Efficient one-dimensional turbomachinery design method based on transfer learning and bayesian optimization

HIGHLIGHTS

SUMMARY

With the development of aerodynamic design method, combining optimization algorithms with 1D mean-line aerodynamic performance evaluation methods or loss models have become an important path toward improving turbine efficiency. Juangphanich combined the 1D design directly with the three-dimensional design to reduce the design cycle, and utilized the differential evolution algorithm for multiobjective optimization of load and stage efficiency in the 1D design. Each aeroengine design generation needs to accumulate a large quantity of data and establish new loss models to expand the design space and improve model accuracy. As a result, if . . .

 



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