A method for monitoring icing behavior on structural surfaces based on computational fluid dynamics

Abstract

The present invention provides a method for monitoring the icing behavior of structural surfaces based on computational fluid dynamics. The method uses Ansys-Fluent software to establish computational domains for different microstructures, selects corresponding boundary conditions and solvers based on different incoming flow velocities, and calculates the distribution of tiny droplets and analyzes icing behavior based on the steady-state flow field, thereby reliably predicting the icing behavior of microstructure surfaces. The icing behavior monitoring method proposed in the present invention is not limited to the incoming flow environment and the physical properties of the structure itself, but instead fills a technical gap in directly tracking the evolution of ice layers on microstructure surfaces under low-temperature, high-speed incoming flow conditions. While ensuring high computational accuracy, it has high computational efficiency, significantly reducing the time and economic costs of evaluating the icing behavior of microstructure surfaces, and providing important theoretical support for the microstructural design of surfaces for efficient anti-icing and de-icing materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of material surface performance evaluation, and in particular relates to a method for monitoring icing behavior of a structure surface based on computational fluid dynamics. Background Art

[0002] In recent years, the large number of extreme icing behaviors faced by aircraft during service in high-altitude and low-temperature environments can easily affect the shape and weight distribution of the fuselage surface, change the aerodynamic performance of the fuselage, hinder observation lines of sight, and reduce the controllability and stability of the aircraft, increasing flight risks. In order to effectively reduce the threat of icing behavior to flight safety, researchers have proposed a series of construction concepts for anti-icing materials, among which surface microstructure design is an important link in obtaining anti-icing materials. Different microstructures can easily induce selective icing trends on the surface, resulting in complex icing phenomena on the surface of the material. Microstructures with specific morphologies can effectively increase the freezing delay time of droplets, reduce the adhesion between the ice layer and the material, and exhibit excellent ice-repellent properties. Therefore, in order to obtain high-performance anti-icing materials for service environments, it is urgent to fully explore the influence of microstructure on surface icing behavior.

[0003] Currently, research on the icing behavior of microstructured surfaces remains primarily at the experimental testing stage, aiming to predict the anti-icing performance of macroscopic materials by observing the ice morphology on microstructured surfaces under different conditions. This method not only requires significant manpower and material resources, but also struggles to fully cover all operating conditions. Furthermore, due to limitations in objective experimental conditions, current icing behavior observations typically focus on zero or low wind speed test conditions. Further exploration of the icing patterns on material surfaces under high-speed inflow conditions is lacking, and the evolution of the icing interface also requires further analysis.

[0004] However, the advent of computational fluid dynamics has significantly reduced the difficulty of monitoring surface icing behavior, enabling high-throughput, multi-condition icing behavior prediction with minimal computational resources. Therefore, in order to efficiently analyze the icing behavior of microstructured surfaces in low-temperature, high-velocity inflow environments, it is urgent to develop a method for predicting icing behavior on microstructured surfaces in low-temperature, high-speed inflow environments. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for monitoring icing behavior on a structure surface based on computational fluid dynamics.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention relates to a method for monitoring icing behavior on a structure surface based on computational fluid dynamics, comprising the following steps:

[0008] Step 1: A three-dimensional computational domain model was established using Ansys ICEM software. To avoid interference between the top of the computational domain and the flow field at the bottom, the height and width of the computational domain were set according to the characteristic length of the microstructure region, and the microstructure region was placed at the bottom center of the computational domain. To stabilize the flow field before and after the structure region, walls of a certain length were set before and after the microstructure region as transition areas. Considering the compressibility of air under high-speed conditions, a certain amount of space was reserved at the inlet and outlet of the computational domain to improve calculation accuracy.

[0009] Step 2: Use the 3D computational domain model to determine the fluid classification based on the incoming Mach number. If it is a compressible fluid, select a density-based solver; if it is an incompressible fluid, select a pressure-based solver.

[0010] Step 3: Set boundary conditions. Set the upper boundary of the computational domain, the front and rear side walls, and the boundaries of the bottom reserved area as symmetry boundaries, and set the boundary conditions of the bottom structure area and the wall area as wall boundaries. For incompressible fluids, set the boundary conditions of the inlet and outlet as velocity inlet and pressure outlet, respectively. For compressible fluids, set the boundary conditions of the inlet and outlet as pressure far field.

[0011] Step 4: Use structured grids to generate all grids in the computational domain. To meet the accuracy requirements for near-wall calculations, a boundary layer controlled by the initial height and number of layers is set at the bottom of the flow field to ensure that the grid quality values ​​are all higher than 0.3.

[0012] Step 5: Use steady-state calculations to verify the reliability of different turbulence calculation models. Select the turbulence model with the highest calculation accuracy based on the deviation between the calculation results and the experimental results, and perform grid-independent verification.

[0013] Step 6: In Ansys Fluent software, the incoming flow velocity, incoming flow temperature, and wall temperature are set according to the actual working conditions, the flow field is calculated, and the calculation results are read into Ansys Fluent Icing software;

[0014] Step 7: Set the wall temperature again in the Particles module in Ansys Fluent Icing. In the Droplet module, set the Liquid Water Content (LWC), Droplet Diameter, and Particle Distribution modules to calculate the droplet distribution behavior in the low-temperature environment.

[0015] In step 8, based on the Particles calculation results, enable the Icing module, confirm the icing temperature, determine the ice type under the Ice Accretion Conditions, and select an appropriate icing time for calculation. This will yield predictions for the icing range, icing morphology, and icing quality on the structural surface.

[0016] Preferably, in step 1, the height and width of the calculation domain are both more than 20 times the characteristic length of the structural region.

[0017] Preferably, in step 1, the bottom wall length of the calculation domain is 1 to 1.5 times the characteristic length of the structural region.

[0018] Preferably, in step 1, the reserved spaces at the inlet and outlet positions of the calculation domain are both 2 to 5 times the characteristic length of the structural region.

[0019] Preferably, in step 2, the standard for determining the fluid classification is: a fluid with an incoming flow Mach number higher than 0.3 is determined to be a compressible fluid, and a fluid with an incoming flow Mach number lower than 0.3 is determined to be an incompressible fluid.

[0020] Preferably, in step 4, the boundary layer is set as follows: the number of boundary layers is 10, the expansion coefficient is 1.2, and the initial height is y, as shown in formula (1):

[0021]

[0022] In formula (1), y + is the dimensionless height from the bottom wall, U τ is the tangential velocity controlled by the wall shear stress, y + Usually 0.8-1.2;

[0023] Among them U τ It is expressed by formula (2):

[0024]

[0025] In formula (2), τ ω is the wall shear stress, ρ is the air density;

[0026] τ ω The expression of is shown in formula (3),

[0027]

[0028] In formula (3), U ∞ is the incoming flow velocity.

[0029] Preferably, in step 5, the turbulence model is Spalart-Allmaras (SA), k-ε, k-ω, Transition SST, Reynolds stress (RSM) and Detached eddy simulation (DES), etc.

[0030] Preferably, in step 6, the incoming flow temperature is -60°C to 0°C, and the wall temperature is the adiabatic stagnation temperature + 10K.

[0031] Preferably, in step 7, the LWC is 0.00015 kg / m 3 ~0.001kg / m 3 , with droplet diameters ranging from 10μm to 100μm. Also, keep the Monodispersed option enabled in the Particles distribution module to capture droplet motion using a single droplet. To ensure that the droplet velocity matches the incoming flow velocity, disable the Droplet velocity vector option.

[0032] Preferably, in step 8, the ice types are glaze, rime and mixed ice.

[0033] The present invention has the following advantages:

[0034] (1) The icing monitoring method of the present invention can obtain the icing characteristics of different structural surfaces through computational fluid dynamics, without the need for expensive test equipment and a large number of testers, thus significantly reducing time and economic costs.

[0035] (2) The ice monitoring method involved in the present invention can directly capture the droplet cluster distribution behavior and ice layer growth pattern on the surface of the microstructure, filling the technical gap of current experimental means in tracking the evolution law of ice layer on the surface of the microstructure under low-temperature and high-speed flow conditions.

[0036] (3) The monitoring method involved in the present invention proposes an efficient and reasonable calculation parameter range, which effectively reduces the demand for computing resources and improves the evaluation efficiency while ensuring the calculation accuracy.

[0037] (4) The evaluation method involved in the present invention provides theoretical guidance for the microstructure design of anti-icing materials required in the fields of aerospace, rail transportation, and power transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the computational domain in the method for monitoring icing behavior on a structural surface based on computational fluid dynamics according to the present invention;

[0039] Figure 2 This is a flow chart of the method for monitoring icing behavior on a structural surface based on computational fluid dynamics involved in the present invention;

[0040] Figure 3 The microstructure grid diagram established in Example 1;

[0041] Figure 4 This is a diagram of ice accumulation on the microstructure surface obtained in Example 1;

[0042] Figure 5 This is the ice mass distribution diagram on the microstructure surface obtained in Example 1. DETAILED DESCRIPTION

[0043] The present invention will be described in detail below with reference to specific embodiments. It should be noted that the following embodiments are only for further explanation of the present invention, but the protection scope of the present invention is not limited to the following embodiments.

[0044] Example 1

[0045] This embodiment relates to a method for monitoring icing behavior on a structural surface based on computational fluid dynamics. Figure 2 The specific steps are as follows:

[0046] Step 1: Establish a three-dimensional computational domain model based on Ansys ICEM software. Set the characteristic length of the wedge-shaped micro-array structure area to 40 mm, the computational domain height to 800 mm, the width to 800 mm, the wall length before and after the structure area to 60 mm, and the reserved length at the inlet and outlet positions to 100 mm.

[0047] Step 2: Set the incoming flow velocity to 70 m / s and select the pressure-based solver.

[0048] Step 3: Set the boundary conditions. Set the upper boundary of the computational domain and the boundary of the bottom reserved area as symmetry boundary (Symmetry Boundary), set the boundary conditions of the bottom structure area and the wall area as wall-no-slip boundary (Wall-no-slip boundary), and set the boundary conditions of the inlet and outlet as velocity inlet (Velocityinlet) and pressure outlet (Pressure outlet) respectively. Figure 1 As shown;

[0049] Step 4: Use structured grid to generate all grids in the computational domain. In order to meet the accuracy requirements of near-wall calculations, 10 boundary layers are set at the bottom of the flow field with an initial height of 1×10 -6m, the expansion coefficient is 1.2, and then the element quality index is guaranteed to be higher than 0.3. The calculation grid of the structure part is as follows Figure 3 As shown;

[0050] The initial height is y, as shown in formula (1):

[0051]

[0052] In formula (1), y + is the dimensionless height from the bottom wall, U τ is the tangential velocity controlled by the wall shear stress;

[0053] Among them U τ It is expressed by formula (2):

[0054]

[0055] In formula (2), τ ω is the wall shear stress, ρ is the air density;

[0056] τ ω The expression of is shown in formula (3),

[0057]

[0058] In formula (3), U ∞ is the incoming flow velocity.

[0059] Step 5: Use steady-state calculations to verify the reliability of different turbulence calculation models. Select the k-ω turbulence model based on the calculation results, and then perform grid-independent verification to determine the minimum number of grids to be 1.35 million.

[0060] Step 6: In Ansys Fluent software, set the incoming flow velocity to 70 m / s, the incoming flow temperature to 253.15 K, and the wall temperature to 265.54 K, perform flow field calculations, and read the calculation results into Ansys Fluent Icing software.

[0061] Step 7: In the Particles module of Ansys Fluent Icing, set the wall temperature to 265.54K again. In the Droplet module, set the liquid water content (LWC) to 0.00055kg / m 3, the droplet diameter is 20 μm, keep it monodispersed in the Particles distribution module, and disable the Droplet velocity vector option to calculate the droplet distribution behavior of the incoming flow in a low temperature environment.

[0062] Step 8: Based on the particle flow calculation results, open the icing module, confirm that the icing temperature is 253.15K, determine the ice type as Glaze in the ice accretion conditions, select the icing time as 60s, start the calculation, and then obtain the ice morphology and ice mass on the structure surface. Figure 4 、 Figure 5 shown.

[0063] Example 2

[0064] This embodiment relates to a method for monitoring icing behavior on a structural surface based on computational fluid dynamics. Figure 2 The specific steps are as follows:

[0065] Step 1: Establish a three-dimensional computational domain model based on Ansys ICEM software. Set the characteristic length of the cylindrical micro-array structure area to 80 mm, the computational domain height to 1600 mm, the width to 1600 mm, the wall length before and after the structure area to 100 mm, and the reserved length at the inlet and outlet positions to 320 mm.

[0066] Step 2: Set the incoming flow velocity to 70 m / s and select the pressure-based solver.

[0067] Step 3: Set boundary conditions. Set the upper boundary of the computational domain and the boundary of the bottom reserved area as symmetry boundary, set the boundary conditions of the bottom structure area and the wall area as wall-no-slip boundary, and set the boundary conditions of the inlet and outlet as velocity inlet and pressure outlet, respectively.

[0068] Step 4: Use structured grid to generate all grids in the computational domain. In order to meet the accuracy requirements of near-wall calculations, 10 boundary layers are set at the bottom of the flow field with an initial height of 1×10 -6 m, and the expansion coefficient is 1.2; then, the element quality indicators are guaranteed to be higher than 0.3.

[0069] Step 5: Use steady-state calculations to verify the reliability of different turbulence calculation models. Select the k-ε turbulence model based on the calculation results, and then perform grid-independent verification to determine the minimum number of grids to be 4.37 million.

[0070] Step 6: In Ansys Fluent software, set the incoming flow velocity to 70 m / s, the incoming flow temperature to 253.15 K, and the wall temperature to 265.54 K, perform flow field calculations, and read the calculation results into Ansys Fluent Icing software.

[0071] Step 7: In the Particles module of Ansys Fluent Icing, set the wall temperature to 265.54K again. In the Droplet module, set the liquid water content (LWC) to 0.00085kg / m 3 , the droplet diameter is 60 μm, keep it monodispersed in the Particles distribution module, and disable the Droplet velocity vector option to calculate the droplet distribution behavior of the incoming flow in a low temperature environment.

[0072] Step 8: Based on the Particles calculation results, enable the Icing module, confirm that the icing temperature is 253.15 K, select Glaze as the ice type under Ice Accretion Conditions, select a freezing time of 120 seconds, and start the calculation to obtain the ice morphology and mass on the structure surface.

[0073] Example 3

[0074] This embodiment relates to a method for monitoring icing behavior on a structural surface based on computational fluid dynamics. Figure 2 The specific steps are as follows:

[0075] Step 1: Establish a three-dimensional computational domain model based on Ansys ICEM software. Set the characteristic length of the rectangular micro-array structure area to 40 mm, the computational domain height to 800 mm, the width to 800 mm, the wall length before and after the structure area to 60 mm, and the reserved length at the inlet and outlet positions to 100 mm.

[0076] Step 2: Set the incoming flow velocity to 138 m / s and select the density-based solver.

[0077] Step 3: Set boundary conditions. Set the upper boundary of the computational domain and the boundary of the bottom reserved area as symmetry boundaries. Set the boundary conditions of the bottom structure area and the wall area as wall-no-slip boundaries. Set the boundary conditions of the inlet and outlet to pressure far field.

[0078] Step 4: Use structured grid to generate all grids in the computational domain. In order to meet the accuracy requirements of near-wall calculations, 10 boundary layers are set at the bottom of the flow field with an initial height of 1×10 -7 m, the expansion coefficient is 1.2, and then the element quality indicators are guaranteed to be higher than 0.3.

[0079] Step 5: Use steady-state calculations to verify the reliability of different turbulence calculation models. Select the k-ε turbulence model based on the calculation results, and then perform grid-independent verification to determine the minimum number of grids to be 1.44 million.

[0080] Step 6: In Ansys Fluent software, set the incoming flow velocity to 138 m / s, the incoming flow temperature to 265.67 K, and the wall temperature to 280.93 K, perform flow field calculations, and read the calculation results into Ansys Fluent Icing software.

[0081] Step 7: In the Particles module of Ansys Fluent Icing, set the wall temperature to 280.93K again. In the Droplet module, set the liquid water content (LWC) to 0.00045 kg / m 3 , the droplet diameter is 40 μm, keep it monodispersed in the Particles distribution module, and disable the Droplet velocity vector option to calculate the droplet distribution behavior of the incoming flow in a low temperature environment.

[0082] Step 8: Based on the Particles calculation results, enable the Icing module, confirm that the icing temperature is 265.67 K, select Glaze as the ice type under Ice Accretion Conditions, select a freezing time of 420 seconds, and start the calculation to obtain the ice morphology and mass on the structure surface.

[0083] The present invention establishes a calculation domain for different microstructures through Ansys-Fluent software, and selects corresponding boundary conditions and solvers according to different incoming flow velocities. On the basis of obtaining a steady-state flow field, it performs tiny droplet distribution calculations and freezing behavior analysis, thereby realizing a reliable prediction of freezing behavior on the surface of the microstructure. The freezing behavior monitoring method proposed in the present invention is not limited to the incoming flow environment and the physical properties of the structure itself, filling the technical gap of directly tracking the evolution law of ice layers on the surface of microstructures under low-temperature and high-speed incoming flow conditions. It has high computational efficiency while ensuring high computational accuracy, significantly reducing the time and economic costs of evaluating freezing behavior on the surface of microstructures, and providing important theoretical support for the microstructural design of the surface of efficient anti-icing materials. It is worth noting that the present invention focuses on proposing a method for monitoring freezing behavior on the surface of a structure based on computational fluid dynamics, and is not limited to the incoming flow environment and the physical properties of the structure itself.

[0084] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various variations or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for monitoring icing behavior on a structural surface based on computational fluid dynamics, characterized in that: The following steps are involved: Step 1: Establish a 3D computational domain model based on Ansys ICEM software. Set the height and width of the computational domain according to the characteristic length of the microstructure region, and place the microstructure region at the bottom center of the computational domain. Set walls of a certain length before and after the microstructure region as transition areas. Reserve a certain amount of space at the inlet and outlet of the computational domain. Step 2: Use a three-dimensional computational domain model to determine the fluid classification based on the incoming flow Mach number. For compressible fluids, a density-based solver is selected, while for incompressible fluids, a pressure-based solver is selected. Step 3: Set the boundary conditions. Set the upper boundary of the computational domain, the front and rear side walls, and the boundaries of the bottom reserved area as symmetric boundaries. Set the boundary conditions of the bottom structure area and the wall area as wall boundaries. For incompressible fluids, set the boundary conditions of the inlet and outlet as velocity inlet and pressure outlet, respectively. For compressible fluids, set the boundary conditions of the inlet and outlet as pressure far field. Step 4: Use structured grid to generate all grids in the computational domain, set a boundary layer at the bottom of the flow field controlled by the initial height and number of layers, and ensure that the grid quality value is higher than 0.3; Step 5: Use steady-state calculations to verify the reliability of different turbulence calculation models. Select the turbulence model with the highest calculation accuracy based on the deviation between the calculation results and the experimental results, and perform grid-independent verification. Step 6: In Ansys Fluent software, the incoming flow velocity, incoming flow temperature, and wall temperature are set according to the actual working conditions, the flow field is calculated, and the calculation results are read into Ansys Fluent Icing software; Step 7: Set the wall temperature again in the particle flow module of Ansys Fluent Icing software. In the droplet module, set the liquid water content, droplet diameter, and particle distribution modules to calculate the droplet distribution behavior of the incoming flow under low temperature environment. In step 8, based on the Particles calculation results, the icing module is enabled, the icing temperature is confirmed, the ice type is determined under the icing conditions, and an appropriate icing time is selected for calculation, thereby obtaining predicted data such as the icing range, icing morphology, and icing quality on the structure surface.

2. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, characterized in that: In step 1, the height and width of the calculation domain are both more than 20 times the characteristic length of the structural region.

3. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, characterized in that: In step 1, the bottom wall length of the calculation domain is 1 to 1.5 times the characteristic length of the structural region.

4. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, wherein: In step 1, the reserved space at the inlet and outlet positions of the calculation domain is 2 to 5 times the characteristic length of the structural region.

5. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, characterized in that: In step 2, the standard for determining the fluid classification is: a fluid with an incoming flow Mach number higher than 0.3 is determined to be a compressible fluid, and a fluid with an incoming flow Mach number lower than 0.3 is determined to be an incompressible fluid.

6. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, wherein: In step 4, the boundary layer is set as follows: the number of boundary layers is 10, the expansion coefficient is 1.2, and the initial height is y, as shown in formula (1): In formula (1), y + is the dimensionless height from the bottom wall, U τ is the tangential velocity controlled by the wall shear stress; Among them U τ It is expressed by formula (2): In formula (2), τ ω is the wall shear stress, ρ is the air density; τ ω The expression of is shown in formula (3), In formula (3), U ∞ is the incoming flow velocity.

7. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, characterized in that: In step 5, the turbulence models are Spalart-Allmaras, k-ε, k-ω, Transition SST, Reynoldsstress, and Detached eddy simulation.

8. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, characterized in that: In step 6, the incoming flow temperature is -60°C to 0°C, and the wall temperature is the adiabatic stagnation temperature + 10K.

9. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, characterized in that: In step 7, LWC is 0.00015 kg / m 3 ~0.001kg / m 3 , the droplet diameter is 10μm~100μm.

10. The method for monitoring icing behavior of a structure surface based on computational fluid dynamics as claimed in claim 1, characterized in that: In step 8, the ice types are clear ice, frost ice and mixed ice.

Images