Distributed collaborative extremum response surface method for mechanical dynamic assembly reliability analysis

To make the dynamic assembly reliability analysis more effective for complex machinery of multi-object multi-discipline(MOMD),distributed collaborative extremum response surface method(DCERSM)was proposed based on extremum response surface method(ERSM).Firstly,the basic theories of the ERSM and DCERSM were investigated,and the strengths of DCERSM were proved theoretically.Secondly,the mathematical model of the DCERSM was established based upon extremum response surface function(ERSF).Finally,this model was applied to the reliability analysis of blade-tip radial running clearance(BTRRC)of an aeroengine high pressure turbine(HPT)to verify its advantages.The results show that the DCERSM can not only reshape the possibility of the reliability analysis for the complex turbo machinery,but also greatly improve the computational speed,save the computational time and improve the computational efficiency while keeping the accuracy.Thus,the DCERSM is verified to be feasible and effective in the dynamic assembly reliability(DAR)analysis of complex machinery.Moreover,this method offers an useful insight for designing and optimizing the dynamic reliability of complex machinery.

Prediction of Extreme Significant Wave Height from Daily Maxima

For prediction of the extreme significant wave height in the ocean areas where long term wave data are not available, the empirical method of extrapolating short term data (1 similar to3 years) is used in design practice. In this paper two methods are proposed to predict extreme significant wave height based on short-term daily maxima. According to the daa recorded by the Oceanographic Station of Liaodong Bay at the Bohai Sea, it is supposed that daily maximum wave heights are statistically independent. The data show that daily maximum wave heights obey log-normal distribution, and that the numbers of daily maxima vary from year to year, obeying binomial distribution. Based on these statistical characteristics, the binomial-log-normal compound extremum distribution is derived for prediction of extreme significant wave heights (50 similar to 100 years). For examination of its accuracy and validity, the prediction of extreme wave heights is based on 12 years' data at this station, and based on each 3 years' data respectively. The results show that with consideration of confidence intervals, the predicted wave heights based on 3 years' data are very close to those based on 12 years' data. The observed data in some ocean areas in the Atlantic Ocean and the North Sea show it is not correct to assume that daily maximum wave heights are statistically independent; they are subject to Markov chain condition, obeying log-normal distribution. In this paper an analytical method is derived to predict extreme wave heights in these cases. A comparison of the computations shows that the difference between the extreme wave heights based on the assumption that daily maxima are statistically independent and that they are subject to Markov Chain condition is smaller than 10%.