Composite pressure structures refer to components made of composite materials designed to withstand pressure, such as hydrogen storage tanks for vehicles, liquid oxygen tanks, and solid rocket engines. These structures possess numerous advantages including lightweight, high strength, and excellent design flexibility, making them widely employed in aerospace, automotive, and petrochemical industries. However, under severe service conditions, the accumulation and propagation of damage in composite pressure structures can easily lead to component failure. Therefore, the development of advanced structural health monitoring technologies is crucial for enhancing their in-service safety. This paper begins by comprehensively comparing the advantages and disadvantages of monitoring methods such as ultrasonic guided waves, acoustic emission, infrared, and fiber optic gratings, focusing particularly on the methods and research progress of structural health monitoring for composite pressure structures. Subsequently, addressing the material characteristics of composite pressure structures, we discuss the application and key challenges of implanted micro/nano-material sensors in monitoring the interfacial and interlaminar properties of fiber/matrix. Finally, we explore the development progress of novel sensing technologies and artificial intelligence methods, and analyze their application prospects in the in-service safety of composite pressure structures.

Upon charge-discharge cycling, lithium-ion batteries inevitably undergo capacity fading, which is a common and well-acknowledged phenomenon. However, the mechanisms behind this apparent performance degradation are inherently complex. In this review, the mechanical-electrochemical coupling degradation mechanisms of lithium-ion batteries in multi-scale, multi-field, and multi-process are comprehensively reviewed, starting from the particle scale to the electrode scale, with a specific focus on degradation in solid-state batteries. Furthermore, the degradation models used to describe the mechanical-electrochemical coupling degradation behavior of lithium-ion batteries are reviewed. It should be noted that due to the complexity of battery degradation mechanisms and the scarcity of externally measurable parameters, establishing degradation models is challenging, and there is still a considerable research gap. In light of this, a conceptual bidirectional degradation model that integrates physical models and data-driven models is proposed. Serving as links, internal variables with meaningful physical implications should be introduced to establish a comprehensive mapping of "mechanical behavior-internal variables-degradation behavior", to provide a novel approach for objectively and accurately describing and predicting battery degradation behavior.

With the development of aerospace technology, large membrane diffraction space telescopes have gained widespread attention and research due to their advantages such as small mass and volume, high optical imaging capability, and ease of folding and unfolding. In this paper, we conduct vibration control research on a large membrane diffraction space telescope and provide a vibration control strategy based on piezoelectric actuators. Firstly, the finite element method is used to establish the dynamic model of the structure. Then, the position of the actuator is studied based on the controllability criterion. The control law is designed based on the fuzzy proportional-derivative (PD) algorithm. Finally, the effectiveness of the proposed method in this paper is verified through numerical simulation. In this paper, the corresponding relationship between the number of piezoelectric actuators and the stability time is studied through numerical simulation, and the robustness of the control law is also investigated.

With the rapid development of aeroengine field, the service environment of hot end turbine blade is becoming increasing severe. In order to improve the heat bearing capacity of blade, the double-walled blade structure is proposed in the industry, which is based on the principle of "outer wall heat bearing, inner wall load bearing". In this paper, aiming at ensuring the structural strength of double-walled blade film hole, a parametric modeling is used to construct the blade shape and the outer wall hole structure, and the influences of hole design parameters on the perimeter structure strength of the cylindrical film hole, the conical expansion hole and the dustpan expansion hole are determined through simulation analysis. Then, according to the parameter input and the corresponding response, a Radial Basis Function (RBF) neural network model is constructed and further optimized through adjusting the weights and thresholds using the genetic algorithm to improve the accuracy of the surrogate model. Finally, the optimal hole designs for three film holes are obtained. Compared with the original cylindrical hole, the perimeter stress is reduced by 25.12 % and 22.54 %, respectively for the conical expansion hole and the dustpan expansion hole.