Abstract

With the development of the high performance and high reliability of aeroengine, the blade-tip radial running clearance (BTRRC) of high pressure turbine seriously influences the reliability and performance of aeroengine, wherein the radial deformation control of turbine casing has to be concerned in BTRRC design. To improve BTRRC design, the optimum control-based probabilistic optimization of turbine casing radial deformation was implemented using time-varying least square support vector machine (T-LSSVM) by considering nonlinear material properties and dynamic thermal load. First the T-LSSVM method was proposed and its mathematical model was established. And then the nonlinear dynamic optimal control model of casing radial deformation was constructed with T-LSSVM. Thirdly, through the numerical experiments, the T-LSSVM method is demonstrated to be a promising approach in reducing additional design samples and improving computational efficiency with acceptable computational precision. Through the optimum control-based probabilistic optimization for nonlinear dynamic radial turbine casing deformation, the optimum radial deformation is 7.865 × 10−4 m with acceptable reliability degree 0.995 6, which is reduced by 7.86 × 10−5 m relative to that before optimization. These results validate the effectiveness and feasibility of the proposed T-LSSVM method, which provides a useful insight into casing radial deformation, BTRRC control, and the development of gas turbine with high performance and high reliability.

Highlights

  • Blade-tip radial running clearance (BTRRC) of high pressure turbine (HPT) seriously influences the performance and reliability of gas turbine [1]

  • From the test results of two response surface models by 200 times simulations, it is revealed that T-least square SVM (LSSVM) saves computing time 1.411 s and improves computational precision 3.611% relative to LSSVM, Table 5: Optimization results of casing radial deformation based on time-varying LSSVM (T-LSSVM)

  • (1) The mathematical model of T-LSSVM method is established for structural dynamic probabilistic optimization

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Summary

Introduction

Blade-tip radial running clearance (BTRRC) of high pressure turbine (HPT) seriously influences the performance and reliability of gas turbine [1]. An effective optimization method-based probabilistic analysis is required for the optimum control of turbine casing radial deformation. The optimum control-based probabilistic optimization for nonlinear dynamic radial deformation of turbine casing was performed with the proposed T-LSSVM, subject to constraints on reliability degree and other practical conditions with nonlinear material property and dynamic thermal load. For time-varying probabilistic optimization problem, the response of each calculation is a stochastic process so that it is difficult to finish the probabilistic analysis of complex structure with response surface method. In all of the loop computations of probabilistic optimization, it is difficult to make this computational point reasonable and feasible, so that the computational accuracy of probabilistic design is low To resolve this issue using LSSVM, this paper advances the T-LSSVM method for the dynamic probabilistic optimization of turbine casing radial deformation.

Problem Formulation
Experimental Study
Method
Optimization Design of Turbine Casing
Findings
Conclusions
Full Text
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