Turbocharger design involves adjustment of various geometric parameters to
improve the performance and suit mechanical constraints, depending on the
application-specific requirements. In designing the turbine stage, these
parameters are optimized to maximize durability and efficiencies at the required
operating points. For a heavy-duty class eight truck, “road load” and “rated
power” are generally considered the two most important operating points. The
objective of this article is to improve the efficiencies of these two operating
points.
The common challenge in the development of a turbine wheel design is the large
number and interdependence of parameters to optimize. For example, increasing
the blade thickness improves structural strength but reduces the mass flow
capacity, thus influencing its performance. It is general practice to optimize
the wheel geometry using iterative CFD analysis. However, running simulations
for every single change in geometry involves significant computation time and
does not guarantee the global optimum.
This study proposes a machine learning (ML)-driven optimization process for
variable turbine geometry (VTG) wheels with two target operating points. Initial
CFD data is collected and used to train/validate a ML model for each operating
point. A multi-objective optimization algorithm utilizes these ML models to
provide a list of ideal turbine wheel designs, and the best candidates are
validated in CFD for accuracy. The results are a balanced maximization in
efficiency for both operating points, with an improvement in the pareto front by
1.4% points over CFD optimization alone.
