Junhua Zhang

This study investigates the structural design, performance prediction and inverse design of gradient chiral petal honeycombs. Firstly, based on the parametric modeling method, four gradient configurations, concave, convex, increasing, and decreasing, are designed to reveal the deformation evolution mechanism under the coupling effect of stiffness gradient and chiral topology, elucidating the regulatory influence of different gradient patterns on shape-morphing curves. Secondly, a grouped parallel, feature-enhanced ensemble multi-output machine learning framework is constructed to achieve high-precision forward prediction ( R 2 > 99 % ) from 63-dimensional geometric parameters to 24-dimensional mechanical properties (including shape-morphing curves, Poisson’s ratio, and macroscopic mechanical indices). A grouped decoupling inverse prediction model is also established to achieve reliable inversion from performance metrics to geometric parameters (average R 2 > 98 % ), overcoming the mapping challenge between high-dimensional nonlinear parameters and multi-dimensional properties. Finally, a two-stage optimization system integrating data-driven prediction and simulation verification is proposed, employing a progressive algorithmic strategy designed for rapid convergence and refined optimization. This system enables the inverse design of four typical target shape-morphing curves: linear, quasi-sinusoidal wave-type, cosine-like double-peak wave-type, and trapezoidal. The average relative error for all cases is controlled within 2%. The research outcomes provide methodologies and tools for the programmable deformation design of gradient honeycomb structures, facilitating a paradigm shift in structural design from empirical trial-and-error to data-driven, closed-loop intelligence.

Modeling of dynamic systems with nonholonomic constraints usually involves constraint multipliers. Consequently, the dynamic equations in the laboratory coordinate system have a complex form, and as a result, the corresponding numerical algorithms need to be improved in terms of both efficiency and accuracy. This paper addresses establishing the mathematical model of the hydrodynamic sleigh in the Hamel framework. Firstly, the Lie symmetry and the Noether theorem conserved quantities of classic Chaplygin sleigh in which the inertial frame is reviewed. Based on the symmetries and the nonholonomic constraints, the frame of the sleigh can be directly realized in the algebraic space. Based on the mutual coupling mechanism between the fluid and the sleigh in a potential flow environment, the reduced equations in the moving frame are proposed in nonintegrable constraint distributions. The corresponding Hamel integrator is constructed based on the discrete variational principle. For the sleigh model in potential flow, the Hamel integrator is used to verify the feasibility of parameter control based on rotation angles and mass distribution, and to obtain the dynamic characteristics of the sleigh blade with both a rotational offset and translational offset. Numerical results indicate that the modeling method in the Hamel framework provides a more concise and efficient approach for exploring the dynamic behavior of the hydrodynamic sleigh.

This study investigates the mechanical properties of a category of honeycomb structures characterized by rose‐shaped cells under various loading conditions. Analytical expressions for the equivalent mechanical parameters of curved‐wall rose honeycombs are derived, significantly reducing the errors associated with employing star‐shaped honeycombs in prior calculations reported in the literature. Subsequently, mechanical parameters such as Poisson's ratio and Young's modulus, computed using the derived formulas, are compared with results obtained from finite‐element models. The close agreement between these two sets of results confirms the accuracy and reliability of the proposed theoretical formulas. Furthermore, both forward and inverse neural network models of the rose‐shaped honeycombs are developed to investigate the relationship between geometric configurations and mechanical performance parameters, facilitating the programed design of rose‐shaped honeycomb structures. The validation results demonstrate that the average correlation coefficients exceed 98.48%, and prediction errors remain below 6%, thereby validating the effectiveness of the models. Finally, the study compares the performance differences of Kinesio tape (KT) with and without honeycomb structures across various arm models to assess the potential application of auxetic structures in KT. Additionally, performance comparisons among rose‐shaped, star‐shaped, and re‐entrant honeycombs are conducted. This research provides a theoretical foundation for the application of rose‐shaped honeycombs in engineering contexts.

Traditional loss functions such as mean squared error (MSE) are widely employed, but they often struggle to capture the dynamic characteristics of high-dimensional nonlinear systems. To address this issue, we propose an improved loss function that integrates linear multistep methods, system-consistency constraints, and prediction-phase error control. This construction simultaneously improves training accuracy and long-term stability. Furthermore, the introduction of recursive loss and interpolation strategies brings the model closer to practical prediction scenarios, broadening its applicability. Numerical simulations demonstrate that this construction significantly outperforms both mean square error and existing custom loss functions in terms of performance.

Wind turbine blade is one of the most critical components of a wind turbine, has a crucial impact on the overall performance. Therefore, designing high-strength and lightweight blade structures is of great significance. In this paper, a 2MW wind turbine blade model is optimal designed by using MATLAB and UGNX software through an iterative process. The webs of the wind turbine blades are filled with honeycombs, it is found that the maximum stress of the wind turbine blades with honeycomb is significantly reduced through stress-strain analysis. Layered design is carried out on the leading edge, trailing edge, web plate, and main beam of the blade to make it closer to the actual blade application, with honeycomb layers are added. The results demonstrates that blades incorporating honeycombs exhibited higher strength and stiffness. When the blade subjects to extreme wind loads, the deflection at the tip of blades featuring negative Poisson's ratio honeycombs is only 55.60% of the regular blades. It is indicated that the application of honeycomb structures to blades can significantly mitigate the risk of blade-tower collisions, enhance durability, and prolong the lifespan of wind turbines.

A vibration absorption meta-structure with strong nonlinear stiffness is proposed by combining concepts of the vibration absorber and meta-structure. An experimental setup comprising the meta-structure and a primary oscillator is designed. The results of ultraslow linear sweep and fixed-frequency harmonic excitation tests show that the meta-structure can suppress the vibrations of the primary oscillator with different stiffnesses. Moreover, the meta-structure changes the resonance frequency band of the test system to an acceptable range when softer springs are used in the primary oscillator. The primary oscillator and meta-structure have almost the same phase when the resonance occurs, resulting in an acceptable change. Another meta-structure mode is activated as its frequency increases, inducing an additional resonance band when a harder spring is attached. Furthermore, permanent magnets are installed in the meta-structure to form a bistable characteristic, and the influences of the number of magnets on the vibration reduction are analyzed. When the meta-structure has twelve magnets, the high-frequency resonance peak is eliminated because the magnets can suppress the second mode of the meta-structure. The wavelet analysis is conducted to detect the frequency components. The meta-structure results in energy spread from the excitation frequency to the other frequencies.