Ford: Multidisciplinary Design Optimization of a Turbocharger Compressor

There are several reasons why optimization methods can, at this time, not be considered in routine design work:

1. Lack of computational resources: Everybody is enjoying that those resources have considerably increased recently; nevertheless, design optimization work is still seen as too costly
2. The need for multi-operating points: For quite some time, optimization and inverse design techniques would be focused on one single operating point, with no guarantee or control of the off-design performance, choke flow, performance at lower speeds, etc... Investing in a significant effort without guaranteed ROI is limiting the interest of designers working under pressure and with time constraints.
3. The multi-disciplinary need: Especially for designs working under strong mechanical pressure, such as turbochargers, an optimization limited to the aerodynamic performance and not offering control or verification of the mechanical integrity is also not so interesting.

Cadence’s Fidelity Optimization software offers solutions to all three items mentioned above:

1. We considerably optimized our solver speeds: The Fidelity Flow solver converges in typically 30 minutes to 2 hours per million nodes and per core. With a dozen cores, the aero-analysis of a new design at 3-4 operating conditions can be done in 2 hours! An impressive result compared to standard commercial solvers that need an entire day for this.
2. Optimization is flexible and can easily address multi-operating point problems. In the example below, performance is investigated and controlled from stall to choke at different speeds.
3. The flexibility of the optimization allows for the analysis phase to include a CFD solver and a mechanical tool (or other solvers). Cadence developed a partnership with the company Open Engineering, and we have coupled Fidelity Optimization with the FEA mechanical solver Oophelie.

The optimization example case described below was presented at the 2015 IGTI Turbo Expo conference (GT 2015-43631). Cadence (previously NUMECA-USA) collaborated with the Ford turbocharger research group on this project. We started from an initial compressor blade design that already presented relatively high performance, with the objective of decreasing the mechanical stress levels by 20%. No clear objectives were set on the aero performance besides trying to maintain it if possible.

Mechanical Optimization

The process started with an optimization of the back plate and bore zones, which only necessitated running the mechanical analysis tool Oophelie. Once the design parameters were selected and variation bounds applied, our optimization process worked in 2 steps: random geometries were generated and analyzed by the FEA tool (DOE process), and the information resulting from this first process was used by the optimizer to find the optimum.

Parameterization of the back plate

The whole analysis process was executed in batch mode. Once the parameters were selected, a CAD definition of the geometry was generated and provided to the mesh generator (see resulting mesh below; note that fillets were automatically applied to the blades, even if not included in the geometry).

Unstructured mesh of the solid domain

The resulting mesh was then resolved by the FEA tool. In the present case a quite significant reduction of the stress levels was achieved, as shown in the Figure below.

Results of the mechanical optimization

Aero-Mechanical optimization

The mechanical optimization process was done in one night. It was followed by a complete optimization of the blade shape and meridional channel, involving both the CFD analysis tool Fidelity Flow and the FEA solver Oophelie.

Several speed lines of the initial design were analyzed. The computational domain included not only the wheel but also the volute and the casing treatment. We then decided to focus on the choke and stall conditions at design speed and on the near-stall conditions at a lower speed. This decision was made based on the assumption that the design performance would be indirectly controlled if the choke flow and near-stall performance were maintained or improved.

The blade camber lines were parameterized, as well as the hub path, which was controlled by Bezier points. Also, we included the camber line profiles of the splitter blades and their stream-wise and tangential positions. This led us to a total number of 19 parameters.

Hub Parameterization with Bezier curve

450 geometries were randomly generated. We decided to first calculate them with the mechanical tool. This allowed us to eliminate 300 of them, as they were providing higher maximum stresses. We associated poor aero performance to those (without actually running the CFD) and then applied the CFD solver only to the 150 better ones, saving a lot of computational time. After optimization, a new blade design was selected, presenting slightly better pressure ratio performance and with 20% lower mechanical stresses. The pressure ratio curves are shown below.

Performance curves before and after optimization

Would you like to try out Fidelity Optimization and/or Fidelity Flow for yourself?