The Challenges of Meshing Ice Accretion for CFD

Figure 1: Hexahedral mesh for an aircraft icing surface.

1228 words / 6 minutes read

Complex ice shapes make generating well-resolved mesh extremely difficult, compelling CFD practitioners to make geometric and meshing compromises to understand the effect of Ice accretion on UAVs.

Introduction

Flying safely and reliably depends on how well icing conditions are managed. Atmospheric icing is one of the main reasons for the operational limitations, Icing disturbs the aerodynamics and limits the flight capabilities such as range and duration. In some scenarios, it can even lead to crashes.

Icing has been under research for manned aircraft since the 1940s. However, the need to understand icing effects for different flying scenarios in unmanned aerial vehicles (UAVs) or drones has reignited the research. Drones are used for a wide range of applications like package delivery, military, glacier studies, pipeline monitoring, search and rescue, etc.

Ice accumulation on different aircraft parts such as nose cone, engine, pitot probe. — **Figure 2: a.** Ice on nose cone. b. Ice on an engine. c. Ice on a pitot probe. Image source – Ref [4]

The well-understood icing process of manned civil and military aircraft does not hold good for most UAVs. UAVs fly at a lower air speed and are smaller in size. They operate at a low Reynolds Number in the range of 0.1-1.0 million as against manned aviation which fly at Reynolds Numbers of the order of 10-100 million. This huge difference necessitates the need to gain a better understanding of the icing process at low Reynolds numbers.

CFD simulation of aircraft ice accretion is a natural choice for researchers due to its cost-effective approach when compared to flight testing. In this article, we will discuss how researchers navigate through geometry and meshing challenges to understand the icing effects.

Ice Accretion Analysis

Icing analysis covers a large variety of physical phenomena. From droplet or ice crystal impact on cold surfaces to solidification process at different scales. Ice accumulation degrades aerodynamic performances such as the lift, drag, stability and stall behaviour of lifting surfaces by modifying the leading-edge geometry and the state of the boundary layer downstream. This results in premature and highly undesirable flow separation.

**Figure 3:** Aircraft Icing: Flow field around an iced airfoil. *Image source – [Ref 5, 6]*

Such flow transition and turbulently active regions need well-resolved grids. However, the complex icing undulations make meshing very hard, forcing the CFD practitioners to face geometric and meshing challenges.

Complex Geometric Shapes

Icing develops different kinds of geometric features such as conic shapes, jagged ridges, narrow, deep valleys and concave regions. In 3D, the spanwise variation of these features creates further complexities.

Meshes for aircraft icing simulation: Inviscid unstructured mesh using tetrahedral elements to discretize the complex 3D iced wing. — **Figure 4:** Inviscid unstructured mesh using tetrahedral elements to discretize the complex 3D iced wing. *[Image source: Ref 3]*

Geometric simplification is more often done while attempting 3D simulations. Even though fine resolution 3D scanned ice feature data is available, incapability to create quality normal wall resolved cells compels CFD practitioners to either simplify the ice features or settle down for some kind of inviscid simulation without capturing the viscous effects. Figure 4 shows such a compromised unstructured mesh without viscous padding for a DLES simulation. Figure 5 shows the extraction of a smoothened and simplified ice geometry from an actual icing surface.

Aircraft icing: Geometric simplification done to 3D ice surface to ease meshing difficulties. — **Figure 5** Geometric simplification done to 3D ice surface to ease meshing difficulties. *[Image source- Ref 9].*

It is extremely difficult to mesh such realistic ice shapes for any mesh generation algorithm let alone the aspect of mesh quality.

As a compromise, the sub-scale surface roughness is smoothened out and is not captured. As a consequence, the turbulence effects due to sub-scale geometric features get ignored.

Wide-Ranging Geometric Scales

Ice features range widely in geometric scales. For, e.g., ice horns can be as big as 1-2 centimetres, while sub-scale surface roughness can be as small as a few microns.

The level of deterioration in performance is directly related to the ice shapes and to the degree of aerodynamic flow disruption they rake up. Sub-scale ice surface roughness triggers laminar to turbulent transition while large size ice-horns cause large-scale separation.

**Figure 6:** *Orthogonal boundary layer padding to capture the viscous activities near the wall.*

Meshing such wide-ranging geometric scales poses a few challenges. Firstly, they will need a massive number of cells to capture the micron-level features, directly posing a challenge to the computational power and considerable time for both meshing and CFD.

Literature review shows that certain CFD practitioners, foreseeing these challenges, settle down for 2D simulations to avoid computationally expensive 3D simulations. Even at the 2D level, finer ice-roughness features are smoothened to make viscous padding creation more manageable.

Finely refined flow aligned hexahedral grid to capture the ice horn wake using GridPro. — **Figure 7:** Finely refined flow-aligned hexahedral grid to capture the ice horn wake.

Horns and Crevices

Crevices and concave regions are home to re-circulation flows. These viscous regions need finely resolved unit aspect ratio cells to capture them. But since many grid generators find it difficult to mesh these regions, the crevices are removed and replaced by a small depression.

Hexahedral meshing of the narrow crevices and concave regions of the aircraft icing surface using GridPro. — **Figure 8:** Hexahedral meshing of the narrow crevices and concave regions of the aircraft icing surface.

Aft of the horns, large-scale wakes are created, which are highly unsteady and three-dimensional in nature. Also, with an increase in the angle of attack, these turbulent features grow in size and start to extend further in the normal and axial direction w.r.t the wing surface. In concave regions and narrow crevices, recirculation flows can be observed.

Boundary-Layer Mesh

The boundary layer padding needs to have a good wall-normal resolution with first spacing equivalent to Y+ not more than 1. The rough ice surfaces aggravate flow separation and adequate viscous padding with a uniform number of layers with orthogonal cells is necessary at all locations.

Growing wall-normal quadrilateral or hexahedral cells from the ice walls for the entire region is a challenge since the crevices are very narrow with irregular protrusions, and generating continuous viscous padding causes cells to collapse one over the other.

Aircraft icing meshes: Viscous boundary layer padding in narrow crevices. a. Hybrid unstructured mesh. b. Hexahedral mesh. — **Figure 9:** Viscous boundary layer padding in narrow crevices. a. Hybrid unstructured mesh. Image source [Ref 7] b. Hexahedral mesh.

To overcome this some grid generators resort to partial normal wall padding to the extent the local geometry permits and quickly transition to unstructured meshing, as shown in Figure 9a.

Meshing Transient Ice Accumulation

Research has shown that airframe size and air speed are two main important parameters influencing ice accretion.

One of the icing simulation requirements is computing ice accumulation for a finite time period spanning 15 to 20 minutes. Multiple CFD simulations are done for different chord lengths and air velocities. As one can perceive, this is a numerically intensive job requiring automated geometry building and mesh generation. In such studies, it is necessary to generate new mesh for every minute or even less to make a CFD run for newer instances of ice deposition.

**Figure 10:** Ice accumulation due to change in a. Airframe. b. Airspeed. Image source Ref [5].

With each time step the shape of the ice-feature changes and with time, they take fairly complex shapes with horns and crevices, making local manual intervention an inevitable necessity.

GridPro's single-topology multiple grid approach helps to rapidly generate high-quality meshes for multiple icing variants. — **Figure 11:** GridPro’s single-topology multiple grid approach helps to rapidly generate high-quality meshes for multiple icing variants-ice accretion analysis automatically.

Parting Remarks

For the safe operation of UAVs without an icing protection system, the common solution is to ground the aircraft when icing conditions prevail. This limitation can be overcome by having a better de-icing system. Through CFD analysis of ice accretion at different atmospheric conditions, the amount of optimal onboard electrical power needed to do de-icing can be known.

However, accurate CFD analysis hinges on precise capturing of the ice features by the mesh. A meshing system which can aptly meet this requirement without making geometric or meshing compromises is the need of the hour.

For structured meshing needs for icing analysis reach out to GridPro, please contact: gridpro@gridpro.com.