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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
2023, 13(6) :100465-100465. doi: 10.1016/j.taml.2023.100465
[Abstract](110) [PDF 4208KB](10)
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
2023, 13(6) :100469-100469. doi: 10.1016/j.taml.2023.100469
[Abstract](70) [PDF 2460KB](6)
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
2023, 13(6) :100480-100480. doi: 10.1016/j.taml.2023.100480
[Abstract](298) [PDF 1486KB](16)
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
2023, 13(6) :100482-100482. doi: 10.1016/j.taml.2023.100482
[Abstract](240) [PDF 1985KB](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 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
2023, 13(6) :100485-100485. doi: 10.1016/j.taml.2023.100485
[Abstract](300) [PDF 3045KB](5)
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
2023, 13(6) :100475-100475. doi: 10.1016/j.taml.2023.100475
[Abstract](71) [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
2023, 13(6) :100477-100477. doi: 10.1016/j.taml.2023.100477
[Abstract](63) [PDF 3248KB](0)
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
2023, 13(6) :100479-100479. doi: 10.1016/j.taml.2023.100479
[Abstract](263) [PDF 6046KB](6)
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
2023, 13(6) :100478-100478. doi: 10.1016/j.taml.2023.100478
[Abstract](248) [PDF 1233KB](2)
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
2023, 13(6) :100481-100481. doi: 10.1016/j.taml.2023.100481
[Abstract](258) [PDF 3458KB](5)
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.