Sensitivity analysis for probabilistic anti-resonance design of aeronautical hydraulic pipelines

In an uncertainty scheme, reliability and global sensitivity analysis is studied in this work, to provide helpful information for probabilistic anti-resonance design of vibration systems.Discussions show that the resonance failure problem can be viewed as a series system, in which input uncertainties are modeled by random variables. In order to quantitatively measure the contributions of input variables to the system reliability, a global sensitivity index is proposed, the properties of which are also discussed. Then the proposed index is tested with an aeronautical hydraulic pipeline system, which is under the excitation of pump vibration and at a risk of resonance failure. Sensitivity results under different failure criteria and variation coefficients are obtained and studied, from which significant and insignificant input variables can be identified.The proposed method provides a relatively new insight for anti-resonance design of engineering structures.

Reliability and global sensitivity analysis for an airplane slat mechanism considering wear degradation

This paper focuses on the issue of reliability and global sensitivity analysis for an airplane slat mechanism considering the uncertainties in the wear process of mechanical components.First,the multi-body kinematic model of the slat mechanism is built in the ADAMS software.The geometrical sizes of the roller wheels after wear degradation are considered as input variables and the angle the slat should turn is considered as the output response.To accurately identify the influential roller wheels to the reliability and robustness of the slat mechanism,the failure probability based sensitivity and variance-based sensitivity indices are introduced.Comprehensive analysis of the results have shown that the reliability analysis and global sensitivity theory can help engineers find significant parts by their contributions,thus provide guidance for mechanical design and maintenance.

Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical models.This paper proposes an improved active learning surrogate model method,which combines the advantages of the classical Active Kriging–Monte Carlo Simulation(AK-MCS)procedure and the Adaptive Linked Importance Sampling(ALIS)procedure.The proposed procedure can,on the one hand,adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling(IS)density,on the other hand,adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event.Then,the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models.Compared with the classical AK-MCS and Active Kriging–Importance Sampling(AK-IS)procedure,the proposed method neither need to build very large sample pool even when the failure probability is extremely small,nor need to estimate the Most Probable Points(MPPs),thus it is computationally more efficient and more applicable especially for problems with multiple MPPs.The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.

Regional moment-independent sensitivity analysis with its applications in engineering

Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function（RIMPDF） and Regional Importance Measure based on Cumulative Distribution Function（RIMCDF）,are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.

Safety lifetime analysis method for multi-mode time-dependent structural system

It is important to determine the safety lifetime of Multi-mode Time-Dependent Structural System(MTDSS). However, there is still a lack of corresponding analysis methods.Therefore, this paper establishes MTDSS safety lifetime model firstly, and then proposes a Kriging surrogate model based method to estimate safety lifetime. The first step of proposed method is to construct the Kriging model of MTDSS performance function by using extremum learning function. By identifying possible extremum mode of MTDSS, the performance function of MTDSS can be equivalently transformed into the one of Single-mode Time-Dependent Structure(STDS).The second step is to use the Advanced First Failure Instant Learning Function(AFFILF) to train the Kriging model constructed in the first step, so that the convergent Kriging model can identify the possible First Failure Instant(FFI) of STDS. Then safety lifetime can be searched quickly by dichotomy search. By using AFFILF, the minimum instant that the state is not accurately identified by the current Kriging model is selected as the training point, which avoids the unnecessary calculation which may be introduced into the existing First Failure Instant Learning Function(FFILF).In addition, the Candidate Sample Pool(CSP) reduction strategy is also adopted. By adaptively deleting the random candidate sample points whose FFI have been accurately identified by the current Kriging model, the training efficiency is further improved. Three cases show that the proposed method is accurate and efficient.