Mechanical properties and failure behaviors of T1100/5405 composite T-joint under in-plane shear load coupled with initial defect and high-temperature

Li, Guowei; Cao, Ertai; Jia, Ben; Zhang, Xuanjia; Wang, Wenzhi; Huang, Heyuan

doi:10.1016/j.compstruct.2023.117722

Cited by 3 publications

(1 citation statement)

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“…These advancements underscore the evolving landscape of anomaly detection in aviation where data mining and deep learning techniques are paving the way for enhanced safety and operational efficiency. Furthermore, Puranik et al have proposed a framework for aircraft-level abnormal energy state detection based on energy metrics [9,[26][27][28]. This framework has been further enhanced by integrating sliding window preprocessing technology and cluster analysis techniques based on the Gaussian Mixture Model for transient anomaly detection during the approach phase [17].…”

Section: Introductionmentioning

confidence: 99%

Approach and Landing Energy Prediction Based on a Long Short-Term Memory Model

Hu,

Yan,

Cao

et al. 2024

Aerospace

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The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages.

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Section: Introductionmentioning

confidence: 99%

Approach and Landing Energy Prediction Based on a Long Short-Term Memory Model

Hu,

Yan,

Cao

et al. 2024

Aerospace

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Thermal‐Mechanical Coupling Performance of Heat‐Resistant, High‐Strength and Printable Al‐Si Alloy Antisymmetric Lattice Structure

Yan,

Dong,

Jia

et al. 2024

Advanced Science

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The unsatisfactory mechanical performance at high temperatures limits the broad application of 3D‐printed aluminum alloy structures in extreme environments. This study investigates the mechanical behavior of 4 different lattice cell structures in high‐temperature environments using AlSi12Fe2.5Ni3Mn4, a newly developed, heat‐resistant, high‐strength, and printable alloy. A novel Antisymmetric anti‐Buckling Lattice Cell (ASLC‐B) based on a unique rotation reflection multistage design is developed. Micro‐CT (Computed Tomography) and SEM (Scanning Electron Microscope) analyses revealed a smooth surface and dense interior with an average porosity of less than 0.454%. Quasi‐static compression tests at 25, 100, and 200 °C showed that ASLC‐B outperformed the other 3 lattice types in load‐bearing capacity, energy absorption, and heat transfer efficiency. Specifically, the ASLC‐B demonstrated a 51.56% and 44.14% increase in compression load‐bearing capacity at 100 and 200 °C compared to ASLC‐B(AlSi10Mg), highlighting its excellent high‐temperature mechanical properties. A numerical model based on the Johnson‐Cook constitutive relationship revealed the damage failure mechanisms, showing ASLC‐B's effectiveness in preventing buckling, enhancing load‐transfer efficiency, and reducing stress concentrations. This study emphasizes the importance of improving energy absorption and mechanical performance for structural optimization in extreme conditions. The ASLC‐B design offers significant advancements in maintaining structural integrity and performance under high temperatures.

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