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....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.展开更多
The alternative working modes and flexible working states are the outstanding features of an adaptive cycle engine, with a proper control schedule design being the only way to exploit the performance of such an engine...The alternative working modes and flexible working states are the outstanding features of an adaptive cycle engine, with a proper control schedule design being the only way to exploit the performance of such an engine. However, unreasonable design in the control schedule causes not only performance deterioration but also serious aerodynamic stability problems. Thus, in this work,a hybrid optimization method that automatically chooses the working modes and identifies the optimal and smooth control schedules is proposed, by combining the differential evolution algorithm and the Latin hypercube sampling method. The control schedule architecture does not only optimize the engine steady-state performance under different working modes but also solves the control-schedule discontinuity problem, especially during mode transition. The optimal control schedules are continuous and almost monotonic, and hence are strongly suitable for a control system, and are designed for two different working conditions, i.e., supersonic and subsonic throttling,which proves that the proposed hybrid method applies to various working conditions. The evaluation demonstrates that the proposed control method optimizes the engine performance, the surge margin of the compression components, and the range of the thrust during throttling.展开更多
基金the financial support of the National Natural Science Foundation of China(Nos.52076180,51876176 and 51906204)National Science and Technology Major Project,China(No.2017-I0001-0001)。
文摘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.
基金funded by National Nature Science Foundation of China(Nos.51776010 and 91860205)supported by the Academic Excellence Foundation of BUAA for PhD Students,China。
文摘The alternative working modes and flexible working states are the outstanding features of an adaptive cycle engine, with a proper control schedule design being the only way to exploit the performance of such an engine. However, unreasonable design in the control schedule causes not only performance deterioration but also serious aerodynamic stability problems. Thus, in this work,a hybrid optimization method that automatically chooses the working modes and identifies the optimal and smooth control schedules is proposed, by combining the differential evolution algorithm and the Latin hypercube sampling method. The control schedule architecture does not only optimize the engine steady-state performance under different working modes but also solves the control-schedule discontinuity problem, especially during mode transition. The optimal control schedules are continuous and almost monotonic, and hence are strongly suitable for a control system, and are designed for two different working conditions, i.e., supersonic and subsonic throttling,which proves that the proposed hybrid method applies to various working conditions. The evaluation demonstrates that the proposed control method optimizes the engine performance, the surge margin of the compression components, and the range of the thrust during throttling.
