Aircraft Ice Shape Prediction Method, Device, Computer Equipment and Storage Medium

Abstract

The present application relates to an aircraft icing shape prediction method, device, computer device, storage medium, and computer program product. The method includes: obtaining wing grid information of a target aircraft; performing air flow field calculation and water droplet field calculation according to the wing grid information, a preset Reynolds-averaged model, and a preset water droplet field calculation model to obtain an icing air flow field result of the target aircraft and a water droplet field result of the target aircraft; the preset Reynolds-averaged model includes an SPF turbulence model and an N-S equation solving model; calculating the current icing shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft, and updating the second wing grid information in the wing grid information; when the cumulative icing time of the target aircraft meets a preset icing stop condition, taking the currently calculated icing shape as the icing shape of the target aircraft. Using this method can accurately predict the aircraft icing shape.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and particularly to an aircraft ice shape prediction method, device, computer device, storage medium, and computer program product. Background Art

[0002] During aircraft flight, ice sometimes forms on certain parts of the aircraft due to the freezing of water droplets or the accumulation of sublimated water vapor, that is, aircraft icing. Aircraft icing has an important impact on the aerodynamic and handling stability characteristics of the aircraft. A certain degree of aircraft icing will not only cause insufficient lift provided by the wing, resulting in the aircraft falling, but also cause insufficient trimming moment provided by the horizontal tail, making the aircraft uncontrollable and causing irreparable disasters.

[0003] To ensure flight safety, during the aircraft design process, engineers need to arrange and design the anti-icing device according to the loss degree of the aerodynamic characteristics of the aircraft with ice. The premise of evaluating the aerodynamic characteristics of the aircraft with ice is to be able to predict the ice shape of the aircraft. Therefore, it is very necessary to provide an aircraft ice shape prediction method that can accurately predict the ice shape of the aircraft. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide an aircraft ice shape prediction method, device, computer device, computer-readable storage medium, and computer program product that can accurately predict the ice shape of the aircraft.

[0005] In a first aspect, the present application provides an aircraft ice shape prediction method. The method includes:

[0006] Obtain the wing grid information of the target aircraft; the wing grid information of the target aircraft includes the first wing grid information of the target aircraft when it is not iced and the second wing grid information of the target aircraft after it is iced;

[0007] According to the wing grid information, a preset Reynolds-averaged model, and a preset water droplet field calculation model, perform air flow field calculation and water droplet field calculation to obtain the icing air flow field result of the target aircraft and the water droplet field result of the target aircraft; the preset Reynolds-averaged model includes the SPF turbulence model and the N-S equation solution model; the icing air flow field result of the target aircraft includes the icing convective heat transfer coefficient and icing shear stress corresponding to the target aircraft; the water droplet field result of the target aircraft includes the local water collection coefficient corresponding to the target aircraft;

[0008] According to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft, calculate the current ice shape of the target aircraft and update the second wing grid information in the wing grid information;

[0009] When the cumulative icing time of the target aircraft meets the preset icing stop condition, the currently calculated ice shape is used as the ice shape of the target aircraft.

[0010] In one embodiment, the process of calculating the air flow field includes:

[0011] Input the wing grid information, the first icing mean field variable, and the first icing eddy viscosity into the N-S equation solving model to obtain the second icing mean field variable;

[0012] Input the second icing mean field variable into the SPF turbulence model to obtain the second icing eddy viscosity, and update the first icing mean field variable and the first icing eddy viscosity;

[0013] When the second icing mean field variable meets the preset air flow field calculation stop condition, the second icing mean field variable is used as the icing air flow field result of the target aircraft.

[0014] In one embodiment, the execution process of the SPF turbulence model includes:

[0015] Calculate the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary according to the second icing mean field variable and the roughness Reynolds number;

[0016] Calculate the first pulsating energy, the first turbulent kinetic energy, and the first unit turbulent kinetic energy dissipation rate in the space according to the second icing mean field variable;

[0017] Input the first pulsating energy, the first turbulent kinetic energy, the first unit turbulent kinetic energy dissipation rate in the space, and the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary into a preset transport equation to obtain the second pulsating energy, the second turbulent kinetic energy, and the second unit turbulent kinetic energy dissipation rate;

[0018] Calculate the second icing eddy viscosity according to the second pulsating energy, the second turbulent kinetic energy, and the second unit turbulent kinetic energy dissipation rate.

[0019] In one embodiment, the preset transport equations include: a pulsating energy transport equation, a turbulent kinetic energy transport equation, and a unit turbulent kinetic energy dissipation rate transport equation; the unit turbulent kinetic energy dissipation rate transport equation includes a non-equilibrium turbulence measure index correction term; the non-equilibrium turbulence measure index correction term is used for correcting the non-equilibrium turbulence measure index; the non-equilibrium turbulence measure index correction term can be expressed as:

[0020] f NE = min(max(300Re Ω ΓSSL ,1),3.3)

[0021] where f NE is the correction term of the non - equilibrium turbulence measurement index, Γ SSL is the switching function, and Re Ω is an expression for measuring the large eddy region far from the wall surface.

[0022] In one embodiment, the method further includes:

[0023] Determine the ice - shape grid information of the target aircraft according to the ice - formation ice shape of the target aircraft;

[0024] Perform air flow field calculation according to the ice - shape grid information and the preset Reynolds - averaged model to obtain the ice - shape air flow field result of the target aircraft; the ice - shape air flow field result of the target aircraft includes the ice - shape pressure and ice - shape shear stress corresponding to the target aircraft;

[0025] Calculate the ice - formation characteristics of the target aircraft according to the ice - shape air flow field result of the target aircraft.

[0026] In one embodiment, the calculating the current ice - formation ice shape of the target aircraft according to the ice - formation convective heat transfer coefficient, ice - formation shear stress and local water collection coefficient corresponding to the target aircraft includes:

[0027] Input the ice - formation convective heat transfer coefficient, ice - formation shear stress and local water collection coefficient corresponding to the target aircraft into a preset ice - formation thermodynamics model to obtain the current ice - formation ice shape of the target aircraft.

[0028] In a second aspect, the present application also provides an aircraft ice - formation ice - shape prediction device. The device includes:

[0029] An acquisition module, configured to acquire the wing grid information of the target aircraft; the wing grid information of the target aircraft includes the first wing grid information of the target aircraft when not iced and the second wing grid information of the target aircraft after icing;

[0030] A first calculation module, configured to perform air flow field calculation and water - droplet field calculation according to the wing grid information, a preset Reynolds - averaged model, and a preset water - droplet field calculation model to obtain the ice - formation air flow field result of the target aircraft and the water - droplet field result of the target aircraft; the preset Reynolds - averaged model includes an SPF turbulence model and an N - S equation solving model; the ice - formation air flow field result of the target aircraft includes the ice - formation convective heat transfer coefficient and ice - formation shear stress corresponding to the target aircraft; the water - droplet field result of the target aircraft includes the local water collection coefficient corresponding to the target aircraft;

[0031] A second calculation module, configured to calculate a current ice shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft, and update second wing grid information in the wing grid information;

[0032] A determination module, configured to use the ice shape calculated currently as the ice shape of the target aircraft when the cumulative icing time of the target aircraft meets a preset icing stop condition.

[0033] In one embodiment, the first calculation module is specifically configured to:

[0034] Input the wing grid information, first icing mean field variables, and first icing eddy viscosity into the N - S equation solving model to obtain second icing mean field variables;

[0035] Input the second icing mean field variables into the SPF turbulence model to obtain second icing eddy viscosity, and update the first icing mean field variables and the first icing eddy viscosity;

[0036] When the second icing mean field variables meet a preset air flow field calculation stop condition, use the second icing mean field variables as the icing air flow field result of the target aircraft.

[0037] In one embodiment, the first calculation module is specifically configured to:

[0038] Calculate a first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary according to the second icing mean field variables and the roughness Reynolds number;

[0039] Calculate a first pulsating energy, a first turbulent kinetic energy, and a first unit turbulent kinetic energy dissipation rate in space according to the second icing mean field variables;

[0040] Input the first pulsating energy, the first turbulent kinetic energy, the first unit turbulent kinetic energy dissipation rate in space, and the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary into a preset transport equation to obtain a second pulsating energy, a second turbulent kinetic energy, and a second unit turbulent kinetic energy dissipation rate;

[0041] Calculate the second icing eddy viscosity according to the second pulsating energy, the second turbulent kinetic energy, and the second unit turbulent kinetic energy dissipation rate.

[0042] In one embodiment, the preset transport equation includes: a pulsating energy transport equation, a turbulent kinetic energy transport equation, and a unit turbulent kinetic energy dissipation rate transport equation; the unit turbulent kinetic energy dissipation rate transport equation includes a non-equilibrium turbulence measurement index correction term; the non-equilibrium turbulence measurement index correction term is used for correcting the non-equilibrium turbulence measurement index; the non-equilibrium turbulence measurement index correction term can be expressed as:

[0043] f NE =min(max(30ORe Ω Γ SsL ,1),3.3)

[0044] where f NE is the non-equilibrium turbulence measurement index correction term, Γ SSL is the switching function, and Re Ω is an expression for measuring the large eddy region far from the wall.

[0045] In one embodiment, the device further includes:

[0046] Determine the ice shape grid information of the target aircraft according to the ice formation shape of the target aircraft;

[0047] Perform air flow field calculation according to the ice shape grid information and the preset Reynolds-averaged model to obtain the ice shape air flow field result of the target aircraft; the ice shape air flow field result of the target aircraft includes the ice shape pressure and ice shape shear stress corresponding to the target aircraft;

[0048] Calculate the icing characteristics of the target aircraft according to the ice shape air flow field result of the target aircraft.

[0049] In one embodiment, the second calculation module is specifically configured to:

[0050] Input the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft into a preset icing thermodynamics model to obtain the current ice formation shape of the target aircraft.

[0051] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps described in the first aspect above are implemented.

[0052] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps described in the first aspect above are implemented.

[0053] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program which, when executed by a processor, implements the steps described in the first aspect above.

[0054] For the above aircraft icing shape prediction method, device, computer equipment, storage medium and computer program product, wing grid information of a target aircraft is obtained; the wing grid information of the target aircraft includes first wing grid information of the target aircraft when not iced and second wing grid information of the target aircraft after icing; based on the wing grid information, a preset Reynolds-averaged model, and a preset water droplet field calculation model, air flow field calculation and water droplet field calculation are performed to obtain an icing air flow field result of the target aircraft and a water droplet field result of the target aircraft; the preset Reynolds-averaged model includes an SPF turbulence model and an N-S equation solving model; the icing air flow field result of the target aircraft includes an icing convective heat transfer coefficient and an icing shear stress corresponding to the target aircraft; the water droplet field result of the target aircraft includes a local water collection coefficient corresponding to the target aircraft; based on the icing convective heat transfer coefficient, icing shear stress and local water collection coefficient corresponding to the target aircraft, the current icing shape of the target aircraft is calculated, and the second wing grid information in the wing grid information is updated; when the cumulative icing time of the target aircraft meets a preset icing stop condition, the currently calculated icing shape is used as the icing shape of the target aircraft. In this way, through the Reynolds-averaged model including the SPF turbulence model and the water droplet field calculation model, the icing air flow field result and the water droplet field result of the target aircraft can be accurately calculated, and then the accurate icing shape of the target aircraft can be obtained, realizing accurate prediction of the aircraft icing shape. Description of the Drawings

[0055] Figure 1 It is a schematic flow chart of the aircraft icing shape prediction method in an embodiment;

[0056] Figure 2 It is a schematic flow chart of the air flow field calculation step in an embodiment;

[0057] Figure 3 It is a schematic flow chart of the execution process steps of the SPF turbulence model in an embodiment;

[0058] Figure 4 It is a schematic flow chart of the aircraft icing shape prediction method in another embodiment;

[0059] Figure 5 For an embodiment, the Schematic diagram of the results of ice shape prediction using the model, SST model and commercial software LEWICE, as well as experimental results;

[0060] Figure 6In an embodiment, at different angles of attack, the results of predicting icing characteristics using the model and the SST model, as well as a schematic diagram of the experimental results;

[0061] Figure 7 In an embodiment, using the model, the SST model, and the commercial software LEWICE3D to predict the ice shape, as well as a schematic diagram of the experimental results;

[0062] Figure 8 The structural block diagram of an aircraft icing shape prediction device in an embodiment;

[0063] Figure 9 The internal structure diagram of a computer device in an embodiment. Specific embodiments

[0064] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] In an embodiment, as Figure 1 shown, a method for predicting the icing shape of an aircraft is provided. In this embodiment, the application of this method to a terminal is taken as an example for illustration. It can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0066] Step 101, obtain the wing grid information of the target aircraft.

[0067] Among them, the wing grid information of the target aircraft includes the first wing grid information of the target aircraft when it is not iced and the second wing grid information of the target aircraft after icing.

[0068] In the embodiments of the present application, the terminal may first obtain the wing geometric information of the target aircraft. Among them, the wing geometric information of the target aircraft includes the first wing geometric information when the target aircraft is not iced and the second wing geometric information after the target aircraft is iced. The wing geometric information of the target aircraft may be the geometric coordinate points of the wings of the target aircraft. The wing geometric information may include: the two-dimensional geometric information of the airfoil of the wing and the three-dimensional geometric information of the wing. The airfoil is the cross-sectional shape parallel to the symmetry plane of the aircraft or perpendicular to the leading edge (or the line connecting the 1 / 4 chord length points) on the wings, tail wings, missile wing surfaces, helicopter rotor blades, and propeller blades of the aircraft, also known as the wing section or blade section. Then, the terminal may generate a body-fitted grid around the wings of the target aircraft or the airfoil of the target aircraft according to the wing geometric information of the target aircraft, and determine the wing grid information of the target aircraft. Among them, the wing grid information of the target aircraft includes the two-dimensional grid information of the airfoil of the target aircraft and the three-dimensional grid information of the wings of the target aircraft. The body-fitted grid is a grid composed of coordinate lines in an arbitrary curvilinear coordinate system, and all boundaries of the calculation region are grid lines of a curvilinear coordinate.

[0069] Step 102, perform air flow field calculation and water droplet field calculation according to the wing grid information, the preset Reynolds-averaged model, and the preset water droplet field calculation model, to obtain the icing air flow field result of the target aircraft and the water droplet field result of the target aircraft.

[0070] Among them, the preset Reynolds-averaged model includes the SPF turbulence model and the N-S equation solving model. The icing air flow field result of the target aircraft includes the icing convective heat transfer coefficient and the icing shear stress corresponding to the target aircraft. The water droplet field result of the target aircraft includes the local water collection coefficient corresponding to the target aircraft.

[0071] In the embodiments of the present application, the terminal may first perform air flow field calculation according to the wing grid information of the target aircraft and the preset Reynolds-averaged model, to obtain the icing air flow field result of the target aircraft. Then, the terminal may input the wing grid information of the target aircraft and the icing air flow field result into the preset water droplet field calculation model to perform water droplet field calculation, to obtain the water droplet field result of the target aircraft. Among them, the icing shear stress is the shear stress of the un-iced wing or the iced wing during the icing process. The icing shear stress is the shear stress of the air flowing over the object surface or the ice surface. The icing convective heat transfer coefficient is the convective heat transfer coefficient during the icing process.

[0072] In one example, the terminal can input the wing grid information of the target aircraft and the icing air flow field result into a preset water droplet field calculation model to perform water droplet field calculation, obtaining the volume fraction at each point in space. Then, the terminal can extract the volume fraction near the wall of the target aircraft and calculate the water collection characteristics of the wall based on the volume fraction near the wall. Next, the terminal can obtain the local water collection coefficient according to the water collection characteristics of the wall. Specifically, the terminal can use the water collection characteristics of the wall as the local water collection coefficient corresponding to the target aircraft.

[0073] In one example, the terminal can perform air flow field calculation according to the wing grid information of the target aircraft and a preset Reynolds-averaged model to obtain the icing air flow field result of the target aircraft. During the air flow field calculation, each time an iteration is performed, a second icing mean field variable can be calculated. For each second icing mean field variable, the terminal can input the second icing mean field variable and the wing grid information of the target aircraft into a preset water droplet field calculation model to perform water droplet field calculation, obtaining an intermediate result of the water droplet field. In this way, multiple intermediate results of the water droplet field can be obtained during the calculation of the water droplet field. When the intermediate result of the water droplet field meets the preset water droplet field calculation stop condition, the intermediate result of the water droplet field is used as the water droplet field result of the target aircraft. Among them, the preset water droplet field calculation stop condition can be the convergence of the intermediate result of the water droplet field.

[0074] Step 103: Calculate the current ice shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft, and update the second wing grid information in the wing grid information.

[0075] In the embodiment of the present application, the terminal can first calculate the current ice shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft. In one example, the terminal can calculate the current ice shape of the target aircraft based on the icing convective heat transfer coefficient, icing shear stress, local water collection coefficient, and icing thermodynamic model corresponding to the target aircraft. Then, the terminal can determine the second wing geometric information of the target aircraft after icing according to the current ice shape of the target aircraft. Next, the terminal can generate a body-fitted grid around the wing of the target aircraft or the airfoil of the target aircraft according to the second wing geometric information of the target aircraft to determine the second wing grid information of the target aircraft after icing. Then, the terminal can use the current second wing grid information as the wing grid information of the target aircraft. Next, the terminal can repeat steps 102-103.

[0076] Step 104: When the cumulative icing time of the target aircraft meets the preset icing stop condition, use the currently calculated ice shape as the ice shape of the target aircraft.

[0077] In the embodiments of the present application, the terminal can preset the icing stop condition in advance. Among them, the icing stop condition may include the target icing time of the target aircraft. Then, during the process of repeating steps 102-103, when the cumulative icing time of the target aircraft reaches the target icing time, the terminal can use the currently calculated ice shape as the ice shape of the target aircraft. The ice shape of the target aircraft includes the two-dimensional ice shape of the airfoil of the target aircraft and the three-dimensional ice shape of the wing of the target aircraft.

[0078] In the above aircraft ice shape prediction method, the Reynolds-averaged model including the SPF turbulence model can accurately calculate the icing air flow field result of the target aircraft, so that the water droplet field calculation model can accurately calculate the water droplet field result, and then obtain the accurate ice shape of the target aircraft, realizing the accurate prediction of the aircraft ice shape.

[0079] In one embodiment, as Figure 2 shown, the specific process of air flow field calculation includes the following steps:

[0080] Step 201, input the wing grid information, the first icing mean field variable, and the first icing eddy viscosity into the N-S equation solving model to obtain the second icing mean field variable.

[0081] In the embodiments of the present application, the terminal can preset the first icing mean field variable and the first icing eddy viscosity in advance. Then, the terminal can input the wing grid information, the first icing mean field variable, and the first icing eddy viscosity into the N-S equation solving model to obtain the second icing mean field variable. Among them, both the first icing mean field variable and the second icing mean field variable include the icing mean field thermodynamic coefficient, the icing mean field velocity, and the icing mean field pressure. The icing mean field thermodynamic coefficient includes the icing mean field convective heat transfer coefficient and the icing mean field shear stress.

[0082] Step 202, input the second icing mean field variable into the SPF turbulence model to obtain the second icing eddy viscosity, and update the first icing mean field variable and the first icing eddy viscosity.

[0083] In the embodiments of the present application, the terminal can first input the second icing mean field variable into the SPF turbulence model to obtain the second icing eddy viscosity. Then, the terminal can use the current second icing mean field variable as the first icing mean field variable. At the same time, the terminal can use the current second icing eddy viscosity as the first icing eddy viscosity. Then, the terminal can repeat steps 201-202.

[0084] Step 203, when the second icing mean field variable meets the preset air flow field calculation stop condition, use the second icing mean field variable as the icing air flow field result of the target aircraft.

[0085] In the embodiments of the present application, the terminal may preset the stop condition for the air flow field calculation. Among them, the stop condition for the air flow field calculation may be the convergence of the second icing mean field variable. Then, during the process of repeating steps 201-202, when the second icing mean field variable converges, the terminal may use the currently calculated second icing mean field variable as the icing air flow field result of the target aircraft.

[0086] In the above aircraft icing shape prediction method, the SPF turbulence model is used to assist the N-S equation solving model for air flow field calculation, which can accurately calculate the icing air flow field result of the target aircraft. And the water droplet field result is affected by the air flow field calculation. Therefore, the above aircraft icing shape prediction method can accurately calculate the icing air flow field result of the target aircraft while accurately calculating the water droplet field result of the target aircraft, and then obtain the accurate icing shape of the target aircraft, realizing the accurate prediction of the aircraft icing shape.

[0087] In one embodiment, as Figure 3 shown, the specific process of the execution process of the SPF turbulence model includes the following steps:

[0088] Step 301, calculate the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary according to the second icing mean field variable and the roughness Reynolds number.

[0089] In the embodiments of the present application, the terminal may calculate the equivalent sand grain roughness height according to a preset equivalent sand grain roughness height calculation model. Among them, the preset equivalent sand grain roughness height calculation model may be the model of Shin et al. In one example, the terminal may calculate the equivalent sand grain roughness height k according to the following formula s .

[0090]

[0091] Among them, the unit of LWC is g / m 3 , and other units are all in the SI unit system.

[0092]

[0093]

[0094]

[0095] (k s / C) base = 0.00117

[0096] Among them, c is the reference chord length, generally the chord length of the airfoil of the wing. LWC is the liquid water content, MVD is the mean supercooled water droplet diameter, and T s is the surface temperature.

[0097] Then, the terminal can calculate the roughness Reynolds number according to the equivalent sand grain roughness height. Among them, the roughness Reynolds number measures the roughness influence in the boundary layer buffer zone where the fluid is close to the wall surface, as well as the ratio of the boundary layer thickness to the roughness height, and determines whether the logarithmic region exists. The boundary layer refers to the thin flow layer where viscous forces dominate near the wall surface. In one example, the terminal can calculate the roughness Reynolds number according to the following formula and the equivalent sand grain roughness height.

[0098]

[0099]

[0100]

[0101] Among them, is the roughness Reynolds number, u τ is the wall friction velocity, v is the molecular viscosity, τ w is the shear stress of air on the wall, ρ is the air density, y + is the dimensionless height, and y is the distance to the wall.

[0102] Next, the terminal can calculate the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary according to the shear stress, molecular dynamic viscosity, and roughness Reynolds number in the second icing mean field variable through the following formula. Among them, the shear stress in the second icing mean field variable is the shear stress of air and the wall.

[0103]

[0104]

[0105] Among them, ω is the first unit turbulent kinetic energy dissipation rate, S R is the equivalent roughness height Reynolds number, τ w is the shear stress of air and the wall, μ is the molecular dynamic viscosity, is the roughness height Reynolds number.

[0106] Step 302, calculate the first pulsating energy, the first turbulent kinetic energy, and the first unit turbulent kinetic energy dissipation rate in the space according to the second icing mean field variable.

[0107] In an embodiment of the present application, the terminal can obtain the first pulsating energy, the first turbulent kinetic energy, and the first unit turbulent kinetic energy dissipation rate in the space through time marching iteration according to the second icing mean field variable and the transport equation of the SPF turbulence model. Specifically, the terminal can calculate the right-hand side term of the transport equation of the SPF turbulence model according to the second icing mean field variable, and then obtain the first pulsating energy, the first turbulent kinetic energy, and the first unit turbulent kinetic energy dissipation rate in the space based on the time marching method.

[0108] Step 303: Input the first pulsating energy, the first turbulent kinetic energy, the first unit turbulent kinetic energy dissipation rate in the space, and the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary into a preset transport equation to obtain the second pulsating energy, the second turbulent kinetic energy, and the second unit turbulent kinetic energy dissipation rate.

[0109] In an embodiment of the present application, the SPF turbulence model is a model. The terminal can input the first pulsating energy, the first turbulent kinetic energy, the first unit turbulent kinetic energy dissipation rate in the space, and the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary into a preset transport equation to obtain the second pulsating energy, the second turbulent kinetic energy, and the second unit turbulent kinetic energy dissipation rate. Among them, the preset transport equation includes a pulsating energy transport equation, a turbulent kinetic energy transport equation, and a unit turbulent kinetic energy dissipation rate transport equation. The preset transport equation can be expressed as:

[0110]

[0111]

[0112]

[0113] Among them, k is the pulsating energy, t is the time, u j is the local mean field velocity, P k is the generation term of the pulsating energy, D k is the destruction term of the pulsating energy, ρ is the air density, x j is the local position coordinate, μ is the aerodynamic viscosity, α T is the first diffusion coefficient, σ k is the second diffusion coefficient. is the turbulent kinetic energy, is the generation term of the turbulent kinetic energy, R BP is the control bypass transition term, R NAT is the control natural transition term, ω is the dissipation rate of the unit turbulent kinetic energy, is the destruction term of the turbulent kinetic energy, P ω is the generation term of ω, C ωR is the model constant coefficient, f W is the wall shielding function, C ω2is the mode constant coefficient, β * is the cross-diffusion term coefficient, is the boundary layer switching function, σ ω is the second diffusion coefficient of ω.

[0114] Step 304: Calculate the second icing eddy viscosity based on the second pulsating energy, the second turbulent kinetic energy, and the second unit turbulent kinetic energy dissipation rate.

[0115] In the embodiment of the present application, the terminal can first calculate the effective small-scale turbulent kinetic energy according to the second turbulent kinetic energy and the second unit turbulent kinetic energy dissipation rate. Then, the terminal can calculate the small-scale eddy viscosity according to the effective small-scale turbulent kinetic energy. At the same time, the terminal can calculate the effective large-scale turbulent kinetic energy according to the effective small-scale turbulent kinetic energy and the second turbulent kinetic energy. Then, the terminal can calculate the large-scale eddy viscosity according to the effective large-scale turbulent kinetic energy and the second pulsating energy. Then, the terminal can add the small-scale eddy viscosity and the large-scale eddy viscosity to obtain the second icing eddy viscosity.

[0116] In one example, the terminal can calculate the second icing eddy viscosity according to the following formula.

[0117]

[0118] λ eff = min(C λ d, λ T ),

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] μT = μ T，s + μ T，l

[0126] where μ T,s is the small-scale eddy viscosity, f v is the viscous shielding part for calculating μ T,s is the intermittency factor part for calculating μ INT is the viscous shielding part for calculating μ T,s is the intermittency factor part for calculating μ is the effective small-scale turbulent kinetic energy, λ eff is the characteristic wall effective distance, fss For calculating the shielding function at time, f W is the wall shielding function, is the second turbulent kinetic energy, C λ is the model constant coefficient, d is the distance to the wall, λ T is the turbulent characteristic length, d eff is the effective wall distance further obtained from λ eff is the model constant coefficient, v is the kinematic viscosity of air molecules, Ω is the vorticity, Re ss is the dimensionless number characterized by T and ω, C is the model constant coefficient, f INT is the shear stress shielding function, C τ,l is the shear stress shielding function, C 11 and C 12 are the large-scale eddy viscosity calculation coefficients; β TS is used to represent the T-S instability, S is the velocity deformation rate, C μ is the coefficient used for the eddy viscosity calculation, A0 and A S are the model constant coefficients, C τ,l is the model constant coefficient, is the effective large-scale turbulent kinetic energy, μ T , s is the small-scale eddy viscosity, μ T,l is the large-scale eddy viscosity, μ T is the eddy viscosity.

[0127] In the above aircraft icing shape prediction method, the eddy viscosity calculated by the SPF turbulence model is used to assist the N-S equation solving model to calculate the air flow field, and the icing air flow field result of the target aircraft can be accurately calculated. And the water droplet field result is affected by the air flow field calculation. Therefore, while accurately calculating the icing air flow field result of the target aircraft, the above aircraft icing shape prediction method also accurately calculates the water droplet field result of the target aircraft, and then obtains the accurate icing shape of the target aircraft, realizing the accurate prediction of the aircraft icing shape.

[0128] In one embodiment, the preset transport equations include: the pulsating energy transport equation, the turbulent kinetic energy transport equation, and the unit turbulent kinetic energy dissipation rate transport equation. The unit turbulent kinetic energy dissipation rate transport equation includes a non-equilibrium turbulence measure index correction term. The non-equilibrium turbulence measure index correction term is used for correcting the non-equilibrium turbulence measure index. The non-equilibrium turbulence measure index correction term can be expressed as:

[0129] f NE = min(max(300Re Ω Γ SsL , 1), 3.3)

[0130] Among them, f NE is the correction term of the non-equilibrium turbulence measurement index, and Γ SSL is the switching function, and Re Ω is an expression for measuring the large vorticity region far from the wall.

[0131] In the embodiment of the present application, the correction term of the non-equilibrium turbulence measurement index is located on the destruction term of the transport equation of the unit turbulent kinetic energy dissipation rate. The transport equation of the unit turbulent kinetic energy dissipation rate including the correction term of the non-equilibrium turbulence measurement index can be expressed as:

[0132]

[0133] Among them, k is the total pulsating energy, t is the time, u j is the local mean field velocity, ρ is the air density, x j is the local position coordinate, μ is the aerodynamic viscosity, α T is the first diffusion coefficient, σ ω is the second diffusion coefficient. is the turbulent kinetic energy, R BP is the control bypass transition term, R nAT is the control natural transition term, ω is the dissipation rate of the unit turbulent kinetic energy, is the destruction term of the turbulent kinetic energy, P ω is the generation term of ω, C ωR is the model constant coefficient, f W is the wall shielding function, C ω2 is the model constant coefficient, β * is the cross-diffusion term coefficient, is the boundary layer switching function, f NE is the correction term.

[0134] The terminal can first calculate the expression for measuring the large vorticity region far from the wall according to the molecular viscosity and vorticity. At the same time, the terminal can calculate the switching function according to the small-scale eddy viscosity, the modulus of the velocity deformation rate, the air density, the turbulent kinetic energy, the dissipation rate of the unit turbulent kinetic energy, and the threshold for judging the separated shear layer. Then, the terminal calculates the correction term of the non-equilibrium turbulence measurement index according to the expression for measuring the large vorticity region far from the wall and the switching function.

[0135] In an example, the expression for measuring the large vorticity region far from the wall can be expressed as:

[0136]

[0137] The switching function can be expressed as:

[0138]

[0139] Among them, Γ SSLis the switching function, Re Ω is an expression for measuring the large eddy region far from the wall, d is the distance from the current grid point to the wall, Ω is the vorticity, ν is the molecular viscosity, is the generation term of the turbulent kinetic energy, ε is the dissipation rate of the turbulent kinetic energy, C SSL is the threshold for judging the separated shear layer (set to 2.5), μ T,s is the eddy viscosity of the small scale, S 2 is the modulus of the velocity deformation rate, ρ is the density of air, is the turbulent kinetic energy, ω is the dissipation rate of the unit turbulent kinetic energy.

[0140] In the above aircraft icing shape prediction method, by setting a correction term in the transport equation of the SPF turbulence model, the generation to dissipation ratio value of the SPF turbulence model is adjusted, that is, the non-equilibrium turbulence measurement index of the SPF turbulence model is adjusted, significantly improving the ability to calculate the separated shear layer of non-streamlined body leading edges such as aircraft icing, and further improving the calculation ability for separation bubbles, thereby improving the accuracy of aircraft icing shape prediction. Moreover, the SPF turbulence model can achieve improved accuracy at large angles of attack or high lift coefficients, making this method capable of improving the accuracy of icing shape prediction compared to the SST model.

[0141] In one embodiment, in the SPF turbulence model, a turbulent shear stress limiter can be introduced into the calculation of the small scale eddy viscosity μ T,s Specifically, two sets of proportionality coefficients can be used to control the ratio of turbulent shear stress to turbulent kinetic energy in the boundary layer, free shear layer, and separated shear layer respectively. As shown in the following formula, in flows such as the boundary layer, a1 = 0.31, in the separated shear layer, a2 = 0.23, and the switching function between a1 and a2 is Γ SSL .

[0142]

[0143] In this way, introducing the turbulent shear stress limiter can improve the effect of the SPF turbulence model in predicting separated flows. Moreover, the shear stress limiter can also play a role in the separated shear layer behind the ice angle. This can further improve the accuracy of the SPF turbulence model, and further improve the accuracy of aircraft icing shape prediction.

[0144] In one embodiment, in the SPF turbulence model, the λ T used to calculate the large scale eddy viscosity can be replaced with λ eff , and a correction term for the large scale eddy viscosity μ T,l can be obtained, which can be expressed as:

[0145]

[0146] This can significantly improve the calculation of the reattachment point of the separation bubble, further improve the accuracy of the SPF turbulence model, and then further improve the accuracy of aircraft ice accretion shape prediction.

[0147] In one embodiment, the coefficients in the SPF turbulence model can be readjusted. Among them, the coefficient C R should be 3.2, and the large-scale eddy viscosity calculation coefficient C 11 should be 3.4×10 -7 . This conforms to the actual situation that the bypass transition energy coefficient of the turbulence model can be strengthened in the flow including separation and transition, can further improve the accuracy of the SPF turbulence model, and then further improve the accuracy of aircraft ice accretion shape prediction.

[0148] In one embodiment, as Figure 4 shown, the specific process of the aircraft ice accretion shape prediction method further includes the following steps:

[0149] Step 401, determine the ice shape grid information of the target aircraft according to the ice accretion shape of the target aircraft.

[0150] In the embodiment of the present application, the terminal can first determine the ice shape geometric information of the target aircraft according to the ice accretion shape of the target aircraft. Then, the terminal can generate a body-fitted grid around the ice accretion shape of the target aircraft according to the ice shape geometric information of the target aircraft to determine the ice shape grid information of the target aircraft. Among them, the ice shape geometric information of the target aircraft can be the geometric coordinate points of the ice shape of the target aircraft.

[0151] Step 402, perform air flow field calculation according to the ice shape grid information and the preset Reynolds-averaged model to obtain the ice shape air flow field result of the target aircraft.

[0152] Among them, the ice shape air flow field result of the target aircraft includes the ice shape pressure and ice shape shear stress corresponding to the target aircraft.

[0153] In the embodiment of the present application, the terminal can first preset the first ice shape mean field variable and the first ice shape eddy viscosity.

[0154] Then, the terminal can input the ice shape grid information, the first ice shape mean field variable, and the first ice shape eddy viscosity into the N-S equation solving model to obtain the second ice shape mean field variable. Among them, both the first ice shape mean field variable and the second ice shape mean field variable include the ice shape mean field pressure and the ice shape mean field shear stress.

[0155] Next, the terminal can input the second ice shape mean field variables into the SPF turbulence model to obtain the second ice shape eddy viscosity. Then, the terminal can use the current second ice shape mean field variables as the first ice shape mean field variables. At the same time, the terminal can use the current second ice shape eddy viscosity as the first ice shape eddy viscosity. Next, the terminal can repeat the above steps.

[0156] Then, during the process of repeating the above steps, when the second ice shape mean field variables converge, the terminal can use the currently calculated second ice shape mean field variables as the ice shape air flow field results of the target aircraft. Among them, the ice shape pressure is the pressure after icing. The ice shape shear stress is the shear stress of the wing after icing. The ice shape shear stress is the shear stress of the air flowing over the ice surface.

[0157] Step 403, calculate the icing characteristics of the target aircraft according to the ice shape air flow field results of the target aircraft.

[0158] In the embodiments of the present application, the icing characteristics of the target aircraft include the lift coefficient, drag coefficient, and moment coefficient of the target aircraft.

[0159] The terminal can calculate the lift coefficient of the target aircraft according to the ice shape pressure corresponding to the target aircraft. Specifically, the terminal can integrate the ice shape pressure corresponding to the target aircraft to calculate the lift coefficient of the target aircraft.

[0160] The terminal can calculate the friction coefficient of the target aircraft according to the ice shape shear stress corresponding to the target aircraft. At the same time, the terminal can calculate the pressure difference drag coefficient of the target aircraft according to the ice shape pressure corresponding to the target aircraft. Then, the terminal can form the drag coefficient of the target aircraft by combining the friction coefficient and the pressure difference drag coefficient of the target aircraft.

[0161] The terminal can calculate the distribution force conditions of each point on the ground corresponding to the target aircraft according to the ice shape shear stress corresponding to the target aircraft. At the same time, the terminal can obtain the moment reference point. Then, the terminal can calculate the moment coefficient of the target aircraft according to the distribution force conditions of each point on the ground corresponding to the target aircraft and the moment reference point.

[0162] In the above aircraft icing shape prediction method, by using the eddy viscosity obtained from the SPF turbulence model to assist the N-S equation solving model for air flow field calculation, the ice shape air flow field results of the target aircraft can be accurately calculated. Furthermore, on the premise of obtaining the accurate icing shape of the target aircraft, the accurate prediction of the aircraft icing characteristics can be realized. Moreover, the SPF turbulence model with non-equilibrium characteristic correction at large angles of attack shows obvious advantages, making the prediction effect of the SPF turbulence model on icing characteristics much improved compared with traditional SA or SST models.

[0163] In one embodiment, the specific process of calculating the current ice shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft includes the following steps: Input the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft into a preset icing thermodynamic model to obtain the current ice shape of the target aircraft.

[0164] In the embodiments of the present application, the terminal can first pre-store the icing thermodynamic model. Then, the terminal can input the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft into the preset icing thermodynamic model to obtain the current ice shape of the target aircraft. Among them, the icing thermodynamic model can be the Messinger model and the Myers model.

[0165] In the above aircraft ice shape prediction method, by inputting the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft into a preset icing thermodynamic model, an accurate ice shape of the target aircraft is obtained, realizing accurate prediction of the aircraft ice shape.

[0166] In one embodiment, the terminal respectively uses the model to predict the ice shape of the two-dimensional airfoil GLC305. At this time, the oncoming flow velocity is 90 m / s, the temperature is 268.15 K, the supercooled water droplet content is 0.54 g / m 3 , the supercooled water droplet diameter is set to 20 μm, the icing time is 22.5 minutes, and the calculated angle of attack is 5°. Using the model, the SST model, and the commercial software LEWICE to predict the results of this ice shape, and the experimental results are as Figure 5 shown. Using the model to calculate the ice height is greater than the SST model and is closer to the experimental result, that is, the ice shape prediction result is more accurate. At different angles of attack, using the model and the SST model to predict the icing characteristics of this ice shape, and the experimental results are as Figure 6 shown. The lift line predicted by using the model is closer to the experimental result, that is, the lift characteristic prediction is accurate and the icing characteristic prediction result is more accurate.

[0167] In one embodiment, the terminal respectively uses Predict the ice shape of the hybrid wing icing of the model and the three-dimensional CRM (Common Research Model) standard model. The hybrid wing is designed for the inner wing section of the CRM65 wing. Among them, compared with the original section of the wing, the head shape of the hybrid wing remains the same, but the airfoil of the second half and the additional flaps are different from the original cruise wing. Its shape is optimized so that the pressure distribution at the head of the airfoil is consistent with the water collection of the original wing. The icing test of the large aircraft wing is usually carried out on the hybrid wing platform. At this time, the typical clear ice condition is adopted for the icing condition, the oncoming flow velocity is 66.36 m / s, the temperature is 264.55 K, and the supercooled water droplet content is 1.0 g / m 3 , the diameter of the supercooled water droplets is set to 25 μm, the icing time is 29 minutes, and the calculated angle of attack is 3.7°. Using The results of the middle section predicted by the model, the SST model and the commercial software LEWICE3D for this ice shape, and the results of the experimental middle section are as Figure 7 shown. Using The model can obtain higher ice than SST, is closer to the test results, and is significantly better than the results of LEWICE3D, that is, the prediction result of the ice shape is more accurate.

[0168] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0169] Based on the same inventive concept, the embodiment of the present application also provides an aircraft ice shape prediction device for implementing the aircraft ice shape prediction method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the aircraft ice shape prediction device provided below can refer to the limitations on the aircraft ice shape prediction method in the above text, and will not be repeated here.

[0170] In one embodiment, as Figure 8 shown, an aircraft ice shape prediction device 800 is provided, including: an acquisition module 810, a first calculation module 820, a second calculation module 830, and a determination module 840, where:

[0171] An acquisition module 810, configured to acquire wing grid information of a target aircraft; the wing grid information of the target aircraft includes first wing grid information when the target aircraft is not iced and second wing grid information after the target aircraft is iced.

[0172] A first calculation module 820, configured to perform an air flow field calculation and a water droplet field calculation according to the wing grid information, a preset Reynolds-averaged model, and a preset water droplet field calculation model, so as to obtain an icing air flow field result of the target aircraft and a water droplet field result of the target aircraft; the preset Reynolds-averaged model includes an SPF turbulence model and an N-S equation solving model; the icing air flow field result of the target aircraft includes an icing convective heat transfer coefficient and an icing shear stress corresponding to the target aircraft; the water droplet field result of the target aircraft includes a local water collection coefficient corresponding to the target aircraft.

[0173] A second calculation module 830, configured to calculate a current ice shape of the target aircraft according to the icing convective heat transfer coefficient, the icing shear stress, and the local water collection coefficient corresponding to the target aircraft, and update the second wing grid information in the wing grid information.

[0174] A determination module 840, configured to use the currently calculated ice shape as the ice shape of the target aircraft when the cumulative icing time of the target aircraft meets a preset icing stop condition.

[0175] Optionally, the first calculation module 820 is specifically configured to:

[0176] Input the wing grid information, a first icing mean field variable, and a first icing eddy viscosity into the N-S equation solving model to obtain a second icing mean field variable.

[0177] Input the second icing mean field variable into the SPF turbulence model to obtain a second icing eddy viscosity, and update the first icing mean field variable and the first icing eddy viscosity.

[0178] When the second icing mean field variable meets a preset air flow field calculation stop condition, use the second icing mean field variable as the icing air flow field result of the target aircraft.

[0179] Optionally, the first calculation module 820 is specifically configured to:

[0180] Calculate a first unit turbulent kinetic energy dissipation rate corresponding to a wall boundary according to the second icing mean field variable and a roughness Reynolds number.

[0181] Calculate a first pulsation energy, a first turbulent kinetic energy, and a first unit turbulent kinetic energy dissipation rate in space according to the second icing mean field variable;

[0182] Input the first pulsation energy, the first turbulent kinetic energy, the first unit turbulent kinetic energy dissipation rate in space, and the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary into a preset transport equation to obtain a second pulsation energy, a second turbulent kinetic energy, and a second unit turbulent kinetic energy dissipation rate;

[0183] Calculate the second icing eddy viscosity according to the second pulsation energy, the second turbulent kinetic energy, and the second unit turbulent kinetic energy dissipation rate.

[0184] Optionally, the preset transport equation includes: a pulsation energy transport equation, a turbulent kinetic energy transport equation, and a unit turbulent kinetic energy dissipation rate transport equation; the unit turbulent kinetic energy dissipation rate transport equation includes a non-equilibrium turbulence measure index correction term; the non-equilibrium turbulence measure index correction term is used for correcting the non-equilibrium turbulence measure index; the non-equilibrium turbulence measure index correction term can be expressed as:

[0185] f NE =min(max(30ORe Ω Γ SsL ,1),3.3)

[0186] where f NE is the non-equilibrium turbulence measure index correction term, Γ SSL is a switching function, and Re Ω is an expression for measuring the large eddy region far from the wall.

[0187] Optionally, the device 800 further includes:

[0188] Determine ice shape grid information of the target aircraft according to the icing ice shape of the target aircraft;

[0189] Perform air flow field calculation according to the ice shape grid information and the preset Reynolds-averaged model to obtain an ice shape air flow field result of the target aircraft; the ice shape air flow field result of the target aircraft includes an ice shape pressure and an ice shape shear stress corresponding to the target aircraft;

[0190] Calculate the icing characteristics of the target aircraft according to the ice shape air flow field result of the target aircraft.

[0191] Optionally, the second calculation module 830 is specifically configured to:

[0192] Input the icing convective heat transfer coefficient, the icing shear stress, and the local water collection coefficient corresponding to the target aircraft into a preset icing thermodynamics model to obtain the current icing ice shape of the target aircraft.

[0193] Each module in the above aircraft icing shape prediction device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0194] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structural diagram can be as Figure 9 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes 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 can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an aircraft icing shape prediction method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad provided on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0195] Those skilled in the art can understand that Figure 9 the structure shown in

[0196] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0197] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps in each of the above method embodiments.

[0198] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps in each of the above method embodiments.

[0199] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0200] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0201] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0202] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An aircraft icing shape prediction method, characterized in that, The method includes: Obtaining the wing grid information of the target aircraft; the wing grid information of the target aircraft includes the first wing grid information when the target aircraft is not iced and the second wing grid information after the target aircraft is iced; Performing air flow field calculation and water droplet field calculation according to the wing grid information, a preset Reynolds-averaged model, and a preset water droplet field calculation model to obtain the icing air flow field result of the target aircraft and the water droplet field result of the target aircraft; the preset Reynolds-averaged model includes an SPF turbulence model and an N-S equation solving model; the icing air flow field result of the target aircraft includes the icing convective heat transfer coefficient and icing shear stress corresponding to the target aircraft; the water droplet field result of the target aircraft includes the local water collection coefficient corresponding to the target aircraft; Calculating the current ice shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft, and updating the second wing grid information in the wing grid information; When the cumulative icing time of the target aircraft meets a preset icing stop condition, taking the currently calculated ice shape as the ice shape of the target aircraft; The process of the air flow field calculation includes: Inputting the wing grid information, a first icing mean field variable, and a first icing eddy viscosity into the N-S equation solving model to obtain a second icing mean field variable; Inputting the second icing mean field variable into the SPF turbulence model to obtain a second icing eddy viscosity, and updating the first icing mean field variable and the first icing eddy viscosity; When the second icing mean field variable meets a preset air flow field calculation stop condition, taking the second icing mean field variable as the icing air flow field result of the target aircraft; The process of obtaining the second icing eddy viscosity by executing the SPF turbulence model includes: Calculating a first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary according to the second icing mean field variable and the roughness Reynolds number; Calculating a first pulsating energy, a first turbulent kinetic energy, and a first unit turbulent kinetic energy dissipation rate in space according to the second icing mean field variable; Inputting the first pulsating energy, the first turbulent kinetic energy, the first unit turbulent kinetic energy dissipation rate in space, and the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary into a preset transport equation to obtain a second pulsating energy, a second turbulent kinetic energy, and a second unit turbulent kinetic energy dissipation rate; Calculating an effective small-scale turbulent kinetic energy according to the second turbulent kinetic energy and the second unit turbulent kinetic energy dissipation rate, calculating a small-scale eddy viscosity according to the effective small-scale turbulent kinetic energy; calculating an effective large-scale turbulent kinetic energy according to the effective small-scale turbulent kinetic energy and the second turbulent kinetic energy; calculating a large-scale eddy viscosity according to the effective large-scale turbulent kinetic energy and the second pulsating energy; adding the small-scale eddy viscosity and the large-scale eddy viscosity to obtain a second icing eddy viscosity.

2. The method according to claim 1, characterized in that, The preset transport equations include: a pulsating energy transport equation, a turbulent kinetic energy transport equation, and a unit turbulent kinetic energy dissipation rate transport equation; the unit turbulent kinetic energy dissipation rate transport equation includes a non-equilibrium turbulence measurement index correction term; the non-equilibrium turbulence measurement index correction term is used for correcting the non-equilibrium turbulence measurement index; the non-equilibrium turbulence measurement index correction term can be expressed as: f NE = min(max(300Re Ω Γ SSL , 1), 3.3) where, f NE is the correction term of the non-equilibrium turbulence measurement index, Γ SSL is the switching function, and Re Ω is an expression for measuring the large eddy region far from the wall.

3. The method according to claim 1, characterized in that, The method further includes: Determining ice shape grid information of the target aircraft according to the icing shape of the target aircraft; Performing an air flow field calculation according to the ice shape grid information and the preset Reynolds-averaged model to obtain an ice shape air flow field result of the target aircraft; the ice shape air flow field result of the target aircraft includes the ice shape pressure and ice shape shear stress corresponding to the target aircraft; Calculating the icing characteristics of the target aircraft according to the ice shape air flow field result of the target aircraft.

4. The method according to claim 1, characterized in that, The calculating the current icing shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft includes: Inputting the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft into a preset icing thermodynamics model to obtain the current icing shape of the target aircraft.

5. An aircraft icing shape prediction device, characterized in that, The device includes: An acquisition module, configured to acquire wing grid information of a target aircraft; the wing grid information of the target aircraft includes first wing grid information when the target aircraft is not iced and second wing grid information after the target aircraft is iced; A first calculation module, configured to perform an air flow field calculation and a water droplet field calculation according to the wing grid information, a preset Reynolds-averaged model, and a preset water droplet field calculation model to obtain an icing air flow field result of the target aircraft and a water droplet field result of the target aircraft; the preset Reynolds-averaged model includes an SPF turbulence model and an N-S equation solving model; the icing air flow field result of the target aircraft includes the icing convective heat transfer coefficient and icing shear stress corresponding to the target aircraft; the water droplet field result of the target aircraft includes the local water collection coefficient corresponding to the target aircraft; A second calculation module, configured to calculate the current icing shape of the target aircraft according to the icing convective heat transfer coefficient, icing shear stress, and local water collection coefficient corresponding to the target aircraft, and update the second wing grid information in the wing grid information; A determination module, configured to use the currently calculated icing shape as the icing shape of the target aircraft when the cumulative icing time of the target aircraft meets a preset icing stop condition; The first calculation module is specifically configured to: Input the wing grid information, a first icing mean field variable, and a first icing eddy viscosity into the N-S equation solving model to obtain a second icing mean field variable; Input the second icing mean field variable into the SPF turbulence model to obtain a second icing eddy viscosity, and update the first icing mean field variable and the first icing eddy viscosity; When the second icing mean field variable satisfies a preset air flow field calculation stop condition, the second icing mean field variable is used as the icing air flow field result of the target aircraft; The first calculation module 820 is specifically configured to: calculate a first unit turbulent kinetic energy dissipation rate corresponding to a wall boundary according to the second icing mean field variable and the roughness Reynolds number; calculate a first pulsating energy, a first turbulent kinetic energy, and a first unit turbulent kinetic energy dissipation rate in space according to the second icing mean field variable; input the first pulsating energy, the first turbulent kinetic energy, the first unit turbulent kinetic energy dissipation rate in space, and the first unit turbulent kinetic energy dissipation rate corresponding to the wall boundary into a preset transport equation to obtain a second pulsating energy, a second turbulent kinetic energy, and a second unit turbulent kinetic energy dissipation rate; calculate an effective small-scale turbulent kinetic energy according to the second turbulent kinetic energy and the second unit turbulent kinetic energy dissipation rate, calculate a small-scale eddy viscosity according to the effective small-scale turbulent kinetic energy; calculate an effective large-scale turbulent kinetic energy according to the effective small-scale turbulent kinetic energy and the second turbulent kinetic energy; calculate a large-scale eddy viscosity according to the effective large-scale turbulent kinetic energy and the second pulsating energy; add the small-scale eddy viscosity and the large-scale eddy viscosity to obtain a second icing eddy viscosity.

6. The device according to claim 5, wherein The device further includes: determine ice shape grid information of the target aircraft according to the ice shape of the target aircraft; perform air flow field calculation according to the ice shape grid information and the preset Reynolds-averaged model to obtain an ice shape air flow field result of the target aircraft; the ice shape air flow field result of the target aircraft includes an ice shape pressure and an ice shape shear stress corresponding to the target aircraft; calculate the icing characteristics of the target aircraft according to the ice shape air flow field result of the target aircraft.

7. The device according to claim 5, characterized in that The second calculation module is specifically configured to: input the icing convective heat transfer coefficient, the icing shear stress, and the local water collection coefficient corresponding to the target aircraft into a preset icing thermodynamic model to obtain the current ice shape of the target aircraft.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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