GB2539499A - Wading simulation method - Google Patents

Abstract

A simulated wading event for a vehicle involves defining a multi-body dynamics model of the wading event for generating motion-related data for a multi-body simulated vehicle as it traverses a wade trough; defining a computational fluid dynamics model of the wading event for generating force-related data for a fluid dynamics simulated vehicle as it wades through water in a trough region; receiving, at the multi-body dynamics model, force-related data from the computational fluid dynamics model and using the received force-related data as an input to the multi-body dynamics model; receiving, at the computational fluid dynamics model, motion-related data from the multi-body dynamics model and using the received motionrelated data as an input to the computational fluid dynamics model.

Description

WADING SIMULATION METHOD

TECHNICAL FIELD

The present disclosure relates to a wading simulation method and particularly, but not exclusively, to the simulation of a vehicle/vehicle components when a vehicle is travelling through different water depths at varying speeds. Aspects of the invention relate to a method, such as a method of testing vehicle functional part integrity using the wading simulation method, to a system and to a computer program product.

BACKGROUND

Vehicle wading may occur when a vehicle encounters a body of water. Water levels during wading may be low and comprise a splash effect where water hits the underside of the vehicle and drag force/water pressure on the under body of the vehicle are due to air and water combined. Wading may also occur with higher water levels in which the lower part of the vehicle may be submerged in water and the under parts of the vehicle may experience hydrodynamic force and drag.

Vehicle water wading capability refers to vehicle functional part integrity (e.g. engine under-tray, bumper cover, plastic sill cover etc.) when travelling through water. Wade testing involves a vehicle, comprising a functional part for testing, being driven through different depths of water at various speeds. The wade test may be repeated with a variety of different functional part designs and these functional parts may be inspected afterwards for damage. Wade testing is of particular use in testing under-body functional parts.

Traditionally wade testing has involved the physical manufacture of functional part designs which are then tested in a wading test. Such a testing process can lead to the late detection of failure modes which inevitably leads to expensive design change, and potentially affects program timing.

The present Invention has been devised to mitigate or overcome at least some of the above-mentioned problems.

SUMMARY OF THE INVENTION

According to an aspect of the present invention there is provided a method of performing a computer implemented analysis of a vehicle in a simulated wading event. The method may comprise defining a multi-body dynamics model of the wading event for generating motion-related data for a multi-body simulated vehicle as it traverses a wade trough. The method may comprise defining a computational fluid dynamics model of the wading event for generating force-related data for a fluid dynamics simulated vehicle as it wades through water in a trough region. The method may comprise receiving, at the multi-body dynamics model, force-related data from the computational fluid dynamics model and using the received force-related data as an input to the multi-body dynamics model. The method may comprise receiving, at the computational fluid dynamics model, motion-related data from the multi-body dynamics model. The method may comprise using the received motion-related data as an input to the computational fluid dynamics model.

In embodiments of the present invention there is provided a method of simulating the motion of a vehicle in a wading event in which a multi-body dynamics (MBD) model and a computational fluid dynamics (CFD) model are coupled together such that each model uses outputs (e.g. output results) from the other model as an input to its own calculations.

The effects of water pressure and waves etc. on a vehicle may be simulated using a CFD model but such an approach does not account for the jumping behaviour of the car due to hydrodynamic forces and the vehicle's reaction when diving into water trough is calculated in a MBS code. The present invention provides a form of co-simulation in which force-related data that is generated by the CFD model is used as an input (in addition to other input variables) to the MBD model. Motion-related data that is generated by the MBD model is additionally used as an input (in addition to other input variables) to the CFD model.

Optionally, the two models may be coupled together via an MpCCI coupling environment.

Optionally, the multi-body dynamics model and computational fluid dynamics model generates data with respect to a common time step.

Optionally, the method comprises using an adaptive time step to control the size of the common time step.

Optionally, the method comprises controlling the size of the time step using a courantfriedrichs-lewy condition Optionally, each model generates and outputs data to the other model once per time step.

Optionally, within a given time step, the computational fluid model: (i) generates force-related data using motion-related data output by the multi-body dynamics model in a previous time step and (H) outputs force-related data to the multi-body dynamics model, and the multi-body dynamics model (Hi) generates motion-related data using force-related data output by the computational fluid model in the previous time step and (iv) outputs motion-related data to the computational fluid dynamics model. In this manner each model may run in parallel using data generated by the other model in the previous time step (relative to the current, given time step) to generate results for the current (given) time step.

Optionally, the models output generated data to and receive data from a control server.

Optionally, the method comprises defining coupling regions between the multi-body simulated vehicle and the fluid dynamics simulated vehicle. The coupling regions between the two models may be defined during a set up phase.

Optionally, the force-related data comprises force and torque data relating to the simulated vehicle.

Optionally, the motion-related data comprises velocity and angular velocity data relating to the simulated vehicle. It is noted that the motion-related data may also comprise a positional element such that the location of the vehicle and other vehicle components such as the 20 wheels may be communicated between the models.

Optionally, the model comprises storing force-related data generated by the computational fluid dynamics model over time.

Optionally, defining the computational fluid dynamics model comprises simulating underbody components of the vehicle, the method comprising defining a finite element analysis of the underbody components of the vehicle and using the stored force-related data as an input to the finite element analysis.

Optionally, the method comprises determining deformation of components of the vehicle from the finite element analysis. Optionally, determining deformation of vehicle components may be run in parallel with the CFD-MBD coupled model and the results of the finite element analysis used as an input to either or both of the CFD and MBD models.

According to another aspect of the invention there is provided a system for performing a computer implemented analysis of a vehicle in a simulated wading event. The system may comprise a multi-body dynamics model of the wading event arranged to generate motion-related data for a multi-body simulated vehicle as it traverses a wade trough. The system may comprise a computational fluid dynamics model of the wading event arranged to generate force-related data for a fluid dynamics simulated vehicle as it wades through water in a trough region. The system may comprise a first input, at the multi-body dynamics model, arranged to receive force-related data from the computational fluid dynamics model and use the received force-related data as an input value to the multi-body dynamics model. The system may comprise a second input, at the computational fluid dynamics model, arranged to receive motion-related data from the multi-body dynamics model and using the received motion-related data as an input value to the computational fluid dynamics model.

According to a further aspect of the present invention there is provided a method of assessing the performance of a functional part of a vehicle during a wading event. The method may comprise simulating the wading event according to the above of performing a computer implemented analysis of a vehicle in a simulated wading event. The method may comprise obtaining transient pressure data from the simulation of the wading vehicle. The method may comprise modelling the effects of the transient pressure data on the functional part. The method may comprise determining loading data on the functional part from the transient pressure modelling. The method may comprise assessing the performance of the functional part from the determined loading data.

The functional part may comprise a part and a number of fixings. The loading of the part may then also include the retention of fixings or their failure to fully retain the part. These fixings often comprise trim clips and the failure mode comprises the failure of the clips and subsequent detachment of the part. Another failure mode is bending, folding or tearing of heat shields which comprise thin aluminium sheets The deformation then moves the heat shield out of position and it fails to correctly perform its shielding function.

Optionally assessing the performance of the functional part comprises comparing the performance of the assessed functional part with previously assessed functional part designs.

Optionally, the method comprises assessing the performance of the functional part against predefined failure modes of the functional part. Predefined failure modes may comprise plastic deformation or fixing retention failure.

Optionally, assessing the performance of the functional part comprises comparing the determined loading data with physical testing data.

Optionally, modelling the surface of the vehicle comprises stitching gaps in the surface of the vehicle to create a water tight assembly.

According to a still further aspect of the present invention there is provided a system for assessing the performance of a functional part of a vehicle during a wading event. The system may comprise an input arranged to receive data relating to a vehicle and a trough region to be waded by the vehicle. The system may comprise a processor arranged to simulate the wading event according to the system for performing a computer implemented analysis of a vehicle in a simulated wading event, obtain transient pressure data from the simulation of the wading vehicle, model the effects of the transient pressure data on the functional part, determine loading data on the functional part from the transient pressure modelling, and assess the performance of the functional part from the determined loading data. The system may comprise an output arranged to output a performance indication for the functional part.

Additionally, a computer program product may comprise computer readable code for controlling a computing device to carry out the method of the above method aspects of the present invention.

Within the scope of this application it is expressly intended that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. That is, all embodiments and/or features of any embodiment can be combined in any way and/or combination, unless such features are incompatible. The applicant reserves the right to change any originally filed claim or file any new claim accordingly, including the right to amend any originally filed claim to depend from and/or incorporate any feature of any other claim although not originally claimed in that manner.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which: Figure 1 shows the ride height of a vehicle in a wading event; Figure 2 shows coupled simulation models in accordance with embodiments of the present invention; Figure 3 shows coupled simulation models in accordance with embodiments of the present invention; Figure 4 shows a block in a multi-body dynamics model; Figure 5 shows a block in a computational fluid dynamics model; Figure 6 shows coupled simulation models and a control server in accordance with embodiments of the present invention; Figure 7 shows velocity versus time traces for a coupled model method according to embodiments of the present invention in comparison with a MBD model; Figures 8 to 11 shows results from coupled models in accordance with embodiments of the present invention; Figure 12 shows an overset mesh in accordance with an embodiment of the present invention (Overset mesh approach); Figure 13 shows a simulation of a vehicle within a wading trough in accordance with an embodiment of the present invention; Figure 14 is a flow chart of a testing process in accordance with an embodiment of the present invention; Figure 15 shows vehicle motion and wheel rotation in accordance with an embodiment of the present invention (Motion definition of vehicle and wheels); Figure 16 is a bar chart comparing simulated pressures on an under-tray component with pressures measured in a test (Correlation of peak pressure data on sensor location for undertray at 250 mm, 4.167 m/s); Figure 17 shows simulated pressures on an under-tray functional part (Mapped Static pressure on structural mesh at T= 0.675 sec); Figure 18 shows loading stresses on the under-tray component of Figure 20 (Von Mises stresses on undertray at T= 0.675 sec); Figure 19 shows a moving domain; Figure 20 shows a moving domain at different time instances; Figure 21 shows a mesh morphing approach; Figure 22 shows the location of pressure transducers; Figure 23 shows a CAD model of block and tank domain; Figure 24 shows mid-plane cross section of domain mesh; Figure 25 shows a simulation model of block and tank domain; Figure 26 shows co-relation of peak pressure data (in mm of H 0) at sensor locations for 180 mm, 1.85 m/s; Figure 27 shows co-relation of transient pressure data (in Pa) in sensor 6 (base centre) at 180 mm, 1.85 m/s; Figure 28 shows a test clip taken at immersion depth 180 mm and speed 1.85 m/s; Figure 29 shows a simulation clip at immersion depth 180 mm and speed 1.85 m/s; Figure 30 shows sensor location (white marks) on undertray; Figure 31 shows experimental testing of vehicle; Figure 32 shows co-relation of transient pressure data in sensor 2 (undertray) at 450 mm, 1.944 m/s; Figure 33 shows co-relation of peak pressure data on sensor location for undertray at 450 mm, 1.944 m/s; and Figure 34 shows co-relation of peak pressure data on sensor location for undertray at 200 mm, 3.33 m/s.

DETAILED DESCRIPTION

Embodiments of the present invention provide a method of modelling the motion of a vehicle through a body of water. Modelling the vehicle according to embodiments of the present invention provides the ability to test the effects of wading on vehicle functional parts such as under-tray components.

Vehicle wading at different depths of water and at different vehicle speeds is an important test procedure in a vehicle development program. Such a test procedure may be arranged to determine functional integrity of various components in a vehicle such as bumper, engine undertray, and transmission scoop, radiator, plastic sills etc. when traversing through water.

A computational fluid dynamics based model (such as described in the Applicant's co-pending UK patent application 1405761.6) may calculate a pressure field with respect to rigid body motion. Such a CFD code based approach may calculate the pressure field around a vehicle as it wades into water. In deep water wading such an approach may accurately model the vehicle's behaviour (as described in relation to Figures 12 to 34 below).

However, under certain wading conditions (e.g. splash modelling) the vehicle's vertical position may play a more prominent role and the present invention is arranged to account for changes in the vertical position of the vehicle.

Vehicle suspension behaviour -when a vehicle is traversing through water at different depths and speeds due to force acting on the vehicle components, it is understood that there is definite movement in suspension components affecting the traction of the vehicle. The ride height of a mid-sized SUV driven through a wading trough profile at 8 [km/h] is as shown in Figure 1 which illustrates that dynamic behaviour of the suspension is an important factor which needs to be considered during virtual simulation of wading.

As shown in Figure 1 the relative vertical position of the vehicle is shown on the y axis over time (x axis). As the vehicle first hits the water within the trough the vehicle experiences a drop in ride height (see peak 2) followed by a recovery in ride height (see trough 4). The vehicle will then rebound downwards again (see peak 6).

The present invention provides a method of simulating wading in which two simulation models (a computational fluid dynamics model and a multi-body dynamics model) are coupled together in order to model the movement of a vehicle as it moves through a wading region.

As described herein multi-body dynamics (MBD) simulation was implemented using the Simpack software package and the computational fluid dynamics (CFD) simulation was implemented using the STAR-CCM+ software package.

The co-simulation arrangement according to embodiments of the present invention is shown in Figures 2 and 3. The MBD model 10 and CFD model 12 are arranged to run in parallel with a control server 14 interposed between the two models in order to control the two simulation models.

The control server 14 is arranged to exchange data relating to the synchronisation time between the two models and one or more of the following parameters may be specified via a graphical user interface 16 at the server 14: synchronization time step, co-simulation scheme, job parameters. The GUI 16 may also be used to define coupling regions on the modelled vehicle within the two models.

It is noted that other parameters, especially the solver set-up (e.g. which solver is used, error tolerance, maximum time step), are not affected by the control server 14.

Each model 10, 12 is arranged to use its own error estimation and time step size control algorithms.

During a vehicle simulation, at a given time step, the CFD model 12 is arranged to model pressure (force and torque) on the vehicle model and to send these modelled parameters to the MBD model 10 via the control server. The MBD model 10 in turn takes the force and torque parameters as input and models the motion of the vehicle in response. Linear and angular motion parameters are then generated by the MBD model 10 which sends these parameters back to the CFD model 12 (via the control server 14) to be used as inputs at the subsequent time step.

As a result the pressure on the components of the modelled vehicle may be modelled over time along with the associated vehicle motion. A finite element (FE) mesh model may be utilised at the end of the co-simulation to determine deformation of vehicle components.

To improve the performance of the co-simulation, adaptive time steps (in which the size of the time step is changed to improve accuracy, e.g. reducing time step when result is rapidly changing) may be used by both models 10,12. During the simulation each model 10, 12 sends exchanged quantities to the control (MpCCI) server 14. Interpolation in time may then take place to provide requested information.

Within the MBD model 10 a full vehicle multi-body model with car body, suspension components and tyres is moved through the wading trough profile. An additional force element 18 to enable the co-simulation to operate may be included in the MBD model 10 via suitable sub routines. This force element 18 exchanges physical quantities with the control server 14 as depicted in Figures 2 and 3. In such a manner it is possible to determine the velocities/angular velocities and forces/torques acting on the vehicle that are needed to drive the co-simulation.

Within the CFD model 12 a full vehicle model with detailed underbody components is moved through the wading trough profile with water. Motion to the car body is imparted employing mesh overset method as described in relation to Figures 12 to 34 below. The movement and current orientation of the vehicle are calculated within the MBD model 10.

It is noted that the computational time and power required to solve a CFD simulation is higher than for an MBD model. In order to reduce the total simulation time, an adaptive time step size may be used for the CFD model 12 in which the time step size is controlled by a Courant-Friedrichs-Lewy (CFL) number.

Data exchange between the two models 10,12 and the control server 14 is performed at each single time step.

Within the MBD model 10, a predictor-corrector-approach may be implemented for the stiff-solvers to evaluate the equation of motion. This algorithm makes usage of partial derivatives for solving nonlinear equations, which arise during numerical time integration. In the considered example of a wading vehicle these partial derivatives would be derivatives of force with respect to velocity. The solver has to calculate the Jacobian matrix which may be done with a finite difference scheme. As consequence, there are variations of local state at a single time point, which have to be considered. In the case of a coupled MBS-CFD simulation this means evaluation of the CFD-model 12 for a new state. This may be computationally expensive. In the wading vehicle example this means simulation of the CFD model 12 with new kinematic constraints which requires use of the restart capabilities of the STAR-CCM+ software package.

Another approach is to introduce an approximation for exchanged quantities. These approximations then may be used to interpolate received values for internal state changes due to iteration at a single time point.

A semi-implicit approach in which approximations of the behaviour of exchanged quantities is calculated may improve stability of the co-simulation, especially on the MBD side, as it provides updated information about the force values in each iteration of the solver.

Figures 4 to 11 provide validation results relating to the coupled MFD-CFD approach according to embodiments of the present invention.

Within the MBD model 10 a vehicle was modelled as a simple block 30 with four wheels 32, with suspension (e.g. springs) directly connected to the block. This simplified vehicle was driven within the MBD model 10 through a standard wading trough profile 34 as depicted in Figure 4. (It is noted that the suspension within the MBD model may be expanded to include springs, dampers, steering elements, bushes, non-linear kinematics and other elements.) Within the CFD model 12 was modelled as a simple box 40 traversing through the trough 42 with water 44 as depicted in Figure 5. Figure 6 is a schematic diagram showing the physical quantities that are being exchanged between the CFD model 12 and MBD model 10 via the control server 14.

The results of the co-simulation approach according to embodiments of the present invention are shown in Figure 7 in which the velocity of the vehicle over time as it enters the trough 34 is compared to the velocity of the vehicle using a standalone multi-body dynamics model.

During the co-simulation, two important phenomena were noticed; one being the damping effect of water on forces generated/observed on different components of the simple block 40, i.e. when the block is traversing through water 44 in downward slope of wading trough 42 due to the presence of water around the block there was reduced amount of force acting at the components. Forces acting at spring (force elements) and tyre components are depicted in Figure 8 and Figure 9 respectively. It can be seen that the amount of force generated in the co-simulation approach according to embodiments of the present invention is less relative to that from standalone Multi-body simulation since the surrounding water is acting like a damping device (a dashpot).

The second phenomenon was observed in the modelled results after the first phenomenon. As the vehicle goes deeper into the water trough, the area of the block surface in contact with the water 44 increases. The vehicle/block 40 attempts to push the water but water being incompressible and heavier (relative to air) generates a reaction force on the block. This reaction force is in the form of pressure acting on the block and the force acting on the block causes it to lose/reduce the traction on the surface of the trough profile resulting in aquaplaning. This phenomenon repeats itself until the reaction forces fall short of the gravitational forces. It is usually seen, see Figure 1, that vehicles at high speeds usually enter the trough with a couple of bounces. Figures 10 and 11 illustrate the second phenomena wherein, just after two seconds simulation time, the block is lifted up due to hydrodynamic force acting on it.

As described above in relation to Figures 1 to 11, embodiments of the present invention use a computational fluid dynamics model coupled to a multi-body dynamics model to simulate the forces acting upon and motion of a vehicle during a wading event. Further details useful for understanding the present invention are presented below. It is noted that the following description relates to the operation and use of a computational fluid dynamics model only to model a vehicle during a wading event. However, it is to be appreciated by the skilled person that the following teaching may also be applied to the operation of the CFD model in the above described embodiments of the present invention.

The present example useful for understanding the present invention utilises an overset mesh (Chimera) technique in which two different domains 110, 120 are modelled (see Figure 12). The domain with the object of interest (the vehicle, referred to as the vehicle domain 20 below) is meshed separately to the background domain 110 (referred to as the trough domain).

Within the vehicle simulation according to examples useful for understanding the present invention, at every time step when the field grid (vehicle domain) moves over the background grid (trough domain), the region of the background grid overlapping with the field grid may be cut out leaving only the fringe cells (or acceptor cells) of the cut region in the background grid. Likewise, the outer cells of the field grid may also be acceptor cells. The acceptor cells of both grids may be used to couple the two grids through the use of interpolation in order to allow two way communications between the vehicle domain and the trough domain.

The overset mesh technique has the advantage of being robust with respect to large amounts of motion as well as complex motion. Furthermore, mesh motion handling needed comparatively less computational effort and, in turn, the computational run time was relatively less for the overset mesh technique in accordance with an example useful for understanding the present invention compared to other available modelling techniques such as mesh morphing and re-meshing and moving domain approaches.

Figure 13 depicts a simulated vehicle 130 within a trough 140 that represents the region where wading occurs in the simulation (the trough domain 110).

For wading, the surface of the trough domain may be modelled as a wall with no-slip boundary conditions. The other five sides of the wading trough 110 domain may be modelled as pressure outlet at atmospheric boundary conditions.

Figure 14 depicts a method of testing a functional part of a vehicle 130, for example an under-tray component during a simulated wading test. Figure 15 additionally shows motion of the vehicle within the overset mesh approach.

In step 200 of Figure 14 the vehicle 130 is modelled. The surface mesh model of the vehicle may comprise data from a computer aided engineering database. The model may be suitably cleaned for use in the testing method of Figure 14 by, for example, stitching gaps in the vehicle body to create a water-tight assembly. The surface mesh data from the CAE database may be imported to Hypermesh (a high-performance finite element pre-processor that provides a highly interactive and visual environment to analyze product design performance) and ANSA ( is a computer-aided engineering tool for Finite Element Analysis and CFD Analysis widely used in the automotive industry). It is noted that when cleaning the mesh model, it is important to keep geometrical details that might be important to the results of the testing process, for example under-trays, wheel arch liners etc. It is also important to not include too many details that make the computational model unnecessarily big. An example of the size of the elements within the model is shown in Table 1 below.

Area Characteristic length (mm) Exterior surface 10 Front Grill 2 Engine & Transmission 5 Cooling packs 5 Floor 5 Under trays 5 Wheels arches 3-5 Wheels & Suspension assm. 5-10 Global mesh 20 Table 1 -Characteristic length of element size of triangular surface mesh in HYPERMESH In step 202, the area that the vehicle is to be simulated moving though may be defined as a trough domain 110. Additionally the vehicle may be defined within a vehicle domain 120.

As discussed above in relation to Figure 12, two different domains 110, 120, one housing the vehicle and the other representing the trough domain may be created to allow an overset mesh modelling technique to be employed. A hexahedral dominant mesh may be generated in both domains. Prism layers may also generated on the vehicle domain surface to resolve the boundary layer.

In addition to the vehicle and the trough domains, separate domains for other vehicle components such as the intercooler, condenser and radiator (the coolpacWcooling pack) may be defined in order to solve porous media physics in these regions.

In the vehicle domain normal physics is solved, while in the coolpack domain regions porous physics may be additionally solved along with the normal physics. The coolpack components and the vehicle region may be connected by the front, rear and side faces of the porous media core and internal interfaces at these boundaries may be defined. The interfaces may be defined as being non conformal and information exchanged between the vehicle and the coolpack regions using interpolation. The wheels are kept floating to permit local rotation. A hexahedral dominant mesh may be generated in all the domains (vehicle, coolpack, trough domains).

Certain components of interest (such as undertrays) may be meshed with prism layers to resolve the boundary layer and the vehicle domain suitably refined to capture the near object flow physics.

Three mesh refinement regions may be created. In a first refinement region, the region of the trough domain through which the vehicle moved may be refined to capture the flow physics and more importantly to reduce interpolation errors during the overset mesh process.

A second refinement area v defined around the coolpacks to accurately capture the flow physics in this area.

A third refinement area may be defined in the region occupied by the water in the trough in order to help capture the transient water/air interface accurately.

A segregated implicit unsteady solver may be selected to resolve a flow field around the vehicle, a volume of fluid (VOF) model used to solve the multiphase flow physics and a shear stress transport model (K-Omega SST) used to solve the turbulence.

Porous inertial and viscous resistance coefficients can be calculated from experimental test data of pressure drop versus velocity for the coolpacks and to solve the porous physics in the coolpack region.

Side and upper boundaries of the trough domain may be modelled as pressure outlets. A field function may be hooked to the VOF model to supply the initial water level as in the case of the block and the tank.

The motion of the vehicle as it moves through the trough is a combination of rotation and translation motion. Co-ordinate systems may be defined at the front and rear axles of the vehicle and moved with the vehicle (see Figure 15) such that the front and rear axle co-ordinate systems are maintained parallel with the ground. The motion of the vehicle is defined using the axis on the front axle. The vehicle is translated along the positive y axis of the front axle co-ordinate system and rotated about the x axis when it approaches the trough as shown in Figure 15. This is made possible by using a time dependent rotation rate. This entire motion profile may be applied to the overset mesh using the rigid body motion solver.

The wheels of the vehicle are also given a tangential velocity boundary condition which is defined using a local rotation rate about the front and rear axle co-ordinate systems.

Within Figure 15 the following is noted, arrows 150 and 160 define the vehicle motion (vehicle overset domain). The straight arrow 150 defines the linear motion of the vehicle, whereas the curved arrow motion (arrow 160) defines the vehicle rotation as it moves over the slope. (The vehicle has to correct its position when it moves over the slope as the slope angle decreases as it approaches the trough throat.) The arrows 170 and 180 define the wheel rotation rate. Wheel motion was defined in a Moving Reference frame within the vehicle domain mesh (in other words it was given a local tangential velocity relative to the mesh).

The overmesh model generates transient pressure data as the vehicle is moved into and through the water within the trough region. In the modelled test (see discussion in relation to Figures 16 to 18 below) the pressure was monitored at sixteen different locations on the underfloor components and front bumper of the vehicle. This pressure data may be obtained in step 204.

At step 206 the transient pressure data may be coupled to functional parts of interest on the vehicle via a further model.

Loading stresses on the function part of the vehicle may then be determined in step 208 and the performance of the function part assessed in step 210. A number of functional parts may be modelled and tested according to the testing method of Figure 14 and the relative performances of the functional parts compared. For example, different design options for a new under-tray component for a vehicle may be tested and the output of the test may be used to direct physical testing. In this manner the designs may be rated prior to physical testing and poorly performing designs can be dropped from consideration.

Figure 16 shows a comparison between transient pressure data generated in accordance with the present invention and pressure data measured during a test of a real vehicle in a wading pool. It can be seen that there is close correlation between the test and the simulation.

Figures 17 and 18 show, respectively, the simulated static pressures on a structural mesh representing a vehicle under-tray component and the stresses on the same component.

Pressure data on the under-tray component as seen in Figure 17 generated from steps 212 and 214 of Figure 14 was mapped at various time intervals onto its corresponding structural mesh. This mapped pressure data was taken as a transient load input into a finite element analysis (FEA) structural solver and the loads at the fixtures were obtained. High stress areas and deflection of the component were also obtained as seen in Figure 18.

Further aspects of the example useful for understanding the present invention are described below: Vehicle water wading capability refers to vehicle functional part integrity (e.g. engine under-tray, bumper cover, plastic sill cover etc.) when travelling through water. Wade testing involves vehicles being driven through different depths of water at various speeds. The test is repeated and under-body functional parts are inspected afterwards for damage. Lack of CAE capability for wading equates to late detection of failure modes which inevitably leads to expensive design change, and potentially affects program timing.

It is thus of paramount importance to have a CAE capability in this area to give design loads to start with. Computational fluid dynamics (CFD) software is used to model a vehicle travelling through water at various speeds. A non-classical CFD approach was deemed necessary to model this. To validate the method, experimental testing with a simplified block was done and then verified with CFD modelling. The simple rectangular block at two different speeds and three immersion depths in water was utilized for the purpose. As a next step a full vehicle test was conducted and was used to validate the simulation method. Fluid structure interaction and coupling between MBS model of the vehicle and CFD loads is also explored.

Vehicle wading at different depths of water and at different vehicle speeds is an important test procedure for a vehicle development program. The test procedure looks at various vehicle attributes for failures and functionality. As the development of a vehicle program progresses, test results can give open ended answers for functionality and failures.

The first target was to understand the physical testing. The vehicle wading test is done at proving grounds which has a wading trough with an inlet ramp and exit ramp. The wading test procedure is done for a combination of speeds and depths. The vehicle approaches the water trough at constant speed and enters the trough over the ramp. The impact force on the vehicle when it reaches the trough is of a large magnitude. The various test scenarios exhibit different behaviours. The low depth water and high speed test runs see high splash pattern and the vehicle maintaining the entry speed. A bow wave is seen in the front of the vehicle. The high depth and high speed runs have different splash pattern. The initial splash is bigger however as the vehicle decelerates the splash diminishes and at a much slower speed a front bow wave pattern is seen.

We looked at the capabilities of various CAE tools to model this scenario such as LS DYNA, STAR CCM+, and smoothed particle hydrodynamics (SPH).

LS DYNA has a fluid flow model which can be utilized however it has no proven track record about its fluid solver. The solver is based on finite element method and does not have many turbulence models which are one of the main drawbacks. The turbulence model will be of importance because it will play a role in modelling splash in wading analysis. Khatib-Shahidi et al pointed out some limitations of LS DYNA as opposed to CFD while obtaining blast peak pressure. The peak pressure was under predicted using LS DYNA. They also described the sensitivity of the peak pressure results to the courant number in LS-DYNA.

Smoothed-particle hydrodynamics (SPH) is a computational method used for simulating fluid flows. It is a mesh free Lagrangian method. SPH computes pressure from weighted contributions of neighbouring particles rather than by solving linear systems of equations.

And this makes the pressure results dependent on the number of particles used to model the flow physics. And with more of number of particles it becomes computationally intensive and expensive.

The Star CCM+ code is finite volume based code. The flow physics is solved by the linear equations to obtain flow filed and pressure field. It has vast array of turbulence models available to model the turbulent flow. And it is proven CFD tool in its field.

After looking at the background of these codes and their capabilities, the clear winner was the CFD code STAR CCM+.

On scrutinizing the physical test we observed that the failure modes (and hence pressure field) were very dynamic and transient in nature. As the vehicle entered water the under floor components were subjected to impact load and then the vehicle decelerated. So the pressure field was very different from pressure fields obtained from classical CFD modelling (Object is stationary and fluid is moving). To obtain the exact dynamic transient results, the motion of the object needed to be modeled in CFD. The motion would give the transient pressure field which would help us understand the failure modes and the splash patterns at different wading speeds and depths of water.

Historically the vehicle wading work literature is limited. Most of the work conducted is test procedures during the vehicle development programs. Some reports in different forums exhibit only the water level management in and around the under hood compartment and air intake system. The major reference is from Zheng et al. The paper describes modelling of vehicle water wading using commercial CFD software and focuses mostly on the firewall water scouring and its remedies. It describes very briefly the addition of under body panels to avoid water scouring and seepage through firewall.

Modelling the motion of the object in CFD was one of the biggest challenges. The aim was to get a motion modelling technique to model the motion of vehicles which would be close to the test scenario as well as be robust and efficient with respect to the transient conditions, physics involved, turnaround time and the results expected. Some of the modelling techniques considered are as follows: 1. Moving domain The first approach consisted of modelling the object in a separate domain to which a velocity was imparted. The domains trailing and leading the moving domain were allowed to morph by expanding and compressing the mesh respectively. The internal interfaces between the moving and the morphing domains were used to exchange data between them [Figure 19 and Figure 20].

The motion worked well however this method had a number of problems. Since re-meshing or re-layering would complicate and increase the run time, the leading domain would go on compressing the layers of the mesh and finally fail. Likewise, the trailing domain would expand to a very large volume cells making it impossible to capture the wake region. More so, the morphing domain would not morph with change in elevations (i.e. as vehicle enters and leaves the water trough) leaving this method incapable of resolving the test scenario.

Secondly, since the whole of the domain in which the object was present was moving the water-air interface did not develop as expected. Both of the above issues, led to poor capturing of flow physics.

2. Mesh morphing The second approach was relatively straight forward. The object was imparted with the rigid body motion and the mesh around the object was allowed to morph. The main problem of this method is that it worked well for simple and small amount of motion but in the case of larger and complex motions and sharper 3D feature angles (as in a vehicle), the mesh failed after degenerating the cells [Figure 21].

3. Mesh morphing and re-meshing The third approach took the second approach as initial step and then a macro was written for remeshing the domain which was run dynamically. A script was written for checking the face validity at every time step. The condition for a good quality cell was that the face normal should point away from the attached cell centroid. A face validity of 1.0 meant that all face normals were properly pointing away from the centroid while values below 1.0 meant that some portions of the face were not properly pointing away from the centroid, indicating some form of concavity. If the face validity was breached (i.e. less than 0.8) the script would re-mesh the whole domain and continue the solution from the last time step else it would continue directly to the next time step. This approach worked well.

However the looping and re-meshing was very computationally intensive and thus was not practical for very large displacements of bodies as in our case.

4. Overset mesh The fourth approach was the overset mesh (Chimera) technique. The modeling of this technique needed two different meshes. The domain with the object of interest (referred to as the field grid) was meshed separately, whereas the background domain (referred to as the background grid) was meshed separately [Figure 12]. At every time step when the field grid moved over the background grid, the region of the background grid overlapping with the field grid would be cut out leaving only the fringe cells (or acceptor cells) of the cut region in the background grid. Likewise, the outer cells of the field grid were also acceptor cells. The acceptor cells of both grids would be used to couple both the grids implicitly through the use of interpolation. Thus two way communications between the field and the background grid was possible.

The overset mesh showed promising results. It was robust with respect to large amounts of motion as well as complex motion. Mesh motion handling needed comparatively less computational effort. In turn the computational run time was relatively less for overset mesh. This technique had all positive outcomes for application of large motion with reduced computational effort. Care had to be taken that certain meshing and time step criteria were satisfied for it to perform properly.

It was thus decided to use overset meshing to model the motion of the object through the domain In order to validate the non-traditional approach we decided to prepare a scaled down model in the lab. Guidelines around one of our vehicle were drawn and 1:5 scaled down rectangle was prepared. The motion model in CFD performed robustly with promising initial results.

We placed pressure sensors at six different locations and compared pressure measured at various locations.

The test was conducted using a towing tank setup. A simplified rectangular block was constructed from 12mm thick acrylic sheet, scaled 1:5 times the vehicle dimensions.

However the experimental setup constrained the height of the block to 500mm. so the final dimensions of the box were 1000mm x 400mm x 500 mm. The speed and the depth of the tests were also scaled down compared to original test conditions. Thus, tests were performed at water depths of 50mm, 100mm and 180mm, each at speeds of 0.87m/sec and 1.86m/sec. The test was carried out in a tank (60m x 3.7m. x 1.8m). Turbulence stimulating pins were positioned round the girth 50mm aft of the leading face of the box, each at 25mm centres. The pins were cylindrical, .54mm high and 3.15mm in diameter and can be seen in Figure 22. The block had six diaphragm pressure transducers, three on the front face and three on the base of the block and were 3 mm in diameter. They were positioned flush to the block surface and can be seen in Figure 22. Pressure readings from these sensors were collected by the data acquistion system and were stored as whole time history data, thus allowing the average values to be obtained. A grid was also drawn on the sides of the box to allow the wave profile to be determined. The grid consisted of vertical and horizontal grading lines equi-spaced from the base at 40 mm and 50 mm respectively. In addition to the measurements, still photographs of the block were taken when the test was underway and a motion video was captured at each combination of speed and immersion to observe the bow wave formation and water levels at different locations on the block. The block was also mounted on dynamometers which measured drag and lift forces as well as pitch moment. The block was fixed in space with zero yaw, pitch and roll angle (orthogonal to the tank axes) at various water depths.

Similar to the test setup, a three dimensional geometric CAD model of the block and tank was built in ANSA. Instead of modeling 60 metres of the tank domain only 15 metres were modeled since a fully developed flow around a block would be attained within a travelling distance of two or three metres of the block. The region of the tank above the block was taken into consideration and the air domain above the tank was modeled for a height equivalent to the tank depth [Figure 23]. The block was modeled with the same dimensions as in the test. The CAD model was imported in the CFD software for meshing.

A box was modeled around the block to have the overset mesh successfully defined around the block and tank. Two different domains one housing the block and the enclosing box and the other housing the tank were created for overset meshing. A hexahedral dominant mesh was generated in the both the domains. Prism layers were also generated on the block surface to resolve the boundary layer. The block domain was suitably refined to capture the near object flow physics. Likewise, the region of the tank domain through which the block would pass was suitably refined to capture the flow physics and more importantly to reduce the interpolation error during the overset mesh process [Figure 24]. The total mesh count was 20.88 million.

A segregated implicit unsteady solver was selected to resolve a flow field and pressure field around the block. To better resolve the turbulent flow near the wall as well as in the far field, the SST K-Omega model was used. The SST K-Omega model is used a lot in marine CFD since it blends a K-Epsilon model in the far-field with a K-Omega model near the wall. An overset mesh was defined between the tank domain and the block domain and a linear motion for the block moving through the tank was solved with a rigid body motion solver. A volume of fraction (VOF) model was used to solve the multiphase flow physics and capture the water air interface [Figure 25]. A field function was hooked to the VOF model to supply the initial water level. Six points at the position of the experimental pressure sensor locations were used as monitors to obtain the transient pressure. In addition to them, drag plots were also recorded for the different speeds and depths of water.

The transient pressure data from the pressure monitors at the sensor locations were obtained from CFD. These were compared with the test pressure readings. The pressure readings from the test were averaged so as to reduce the noise from test signals. The percentage difference between the test and CFD results varied on an average by around 10 percent. The largest margin of error was for the shallow depths, which was 19 percent. The main reason for this comparatively larger discrepancy was due to the splash generated during shallow water wading. Capturing splash was highly mesh dependent as the mesh at that location would have to be much finer than the size of the droplets. Figure 26 and Figure 27 show a comparison between experimental data and CFD data for an immersion depth of 180 mm and a speed of 1.85 m/s. The visual attribute of the bow wave formation around the block was compared. Figure 28 and Figure 29 show the water level comparison for an immersion depth of 180 mm and speed of 1.85 m/s. This comparison was done by calculating the height of water at two locations, front face centre line and the rear corner. The height from the experiment was calculated by visually inspecting the water level from the photographs recorded during the testing. Visual observations tell us that water level is between lines marked 3 and 4. The lines are equispaced 40 mm apart (0-9), so the height of line 4 is 0.16 mtrs. In CFD the height of the water level is determined by measuring the centerline from the free surface (corresponding to a volume fraction of water equal to 0.5).The value is 0.158 m. The water level comparison was very promising showing a maximum error of 5% and minimum of 1%.

The next stage was to model vehicle wading similar to the real-life test procedure. The CFD model would give us some results; however correlation with the real life test and with the complete vehicle was essential. The next stage started with testing the vehicle by instrumenting it and recording the transient pressure data at different locations followed by building the CFD model and co-relating the results.

Waterproof pressure transducers were fitted at sixteen locations on the underside panels and bumper of the test vehicle [Figure 30]. The pressure transducers were capable of measuring up to 93.15kPA (9.5mH20) and were mounted such that the sensing diaphragm was parallel to the body panels and recessed approximately 5mm behind the outer face of the panels. A protective stainless steel mesh was fixed over the diaphragms. The signal conditioning and data acquisition system was mounted in the rear of the vehicle and the signal wires were lead around the bodywork to the pressure transducers. All the signal wires were shielded in order to minimise electrical noise contamination of the signals.

The tests were conducted in the wading trough at Millbrook Vehicle Proving Ground in Bedfordshire [Figure 31]. The vehicle used was one of the Jaguar XJ. The transducers were 'zeroed' while the vehicle was at standstill immediately prior to the test run. The vehicle accelerated up to the required wading speed immediately prior to entering the wading pool and then a constant wading speed was maintained by the driver. Data acquisition commenced several seconds before entering the water and was stopped once the vehicle was clear of the water and had come to a standstill. This procedure was repeated over the test matrix of vehicle speeds and wading depths.

The wading trough was built in a CAD software resembling the one used for the test. The surface mesh model of the vehicle used in the test was received from the crash team. The crashmodel was suitably cleaned for CFD use (such as stitching gaps to create a water-tight assembly) in Hypermesh and ANSA. The vehicle is aligned with the entry ramp of the trough. Both the wading trough CAD data and the vehicle model surface mesh data were imported into STAR-CCM+ for additional surface preparation and volume mesh generation.Similar to the CFD model of the block and tank, a box was modeled around the vehicle to have the overset mesh successfully defined between the vehicle and the trough.

In addition to the vehicle and the trough domains, we defined separate domains for the coolpacks (intercooler, condenser and radiator) to solve porous media physics in these regions.

In the vehicle region the normal physics were solved while in the coolpack regions the porous physics were solved along with the normal physics. Since in reality the coolpacks and the vehicle region were connected by the front, rear and side faces of the porous media core, we defined internal interfaces at these boundaries. These interfaces were non conformal and exchanged information between the vehicle and the coolpack regions using interpolation. The wheels were kept floating to permit local rotation. A hexahedral dominant mesh was generated in the all the domains. Certain components of interest (such as undertrays) were meshed with prism layers to resolve the boundary layer. The vehicle domain was suitably refined to capture the near object flow physics. Three mesh refinement regions were created. Similar to the block and tank model, the region of the trough domain through which the vehicle would move was suitably refined to capture the flow physics and more importantly to reduce the interpolation error during the overset mesh process. The second refinement area was defined around the coolpacks to accurately capture the flow physics in this area as well. The third refinement area was defined in the region occupied by the water in the trough. This would help capture the transient water air interface accurately. The total mesh count was 40 million+.

As done with the block and tank, the segregated implicit unsteady solver was selected to resolve a flow field around the vehicle. The VOF model was used to solve the multiphase flow physics and the K-Omega SST model was used to solve the turbulence. Overset mesh was defined between the wading trough and the vehicle domain. The porous inertial and viscous resistance coefficients were calculated from the experimental test data of pressure drop vs velocity for the coolpacks and were used to solve the porous physics in the coolpack region.The side and upper boundaries of the the trough domain were modelled as pressure outlets. A field function was hooked to the VOF model to supply the initial water level as in the case of the block and the tank.

The motion of the vehicle as it moves through the trough is a combination of rotation and translation motion. Co-ordinate systems are defined at the front and rear axles of the vehicle and are moved with the vehicle. Thus, the front and rear axle co-ordinate systems are always maintained parallel with the ground. The motion of the vehicle is defined using the axis on the front axle. The vehicle is translated along the positive y axis of the front axle coordinate system and rotated about the x axis when it approaches the trough as shown in Figure 15 This is made possible by using a time dependent rotation rate. This entire motion profile is applied to the overset mesh using the rigid body motion solver. The wheels of the vehicle are also given a tangential velocity boundary condition which is defined using a local rotation rate about the front and rear axle coordinate systems [Figure 15]. The pressure was monitored at sixteen different locations on the underfloor components and front bumper.

These CFD pressure readings were compared to the experimental readings of the test vehicle.

The comparison between transient pressure data of test and CFD at the sensor locations showed percentage errors within acceptable limits. The error margin was expected as the reallife scenario involved multi-disciplinary physics which was just partly taken into account by the CFD model.

Comparatively larger pressures were recorded by CFD on flexible components (such as the aeroflips) since they were modelled as rigid in CFD whereas during the test these componenets would deflect upon loading and thus the pressure measured would be less.

This discrepancy could be resolved by modelling fluid structure interaction (two way coupling). But for stiff components like the undertray good co-relation was achieved [Figure 32, 33 and 34]. The front bow wave seen in the CFD model also showed good agreement with what was seen in the test.

Since aiding structural design of the underbody components for wading was one of the main purposes of this method, obtaining loads at fixtures and high stress areas on the underbody components was the next logical step. To do this, the pressure data on the undertray as seen in Figure 17 from STAR-CCM+ was mapped at various time intervals onto its corresponding structural mesh. This mapped pressure data was taken as a transient load input into Abaqus, a FEA structural solver and the loads at the fixtures were obtained. High stress areas and deflection of the component were also obtained as seen in Figure 18. Currently only one way coupling between the fluid and the structure was modeled.

It was seen that for the block and tank test good co-relation was achieved between the test and CFD results validating the use of the overset mesh to model motion in CFD as well as the other physics models used in the simulation. On the vehicle level as well, the CFD model was able to deliver results in close comparison with the test. Few discrepancies were observed and potential ways to overcome them in the future were also formulated. The first was to work on two way coupling between the CFD and the structural solver as opposed to the current process of one way coupling to capture the pressure field accurately around flexible components. The second was to accurately model the splash and water ingress on components within the engine bay specifically for shallow water depths and high speeds. The third was to model the jumping behaviour of the vehicle as it traverses through the water (especially at high speeds and high depths). It would be necessary to couple the CFD model with an MBS model to replicate this behaviour. Nonetheless, the above CFD-only results did give us insight into the underbody component loading and potential failure modes. With these insights the design loads for the components could be estimated which could aid structural design of the part for wading during the initial phase of design.

Further aspects of the computational fluid dynamics model that is used according to embodiments of the present invention are set out in the following numbered paragraphs: 1. A method of performing a computer implemented analysis of a vehicle in a simulated wading event, the method comprising: defining a trough domain representing a region comprising a water level to be waded by the vehicle; defining a vehicle domain comprising a simulation of the vehicle; the method further comprising generating a first mesh comprising a plurality of finite mesh elements representing the trough domain; generating a second mesh comprising a plurality of finite mesh elements representing the vehicle domain; defining an overset between the first and second meshes; simulating the wading event by moving the second mesh representing the vehicle domain through the first mesh representing the trough domain, resolving the forces on at least a subset of the finite mesh elements to obtain transient pressures on at least a part of said vehicle domain, and outputting data indicative of said transient pressures.

2. A method as claimed in Paragraph 1, wherein defining the overset mesh comprises determining an overlap region between the first and second meshes and cutting the overlap region out from the first mesh.

3. A method as claimed in Paragraph 2, wherein simulating the wading event comprises stepping the second mesh through the first mesh in time periods, and wherein for each time period, the overlap region is determined and cut out from the first mesh to define fringe cells in the cut overlap region 4. A method as claimed in Paragraph 3, comprising coupling outer cells of the second mesh to the fringe cells of the first mesh.

5. A method as claimed in Paragraph 4, wherein coupling the first and second meshes comprises using an interpolation function.

6. A method as claimed in Paragraph 1, wherein further meshes are generated for each functional part of the vehicle.

7. A method as claimed in Paragraph 1, wherein the vehicle domain defines a functional part of the vehicle, the method comprising defining a prism layer between the functional part of the vehicle in the vehicle domain and the first mesh representing the trough domain.

8. A method as claimed in Paragraph 7, comprising resolving a boundary layer within the prism region.

9. A method as claimed in Paragraph 1, comprising creating a first mesh refinement region corresponding to a region of the first mesh representing the trough domain through which the first mesh representing the vehicle domain is to be moved.

10. A method as claimed in Paragraph 1, comprising creating a second mesh refinement region corresponding to region surrounding coolpacks.

11.A method as claimed in Paragraph 1, comprising creating a third refinement region corresponding to the water within the trough domain.

12. A method as claimed in Paragraph 1, wherein simulating the wading event comprises resolving flow field around the second mesh representing the vehicle domain.

13. A method as claimed in Paragraph 1, wherein simulating the wading event comprises solving multiphase flow using a volume of fluid model.

14. A method as claimed in Paragraph 1, wherein simulating the wading event comprises solving turbulence using a shear stress transport model.

15. A method as claimed in Paragraph 1, comprising calculating transient pressures at one or more locations on the vehicle domain.

16. A method as claimed in Paragraph 1, wherein motion of the second mesh through the first mesh comprises a combination of rotation and translation motion.

17. A method as claimed in Paragraph 16, comprising defining coordinate systems at front and rear axles of vehicle. 10 18. A method as claimed in Paragraph 17, comprising maintaining the coordinate systems parallel with the ground of the trough domain.

19. A system for performing a computer implemented analysis of a vehicle in a simulated wading event, the system comprising: an input arranged to receive data relating to a vehicle and a trough region to be waded by the vehicle; a processor arranged to: define a trough domain representing the trough region comprising a water level to be waded by the vehicle; define a vehicle domain comprising a simulation of the vehicle; generate a first mesh comprising a plurality of finite mesh elements representing the trough domain; generate a second mesh comprising a plurality of finite mesh elements representing the vehicle domain; define an overset between the first and second meshes; simulate the wading event by moving the second mesh representing the vehicle domain through the first mesh representing the trough domain, resolve the forces on at least a subset of the finite mesh elements to obtain transient pressures on at least a part of said vehicle domain, and an output arranged to output data indicative of said transient pressures.

20. A method of assessing the performance of a functional part of a vehicle during a wading event, the method comprising modelling the surface of the vehicle, the model comprising the functional part to be tested; simulating the wading event according to the method of any one of Paragraphs 1 to 18: obtaining transient pressure data from the simulation of the wading vehicle; modelling the effects of the transient pressure data on the functional part; determining loading data on the functional part from the transient pressure modelling; assessing the performance of the functional part from the determined loading data.

21. A method as claimed in Paragraph 20, wherein assessing the performance of the functional part comprises comparing the performance of the assessed functional part with previously assessed functional part designs.

22. A method as claimed in Paragraph 20, wherein assessing the performance of the functional part comprises comparing the determined loading data with physical testing data.

23. A method as claimed in Paragraph 20, wherein modelling the surface of the vehicle comprises stitching gaps in the surface of the vehicle to create a water tight assembly.

24. A system for assessing the performance of a functional part of a vehicle during a wading event, the system comprising an input arranged to receive data relating to a vehicle and a trough region to be waded by the vehicle a processor arranged to: model the surface of the vehicle, the model comprising the functional part to be tested; simulate the wading event according to the system of Paragraph 20; obtain transient pressure data from the simulation of the wading vehicle; model the effects of the transient pressure data on the functional part; determine loading data on the functional part from the transient pressure modelling; assess the performance of the functional part from the determined loading data an output arranged to output a performance indication for the functional part.

25. A non-transitory computer readable medium storing a program for controlling a computing device to carry out the method of Paragraph 1. 35 26. A non-transitory computer readable medium storing a program for controlling a computing device to carry out the method of Paragraph 20.

Claims (21)

1. CLAIMS1. A method of performing a computer implemented analysis of a vehicle in a simulated wading event, the method comprising: defining a multi-body dynamics model of the wading event for generating motion-related data for a multi-body simulated vehicle as it traverses a wade trough; defining a computational fluid dynamics model of the wading event for generating force-related data for a fluid dynamics simulated vehicle as it wades through water in a trough region; receiving, at the multi-body dynamics model, force-related data from the computational fluid dynamics model and using the received force-related data as an input to the multi-body dynamics model; and receiving, at the computational fluid dynamics model, motion-related data from the multi-body dynamics model and using the received motion-related data as an input to the computational fluid dynamics model.
2. 2. A method as claimed in Claim 1, wherein the multi-body dynamics model and computational fluid dynamics model generate data with respect to a common time step.
3. 3. A method as claimed in Claim 2, comprising using an adaptive time step to control the size of the common time step.
4. 4. A method as claimed in Claim 3, comprising controlling the size of the time step using a courant-friedrichs-lewy condition
5. 5. A method as claimed in any one of Claims 2 to 4, wherein each model generates and outputs data to the other model once per time step.
6. 6. A method as claimed in any one of Claims 2 to 5, wherein, within a given time step, the computational fluid model: (I) generates force-related data using motion-related data output by the multi-body dynamics model in a previous time step; and (H) outputs force-related data to the multi-body dynamics model; and the multi-body dynamics model: (Hi) generates motion-related data using force-related data output by the computational fluid model in the previous time step; and (iv) outputs motion-related data to the computational fluid dynamics model.
7. 7. A method as claimed in any preceding claim, wherein the models output generated data to and receive data from a control server.
8. 8. A method as claimed in any preceding claim, comprising defining coupling regions between the multi-body simulated vehicle and the fluid dynamics simulated vehicle.
9. 9. A method as claimed in any preceding claim, wherein the force-related data comprises force and torque data relating to the simulated vehicle.
10. 10. A method as claimed in any preceding claim, wherein the motion-related data comprises velocity and angular velocity data relating to the simulated vehicle.
11. 11. A method as claimed in any preceding claim comprising storing force-related data generated by the computational fluid dynamics model over time.
12. 12. A method as claimed in Claim 11, wherein defining the computational fluid dynamics model comprises simulating underbody components of the vehicle, the method comprising defining a finite element analysis of the underbody components of the vehicle and using the stored force-related data as an input to the finite element analysis.
13. 13. A method as claimed in Claim 12, comprising determining deformation of components of the vehicle from the finite element analysis. 25
14. 14. A system for performing a computer implemented analysis of a vehicle in a simulated wading event, the system comprising: a multi-body dynamics model of the wading event arranged to generate motion-related data for a multi-body simulated vehicle as it traverses a wade trough; a computational fluid dynamics model of the wading event arranged to generate force-related data for a fluid dynamics simulated vehicle as it wades through water in a trough region; a first input, at the multi-body dynamics model, arranged to receive force-related data from the computational fluid dynamics model and using the received force-related data as an input value to the multi-body dynamics model; and a second input, at the computational fluid dynamics model, arranged to receive motion-related data from the multi-body dynamics model and using the received motion-related data as an input value to the computational fluid dynamics model.
15. 15. A method of assessing the performance of a functional part of a vehicle during a wading event, the method comprising simulating the wading event according to the method of any one of Claims 1 to 13; obtaining transient pressure data from the simulation of the wading vehicle; modelling the effects of the transient pressure data on the functional part; determining loading data on the functional part from the transient pressure modelling; and assessing the performance of the functional part from the determined loading data.
16. 16. A method as claimed in Claim 15, wherein assessing the performance of the functional part comprises comparing the performance of the assessed functional part with previously assessed functional part designs.
17. 17. A method as claimed in Claim 16, comprising assessing the performance of the functional part against predefined failure modes of the functional part.
18. 18. A method as claimed in any one of Claims 15 to 17, wherein assessing the performance of the functional part comprises comparing the determined loading data with physical testing data.
19. 19. A method as claimed in any one of Claims 15 to 18, wherein modelling the surface of the vehicle comprises stitching gaps in the surface of the vehicle to create a water tight assembly.
20. 20. A system for assessing the performance of a functional part of a vehicle during a wading event, the system comprising: an input arranged to receive data relating to a vehicle and a trough region to be waded by the vehicle; a processor arranged to: simulate the wading event according to the system of Claim 14; obtain transient pressure data from the simulation of the wading vehicle; model the effects of the transient pressure data on the functional part; determine loading data on the functional part from the transient pressure modelling; and assess the performance of the functional part from the determined loading data; and an output arranged to output a performance indication for the functional part.
21. 21. A computer program product comprising computer readable code for controlling a computing device to carry out the method of any one of Claims 1 to 13 or 15 to 19.