Integration of high-fidelity model of forward variable area bypass injector into zero-dimensional variable cycle engine model

Forward Variable Area Bypass Injector(FVABI)is one of key components which contributes to modulate the cycle parameters of Variable Cycle Engine(VCE)under various operation conditions.The modeling method of zero-dimensional FVABI was reviewed and its deficiency was analyzed based on FVABI flow characteristic.In order to improve the accuracy of VCE performance simulation,the high-fidelity modeling method of FVABI was developed based on its working characteristics.Then it was coupled with the zero-dimensional VCE model and the multi-level VCE model was built.The results indicate that the geometric and aerodynamic parameters can affect the interaction between the two airflows and the zero-dimensional FVABI model is too simple to predict the component performance accurately,especially when the FVABI inner bypass is chocked.Based on the performance curves for single bypass mode and the regression model of multi-scale support vector regression for double bypass mode,the high-fidelity model can predict FVABI performance accurately and rapidly.The integration of high-fidelity FVABI model into zerodimensional VCE model can be done by adjusting iterative variables and balance equations.The multi-level model has good convergence and it can predict VCE performance when the FVABI inner bypass is chocked.

Sequential ensemble optimization based on general surrogate model prediction variance and its application on engine acceleration schedule design

The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.In this paper,a Sequential Ensemble Optimization(SEO)algorithm based on the ensemble model is proposed.In the proposed algorithm,there is no limitation on the selection of an individual surrogate model.Specifically,the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model.Also,a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator(GUE)is proposed.The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions.The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate.Further,the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.