Linear stability analysis of surface waves of liquid jet injected in transverse gas flow with different angles

Kasmaiee, Si.; Tadjfar, M.; Kasmaiee, Sa.; Ahmadi, G.

doi:10.1007/s00162-024-00685-2

Cited by 7 publications

(1 citation statement)

References 67 publications

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“…Specifically, the CNN-based approach significantly improves the mean bit error rate (BER) performance, tripling its efficiency. Furthermore, the significance of feature optimization or selection is highlighted in numerous studies as a key determinant of performance [27][28][29]. The research presented in [30] investigates the use of deep learning (DL) algorithms for differentiating between normal and abnormal BBI traffic, providing dynamic and scalable approaches to feature engineering.…”

Section: Related Workmentioning

confidence: 99%

Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model

Er,

Isik,

Kuran

et al. 2024

Arab J Sci Eng

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Molecular communication (MC) holds promise for enabling communication in scenarios where traditional wireless methods may be impractical or ineffective, offering unique capabilities for a range of applications in both natural and engineered systems. In this research, a novel approach to MC is explored, diverging from the standard use of stationary transmitter and receiver models typically found in the field. The study introduces a dynamic MC model, where both the transmitter and receiver are mobile within a diffusion environment. This model operates using a 5-bit system. The key finding is that the mobility of these nanodevices alters their distance, which in turn impacts the likelihood of molecule reception at the receiver. The study employs deep learning techniques, specifically a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to categorize the mobility patterns of the receiver (Rx) and transmitter (Tx). By analyzing various mobility rates (Drx and Dtx) and distances between the Tx and Rx, the research successfully identifies the most efficient mobile MC model in terms of molecule reception rates. The use of Linear Support Vector Machine alongside the CNN and LSTM hybrid feature vector resulted in an 87.68% accuracy in predicting diffusion coefficients. Moreover, using a Cubic Support Vector with the same hybrid feature vector, the study achieved an 88.09% accuracy in estimating the distance between the transmitter and receiver. The study concludes that an increase in the mobilities of Rx and Tx correlates with a higher rate of molecule reception.

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Section: Related Workmentioning

confidence: 99%

Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model

Er,

Isik,

Kuran

et al. 2024

Arab J Sci Eng

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A Novel Feature Selection Approach-Based Sampling Theory on Grapevine Images Using Convolutional Neural Networks

Özaltın,

Koyuncu

2024

Arab J Sci Eng

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Feature selection, reducing number of input variables to develop classification model, is an important process to reduce computational and modeling complexity and affects the performance of image process. In this paper, we have proposed new statistical approaches for feature selection based on sample selection. We have applied our new approaches to grapevine leaves data that possess properties of shape, thickness, featheriness, and slickness that are investigated in images. To analyze such kind of data by using image process, thousands of features are created and selection of features plays important role to predict the outcome properly. In our numerical study, convolutional neural networks have been used as feature extractors and then obtained features from the last average pooling layer to detect the type of grapevine leaves from images. These features have been reduced by using our suggested four statistical methods: simple random sampling, ranked set sampling, extreme ranked set sampling, moving extreme ranked set sampling. Then, selected features have been classified with artificial neural network and we obtained the best accuracy of 97.33% with our proposed approaches. Based on our empirical analysis, it has been determined that the proposed approach exhibits efficacy in the classification of grapevine leaf types. Furthermore, it possesses the potential for integration into various computational devices.

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Unsteady CFD simulation of a rotor blade under various wind conditions

Kasmaiee,

Farshad

2024

Sci Rep

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Wind and gusts can significantly impact the performance of rotors and turbines. The transient behavior of the rotor should be carefully examined to account for these effects. This paper investigates the unsteady aerodynamic characteristics of a rotor blade under different wind conditions, such as direction, speed, and angle. A 3D transient Computational Fluid Dynamics (CFD) simulation using the dynamic mesh technique is performed to analyze the rotor dynamics. The unsteady Reynolds-averaged Navier–Stokes (URANS) equations with the k-ω SST turbulence model are solved. The rotor blade used for this study is the U15XXL Combo KV29 industrial blade, which has not been numerically analyzed before. The results show that wind in the same direction as the rotation reduces the thrust more than lateral or opposite wind. Lateral wind with a speed lower than 5 m/s decreases the blade performance, but higher speeds increase it. Higher lateral wind speeds also cause two peaks in the torque curve, forming a butterfly wing shape in the polar torque plot and multiple extrema in the torque curve with increasing speed. The maximum thrust shifts slightly to the left with increasing lateral wind speed. The lateral angle does not affect the average thrust produced in one blade revolution but only causes a spatial shift. The thrust production decreases as the angle approaches the opposite direction of rotation. The motion amplitude decreases, and the curve becomes smoother as this angle increases. A nearly straight line similar to no side wind is observed at 60 and 90 degrees, which is attributed to the constant effective angle of attack during the rotation cycle.

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Linear stability analysis of surface waves of liquid jet injected in transverse gas flow with different angles

Cited by 7 publications

References 67 publications

Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model

Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model

A Novel Feature Selection Approach-Based Sampling Theory on Grapevine Images Using Convolutional Neural Networks

Unsteady CFD simulation of a rotor blade under various wind conditions

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