*Authors: Lohitasyudu Gorli, Aero, Multiphysics & Combustion Products & Applications Engineer, Kilian Claramunt, Multiphysics Head of Group and Yingchen Li, Openlabs & Adjoints Responsible - Cadence Design Systems*

Development cycles in the automotive industry are constantly getting shorter, driving the demand for reliable, automated simulation processes providing accurate results within a short time frame. Moreover with the growing availability and cost-effectiveness of computational resources, computational fluid dynamics (CFD) simulations are able to smoothly handle ever more complex physics and their interactions.

Against this background, Honda was searching for a comprehensive toolchain for fully coupled simulations to address the thermal management of a complete vehicle for the design of their Honda CR-V model. They needed accurate, real-world results and wanted to drastically reduce the overall engineering time needed to obtain those. They chose Cadence Omnis and in the case described below you can read how they obtained their objectives.

**Project Description**

For the design of the Honda CR-V SUV model, Honda wanted to obtain a fully coupled thermal 3D-CFD RANS simulation of a detailed car geometry. The main goal of the simulation was to get a clear understanding of all thermal aspects of the under hood of the car. All relevant heat sources needed to be taken into account: the engine, the exhaust system, the radiator, the condenser and the fans.

They needed a solver that can handle large scale coupled simulations while taking into account external flow, rotating components, porous media, conjugate heat transfer, heat exchanger modelling and radiation, in one single simulation. Omnis Open-DBS was their answer. For meshing they used Omnis Hexpress, with special focus on unstructured, conformal, multi-block meshing, offering impressive turn-around speeds for a complex, detailed mesh.

**Meshing**

The simulation required a high quality mesh containing blocks for all parts of the car that either act as heat sinks or sources or which play a significant role in heat conduction, convection or radiation.

The definition of adjacent mesh blocks and corresponding interfaces was done automatically, reducing set-up engineering time significantly. This also ensures connections between all blocks are conformal and matching, eliminating inaccuracies caused by interpolation.

With Omnis Hexpress, original, imperfect CAD data can be imported immediately, without any need for manual preprocessing or adaptations. That means further significant savings in engineering time.

*FIGURE 1: Combined view of geometry and mesh of a Honda CR-V model*

*FIGURE 2: Cut section of the engine block with air inside the exhaust manifold and external air where the mesh in fully conformal*

In total, the resulting multi-block mesh contained 57 different blocks, all of which were connected through nodal-conformal interfaces. For the feasibility study, no viscous layers were inserted in the first attempt. Instead, wall functions were used to model the flow in the boundary layer. This approach led to a mesh size of 420 million cells, covering fluid and solid domains, created in less than 9 hours on 32 cores.

**Conjugate Heat Transfer**

In order to obtain realistic and accurate heat transfer predictions, the different solid parts of the geometry were taken into account in the energy equation with the resolution of the heat conduction equation. The thermal properties of the solid bodies were characterized by their conductivity coefficient. At the solid-fluid interface, the heat flux was implicitly applied based on the gradient of the temperature between the solid and fluid bodies.

**Surface-to-Surface Radiation**

The exchange of radiative energy between the surfaces of the engine, exhaust pipe and other frames is virtually unaffected by the air flow and a surface-to-surface (S2S) radiation model was therefore chosen to simulate the radiative heat exchange between hot and cold components.

It is usual to assume that all surfaces are gray diffuse emitters (and thus, absorbers), as well as gray diffuse reflectors. With these assumptions, the radiosity-irradiosity method [2] can be applied, requiring calculation of the view factors. In OMNIS Open-DBS, the Stochastic Ray Tracing method [3,4,5,6] is used to calculate these view factors.

Thanks to the high performance of the ray tracing algorithm, the view factor of the Honda car could be computed in one single hour, with more than 200 processors, using 1,000 rays shooting per boundary facet. Coupling of the radiation with the flow can be applied to a selectable level. When the radiation is strong, more coupling cycles may be needed.

**Heat Exchanger Model**

The radiator and condenser were modelled as porous media with isotropic pressure loss. Omnis Open-DBS' OpenLabs was used to customize these two blocks, ensuring that pressure drop across each block matched experimental data. For the radiator block, in addition to the pressure drop, a coupled strategy between the steady-state CFD calculation and a thermal 1D calculation was defined. Coolant temperature varied throughout its flow path, with a non-uniform heat rejection from the radiator over the block. The heat exchanger subsystem was formed by the CFD mesh for the primary fluid, the air, and an overlapped 2D coarser mesh along the direction of the auxiliary fluid flow defined by the coolant. This approach of modelling the heat exchanger core by splitting it into macroscopic cells, provides more realistic solutions for the heat rejection compared to a uniform heat source term.

**Rotating Machinery**

For the counter-rotating fans, two models are available in OMNIS Open-DBS. Added momentum and energy can be introduced using an actuator disk model that doesn’t contain the entire geometry, but only models the effect on the flow. The definition of the source terms is then made possible through the programmable OpenLabs interface. Another method to do this is to build cylindrical blocks containing the fans and then connecting them to the surrounding domain using a rotor/stator interface. Honda chose this second approach, in combination with a frozen rotor interface. Although this represents only a snapshot in time, the advantage is the high robustness and low computational cost. The blocks containing the fans were conformally connected to the outer air domain.

*FIGURE 3: Counter-rotating fans in front of radiator and condenser*

**Results**

The fully coupled CHT simulation provided highly realistic results both in terms of aerodynamic performance and thermal management prediction.

Figure 4 shows the external aerodynamics of the car, showing pressure distribution and streamlines around the car. The pressure distribution in front the wheel clearly shows the complexity of the flow under the car, which has a strong effect on the thermal prediction of the underbody, which is why a fully coupled CHT simulation was adopted here.

*FIGURES 4: Static pressure distribution in the front part of the car and external aerodynamic view*

Figures 5 clearly show that neither temperature nor heat flux are constant on the surfaces of the engine and mufflers. Accurately modeling thermal interactions in both engine and exhaust system can decidedly not be done without a coupled CFD simulation that also captures Conjugate Heat Transfer effects.

*FIGURE 5a: Temperature of engine surface*

*FIGURE 5b: Horizontal cutting plane around engine*

Large temperature differences cause strong radiation heat transfer as can be seen in Figures 6 and 7. Achieving accurate thermal predictions here require direct coupling of Conjugate Heat Transfer with the radiation model.

*FIGURE 6: Temperature of the exhaust pipe*

*FIGURE 7: Static temperature on the exhaust system and flow structure at the underbody*