Integral inference method for da／dN-ΔK and P-da／dN-ΔK curves

The integral inference method for fatigue crack growth(FCG) curve was established based on the two-variance regression theory.The estimates of da/dN-ΔK curve and P-da/dN-ΔK curve with high confidence level and high survival probability were also given.FCG data scatter with stress intensity factor ranges in reality,which limits the group test method’s application.The presented method analyzes FCG curve as an integration,thus it can exploit the lengthwise information among the FCG rates corresponding to different stress intensity factor ranges.Therefore,compared with group test method,the presented method can not only save specimens,but also have higher precision in the evaluations of da/dN-ΔK and P-da/dN-ΔK curves.     

Nonlinear flutter wind tunnel test and numerical analysis of folding fins with freeplay nonlinearities

The flutter characteristics of folding control fins with freeplay are investigated by numerical simulation and flutter wind tunnel tests. Based on the characteristics of the structures, fins with different freeplay angles are designed. For a 0° angle of attack, wind tunnel tests of these fins are conducted, and vibration is observed by accelerometers and a high-speed camera. By the expansion of the connected relationships, the governing equations of fit for the nonlinear aeroelastic analysis are established by the free-interface component mode synthesis method. Based on the results of the wind tunnel tests, the flutter characteristics of fins with different freeplay angles are analyzed. The results show that the vibration divergent speed is increased, and the divergent speed is higher than the flutter speed of the nominal linear system. The vibration divergent speed is increased along with an increase in the freeplay angle. The developed free-interface component mode synthesis method could be used to establish governing equations and to analyze the characteristics of nonlinear aeroelastic systems. The results of the numerical simulations and the wind tunnel tests indicate the same trends and critical velocities.     

Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis

In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators(CIs) are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann–Kendall test. Afterwards, the selected CIs are used to generate a health indicator(HI)through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitoring of helicopter transmission systems, and it is effective to reduce the test cost and improve the system's reliability.     

Neural Networks Combined with Importance Sampling Techniques for Reliability Evaluation of Explosive Initiating Device

Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function (RBF) neural network is used, and the RBF neural network based on first-order reliability method (FORM) is to approximate the unknown implicit limit state functions and calculate the most probable point (MPP) with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm.     

Safety Analysis of Flow Parameters in a Rotor-stator Cavity

In order to ensure the safety of engine life limited parts (ELLP) according to airworthiness regulations, a numerical approach integrating one-way fluid structure interaction (FSI) and probabilistic risk assessment (PRA) is developed, by which the variation of flow parameters in a rotor-stator cavity on the safety of gas turbine disks is investigated. The results indicate that the flow parameters affect the probability of fracture of a gas turbine disk since they can change the distribution of stress and temperature of the disk. The failure probability of the disk rises with increasing rotation Reynolds number and Chebyshev number, but descends with increasing inlet Reynolds number. In addition, a sampling based sensitivity analysis with finite difference method is conducted to determine the sensitivities of the safety with respect to the flow parameters. The sensitivity estimates show that the rotation Reynolds number is the dominant variable in safety analysis of a rotor-stator cavity among the flow parameters.     

Reliability Analysis of Aircraft Condition Monitoring Network Using an Enhanced BDD Algorithm

The aircraft condition monitoring network is responsible for collecting the status of each component in aircraft. The reliability of this network has a significant effect on safety of the aircraft. The aircraft condition monitoring network works in a real-time manner that all the data should be transmitted within the deadline to ensure that the control center makes proper decision in time. Only the connectedness between the source node and destination cannot guarantee the data to be transmitted in time. In this paper, we take the time deadline into account and build the task-based reliability model. The binary decision diagram (BDD), which has the merit of efficiency in computing and storage space, is introduced when calculating the reliability of the network and addressing the essential variable. A case is analyzed using the algorithm proposed in this paper. The experimental results show that our method is efficient and proper for the reliability analysis of the real-time network.     

A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters

Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction uncertainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.     

载人航天器出舱活动氧气分压控制研究

载人航天器出舱活动期间氧气分压控制是保证航天员安全和完成出舱任务的重要因素.文章通过对出舱过程中引起氧气分压变化的因素分析,进行了出舱活动低压情况下的氧气分压安全限的试验验证研究,建立了出舱活动氧气分压变化趋势仿真模型,并在此基础上确定了出舱活动阶段舱压和氧气分压的调控方案     

Drop test and crash simulation of a civil airplane fuselage section

Crashworthiness of a civil airplane fuselage section was studied in this paper. Firstly, the failure criterion of a rivet was studied by test, showing that the ultimate tension and shear failure loads were obviously affected by the loading speed. The relations between the loading speed and the average ultimate shear, tension loads were expressed by two logarithmic functions. Then, a vertical drop test of a civil airplane fuselage section was conducted with an actual impact velocity of6.85 m/s, meanwhile the deformation of cabin frame and the accelerations at typical locations were measured. The finite element model of a main fuselage structure was developed and validated by modal test, and the error between the calculated frequencies and the test ones of the first four modes were less than 5%. Numerical simulation of the drop test was performed by using the LS-DYNA code and the simulation results show a good agreement with that of drop test. Deforming mode of the analysis was the same as the drop test; the maximum average rigid acceleration in test was 8.81 g while the calculated one was 9.17 g, with an error of 4.1%; average maximum test deformation at four points on the front cabin floor was 420 mm, while the calculated one was 406 mm, with an error of 3.2%; the peak value of the calculated acceleration at a typical location was 14.72 g, which is lower than the test result by 5.46%; the calculated rebound velocity result was greater than the test result 17.8% and energy absorption duration was longer than the test result by 5.73%.     

Inflatable Wing Design Parameter Optimization Using Orthogonal Testing and Support Vector Machines

The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines (SVMs). Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio (SNR), can be obtained. This can guide general technological design optimization.