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Solid rocket motors (SRMs) are widely used as propulsion devices in the aerospace industry. The SRM nozzle and combustion chamber are connected with a plugged-in structure, which makes it difficult to use the existing technology to investigate the internal conditions of the SRM during docking and assembly. The unknown deformation of the O-ring inside the groove caused by different assembly conditions will prevent the engine assembly quality from being accurately predicted. Algorithms such as machine learning can be used to fit mechanical simulation data to create a model that can be used to make predictions during assembly. In this paper, the prediction method uses the sampled parameters as boundary conditions and applies the finite element method (FEM) to calculate the stresses and strains of the O-ring under different assembly conditions. The simulation data are fitted using the gradient-enhanced Kriging (GEK) model, which is more suitable for high-dimensional data than the ordinary Kriging model. A genetic algorithm (GA) and conditional tabular generative adversarial networks (CTGAN) are used to optimize the prediction model and improve its accuracy as new data are incorporated. The proposed method is not only accurate but also efficient, allowing for a significant reduction in assembly time. The use of the surrogate model and FEM makes it possible to predict the stresses and strains of the O-ring in real-time, making the assembly process smoother and more efficient. In conclusion, the proposed method provides a promising solution to the challenges associated with the assembly process of SRM in the aerospace industry.

Importance sampling methods are extensively used in time-independent reliability analysis. However, the kind of methods is barely studied in the field of time-variant reliability analysis. This article presents an importance sampling method for time-variant reliability analysis. It increases the probability of sampling failure trajectories of a time-variant performance function. To develop the method, the instantaneous performance function at a predefined time instant is regarded as a time-independent one. A time-independent importance sampling is first implemented on the instantaneous performance function in order to obtain instantaneous samples of stochastic processes and random variables. Then, conditional trajectories of stochastic processes are generated on the condition of instantaneous samples achieved above, which utilizes the correlationship among instantaneous uncertainties at different time instants associated with stochastic processes. Subsequently, trajectories of the time-variant performance function are obtained. Validation results show that comparing with crude Monte Carlo simulation, the proposed method remarkably increases the probability of sampling failure trajectories. The efficiency and accuracy of the proposed method are demonstrated.

Even though a great number of methods have been developed for time-variant reliability analysis (TRA), crude Monte Carlo simulation (MCS) is still widely used alone or combined with other methods to enhance the efficiency and accuracy of TRA. Multilevel Monte Carlo (MLMC) is introduced to TRA herein in order to reduce the computational complexity of MCS. MLMC first discretizes the time interval of interest by a geometric sequence of different timesteps. The finer grid is associated with smaller discretization error but larger computational cost. Accounting for the computational complexity of each level and the contribution that each level makes to the time-variant failure probability, the allocation of computational resource is then optimized in order to achieve a satisfying estimation of failure probability at the minimal computational cost. MLMC retains the robustness of crude MCS with respect to nonlinearity and dimensions of the time-variant performance function. Two benchmark examples are adopted to validate the effectiveness of the proposed MLMC. The results show that achieving the same accuracy level of results, the proposed method saves considerably computational cost relative to crude MCS.

With the development of production technology, the mechanical structure has become more and more complicated, which makes the simulation process of the corresponding computer model very time-consuming. As a result, the reliability analysis needs to consume huge time resources. To deal with this problem, an improved method, which balances the accuracy of failure probability and the efficiency of computation, is proposed. The novelty of the proposed method is the use of an efficient point selection strategy, the k-means algorithm, and the constraint of correlation among the training points. The k-means algorithm can divide the candidate points into a few groups. Therefore, we can update the Kriging model by selecting several sample points which have large contributions to improve the accuracy of failure probability in each iteration. Meanwhile, a constraint is applied to control the relative location among points of DoE to avoid redundant information. The efficiency and accuracy of the proposed method are verified through two numerical examples. Finally, a type of artillery coordination system as a representative of the complex mechanisms is mentioned. And the proposed method is applied to calculate the reliability of the position accuracy of the coordination process, which proves the significance of the proposed method in engineering practice.

Organic-inorganic halide perovskite semiconductors with the attractive physics properties, including strong photoluminescence (PL), huge oscillator strengths, and low nonradiative recombination losses, are ideal candidates for studying the light-matter interaction in nanostructures. Here, we demonstrate the coupling of the exciton state and the cavity mode in the lead halide perovskite microcavity system at room temperature. The Purcell effect in the coupling system is clearly observed by using angle-resolved photoluminescence spectra. Kinetic analysis based on time-resolved PL reveals that the spontaneous emission rate of the halide perovskite semiconductor is significantly enhanced at resonance of the exciton energy and the cavity mode. Our results provide the way for developing electrically driven organic polariton lasers, optical devices, and on-chip coherent quantum light sources. [ABSTRACT FROM AUTHOR]