A numerical simulation method for rapid evaluation of ice characteristics of airfoil

Abstract

The present invention belongs to the technical field of aircraft airfoil ice type simulation, and relates to a numerical simulation method for rapid evaluation of airfoil ice type characteristics, including: determining the airfoil curve used for ice accumulation simulation, generating corresponding airfoil data points; meshing the airfoil data points, generating a 2.5D grid of the initial clean airfoil; solving the flow field information of the 2.5D grid, performing batch processing and solving calculations in a multi-node parallel manner, and saving the corresponding flow field solution data; performing Euler water droplet trajectory calculations to obtain surface water droplet collection distribution; reading the water droplet collection distribution, judging the ice type according to the calculated temperature, calculating the single ice result of the airfoil leading edge, obtaining the airfoil data point after ice formation, and recording the ice time as t; according to the ice time calculation condition, it should be satisfied that the n-time ice time nt is not less than the total ice time T, and if it is satisfied, the calculation is terminated, otherwise, step 2-step 5 is repeated until the condition is satisfied. The problem that batch fast multiple iteration ice type solving cannot be achieved is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of aircraft airfoil ice type simulation, and in particular relates to a numerical simulation method for rapid evaluation of airfoil ice type characteristics. Background Art

[0002] When an aircraft passes through a cloud layer rich in supercooled water droplets, the surface area of ​​the water droplets suddenly increases when the supercooled water droplets hit and cover the windward surface below zero degrees, the surface tension decreases, and the internal pressure decreases, causing the freezing point inside the water droplets to rise, making them very easy to freeze into ice. Clear ice can reduce the lift of the wing by 35%. When ice accumulation is severe, the flight resistance can increase by as much as 3 times compared to a smooth wing. Therefore, it is very important to be able to quickly predict the shape of ice accumulation for the flight safety of the aircraft. Existing technologies mostly solve and iterate the ice shape of a single airfoil in a single solution. The simulation of frost ice and angular clear ice requires different grid ideas. Each part of the ice accumulation calculation requires manual intervention. It takes a long time to calculate the ice shape in a single solution. The data interaction speed of different modules is slow, and the universality is poor. It cannot achieve batch rapid multiple iterations of ice type solutions. Summary of the invention

[0003] The purpose of the present invention is to provide a numerical simulation method for rapid evaluation of ice type characteristics of an airfoil, which solves the problem that batch rapid multiple iterative ice type solutions cannot be achieved.

[0004] The present invention is achieved through the following technical solutions:

[0005] A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil comprises the following steps:

[0006] Step 1, determine the airfoil curve used for ice accumulation simulation and generate corresponding airfoil data points;

[0007] Step 2, meshing the airfoil data points, locally encrypting the leading edge and trailing edge of the airfoil data points, determining the first layer height value of the boundary layer according to y+ being less than 1, and generating a 2.5D mesh of the initial clean airfoil;

[0008] Step 3, solve the flow field information of the 2.5D grid, use Python to change the flow field calculation control parameters, given the flow field calculation control parameters under different calculation conditions, determine the flow field iteration with the same boundary conditions, turbulence model and Couple pressure-velocity coupling, perform batch solution calculation in a multi-node parallel manner, save the corresponding flow field solution data for data comparison before and after icing, and output the flow field file for icing decoupling calculation;

[0009] Step 4: Use the Python framework to establish data interaction between the ice accumulation solution module and the flow field file, control the water drop parameters in the form of a script, perform Euler water drop trajectory calculation, and obtain the surface water drop collection distribution;

[0010] Step 5: read the water droplet collection distribution, determine the icing type according to the calculated temperature, calculate the single icing result of the leading edge of the airfoil, obtain the airfoil data point after icing, and record the icing time as t;

[0011] Step 6: According to the icing time calculation conditions, the n-th icing time nt should not be less than the total icing time T. If it is met, the calculation is terminated. Otherwise, the airfoil data point after icing is returned to step 2, and steps 2 to 5 are repeated until the conditions are met.

[0012] Further, in step 1, the corresponding airfoil data points are outputted through airfoil generation software or an online airfoil database.

[0013] Further, step 2 specifically includes the following steps:

[0014] Step 2.1, airfoil curve segmentation: read the airfoil data points, draw a smooth airfoil curve through a spline curve, and segment the curve into upper and lower parts at the leading edge point to obtain an upper airfoil curve and a lower airfoil curve;

[0015] Step 2.2, surface mesh generation: evenly distribute multiple mesh nodes on the upper airfoil curve and the lower airfoil curve, set the distance between the two starting points of the curve, refine the leading edge and the trailing edge, and set the first layer mesh height to 10 -6 The size of the order of magnitude m is based on the airfoil curve and expands outward to generate a two-dimensional surface mesh;

[0016] Step 2.3, 2.5D mesh drawing: stretch the surface mesh by unit length along the extension direction, name the calculation boundaries as far field, wall and symmetry boundary, and complete the 2.5D mesh for flow field calculation.

[0017] Furthermore, in step 3, the flow field calculation control parameters include velocity, atmospheric temperature, angle of attack and wall temperature.

[0018] Furthermore, in step 4, the Euler droplet trajectory is calculated to obtain the droplet collection coefficient and impact boundary, and the droplet field result file is output.

[0019] Further, step 5 is specifically as follows: obtaining the water drop field result file and water drop parameters, judging the ice shape type according to the temperature conditions, above -15°C is clear ice or mixed ice, using the Beading model to automatically update the roughness, calculating the ice shape boundary and heat transfer coefficient in the icing unit control body, and outputting new icing airfoil data points for a single icing;

[0020] If the temperature is below -15℃, it is considered frost ice. In this case, the ice shape boundary and heat transfer coefficient in the icing unit control body are calculated, and new icing airfoil data points are output for each icing event.

[0021] Further, in step 4, the water droplet parameters include the liquid water droplet content and the average water droplet effective diameter.

[0022] Further, in step 4, the calculation formula for the liquid water droplet content is:

[0023] 99% LWC = 0.553-0.089log 10 (H)

[0024] Where LWC is the liquid water drop content, H is the altitude;

[0025] The atmospheric water droplet content at the corresponding altitude is calculated according to the 99% confidence level, and the average effective diameter of water droplets is determined according to the airworthiness standards of different types of aircraft to obtain the water droplet parameters.

[0026] Furthermore, in step 2, the airfoil data points are imported into the mesh generation software to perform meshing operations.

[0027] Compared with the prior art, the present invention has the following beneficial technical effects:

[0028] The present invention discloses a numerical simulation method for rapidly predicting ice type characteristics of an airfoil. The grid is automatically generated, and by solving the Euler droplet trajectory and the icing phase change model of shallow water flow, the open ice simulation with water film flow can be well realized. The data is transferred by calling a program in Python, and the rapid calculation from airfoil data points to ice type characteristics can be completed simply and efficiently. The ice accumulation type can be automatically selected, and different surface roughness updating methods and multi-step methods can better fit the wind tunnel test data. All calculation modules are public software, which is simple and easy to operate, has high calculation accuracy, and is stable and reliable.

[0029] The present invention uses Python to write interfaces and running scripts for different software, realizes rapid calculation and simulation from airfoil data points to aerodynamic characteristics of ice-accumulated airfoils in different atmospheric environments, and simulates unsteady ice formation by solving multiple steady-state ice accretion solutions. It can not only fit wind tunnel test data well, but also quickly obtain aerodynamic parameters such as lift and drag coefficients, pressure distribution, and ice-accretion before and after ice accretion. The changes in aerodynamic characteristics caused by ice growth and thickness changes can meet the simulation requirements of various clear ice and frost ice. The operation process is simple and efficient, which is conducive to quickly judging flight safety in different atmospheric environments, and provides a rapid evaluation method for ice accretion characteristics for aircraft aerodynamic design ideas.

[0030] Furthermore, no matter what kind of ice shape it is, ice accumulation and reflux are required. However, if the temperature is above -15℃, it is clear ice or mixed ice, and the Beading model must be used to automatically update the roughness first, which can be calculated more accurately. If the temperature is below 15℃, the results calculated with or without the Beading model are the same. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a process framework diagram of the present invention.

[0032] Figure 2 It is the multi-step ice type curve of the present invention;

[0033] The ice type curve in the figure is realized through 7 steady-state simulations. The last ice accumulation result is used as the input of the next ice accumulation airfoil, and the ice type growth calculation is carried out on the basis of the previous simulation.

[0034] Figure 3 It is the flow field cloud diagram information corresponding to the multi-step ice type of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present invention more clear, the following is further described in detail 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 invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, not all embodiments.

[0036] The components described and shown in the drawings and embodiments of the present invention may be arranged and designed in various configurations. Therefore, the detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the claimed invention, but merely represents a selected embodiment of the present invention. Based on the drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0037] The features and performances of the present invention are further described in detail below in conjunction with the embodiments.

[0038] like Figure 1 As shown, the present invention discloses a numerical simulation method for rapid evaluation of ice type characteristics of an airfoil, comprising the following steps:

[0039] Step 1. Determine the airfoil curve used for ice accumulation simulation, output the corresponding airfoil data points through Profili, and save them as dat files;

[0040] Step 2. Import the airfoil data points into Pointwise software for meshing, locally encrypt the leading edge and trailing edge, determine the first layer height value of the boundary layer based on y+ being less than 1, and finally generate a 2.5D mesh of the initial clean airfoil with a span length of 1, and output the case file;

[0041] Step 3. Solve the airfoil flow field information in Fluent, output the initial clean airfoil flow field as a plt file, save the corresponding lift, drag, torque and pressure distribution parameters for data comparison before and after icing. The change of airfoil pressure cloud map after icing is shown in Figure 3 , and output the flow field file for ice decoupling calculation;

[0042] Step 4. Use the Python framework to establish data interaction between the ice accretion solution modules DROP3D and ICE3D, control the values ​​of ice accretion parameters such as LWC, MVD, and ice accretion time t in the form of scripts, and output the ice type geometry plt file;

[0043] Step 5. Read the water droplet collection distribution, determine the ice type based on the calculated temperature, calculate the single ice result of the airfoil leading edge, and obtain the airfoil data points after ice formation. The ice geometry calculated multiple times is as follows: Figure 2 , record the freezing time as t;

[0044] Step 6. According to the freezing time calculation conditions, the n-time freezing time nt should not be less than the total freezing time T. If it is met, the calculation is terminated. Otherwise, the airfoil data points after freezing are returned to Pointwise, and the grid is regenerated based on the frozen airfoil. Steps 2 to 5 are repeated until the conditions are met.

[0045] In step 2, the grid is divided using Pointwise. The specific implementation process includes the following steps:

[0046] Step 2.1, airfoil curve segmentation: read the airfoil data points, draw the shape curve through the spline curve, and divide the airfoil curve into upper and lower parts at the leading edge point.

[0047] Step 2.2, surface mesh generation: 160 mesh nodes are evenly distributed on the upper and lower airfoil curves, and the leading and trailing edges are encrypted. The initial distance between the two points is 1x10-5m, and the leading and trailing edges are refined at a growth rate of 1.2. The height of the first layer of mesh is 3x10-6m, based on the airfoil curve shape, and extends outward 80 steps.

[0048] Step 2.3, 2.5D grid: The computational grid used in ice accumulation simulation is of three-dimensional type. To improve computational efficiency, a 2.5D grid is generated instead. The surface grid and geometric boundary generated in the previous step are stretched by unit length along the span direction, named as far field, airfoil wall and symmetric boundary respectively, and fluent is selected as the solver output grid.

[0049] Fluent fluid solution includes the following steps, where all operations are executed through TUI script commands to start Fluent in batch mode:

[0050] Step 3.1, Startup method: Read a specific journal file, including command line startup and multi-core parallel computing of the software. Set the number of CPUs to run, and start Fluent without running the graphical interface. The command is fluent 3ddp-g-t8-ixxx.jou.

[0051] Step 3.2, preprocessing: Set the corresponding turbulence model. FENSAP-ICE air-water droplet two-phase flow supports k-ωSST turbulence model and SA turbulence model. Activate viscous heating, set the energy Prandtl number and wall Prandtl number to 0.9, change the gas parameters to ideal-gas, set the calculation boundary conditions to pressure-far-field, the velocity to 0.3ma, the temperature to 262.166K, and set the surface temperature of the airfoil to the atmospheric temperature + 10K.

[0052] Step 3.3, iterative solution: Select the pressure-velocity coupling mode, use the SIMPLEC algorithm, set the monitor of the lift-drag coefficient, set the number of iterations to 1000, and output the corresponding lift-drag coefficient and flow field pressure cloud map results.

[0053] In step 4, the Euler droplet trajectory is calculated to obtain the droplet collection coefficient and impact boundary, and the droplet field result file is output;

[0054] In step 5, specifically: obtain the water drop field results and water drop parameters, determine the ice shape type according to the temperature conditions, less than 15°C is clear ice or mixed ice, use the Beading model to automatically update the roughness, greater than 15°C is frost ice, calculate the ice shape boundary and heat transfer coefficient in the control body, and output new ice airfoil data points for a single freezing.

[0055] In step 4, the water droplet parameters include the liquid water droplet content and the average water droplet effective diameter.

[0056] The calculation formula for the liquid water droplet content is:

[0057] 99% LWC = 0.553-0.089log 10 (H)

[0058] Where LWC is the liquid water drop content, H is the altitude;

[0059] The atmospheric water droplet content at the corresponding altitude is calculated according to the 99% confidence level, and the average effective diameter of water droplets is determined according to the airworthiness standards of different types of aircraft to obtain the water droplet parameters.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A numerical simulation method for rapid evaluation of ice type characteristics of airfoil, characterized in that: The following steps are involved: Step 1, determine the airfoil curve used for ice accumulation simulation and generate corresponding airfoil data points; Step 2, meshing the airfoil data points, locally encrypting the leading edge and trailing edge of the airfoil data points, determining the first layer height value of the boundary layer according to y+ being less than 1, and generating a 2.5D mesh of the initial clean airfoil; Step 3, solve the flow field information of the 2.5D grid, use Python to change the flow field calculation control parameters, given the flow field calculation control parameters under different calculation conditions, determine the flow field iteration with the same boundary conditions, turbulence model and Couple pressure-velocity coupling, perform batch solution calculation in a multi-node parallel manner, save the corresponding flow field solution data for data comparison before and after icing, and output the flow field file for icing decoupling calculation; Step 4: Use the Python framework to establish data interaction between the ice accumulation solution module and the flow field file, control the water drop parameters in the form of a script, perform Euler water drop trajectory calculation, and obtain the surface water drop collection distribution; Step 5, read the water drop collection distribution, determine the icing type according to the calculated temperature, calculate the single icing result of the leading edge of the airfoil, obtain the airfoil data point after icing, and record the icing time as t; Step 6: According to the icing time calculation conditions, the n-th icing time nt should not be less than the total icing time T. If it is met, the calculation is terminated. Otherwise, the airfoil data point after icing is returned to step 2, and steps 2 to 5 are repeated until the conditions are met.

2. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 1, characterized in that: In step 1, the corresponding airfoil data points are output through airfoil generation software or an online airfoil database.

3. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 1, characterized in that: Step 2 specifically includes the following steps: Step 2.1, airfoil curve segmentation: read the airfoil data points, draw a smooth airfoil curve through a spline curve, and segment the curve into upper and lower parts at the leading edge point to obtain an upper airfoil curve and a lower airfoil curve; Step 2.2, surface mesh generation: evenly distribute multiple mesh nodes on the upper airfoil curve and the lower airfoil curve, set the distance between the two starting points of the curve, refine the leading edge and the trailing edge, and set the first layer mesh height to 10 -6 The size of the order of magnitude m is based on the airfoil curve and expands outward to generate a two-dimensional surface mesh; Step 2.3, 2.5D mesh drawing: Stretch the surface mesh by unit length along the extension direction, name the calculation boundaries as far field, wall and symmetry boundary, and complete the 2.5D mesh for flow field calculation.

4. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 1, characterized in that: In step 3, the flow field calculation control parameters include velocity, atmospheric temperature, angle of attack and wall temperature.

5. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 1, characterized in that: In step 4, the Euler droplet trajectory is calculated to obtain the droplet collection coefficient and impact boundary, and the droplet field result file is output.

6. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 1, characterized in that: Step 5 is as follows: obtain the water drop field result file and water drop parameters, determine the ice shape type according to the temperature conditions, above -15℃ is clear ice or mixed ice, use the Beading model to automatically update the roughness, calculate the ice shape boundary and heat transfer coefficient in the icing unit control body, and output new icing airfoil data points for a single icing; If the temperature is below -15℃, it is considered frost ice. In this case, the ice shape boundary and heat transfer coefficient in the icing unit control body are calculated, and new icing airfoil data points are output for each icing event.

7. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 1, characterized in that: In step 4, the water droplet parameters include the liquid water droplet content and the average water droplet effective diameter.

8. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 7, characterized in that: In step 4, the calculation formula for the liquid water droplet content is: 99%LWC=0.553-0.089log 10 (H) Where LWC is the liquid water drop content, H is the altitude; The atmospheric water droplet content at the corresponding altitude is calculated according to the 99% confidence level, and the average effective diameter of water droplets is determined according to the airworthiness standards of different types of aircraft to obtain the water droplet parameters.

9. A numerical simulation method for rapid evaluation of ice type characteristics of an airfoil according to claim 1, characterized in that: In step 2, the airfoil data points are imported into the mesh generation software to perform meshing operations.

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