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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] (0) [PDF 1676KB] (0)
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] (11) [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] (9) [PDF 3273KB] (0)
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] (9) [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] (9) [PDF 1301KB] (0)
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] (17) [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] (28) [PDF 1538KB] (0)
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] (13) [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] (48) [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] (36) [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] (60) [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] (51) [PDF 3009KB] (1)
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] (78) [PDF 1767KB] (4)
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] (54) [PDF 1694KB] (5)
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] (62) [PDF 1341KB] (3)
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] (64) [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] (105) [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] (163) [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] (181) [PDF 3153KB] (7)
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.
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] (217) [PDF 1872KB] (4)
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] (158) [PDF 979KB] (2)
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] (186) [PDF 1241KB] (2)
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] (174) [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] (180) [PDF 1443KB] (2)
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] (169) [PDF 2418KB] (2)
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] (164) [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] (170) [PDF 1998KB] (1)
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:          |     

Space-time correlations of passive scalar in Kraichnan model
Ping-Fan Yang, Liubin Pan, Guowei He
Theoretical and Applied Mechanics Letters  13 (2023) 100470.   doi: 10.1016/j.taml.2023.100470
[Abstract] (72) [PDF 345KB] (7)
We consider the two-point, two-time (space-time) correlation of passive scalar R(r, τ) in the Kraichnan model under the assumption of homogeneity and isotropy. Using the fine-gird PDF method, we find that R(r, τ) satisfies a diffusion equation with constant diffusion coefficient determined by velocity variance and molecular diffusion. Its solution can be expressed in terms of the two-point, one time correlation of passive scalar, i.e.,. R(r, 0) Moreover, the decorrelation of Ȓ(k, τ), which is the Fourier transform of R(r, τ), is determined by Ȓ(k, 0) and a diffusion kernal.
A data-driven machine learning approach for yaw control applications of wind farms
Christian Santoni, Zexia Zhang, Fotis Sotiropoulos, Ali Khosronejad
Theoretical and Applied Mechanics Letters  13 (2023) 100471.   doi: 10.1016/j.taml.2023.100471
[Abstract] (50) [PDF 3763KB] (6)
This study proposes a cost-effective machine-learning based model for predicting velocity and turbulence kinetic energy fields in the wake of wind turbines for yaw control applications. The model consists of an auto-encoder convolutional neural network (ACNN) trained to extract the features of turbine wakes using instantaneous data from large-eddy simulation (LES). The proposed framework is demonstrated by applying it to the Sandia National Laboratory Scaled Wind Farm Technology facility consisting of three 225 kW turbines. LES of this site is performed for different wind speeds and yaw angles to generate datasets for training and validating the proposed ACNN. It is shown that the ACNN accurately predicts turbine wake characteristics for cases with turbine yaw angle and wind speed that were not part of the training process. Specifically, the ACNN is shown to reproduce the wake redirection of the upstream turbine and the secondary wake steering of the downstream turbine accurately. Compared to the brute-force LES, the ACNN developed herein is shown to reduce the overall computational cost required to obtain the steady state first and second-order statistics of the wind farm by about 85%.
A dynamic-mode-decomposition-based acceleration method for unsteady adjoint equations at low Reynolds numbers
Wengang Chen, Jiaqing Kou, Wenkai Yang
Theoretical and Applied Mechanics Letters  13 (2023) 100472.   doi: 10.1016/j.taml.2023.100472
[Abstract] (44) [PDF 1407KB] (5)
The computational cost of unsteady adjoint equations remains high in adjoint-based unsteady aerodynamic optimization. In this letter, the solution of unsteady adjoint equations is accelerated by dynamic mode decomposition (DMD). The pseudo-time marching of every real-time step is approximated as an infinite-dimensional linear dynamical system. Thereafter, DMD is utilized to analyze the adjoint vectors sampled from these pseudo-time marching. First-order zero frequency mode is selected to accelerate the pseudo-time marching of unsteady adjoint equations in every real-time step. Through flow past a stationary circular cylinder and an unsteady aerodynamic shape optimization example, the efficiency of solving unsteady adjoint equations is significantly improved. Results show that one hundred adjoint vectors contains enough information about the pseudo-time dynamics, and the adjoint dominant mode can be precisely predicted only by five snapshots produced from the adjoint vectors, which indicates DMD analysis for pseudo-time marching of unsteady adjoint equations is efficient.
Fault-tolerant FADS system development for a hypersonic vehicle via neural network algorithms
Qian Wan, Minjie Zhang, Guang Zuo, Tianbo Xie
Theoretical and Applied Mechanics Letters  13 (2023) 100464.   doi: 10.1016/j.taml.2023.100464
[Abstract] (44) [PDF 3079KB] (2)
Hypersonic vehicles suffer from extreme aerodynamic heating during flights, especially around the area of leading edge due to its small curvature. Therefore, flush air data sensing (FADS) system has been developed to perform accurate measurement of the air data parameters. In the present study, the method to develop the FADS algorithms with fail-operational capability for a sharp-nosed hypersonic vehicle is provided. To be specific, the FADS system implemented with 16 airframe-integrated pressure ports is used as a case study. Numerical simulations of different freestream conditions have been conducted to generate the database for the FADS targeting in 2 ≤Ma≤ 5 and 0 km ≤H≤ 30 km. Four groups of neural network algorithms have been developed based on four different pressure port configurations, and the accuracy has been validated by 280 groups of simulations. Particularly, the algorithms based on the 16-port configuration show an excellent ability to serve as the main solver of the FADS, where 99.5% of the angle-of-attack estimations are within the error band ±0.2°. The accuracy of the algorithms is discussed in terms of port configuration. Furthermore, diagnosis of the system health is present in the paper. A fault-tolerant FADS system architecture has been designed, which is capable of continuously sensing the air data in the case that multi-port failure occurs, with a reduction in the system accuracy.
Experimental observation on water entry of a sphere in regular wave
Qian Wang, Changze Zhao, Haocheng Lu, Hua Liu
Theoretical and Applied Mechanics Letters  13 (2023) 100473.   doi: 10.1016/j.taml.2023.100473
[Abstract] (39) [PDF 3290KB] (2)
This paper presents a novel experiment to observe the whole water entry process of a free-falling sphere into a regular wave. A time-accurate synchronizing system modulates the moment elaborately to ensure the sphere impacting onto the water surface at the desirable wave phase. Four high-speed cameras focus locally to measure the high-precision size of the cavity evolution. Meanwhile, the aggregated field view of the camera array covers both the splash above the free surface and the entire cavity in the wave. The detailed methodologies are described and verified for the hardware set-up and the image post-processing. The theoretical maximum deviation is 1.7% on the space scale. The integral morphology of the cavity is captured precisely in the coordinate system during the sphere penetrates through the water at four representative wave phases and the still water. The result shows that the horizontal velocity of the fluid particle in the wave impels the cavity and changes the shape distinctly. Notably, the wave motion causes the cavity to pinch off earlier at the wave trough phase and later at the wave crest phase than in the still water. The wave motion influences the falling process of the sphere slightly in the present parameters.
Numerical study of the splashing wave induced by a seaplane using mesh-based and particle-based methods
Yang Xu, Peng-Nan Sun, Xiao-Ting Huang, Salvatore Marrone, Lei-Ming Geng
Theoretical and Applied Mechanics Letters  13 (2023) 100463.   doi: 10.1016/j.taml.2023.100463
[Abstract] (39) [PDF 3269KB] (0)
In recent years, forest fires and maritime accidents have occurred frequently, which have had a bad impact on human production and life. Thus, the development of seaplanes is an increasingly urgent demand. It is important to study the taxiing process of seaplanes for the development of seaplanes, which is a strong nonlinear fluidstructure interaction problem. In this paper, the smoothed particle hydrodynamics (SPH) method based on the Lagrangian framework is utilized to simulate the taxiing process of seaplanes, and the SPH results are compared with those of the finite volume method (FVM) based on the Eulerian method. The results show that the SPH method can not only give the same accuracy as the FVM but also have a strong ability to capture the splashing waves in the taxiing process, which is quite meaningful for the subsequent study of the effect of a splash on other parts of the seaplane.
The effect of gravity on self-similarity of Worthington jet after water entry of a two-dimensional wedge
Yan Du, Jingzhu Wang, Zhiying Wang, Yiwei Wang
Theoretical and Applied Mechanics Letters  13 (2023) 100462.   doi: 10.1016/j.taml.2023.100462
[Abstract] (43) [PDF 2093KB] (1)
The effect of gravity on the self-similarity of jet shape at late stage of Worthington jet development is investigated by experiment in the study. In addition, the particle image velocimetry (PIV) method is introduced to analyze the development of flow field. There is a linear scaling regarding the axial velocity of the jet and the scaling coefficient increases with the Froude number.
An incompressible flow solver on a GPU/CPU heterogeneous architecture parallel computing platform
Qianqian Li, Rong Li, Zixuan Yang
Theoretical and Applied Mechanics Letters  13 (2023) 100474.   doi: 10.1016/j.taml.2023.100474
[Abstract] (44) [PDF 1882KB] (2)
A computational fluid dynamics (CFD) solver for a GPU/CPU heterogeneous architecture parallel computing platform is developed to simulate incompressible flows on billion-level grid points. To solve the Poisson equation, the conjugate gradient method is used as a basic solver, and a Chebyshev method in combination with a Jacobi sub-preconditioner is used as a preconditioner. The developed CFD solver shows good performance on parallel efficiency, which exceeds 90% in the weak-scalability test when the number of grid points allocated to each GPU card is greater than 2083. In the acceleration test, it is found that running a simulation with 10403 grid points on 125 GPU cards accelerates by 203.6x over the same number of CPU cores. The developed solver is then tested in the context of a two-dimensional lid-driven cavity flow and three-dimensional Taylor-Green vortex flow. The results are consistent with previous results in the literature.
Artificial boundary condition for Klein-Gordon equation by constructing mechanics structure
Pang Gang, Zheng Zijun
Theoretical and Applied Mechanics Letters  13 (2023) 100459.   doi: 10.1016/j.taml.2023.100459
[Abstract] (37) [PDF 946KB] (1)
An innovative local artificial boundary condition is proposed to numerically solve the Cauchy problem of the Klein-Gordon equation in an unbounded domain. Initially, the equation is considered as the axial wave propagation in a bar supported on a spring foundation. The numerical model is then truncated by replacing the half-infinitely long bar with an equivalent mechanical structure. The effective frequency-dependent stiffness of the half-infinitely long bar is expressed as the sum of rational terms using Pade approximation. For each term, a corresponding substructure composed of dampers and masses is constructed. Finally, the equivalent mechanical structure is obtained by parallelly connecting these substructures. The proposed approach can be easily implemented within a standard finite element framework by incorporating additional mass points and damper elements. Numerical examples show that with just a few extra degrees of freedom, the proposed approach effectively suppresses artificial reflections at the truncation boundary and exhibits first-order convergence.
A new passive transfemoral prosthesis mechanism based on 3R36 knee and ESAR foot providing walking and squatting
Amer Imran, Borhan Beigzadeh, Mohammad Reza Haghjoo
Theoretical and Applied Mechanics Letters  13 (2023) 100476.   doi: 10.1016/j.taml.2023.100476
[Abstract] (33) [PDF 2772KB] (1)
Researchers have proposed various linkage mechanisms to connect knee and ankle joints for above-knee prostheses, but most of them only offer natural walking. However, studies have shown that people assume a squatting posture during daily activities. This paper introduces a novel mechanism that connects the knee joint with the foot-ankle joint to enable both squatting and walking. The prosthetic knee used is the well-known 3R36, while the energy storing and return (ESAR) prosthetic foot is used for the ankle-foot joint. To coordinate knee and ankle joint movements, a six-bar linkage mechanism structure is proposed. Simulation results demonstrate that the proposed modular transfemoral prosthesis accurately mimics the motion patterns of a natural human leg during walking and squatting. For instance, the prosthesis allows a total knee flexion of more than 140° during squatting. The new prosthesis design also incorporates energy-storing mechanisms to reduce energy expenditure during walking for amputees.
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](2285) [FullText HTML](1225) [PDF 3845KB](108)
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](2138) [FullText HTML](993) [PDF 3192KB](99)
Mechanistic Machine Learning: Theory, Methods, and Applications
2020, 10(3): 141-142   doi: 10.1016/j.taml.2020.01.041
[Abstract](9901) [FullText HTML](1094) [PDF 4844KB](96)
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](1833) [FullText HTML](915) [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](2253) [FullText HTML](1061) [PDF 4226KB](85)
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](1897) [FullText HTML](1026) [PDF 2862KB](69)
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](2134) [FullText HTML](1279) [PDF 2725KB](69)
Multiscale mechanics
G.W. He, G.D. Jin
11 (2021) 100238   doi: 10.1016/j.taml.2021.100238
[Abstract](1173) [FullText HTML](827) [PDF 2196KB](68)
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](1836) [FullText HTML](986) [PDF 2494KB](66)
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](1310) [FullText HTML](815) [PDF 2541KB](62)