Zhang, Chuanzeng
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Publication Access status: Metadata only , A simple active adaptive control method for mitigating and isolating mechanical vibrations of the pyramid-core lattice sandwich structuresIn this paper, we propose a simple active adaptive feedback (AAF) control strategy to enhance the vibration mitigation and isolation properties of the conventional pyramid-core lattice sandwich structures (PSSs) and the modified chessboard-designed pyramid-core lattice sandwich structures (CPSSs) via the lead zirconate titanate (PZT) ceramic patches. The proposed simple AAF control strategy is based on the active feedback (AF) control by relating the AF control parameters to the frequency. Firstly, the effects of the AF control parameters on the band structures are analyzed. Subsequently, the mathematical expressions of the frequency-dependent AAF control parameters are derived by the curve fitting technique. The results show that the proposed simple AAF control strategy is sufficiently accurate, highly efficient and can significantly expand the band-gaps compared to the conventional AF control, hence leading to improved vibration mitigation and isolation properties of the PSSs and CPSSs. The proposed simple AAF control strategy provides a simplified and generalized method to enhance the vibration mitigation and isolation properties of the lightweight lattice sandwich structures (LSSs) while maintaining their high stiffness-to-density ratio, which may offer various potential applications in engineering sciences.Source Type:Article3 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Analysis of the sound insulation performance of periodic wall structures by a virtual acoustic laboratoryThe propagation of acoustic waves in periodic structures, also known as phononic crystals or PCs, is prohibited in certain frequency ranges, which are referred to as the frequency band-gaps. The existence, the location and the width of the frequency band-gaps are mainly determined by the geometrical parameters and the material properties of the PCs. In this work, a virtual acoustic laboratory based on a fully coupled fluid-structure interaction (FSI) model is developed to determine the sound insulation capacity of one-dimensional (1D) and two-dimensional (2D) periodic walls. The FSI model is discretized using the frequency-domain spectral element method (FDSEM), which is an advanced finite element method (FEM) using special high-order shape functions. Following the guidelines of the ISO10140, the setup of the developed FSI model allows us to take into account the essential physical phenomena, especially the interaction of the wall structure with the fluid domains (air). The FSI model based on the FDSEM increases the computational efficiency and accuracy in comparison with the standard FEM. Several numerical examples will be presented and discussed to show that the designed periodic walls in certain frequency ranges within the band-gaps may exhibit much better sound insulation capabilities than monolithic walls.Source Type:Article3 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Boundary integrated neural networks for 2D elastostatic and piezoelectric problems(2024); ; ; ; In this paper, we make the first attempt to adopt the boundary integrated neural networks (BINNs) for the numerical solution of two-dimensional (2D) elastostatic and piezoelectric problems. The proposed BINNs combine the artificial neural networks with the exact boundary integral equations (BIEs) to effectively solve the boundary value problems based on the corresponding partial differential equations (PDEs). The BIEs are utilized to localize all the unknown physical quantities on the boundary, which are approximated by using artificial neural networks and resolved via a training process. In contrast to many traditional neural network methods based on a domain discretization, the present BINNs offer several distinct advantages. Firstly, by embedding the analytical BIEs into the learning procedure, the present BINNs only need to discretize the boundary of the problem domain, which reduces the number of the unknowns and can lead to a faster and more stable learning process. Secondly, the differential operators in the original PDEs are substituted by integral operators, which can effectively eliminate the need for additional differentiations of the neural networks (high-order derivatives of the neural networks may lead to instabilities in the learning process). Thirdly, the loss function of the present BINNs only contains the residuals of the BIEs, as all the boundary conditions have been inherently incorporated within the formulation. Therefore, there is no necessity for employing any weighting functions, which are commonly used in most traditional methods to balance the gradients among the different objective functions. Extensive numerical experiments show that the present BINNs are much easier to train and can usually provide more accurate solutions as compared to many traditional neural network methods.Source Type:Article3 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Performance analysis and material distribution optimization for sound barriers using a semianalytical meshless method(2023); ; With the increase in car ownership, traffic noise pollution has increased considerably and is one of the most severe types of noise pollution that affects living standards. Noise reduction by sound barriers is a common protective measure used in this country and abroad. The acoustic performance of a sound barrier is highly dependent on its shape and material. In this paper, a semianalytical meshless Burton–Miller‐type singular boundary method is proposed to analyze the acoustic performance of various shapes of sound barriers, and the distribution of sound‐absorbing materials on the surface of sound barriers is optimized by combining a solid isotropic material with a penalization method. The acoustic effect of the sound‐absorbing material is simplified as the acoustical impedance boundary condition. The objective of optimization is to minimize the sound pressure in a given reference plane. The volume of the sound‐absorbing material is used as a constraint. The density of the nodes covered with the sound‐absorbing material is used as the design variable. The method of moving asymptotes was used to update the design variables. This model completely avoids the mesh discretization process in the finite element method and requires only boundary nodes. In addition, the approach also does not require the singular integral calculation in the boundary element method. The method is illustrated and validated using numerical examples to demonstrate its accuracy and efficiency.Source Type:Article6
