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PLA-based additively manufactured samples with different infill percentages under freeze-thaw cycles; mechanical, cracking, and microstructure characteristics
Reza Ale Ali, Hamid Reza Karimi, Razie Mohamadi
Accepted Manuscript , doi: 10.1016/j.taml.2024.100536
[Abstract] (0) [PDF 1122KB] (0)
The layered nature of the parts produced by 3D printing makes them susceptible to freezethaw damage. This research investigates the effect of the freeze-thaw cycles on the tensile, bending, and fracture resistance of samples made of Polylactic acid (PLA) material. For this purpose, the samples with 100, 75, 50, and 25 infill percentages were subjected to 4, 8, and 12 freeze-thaw cycles. The results show that the infill percentage and cycle affect freeze-thaw resistance. So, although for 100% infill samples, the 4, 8, and 12 cycles averagely reduce the tensile strength by 5, 15, and 25. The same trends can also be seen for flexural strength and, more severely, fracture resistance. Reviewing the microstructure with a Scanning electron microscopy (SEM) device shows freeze-thaw’s destructive effect (both the strand’s surface and their joints). In the end, simple statistical analyses were presented to evaluate a model for anticipating the effect of freeze-thaw on mechanical resistance.
Complex Dynamics of a Magnetic Microrobot Driven by Single Deformation Soft Tail in Random Environment
Xinpeng Shi, Yongge Li, Yong Xu, Qi Liu
Accepted Manuscript , doi: 10.1016/j.taml.2024.100534
[Abstract] (6) [PDF 22238KB] (0)
This study focuses on exploring the complex dynamical behaviors of a magnetic microrobot in a random environment. The purpose is to reveal the mechanism of influence of random disturbance on microrobot dynamics. This paper establishes the stochastic dynamic models for the microrobot before and after deformation, considering the influence of Gaussian white noise. The system responses are analyzed via steady-state probability density functions and first deformation time. The effects of different magnetic field strengths and fluid viscosities on the movement speed and angular velocity of the microrobot are studied. The results indicate that random disturbances can cause deformation of microrobots in advance compared to the deterministic case. This work contributes to the design and motion control of microrobots and enhances the theoretical foundation of microrobots.
Modeling and characterization of contact behavior of asperities with irregular shapes
Jiaxin Huanga, Chen Suna, Jubing Chena
Accepted Manuscript , doi: 10.1016/j.taml.2024.100535
[Abstract] (14) [PDF 23416KB] (1)
This paper introduces a model for characterizing the contact behavior of irregular asperities, transforming it into a superposition of sinusoidal asperity contact behaviors. A new sinusoidal asperity model is developed for bilinear hardening under plane strain conditions. Empirical equations are proposed, considering geometric shapes, tangent modulus, and Young's modulus. The frequency of asperity height is extracted through Fourier transform for irregular asperities. Contact area and pressure are predicted using the sinusoidal asperity model, and the behavior of irregular asperities is obtained by superimposing those with the first three frequencies. Experimental validation is conducted with milling and knurling-formed asperities, showing good alignment between the model and experimental results. In rough surface models, the proposed irregular asperity model exhibits greater accuracy in predicting contact behavior than a single sinusoidal asperity when interference exceeds 10% of the amplitude.
Evaporation Mechanisms during Droplet Levitation and Coalescence Based on Molecular Dynamics
Fengming Chen, Tieqiang Gang, Lijie Chen
Accepted Manuscript , doi: 10.1016/j.taml.2024.100533
[Abstract] (88) [PDF 2706KB] (2)
Droplet levitation and coalescence mechanisms have consistently been a focal point in scientific research. From the perspective of molecular interactions, we investigated the influence of vapor pressure on the levitation and coalescence of droplets. The evaporation of nanodroplets and liquid pools under different initial environments was simulated by nonequilibrium molecular dynamics. The study analyzed the effects of evaporation during the processes of droplet levitation and coalescence and determined the existence of the Knudsen layer. Knudsen layers consisting of water molecules rather than clusters are generated during the evaporation. The definition, properties, and mechanisms of the layers were investigated.
Multi-objective optimization of a bistable curved shell with controllable thickness based on machine learning, Theoretical and Applied Mechanics Letters
Shiqing Huang, Chenjie Zhao, Xiaoqian Ning, Wenhua Zhang, Huifeng Xi, Zhiwei Wang, Changxian Wang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100532
[Abstract] (66) [PDF 1134KB] (2)
Bistable curved shells have become a promising low-cost application in energy absorption fields owing to recent advances in material and technology. Significant research has been conducted to improve their energy absorption effect through forward prediction and singleobjective optimization. However, these approaches may not fully explore their functional potential. In this study, we propose a multi-objective optimization framework based on the principle of main objective optimization that combines neural networks and genetic algorithms. The energy absorption effect and backward snapping force of the bistable curved shell are improved synchronously. Meanwhile, a reverse design algorithm is developed to generate the preset load-displacement curve, which further expands the application of machine learning methods in the field of multi-objective optimization. The combination of machine learning and multi-objective optimization is highly effective for building meta-structures with specific performance requirements and has potential applications in solving complex optimization tasks in various fields.
A New Cyclic Cohesive Zone Model for Fatigue Damage Analysis of Welded Vessel
Changyuan Shen, Xiaozhou Xia, Dake Yi, Zhongmin Xiao
Accepted Manuscript , doi: 10.1016/j.taml.2024.100531
[Abstract] (86) [PDF 2857KB] (1)
A new cyclic cohesive zone fatigue damage model is proposed to address the fatigue problem spanning high and low cycle stages. The new damage model is integrated with the damage extrapolation technique to improve calculation efficiency. The model's effectiveness in regulating the low-cycle fatigue evolution rate, overall fatigue damage evolution rate, and stress level at the fatigue turning point is assessed through the comparison of the S-N curves. The fatigue damage model's high precision is proved based on the minor deviation of stress at the turning point of the S-N curve from the actual scenario. Finally, the fatigue damage evolution is simulated considering the effects of pre-load pressure and welding residual stress. It is observed that laser welding induces a significant residual tensile stress, accelerating fatigue damage evolution, while compressive loading impedes fatigue damage progression.
An Implicit Factorized Transformer with Applications to Fast Prediction of Three-dimensional Turbulence
Huiyu Yang, Zhijie Li, Xia Wang, Jianchun Wang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100527
[Abstract] (89) [PDF 6789KB] (0)
Transformer has achieved remarkable results in various fields, including its application in modeling dynamic systems governed by partial differential equations. However, transformer still face challenges in achieving long-term stable predictions for three-dimensional turbulence. In this paper, we propose an implicit factorized transformer (IFactFormer) model, which enables stable training at greater depths through implicit iteration over factorized attention. IFactFormer is applied to large eddy simulation of three-dimensional homogeneous isotropic turbulence (HIT), and is shown to be more accurate than the FactFormer, Fourier neural operator (FNO), and dynamic Smagorinsky model (DSM) in the prediction of the velocity spectra, probability density functions of velocity increments and vorticity, temporal evolutions of velocity and vorticity root-mean-square value and isosurface of the normalized vorticity. IFactFormer can achieve long-term stable predictions of a series of turbulence statistics in HIT. Furthermore, IFactFormer showcases superior computational efficiency compared to the conventional DSM in large eddy simulation.
Motion of a small bubble in forced vibrating sessile drop
Jia-Qi Cheng, Fei Zhang, Chun-Yu Zhang, Hang Ding
Accepted Manuscript , doi: 10.1016/j.taml.2024.100529
[Abstract] (87) [PDF 16530KB] (7)
In this letter, the motion of small gas bubbles within sessile water drops on a vibrating substrate is investigated numerically using a two-phase diffuse interface method. Depending on the amplitude of the plate vibration, the motion of the gas bubbles falls into three distinct regimes: oscillating within the drop, sticking to the substrate, or escaping from the drop. In particular, the motion of oscillating bubbles follows a harmonic function, and is found to be closely related to a combined effect of the deformation of the sessile drop and the vibration of the plate. To interpret the underlying mechanism, we analyze the dominant forces acting on the bubbles in the non-inertial framework fixed to the plate, and take account of the periodic deformation of the drop, which effectively induces flow acceleration inside the drop. As a result, we establish a theoretical model to predict the bubble motion, and correlate the amplitude and phase difference of the bubble with the Bond and Strouhal numbers. The theoretical prediction agrees with our numerical results. These findings and theoretical analysis provide new insights into controlling bubble motion in sessile drops.
Cut layout optimization for design of kirigami metamaterials under large stretching
Chen Du, Yiqiang Wang, Zhan Kang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100528
[Abstract] (85) [PDF 1608KB] (1)
Kirigami metamaterials have gained increasing attention due to their unusual mechanical properties under large stretching. However, most metamaterial designs obtained with trial-and-error approaches tend to lose their desirable properties under large tensile strains due to occurrence of instability caused by out-of-plane buckling. To cope with this limitation, this paper presents a systematic approach of cut layout optimizing for designing kirigami metamaterials working at large tensile strains by fully exploiting their out-of-plane buckling behaviors. This method can also mitigate the local stress concentration issue at the hinges of conventional kirigami designs working at in-plane deformation modes. The effectiveness of the proposed method is demonstrated through several examples regarding metamaterial design with negative Poisson's ratio and specified flip angle pattern. It is shown that the proposed method is capable of addressing the highly nonlinear deformation impacts on the mechanical performance under large stretching, to meet the growing and diverse demands in the field of kirigami metamaterials.
A two-scale method to include essential behavior of bolted connections in structures including elevated temperatures
Qingfeng Xu, Hèrm Hofmeyer, Johan Maljaars
Accepted Manuscript , doi: 10.1016/j.taml.2024.100526
[Abstract] (99) [PDF 14816KB] (1)
A two-scale method is proposed to simulate the essential behavior of bolted connections in structures including elevated temperatures. It is presented, verified, and validated for the structural behavior of two plates, connected by a bolt, under a variety of loads and elevated temperatures. The method consists of a global-scale model that simulates the structure (here the two plates) by volume finite elements, and in which the bolt is modelled by a spring. The spring properties are provided by a small-scale model, in which the bolt is modelled by volume elements, and for which the boundary conditions are retrieved from the global-scale model. To ensure the small-scale model to be as computationally efficient as possible, simplifications are discussed regarding the material model and the modelling of the threads. For the latter, this leads to the experimentally validated application of a non-threaded shank with its stress area. It is shown that a non-linear elastic spring is needed for the bolt in the global-scale model, so the post-peak behavior of the structure can be described efficiently. All types of bolted connection failure as given by design standards are simulated by the two-scale method, which is successfully validated (except for net section failure) by experiments, and verified by a detailed system model, which models the structure in full detail. The sensitivity to the size of the part of the plate used in the small-scale model is also studied. Finally, multi-directional load cases, also for elevated temperatures, are studied with the two-scale method and verified with the detailed system model. As a result, a computationally efficient finite element modelling approach is provided for all possible combined load actions (except for nut thread failure and net section failure) and temperatures. The two-scale method is shown to be insightful, for it contains a functional separation of scales, revealing their relationships, and consequently, local small-scale non-convergence can be handled. Not presented in this paper, but the two-scale method can be used in e.g. computationally expensive two-way coupled fire-structure simulations, where it is beneficial for distributed computing and densely packed bolt configurations with stiff plates, for which a single small-scale model may be representative of several connections.
Effective Permittivity of Compacted Granular Materials: Effects of Interfacial Polarization and Pore-filling Fluids
Xu Wang, Chongpu Zhai, Yixiang Gan
Accepted Manuscript , doi: 10.1016/j.taml.2024.100525
[Abstract] (126) [PDF 1401KB] (9)
Interfacial polarization dominates the permittivity spectra of heterogeneous granular materials for the intermediate frequency range (i.e., from kHz to MHz). In this study, we examine the corresponding dielectric responses of compacted glass sphere packings saturated with pore-filling fluids under various compressive stresses. The effective permittivity spectra are observed to exhibit consistently a plateau-to-plateau drop, described by low-frequency permittivity, characteristic frequency, and high-frequency permittivity. The permittivity spectra under different compressive levels are found to be influenced by the packing structure, compressive stress, and electrical property contrasts between solid and fluid (specifically permittivity and conductivity). For considered measurement conditions, the variation of packing structure and its associated porosity is found to be more significant than the stress evolution in controlling the interfacial polarization, thus the permittivity spectra, as supported by analytical and numerical results for unit cells. Furthermore, to gain a general rule for dielectric responses for saturated granular materials, we train multi-layer artificial neural network (ANN) models based on a series of simulations for unit cells with various structures, stresses, and electrical and dielectric properties. The predictions with two-layer ANN agree well with experimental measurements, presenting errors smaller than 5% for both low-frequency and high-frequency permittivity. This study offers an effective predicting approach for the dielectric behaviour of heterogeneous and multiphase materials.
Tunable Supra-Transmission of a Stacked Miura-Origami Based Meta-Structure
Qiwei Zhang, Hongbin Fang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100523
[Abstract] (122) [PDF 2295KB] (9)
The origami-based meta-structure has wide application in areas such as energy and wave transmission. Existing research has demonstrated the occurrence of supra-transmission in origami meta-structures and revealed its underlying mechanism. However, studies on how to regulate the phenomenon of supra-transmission are still very limited. In this work, we choose the meta-structure composed of stacked-Miura ori (SMO) as the subject. The SMO unit possesses two topologically distinct stable configurations, enabling the meta-structure to possess a rich variety of periodic layouts. Based on the established equivalent dynamic model of the SMO-based meta-structure, we employ numerical simulation methods and find that the supra-transmission threshold could be adjusted by tuning the periodic layout of the meta-structure. Furthermore, the probability of supra-transmission is also highly dependent on the periodic layout. Increasing the number of SMO units under the bulged-out configuration in each periodic layout decreases the likelihood of supratransmission occurring. The findings of this study yield an extensive array of foundational insights into the wave dynamics of origami structures. Furthermore, these insights translate into practical guidelines for designing origami-based meta-structure with tunable and programmable dynamic characteristics.
On the impact of debris accumulation on power production of marine hydrokinetic turbines: Insights gained via LES
Mustafa Meriç Aksen, Kevin Flora, Hossein Seyedzadeh, Mehrshad Gholami Anjiraki, Ali Khosronejad
Accepted Manuscript , doi: 10.1016/j.taml.2024.100524
[Abstract] (118) [PDF 5464KB] (1)
We present a series of large-eddy simulations to systematically investigate the impact of debris accumulation on the hydrodynamics and power production of a utility-scale marine hydrokinetic (MHK) turbine under various debris loads lodged on the upstream face of the turbine tower. The turbine blades are modeled using turbine resolving, actuator line, and actuator surface methods. Moreover, the influence of debris on the flow field is captured by directly resolving individual logs and employing a novel debris model. Analyzing the hydrodynamics effects of various debris accumulations, we show that an increase in the density of debris accumulation leads to more flow bypassing beneath the turbine blade. This, in turn, reduces the flow momentum that reaches the MHK blades at the lower depths, inducing significant fluctuation in power production. Further, it is shown that debris-induced turbulent fluctuations contribute to significant variability in the MHK turbine's power production.
Analysis and optimization of stamping and forming process of bearing outer ring
Shichao Zhu, Yulu Ding, Bing Long, Hun Guo, Linhan Ouyang, Wengang Chen, Zhengyi Jiang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100522
[Abstract] (168) [PDF 2260KB] (7)
To address the common issues of wrinkling, tearing, and uneven wall thickness in the actual sheet metal stamping process of the outer ring of needle roller bearings, this study analyzes critical technical indicators such as forming limits, thickness distribution, and principal strains in the forming process in detail. Three-dimensional models of the concave and convex dies were constructed. The effects of different process parameters, including stamping speed, edge pressure, sheet metal thickness, and friction coefficient, on the quality of the forming parts were investigated by varying these parameters. Subsequently, the orthogonal experimental method was used to determine an optimal experimental group from multiple sets of experiments. It was found that under the process parameters of a stamping speed of 3000 mm/s, edge pressure of 2000 N, sheet metal thickness of 0.9 mm, and friction coefficient of 0.125, the forming quality of the outer ring of the bearing is ideal.
The Significant Contribution of Stochastic Forcing to Nonlinear Energy Transfer in Resolvent Analysis
Youhua Wang, Ting Wu, Guowei He
Accepted Manuscript , doi: 10.1016/j.taml.2024.100521
[Abstract] (220) [PDF 1959KB] (25)
Nonlinear energy transfer is represented through eddy viscosity and stochastic forcing within the framework of resolvent analysis. Previous investigations estimate the contribution of eddyviscosity-enhanced resolvent operator to nonlinear energy transfer. The present article estimates the contribution of stochastic forcing to nonlinear energy transfer and demonstrates that the contribution of stochastic forcing cannot be ignored. These results are achieved by numerically comparing the eddy-viscosity-enhanced resolvent operator and stochastic forcing with nonlinear energy transfer in turbulent channel flows. Furthermore, the numerical results indicate that composite resolvent operators can improve the prediction of nonlinear energy transfer.
A sub-grid scale model for Burgers turbulence based on the artificial neural network method
Xin Zhao, Kaiyi Yin
Accepted Manuscript , doi: 10.1016/j.taml.2024.100519
[Abstract] (169) [PDF 1308KB] (5)
The present study proposes a sub-grid scale (SGS) model for the one-dimensional Burgers turbulence based on the neural network and deep learning method. The filtered data of the direct numerical simulation is used to establish the training data set, the validation data set, and the test data set. The artificial neural network (ANN) method and Back Propagation method are employed to train parameters in the ANN. The developed ANN is applied to construct the sub-grid scale model for the large eddy simulation (LES) of the Burgers turbulence in the one-dimensional space. The proposed model well predicts the time correlation and the space correlation of the Burgers turbulence.
Simulation of wave scattering over a floating platform in the ocean with a coupled CFD-IBM model
Pengxuan Luo, Jingxin Zhang, Yongyong Cao, Shaohong Song
Accepted Manuscript , doi: 10.1016/j.taml.2024.100516
[Abstract] (180) [PDF 1113KB] (1)
A numerical study of linear wave scattering over a floating platform has been simulated by an efficient numerical model in this letter. The non-hydrostatic model is used to simulate the free surface and the uneven bottom. For the solid body modelling, the immersed boundary method (IBM) is implemented by introducing a virtual boundary force into the momentum equations to emulate the boundary conditions. This implementation enhances the ability of the model to simulate interactions between waves and floating structures. A numerical case involving wave interactions with a floating platform is studied to validate the numerical model. By simulating the wave propagation, the numerical model captures the variation of the wave scattering very well, which verifies the performance of the numerical model and the robust strategy of the IBM.
Multi-head neural networks for simulating particle breakage dynamics
Abhishek Gupta, Barada Kanta Mishra
Accepted Manuscript , doi: 10.1016/j.taml.2024.100515
[Abstract] (167) [PDF 1904KB] (4)
The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation. In this paper, the emerging computational science paradigm of physics-informed neural networks is studied for the first time for solving both linear and nonlinear variants of the governing dynamics. Unlike conventional methods, the proposed neural network provides rapid simulations of arbitrarily high resolution in particle size, predicting values on arbitrarily fine grids without the need for model retraining. The network is assigned a simple multi-head architecture tailored to uphold monotonicity of the modelled cumulative distribution function over particle sizes. The method is theoretically analyzed and validated against analytical results before being applied to real-world data of a batch grinding mill. The agreement between laboratory data and numerical simulation encourages the use of physics-informed neural nets for optimal planning and control of industrial comminution processes.
Vibration of Black Phosphorus Nanotubes via Orthotropic Cylindrical Shell Model
Minglei HE, Lifeng WANG
Accepted Manuscript , doi: 10.1016/j.taml.2024.100513
[Abstract] (167) [PDF 1477KB] (6)
Black phosphorus nanotubes (BPNTs) may have good properties and potential applications. Determining the vibration property of BPNTs is essential for gaining insight into the mechanical behaviour of BPNTs and designing optimized nanodevices. In this paper, the mechanical behaviour and vibration property of BPNTs are studied via orthotropic cylindrical shell model and molecular dynamics (MD) simulation. The vibration frequencies of two chiral BPNTs are analysed systematically. According to the results of MD calculations, it is revealed that the natural frequencies of two BPNTs with approximately equal sizes are unequal at each order, and that the natural frequencies of armchair BPNTs are higher than those of zigzag BPNTs. In addition, an armchair BPNTs with a stable structure is considered as the object of research, and the vibration frequencies of BPNTs of different sizes are analysed. When comparing the MD results, it is found that both the isotropic cylindrical shell model and orthotropic cylindrical shell model can better predict the thermal vibration of the lower order modes of the longer BPNTs better. However, for the vibration of shorter and thinner BPNTs, the prediction of the orthotropic cylindrical shell model is obviously superior to the isotropic shell model, thereby further proving the validity of the shell model that considers orthotropic for BPNTs.
A symmetric substructuring method for analyzing the natural frequencies of conical origami structures
Chenhao Lu, Yao Chen, Weiying Fan, Jian Feng, Pooya Sareh
Accepted Manuscript , doi: 10.1016/j.taml.2024.100517
[Abstract] (149) [PDF 2421KB] (3)
Conical origami structures are characterized by their considerable out-of-plane stiffness and energy-absorption capacity. Previous investigations have commonly focused on the static characteristics of these lightweight structures. However, efficient analysis of the natural vibrations of these structures is pivotal for designing conical origami structures with programmable stiffness and mass. In this paper, we propose a novel method to analyze the natural vibrations of such structures by combining a symmetric substructuring method (SSM) and a generalized eigenvalue analysis. SSM exploits the inherent symmetry of the structure to decompose it into a finite set of repetitive substructures. In doing so, we reduce the dimensions of matrices and improve the computational efficiency by adopting the stiffness and mass matrices of the substructures in the generalized eigenvalue analysis. The simulated results of pin-jointed models implemented using finite element analyses are used to validate the computational results of the proposed approach. Moreover, the parametric analysis of the structures demonstrates the influences of the number of segments along the circumference and the radii of the cone on the structural mass and natural frequencies of the structures. Furthermore, a comparison between six-fold and four-fold conical origami structures and the influence of various geometric parameters on their natural frequencies is presented. This work provides a strategy for efficiently analyzing the natural vibration of symmetric origami structures and has the potential to contribute to the efficient design and customization of origami structures with programmable stiffness.
A Real Space Moiré Inversion Technique and Its Practical Applications in Real Space for Lattice Reconstruction
Bo Cui, Hongye Zhang, Miao Li, Dong Zhao, Huimin Xie, Zhanwei Liu
Accepted Manuscript , doi: 10.1016/j.taml.2024.100518
[Abstract] (178) [PDF 1551KB] (0)
Distinct physical properties emerge at the nanoscale in Moiré materials, such as bilayer graphene and layered material superposition. This study explores similar structural features within a second-generation nickel-based superalloy, unveiling potential formation mechanisms. Introducing the real space Moiré inversion method (RSMIM) for nanoscale imaging, combined with the transmission electron microscopy (TEM) nano-Moiré inversion method, we reveal spatial angles between specimen and reference lattices in 3D. Simultaneously, we reconstruct the Moiré pattern region to deepen us understand the phenomenon of Moiré formation. Focused on face-centered cubic structures, the research identifies six spatial angles, shedding light on Moiré patterns in the superalloy. The RSMIM not only enhances understanding but also expands 3D structure measurement capabilities. The RSMIM served to validate TEM nano-Moiré inversion results, ascertaining the spatial relative angle between lattices, and establishing a theoretical simulation model for Moiré patterns. This study marks a substantial step toward designing high-performance nanomaterials by uncovering dynamic Moiré variations.
Study on cumulative effects of biological craniocerebral trauma under repeated blast
Xingyuan Huang, Bingchen Xia, Lijun Chang, Zhikang Liao, Hui Zhao, Zhihua Cai
Accepted Manuscript , doi: 10.1016/j.taml.2024.100514
[Abstract] (154) [PDF 2081KB] (0)
Repeated blast impacts on personnel in explosive environments can exacerbate craniocerebral trauma. Most existing studies focus on the injury effects of a single blast, lacking in-depth analysis on the injury effects and cumulative effects of repeated blasts. Therefore, rats were used as the experimental samples to suffer from explosion blasts with different peak air overpressures (167 kPa~482 kPa) and varying number of repeated blasts. The cumulative effect of craniocerebral trauma was most pronounced for moderate repeated blast, showing approximately 95% increase of trauma severity with penta blast, and an approximately 85% increase of trauma severity with penta minor blast. The cumulative effect of craniocerebral trauma from severe, repeated blast has a smaller rate of change compared to the other two conditions. The severity of trauma from penta blast increased by approximately 69% compared to a single blast. Comprehensive physiological, pathological and biochemical analysis show that the degree of neurological trauma caused by repeated blasts is higher than that of single blasts, and the pathological trauma to brain tissue is more extensive and severe. The trauma degree remains unchanged after double blast, increases by one grade after triple or quadruple blast, and increases by two grades after penta blast.
Theoretical optimization of micropillar arrays for structurally stable bioinspired dry adhesives
Ke Ni, Zhengzhi Wang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100512
[Abstract] (168) [PDF 872KB] (2)
Inspired by the excellent adhesion performances of setae structure from organisms, micro/nano-pillar array has become one of the paradigms for adhesive surfaces. The micropillar arrays are composed of the resin pillars for adhesion and the substrate with different elastic modulus for supporting. The stress singularity at the bi-material corner between the pillars and the substrate can induce the failure of the micropillar-substrate corner and further hinder the fabrication and application of micropillar arrays, yet the design for the stability of the micropillar array lacks systematical and quantitative guides. In this work, we develop a semi-analytical method to provide the full expressions for the stress distribution within the bi-material corner combining analytical derivations and numerical calculations. The predictions for the stress within the singularity field can be obtained based on the full expressions of the stress. The good agreement between the predictions and the FEM results demonstrates the high reliability of our method. By adopting the strain energy density factor approach, the stability of the pillar-substrate corner is assessed by predicting the failure at the corner. For the elastic mismatch between the pillar and substrate given in this paper, the stability can be improved by increasing the ratio of the shear modulus of the substrate to that of the micropillar. Our study provides accurate predictions for the stress distribution at the bi-material corner and can guide the optimization of material combinations of the pillars and the substrate for more stable bioinspired dry adhesives.
Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks
Hanyu Zhou, Haochen Li, Yaomin Zhao
Accepted Manuscript , doi: 10.1016/j.taml.2024.100511
[Abstract] (175) [PDF 7302KB] (0)
Identification of underlying partial differential equations (PDEs) for complex systems remains a formidable challenge. In the present study, a robust PDE identification method is proposed, demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge. Specifically, the proposed method combines gene expression programming, one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms, with symbolic regression neural networks. These networks are designed to represent explicit functional expressions and optimize them with data gradients. In particular, the specifically designed neural networks can be easily transformed to physical constraints for the training data, embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification. The proposed method has been tested in four canonical PDE cases, validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.
A Study of the Effect of Local Scour on the Flow Field Near the Spur Dike
Yu-tian Li, Jie-min Zhan, Onyx WH Wai
Accepted Manuscript , doi: 10.1016/j.taml.2024.100510
[Abstract] (198) [PDF 1387KB] (0)
The flow field near a spur dike such as down flow and horseshoe vortex system(HVS) are susceptible to the topographic changes in the local scouring process, resulting in variation of the sediment transport with time.In this study, large eddy simulations with fixed-bed at different scouring stages were conducted to investigate the changes in flow field. The results imply that the bed deformation leads to an increase in flow rate per unit area, which represent the capability of sediment transportation by water, in the scour hole. Moreover, the intensity of turbulent kinetic energy (TKE) and bimodal motion near the sand bed induced by the HVS were also varied. However, the peak moments between the two sediment transport mechanisms were different. Hence, understanding the complex feedback mechanism between topography and flow field is essential for the local scour problem.
Analysis of pulse-wave propagation characteristics in abdominal aortic sclerosis disease
Xuehang Sun, Bensen Li, Yicheng Lu, Xiabo Chen, Wenbo Gong, Fuxing Miao
Accepted Manuscript , doi: 10.1016/j.taml.2024.100507
[Abstract] (171) [PDF 1495KB] (1)
In this work, a bidirectional fluid‒structure coupling finite element analysis model of the abdominal aorta was established with the various vascular elastic modulus as the main parameter of atherosclerosis, in consideration of blood dynamic viscosity and compressibility. Pressure and velocity pulse-wave propagation were investigated by the application of full-coupling analysis algorithm. The effect of atherosclerosis degree on the propagation characteristics of pulse waves in the bifurcated abdominal aorta was quantitatively analyzed. Arterial bifurcation can cause a substantial attenuation on the peak of pressure pulse waveform and an increase in wave velocity during the cardiac cycle. The elastic modulus and bifurcation properties of the arterial wall directly affected the peak value and wave propagation velocity of the pressure pulse wave. The preliminary results of this work will be crucial in guiding the evolution of the pressure pulse wave and the initial diagnosis of atherosclerotic disease through the waveform.
Design and Mechanical Properties Analysis of a Cellular Waterbomb Origami Structure
Yongtao Bai, Zhaoyu Wang, Yu Shi
Accepted Manuscript , doi: 10.1016/j.taml.2024.100509
[Abstract] (205) [PDF 1535KB] (8)
Cellular structures are commonly used to design energy-absorbing structures, and origami structures are becoming a prevalent method of cellular structure design. This paper proposes a foldable cellular structure based on the Waterbomb origami pattern. The geometrical configuration of this structure is described. Quasi-static compression tests of the origami tube cell of this cellular structure are conducted, and load-displacement relationship curves are obtained. Numerical simulations are carried out to analyze the effects of aspect ratio, folding angle, thickness and number of layers of origami tubes on initial peak force and specific energy absorption (SEA). Calculation formulas for initial peak force and SEA are obtained by the multiple linear regression method. The degree of influence of each parameter on the mechanical properties of the single-layer tube cell is compared. The results show that the cellular structure exhibits negative stiffness and periodic load-bearing capacity, as well as folding angle has the most significant effect on the load-bearing and energy-absorbing capacity. By adjusting the design parameters, the stiffness, load-bearing capacity and energy absorption capacity of this cellular structure can be adjusted, which shows the programmable mechanical properties of this cellular structure. The foldability and the smooth periodic load-bearing capacity give the structure potential for application as an energy-absorbing structure.
Experimental study of solid-liquid origami composite structures with improved impact resistance
Shuheng Wang, Zhanyu Wang, Bei Wang, Zhi Liu, Yunzhu Ni, Wuxing Lai, Shan Jiang, YongAn Huang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100508
[Abstract] (212) [PDF 1519KB] (2)
In this paper, a liquid-solid origami composite design is proposed for the improvement of impact resistance. Employing this design strategy, Kresling origami composite structures with different fillings were designed and fabricated, namely air, water, and shear thickening fluid (STF). Quasi-static compression and drop-weight impact experiments were carried out to compare and reveal the static and dynamic mechanical behavior of these structures. The results from drop-weight impact experiments demonstrated that the solid-liquid Kresling origami composite structures exhibited superior yield strength and reduced peak force when compared to their empty counterparts. Notably, the Kresling origami structures filled with STF exhibited significantly heightened yield strength and reduced peak force. For example, at an impact velocity of 3 m/s, the yield strength of single-layer STF-filled Kresling origami structures increased by 772.7% and the peak force decreased by 68.6%. This liquid-solid origami composite design holds the potential to advance the application of origami structures in critical areas such as aerospace, intelligent protection and other important fields. The demonstrated improvements in impact resistance underscore the practical viability of this approach in enhancing structural performance for a range of applications.
A theoretical model for impact protection of flexible polymer material
Huifeng Xi, Hui Pan, Song Chen, Heng Xiao
Accepted Manuscript , doi: 10.1016/j.taml.2024.100506
[Abstract] (191) [PDF 1676KB] (3)
The relationship between the protective performance of flexible polymer material and material parameters (elastic modulus, viscosity coefficient) is explored, an impact collision motion equation between two bodies is established from the viscoelastic material constitutive, and the relationship between the kinematic response and the material parameters is obtained. Based on the Kelvin constitutive model, a theoretical model for impact between the protective body and the protected body is established, then the dynamic response is obtained. The feasibility of the model was verified by drop hammer experiment, and the material parameters (elastic modulus, viscosity coefficient) were obtained by formula. The model is discretized and the relationship between local impact response and material parameters is analyzed. The discussion results on the relationship between the impact response and the protective material performance indicate that adjusting the elastic modulus, viscosity coefficient, and thickness of the protective material can effectively improve protective effect.
Numerical Study of Flow and Thermal Characteristics of Pulsed Impinging Jet on a Dimpled Surface
Amin Bagheri, Kazem Esmailpour, Morteza Heydari
Accepted Manuscript , doi: 10.1016/j.taml.2024.100501
[Abstract] (166) [PDF 3446KB] (0)
This research comprehensively investigates the flow and thermal characteristics of a pulsating impinging jet over a dimpled surface. It analyzes the impact of key parameters (e.g., inlet velocity pulsation functions, pulsation frequency, amplitude, dimple pitch, dimple depth, Reynolds number) on flow patterns and heat transfer. Validated computational fluid dynamics (CFD) and the Re-Normalization Group (RNG) turbulence model are employed to accurately simulate complex turbulent flow behavior. Local and average heat transfer coefficients are calculated and compared to steady impingement cases, revealing the potential benefits of pulsation for heat transfer enhancement. The study also examines how pulsationinduced flow modulation and thermal mixing affect heat transfer mechanisms. Results indicate that combining fluctuating flow with a dimpled surface can improve heat transfer rates. In summary, increasing pulsation amplitude consistently enhances heat transfer, while the effect of frequency varies between impinging and wall jet zones.
Constrained re-calibration of two-equation Reynolds-averaged Navier–Stokes models
Yuanwei Bin, Xiaohan Hu, Jiaqi Li, Samuel J. Grauer, Xiang I. A. Yang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100503
[Abstract] (168) [PDF 3273KB] (1)
Machine-learned augmentations to turbulence models can be advantageous for flows within the training dataset but can often cause harm outside. This lack of generalizability arises because the constants (as well as the functions) in a Reynolds-averaged Navier– Stokes (RANS) model are coupled, and un-constrained re-calibration of these constants (and functions) can disrupt the calibrations of the baseline model, the preservation of which is critical to the model’s generalizability. To safeguard the behaviors of the baseline model beyond the training dataset, machine learning must be constrained such that basic calibrations like the law of the wall are kept intact. This letter aims to identify such constraints in two-equation RANS models so that future machine learning work can be performed without violating these constraints. We demonstrate that the identified constraints are not limiting. Furthermore, they help preserve the generalizability of the baseline model.
Quantification and reduction of uncertainty in aerodynamic performance of GAN-generated airfoil shapes using MC dropouts
Kazuo Yonekura, Ryuto Aoki, Katsuyuki Suzuki
Accepted Manuscript , doi: 10.1016/j.taml.2024.100504
[Abstract] (166) [PDF 2212KB] (0)
Generative adversarial network (GAN) models are widely used in mechanical designs. The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance, and conditional GAN is used for that aim. However, the output of GAN contains uncertainties. Additionally, the uncertainties of labels have not been quantified. This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout. In addition, an uncertainty reduction method is proposed based on imbalanced training. The proposed method was evaluated for the airfoil generation task. The results indicated that the uncertainty was appropriately quantified and successfully reduced.
Truly optimal semi-active damping to control free vibration of a single degree of freedom system
La Duc Viet, Nguyen Tuan Ngoc
Accepted Manuscript , doi: 10.1016/j.taml.2024.100505
[Abstract] (162) [PDF 1301KB] (2)
This paper studies a single degree of freedom system under free vibration and controlled by a general semi-active damping. A general integral of squared error is considered as the performance index. A one-time switching damping controller is proposed and optimized. The Pontryagin Maximum Principle is used to prove that no other form of semi-active damping can provide the better performance than the proposed one-time switching damping.
Magnetically-actuated Intracorporeal Biopsy Robot Based on Kresling Origami
Long Huang, Tingcong Xie, Lairong Yin
Accepted Manuscript , doi: 10.1016/j.taml.2024.100500
[Abstract] (155) [PDF 2324KB] (0)
The introduction of wireless capsule endoscopy has brought a revolutionary change in the diagnostic procedures for gastrointestinal disorders. Biopsy, an essential procedure for disease diagnosis, has been integrated into robotic capsule endoscopy to augment diagnostic capabilities. In this study, we propose a magnetically driven biopsy robot based on a Kresling origami. Considering the bistable properties of Krelsing origami and the elasticity of the creases, a foldable structure of the robot with constant force characteristics is designed. The folding motion of the structure is used to deploy the needle into the target tissue. The robot is capable of performing rolling motion under the control of an external magnetic drive system, and a fine needle biopsy technique is used to collect deep tissue samples. We also conduct in vitro rolling experiments and sampling experiments on apple tissues and pork tissues, which verify the performance of the robot.
A New Strain-Based Pentagonal Membrane Finite Element for Solid Mechanics Problems
Koh Wei Hao, Logah Perumal, Kok Chee Kuang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100499
[Abstract] (182) [PDF 1538KB] (3)
Polygonal finite elements remain an attractive option in finite element analysis due to their flexibility in modeling arbitrary shapes compared to triangles. In this study, a pentagonal membrane element was developed with the strain approach for the first time. The element possesses invariance, and the equilibrium constraint was applied to the assumed strain field using corrective coefficients. Inspired by the advancing front technique, a pentagonal mesh was generated, and the mesh quality was enhanced with Laplacian smoothing. The performance of the developed pentagonal element was assessed in a few numerical tests, and the results revealed its suitability in modeling the bending of beams. Besides, the numerical results are enhanced when pentagonal elements are used in mesh transitions along boundaries to smoothen curved edges and capture distributed loads.
A Comparative Study on Kinetics and Dynamics of Two Dump Truck Lifting Mechanisms Using MATLAB Simscape
Thong Duc Hong, Minh Quang Pham, Son Cong Tran, Lam Quang Tran, Truong Thanh Nguyen
Accepted Manuscript , doi: 10.1016/j.taml.2024.100502
[Abstract] (169) [PDF 3031KB] (0)
In this paper, two lifting mechanism models with opposing placements, which use the same hydraulic hoist model and have the same angle of 50 degrees, have been developed. The mechanical and hydraulic simulation models are established using MATLAB Simscape to analyze their kinetics and dynamics in the lifting and holding stages. The simulation findings are compared to the analytical calculation results in the steady state, and both methods show good agreement. In the early lifting stage, Model 1 produces greater force and discharges goods in the container faster than Model 2. Meanwhile, Model 2 reaches a higher force and ejects goods from the container cleaner than its counterpart at the end lifting stage. The established simulation models can consider the effects of dynamic loads due to inertial moments and forces generated during the system operation. It is crucial in studying, designing, and optimizing the structure of hydraulic-mechanical systems.
Physics-data coupling-driven method to predict the penetration depth into concrete targets
Qin Shuai, Liu Hao, Wang Jianhui, Zhao Qiang, Zhang Lei
Accepted Manuscript , doi: 10.1016/j.taml.2024.100495
[Abstract] (181) [PDF 1112KB] (0)
The projectile penetration process into concrete target is a nonlinear complex problem. With the increase of experiment data, the data-driven paradigm has exhibited a new feasible method to solve such complex problem. However, due to poor quality of experimental data, the traditional machine learning (ML) methods, which are driven only by experimental data, have poor generalization capabilities and limited prediction accuracy. Therefore, this study intends to exhibit a ML method fusing the prior knowledge with experiment data. The new ML method can constrain the fitting to experimental data, improve the generalization ability and the prediction accuracy. Experimental results show that integrating domain prior knowledge can effectively improve the performance of the prediction model for penetration depth into concrete targets.
Assessment of scale interactions associated with wake meandering using bispectral analysis methodologies
Dinesh Kumar Kinjangi, Daniel Foti
Accepted Manuscript , doi: 10.1016/j.taml.2024.100497
[Abstract] (180) [PDF 14430KB] (2)
Large atmospheric boundary layer fluctuations and smaller turbine-scale vorticity dynamics are separately hypothesized to initiate the wind turbine wake meandering phenomenon, a coherent, dynamic, turbine-scale oscillation of the far wake. Triadic interactions, the mechanism of energy transfers between scales, manifest as triples of wavenumbers or frequencies and can be characterized through bispectral analyses. The bispectrum, which correlates the two frequencies to their sum, is calculated by two recently developed multi-dimensional modal decomposition methods: scale-specific energy transfer method and bispectral mode decomposition. Large-eddy simulation of a utility-scale wind turbine in an atmospheric boundary layer with a broad range of large length-scales is used to acquire instantaneous velocity snapshots. The bispectrum from both methods identifies prominent upwind and wake meandering interactions that create a broad range of energy scales including the wake meandering scale. The coherent kinetic energy associated with the interactions shows strong correlation between upwind scales and wake meandering.
Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions
Jianlin Huang, Rundi Qiu, Jingzhu Wang, Yiwei Wang
Accepted Manuscript , doi: 10.1016/j.taml.2024.100496
[Abstract] (199) [PDF 5133KB] (0)
Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed neural networks (msPINNs) is proposed to solve the boundary layer flow at high Reynolds numbers without any data. The flow is divided into several regions with different scales based on Prandtl’s boundary theory. Different regions are solved with governing equations in different scales. The method of matched asymptotic expansions is used to make the flow field continuously. A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale. The results are compared with the reference numerical solutions, which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows. This scheme can be developed for more multi-scale problems in the future.
Multi-Scale-Matching neural networks for thin plate bending problems
Lei Zhang, Guowei He
Accepted Manuscript , doi: 10.1016/j.taml.2024.100494
[Abstract] (213) [PDF 3009KB] (3)
Physics-informed neural networks (PINN) are a useful machine learning method for solving differential equations, but encounter challenges in effectively learning thin boundary layers within singular perturbation problems. To resolve this issue, Multi-Scale-Matching Neural Networks (MSM-NN) are proposed to solve the singular perturbation problems. Inspired by matched asymptotic expansions, the solution is decomposed into inner solutions for small scales and outer solutions for large scales, corresponding to boundary layers and outer regions, respectively. Moreover, to conform neural networks, we introduce exponential stretched variables in the boundary layers to avoid semi-infinite region problems. Numerical results for the thin plate problem validate the proposed method.
Towards Data-efficient Mechanical Design of Bicontinuous Composites Using Generative AI
Milad Masrouri, Zhao Qin
Accepted Manuscript , doi: 10.1016/j.taml.2024.100492
[Abstract] (208) [PDF 1767KB] (5)
The distribution of material phases is crucial to determine the composite’s mechanical property. While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases, this relationship is difficult to be revealed for complex irregular distributions, preventing design of such material structures to meet certain mechanical requirements. The noticeable developments of artificial intelligence (AI) algorithms in material design enables to detect the hidden structuremechanics correlations which is essential for designing composite of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low Rank Adaptation models, can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness, fracture and robustness of the material with one model, and such has to be done by several different experimental or simulation tests. This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.
A grouping strategy for reinforcement learning-based collective yaw control of wind farms
Chao Li, Luoqin Liu, Xiyun Lu
Accepted Manuscript , doi: 10.1016/j.taml.2024.100491
[Abstract] (188) [PDF 1694KB] (6)
Reinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiting its potential in practical applications. In this Letter, we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms. To reduce the computational complexity, the turbines in the wind farm are grouped according to the strength of the wake interaction. Meanwhile, to improve the control efficiency, each subgroup is treated as a whole and controlled by a single agent. Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency.
Mechanical Janus lattice with plug-switch orientation
Yupei Zhang, Jiawei Zhong, Zhengcai Zhao, Ruiyu Bai, Yanqi Yin, Yang Yu, Bo Li
Accepted Manuscript , doi: 10.1016/j.taml.2024.100493
[Abstract] (195) [PDF 1341KB] (5)
In recent years, materials with asymmetric mechanical response properties (mechanical Janus materials) have been found possess numerous potential applications, i.e. shock absorption and vibration isolation. In this study, we propose a novel mechanical Janus lattice whose asymmetric mechanical response can be switched in orientation by a plug. Through finite element analysis (FEA) and experimental verification, this lattice exhibits asymmetric displacement responses to symmetric forces. Furthermore, with such a plug structure inside, individual lattices can switch the orientation of asymmetry and thus achieve reprogrammable design of a mechanical structure with chained lattices. The reprogrammable asymmetry of this material will offer multiple functions in design of mechanical metamaterials
In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning
Jiaxuan Ma, Sheng Sun
Accepted Manuscript , doi: 10.1016/j.taml.2024.100490
[Abstract] (243) [PDF 2605KB] (1)
Dielectric elastomers (DEs) require balanced electric actuation performance and mechanical integrity under applied voltages. Incorporating high dielectric particles as fillers provides extensive design space to optimize concentration, morphology, and distribution for improved actuation performance and material modulus. This study presents an integrated framework combining finite element modeling (FEM) and deep learning to optimize the microstructure of DE composites. FEM first calculates actuation performance and the effective modulus across varied filler combinations, with these data used to train a convolutional neural network (CNN). Integrating the CNN into a multi-objective genetic algorithm (NSGA-II) generates designs with enhanced actuation performance and material modulus compared to the conventional FEM-NSGA-II approach within the same time. This framework harnesses artificial intelligence to navigate vast design possibilities, enabling optimized microstructures for high-performance DE composites.
An adaptive machine learning based optimization methodology in the aerodynamic analysis of a finite wing under various cruise conditions
Zilan Zhang, Yu Ao, Shaofan Li, Grace X. Gu
Accepted Manuscript , doi: 10.1016/j.taml.2023.100489
[Abstract] (243) [PDF 1722KB] (2)
Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions. Plenty of existing literature has considered twodimensional infinite airfoil optimization, while three-dimensional finite wing optimizations are subject to limited study because of high computational costs. Here we create an adaptive optimization methodology built upon a digitized wing shape deformation and deep learning algorithms, which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions. This methodology unfolds in three stages: radial basis function (RBF) interpolated wing generation, collection of inputs from computational fluid dynamics (CFD) simulations, and deep neural network that constructs the surrogate model for the optimal wing configuration. To this end, the proposed methodology can significantly reduce the computational cost of numerical simulations. It also has potential to optimize various aerial vehicles undergoing different mission environments, loading conditions, and safety requirements.
A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics
Coleman Moss, Romit Maulik, Giacomo Valerio Iungo
Accepted Manuscript , doi: 10.1016/j.taml.2023.100488
[Abstract] (346) [PDF 2093KB] (3)
With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations, machine learning (ML) models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays, the generated wakes and their interactions, and wind energy harvesting. However, the majority of the existing ML models for predicting wind turbine wakes merely recreate CFD-simulated data with analogous accuracy but reduced computational costs, thus providing surrogate models rather than enhanced data-enabled physics insights. Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models, using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue. In this letter, we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations, along with new promising research strategies.
Inverse design of mechanical metamaterial achieving a prescribed constitutive curve
Zongliang Du, Tanghuai Bian, Xiaoqiang Ren, Yibo Jia, Shan Tang, Tianchen Cui, Xu Guo
Accepted Manuscript , doi: 10.1016/j.taml.2023.100486
[Abstract] (346) [PDF 3153KB] (8)
Besides showing excellent abilities such as energy absorption, phase-transforming metamaterials provide a rich design space for achieving nonlinear constitutive relations by switching between different patterns under deformation. The related inverse design problem, nevertheless, is quite challenging due to the lack of appropriate mathematical formulation and the convergence issue of post-buckling analysis of intermediate designs. In the present work, periodic unit cells are explicitly described by the moving morphable voids method and effectively analyzed by removing the DOFs of void regions. Furthermore, exploring the Pareto frontiers between error and cost, an inverse design formulation is proposed for unit cells achieving a prescribed constitutive curve and validated by numerical examples and experimental results. The present design approach can be extended to the inverse design of other types of mechanical metamaterials with prescribed nonlinear effective properties.
Generative optimization of bistable plates with deep learning
Hong Li, Qingfeng Wang
Accepted Manuscript , doi: 10.1016/j.taml.2023.100483
[Abstract] (289) [PDF 979KB] (3)
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] (325) [PDF 1241KB] (4)
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.

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Investigation and simulation of parabolic trough collector with the presence of hybrid nanofluid in the finned receiver tube
M. Javidan, M. Gorji-Bandpy, A. Al-Araji
Theoretical and Applied Mechanics Letters  13 (2023) 100465.   doi: 10.1016/j.taml.2023.100465
[Abstract] (263) [PDF 4208KB] (26)
The present study discusses the thermal performance of the receiver tube, which contains a wall with various fin shapes in the parabolic trough collector. Inserted fins and bulge surfaces of the inner wall of the receiver tube increase the turbulent fluid flow. In pursuance of uniform distribution of heat transfer, various fin shapes such as square-shape, circle-shape, triangle-shape, and combined square-circle shapes were inserted, examined, and compared. A study of the temperature differences and fluid flow is meaningful for this project therefore finite volume method was used to investigate heat transfer. Also, hybrid Nano-Fluid AL2O3CuO, TiO2Cu, and Ag-MgO were applied to increase thermal diffusivity. When the combined square-circle-shaped fin was inserted, the thermal peak of fluid flow in the receiver tube was lower than the other studied fin shapes by almost 1%. Besides, the hybrid nano-fluid Ag-MgO Syltherm-oil-800 has lower thermal waste in comparison to others by more than 3%.
Large eddy simulation of supersonic flow in ducts with complex cross-sections
Huifeng Chen, Mingbo Sun, Dapeng Xiong, Yixin Yang, Taiyu Wang, Hongbo Wang
Theoretical and Applied Mechanics Letters  13 (2023) 100469.   doi: 10.1016/j.taml.2023.100469
[Abstract] (179) [PDF 2460KB] (12)
Large Eddy Simulation (LES) has been employed for the investigation of supersonic flow characteristics in five ducts with varying cross-sectional geometries. The numerical results reveal that flow channel configurations exert a considerable influence on the mainstream flow and the near-wall flow behavior. In contrast to straight ducts, square-to-circular and rectangular-to-circular ducts exhibit thicker boundary layers and a greater presence of vortex structures. Given the same inlet area, rectangular-to-circular ducts lead to higher flow drag force and total pressure loss than square-to-circular ducts. Characterized by the substantial flow separation and shock waves, the “S-shaped duct shows significant vertically-asymmetric characteristics.
Machine learning of partial differential equations from noise data
Wenbo Cao, Weiwei Zhang
Theoretical and Applied Mechanics Letters  13 (2023) 100480.   doi: 10.1016/j.taml.2023.100480
[Abstract] (421) [PDF 1486KB] (26)
Machine learning of partial differential equations (PDEs) from data is a potential breakthrough for addressing the lack of physical equations in complex dynamic systems. Recently, sparse regression has emerged as an attractive approach. However, noise presents the biggest challenge in sparse regression for identifying equations, as it relies on local derivative evaluations of noisy data. This study proposes a simple and general approach that significantly improves noise robustness by projecting the evaluated time derivative and partial differential term into a subspace with less noise. This method enables accurate reconstruction of PDEs involving high-order derivatives, even from data with considerable noise. Additionally, we discuss and compare the effects of the proposed method based on Fourier subspace and POD (proper orthogonal decomposition) subspace. Generally, the latter yields 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 K. Eldridge-Allegra, Earl H. Dowell
Theoretical and Applied Mechanics Letters  13 (2023) 100482.   doi: 10.1016/j.taml.2023.100482
[Abstract] (345) [PDF 1985KB] (4)
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 networks 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, quasi-periodic 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-based stiffness optimization of digital composite metamaterials with desired positive or negative Poisson's ratio
Xihang Jiang, Fan Liu, Lifeng Wang
Theoretical and Applied Mechanics Letters  13 (2023) 100485.   doi: 10.1016/j.taml.2023.100485
[Abstract] (404) [PDF 3045KB] (15)
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 multi-objective 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.
Reinforcement learning for wind-farm flow control: Current state and future actions
Mahdi Abkar, Navid Zehtabiyan-Rezaie, Alexandros Iosifidis
Theoretical and Applied Mechanics Letters  13 (2023) 100475.   doi: 10.1016/j.taml.2023.100475
[Abstract] (163) [PDF 1864KB] (2)
Wind-farm flow control stands at the forefront of grand challenges in wind-energy science. The central issue is that current algorithms are based on simplified models and, thus, fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere interactions. Reinforcement learning (RL), as a subset of machine learning, has demonstrated its effectiveness in solving high-dimensional problems in various domains, and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow control. This review has two main objectives. Firstly, it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL methods. By examining the latest research in this area, the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL techniques. Secondly, it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on RL. By highlighting these challenges, the review aims to identify areas requiring further exploration and potential opportunities for future research.
Statistical learning prediction of fatigue crack growth via path slicing and re-weighting
Yingjie Zhao, Yong Liu, Zhiping Xu
Theoretical and Applied Mechanics Letters  13 (2023) 100477.   doi: 10.1016/j.taml.2023.100477
[Abstract] (146) [PDF 3248KB] (1)
Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditions, making diagnosis and prognosis of fatigue damage challenging. We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events. The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns. The end-to-end approach is validated by representative examples with fatigue cracks in plates, which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.
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
Theoretical and Applied Mechanics Letters  13 (2023) 100479.   doi: 10.1016/j.taml.2023.100479
[Abstract] (384) [PDF 6046KB] (7)
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.
Piezomagnetic vibration energy harvester with an amplifier
João Pedro Norenberg, Americo Cunha Jr, Piotr Wolszczak, Grzegorz Litak
Theoretical and Applied Mechanics Letters  13 (2023) 100478.   doi: 10.1016/j.taml.2023.100478
[Abstract] (337) [PDF 1233KB] (4)
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.
Machine learning potential for Ab Initio phase transitions of zirconia
Yuanpeng Deng, Chong Wang, Xiang Xu, Hui Li
Theoretical and Applied Mechanics Letters  13 (2023) 100481.   doi: 10.1016/j.taml.2023.100481
[Abstract] (345) [PDF 3458KB] (8)
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.
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