow field distribution data, the above formulas (1) and (2) are used to solve and u' x＝-∞ , obtain the initial velocity distribution data at the fan inlet position, and provide the initial flow field conditions for subsequent Mach number calculations.

[0100] Step S330: Generate the fan inlet Mach number based on the initial velocity distribution data.

[0101] In the embodiment, based on the initial velocity distribution data, u is calculated by the above formulas (3)-(5) x＝0 and p x＝0 , and using the relationship between total pressure, static pressure and Mach number (Formula (13)), the fan inlet Mach number is generated.

[0102] Step S340: Calculate the volume force coefficient based on the fan inlet Mach number and the Mach number predicted by the pre-built fan inlet flow field prediction model.

[0103] In the embodiment, based on the fan inlet Mach number, the Mach number predicted by the fan inlet flow field prediction model (UIM) is combined with the body force coefficient penalty function to ultimately determine the three body force coefficients.

[0104] In this process, iterative calculations are performed to make the flow field calculation results consistent with the upstream flow field prediction model results, and a body force coefficient penalty function is constructed to ensure the calculation convergence:

[0105] f i+1 =f i [1+k e (1-u xi / u xp )] (twenty one)

[0106] Among them, f i+1 represents the body force coefficient at the i+1th iteration; f i and u xi represent the body force coefficient and axial velocity at the i-th iteration respectively.

[0107] To ensure the convergence of the calculation, the initial value of f can be determined in the following way:

[0108] Extract the inlet velocity curve from the initial flow field;

[0109] The theoretical initial velocity at the fan inlet position is solved based on the fan inlet flow field prediction model;

[0110] According to the functional relationship between the body force coefficient and the Mach number, the initial value of the body force coefficient is determined.

[0111] In the embodiment, before starting the calculation, it is first necessary to obtain the initial inlet velocity curve.

[0112] Based on the initial inlet velocity distribution, the fan inlet flow field prediction model is used to calculate the theoretical initial velocity at the fan inlet. This theoretical initial velocity is derived from the prediction model using small perturbation theory and the flow field distribution. The prediction model can be used to obtain flow field parameters (such as velocity and pressure) at the fan inlet, which are used in the subsequent calculation of the volume force coefficient.

[0113] Then, based on the functional relationship between the body force coefficient and the Mach number, the corresponding Mach number can be calculated from the theoretical initial velocity at the fan inlet. This functional relationship can then be used to determine the initial value of the body force coefficient. The body force coefficient is a key parameter in the streamline body force model, directly affecting the accuracy and convergence of the numerical calculation. By setting a reasonable initial value, the number of iterations can be reduced, improving computational efficiency.

[0114] In addition, k e It represents the empirical coefficient related to the total inlet pressure, which can usually be specified as the ratio of the total pressure in the distorted area to the total pressure in the non-distorted area, and is used in the iterative process of adjusting the body force coefficient. xp It represents the axial velocity predicted by the prediction model, which is used to compare with the currently calculated axial velocity to adjust the body force coefficient.

[0115] Through iterative calculation, the body force coefficient is gradually adjusted so that the calculated flow field parameters are consistent with the prediction model results, thereby ensuring the convergence and accuracy of the calculation.

[0116] Step S350: Generate performance prediction data based on the body force coefficient and the control equation.

[0117] In the embodiment, the determined body force coefficient is added as a source term of momentum and energy to the control equation (corresponding to the linearized motion equation of the above formulas (3)-(5)), and the flow field parameters are made consistent with the UIM prediction results through iterative calculation, and finally the forward-backward integrated performance prediction data is generated.

[0118] For example, Figure 4a This is a comparison chart of the total pressure distribution between the fan inlet flow field prediction model prediction results and the CFD simulation results provided by the embodiment of the present disclosure. Figure 4a As shown in the figure, the prediction result of the fan inlet flow field prediction model (UIM) is the red line, the CFD simulation result is the blue line, the horizontal axis represents the circumferential angle (θ), and the vertical axis represents the pressure (Pressure).

[0119] At the inlet and mid positions, the fan inlet flow field prediction model results are consistent with the CFD simulation results. At the air inlet position (AIP position), the fan inlet flow field prediction model prediction results have a small error with the CFD simulation results.

[0120] For example, Figure 4b This is a comparison chart of the speed distribution of the fan inlet flow field prediction model prediction results and CFD simulation results provided by the embodiment of the present disclosure. Figure 4b As shown in the figure, the prediction result of the fan inlet flow field prediction model (UIM) is the red line, the CFD simulation result is the blue line, the horizontal axis represents the circumferential angle (θ), and the vertical axis represents the speed (Velocity).

[0121] At the inlet position, mid position, and air inlet position (AIP position), the error between the fan inlet flow field prediction model prediction results and the CFD simulation results is small.

[0122] It can be seen that the fan upstream velocity and total pressure distribution obtained by the fan inlet flow field prediction model are basically consistent with the CFD results.

[0123] For example, Figure 5a This is a comparison chart of thermal insulation efficiency provided by the embodiment of the present disclosure. Figure 5b This is a comparison chart of the total pressure ratio provided by the embodiment of the present disclosure. Figure 5a and Figure 5b As shown in the figure, the fan characteristics of Rotor 67 are calculated by using CFD, Streamwise Body Force Model (SBFM) and Streamwise Body Force Model considering circumferential total pressure inlet distortion (MSBFM).

[0124] Compared to SBFM, MSBFM has an efficiency error of only 3% and a flow error of only 4% across the entire operating range. Furthermore, SBFM consumes 768 core hours of computing resources, while MSBFM consumes only 38.4 core hours, resulting in a nearly 20-fold increase in computing efficiency.

[0125] One or more technical solutions provided in the exemplary embodiments of the present disclosure derive a model for the influence of the fan potential interference effect on the fan inlet flow field parameters under circumferential total pressure inlet distortion based on the small disturbance theory, combine it with the classical streamline volume force model, reconstruct the correlation between the fan inlet Mach number and the volume force coefficient, and then develop an inlet-engine integrated rapid performance prediction method considering circumferential total pressure inlet distortion.

[0126] Among them, the correlation between the fan inlet Mach number and the body force coefficient was established through the small perturbation theory, bypassing the direct dependence on blade geometry information, so that the body force coefficient can be quickly estimated in the early design stage, significantly improving the applicability of the model.

[0127] Furthermore, the impact of circumferential total pressure inlet distortion on the fan inlet flow field is quantified using small perturbation theory, accurately describing the flow field redistribution phenomenon under distorted inlet conditions. Compared with related technologies, this method can better capture the nonlinear effects of the flow field, thereby improving prediction accuracy.

[0128] Finally, by reconstructing the relationship between the fan inlet Mach number and the volume force coefficient, the dependence on complex flow field information is reduced and the computational complexity is lowered.

[0129] Therefore, the fast performance prediction method of the engine-engine integration considering the circumferential total pressure intake distortion provided in the exemplary embodiment of the present disclosure solves the technical problems of difficulty in determining the volume force coefficient, low prediction accuracy and calculation efficiency, and provides a fast performance prediction method of the engine-engine integration considering the circumferential total pressure intake distortion.

[0130] The above mainly introduces the solutions provided by the exemplary embodiments of the present disclosure. It is understandable that in order to implement the above functions, the electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present disclosure.

[0131] The exemplary embodiments of the present disclosure can divide the functional units of the electronic device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the exemplary embodiments of the present disclosure is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0132] In the case of dividing each functional module according to each function, an exemplary embodiment of the present disclosure provides an integrated engine-engine rapid performance prediction device that takes into account circumferential total pressure intake distortion. The integrated engine-engine rapid performance prediction device that takes into account circumferential total pressure intake distortion can be a server or a chip applied to a server. Figure 6 This is a schematic block diagram of the functional modules of the engine-inlet integrated rapid performance prediction device considering circumferential total pressure intake distortion provided by the embodiment of the present disclosure. Figure 6 As shown, the intake-emission integrated rapid performance prediction device 600 considering circumferential total pressure intake distortion includes:

[0133] A data acquisition module 610 is configured to acquire compressor performance map data under uniform intake conditions, and based on the compressor performance map data, acquire slope data of a compressor total pressure rise versus axial velocity curve;

[0134] The data processing module 620 is used to obtain initial velocity distribution data at the fan inlet position based on the inlet flow field distribution data;

[0135] The data processing module 620 is further configured to generate a fan inlet Mach number based on the initial velocity distribution data;

[0136] The data processing module 620 is further configured to determine a volume force coefficient based on the fan inlet Mach number and the Mach number predicted by a pre-built fan inlet flow field prediction model;

[0137] The data processing module 620 is further configured to generate performance prediction data based on the body force coefficient and the control equation.

[0138] In another embodiment provided by the present disclosure, the data processing module 620 is further used to decompose the velocity distortion caused by the total pressure distortion into an average quantity and a disturbance quantity; define the stream function by linearizing the motion equation, and use the Fourier series to analyze the velocity disturbance component; combine the total pressure disturbance quantity equation and the compressor characteristic parameters, solve the unknown constants, and obtain a pre-constructed fan inlet flow field prediction model.

[0139] In another embodiment provided by the present disclosure, the data processing module 620 further includes: the total pressure disturbance equation is:

[0140]

[0141] Where p' represents static pressure disturbance; ρ represents density; m represents the airflow compressibility correction coefficient; represents the axial velocity of uniform flow; u' Ω represents the disturbance component.

[0142] In another embodiment provided by the present disclosure, the data processing module 620 further includes:

[0143]

[0144] Among them, A krepresents the Fourier coefficient; w represents the angular frequency; k represents the order; ρ represents the density; m represents the airflow compressibility correction coefficient; represents the slope parameter; represents the axial velocity of uniform flow.

[0145] In another embodiment provided by the present disclosure, the data processing module 620 further includes: the fan inlet flow field prediction model is:

[0146]

[0147] Among them, A k represents the Fourier coefficient; w represents the angular frequency; k represents the order; ρ represents the density; m represents the airflow compressibility correction coefficient; represents the slope parameter; represents the axial velocity of uniform flow.

[0148] In another embodiment provided by the present disclosure, the data processing module 620 is further used to extract an inlet velocity curve from the initial flow field; solve the theoretical initial velocity at the fan inlet position based on the fan inlet flow field prediction model; and determine the initial value of the volume force coefficient based on the functional relationship between the volume force coefficient and the Mach number.

[0149] In another embodiment provided by the present disclosure, the data processing module 620 further includes: constructing a body force coefficient penalty function:

[0150] f i+1 =f i [1+k e (1-u xi / u xp )]

[0151] Among them, f i+1 represents the body force coefficient at the i+1th iteration; f i and u xi are the body force coefficient and axial velocity at the i-th iteration respectively; k e Indicates the empirical coefficient related to the total inlet pressure; u xp It represents the axial velocity predicted by the fan inlet flow field prediction model.

[0152] The exemplary embodiments of the present disclosure further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being configured to cause the electronic device to perform a method according to an exemplary embodiment of the present disclosure when executed by the at least one processor.

[0153] Exemplary embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0154] Figure 7 The structural block diagram of the electronic device provided for the embodiment of the present disclosure will now be described as a structural block diagram of an electronic device 700 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0155] like Figure 7 As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the electronic device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0156] Multiple components within electronic device 700 are connected to I / O interface 705, including an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. Input unit 706 can be any type of device capable of inputting information into electronic device 700. Input unit 706 can receive input numeric or character information and generate key input signals related to user settings and / or function control of the electronic device. Output unit 707 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 708 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0157] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above. Each of the methods described above can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709.

[0158] Figure 8 This is a schematic diagram of a computer program product provided by an embodiment of the present disclosure. The exemplary embodiment of the present disclosure also provides a computer program product 800, including a computer program 801, wherein the computer program 801, when executed by a processor of a computer, is used to enable the computer to perform a method according to an embodiment of the present disclosure.

[0159] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0160] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0161] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0162] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0163] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0164] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0165] In the above embodiments, they can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is performed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a tape; it can also be an optical medium, such as a digital video disc (DVD); it can also be a semiconductor medium, such as a solid state drive (SSD).

[0166] Although the present disclosure has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely illustrative of the present disclosure as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is intended to include such modifications and variations if they fall within the scope of the claims of the present disclosure and their equivalents.

Claims

1. A rapid performance prediction method for an integrated engine and aerospace engine considering circumferential total pressure intake distortion, characterized in that: The method comprises: Obtaining compressor performance map data under uniform intake conditions, and obtaining slope data of a compressor total pressure rise versus axial velocity curve based on the compressor performance map data; Based on the inlet flow field distribution data, the initial velocity distribution data at the fan inlet position is obtained; generating a fan inlet Mach number based on the initial velocity distribution data; Determining a volume force coefficient based on the fan inlet Mach number and the Mach number predicted by a pre-constructed fan inlet flow field prediction model; Based on the body force coefficients and governing equations, performance prediction data is generated.

2. The method according to claim 1, characterized in that The method further comprises: Decompose the velocity distortion caused by total pressure distortion into average quantity and disturbance quantity; The stream function is defined by linearizing the equations of motion and the velocity disturbance components are resolved using Fourier series. The total pressure disturbance equation and the compressor characteristic parameters are combined to solve the unknown constants and obtain the pre-constructed fan inlet flow field prediction model.

3. The method according to claim 2, characterized in that The total pressure disturbance equation is: Where p′ represents the static pressure disturbance; ρ represents the density; m represents the airflow compressibility correction coefficient; represents the axial velocity of uniform flow; u′ x represents the disturbance component.

4. The method according to claim 3, characterized in that The method of simultaneously solving the total pressure disturbance equation and the compressor characteristic parameters to obtain the unknown constants includes: Among them, A k represents the Fourier coefficient; w represents the angular frequency; k represents the order; ρ represents the density; m represents the airflow compressibility correction coefficient; represents the slope parameter; represents the axial velocity of uniform flow.

5. The method according to claim 4, characterized in that The fan inlet flow field prediction model is: Among them, A k represents the Fourier coefficient; w represents the angular frequency; k represents the order; ρ represents the density; m represents the airflow compressibility correction coefficient; represents the slope parameter; represents the axial velocity of uniform flow.

6. The method according to claim 1, characterized in that The volume force coefficient based on the fan inlet Mach number and the Mach number predicted by the pre-constructed fan inlet flow field prediction model includes: Extract the inlet velocity curve from the initial flow field; Calculating the theoretical initial velocity at the fan inlet position based on the fan inlet flow field prediction model; According to the functional relationship between the body force coefficient and the Mach number, the initial value of the body force coefficient is determined.

7. The method according to claim 6, characterized in that The method further comprises: Construct the body force coefficient penalty function: f i+1 =f i [1+k e (1-u xi / u xp )] Among them, f i+1 represents the body force coefficient at the i+1th iteration; f i and u xi are the body force coefficient and axial velocity at the i-th iteration respectively; k e Indicates the empirical coefficient related to the total inlet pressure; u xp It represents the axial velocity predicted by the fan inlet flow field prediction model.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the method of claim 1.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the method according to claim 1 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to claim 1 is implemented.

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