T E C H N O L O G Y TESTING

JLR’S REVOLUTIONARY APPROACH TO VEHICLE WADE TESTING The ability of a car to maintain its stability and functionality in flooded roads, referred to as vehicle wading, is crucial. Driving through water is detrimental to underbody components, bumper cover, electronic circuits, air intake (causing hydro-lock) and the engine. All cars are designed with wading capability, but the ability to wade through different depths of water, varies based on the design. In this article, Jaguar Land Rover (JLR) talks about their revolutionary use of numerical simulation in vehicle wading testing, leading to better wading performance.

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AUTHORS

DR.-ING. PRASHANT KHAPANE is Manager, Durability & Reliability CAE at Jaguar & Land Rover in Warwick (United Kingdom)

PRASHANTH SHANKARA is Technical Marketing Engineer at CD-adapco in Detroit (United States)

UDAY GANESHWADE is Senior CFD Analyst Engg. at Tata Technologies in Pune (India)

Unlike the aerodynamic design of the vehicle body where numerical simulation plays a major role, vehicle wade testing is still the only design procedure to analyse and establish wade performance. Wading tests involve driving the car through different depths of car at different speeds. Often, the underbody design and placement of components and the structural design of the chassis has already been decided before wading tests commence and numerical simulation is not used. This leads to late detection of failure modes, expensive design changes, and increased cost & time for testing. As a result, this affects the programme timing. An established Computer Aided Engineering (CAE) process for vehicle wade testing can identify failure modes at an earlier stage, provide insight into the structural integrity of underbody components, and analyse multiple designs with confidence, leading to testing of an optimum design. Numerical simulation helps in savings enormous amount of cost and time, and also improves wading capability for the vehicle and structural integrity of the components.

CHOOSING THE RIGHT SIMULATION TOOL

The use of numerical simulation for vehicle wading testing is at a nascent stage in production environments, and hence there is dearth of literature on best-practices and the use of CAE in vehicle wading. In fact, wade testing is still the only procedure used here. The work done by

Zheng, et al [1] is the major reference for JLR’s development of the CAE process. Aside from this, JLR is the first OEM to publish literature on this topic. The need for this process was to understand the failure modes of under-body components early in the design stage and their effect on the vehicle performance and integrity. The current testing procedure at JLR involves driving the vehicle over a ramp into a wading trough, and using another ramp to exit the trough. Testing is done for different speeds and water depths. Various combinations of speed and depth produce differing behaviours in stability, splash pattern and bow wave formation in front of the vehicle. With numerical simulation, JLR aims to understand these different behaviours and optimise the underbody design. With no historic literature or procedure available, JLR’s first challenge was to identify a computational tool capable of accurately modelling the motion of a vehicle through water. STAR-CCM+ was one of the contenders, in addition to a Smoothed Particle Hydrodynamics (SPH) code and LS- DYNA, another popular Navier-Stokes based commercial code. The CAE process needed to accurately simulate the transient pressure forces on the under-body components due to the motion of the vehicle and water relative to each other. To accurately identify failure modes, the tool needed to handle modelling of the motion of the vehicle in a fully-transient analysis. After careful consideration of the tools, STAR-CCM+ was the clear winner due to its proven use in the automotive industry, overset

➊ Mid-plane cross section of Overset Mesh and domain autotechreview

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T E C H N O L O G Y TESTING

➋ Simulation result (Right) at immersion depth of 180 mm and speed of 1.85 m/s compared to test (Left)

mesh capability to model motion and a well validated Volume of Fluid (VOF) model to capture the air-water interface during wading. The motion modelling needed to be robust and be as close to the test scenario as possible. The Overset mesh capability made STAR-CCM+ the clear winner. This technique involves two different mesh domains, one for the vehicle (overset region) and one for the background domain. This Chimera meshing technique will cut out the region of the background grid overlapping with the overset region, leaving the bordering cells (acceptor cells) between the two regions, which can communicate with each other through interpolation. This enables handling of large motions in a robust, accurate manner.

➌ Comparison of peak pressure data (in mm of H2O) at sensor locations for 180 mm, 1.85 m/s

VALIDATING THE OVERSET MESH

Before applying the overset mesh approach to the vehicle wading simulation, it was imperative to validate this methodology for modelling an object motion into water. For this purpose, JLR scaled down one of their vehicles into a rectangular block to be tested in a towing tank. Six pressure sensors were placed on the block in testing to gather transient pressure data, which can be compared with the CFD results to validate the numerical approach. The box in test was 1,000 mm x 400 mm x 500 mm and tests were at water depths of 50 mm, 100 mm and 180 mm, at speeds of 0.87 m/s and 1.86 m/s. ➊ shows the overset mesh with hexa-

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➍ Sensor locations (white marks) on the vehicle undertray www.autotechreview.com

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T E C H N O L O G Y TESTING

➎ Motion definition of vehicle and wheels and initial water in STAR-CCM+

hedral cells around the block in STARCCM+. The SST k-omega turbulence model in STAR-CCM+, well-validated in the marine industry, was used with the VOF model to capture the air-water interface. Pressure monitors were set-up in the simulation at the exact locations as the six pressure sensors. ➋ shows the rectangular block at an immersion depth of 180 mm and speed of 1.85 m/s in both the towing tank and simulation. This shows good comparison of the water level around the block between test and CFD. In ➌, the correlation of peak pressure data (in mm of H2O) between test and simulation at the six sensor locations is represented for 180 mm and 1.85 m/s. The difference between simulation and test results for all scenarios was within 10 %, which was deemed acceptable. In addition, the water level height comparison between CFD (0.158 m) and test (0.16 m) was also satisfactory establishing the validity of this simulation method.

wires to minimise contamination of test data. Different speeds and wading depths were tested. The vehicle started from standstill and data acquisition started before the vehicle entered the water and stopped when it came to a standstill.

CFD MODELING OF VEHICLE WADING

For accurate modelling of the test environment, a CAD representation of the vehicle and the wading trough was built and cleaned in Hypermesh and ANSA and brought into STAR-CCM+. The vehicle was aligned with the ramp entry and the wheels were floating to enable rotation ➎. A rectangular domain around the vehicle was created to be the overset region, which moves and the rest of the domain was modelled as the static background region. The cool packs (intercooler, condenser and radiator) were modelled as separate domains to solve for porous

physics along with normal physics and were connected to other regions by internal interfaces through which data interpolation takes place. A hexahedral trimmed mesh was automatically generated with proper refinement around the cool packs, water region and the motion path of the vehicle. The final mesh count was around 40 mn cells. The Segregated Implicit Unsteady solver was used to resolve the flow field and the VOF model solves for the multiphase flow physics. Turbulence is modelled using the SST k-omega model and experimental data supplied the inertial and viscous resistance coefficients for the porous flow physics. A velocity inlet boundary condition was chosen at the domain inlet and the side and upper faces were designated as pressure outlets. A rotating (while entering trough) and translating motion were prescribed for the vehicle to model test conditions and tangential velocity boundary conditions is

VEHICLE TESTING

With confidence in the simulation strategy established, JLR moved to the vehicle wading testing and modelling. A Jaguar XJ was used for the wading tests, conducted in the wading trough at the Millbrook Vehicle Proving Ground in Bedfordshire. Sixteen waterproof pressure transducers were fitted on the underside panels ➍ and bumpers. Protective stainless steel meshes protected the sensing diaphragm. The data acquisition and signal conditioning system were set up in the rear of the vehicle, with shielded electrical signal

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➏ Comparison of transient pressure data in sensor 2 (undertray) at 450 mm, 1.944 m/s www.autotechreview.com

T E C H N O L O G Y TESTING

➐ Bow shock from the STAR-CCM+ simulation

given at the wheels using local rotation rate. Sixteen pressure monitors were setup in the simulation at the same locations as the test to compare the results. ➏ shows the comparison of transient pressure data in sensor 2 (undertray) between CFD and testing at 450 mm and 1.944 m/s. The transient pressure data from CFD in all scenarios was within acceptable limits in comparison to test data, especially on stiff components like the undertray. In flexible components such as aero flips, the numerical results were significantly higher compared to test data. This is to be expected since these were modelled as rigid bodies in simulation, while in testing, the deflection from loading leads to reduced pressures. The front bow wave structure also corresponded well between CFD and experimental results, ➐. One of the benefits of using STAR-

CCM+ is the fully coupled, two-way, cosimulation capability with Abaqus, a leading finite element analysis (FEA) structural solver from SIMULIA. Pressure data from STAR-CCM+ was mapped at various time intervals to Abaqus and the loads at various fixtures and high stress areas were obtained. This information is crucial in assisting the underbody design at an early stage. JLR modelled one-way coupling between the fluid and structure but future work will model two-way coupling. The Von Mises stresses on the undertray at a time step of 0.675 s from Abaqus are seen in ➑. A full multi-physics procedure is being validated currently on a simplified model using STAR-CCM+ and Simpack, a multi body simulation (MBS) software using a coupling tool called Multi physics Code Coupling Interface (MpCCI). This will allow the forces and torques from STAR-

CCM+ to be transferred to Simpack to calculate jumping behaviour, when the vehicle enters water. Simpack then transfers the corresponding velocities back during jumping behaviour to STAR-CCM+.

A VISIONARY APPROACH

JLR has developed a revolutionary process for vehicle wade testing using numerical simulation, the first published work of its kind among OEMs. The overset mesh capability of STAR-CCM+ and advanced physics models have helped JLR successfully integrate virtual testing into its process, giving better insight into the underbody component loading and potential failure modes at an earlier stage. Future work involving FSI and MBS in addition to CFD will result in an accurate virtual test bed for wade testing. The benefits are many, including, early detection of failure modes, ability to investigate multiple designs, reduced cost of testing, lesser delays in programme timing and better wading capability. REFERENCE:

[1] Zheng, Xin., Qiao, Xin., Kong, Fanhua., “Vehicle Wading Simulation with STAR-CCM+,” presented at FISITA World Automotive Congress, SAE China, Beijing, 2012.

Note: Wading CAE is a patented CAE technique and has shaped the design of many JLR products.

➑ Von Mises stresses on undertray at T=0.675 sec

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