Thermal expansion is inevitable for space structures under the alternating temperature of outer space around the earth. This may lead to the thermal stress and deformation due to the mismatch of the coefficient of thermal expansion. Near-zero thermal expansion (Near-ZTE) is a vitally essential demand for large-scale space telescopes or antennas to preserve their spatial precision and resolution. Recently, mechanical metamaterials with superior and tailorable properties have attracted significant interest with regard to developing negative materials or ultra-property materials. In this paper, the near-ZTE space structure architected by a dual-hourglass bi-material lattice is achieved by the structural optimization method with the gradient-based algorithm. First, an hourglass lattice with adjustable structural parameters is optimized to seek the design of effective negative thermal expansion (NTE) in the thickness direction. Then, two building blocks with both NTE and legacy positive thermal expansion (PTE) are combined as a dual-layered lattice to obtain the near-ZTE. Finally, a structure with near-ZTE of about ~10⁻⁹ m/(m·K) is obtained. Furthermore, the various lattice configurations, such as the hexagonal pyramid and triangle pyramid, are investigated in detail. Finally, the natural frequencies of two near-ZTE lattices are calculated by the modal analysis method, and the stiffness is discussed for the optimal solution of space applications. This work demonstrates that the near-ZTE structure can be achieved by utilizing the negative metamaterial and structural optimization method. It provides a novel solution to design the large-scale space structures with the near-zero thermal induced deformation, and may be constructed and assembled by the on-orbit fabrication technology.

To mitigate vibration in thin-walled composite combustion liners of aero-engines, this study proposes an optimization strategy for stiffener design to maximize natural frequencies and suppress resonance. The approach enhances structural dynamics by installing transverse and longitudinal stiffeners along the tubular wall, with their dimensions and orientations systematically optimized. Design variables were chosen: combustion liner wall thickness, stiffener thickness, transverse stiffener width/angle, longitudinal stiffener width, and composite lamination layup scheme. The orthogonal experiments were completed and followed by range analysis and variance analysis. The results demonstrated that wall thickness had the most significant impact on the natural frequency, and the 45° lamination scheme showed a superior performance compared to other configurations. Finally, a predictive equation was developed using a multiple linear regression model. The optimized stiffener configuration markedly enhances natural frequencies, mitigating vibration-induced instability. This methodological framework provides a systematic basis for designing optimized stiffener layouts in composite combustion liners for aero-engines.

Composite materials with 3D architectures are desirable in a variety of applications for the capability of tailoring their properties to meet multiple functional requirements. By the arrangement of materials' internal components, structure design is of great significance in tuning the properties of the composites. However, most of the composite structures are proposed by empirical designs following existing patterns. Hindered by the complexity of 3D structures, it is hard to extract customized structures with multiple desired properties from large design space. Here we report a multi-objective driven Wasserstein generative adversarial network (MDWGAN) to implement inverse designs of 3D composite structures according to given geometrical, structural and mechanical requirements. Our framework consists a GAN based network which generates 3D composite structures possessing with similar geometrical and structural features to the target dataset. Besides, multiple objectives are introduced to our framework for the control of mechanical property and isotropy of the composites. Real time calculation of the properties in training iterations is achieved by an accurate surrogate model. We constructed a small and concise dataset to illustrate our framework. With multiple objectives combined by their weight, and the 3D-GAN act as a soft constraint, our framework is proved to be capable of tuning the properties of the generated composites in multiple aspects, while keeping the selected features of different kinds of structures. The feasibility on small dataset and potential scalability on objectives of other properties make our work a novel, effective approach to provide fast, experience free composite structure designs for various functional materials.