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In-Plane Dynamic Crushing Behaviors of a Vertex-Based Hierarchical Auxetic Honeycomb

Acta Mechanica Solida Sinica 2024年第1期37卷 53-62页

作者： Yichen Zan Xiuhui Hou Zichen Deng School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi'an 710072China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi'an 710072China School of Aeronautics Northwestern Polytechnical UniversityXi'an 710072China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

Auxetic metamaterials,which exhibit the negative Poisson’s ratio(NPR)effect,have found wide applications in many engineering ***,their high porosity inevitably weakens their bearing capacity and impact *** improve the energy absorption efficiency of auxetic honeycombs,a novel vertex-based hierarchical star-shaped honeycomb(VSH)is designed by replacing each vertex in the classical star-shaped honeycomb(SSH)with a newly added self-similar *** analytical model is built to investigate the Young’s modulus of VSH,which shows good agreement with experimental results and numerical *** in-plane dynamic crushing behaviors of VSH at three different crushing velocities are investigated,and empirical formulas for the densification strain and plateau stress are *** results reveal more stable deformation modes for VSH,attributed to the addition of self-similar star-shaped ***,compared with SSH under the same relative densities,VSH exhibits better specific energy absorption and higher plateau ***,VSH is verified to be a better candidate for energy absorption while maintaining the auxetic *** study is expected to provide a new design strategy for auxetic honeycombs.

关键词： Vertex-based hierarchical star-shaped honeycomb Auxetic honeycomb Energy absorption Plateau stress Negative Poisson's ratio

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Fixed-interval smoothing of an aeroelastic airfoil model with cubic or free-play nonlinearity in incompressible flow

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Acta Mechanica Sinica 2021年第7期37卷 1168-1182,I0003页

作者： Qi Liu Yong Xu Yongge Li Jurgen Kurths Xiaochuan Liu School of Mathematics and Statistics Northwestern Polytechnical UniversityXi’an 710072China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an 710072China Potsdam Institute for Climate Impact Research Potsdam 14473Germany Lobachevsky University of Nizhny Novgorod Nizhnij NovgorodRussia AVIC Aircraft Strength Research Institute Xi’an 710065China

Fixed-interval smoothing,as one of the most important types of state estimation,has been concerned in many practical problems especially in the analysis of flight test ***,the existing sequential filters and smoothers usually cannot deal with nonlinear or high-dimensional systems well.A state-of-the-art technique is employed in this study to explore the fixed-interval smoothing problem of a conceptual two-dimensional airfoil model in incompressible flow from noisy measurement ***,the governing equations of the airfoil model are assumed to be known or only partially known.A single objective optimization problem is constructed with the classical Runge–Kutta scheme,and then estimations of the system states,the measurement noise and even the unknown parameters are obtained simultaneously through minimizing the objective *** and feasibility of the method are examined under several simulated measurement data corrupted by different measurement *** the obtained results indicate that the introduced algorithm is applicable for the airfoil model with cubic or free-play structural nonlinearity and leads to accurate state and parameter ***,it is highly robust to Gaussian white and even more complex heavy-tailed measurement *** should be emphasized that the employed algorithm is still effective to high-dimensional nonlinear aeroelastic systems.

关键词： Airfoil model Incompressible flow State estimation Parameter estimation

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Biologically inspired information integration pooling module of spiking neural networks for rolling bearing fault diagnosis

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Expert systems with Applications 2025年 286卷

作者： Mingfei Shi Feng Jiang Yongbo Li Lin Du MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an 710072 China Unmanned System Research Institute Northwestern Polytechnical University Xi’an 710072 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an 710072 China School of Aeronautics Northwestern Polytechnical University Xi’an 710072 China School of Mathematics and Statics Northwestern Polytechnical University Xi’an 710072 China MOE Key Laboratory for Complexity Science in Aerospace Northwestern Polytechnical University Xi’an 710072 China

Rolling bearings constitute critical components in mechanical systems, and their effective fault diagnosis plays a vital role in ensuring operational safety and reliability. Despite substantial advancements in diagnostic methodologies, achieving high-precision, low-latency fault detection in large-scale rolling bearing datasets remains a persistent challenge. Artificial neural networks to spiking neural networks (ANN-to-SNN) conversion algorithms offer promising solutions for reducing computational costs and hardware adaptation barriers compared to conventional approaches. However, inherent information degradation during conversion processes has been demonstrated to significantly undermine model performance in terms of accuracy, stability, and interpretability. To overcome these limitations, we propose IIPooling , a novel I nformation- I ntegration biological neural mechanism-inspired Pooling module designed to enhance global structural feature extraction. The proposed framework incorporates three core neurobiological information processing principles: (1) promotion, (2) lateral inhibition, and (3) longitudinal inhibition, effectively simulating biological neuronal spiking patterns while optimally utilizing ANN activation characteristics. Additionally, we develop two adaptive learning mechanisms — Surrogate Gradient Adaptive Learning and Spiking Spatiotemporal Adaptive Learning — to implement negative feedback mechanism and adaptive parameter adjustment, thereby improving cross-conditional generalization capabilities. Comprehensive evaluations conducted on five bearing datasets (CWRU: DE/FE , PU, and Lab-collected: 6025/N205EW ) demonstrate superior performance across four metrics (ACC, AUC, AUPRC, Mean-ACC). Our method achieves state-of-the-art results, notably attaining ACC values of 0.959 (CWRU-DE) and 0.861 (Lab-N205EW) in IIPooling-ST configurations, confirming its effectiveness for real-time, high-accuracy fault detection in complex operational environments.

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Slowing down critical transitions via Gaussian white noise and periodic force

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Science China(Technological Sciences) 2019年第12期62卷 2144-2152页

作者： MA JinZhong XU Yong XU Wei LI YongGe KURTHS Jirge Department of Applied Mathematics Northwestern Polytechnical UniversityXi'an 710072China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversiyXi'an 710072China Center for Mathematical Sciences&School of Mathematics and Statistics Huazhong University of Science and TechmologyWuhan 430074China Potsdam Institute for Climate Impact Research Potsdam 14412Germany Department of Physics Humboldt UniversityBerlin 12489Germany

Stochastic perturbations and periodic excitations are generally regarded as sources to induce critical transitions in complex systems. However, we find that they are also able to slow down an imminent critical transition. To illustrate this phenomenon, a periodically driven bistable eutrophication model with Gaussian white noise is introduced as a prototype class of real *** residence probability(RP) is presented to measure the possibility that the given system stays in the oligotrophic state versus Gaussian white noise and periodic force. Variations in the mean first passage time(MFPT) and the mean velocity(MV) of the first right-crossing process are also calculated respectively. We show that the frequency of the periodic force can increase the MFPT while reduce the MV under different control parameters. Nevertheless, the noise intensity or the amplitude may result in an increase of the RP only in the case of control parameters approaching the critical values. Furthermore, for an impending critical transition, an increase of the RP appears with the interaction between the amplitude and noise intensity or the combination of the noise intensity and frequency, while the interaction of the frequency and amplitude leads to an extension of the MFPT or a decrease of the MV. As a result, an increase of the RP and MFPT, and a decrease of the MVobtained from our results claim that it is possible to slow down an imminent critical transition via Gaussian white noise and periodic force.

关键词： critical transition slowing down bistable eutrophication model Gaussian white noise periodic force residence probability mean first passage time mean velocity

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Data-driven identification of variational equations for vortex-induced vibration systems

Data-driven identification of variational equations for vort...

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第十三届全国流体力学学术会议

作者： Kang Lu Zheng Zeng Rongchun Hu Zichen Deng Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex SystemsNorthwestern Polytechnical University

In this study, a data-driven approach using the embedded variational principle is used to identify the variational equations of vortex-induced vibration fluid-solid coupled systems, in particular the coupling term and the aerodynamic damping term. Under the data-driven paradigm,variational equation identification is primarily accomplished through five steps: collecting discrete data, setting variational functions, building the product function, solving linear equations, and evaluating errors. The explicit variational equations of the system are eventually determined automatically from the excitation and response. Gaussian white noise is added to the excitation to evaluate the method's noise robustness. The findings demonstrate that numerical estimation which stays away from higher-order derivatives significantly enhances the variational law identification's noise robustness by taking advantage of the variational law's lower order time ***, the arbitrariness of the variational setting inherent in the variational law significantly improves the effectiveness of data utilization and lowers the necessary data volume. In addition, a system of linear equations is solved by identifying connected nonlinear equations, which significantly increases modelling efficiency. The basis for engineering modelling, optimization, and control of intricate fluid-solid coupled systems are provided by these benefits.

关键词： Data-driven System identification vortex-induced vibration fluid-solid coupled variational principle

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Symplectic analysis for regulating wave propagation in a one-dimensional nonlinear graded metamaterial

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Applied Mathematics and Mechanics(English Edition) 2023年第5期44卷 745-758页

作者： Yunping ZHAO Xiuhui HOU Kai ZHANG Zichen DENG Department of Applied Mathematics Northwestern Polytechnical UniversityXi’an 710072China School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an 710072China Ministry of Industry and Information Technology Key Laboratory of Dynamics and Control of Complex SystemsNorthwestern Polytechnical UniversityXi’an 710072China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of TechnologyDalian 116024China

An analytical method,called the symplectic mathematical method,is proposed to study the wave propagation in a spring-mass chain with gradient arranged local resonators and nonlinear ground *** with the linearized perturbation approach,the symplectic transform matrix for a unit cell of the weakly nonlinear graded metamaterial is derived,which only relies on the state *** results of the dispersion relation obtained with the symplectic mathematical method agree well with those achieved by the Bloch *** is shown that wider and lower frequency bandgaps are formed when the hardening nonlinearity and incident wave intensity ***,the displacement response and transmission performance of nonlinear graded metamaterials with finite length are *** dual tunable effects of nonlinearity and gradation on the wave propagation are explored under different excitation *** small excitation frequencies,the gradient parameter plays a dominant role compared with the *** reason is that the gradient tuning aims at the gradient arrangement of local resonators,which is limited by the critical value of the local resonator *** contrast,for larger excitation frequencies,the hardening nonlinearity is dominant and will contribute to the formation of a new bandgap.

关键词： symplectic mathematical method nonlinear graded metamaterial tunable bandgap

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Statistical solution to SDEs with α -stable Lévy noise via deep neural network

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International Journal of dynamics and control 2020年第4期8卷 1129-1140页

作者： Zhang, Hao Xu, Yong Li, Yongge Kurths, Jürgen Department of Applied Mathematics Northwestern Polytechnical University Xian710072 China Department of Engineering Mechanics Northwestern Polytechnical University Xian710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xian710072 China Potsdam Institute for Climate Impact Research Potsdam14412 Germany Department of Physics Humboldt University Berlin Berlin12489 Germany

The probability density function of the stochastic differential equations with α-stable Lévy noise is determined by the deterministic fractional FokkerPlanck (FFP) equation with the Riesz fractional derivative. In this paper, to solve the FFP equation, a new algorithm which is an efficient numerical approach using the deep neural network is proposed in view of the mesh-dependence phenomenon in the traditional discrete scheme and the nonsmooth solution of the Monte Carlo method. Under the framework of the DL-FP algorithm, the "fractional centered derivative" approach is applied to approximate the Riesz fractional derivative of the output in the neural network, which is the major novelty of this approach. Numerical results are presented to demonstrate the accuracy of the approach. Comparing with the traditional discrete scheme and the Monte Carlo method, the proposed algorithm is mesh-less and smoother. Furthermore, the proposed technique performs well in dealing with the heavy-tail case through comparing with the analytical solution. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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基于反常热传导的分数阶热弹耦合理论

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Acta Mechanica Sinica 2024年第5期40卷 141-149页

作者：尉亚军吴华邓子辰 School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an 710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an 710072 China State Key Laboratory for Strength and Vibration of Mechanical Structures Xi’an Jiaotong University Xi’an 710049 China

原子模拟和实验研究表明低维纳米材料具有反常热传导特性,建立唯象反常热弹耦合模型,研究强热传导导致的热力耦合响应,对微纳器件的安全稳定运行至关重要.本文旨在从反常热传导的视角重新审视分数阶热弹耦合模型。反常热传导由第二声效... 详细信息

原子模拟和实验研究表明低维纳米材料具有反常热传导特性,建立唯象反常热弹耦合模型,研究强热传导导致的热力耦合响应,对微纳器件的安全稳定运行至关重要.本文旨在从反常热传导的视角重新审视分数阶热弹耦合模型。反常热传导由第二声效应引起,考虑波动热传导理论,建立了波动热传导与粘弹性理论的类比,阐明了Cattaneo-Vernotte模型和Green-Naghdi模型的关联.通过引入分数阶导数,构建了基于Cattaneo-Vernotte和Green-Naghdi理论的分数阶热弹耦合模型。数值结果表明:对分数阶次区间[O,1],分数阶Cattaneo-Vernotte(FCV)I模型和分数阶Green-Naghdi(FGN)I-III模型都能预测反常热弹耦合响应:比经典热弹性理论更高的温度和应力;温度和应力随时间的变化显示:FGNII模型在所有时间范围内都能得到反常响应.对Green-Naghdi模型及其分数阶模型的进一步系统研究,有助于揭示反常热传导和热弹耦合机理,并促进纳米材料的广泛应用.

关键词：粘弹性理论分数阶导数热弹耦合热弹性理论热传导低维纳米材料分数阶模型微纳器件

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Stability switch of tumor-immune system driven by delayed response in cell-to-cell contact

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The European Physical Journal Special Topics 2024年第1期234卷 165-178页

作者： Xu, Qian Fan, Aili Qu, Lijin Miao, Yuchen Du, Lin School of Mathematics and Statistics Northwestern Polytechnical University Xi’an China MIIT Key Laboratory of Dynamics and Control of Complex Systems Xi’an China MOE Key Laboratory for Complexity Science in Aerospace Xi’an China

Considering the delayed response in cell-to-cell contact is essential for accurately predicting the progression of cancer. In this paper, the stability switch phenomenon of a tumor-immune interaction system induced by the delayed response is presented. Firstly, the qualitative properties of the system including non-negativity and boundedness of the solutions are discussed. Subsequently, a complete discriminant system for polynomials method is employed to scrutinize the local stability switch conditions, which circumvents the complicated calculation procedures and enhances the analytical accuracy for high-dimensional nonlinear time-delay systems. The delayed response in cell-to-cell contact is observed to exert a suppressive influence on system stability. Specifically, the system with stable initial state will switch to instability when the delayed responding time of cell-to-cell contact exceeds a critical threshold, indicating a weakened resistance of the immune system against tumor cells. While the system with unstable initial state will maintain and even exhibit chaotic behavior, implying uncontrolled tumor proliferation. Finally, the major findings related to cancer therapy are summarized in the conclusion. These results help to understand the complex mechanism of the tumor-immune system and provide ideas for the clinical treatment of tumor.

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Uncertainty analysis of hybrid vibration energy harvester

Uncertainty analysis of hybrid vibration energy harvester

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第八届国际振动工程会议

作者： Yu Shi Xiaxia Duan Ying Zhang School of Mathematics and Statistics Northwestern Polytechnical University MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University

Owing to various inevitable uncertain errors, the system parameter of the energy harvesting system should be treated as uncertainties in design. The influence of the uncertain system parameters on the hybrid vibration energy harvester(HVEH) is revealed by the uncertainty analysis. Firstly, the HVEH with an uncertain parameter is transformed into an equivalent deterministic system by the orthogonal polynomial approximation, and the ensemble mean response of the equivalent system is proposed to explore the dynamic behavior of stochastic HVEH. Then,the effectiveness of the orthogonal polynomial approximation is verified by numerical ***, the stochastic dynamics of the HVEH under uncertain parameters is investigated in detail through bifurcation diagrams, top Lyapunov exponent, etc. The results reveal the mechanism of the influence of random parameters on dynamic behavior and output voltage, which is significant to the design of the energy harvester.

关键词： Uncertainty analysis of hybrid vibration energy harvester

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