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WATER INGRESS ANALYSIS FOR VEHICLE WADING USING THE PASSIVE SCALAR MODEL

Dr.-Ing. Prashant Khapane Vivek Chavan, Uday Ganeshwade

D & R CAE, VE Group, JLR-ODEC, Pune

Abstract

As a vehicle wades through water, the water enters the engine bay through the openings/gaps present on the vehicle underbody (henceforth referred to as water ingress). This paper demonstrates the capability of modelling the water ingress in a non-conventional CFD analysis of vehicle wading, using the passive scalar model. Passive scalars act as a pseudo catalyst in the target fluid phase of the virtual CFD simulation. A quantitative analysis of the water ingress is carried out using this model in conjunction with the established wading simulation model. The quantitative results thus obtained give the amount of water ingress from specific locations on the vehicle and also the amount of water subsequently splashing on the pre-set target locations or components in the engine bay. This facilitates a better understanding of splash protection (for alternator, starter motor or any such electrical/electronic component) and splash guard structuring in the design of the vehicle for the given packaging constraints. An enquiry is also made at understanding splash and water impingement characteristics of the default VOF (Volume Of Fluid) multiphase model and the LSI (Large Scale Interface) segregated flow model; and a comparison is made between the two.

Keywords:

CFD, Vehicle wading, turbulence, water ingress, wetting contribution, Passive scalars, Large Scale Interface (LSI), Volume Of Fluid (VOF), Eulerian multiphase modelling.

1. Introduction

Automotive OEMs carry out vehicle wading tests so as to analyse its capability to wade through a flooded road or a similar circumstance. A CAE analysis method has been established [1.], wherein the wading process is simulated virtually using FVM and then the possible failure modes of the underbody components are determined by means of FSI. The method enables a better design of the vehicle components sensitive to failure in the wading test, thereby saving redesigning cost and time.

The splashing of water on a vehicle part can be observed visually by postprocessing the results of the CFD simulation. However, a comprehensive analysis of the water splashing on a component is not possible using the current capabilities offered by the established CAE method. Such a comprehensive analysis would involve the understanding of the water ingress in the engine bay and other areas of the vehicle and its quantification, using virtual simulation modelling.

The proposed simulation methodology can be carried out in conjunction with the already standardized wading CAE analysis method, by incorporating a few modifications in the simulation setup. As a result of the analysis, the amount of water splashing on a given predefined component can be analysed. For e.g., in case of splashing on a critical electrical component (such as the alternator or starter motor), the virtual analysis can be used to

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determine the quantity of water impinging on it and the sources of water that contribute to the impingement. Thus, within the given packaging constraints, a better design of splash guards and/or relocation of critical components can be accomplished via the proposed design modifications.

The passive scalars model is used in conjunction with the other physics model in the simulation method. This model is traditionally used to study mixing and/or diffusion between fluids of different phases and for studying their characteristic at certain predefined locations in the fluid domain [2.]. The use of this model elaborated in this paper is a first attempt of its kind. The data obtained from the computational analysis of the passive scalar is needed to be interpreted, via postprocessing in a way that would give an accurate assessment of the water splashing. Without the necessary filtering of the solution output data, the results obtained would be irrelevant.

The scope of this paper is as follows:

• A brief description of the research work done in this domain, and the contextual information on wading CAE and passive scalars has been provided in Sections 2 and 3

• The method development philosophy and process has been elaborated in section 4.

• Subsequent details on the simulation results, experimental testing and validation process have been given in sections 5 and 6, respectively.

2. Literature Survey

The JLR standard TPJLR.00.105 [3.] has been established so as to clearly outline the test procedures to be undertaken when testing a vehicle for its wade capabilities. The standard defines the various vehicle speeds and water depths at which the tests need to be conducted, depending upon the vehicle type and features.

A CAE method has also been established (JLR-CAE-D&R-0001 [4.]) so as to model the water wading tests in accordance with TJLR.00.105. Khapane et. al. [5.] have elaborated on the challenges faced in the development of a robust virtual analysis method of vehicle water wading and the devised solutions for the same.

Relevant literature on the passive scalars and their physical characteristics is sparse. Warhaft [6.] presents one such study, wherein the behaviour of passive scalars in a simple turbulent flow has been discussed. Much of the knowledge gained about the passive scalars model has been taken from Star CCM+ Documentation [2.].

3. Background Study:

3.1 Wading CAE procedure:

JLR uses a patented CAE method [1.] for carrying out the virtual simulations of vehicle wading. The method has been developed in the Star CCM+, which is a comprehensive CFD solver package, using the ‘overset mesh’ capability. In this non-traditional analysis, the vehicle (set in the overset region) moves through a stationary water trough. The speed of the vehicle and the water depth can be readily altered during the pre-processing of the simulation.

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Figure 1. Block wading through a simple water trough. (Above: The block region superimposed on the background domain mesh| Below: An isometric view of the simulation)

The overset mesh operates through the Chimera interpolation technique, where the background (water trough domain) cells that overlap with the overset (vehicle region) cells are cut out [1.]. The cells at the borders of the overset region (directly in contact with the background region) are called the acceptor cells, which communicate computational data between the two regions via interpolation.

The complete wading CAE process can be divided into two major steps [1.]:

• CFD simulation of the vehicle wading through a trough of water.

This is carried out in Star CCM+ via the FVM. The output data obtained at the end of this simulation comprises of visual data of the vehicles traverse through the path flooded with water (obtained in the form of images/scenes), and the numerical data of the pressure on the various predefined components of the underbody components due to the impinging water (obtained in the form of ‘.trn’ files). Figure 2 shows a simulation scene of vehicle wading. The simulation was carried out in Star CCM+ 10.02.

Figure 2. Zoomed out view of a full-scale vehicle wading through water

• FEA assessment of the stress on the underbody components and the loads on the fixtures.

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Figure 3. Von Misses Stress plotted on the Bumper-Lower Cover of the vehicle. High stress areas (prone to failure) have been highlighted.

Abaqus FEA package is used for this purpose. The Step 1 connects with Step 2 via FSI in an open loop manner. This assessment enables the CAE analyst to find out whether the component, subject to the water pressure at given vehicle speed and water depth, would undergo failure.

The water ingress analysis methodology discussed in this paper deals with alterations and additions made to the Step 1, since this analysis has to do with the fluid flow assessment in the regions rather than the structural assessment.

3.2 Passive Scalar:

A passive scalar is a diffusive contaminant in a fluid flow that is present in such low concentration that it has no effect on the fluid motion, or any of its properties [6.]. For e.g., tracer dye in a flowing stream of fluid would not affect the physical properties of the flow, but rather would help in analysing the fluid flow.

In the CFD environment, passive scalars are user-defined variables of arbitrary value, assigned to fluid phases or individual particles [2.]. Taking the example of the tracer dye in a fluid, the passive scalar model would impart it numerical values in the virtual analysis, instead of the dye colour. Thus, by a careful definition of the PS source term, elaborate physical phenomena can be modelled and its effects can be studied in a far greater detail.

3.2.1 Modelling the Passive Scalars:

The passive scalar model is an optional model in the physics continua of the simulation. In case of a multiphase flow scenario (such as wading), the passive scalar model can be selected for the given mesh domain as a whole, or can be applied to an individual phase/s. One or more passive scalar entities can be set in the fluid domain, depending on the simulation geometry, interaction between the phases and the desired outputs.

The output obtained from the passive scalar model would depend on the initial values set, the source term and the specific locations of the passive scalar source, if any.

3.2.2 Passive scalar formulation:

The transport equation for the passive scalar [5.] component Øj is:

𝜕𝜕𝜕𝜕𝜕𝜕

�𝜌𝜌∅𝑗𝑗𝑑𝑑

𝑉𝑉�𝑉𝑉� + �𝜌𝜌∅𝑗𝑗�𝑣𝑣 − 𝑣𝑣𝑔𝑔�.𝑑𝑑𝑎𝑎�

𝐴𝐴= �𝐽𝐽𝑗𝑗.

𝐴𝐴𝑑𝑑𝑎𝑎� + �𝑆𝑆∅𝑗𝑗𝑑𝑑𝑉𝑉�

𝑉𝑉�

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… (1)

Where:

• j is the component index. • 𝑑𝑑𝑉𝑉� =∝𝑖𝑖 ℵ𝑑𝑑𝑉𝑉, where ∝𝑖𝑖 is the volume fraction of phase i (set to 1 for single phase flows) and ℵ is the void

fraction. • 𝐽𝐽𝑗𝑗 is the diffusion flux. See the following section. • 𝑑𝑑𝑎𝑎� =∝𝑖𝑖 ℵ𝑑𝑑𝑎𝑎 • 𝑆𝑆∅𝑗𝑗 is a source term for passive scalar component j. The source term is not multiplied internally by the volume

fraction. ∅𝑗𝑗is assumed to be positive-definite.

Figure 4. Mixing of two streams of a fluid studied using passive scalar model. Infusion of the smaller stream in the parent stream can be analysed.

As seen from equation (1), the transport equation for the passive scalar contains a convection term as well as a diffusion term. Depending upon the physics of the simulation, either or both of the term can be activated or deactivated via the property node of the particular passive scalar during the pre-processing.

The passive scalars, by default, use the Schmidt number function as a property for specifying the molecular diffusivity. In case of turbulent flows, an additional term, viz. the Turbulent Schmidt number gets incorporated.

4. Passive scalars applied to wading CAE:

The key objectives for conducting the water ingress analysis for wading CAE applications are listed below.

• When water impinges on a particular component/region in the vehicle, it first enters the vehicle from one of the openings present on the vehicle outer body (e.g., front grille opening, underbody gaps). The analysis should yield the amount of water entering from each of these openings.

• To compute the amount of water that hits a certain pre-set component (mainly in the engine bay) w.r.t. time.

• Linking the above two, the simulation should also give the percentage contribution, from each source, to the wetting of the component.

4.1 Geometry and Mesh Setup

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A simplified geometry of a vehicle was preferred for conducting the water ingress analysis. The reasons for the same have been listed below.

• To rid the simulation of any complications and errors, which would arise due to a complex geometry and a large mesh count

• To reduce the computational time, since it is possible that a large number of simulations would be required to run before a decision is reached on the best physics setup.

• The accuracy of results can be assessed, with a fair degree of precision, via logical guesswork and reasoning.

The simplified geometry of the vehicle is given in Fig. 5. The dimensions of the vehicle are such that it serves as a scaled down model of an actual vehicle programme. Accordingly, the background domain (water trough) was scaled down as well.

Figure 5. Details of the simplified vehicle model. Openings on the vehicle body and the target component have been labelled.

The openings which from which the water could possibly enter were determined and a passive scalar with a controlled source term was to be set at these openings. For this purpose, an independent mesh region was established at the periphery of each of the openings, and the passive scalar source flux in that region was matched to the estimated mass flow rate (M) of the water (𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝜕𝜕𝑑𝑑, 𝜌𝜌 = 997 𝑘𝑘𝑘𝑘/𝑚𝑚3) through the opening.

𝑀𝑀 = 𝜌𝜌 × 𝐴𝐴 × 𝑉𝑉 × cos𝜃𝜃

Where,

𝐴𝐴 = Mean cross sectional area of the opening (m2) 𝑉𝑉 = Water velocity relative to the vehicle (m/s) 𝜃𝜃 = Inclination of the opening cross sectional area with the relative fluid velocity (deg)

4.2 Physics Setup

Water wading simulations are conventionally set-up as Eulerian multiphase flow cases, where the two phases, viz. water and air interact via a VOF-VOF phase interaction model. Since only water ingress is meant to be analysed, the passive scalar model is triggered only for the water phase, and the air phase is left as is. The details about the simulation setup of the case are given in Table 1 below.

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Table 1: An overview of the properties and values set in the physics setup of the simulation

Number of passive

3 Passive scalar source

Mass flux (calculated

Passive scalar source

0.0 kg/m3-s Passive scalar transport t /

Both convection and diff i Initial value 0.0

4.3 Observations

A few test cases were run for detecting any preliminary miscellaneous errors that would come up in the simulation. The following issues were encountered:

• As the simulation progresses, the passive scalar no longer remains confined to the water phase and diffuses into the air phase as well, thus giving an erroneous representation of the fluid flow.

• The interfaces defined between the passive scalar source regions at the periphery of the vehicle body openings needed re-initialization after each solution time step. This behaviour is atypical of the interfaces wherein there is no relative motion between the interface boundaries.

• The residuals of the simulation quickly shot up to very high values, and eventually, a floating point exception error was encountered.

The simulation setup was altered slightly and was re-run, with several setup iterations, so as to understand the reasons for each of these issues and to resolve them. It was observed that the passive scalars exhibited a greater diffusive tendency than was assumed earlier. Hence, the diffusive term in their transport equations was set to zero, such that their flow would purely be a result of convection of fluid. Secondly, the passive scalar source term was controlled by a user defined field function, such that the passive scalar in the given peripheral region was active when the vehicle enters the water trough, and no sooner. Using this approach, the infusion of passive scalars in the air phase was reduced to a large extent.

A bug was encountered in the passive scalar model in the version 8.04 of Star CCM+, to which, the instability in simulation residuals can be attributed. The simulation was run using a more recent version (9.06 or higher), and the simulation ran with a greater stability.

The peripheral regions were set near the boundaries of the vehicle openings, instead of directly being attached to them. It was ensured that at least two cell-thick gap was constantly maintained for all the PS regions. The interfaces were found to function appropriately using this approach and the succeeding simulations ran smoothly without an unexpected jump in the residual values.

5. Preliminary Inferences

The results obtained from the successful CFD simulation of the test car case for water ingress analysis gave satisfactory results, which are given in Table 2 below.

Table 2: The results obtained from the analysis of the simplified car

Total mass of water

6.187 kg Total mass of water

6.308 kg Total mass of water

-0.86 kg**

Total mass of water hitting th t t t

0.784 kg

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Impingement contribution

0.388 kg Impingement contribution

0.396 kg

Impingement contribution

1.32E-4 kg**

**It was originally assumed, that the opening I3 would serve as an inlet for water ingress. However, it was observed that as the vehicle travels further through the flat portion of the trough, a greater quantity of water exits from I3, and also a negligible amount of water actually hits the target component. This can also be seen in Figure 6, where the mass flow rates for each inlet is given against the simulation time.

Figure 6.A plot giving the mass flow rate (kg/s) of water from each of the openings. Integrating each curve w.r.t. time would yield the total mass flow.

This serves as an example that this method of modelling can serve as a means of analysing the mass flow through an opening on the vehicle opening in a far greater detail, than what was possible before.

6. Experimental testing and validation

The simulation methodology explained earlier needed to be implemented on a full vehicle programme with a strong correlation between the results obtained from physical testing and those obtained from CFD simulation.

A physical test was conducted on a certain vehicle programme (Jaguar Land Rover), henceforth referred to, as JLR-x. The study was conducted as a functional test, to determine the amount of water impinging on the alternator (located in the engine bay of the vehicle), and the contributing sources of water.

Figure 7 and Figure 8 show the JLR-x vehicle. A porous jacket was wrapped around the alternator (refer Figure 8), so as to absorb the amount of water incident on the alternator. The water collected by the jacket was then measured and reported out. A camera placed in the engine bay, right in front of the alternator, helped in visually identifying the sources of water and qualitatively assessing their share in the wetting.

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Figure 7. Above: CAD representation of the alternator shows its location w.r.t. the front subframe and the splash shield. | Below: Water impacting on the alternator during physical testing.

The test was carried out for a series of vehicle speeds and water depths. The water collection in each of the scenario for JLR-x is given in Table 5.

Table 3: The quantity of water collected in the porous jacket for each water depth-vehicle speed grouping is given for the RWD and AWD vehicle

Test RWD AWD 50 mm/30kph 26 g N.A. 50 mm/50kph 48 g N.A. 50 mm/65kph 83 g 153 g 100

102 g N.A.

100

104 g 135 g

6.1 Water ingress analysis for JLR-x using CFD simulation

Using the analysis methodology outlined in section 4, a simulation case was prepared for the RWD JLR-x programme. The alternator was encased in a contiguous porous region, which would serve as the jacket for storing the impinging water.

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Figure 8.A view of the Engine Bay, showing the alternator and the surrounding porous jacket.

Note, that using the numerical reports in the CFD analysis, it is possible to directly measure the quantity of water impinging on the alternator. Integrating this value over the solution time would give a more accurate value of the amount of water impinging on the alternator, since a certain amount of water would escape the porous jacket under any circumstance. However, since this output cannot be obtained from experimental testing, the porous region was modelled to calculate the amount of water retained at the end of the simulation.

6.2 Simulation results obtained and further diagnosis:

A striking contrast was observed between the CFD simulation output data and the results obtained from the experimental testing. The simulations show a very negligible splashing in the alternator region, as a result of which, almost no water gets collected in the porous jacket. Specific action was taken so as to correctly capture the flow of water in the alternator location (by providing mesh refinement and curve-control), the details of which are given in Table. 3. The simulation output results are outlined in Table. 4.

Table 4: The properties of the simulation, for the RWD simulation cases are given below.

Global mesh size in the

40 mm Mesh size in the Alternator

5 mm

Cell count in the Fluid

32.4 Million Turbulence Model SST (Menter) K-

Wall Treatment All y+ wall

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Eulerian Multiphase model VOF Multiphase Interaction VOF-VOF Phase

Time-Step for the Implicit

0.0025 s Vehicle speed 30 kph and 50 kph,

Water depth 100 mm

Table 5: A summary of the simulation outputs with regards to solver time and water ingress is given below.

Total CPU solver time for 30 kph run 81 hrs. Total CPU solver time for 30 kph run 74 hrs. Water retained by the jacket for 30

3.1946e-5

Water retained by the jacket for 50

2.49277e-

Mass of water incident on the

9.04e-4

Mass of water incident on the

8.25e-4

6.2.1 Diagnosis:

The VOF Eulerian multiphase model used in the physics setup is diffusive in nature. The droplets of water in the splash are needed to be larger than the prevalent mesh size in the vehicle region for the splash to be modelled. In cases where the water jet (broken off from the main water-air interface) is dimensionally smaller than the cell size, the jet would be completely absorbed in the subsequent cell in the fluid domain. Hence, an adequate water splash does not impact the target component, as a result of which, the amount of water retained in the porous jacket is also insignificant.

6.2.2 Possible solutions for accurately modelling the water splash

Barring case setups that would introduce a major alteration in the geometry and physics setup of the simulation, the following two suggestions were identified as having the best prospect of modelling the water impingement and splash, with the least need to modify the current system.

• Retaining the same physics continua as before; refine the mesh in the vehicle region, such that the water jet would be resolved to an acceptable degree. This would also require the solution time-step to be reduced proportionally.

• For the same mesh continua, replace the VOF model with a suitable multiphase interaction model that would model the water jet in the best possible manner.

After some careful consideration, the LSI multiphase segregated flow model was chosen as the best alternative to replace the VOF-VOF interaction model. This model would resolve the fluid splash droplets whose size is greater than that of the mesh size; and would model the droplets that are too small to be resolved. Hence, the LSI model has the best prospects of successfully modelling the splash and successfully replacing the VOF model in water ingress analysis simulations.

6.2.2.1 VOF model with mesh refinement (modified case I)

A simplified simulation case was set-up for analysing the water jet splash and the effects that mesh refinement would have on it. The setup is shown in Figure. 9. Good amount water splashing is seen for the ‘Modified Case I’

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Figure 9.A modified case designed to study water splashing after impacting on an arbitrary boundary surface.

A baseline case was modelled with mesh refinement proportional to that in the JLR-x case. As expected, a negligible amount of water splashed on the porous jacket and no water was retained at the end of the simulation.

The mesh in the domain was then refined in the region from where the water would hit the tilted plate ‘a’, and would then impinge on the alternator. Table 5 gives the details on the changes between the baseline case and the modified case setup.

Table 6: The simulation characteristics for the ‘baseline case’ and ‘Modified Case I’ are given below.

Property Value for Baseline

Value for Modified Case I Global mesh

10 mm 10 mm

Mesh size in

5 mm 2.5 mm Prism layer mesh

None Two cell thick, at plate

Total Cell

0.8 Million 3.4 Million Time-Step for the Implicit

0.0025 s 1e-4 s Water impingement

Not observed Observed

After running the modified case I, it was seen that the water splash was resolved with a far greater accuracy and the water gets collected inside the porous jacket region. The results obtained herein are the best thus far. It is also worth noting that the solver time in this case increases proportionally. The appropriate mesh refinement in the full vehicle (JLR-x) model domain would increase the solver time by a factor of 10 at least. Such a high increase in the computational time makes this method rather unfeasible.

6.2.2.2 Simulation with LSI model (modified case II)

This model was implemented on the case with a mesh domain which was same as that of the baseline case. A much better (compared to the baseline case) and realistic flow of water was seen from the Volume Fraction scalar plots, taken on a centreline cross section plane of the domain.

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Table 7: The simulation characteristics for the ‘baseline case’ and ‘Modified Case II’ are given below.

Property Baseline Case Modified Case Eulerian

Multiphase

VOF Multiphase Segregated

Multiphase i t ti

VOF-VOF h

Large Scale I t f h

Turbulence modelling

K-Omega model,

K-Omega model,

Simulation

High Very Low Water impingement

Not observed Observed

The water impingement in the porous region is seen. The water retained in this region is approximately equal to that in the modified case I, even though the mesh is significantly coarser. This serves as a sufficient proof, that the water jet/droplets which are smaller than the mesh cell size has been modelled accurately.

The LSI model was then also successfully applied to the wading analysis of a simplified block (with a constant mesh size throughout the fluid domain). However, the LSI model is inherently a very unstable model and it was seen that introducing volumetric mesh refinements in the region, or the presence of cell/s which are skewed or have a high aspect ratio, cause the propagation of very high velocity through that cell and a subsequent floating point error due to unrealistically high residuals. The solution was also seen to diverge eventually in the modified case II, despite best efforts to avoid it.

Figure 10.A cut section showing the Volume Fraction scalar plot for the water phase of the ‘Modified Case II’

7. Conclusion

The water ingress analysis methodology described in this paper is certainly useful for analysing the flow of water through the fluid domain in a greater depth, and can be used subsequently, for giving better design suggestions with regards to splash protection. The results from the simulations need to be strongly correlated with the experimental test data, for the method to be acknowledged and standardised.

A suitable method has not been devised so as to apply this methodology to a full scale vehicle programme. LSI model, even though unstable, still provides the best water impingement results at a much lower computational cost.

8. Further Work

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Following list provides an outline of the further work that is needed in the development of this virtual analysis methodology and is being carried out by the authors.

• Studying the LSI model, so as to obtain a greater understanding of its sensitivity to variations in the mesh size and physics and solver setup.

• To make the LSI model case stable and robust by making suitable tweaks in the simulation setup.

• Sufficient modifications should be made in the VOF model for attaining a better water impingement, such that the computational cost is still practicable.

• To implement the modified multiphase interaction model (LSI or VOF) to the JLR-x programme and validating the virtual analysis methodology.

• To develop enhanced ways to make a better use of this analysis methodology.

• To devise in depth physical testing procedures, so as to further correlate the method.

• To provide design suggestions based on the results obtained from the simulations, providing better overall splash protection for the given packaging constraints.

References

[1.] Khapane, Prashant, (Patents W/1/073Abbey Road, Whitley, Coventry, Warwickshire CV3 4LF, GB) 2015, Method for Simulating a Vehicle Driving Through Water, Jaguar Land Rover Limited, WO/2015/150400

[2.] Star CCM+ 10.04.010 Documentation and User Guide.

[3.] TPJLR.00.105: ‘Test Procedure standard for Jaguar Land Rover’ vehicles, number 00105, Wade and splash assessment.

[4.] JLR-CAE-D&R-0001: CAE based functionality testing standard established for ‘virtual wade and splash simulations’.

[5.] Khapane, P. and Ganeshwade, U., ‘Wading Simulation - Challenges and Solutions,’ SAE Technical Paper 2014-01-0936, 2014, DOI: 10.4271/2014-01-0936.

[6.] Warhaft Z., ‘Passive Scalars in Turbulent Flows’, Annual Review of Fluid Mechanics, Vol. 32: 203 -240, January, 2000

Contact Information

1. Vivek Chavan: Email: vivek.chavan@tatatechnologies.com Tel: +91 20 6652 6283

2. Uday Ganeshwade Email: uday.ganeshwade@tatatechnologies.com Tel: +91 20 6652 6160

Acknowledgements

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The authors of this paper are deeply thankful to their JLR Manager, Dr-Ing. Prashant Khapane, for his valuable support, guidance and patience during the development of this competency. They would also like to thank Gabriel Gainham, D&R, JLR, for providing them with the experimental testing data; and also the CD-Adapco support team for their valuable suggestions and cooperation. Lastly, they would like to thank their fellow teammates, colleagues and their seniors for their constant encouragement and assistance.

Abbreviations and Definitions:

FVM Finite Volume Method

FSI Fluid Structure Interaction

JLR-x A Jaguar Land Rover Vehicle programme, which shall not be specified, for confidentiality purposes

RWD Rear Wheel Drive

AWD All Wheel Drive

D&R Durability and Robustness