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The turbine disc plays a crucial role in aerospace engine, significantly influencing both their fatigue life and overall reliability. Due to the substantial uncertainties present, devising a method for the probabilistic analysis of turbine disc fatigue life becomes crucial. This paper introduces a novel framework that combines adaptive Kriging Monte Carlo simulation (AK‐MCS) with a mean stress correction model to evaluate fatigue life distribution effectively. AK‐MCS, stands out as an algorithm that constructs a Kriging model, effectively reducing the need for an extensive finite element analysis. Furthermore, a mean stress correction model is proposed for the Masson‐Coffin equation tailored for fatigue life prediction of GH4133 disc material. This model has been empirically validated to be effective. The presented framework for probabilistic fatigue life analysis not only holds considerable engineering value but also offers innovative approaches for tackling analogous challenges in the field. [ABSTRACT FROM AUTHOR]

The load history exerts a considerable impact on the low cycle fatigue (LCF) life of turbine discs. Thus, oversimplifying the load history leads to substantial errors in fatigue life prediction. This study introduces a probabilistic fatigue life prediction method for turbine discs, accounting for the randomness inherent in LCF load history. The method involves quantifying the randomness of load history through numerical simulation and employing a surrogate model enhanced with learning functions to balance computational efficiency and accuracy. The probabilistic LCF life prediction of full‐scale turbine disc was conducted, demonstrating that the fatigue life scatter predicted by the proposed method more closely aligns with experimental data compared to the original approach. By refining the numerical simulation process, the proposed method better accounts for uncertainties in load history while maintaining computational efficiency, offering significant insights for the fatigue reliability design of turbine discs. [ABSTRACT FROM AUTHOR]

In view of the complex structure and high cost of traditional chaotic lidar systems, this paper proposes two transmission schemes using Field Programmable Gate Array (FPGA) and Arbitrary Waveform Generator (AWG), based on the improved Logistic mapping formula to dynamically generate digital chaotic signals in real time. The digital signal generated on the electrical domain is modulated to a direct-modulated laser to generate a chaotic signal in the optical domain. Then the chaotic optical signal is used as a lidar transmission signal. Therefore, a complete lidar detection system is built on the optical transceiver system. To measure the distance of an object with a reflectivity of 50%, accurate distance measurement at the centimeter level can be achieved without complicated optical structures. We conduct experiments to compare the ranging effects of continuous and binarized chaotic signals. It is found that the ranging accuracy of binarized chaotic signals is at least doubled than that of continuous chaotic signals. For square wave, sine wave and chaotic interference signals at different frequencies, it has stronger anti-interference ability and shows greater application advantages.

This study utilizes the rank correlation coefficient to examine the multi‐site failure correlation of turbine discs. Drawing from the stress‐strength interference model, reliability models both with and without factoring in the multi‐site failure correlation are constructed. Furthermore, the weakest link theory (WLT) within the context of the Weibull distribution function is invoked to develop a model for predicting the fatigue life of turbine discs, taking into account the quantity of critical sections. The variability in the low cycle fatigue (LCF) of turbine discs is scrutinized, leading to the formulation of a probabilistic fatigue life prediction method for these discs. When comparing theoretical values with experimental ones, it becomes evident that factoring in the multi‐site failure correlation significantly enhances the accuracy of turbine disc life predictions. [ABSTRACT FROM AUTHOR]

Dynamic fault tree (DFT) is a reliable method for modeling complex systems with dynamic characteristics. However, evaluating the reliability of such systems can be complex. The sequential binary decision diagram (SBDD) method simplifies this process by reducing computational complexity. This study investigates factors influencing SBDD size and proposes a new method, the complete priority ordering method, for ordering basic events in SBDDs. Results demonstrate the method's effectiveness in comparison to existing approaches. [ABSTRACT FROM AUTHOR]