Turbulence prediction method suitable for heat insulation wall cross-medium aircraft transition

Abstract

The invention provides a transition turbulence prediction method suitable for a heat insulation wall cross-medium aircraft, which comprises the following steps of: firstly, establishing a three-dimensional model of the cross-medium aircraft, creating a computational grid based on the three-dimensional model of the cross-medium aircraft, and applying a flow field boundary condition to obtain a cross-medium aircraft computational model; and then, carrying out flow field solving on the cross-medium aircraft calculation model established in the step 1 by utilizing a CFD solver embedded with an improved transition turbulence model to obtain a transition position. According to the method, a pressure gradient factor is introduced to modify a localized momentum thickness Reynolds number and a transition momentum thickness Reynolds number involved in a boundary layer transition prediction model, and a model function # imgabs2 # corrected according to the transition momentum thickness Reynolds number # imgabs0 # and a Mach number # imgabs1 # of a boundary layer edge are provided; the boundary layer transition prediction model suitable for the cross-medium fluid environment is obtained, and accurate and efficient boundary layer transition-turbulence prediction can be provided for flow noise control of a cross-medium aircraft so as to meet actual engineering requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of computational fluid dynamics, and in particular relates to a transition turbulence prediction method suitable for an adiabatic wall cross-medium vehicle. Background Art

[0002] An airdrop underwater working robot is a cross-medium vehicle that can quickly reach the marine working area and is widely used in the fields of marine oil exploration, underwater scientific research and target detection. Flow field calculation based on computational fluid dynamics for airdrop underwater working robots is an important means to analyze the working performance of robots, and transition turbulence prediction is an important part of flow field calculation.

[0003] Boundary layer transition is a physical phenomenon widely existing in nature, which describes the transition process from laminar state to turbulent state. When the flow transitions from a stable laminar state to a turbulent state, the physical quantities of the flow will fluctuate violently, thereby affecting the distribution of friction, heat flow, noise, etc. on the surface of the object. For cross-medium vehicles, flow transition will not only affect its resistance performance in the air and underwater cruising stages, but also the flow noise generated by the pressure pulsation in the boundary layer of the transition zone and the pressure pulsation in the turbulent zone at the head of the vehicle will interfere with its underwater sonar detection. Therefore, studying the prediction and control of boundary layer transition is of great significance to improving the performance of cross-medium vehicles.

[0004] With the rapid development of computers, numerical simulation methods have become an important means of studying transitional flows due to their advantages of low cost, short cycle, and freedom from field conditions. Commonly used numerical methods for predicting flow transitions can be divided into four categories: methods based on linear stability theory (LST), parabolic stability equations (PSE), transition criteria, and transition models. Among them, the transition prediction method based on stability theory involves integral operations, which is difficult to apply in simulations involving a large amount of calculations, while the transition criteria are highly dependent on the experience of researchers, and the established transition criteria often lack universality. The transition model represented by the combination of the transition model and the RANS turbulence model cleverly avoids the dependence of the transition empirical formula on non-local variables by using the relationship between the vorticity Reynolds number and the momentum thickness Reynolds number obtained in the Brautius boundary layer. The flow variables in the transition model can be solved locally, so it is easier to parallelize. At the same time, the transition criterion established in the transition model also eliminates the dependence on the researcher's experience, and thus has been widely used in transition flow simulation.

[0005] Traditionally, the prediction of boundary layer transition on the surface of aircraft is mainly carried out in a single medium environment, and there is no effective solution for the prediction of boundary layer transition on the surface of cross-medium aircraft. During the research process, the applicant found that when the mainstream boundary layer transition turbulence model is applied When the model is used to simulate and predict the boundary layer flow of a cross-medium vehicle, there are still the following problems: First, the existing theory believes that the model function It mainly affects the boundary layer transition length, so the model function A fixed value is generally used, but the applicant found that the model function It also has a great influence on the transition triggering moment, and under different Reynolds number conditions, the model function The influence of the transition triggering time is very different. For cross-medium vehicles, since transition prediction needs to be performed in environments with huge Reynolds number differences such as underwater and air, the traditional fixed value model function is used. This will lead to a large difference between the transition prediction results and the actual results; 2. The existing transition momentum thickness Reynolds number The relationship is not sensitive to pressure gradient, while flow often exists in the presence of pressure gradient; when the pressure gradient exists, the transition position in the adverse pressure zone and the forward pressure zone will lead to overestimation and underestimation of the transition momentum thickness Reynolds number respectively. The above problems in the prior art lead to inaccurate transition predictions for cross-media vehicles, and hinder the development and research of cross-media vehicle noise prediction and suppression technology. Therefore, facing the needs of cross-media working environment, constructing an efficient and accurate transition-turbulence prediction method has important academic value and practical engineering significance. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the present invention proposes a transition turbulence prediction method suitable for an adiabatic wall cross-medium vehicle. The model introduces the pressure gradient factor to the intermittent factor according to the characteristics of the cross-medium working environment. The localized momentum thickness Reynolds number and transition momentum thickness Reynolds number involved in the model function in the source term of the transport equation (i.e., the boundary layer transition prediction model) are modified. The pressure gradient information is considered when calculating the transition prediction model function. and the Mach number at the edge of the boundary layer Modified model function , avoid using model functions with fixed values , a boundary layer transition prediction model suitable for cross-medium fluid environment is obtained, which can provide accurate and efficient boundary layer transition-turbulence prediction for flow noise control of cross-medium vehicles to meet actual engineering needs.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for predicting transition turbulence of a vehicle crossing a medium with an adiabatic wall comprises the following steps:

[0009] Step 1: Establish a three-dimensional model of a cross-media vehicle, create a computational grid based on the three-dimensional model of the cross-media vehicle, and apply flow field boundary conditions to obtain a computational model of the cross-media vehicle;

[0010] Step 2: Use the CFD solver embedded with the improved transition turbulence model to solve the flow field of the cross-medium vehicle calculation model established in step 1 to obtain the transition position;

[0011] The improved transition turbulence model is obtained through the following process:

[0012] Step 2.1: Based on Transition turbulence model, establish boundary layer transition prediction model suitable for cross-medium fluid environment

[0013] ∂ (rg) ∂ t + ∂ (r u j c) ∂ x j = P c - D c + ∂ ∂ x j [(μ+ m t s f ) ∂ c ∂ x j ]

[0014] in represents the intermittent factor, represents laminar flow, Indicates turbulence, and its value between 0 and 1 corresponds to the transition process; is the fluid density; For time; For the The coordinate components in the directions, Corresponding to the Cartesian coordinate system , , direction; Indicates that the fluid The velocity components in the directions, Corresponding to the Cartesian coordinate system direction; is the dynamic viscosity coefficient, is the turbulent dynamic viscosity coefficient; is the model constant, generally 1; and They correspond to the generation source term and the destruction source term of the intermittent factor respectively:

[0015]

[0016]

[0017] in is the modulus of the velocity strain rate tensor, is the modulus of vorticity, and are model constants, respectively , and is the model function; Control the starting position of the transition, Control the length of the transition zone and the transition triggering time, The control destruction source term is kept closed outside the laminar boundary layer and the viscous bottom layer of the turbulent boundary layer, and the form is as follows:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] in , , Both Related trigger functions, is the intermediate variable, is the localized momentum thickness Reynolds number, is the transition momentum thickness Reynolds number, is the Mach number at the edge of the boundary layer;

[0025] Step 2.2: According to the boundary layer transition prediction model suitable for cross-medium fluid environment obtained in step 2.1, modify the generation source term and destruction source term of the turbulent kinetic energy transport equation in the Menter SST turbulence model to obtain an improved transition turbulence model; the improved transition turbulence model consists of the turbulent kinetic energy transport equation, the turbulent specific dissipation rate transport equation and the boundary layer transition prediction model suitable for cross-medium fluid environment (i.e., the intermittent factor transport equation).

[0026] The core innovation of the above scheme is to propose a Reynolds number based on the transition momentum thickness and the Mach number at the edge of the boundary layer Modified model function , avoid using model functions with fixed values , making the obtained boundary layer transition prediction model applicable to cross-medium fluid environment, and the accurate transition position and occurrence time can be obtained in different media.

[0027] Furthermore, in step 2.1, the localized momentum thickness Reynolds number By localizing the pressure gradient factor Corrected to get:

[0028]

[0029]

[0030] in is the vorticity Reynolds number, is the localized pressure gradient factor function.

[0031] Furthermore, the localized pressure gradient factor The calculation is done using the following expression:

[0032]

[0033] in is the localized momentum thickness, is the velocity at the edge of the boundary layer, is the displacement of the fluid element.

[0034] Furthermore, the velocity at the edge of the boundary layer According to the formula:

[0035] U e = U ∞ 2 + 2t t-1 [1- ( p p ∞ ) (t-1) / t ] p ∞ r ∞

[0036] Calculated, where is the speed of the free stream at infinity; is the density of the free flow at infinity, For local pressure, The pressure of the infinite free flow, is the specific heat ratio.

[0037] Furthermore, in step 2.1, the transition momentum thickness Reynolds number According to the formula

[0038]

[0039] Calculated, where is the Mach number at the edge of the boundary layer, is the turbulence of the infinite incoming flow, is the pressure gradient factor.

[0040] Furthermore, the Mach number at the edge of the boundary layer is According to the isentropic relationship, we can calculate:

[0041] M e = [ 1+(τ-1) / 2 M ∞ 2 (p / p ∞ ) (t-1) / t -1] 2 t-1

[0042] In the formula, is the Mach number of the incoming flow at infinite distance.

[0043] Furthermore, in step 2.2, the turbulent kinetic energy transport equation is:

[0044]

[0045] In the formula, is the turbulent kinetic energy, is a model constant whose value is consistent with that in the original Menter SST turbulence model. and Generate and destroy source terms for the modified turbulent kinetic energy:

[0046] ,

[0047] in and They are respectively the generation source term and the destruction source term of the turbulent kinetic energy transport equation in the original Menter SST turbulence model.

[0048] Beneficial effects:

[0049] The present invention proposes a transition-turbulence prediction method suitable for an adiabatic wall cross-medium vehicle. The method is based on The model introduces the pressure gradient factor to the intermittent factor according to the characteristics of the cross-medium fluid environment. The localized momentum thickness Reynolds number and transition momentum thickness Reynolds number involved in the model function in the source term of the transport equation are corrected. The influence of pressure gradient on the transition of cross-medium fluid environment is considered when calculating the model parameters for transition prediction. A creative method based on the transition momentum thickness Reynolds number is proposed. and the Mach number at the edge of the boundary layer Modified model function , a boundary layer transition prediction model suitable for cross-medium fluid environment is obtained, which can provide accurate and efficient boundary layer transition turbulence prediction for cross-medium vehicles in air and underwater scenarios to meet actual engineering needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1This is a model diagram of a gyrosome precursor according to an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of the calculation grid and boundary conditions of a gyrosome precursor according to an embodiment of the present invention;

[0052] Figure 3 Comparison of calculation results of surface pressure coefficient of the gyrosome precursor of the embodiment of the present invention;

[0053] Figure 4 The calculation results of the friction resistance coefficient on the symmetric surface of the precursor of the rotating body according to the embodiment of the present invention are compared;

[0054] Figure 5 The figure is a comparison of experimental data of transition prediction positions and different flow states of the swirl precursor of an embodiment of the present invention at different Reynolds numbers using different transition prediction models. DETAILED DESCRIPTION

[0055] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention clearer and more understandable, and to enable those skilled in the art to better understand the solutions of the present invention, the present invention is further described and fully described in conjunction with the accompanying drawings and implementation cases. It should be understood that the specific implementation cases described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] This embodiment takes the 4620-3 rotating body as an example, adopts a transition turbulence prediction method suitable for an insulated wall cross-medium vehicle proposed in the present invention, performs numerical simulation on the experimental state of the 4620-3 rotating body, predicts the transition position, and compares it with the experimental results to verify the beneficial effects of the method of the present invention.

[0057] Specifically, a transition turbulence prediction method applicable to an adiabatic wall cross-medium vehicle of this embodiment includes the following steps:

[0058] Step 1: Establish a three-dimensional model of a cross-media vehicle, create a computational grid based on the three-dimensional model of the cross-media vehicle, and apply flow field boundary conditions to obtain a computational model of the cross-media vehicle;

[0059] In this embodiment, the three-dimensional model of the cross-media aircraft is as follows: Figure 1 As shown, the computational grid and flow field boundary conditions are as follows Figure 2 shown.

[0060] Step 2: Use the CFD solver embedded with the improved transition turbulence model to solve the flow field of the cross-medium vehicle calculation model established in step 1 to obtain the transition position.

[0061] The improved transition turbulence model is obtained by: According to the characteristics of the cross-medium fluid environment, a model based on the transition momentum thickness Reynolds number is proposed. and the Mach number at the edge of the boundary layer Modified model function , and introduce the pressure gradient factor to the intermittent factor The localized momentum thickness Reynolds number and transition momentum thickness Reynolds number involved in the model function in the source term of the transport equation (boundary layer transition prediction model) are modified to obtain a boundary layer transition prediction model suitable for a cross-medium fluid environment, which is then coupled to the Menter SST turbulence model framework to obtain an improved transition turbulence model.

[0062] The process of establishing the improved transitional turbulence model is as follows:

[0063] Step 2.1: Based on Transition turbulence model, establish boundary layer transition prediction model suitable for cross-medium fluid environment

[0064] ∂ (rg) ∂ t + ∂ (r u j c) ∂ x j = P c - D c + ∂ ∂ x j [(μ+ m t s f ) ∂ c ∂ x j ]

[0065] in represents the intermittent factor, represents laminar flow, Indicates turbulence, and its value between 0 and 1 corresponds to the transition process; is the fluid density; For time; For the The coordinate components in the directions, Corresponding to the Cartesian coordinate system , , direction; Indicates that the fluid The velocity components in the directions, Corresponding to the Cartesian coordinate system direction; is the dynamic viscosity coefficient, is the turbulent dynamic viscosity coefficient; is the model constant, generally 1; and They correspond to the generation source term and the destruction source term of the intermittent factor respectively:

[0066]

[0067]

[0068] in is the modulus of the velocity strain rate tensor, is the modulus of vorticity, and are model constants, respectively , and is the model function; Control the starting position of the transition, Control the length of the transition zone and the transition triggering time, The control destruction source term is kept closed outside the laminar boundary layer and the viscous bottom layer of the turbulent boundary layer, and the form is as follows:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] in , , Both Related trigger functions, is the intermediate variable, is the localized momentum thickness Reynolds number, is the transition momentum thickness Reynolds number, is the Mach number at the edge of the boundary layer.

[0076] Localized momentum thickness Reynolds number By localizing the pressure gradient factor Corrected to get:

[0077]

[0078]

[0079] in is the vorticity Reynolds number, is the localized pressure gradient factor function.

[0080] Localized pressure gradient factor The calculation is done using the following expression:

[0081]

[0082] in is the localized momentum thickness, is the velocity at the edge of the boundary layer, is the displacement of the fluid element, is the velocity at the edge of the boundary layer Displacement of the fluid element The derivative of .

[0083] The velocity at the edge of the boundary layer According to the calculation using the compressible Bernoulli equation:

[0084] U e = U ∞ 2 + 2t t-1 [1- ( p p ∞ ) (t-1) / t ] p ∞ r ∞

[0085] Calculated, where is the speed of the free stream at infinity; is the density of the free flow at infinity, For local pressure, The pressure of the infinite free flow, is the specific heat ratio.

[0086] The Simple-AHD criterion is used to calculate the transition momentum thickness Reynolds number based on the incoming flow turbulence, the Mach number at the edge of the boundary layer and the pressure gradient factor. :

[0087]

[0088] in is the Mach number at the edge of the boundary layer, is the turbulence of the infinite incoming flow, is the pressure gradient factor.

[0089] Mach number at the edge of the boundary layer According to the isentropic relationship, we can calculate:

[0090] M e = [ 1+(τ-1) / 2 M ∞ 2 (p / p ∞ ) (t-1) / t -1] 2 t-1

[0091] In the formula, is the Mach number of the incoming flow at infinite distance.

[0092] In this embodiment, the corrected transition momentum thickness Reynolds number The applicable range of the transition criterion is 0.0≤ ≤1.1, ≤1%.

[0093] Step 2.2: According to the boundary layer transition prediction model suitable for cross-medium fluid environment obtained in step 2.1, modify the generation source term and destruction source term of the turbulent kinetic energy transport equation in the Menter SST turbulence model to obtain an improved transition turbulence model; the improved transition turbulence model consists of the turbulent kinetic energy transport equation, the turbulent specific dissipation rate transport equation and the boundary layer transition prediction model suitable for cross-medium fluid environment.

[0094] The turbulent kinetic energy transport equation is:

[0095]

[0096] In the formula, is the turbulent kinetic energy, is a model constant whose value is consistent with that in the original Menter SST turbulence model. and Generate and destroy source terms for the modified turbulent kinetic energy:

[0097] ,

[0098] in and They are respectively the generation source term and the destruction source term of the turbulent kinetic energy transport equation in the original Menter SST turbulence model.

[0099] Turbulence specific dissipation rate The transport equations of follow the original Menter SST turbulence model. Finally, we get The improved transition turbulence model of three equations is embedded in the CFD solver to realize the numerical prediction of the transition turbulence flow field of the cross-medium vehicle.

[0100] In order to verify the transition prediction effect of the above technical solution of the present invention in cross-medium flow, Transition Model, The transition model is used as a control to simulate the transition turbulent flow field of the 4620-3 rotating body and compare it with the data published in the literature. The calculation conditions are shown in the following table:

[0101] Table 1. Inflow velocity and Reynolds number conditions for numerical simulation on the 4620-3 rotating body

[0102]

[0103] Figure 3 The comparison between the surface pressure coefficients of different numerical simulations and those given in the literature is given. Figure 3 visible, Transition Model, The pressure coefficient results calculated by the transition model and the method of the present invention are in good agreement with the results given in the literature, which preliminarily verifies the reliability of the calculation of the method of the present invention.

[0104] Figure 4 and Figure 5 The comparison between the surface friction coefficient and the transition position on the symmetry plane of the 4620-3 gyromorph precursor predicted by the three transition models is given respectively. of The transition model is triggered early because the fixed coefficient is too large, so the predicted transition position is the most forward. Since the transition model is less affected by the adverse pressure gradient, the transition position predicted by it is generally later. The transition position of the method of the present invention is between the transition positions predicted by the above two transition models. Figure 5 The flow states observed at various monitoring points in the experiment are marked above, which are divided into laminar state, disturbance wave development state, intermittent state, turbulent state, and a combination of disturbance wave development state and intermittent state, and a combination of intermittent state and turbulent state. The numerical simulation results of the method of the present invention are highly consistent with the results in the literature and experiments, verifying the high accuracy of the boundary layer transition prediction of the present invention for cross-media flow scenarios, and can provide accurate and efficient boundary layer transition-turbulence prediction for cross-media vehicles to meet actual engineering needs.

[0105] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention.

Claims

1. A method for predicting transition turbulence for a vehicle crossing a medium with an adiabatic wall, characterized by: The following steps are involved: Step 1: Establish a three-dimensional model of a cross-media vehicle, create a computational grid based on the three-dimensional model of the cross-media vehicle, and apply flow field boundary conditions to obtain a computational model of the cross-media vehicle; Step 2: Use the CFD solver embedded with the improved transition turbulence model to solve the flow field of the cross-medium vehicle calculation model established in step 1 to obtain the transition position; The improved transition turbulence model is obtained through the following process: Step 2.1: Based on Transition turbulence model, establish boundary layer transition prediction model suitable for cross-medium fluid environment in represents the intermittent factor, represents laminar flow, Indicates turbulence, and its value between 0 and 1 corresponds to the transition process; is the fluid density; For time; For the The coordinate components in the directions, Corresponding to the Cartesian coordinate system , , direction; Indicates that the fluid The velocity components in the directions, Corresponding to the Cartesian coordinate system direction; is the dynamic viscosity coefficient, is the turbulent dynamic viscosity coefficient; is the model constant, generally 1; and They correspond to the generation source term and the destruction source term of the intermittent factor respectively: in is the modulus of the velocity strain rate tensor, is the modulus of vorticity, and are model constants, respectively , and is the model function; Control the starting position of the transition, Control the length of the transition zone and the transition triggering time, The control destruction source term is kept closed outside the laminar boundary layer and the viscous bottom layer of the turbulent boundary layer, and the form is as follows: in , , Both Related trigger functions, is the intermediate variable, is the localized momentum thickness Reynolds number, is the transition momentum thickness Reynolds number, is the Mach number at the edge of the boundary layer; Step 2.2: According to the boundary layer transition prediction model suitable for cross-medium fluid environment obtained in step 2.1, modify the generation source term and destruction source term of the turbulent kinetic energy transport equation in the Menter SST turbulence model to obtain an improved transition turbulence model; the improved transition turbulence model consists of the turbulent kinetic energy transport equation, the turbulent specific dissipation rate transport equation and the boundary layer transition prediction model suitable for cross-medium fluid environment.

2. A method for predicting transition turbulence for a vehicle crossing a medium with an adiabatic wall according to claim 1, characterized in that: In step 2.1, the localized momentum thickness Reynolds number By localizing the pressure gradient factor Corrected to get: in is the vorticity Reynolds number, is the localized pressure gradient factor function.

3. A method for predicting transition turbulence for a vehicle crossing a medium with an adiabatic wall according to claim 2, characterized in that: Localized pressure gradient factor The calculation is done using the following expression: in is the localized momentum thickness, is the velocity at the edge of the boundary layer, is the displacement of the fluid element.

4. A method for predicting transition turbulence for a vehicle crossing a medium with an adiabatic wall according to claim 3, characterized in that: Velocity at the edge of the boundary layer According to the formula: Calculated, where is the speed of the free stream at infinity; is the density of the free flow at infinity, For local pressure, The pressure of the infinite free flow, is the specific heat ratio.

5. The method for predicting transition turbulence for a vehicle crossing a medium with an adiabatic wall according to claim 1, characterized in that: In step 2.1, the transition momentum thickness Reynolds number According to the formula Calculated, where is the Mach number at the edge of the boundary layer, is the turbulence of the infinite incoming flow, is the pressure gradient factor.

6. A method for predicting transition turbulence for a vehicle crossing a medium with an adiabatic wall according to claim 5, characterized in that: Mach number at the edge of the boundary layer According to the isentropic relationship, we can calculate: In the formula, is the Mach number of the incoming flow at infinite distance, is the specific heat ratio, For local pressure, is the pressure of the free flow at infinity.

7. The method for predicting transition turbulence for a vehicle crossing a medium with an adiabatic wall according to claim 1, characterized in that: In step 2.2, the turbulent kinetic energy transport equation is: In the formula, is the turbulent kinetic energy, is a model constant whose value is consistent with that in the original Menter SST turbulence model. and Generate and destroy source terms for the modified turbulent kinetic energy: ， in and They are respectively the generation source term and the destruction source term of the turbulent kinetic energy transport equation in the original Menter SST turbulence model.

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