Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

• A distribution collaborative probabilistic design method (DCSRM) was proposed.
• The dynamic assembly probabilistic design of BTRRC was accomplished.
• DCSRM has high accuracy and high efficiency in complex mechanical probabilistic analysis.
• δ=1.865×10−3 m is an optimally acceptable option for BTRRC design.
• This paper enriches mechanical dynamic assembly reliability design theory.

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.