Renault Optimizes Turbo Compressor Exhaust Gas Recirculation (EGR) with Fidelity

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By Stephane Guilain, Tech. Expert in PWT Aerodynamic and Engine Air Filling DEA-MA – Advanced Engineering, Renault and Donavan Dieu, Senior Consulting Engineer, Cadence CFD Services and Consulting

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With soaring pollution in cities worldwide, legislators are demanding car manufacturers put systems on the market that are as clean and efficient as possible, whatever the driving style or conditions, from traffic jams to high-load mountain trips and in hot or cold weather. While alternative solutions, such as electric and hydrogen-powered vehicles, are on the rise, thermal engines are still very important in today’s transport portfolio, and to reduce CO2 levels, fuel consumption in all of those real-world usage conditions must be significantly limited.

To achieve these goals, all the consuming parts of cars are being meticulously analyzed to try and decrease losses to the maximum through design improvements while taking into account inherent negative effects such as condensation issues for the compressor. It is within this framework that Renault turned to Cadence’s CFD Services and Consulting department, renowned for its top-level expertise in multiphysics design and analysis. The focus of the first study was to evaluate the impact of Low-Temperature Exhaust Gas Recirculation (LT-EGR) on the efficiency of their turbo compressors through CFD analysis. At the end of the study, Renault performed condensation analyses with dedicated software. Condensation can occur when ambient temperatures are low and, in the long term, can damage the blades and create icing problems.

Pre-Processing and Meshing

The impact of 5 geometrical parameters of the LT-EGR injection on the compressor wheel efficiency was studied:

• Radius of the EGR injection
• Axial distance between the EGR injection and the compressor
• Three angles defining the orientation of the EGR around the inlet pipe

The EGR geometry was generated using the IGG block structured mesher incorporated in Fidelity Automesh through a script. This script automatically generates a new geometry for each new set of the five parameters. The inlet pipe and the compressor wheel were provided by Renault and remained unchanged during the simulation process.

On the numerical side, the meshing of the wheel consisted of a high-fidelity grid created with Fidelity Automesh’s automatic structured mesh generator. Only one periodic channel of the wheel had to be meshed. For the inlet pipe and the EGR, an automatic unstructured mesh was generated with Fidelity Hexpress, also incorporated in Automesh. The meshing process of the inlet pipe and EGR was automated using a dedicated script, ensuring good quality meshes regardless of the position of the EGR. Then, both meshes were reassembled.

Examples of various EGR geometries generated by the IGG script in Fidelity Automesh

Non-Linear Harmonic Approach

The innovative Non-Linear Harmonic (NLH) method in Fidelity Flow was used for analyzing the flow distortion created by the inlet pipe and EGR inside the impeller. This approach solves flow perturbations in the frequency domain, allowing for high accuracy of numerical results compared with state–of–the–art time-marching models at a significantly reduced computation cost. It allows for transmitting the 360° flow distortion generated inside the inlet pipe into the wheel, directly impacting its aerodynamic performance. 

For the simulation set-up, the fluid was considered to be air as a perfect gas, and the following boundary conditions were used:

• Main inlet: Total conditions and the flow direction
• Secondary inlet: Massflow
• Main outlet: Massflow

The first solution was started using constant values per domain.

EGR CFD Optimization Results

The DoE (Design of Experiments) was generated using Fidelity Optimization with its Minamo module. A total of 26 elements were generated randomly using a « Latinized Centroidal Voronoi Tessellation » law. Analysis of the results shows that higher wheel efficiency was obtained for both an EGR located far from the wheel and an EGR with a small radius. Further flow analysis indicated that the best configurations demonstrate an important mixture between the main flow and the flow coming from the EGR, leading to less distortion at the inlet of the compressor wheel.

Based on the Minamo module, it was possible to run a deep database analysis to understand the influence and the relation between the free parameters and their impact on the performances. The below “ANOVA” graphic shows the global sensibility of the free parameters for the considered objective.

ANOVA graph for the 6 free parameters

Comparison of the baseline versus best efficiency design

A “self-organizing map” can also project multidimensional data on a 2D plot. Based on an objective, the engineer can easily check if the available free parameters have (or not) the same impact on the objective. An example is provided below for the free parameters and a given objective. The highest value of the objective (green rectangle) corresponds to high values of “L” and low values of the “GAMMA.”

Self-organizing map showing the correlation between the free parameters and the final objective

Conclusions

The condensation analyses performed by Renault, based on the Cadence CFD results, demonstrated that improved wheel efficiency and condensation issues lead to a selection of opposite values of the free parameters. In other words, if we want to increase wheel efficiency, we inevitably also increase the condensation phenomena.

The figures below show the impact of the EGR's two most important geometrical parameters (distance to compressor plane and EGR injection diameter) on the efficiency losses and condensation index. The size of the bubbles is proportional to the losses and condensation level. From the left graph, it can be seen that losses are minimal for a high distance of the EGR to the compressor plane and a small radius of the EGR. However, this region corresponds to the worst condensation index. A compromise has to be made between best efficiency and low condensation index, as these two objectives are antagonistic.

The next step will be to perform an optimization considering the CFD (aerodynamic phenomenon) and the condensation aspects in a coupled manner.

Antagonist behavior between the two main objectives

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