1 AIAA ICE PREDICTION WORKSHOP - folk.ntnu.no

1ST AIAA ICE PREDICTION WORKSHOP

3D Droplet Impingement and 2D/3D Ice Accretion cases

AIT Austrian Institute of Technology

Alessandro Zanon

James Page

Damiano Tormen

Summary

• 3D droplet impingement

• Cases 131 and 132: axysimmetric inlet

• Focus on effects of particle size distribution

• 2D ice accretion

• Cases 241 and 242: 18-in NACA23012 aerofoil

• Focus on effects of ambient parameters and numerical settings (turbulence and roughness models)


• Cases 361 and 362: 30º swept NACA 0012

• Focus on effects of ice density modelling

• Conclusions

228.07.2021

3D droplet impingement

328.07.2021

Droplet impingement: Axisymmetric inlet, Mach=0.23, α=15º, MVD=20.36 μm

Workflow:

1. Mesh

• provided by the IPW organizers (tetra with prismatic inflation layers)

2. Flow field computation

• Fluent solver mimicking IPW prescribed BC setup

• 𝑘 − 𝜔 SST, production limiter, viscous heating, compressibility effects

• Secon-order accurate numerical schemes

3. Droplet Impingement computation

• FENSAP-ICE DROP3D solver

• Drag law for spheres

• Both Monodispersed and Experimental 7 bin PSD [NASA CR 4257]

Case 131

Case 132

Imposed ሶ𝑚

Case 131: Mass flow rate = 7.8 kg/s

Case 132: Mass flow rate = 10.41 kg/s

428.07.2021

Case 131: Droplet impingement: Axisymmetric inlet

Mach=0.23, α=15º, MVD=20.36 μmMass flow rate = 7.8 kg/s

528.07.2021

Case 132: Droplet impingement: Axisymmetric inlet

Mach=0.23, α=15º, MVD=20.36 μmMass flow rate = 10.41 kg/s

628.07.2021

2D ice accretion

728.07.2021

Workflow for the multi-shot ice accretion simulation:

• Mesh generator: WINGRID, automatic hyperbolic grid generator

• Flow solver: Ansys CFX

• Particle tracking: Ansys CFX

• Ice accretion: ICEAC2D

• Messinger approach, solving the heat and mass balances for a single

control volume

• Adjacent control volumes are coupled by the runback water flow

• HTC derived by CFD simulations, evaporated water mass from

empirical correlation

• Surface displacement

Case 241-242: Ice accretion on 2D geometry

18-in NACA23012 at α = 2º

Multi-shot loop

Meshing

Flow solver

Droplets

Ice accretion

Surface disp.

828.07.2021

Baseline simulation, numerical settings

• Mesh size: • 180k nodes, 361k cells (501 on airfoil and 201 on AB and BC)

• Flow solver:• k - ω SST, Gamma transitional

• High roughness model

• Roughness estimated from Shin et al., k=0.00073 m

• Compressible simulations, ideal gas

• Curvature correction, Kato Launder Production Limiter

• Particle tracking: • Experimental droplet size distribution

• Lagrangian, one way coupling

• Water droplets, constant density 997 kg/m3

• Drag Force with Schiller-Naumann

• Forward Euler with 10 time step for element

• Ice accretion• Ice density 920 kg/m3

• Time step= 60 seconds

Case 241-242: Ice accretion on 2D geometry


928.07.2021

y/c

ho

rd

• Discrepancies between Computed and Experimental

Cp on the suction side are not in line with literature

• Excellent agreement of Baseline result

• Dt/2 = time step of 30s (baseline is 60s)

• Monodispersed = 30 micron

Case 241: Rime ice, Ice accretion on 2D geometry


1028.07.2021

• Baseline results, as per NASA/TM—2015-

218724

• T=265.65 K

• LWC=0.75 g/m3

• T, LWC from IPW dataset

• T=266.05 K

• LWC=0.81 g/m3

• T=264.65 K & T= 263.65 K

• Tinf = Texp - 1 K & Tinf = Texp - 2 K

To visualize impact of bulk flow temperature on

Horns position.

Case 242: Glaze ice, Ice accretion on 2D geometry


1128.07.2021

Impact of different numerical settings on HTC and on Ice shape:

• Transitional model vs Fully turbulent (Baseline result vs FT)

• Imposed surface temperature (baseline Tinf - 10K) to compute the HTC (FT vs FT, Tinf -1)

• Formulation of the wall roughness: high roughness vs Standard Roughness (FT , Tinf -1 vs FT, Tinf -1, SR)

• Value of the roughness (FT , Tinf -1, SR vs FT, Tinf -1, SR, k/10 vs FT, Tinf -1, SR, k*0)

Case 242: Glaze ice, Ice accretion on 2D geometry


1228.07.2021

Tinf

Tinf

Tinf

Tinf

3D ice accretion

1328.07.2021

• FENSAP-ICE

• Multishot sequence with 10 x 2 minute shots:

1. Fluent solver mimicking IPW prescribed setup.

2. DROP3D: no splashing, no break-up.

3. ICE3D: rime model, variable ice density,

constant roughness k=0.0005 m.

4. Fluent remeshing mimicking IPW prescribed

mesh density.

Cut at 55% span

for all other data

50 cuts from 72% to 88%

span (LE) for 20 min ice

MCCS

Post processing with in-house developed Python scripts:

Case 361: 30º swept NACA 0012, 200 kts and 257 K, MVD=34.7 μm

20 minutes rime icing

1428.07.2021

1. Aerofoil Cp: discrepancy possibly

related to mesh density and/or

short length of computational

domain.

2. HTC.

3. Aerofoil surface temperature (flat

top feature).

4. Collection efficiency

5. Ice density: peak around 700

kg/m3)

Ae

rofo

ilsu

rfa

ce

tem

pe

ratu

re(K

)

Streamwise distance from highlight (m) Streamwise distance from highlight (m)Streamwise distance from highlight (m)

Streamwise distance from highlight (m)Normalized chord, x/c

Ice

de

nsity

(kg

/m3)

Colle

ctio

n e

ffic

ien

cy

HT

C [

W/ m

2K

]

Cp

15


20 minutes rime icing

28.07.2021

16


20 minutes glaze icing

• FENSAP-ICE

• Multishot sequence with 10 x 2 minute shots:

1. Fluent solver mimicking IPW prescribed setup.

2. DROP3D: no splashing, no break-up.

3. ICE3D: rime model, variable ice density,

constant roughness k=0.0005 m.

4. Fluent remeshing mimicking IPW prescribed

mesh density.

Cut at 55% span

for all other data

50 cuts from 72% to 88%

span (LE) for 20 min ice

MCCS

Post processing with in-house developed Python scripts:

28.07.2021

17



• Visualization of aerofoil surface mesh ice accretion with remeshing and curvature control

• Clean wing

28.07.2021

18


• Shot 2 of 10 (4 minutes of icing exposure)

28.07.2021



19



28.07.2021



20



28.07.2021



21



28.07.2021



22


• Final ice shape: shot 10 of 10 (20 minutes of icing exposure)

28.07.2021



Conclusions

2328.07.2021

• 3D droplet impingement

• It is fundamental to properly account for PSD to obtain reliable impingment predictions

• Good agreement of the impingment limits obtained, but 𝛽 peaks largely overpredicted at some locations

• Mismatch between predictions and experiments probably due to inaccuracies in experimental data


• For rime ice, excellent agreement between predicted and experimental ice shapes

• For glaze ice, fairly good prediction of the ice shape obtained by imposing Tinf = Texp – 2 K

• The imposed ambient T and surface roughness have a large impact on the predicted ice shape (horn positions

and ice limits)


• For rime ice, fairly good prediction of the ice limits obtained, but maximum ice thickness largely overpredicted

• For glaze ice, fairly good prediction of the ice limits and horn lengths. Inaccuracy in horn angle prediction.

• For both rime and glaze ice conditions, a proper modelling of the ice density is fundamental to improve the

accuracy of the ice shape predictions

THANK YOU!

Questions?

Documents

1 AIAA ICE PREDICTION WORKSHOP - folk.ntnu.no