Shape morphing structures are rapidly appearing at the frontiers of sciences and technologies, yet it is challenging for them to retain their shapes and sustain loads. This paper introduces a novel transforming mechanical meta structure (TMMS) capable of shape morphing and locking. Periodically arranging unit cells and modularly assembling, the TMMS can be fabricated into the customized array in a fast and simple way. Deformation actuating and state switching are imposed through mechanical mechanisms for high reliability. For the morphing behaviors, theoretical analysis is carried out to address the kinematical properties, which shows good agreements with experiments. Meanwhile, mechanical tests are performed to validate the shape locking capacities. The analytical models provide a feasible scheme to determine the configuration of the TMMS, and thereby an optimization scheme for accurate shape fitting is developed. This TMMS concept offers a promising viewpoint to develop shape morphing structures and demonstrates outstanding potentials in a wide range of industrial applications.

Load-Structure Adaptation is proposed in this study as a reward-driven mechanism for developing a self-reconfiguration decision-making system for cellular structures. By integrating neural networks and reinforcement learning methods, a time-constrained reconfiguration path planning approach for cellular structures under imminent loads is established, and a decision matrix traversing the global state space is constructed. The results demonstrate that the protective effectiveness of cellular structures under blast loading critically depends on the adaptation between structural characteristics and load time-frequency features, enabling performance exceeding inherent structural limits. Once load variables are introduced, protection can no longer be evaluated solely by inherent structural properties. A dimensionless response ratio serves as a unified evaluator: it follows a universal form in the elastic phase, while in the plastic phase, three expressions are derived based on the four Load-Structure Types, allowing assessment of either load damage potential or structural protection capacity. The reconfiguration path represents a real-time optimal trajectory toward the global optimum, with each step maximizing the instantaneous protective benefit. For different load characteristics (peak pressure, duration, energy concentration band), the corresponding optimal structural state and its locally optimal path vary: in the elastic phase they are chiefly governed by load duration and energy concentration frequency, whereas in the plastic phase they are determined by the coupled effect of peak pressure and duration. Under a series of idealized yet reasonable assumptions, this study provides a complete decision-making framework from theory to design for the pre-adjustment of cellular structures in dynamic threat environments, such as explosive blasts.

Natural gas pipeline leak detection remains a critical challenge in pipeline logistics safety. Deep learning-based detection methods utilizing operational parameters from supervisory control and data acquisition (SCADA) system have emerged as a non-invasive, economical and efficient detection solution for in-service buried natural gas gathering and transportation pipelines. However, persistent challenges include insufficient data samples, uneven distribution, and inadequate sampling representativeness, particularly in leakage localization and leakage rate estimation. To address these limitations, this paper proposes a generative adversarial network (GAN) based on temporal convolutional network (TCN) for generating pipeline leakage flow data time series samples, named TCN-TimeGAN. By integrating TCN technology into the embedding layer, recovery layer, generator, and discriminator networks, the model retains Time-GAN’s temporal dynamics while expanding its receptive field, thereby enhancing its capability to capture long-term dependencies in pipeline flow time series. The paper employs dimension reduction visualization techniques based on principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), combined with the train synthetic test real (TSTR) quality assessment method, to evaluate the model’s generative fidelity relative to original data. The results show that the TCN-TimeGAN-generated data exhibit excellent alignment with the original distribution characteristics. Further validation through leakage rate estimation experiments under two distinct scenarios in an experimental pipeline confirms that the TCN-TimeGAN-generated data improve the accuracy of the supervised leakage estimation.

The traditional negative Poisson's ratio structures have the unique advantages of high shear strength and specific energy absorption, but they usually lack load-bearing units in the transverse direction, resulting in relatively weak load-bearing capacity during the large deformation stage. In this paper, two types of metallic negative Poisson's ratio assembled composite structures are designed by assembling re-entrant honeycomb cells and double-arrow cells with deflection: the re-entrant double-arrow assembled composite structure and the asymmetrical re-entrant double-arrow assembled composite structure. Single-layer and double-layer metallic NPR assembled composite structures were fabricated using bending-vacuum brazing technology. Meanwhile, an experimental method was used to study the deformation mechanism of assembled composite structures under underwater impulsive loading by utilizing flying plate to strike pistons in a water tank, thereby generating shock wave pulses similar to those produced by explosive detonations. The impact process was numerically simulated using the Euler-Lagrange coupled method, and the simulation results agreed well with the experimental data. The results show that the deformation of the assembled structure's back plate is significantly reduced compared to the conventional re-entrant hexagonal honeycomb. Moreover, foam filling of the assembled structure greatly enhances its impact resistance. The research findings can provide valuable insights for the design of novel protective structures.

Pipelines are crucial infrastructure in the modern natural gas industry, but pipeline leaks often cause catastrophic environmental damage, significant personal injuries, and economic losses. Data-driven machine learning models provide an economical and non-intrusive solution for underground in-service pipeline. However, most existing studies focus on leak detection in single long-range pipelines and have not fully considered the impact of different operating conditions (e.g. changes in wellhead pressure, valve opening and closing, etc.) of pipeline systems on model performance, resulting in their applicability to the actual natural gas gathering and transportation pipeline (NGGTP) remains unclear. To address the aforementioned issues, this study proposes an unsupervised graph-based deep learning framework,the Condition-aware Dual-Stream Topological Graph Neural Network (C-DSTGNN),for leak detection and location in NGGTP. The proposed framework first provides comprehensive condition awareness throughout the framework to represent steady-state system behaviors by encoding supply pressures as operational loads and valve statuses as transport topologies. A dual-stream architecture is developed to optimize node relationship learning, facilitating effective spatio-temporal feature aggregation and signal reconstruction for enhanced leak detection. Furthermore, the framework achieves the localization of pipe segments by mapping node-level reconstruction and structural consistency errors to specific edges through a physics-guided method. The results of the pipeline experiment platform demonstrate that C-DSTGNN significantly improves detection sensitivity and localization precision, providing a robust guaranty for the safety and maintenance of NGGTP under complex multi-condition environments.

ABSTRACT The basic unit of snapping mechanical metamaterials is typically a bistable unit. This study proposes a systematic inverse design framework based on topology optimization for designing snapping structures with specific force‐displacement response curves. Such problems involve large deformations and complex deformation modes, which often lead to numerical instability during the optimization process. The paper proposes two improvements to address these issues. In the nonlinear finite element analysis, an improved displacement‐controlled arc‐length method is proposed. The method effectively handles the snap‐through and snap‐back behaviors exhibited by the structure during loading in the topology optimization process, while simultaneously obtaining the deformed configuration of the structure under the prescribed displacement boundary condition. Furthermore, the paper introduces convex element constraints to constrain the deformation state of elements. This approach mitigates convergence issues caused by severe compressive deformations of the elements during nonlinear topology optimization. This work contributes to the design of snapping structures by providing a stable and effective optimization framework.

Mechanical metamaterials combine ingenious geometric design with material integration to exhibit physical properties that are beyond conventional traditional material paradigms. This paper proposes and systematically studies a series of Rotational-Star Embedded Metamaterials (RSEM) that overcome limitations in multi-cycle applicability, programmability, and adaptive energy absorption. The architecture embeds a pre-slit star-shaped unit within a rotational frame, creating a multi-mechanism coupled system that undergoes "hinge rotation-contact reconfiguration-strut buckling" under compression. Quasi-static experiments and numerical simulations indicate that RSEM demonstrates significant temporal deformation behavior and dual-plateau response characteristics, while also possessing extensive programmability, including Poisson's ratio (-1 to 0.7), dual-plateau stress ratios (1.61 to 6.02), and transition strains (0.08 to 0.2). Drop-weight impact tests show enhanced energy absorption attributable to inertia and strain rate related structural effects. The response follows a bilinear progression, beginning with soft buffering and transitioning to efficient energy absorption. At low impact energies, RSEM exhibits noticeable recovery and potential for reuse. Compared with conventional auxetic honeycombs, RSEM achieves higher specific energy absorption at low relative densities and offers a wider tuning range. Finally, this paper proposes a general embedded metamaterial design methodology and two representative instantiations, demonstrating the applicability and methodological insights of RSEM for lightweight, highperformance energy absorption applications in fields such as aerospace, automotive engineering, and wearable protection.

Mechanical metamaterials are attracting increasing interest as artificial architected/composite materials with unprecedented physical properties and promising engineering applications. To address the gaps in current research, particularly the lack of comprehensive insights into reusable, adaptive behaviors and the potential for enhanced energy absorption in the zero Poisson's ratio (ZPR) domain, a family of 3D ZPR mechanical metamaterials composed of thin-plate and axisymmetric thin-shell segments is introduced. Mechanical performance, self-recovery under large deformations, reusable, and strain rate-dependent adaptive energy absorption are achieved. Furthermore, the mechanical performance and functionality of the semi-closed structure composed of both plates and curved shell (SPCS) are further enhanced by filling shear thickening gel (STG). Quasi-static compression and drop-weight impact tests are carried out to investigate the mechanical properties of the SPCS and the influence of impact velocities on its energy absorption performance, quantifying the enhancement effect of filling STG and demonstrating the resistance of SPCS to secondary and even multiple impacts. The extrusions resulting from spatial constraints between the axisymmetric shell containers and the STG fillers enhance strength and plateau stress, improve impact resistance, and enable adaptive energy absorption. The variation in stiffness and energy absorption of filled-SPCS with strain rate adeptly resolves the conflict between comfort and functionality in designs such as wearable protective devices, blast protection, and reactive armor, thereby paving the way for the development of strain rate-dependent mechanical metamaterials. Collectively, a series of ZPR mechanical metamaterials, which offer better energy absorption, broader applicability, enhanced adaptability, and reusability compared to traditional lattices, are introduced. This contributes to insights and guidance to the development of cushioning energy-absorbing metamaterial designs.

Leak rate of natural gas pipelines provides an important quantitative parameter regarding the severity of the leak and its potential impact on safety, the environment, and system operation. Due to the limited availability of actual samples, most existing data-driven methods for estimating leak rates rely on simulation data to construct the leak estimation model. However, the leak characteristics extracted from simulation data may not be effective in real pipeline environments. To address this issue, firstly this paper proposes a virtual-real data fusion method based on transfer learning that learns shared features between real and virtual leak samples, expanding the scale of leak training samples and increasing the data diversity. Secondly, this paper introduces a fusion method that combines the shared-statistical feature and the leak behavior feature to improve estimation accuracy while maintaining interpretability. Finally, the leak rates of 202 different leak events in a real scenario are estimated from flow sensor data to verify the effectiveness of the proposed method. The accuracy of the estimated leak rate can reach 97.97%.

This study investigates dynamic stall mechanisms of a pitching NACA 0012 airfoil through high-fidelity computational fluid dynamics (CFD) simulations. The improved delayed detached eddy simulation (IDDES) method based on a sliding mesh system is constructed and validated against experimental airload measurements. The results demonstrate a good agreement and the capability to capture three-dimensional flow structures. Comparative analyses at two Mach numbers of 0.283 and 0.5 reveal distinct stall physics. At the Mach number of 0.283, a notable 9.7° delay is observed between the static and dynamic stall. The airfoil experiences a leading-edge stall dominated by a strong adverse pressure gradient and generates rapid airload variations. In addition, trailing-edge vortex (TEV) and secondary leading-edge vortices (LEVs) induce distinct airload fluctuations. After the shedding of primary vortices, secondary vortices develop. In contrast, the airfoil at the Mach number of 0.5 presents a reduced stall delay of 6.4° and a shock-induced dynamic stall characterized by dispersed, smaller vortices, which results in mild airload variations during stall. Aerodynamic damping analysis identifies stall delay as a primary contributor to negative damping. Enhanced pitching stability at the higher Mach number correlates with reduced stall delay and different LEV development characteristics. Results across varying reduced frequencies show that increasing reduced frequency delays the aerodynamic response and stall onset. At Ma = 0.283, this increasement promotes a divergent tendency in pitching motion, whereas at Ma = 0.5, it induces greater oscillatory stability attributed to distinct stall characteristics.

Close-in blast tests are plagued by defects such as difficult data measurement, high safety risks, and poor repeatability. A promising method for studying close-in blast is simulating it through impact loading in a laboratory environment. However, current loading equivalence research primarily focuses on reproducing the target plate’s failure mode, with the equivalent criterion adhering to the impulse criterion, which considers only the single contribution of impulse rather than the combined effects of impulse and peak pressure. Few studies have addressed the consistency of pressure time history and the quantitative prediction of equivalent loading conditions between close-in blast and mass-block impact. In this work, the mapping relationship between close-in blast loading conditions and pressure time history parameters is established, based on the fluid-solid coupling model and strong shock wave theory. An incomplete elastic collision model for the mass-block impact is also developed. Peak pressure and impulse transmission are regulated by adjusting impact velocity and mass, while the restitution coefficient and impulse transmission upper limit are determined. Finally, a quantitative prediction method for equivalent loading conditions is proposed. The results show that the pressure time history curves of close-in blast and mass-block impact under equivalent loading conditions exhibit good agreement.

Shape memory materials retain temporary shapes without external constraints and return to their permanent shape when exposed to an external trigger, e.g., light, humidity, or heat. Current shape memory materials can maintain a modest number of shapes, deliver limited modes of deformation with undesired spring-back, suffer slow response speed, and typically require laborious thermomechanical programming and tuning their glass transition temperatures through alteration in chemical composition. In this work, we demonstrate the attainment of a robust and simplified multi-shape memory effect in a class of 3D-printed kirigami that merely relies on two off-the-shelf polymers with distinct temperature-dependent elastic moduli. By programming the kirigami multistability in the low-temperature regime, our multi-shape memory metamaterials can be reconfigured in-situ to retain a geometrical rich and diverse set of stable temporary shapes in planar and spatial kirigami tessellations before reverting to their permanent shape through a heat-induced stiffness reversal. Through mechanics theory, numerical simulations, and thermomechanical experiments, we first investigate the physical mechanism that marks stability transitions and deformation modes, and then leverage the insights to demonstrate their multifunctionality in a diverse range of applications, including temperature sensors, actuators, and robotic grippers. Unreliant on the chemistry tuning of material composition, their hallmarks include the delivery of multiple deformation modes and combination thereof, rich and robust multi-shape memory effect with no spring-back, reprogrammable shape changes, stiffness switch, and heat-induced swift shape recovery. Our strategy is versatile, can be adapted to other 3D printable materials and physicochemical stimuli, e.g., light, moisture, and solute, and can be up- and down-scaled, paving the way for a wide range of multifunctional applications, including adaptive morphing devices, self-powered sensors and actuators, and reconfigurable soft robots.

The imperative advance towards achieving "carbon neutrality" necessitates the development of porous structures possessing dual acoustic and mechanical properties in order to mitigate energy consumption. Nevertheless, enhancing various functionalities often leads to an increase in the structural weight, which limits the feasibility of using such structures in weight-sensitive applications. In accordance with the outlined specifications, a novel structural design incorporating carbon fiber reinforced polymer (CFRP) composites alongside mechanical and acoustic metamaterials has been introduced for the first time. This innovative construction exhibits a lightweight composition with excellent mechanical and acoustic characteristics. Experimental findings demonstrate that with meticulous planning and fabrication, CFRP composite structures can achieve a balance of lightweight construction, high strength, exceptional energy absorption, and remarkable resilience. By introducing membrane and reasonable cavity design, the structure can produce low broadband noise reduction performance by a local resonance effect and impedance matching mechanism of metamaterials. The structural sound insulation capability breaks traditional mass law, resulting in an exceptionally broadband sound insulation peak (bandwidth of nearly 1000 Hz). Furthermore, the sound absorption characteristic of the structure surpasses that of the melamine sponge at frequencies below 300 Hz, demonstrating superior low-frequency sound absorption properties. The proposed structure provides new approaches for the design of multifunctional lightweight superstructures.

Stimuli-responsive materials are able to alter their physicochemical properties, e.g., shape, color, or stiffness, upon exposure to an external trigger, e.g., heat, light, or humidity, exhibiting environmental adaptability. Their capacity to undergo shape reconfiguration, pattern transformation, and property modulation enables multifunctionality. In this work, two strategies are harnessed, i.e., prestressed assembly and temperature-dependent stiffness reversal, to introduce a class of temperature-responsive metamaterials capable of undergoing topological transformations, endowing them with smart functionality. Through a combination of mechanics theory, numerical simulations, and thermomechanical experiments, the physical mechanisms underlying the temperature-triggered topological transformations leading to pattern switches are first elucidated, and then the insights are leveraged to demonstrate tunable bandgaps and robotic capturers. These findings reveal the attainment of giant negative and positive values of coefficient of thermal expansion, accompanied by isotropic expansion and shrinkage under thermal actuation within a fairly rapid timeframe, below 6 s. The strategy here presented is versatile as it relies on a pair of off-the-shelf 3D printable materials, can be up- and down-scaled, and can also be realized through other physical stimuli, e.g., light and moisture, paving the way for use in multifunctional applications, including stimulus-triggered morphing devices, autonomous sensors and actuators, and reconfigurable soft robots.

Reusable mechanical metamaterials with both high energy-dissipating and load-bearing features are ideal candidates for widespread dynamic applications, ranging from impact mitigation to vibration suppression. Despite great demands, the existing designs either exhibit limited damping or stiffness, and perhaps work well only for one-time use. To reconcile these contradictions, a synchronous enhancements strategy of damping and stiffness is proposed to create novel mechanical metamaterial by replacing the viscous damping in the viscoelastic material Kelvin-Voigt constitutive to frictional damping, exemplified by auxetic self-tensioning friction damping metamaterials (FDM). They trigger an embedded sliding friction behavior through auxetic effect to achieve energy dissipation, which especially shows high stiffness while achieving extreme damping ability synergistically under large compressive strain. This synchronous enhancement mechanism is analysed by combining finite element modelling, theoretical analysis, and experimental validation. These innovative mechanical metamaterials with repeatability and self-recoverability have broad applications in engineering materials-structures-systems for energy-dissipation and load-carrying.

Cylindrical sandwich structure with excellent mechanical properties has attracted extensive attention from researchers and been applied in various fields. The cylindrical sandwich structure meets the mechanical requirements of the application and new requirements emerge at this time. The structure should have multifunctionality which can withstand multiple different loads simultaneously. To improve its sound insulation ability while maintaining its mechanical properties, membrane-type metamaterials are introduced into cylindrical sandwich structure in this article. A theoretical model according to the harmonic expansion method is established. Then, the sound transmission loss (STL) of the structure is predicted. Besides, the influence of boundary conditions on its STL is analyzed and the sound insulation mechanism of the structure is investigated by discussing various parameters effect on the STL curve.

Various cellular materials have been emerging in the past decades, whose design strategy focuses on the periodic arrangements of a representative unit cell. Recent studies indicate that aperiodic tessellations possess the ability to eliminate the risk of catastrophic global failure. However, extracting the exact relation between each localized microstructural features and macroscopic material properties of stochastic structures is not applicable due to the intrinsic randomness and disorderliness. In an effort to break through this challenge, this paper develops a class of aperiodic but ordered cellular materials (AOCMs) inspired by three types of Penrose tilings. Both finite element simulations and quasi-static compressive experiments are carried out to address the macroscopic mechanical performance and the microscopic mechanisms. The results show that the distinct deformation and failure mechanisms are induced by their different topological configurations, including the architectural shapes and tessellating orientations. The proposed AOCMs possess excellent potentials as load carrying structures and energy absorbers, and the outcomes reported here serve to provide a new perspective on the development of advanced cellular materials.

Auxetic materials are attracting increasing interest due to their extraordinary or even abnormal mechanical properties. Different from the traditional truss-lattice structures, this paper proposes a series of novel auxetic plate-lattice structures with an excellent auxetic effect. Utilizing a dual-materials glue-free assembly design involving hyperelastic and plastic materials, these plate-lattices exhibit numerous advantages such as disassembly, assembly, replacement, recyclability, reusability, excellent energy absorption, high specific stiffness, and impact resistance performances. Quasi-static/dynamic tests and simulations are conducted to assess mechanical properties, including elastic constants, deformation modes, and mechanical responses. Simultaneously, the effect of the principal geometric parameter, concave angle, on the structural response was analyzed with various impact velocities. Findings suggest that the concave angle influences the structural elastic constants under compressive loading. The stress–strain response exhibits a distinct dual-plateau phenomenon. Taking the A65 (concave angle is 65°) configuration as an example, the stress on the second plateau is approximately 2.7 times that of the first plateau, significantly enhancing the structure's energy absorption capability. Under dynamic impact loadings, different configurations and varying impact velocities affect the structure's energy absorption performance. This paper introduces a series of assemblable plate-lattice structures with an auxetic effect, offering design insights and guidance tailored to the convenient transportation, unconventional structures, and rapid assembly needs of lightweight, impact-resistant mechanical metamaterials.

Sandwich structures are widely used in various fields. Curved sandwich structures have attracted the attention of researchers owing to their superior stiffness and strength. With the application of the structures in engineering, it is required that the structures can not only meet the needs of mechanical loads but also can cope with thermal, acoustic, optical, and electrical loads. Sound insulation is one of the most common problems in the fields of aerospace, transportation, and architecture, so it is imperative to research new structures with good mechanical and acoustic properties. In this paper, a composite structure which is coupled with curve shell sandwich structure and acoustic metamaterials is proposed to obtain good mechanical and acoustic properties. Based on the harmonic expansion method and principle of virtual work, the theoretical model is established and the sound transmission loss (STL) performance is studied. Then, the effects of the geometry of the structure and material parameters on the STL of the structure are discussed.