Display Method:      

Machine learning-based stiffness optimization of digital composite metamaterials with desired positive or negative Poisson's ratio
Xihang Jiang, Fan Liu, Lifeng Wang
Accepted Manuscript , doi: 10.1016/j.taml.2023.100485
[Abstract] (0) [PDF 1872KB] (0)
Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures. However, these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures. In this work, a convolutional neural network (CNN) based self-learning multiobjective optimization is performed to design digital composite materials. The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials, along with their corresponding Poisson's ratios and stiffness values. Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint. Furthermore, we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio (negative, zero, or positive). The optimized designs have been successfully and efficiently obtained, and their validity has been confirmed through finite element analysis results. This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.
Generative optimization of bistable plates with deep learning
Hong Li, Qingfeng Wang
Accepted Manuscript , doi: 10.1016/j.taml.2023.100483
[Abstract] (8) [PDF 979KB] (0)
Bistate plates have found extensive applications in the domains of smart structures and energy harvesting devices. Most bistable curved plates are characterized by a constant thickness profile. Regrettably, due to the inherent complexity of this problem, relatively little attention has been devoted to this area. In this study, we demonstrate how deep learning can facilitate the discovery of novel plate profiles that cater to multiple objectives, including maximizing stiffness, forward snapping force, and backward snapping force. Our proposed approach is distinguished by its efficiency in terms of low computational energy consumption and high effectiveness. It holds promise for future applications in the design and optimization of multistable structures with diverse objectives, addressing the requirements of various fields.
Micropillar compression using discrete dislocation dynamics and machine learning
Jin Tao, Dean Wei, Junshi Yu, Qianhua Kan, Guozheng Kang, Xu Zhang
Accepted Manuscript , doi: 10.1016/j.taml.2023.100484
[Abstract] (7) [PDF 1241KB] (0)
Discrete dislocation dynamics (DDD) simulations reveal the evolution of dislocation structures and the interaction of dislocations. This study investigated the compression behavior of single-crystal copper micropillars using few-shot machine learning with data provided by DDD simulations. Two types of features are considered: external features comprising specimen size and loading orientation and internal features involving dislocation source length, Schmid factor, the orientation of the most easily activated dislocations and their distance from the free boundary. The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs. It is found that the Machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features. However, the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars. Overall, incorporating internal features, especially the information of most easily activated dislocations, improves predictive capabilities across diverse sample sizes and orientations.
A Method of Convolutional Neural Network Based on Frequency Segmentation for Monitoring the State of Wind Turbine Blades
Weijun Zhu, Yunan Wu, Zhenye Sun, Wenzhong Shen, Guangxing Guo, Jianwei Lin
Accepted Manuscript , doi: 10.1016/j.taml.2023.100479
[Abstract] (17) [PDF 3545KB] (1)
Wind turbine blades are prone to failure due to high tip speed, rain, dust and so on. A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed. On the experimental measurement data, variational mode decomposition filtering and Mel spectrogram drawing are conducted first. The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network. Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients, considering the complexity of the real environment. The surfaces of Wind turbine blades are classified into four types: standard, attachments, polishing, and serrated trailing edge. The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%. In addition to support the differentiation of trained models, utilizing proper score coefficients also permit the screening of unknown types.
Machine Learning of Partial Differential Equations from Noise Data
Wenbo Cao, Weiwei Zhang
Accepted Manuscript , doi: 10.1016/j.taml.2023.100480
[Abstract] (7) [PDF 1443KB] (1)
Machine learning of partial differential equations from data is a potential breakthrough to solve the lack of physical equations in complex dynamic systems, and sparse regression is an attractive approach recently emerged. Noise is the biggest challenge for sparse regression to identify equations because sparse regression relies on local derivative evaluation of noisy data. This study proposes a simple and general approach which greatly improves the noise robustness by projecting the evaluated time derivative and partial differential term into a subspace with less noise. This approach allows accurate reconstruction of PDEs involving high-order derivatives from data with a considerable amount of noise. In addition, we discuss and compare the effects of the proposed method based on Fourier subspace and POD subspace, and the latter usually have better results since it preserves the maximum amount of information.
Feature Identification in Complex Fluid Flows by Convolutional Neural Networks
Shizheng Wen, Michael W. Lee, Kai M. Kruger Bastos, Ian Eldridge-Allegra, Earl H. Dowell
Accepted Manuscript , doi: 10.1016/j.taml.2023.100482
[Abstract] (13) [PDF 2418KB] (1)
Recent advancements have established machine learning’s utility in predicting nonlinear fluid dynamics, with predictive accuracy being a central motivation for employing neural networks. However, the pattern recognition central to the network’s function is equally valuable for enhancing our dynamical insight into the complex fluid dynamics. In this paper, a single-layer convolutional neural network (CNN) was trained to recognize three qualitatively different subsonic buffet flows (periodic, quasiperiodic and chaotic) over a high-incidence airfoil, and a near-perfect accuracy was obtained with only a small training dataset. The convolutional kernels and corresponding feature maps, developed by the model with no temporal information provided, identified large-scale coherent structures in agreement with those known to be associated with buffet flows. Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored. The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.
Machine Learning Potential for Ab Initio Phase Transitions of Zirconia
Yuanpeng Deng, Chong Wang, Xiang Xu, and Hui Li
Accepted Manuscript , doi: 10.1016/j.taml.2023.100481
[Abstract] (8) [PDF 1050KB] (1)
Zirconia has been extensively used in aerospace, military, biomedical and industrial fields due to its unusual combination of high mechanical, electrical and thermal properties. However, the fundamental and critical phase transition process of zirconia has not been well studied because of its difficult first-order phase transition with formidable energy barrier. Here, we generated a machine learning interatomic potential with ab initio accuracy to discover the mechanism behind all kinds of phase transition of zirconia at ambient pressure. The machine learning potential precisely characterized atomic interactions among all zirconia allotropes and liquid zirconia in a wide temperature range. We realized the challenging reversible first-order monoclinic-tetragonal and cubic-liquid phase transition processes with enhanced sampling techniques. From the thermodynamic information, we gave a better understanding of the thermal hysteresis phenomenon in martensitic monoclinic-tetragonal transition. The phase diagram of zirconia from our machine learning potential based molecular dynamics simulations corresponded well with experimental results.
Piezomagnetic vibration energy harvester with an amplifier
Jo£o Pedro Norenberg, Americo Cunha Jr, Piotr Wolszczak, Grzegorz Litak
Accepted Manuscript , doi: 10.1016/j.taml.2023.100478
[Abstract] (7) [PDF 1998KB] (0)
We study the effect of an amplification mechanism in a nonlinear vibration energy harvesting system where a ferromagnetic beam resonator is attached to the vibration source through an additional linear spring with a damper. The beam moves in the nonlinear double-well potential caused by interaction with two magnets. The piezoelectric patches with electrodes attached to the electrical circuit support mechanical energy transduction into electrical power. The results show that the additional spring can improve energy harvesting. By changing its stiffness, we observed various solutions. At the point of the optimal stiffness of the additional spring, the power output is amplified a few times depending on the excitation amplitude.

Display Method:          |     

Advance on stochastic dynamics/nonlinear and stochastic dynamics
Yong Xu, Yongge Li, Stefano Lenci
Theoretical and Applied Mechanics Letters  13 (2023) 100457.   doi: 10.1016/j.taml.2023.100457
[Abstract] (19) [PDF 374KB] (2)
Nonlinear and stochastic dynamics is essential and practical in almost all branches of natural science and engineering. It has been a central subject to understand various complex dynamics, such as random vibration, stochastic transition, synchronization, et al., in the areas of mechanical and aerospace engineering, physics and chemistry. For example, the stochastic resonance has been utilized effectively in mechanical fault diagnosis and weak signal detection. Naturally, many methods have been developed to get the stochastic responses, including stochastic averaging method, multiple scale method and path integral method, and so on. Theories of stochastic transition, including stochastic bifurcation, stochastic resonance and first passage process have been proposed to reveal the action mechanism of noises. Consequently, various applications have been found in mechanical, physical, and biological fields. However, the present framework, is far from revealing the underlying mechanisms and principles for many issues. It requires more attention to the non-Gaussian noise, high-dimensional systems, new combination with deep learning, and applications to frontier areas, to name but a few. Therefore, this special issue is organized to report the recent developments in nonlinear and stochastic dynamics, specially focusing on stochastic methods, stochastic dynamics and applications.
A reconfigurable dynamic Bayesian network for digital twin modeling of structures with multiple damage modes
Yumei Ye, Qiang Yang, Jingang Zhang, Songhe Meng, Jun Wang, Xia Tang
Theoretical and Applied Mechanics Letters  13 (2023) 100440.   doi: 10.1016/j.taml.2023.100440
[Abstract] (202) [PDF 3757KB] (0)
Dynamic Bayesian networks (DBNs) are commonly employed for structural digital twin modeling. At present, most researches only consider single damage mode tracking. It is not sufficient for a reusable spacecraft as various damage modes may occur during its service life. A reconfigurable DBN method is proposed in this paper. The structure of the DBN can be updated dynamically to describe the interactions between different damages. Two common damages (fatigue and bolt loosening) for a spacecraft structure are considered in a numerical example. The results show that the reconfigurable DBN can accurately predict the acceleration phenomenon of crack growth caused by bolt loosening while the DBN with time-invariant structure cannot, even with enough updates. The definition of interaction coefficients makes the reconfigurable DBN easy to track multiple damages and be extended to more complex problems. The method also has a good physical interpretability as the reconfiguration of DBN corresponds to a specific mechanism. Satisfactory predictions do not require precise knowledge of reconfiguration conditions, making the method more practical.
Assessment of the stiffened panel performance in the OTEC seawater tank design: Parametric study and sensitivity analysis
Yogie Muhammad Lutfi, Ristiyanto Adiputra, Aditya Rio Prabowo, Tomoaki Utsunomiya, Erwandi Erwandi, Nurul Muhayat
Theoretical and Applied Mechanics Letters  13 (2023) 100452.   doi: 10.1016/j.taml.2023.100452
[Abstract] (10) [PDF 3322KB] (2)
Ocean thermal energy conversion (OTEC) is a process of generating electricity by exploiting the temperature difference between warm surface seawater and cold deep seawater. Due to the high static and dynamic pressures that are caused by seawater circulation, the stiffened panel that constitutes a seawater tank may undergo a reduction in ultimate strength. The current paper investigates the design of stiffening systems for OTEC seawater tanks by examining the effects of stiffening parameters such as stiffener sizes and span-over-bay ratio for the applied combined loadings of lateral and transverse pressure by fluid motion and axial compression due to global bending moment. The ultimate strength calculation was conducted by using the non-linear finite element method via the commercial software known as ABAQUS. The stress and deformation distribution due to pressure loads was computed in the first step and then brought to the second step, in which the axial compression was applied. The effects of pressure on the ultimate strength of the stiffener were investigated for representative stiffened panels, and the significance of the stiffener parameters was assessed by using the sensitivity analysis method. As a result, the ultimate strength was reduced by approximately 1.5% for the span-over-bay ratio of 3 and by 7% for the span-over-bay ratio of 6.
Torsional postbuckling characteristics of functionally graded graphene enhanced laminated truncated conical shell with temperature dependent material properties
Hamad M. Hasan, Saad S. Alkhfaji, Sattar A. Mutlag
Theoretical and Applied Mechanics Letters  13 (2023) 100453.   doi: 10.1016/j.taml.2023.100453
[Abstract] (11) [PDF 2415KB] (1)
Buckling and postbuckling characteristics of laminated graphene-enhanced composite (GEC) truncated conical shells exposed to torsion under temperature conditions using finite element method (FEM) simulation are presented in this study. In the thickness direction, the GEC layers of the conical shell are ordered in a piece-wise arrangement of functionally graded (FG) distribution, with each layer containing a variable volume fraction for graphene reinforcement. To calculate the properties of temperaturedependent material of GEC layers, the extended Halpin-Tsai micromechanical framework is used. The FEM model is verified via comparing the current results obtained with the theoretical estimates for homogeneous, laminated cylindrical, and conical shells, the FEM model is validated. The computational results show that a piece-wise FG graphene volume fraction distribution can improve the torque of critical buckling and torsional postbuckling strength. Also, the geometric parameters have a critical impact on the stability of the conical shell. However, a temperature rise can reduce the crucial torsional buckling torque as well as the GEC laminated truncated conical shell’s postbuckling strength.
Effects of mean shear on the vortex identification and the orientation statistics
Tianyi Bai, Cheng Cheng, Lin Fu
Theoretical and Applied Mechanics Letters  13 (2023) 100454.   doi: 10.1016/j.taml.2023.100454
[Abstract] (11) [PDF 2904KB] (1)
This work compares the threshold applied to the swirling strength as well as the vortex orientation statistics in the total and fluctuating velocity fields using direct numerical simulations of compressible and incompressible turbulent channel flows. It is concluded that the difference in the swirling strength for vortex identification is minimal in the logarithmic region such that these two situations share the same threshold. Regarding the vortex orientation, the inclination angle remains similar. However, as the wall-normal distance increases, a more and more obvious distinction is noticed for its orientation with respect to the spanwise (z) direction. It is mainly due to their intrinsic differences and attendant contrasting preference for the vortex identification, i.e., vortices rotating in the -z direction for the total velocity field and in the z direction for the fluctuating one. These observations function as a reasonable explanation for various remarks in previous studies.
The capillary pressure curves from upscaling interfacial and unsaturated flows in porous layers with vertical heterogeneity
Zhong Zheng
Theoretical and Applied Mechanics Letters  13 (2023) 100467.   doi: 10.1016/j.taml.2023.100467
[Abstract] (7) [PDF 992KB] (0)
We provide the capillary pressure curves pc(s) as a function of the effective saturation s based on the theoretical framework of upscaling unsaturated flows in vertically heterogeneous porous layers proposed recently (Z. Zheng, Journal of Fluid Mechanics, 950, A17, 2022). Based on the assumption of vertical gravitational-capillary equilibrium, the saturation distribution and profile shape of the invading fluid can be obtained by solving a nonlinear integral-differential equation. The capillary pressure curves pc(s) can then be constructed by systematically varying the injection rate. Together with the relative permeability curves krn(s) that are already obtained. One can now provide quick estimates on the overall behaviours of interfacial and unsaturated flows in vertically-heterogeneous porous layers.
Reconstructing urban wind flows for urban air mobility using reduced-order data assimilation
Mounir Chrit
Theoretical and Applied Mechanics Letters  13 (2023) 100451.   doi: 10.1016/j.taml.2023.100451
[Abstract] (235) [PDF 2129KB] (2)
Advancements in uncrewed aircrafts and communications technologies have led to a wave of interest and investment in unmanned aircraft systems (UASs) and urban air mobility (UAM) vehicles over the past decade. To support this emerging aviation application, concepts for UAS/UAM traffic management (UTM) systems have been explored. Accurately characterizing and predicting the microscale weather conditions, winds in particular, will be critical to safe and efficient operations of the small UASs/UAM aircrafts within the UTM. This study implements a reduced order data assimilation approach to reduce discrepancies between the predicted urban wind speed with computational fluid dynamics (CFD) Reynolds-averaged Navier Stokes (RANS) model with real-world, limited and sparse observations. The developed data assimilation system is UrbanDA. These observations are simulated using a large eddy simulation (LES). The data assimilation approach is based on the time-independent variational framework and uses space reduction to reduce the memory cost of the process. This approach leads to error reduction throughout the simulated domain and the reconstructed field is different than the initial guess by ingesting wind speeds at sensor locations and hence taking into account flow unsteadiness in a time when only the mean flow quantities are resolved. Different locations where wind sensors can be installed are discussed in terms of their impact on the resulting wind field. It is shown that near-wall locations, near turbulence generation areas with high wind speeds have the highest impact. Approximating the model error with its principal mode provides a better agreement with the truth and the hazardous areas for UAS navigation increases by more than 10% as wind hazards resulting from buildings wakes are better simulated through this process.
Improving energy storage by PCM using hybrid nanofluid [(SWCNTs-CuO)/H2O] and a helical (spiral) coil: Hybrid passive techniques
Aliakbar Hosseinpour, Mohsen Pourfallah, Mosayeb Gholinia
Theoretical and Applied Mechanics Letters  13 (2023) 100458.   doi: 10.1016/j.taml.2023.100458
[Abstract] (6) [PDF 5892KB] (0)
The aim of this study is the numerical analysis of the melting process of the phase change material (PCM) in a spiral coil. The space between the inner tube and outer shell is filled with RT-50 as PCM. Moreover, the hybrid nanofluid (with a carbon component) flows through the inner tube. The novelty of this work is to use different configurations of fin and different percentage of hybrid nanoparticles (SWCNTs-CuO) on the PCM melting process. In the numerical model created by ANSYS-Fluent, the effect of various inlet temperatures is investigated. The results indicate that the extended surface created by extra fin has a dominant effect on melting time, so by adding the third fin, the melting time is reduced by 39.24%. The next most influential factor in PCM melting is the inlet temperature of the working fluid, so that 10°C increment of temperature result in the PCM melting time decreased by 35.41%.
Simulation of anchor chain based on lumped mass method
Xiaobin Jiang, Jian Gan, Shiyang Teng
Theoretical and Applied Mechanics Letters  13 (2023) 100460.   doi: 10.1016/j.taml.2023.100460
[Abstract] (7) [PDF 1864KB] (0)
In order to develop a anchoring operation simulation system and improve safety during anchoring operations, a relatively accurate mathematical model of anchoring operations needs to be established. In this paper, the stress condition of anchor chain under environmental and subsea geological conditions is further studied and the stress condition of anchor chain is analyzed based on the previous research. In this paper, a quasi-static model based on catenary method is used as the basis of dynamic analysis, and the dynamic model of anchor chain is established based on the concentrated mass method, which fully considers the influence of anchor chain weight, hydrodynamic force, ocean current and interaction with the seabed. The fourth-order Runge Kutta method was used to solve the model numerically, and a calculation procedure was developed. The accuracy of the model was verified by comparing the calculated results with the experimental results, indicating that the constructed anchor chain dynamics model has a high accuracy.
Research on stability of laminated composite plate under nonlinear aerodynamic load
The Van Tran, Quoc Hoa Pham, Nhan Thinh Hoang
Theoretical and Applied Mechanics Letters  13 (2023) 100461.   doi: 10.1016/j.taml.2023.100461
[Abstract] (9) [PDF 2974KB] (1)
In this paper, we propose an finite element approach based on classical plate theory to investigate the dynamic stability of a layered composite plate subject to nonlinear aerodynamic load. This study considers the influence of temperature, nonlinear geometry, and nonlinear aerodynamic load on composite plate structures simultaneously. Specifically, the present work conduct comparison the results of the critical pressure value between the nonlinear aerodynamic load and the linear aerodynamic load, thereby pointing out some necessary cases which must consider the nonlinearity of aerodynamic load for calculating the aerospace structures. We determine the critical pressure value and vibrational amplitude response of the plate by means of calculation. The outcomes of our calculations can be useful in designing and repairing body shells and wings of aircraft equipment.
An overview of fluids mixing in T-shaped mixers
Huixin Li, Duo Xu
Theoretical and Applied Mechanics Letters  13 (2023) 100466.   doi: 10.1016/j.taml.2023.100466
[Abstract] (8) [PDF 2147KB] (2)
In a T-shaped mixer, the two liquid streams in the inlet channels meet each other at the T-junction, and their liquid-liquid contacting face exhibits planar, swirling folds and the folds breaking to be chaos and turbulence, as the Reynolds number increases. The characteristic mixing scenario attracts long-time attention, given these mixings are of fundamental importance in fluid physics and also have been successfully used in engineering applications. The experimental and numerical studies of flow features and mixing characteristics in T-mixers are overviewed in this manuscript. This review introduces the experimental and numerical techniques in the studies, the flow and mixing characteristics in the corresponding regimes and application examples of the T-mixers at last, aiming at introducing fundamentals to researchers with initial interests on this topic.
Crack propagation simulation in brittle elastic materials by a phase field method
Xingxue Lu, Cheng Li, Ying Tie, Yuliang Hou, Chuanzeng Zhang
2019, 9(6): 339-352   doi: 10.1016/j.taml.2019.06.001
[Abstract](1978) [FullText HTML](1081) [PDF 3845KB](104)
Investigation on Savonius turbine technology as harvesting instrument of non-fossil energy: Technical development and potential implementation
Aditya Rio Prabowo, Dandun Mahesa Prabowoputra
2020, 10(4): 262-269   doi: 10.1016/j.taml.2020.01.034
[Abstract](1873) [FullText HTML](877) [PDF 3192KB](98)
Mechanistic Machine Learning: Theory, Methods, and Applications
2020, 10(3): 141-142   doi: 10.1016/j.taml.2020.01.041
[Abstract](9655) [FullText HTML](985) [PDF 4844KB](95)
On the Weissenberg effect of turbulence
Yu-Ning Huang, Wei-Dong Su, Cun-Biao Lee
2019, 9(4): 236-245   doi: 10.1016/j.taml.2019.03.004
[Abstract](1565) [FullText HTML](809) [PDF 2579KB](87)
Physics-informed deep learning for incompressible laminar flows
Chengping Rao, Hao Sun, Yang Liu
2020, 10(3): 207-212   doi: 10.1016/j.taml.2020.01.039
[Abstract](1941) [FullText HTML](899) [PDF 4226KB](82)
Dynamic mode decomposition and reconstruction of transient cavitating flows around a Clark-Y hydrofoil
Rundi Qiu, Renfang Huang, Yiwei Wang, Chenguang Huang
2020, 10(5): 327-332   doi: 10.1016/j.taml.2020.01.051
[Abstract](1611) [FullText HTML](919) [PDF 2862KB](68)
On the interaction between bubbles and the free surface with high density ratio 3D lattice Boltzmann method
Guo-Qing Chen, A-Man Zhang, Xiao Huang
2018, 8(4): 252-256   doi: 10.1016/j.taml.2018.04.006
[Abstract](1881) [FullText HTML](1170) [PDF 2725KB](67)
Multiscale mechanics
G.W. He, G.D. Jin
11 (2021) 100238   doi: 10.1016/j.taml.2021.100238
[Abstract](928) [FullText HTML](716) [PDF 2196KB](66)
Frame-indifference of cross products, rotations, and the permutation tensor
Maolin Du
2020, 10(2): 116-119   doi: 10.1016/j.taml.2020.01.015
[Abstract](1585) [FullText HTML](864) [PDF 2494KB](65)
A modified Lin equation for the energy balance in isotropic turbulence
W.D. McComb
2020, 10(6): 377-381   doi: 10.1016/j.taml.2020.01.055
[Abstract](1114) [FullText HTML](698) [PDF 2541KB](61)