Case-Studies on CFD Simulation of Windshield De-icing Padmesh Mandloi 1 and Nidhesh Jain 2 Abstract CFD Simulation of windshield de-icing has gained a lot of importance over the last few years. With stricter regulatory norms, both OEMs and suppliers have to constantly work on improving the defroster design [1], [2]. Experimental testing is extremely time-consuming as well as quite expensive. CFD simulations have been able to shorten the development cycle and reduce cost. Windshield deicing simulations involve both geometrical complexities as well as complex physics. It involves interaction between the airflow and two modes of heat transfer, basically conduction and convection [1]. A variety of factors play a very important role in accurately predicting the deicing process and deicing pattern. These factors include defroster angle with the windshield, mesh size and mesh type near the windshield and defroster outlet, thermal conductivity and specific heat considerations due to composite laminate windshield, effect of the melting of ice due to deicing, turbulence modeling etc. Without considering these effects, the deicing rates cannot be correctly predicted. An attempt is made here to study the effect of the above-mentioned factors on deicing patterns. A general purpose CFD solver, FLUENT 6.3 is used to simulate the deicing process. The melting of ice is simulated using phase change model which is based on the enthalpyporosity techniques. Simplified cabin geometry is considered for all the cases. C subroutines are written to model various effects on deicing. Keywords: de-icing, defrost, CFD, vehicle climate control 1 Introduction The task of designing a windshield defroster is a difficult one. The defroster must adhere to government regulations regarding the time to clear a minimum specified area of the windshield. Previously the process involved design, then construction of a trial defroster, followed by a testing program. Based on the test results, adjustments to the initial design were made and the process repeated. CFD simulations have been used to augment the process of defroster design [1], [3]. 1 Lead Engineer, Fluent India Pvt. Ltd., Pune, padmesh.mandloi@ansys.com 2 Engineer Applications, Fluent India Pvt. Ltd., Pune, nidhesh.jain@ansys.com 1
2 CFD Modeling Commercial CFD code FLUENT has been used in the present study. A simplified CFD model is created and all the tests are performed over it. Since the model is non-real, comparison of simulation results with actual experimental results are not available to comment on the accuracy of simulation results. The paper tries to highlight the effect of various parameters by showing only qualitative differences. Geometry and Mesh A simplified cabin geometry is considered for the present work. This is shown in Figure 1 below. symmetry inlet outlet Figure 1: Simplified symmetric geometry with an inlet and outlet The ice layer and the windshield are meshed with prism cells. A few layers of prism shaped cells are grown inside the cabin, attached to the windshield to allow better flow development as the air comes out of the defroster outlet (inlet of the domain). The remaining region is meshed with uniform size tetrahedral cells. The mesh in the geometry is shown in Figure 2 below. On actual models, hexcore type mesh can be generated to reduce computational cost. Figure 2: Mesh in the cabin geometry 2
Case Setup FLUENT v6.3 is used in the present study. Turbulence in the flow-field is modeled using the standard k-epsilon model. Enthalpy-porosity based solidification-melting model is used to model the melting of ice [4]. The ice layer is modeled as an ice-water mixture with both solidus and liquidus temperature specified. The standard CFD procedure for such a problem is to first obtain a steady state constant temperature solution, then freeze the flow field and solve for only energy in unsteady state. 3 Case Studies This section covers studies done by varying different parameters that affect deicing patterns. Effect of mesh near windshield The effect of mesh type near the windshield is considered here. Figure 3 shows the two different types of mesh. A default mesh with only tetrahedral cells in the cabin is created. Full tetrahedral mesh Boundary layered mesh Figure 3: Mesh in the cabin geometry 3
The second mesh has about 10 boundary layered cells grown from the windshield surface to allow better flow development. Figure 4 shows the comparison of velocity contours on a center-plane whereas Figure 5 shows the comparison of deicing rate. Full tetrahedral mesh Boundary layered mesh Figure 4: Velocity field comparison T = 600 s T = 600 s T = 800 s T = 800 s Full tetrahedral mesh Boundary layered mesh Figure 5: Comparison of deicing patterns 4
The velocity field on the full-tetrahedral mesh is quite diffusive and is not very well attached with the windshield whereas on the boundary layered mesh very well aligned with the windshield and is also attached with it. This change is the flow-field translates into a big difference when the deicing patterns are compared. Clearly, the rate of deicing is underpredicted by the full-tetrahedral mesh. Therefore, resolving the near wall mesh properly is very important to correctly predict the deicing patterns. Effect of turbulence models The effect of various turbulence modeling is studied in this section. The case (with prism layered mesh inside the cabin grown from the windshield) was run with standard k-ω model and standard k-є model. The k-ω turbulence model in Fluent is based on the Wilcox k-ω model [4] and is found to be good for wall-bounded flows and free shear flows. Figure 6 shows the velocity field on the mid-plane obtained from the two simulations. The standard k- ω model predicts a flow-filed that is very well attached to the windshield. This results in faster de-icing of the windshield which is shown in Figure 7 below. Standard k-omega model Standard k-epsilon model Effect of Melting of Ice Figure 6: Velocity field comparison In the current de-icing formulation, ice layer is modeled as a semi-solid (mushy) zone with constant values of the specific heat and thermal conductivity. These values correspond to the ice material and remain as they are even when the ice starts melting and the value of liquidfraction is non-zero. In reality, as the ice-melts, the cells that represent partial-ice and partialwater should have averaged properties of specific heat and thermal conductivity. This has been incorporated using a small user defined function (UDF) that calculates averaged value of specific heat and thermal conductivity based on the liquid fraction in a given cell. 5
T = 500 s T = 500 s T = 700 s T = 700 s Standard k-omega Standard k-epsilon Figure 7: Comparison of deicing patterns So, C p eff β + C = C p liq p ice ( 1 β ), where β is the liquid fraction in the cell. Similarly K β + K eff = K liq ice ( 1 β ) Since C p cannot have a user-defined value in Fluent (due to stability concerns), the effect of change in C p is accounted for by an equivalent change in density. Figure 7 shows the effect of accounting for variable K and C p. The figures on left show the de-icing pattern based on constant properties whereas those on the right show the pattern based on variable properties. The melting process once started becomes slower in the case of variable property. This is because the specific heat of water is more than that of ice and so the value of effective specific heat is always greater than that of just ice once the melting starts. Higher value of specific heat means slower transfer of heat and hence slower melting rate 6
Effect of considering composite solids Windshields in automobiles are made up of layers of glass and plastic (called laminates). Some times the windshield glass is also tinted with plastic films to shade it. It is important to consider the thermal effects of these plastic layers to correctly capture the de-icing profile. One way is to actually model these plastic layers. Since the thickness of these laminates and plastic films is so small that to actually resolve this small thickness, we may have to put a very fine mesh in this small thickness, which may not be possible in real geometries. Therefore the other alternative is to model just one solid for the entire windshield glass and then use effective thermal conductivity and effective specific heat for this solid made up of composite material. Again, since deriving an effective specific heat for composites is not so straight forward, the other alternative which is available in FLUENT, is to model the thin laminates as shell conduction walls [4]. Tables 1 and 2 show the material properties of laminate plastic and the effective conductivity of the composite solid. Figure 8 shows the glass-laminate windshield and the geometry when composite solid approach is used. T = 600 s T = 600 s Contant K and Cp Variable K and Cp due to ice melting Figure 7: Comparison of de-icing patterns 7
Table 1: Properties of Plastic Thermal conductivity 3.9 e-02 W/mK Specific Heat 1900 J/kgK Density 1370 kg/m 3 Table 2: Properties of Glass Plastic composite Layer Conductivity (W/mK) Thickness (In) Glass 0.93 0.0044 Plastic 0.039 0.0005 Glass-plastic composite 0.2791 0.0049 Ice layer Plastic layer Glass layer Ice layer Plastic and glass layer merged to create one solid zone Modeling plastic layer Modeling effective conductivity Figure 8: Modeling glass-laminate composite Figure 9 shows the comparison between the two approaches shown in Figure 8. It is clear from this figure that the deicing is fast in the case where the glass-laminate is modeled as a composite. This is because the effective specific is not considered in this case and only the specific heat of glass is used as the effective specific heat. Since the specific heat of glass is lesser than that of plastic, deicing is faster. In the second case, instead of modeling the glass-laminate as composite, the laminate is modeled as a wall with shell conduction effects. The comparison of deicing from this simulation with the baseline simulation (where the laminate is modeled as a separate solid) is shown in Figure 10 below. The rate of deicing in both the simulations is almost the same. This confirms that modeling the laminate solid zone as a shell-conduction wall is a good alternative to modeling the laminate as a separate solid zone. 8
T = 700 s T = 700 s Modeling plastic layer Modeling using K eff of the composite Figure 9: Comparison of de-icing patterns 4 Conclusion This paper summarizes some best-practice techniques involved in the CFD simulation of windshield deicing. There are several key factors like mesh size, turbulence models, variable material properties etc. which need to be taken into account while modeling windshield deicing. Case studies on each of these factors are presented along with a plausible explanation of the effect of these factors. Although FLUENT v6.3 is used to demonstrate the effect of various factors, it must be emphasized here that these factors are physics dependent and are not specific to any particular CFD code. With regulation norms becoming more and more stringent, it is all the more important to achieve accuracy in predicting deicing patterns. This paper can be used as a guide to accurately predict windshield deicing patterns using CFD. 9
T = 700 s T = 700 s Modeling plastic layer Modeling using shell-conduction Figure 10: Comparison of de-icing patterns References [1] A. Farag and L. J. Huang CFD Analysis and Validation of Automotive Windshield De- Icing Simulation. SAE Technical Paper Series. 2003-01-1079, 2003 [2] S. Roy, H. Kumar, R. Anderson Efficient defrosting of an inclined flat surface. Int. Journal of Heat and Mass Transfer. 48: 2613-2624, 2005 [3] Z. Tastan and M. Matthes Windshield Deicing of a passenger car, Fluent Gernany Automotive User Group Meeting, 2002 [4] Fluent 6.3 Users Guide, Ansys Inc., Cannosburg PA, 2006 10