Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades

Ding, Yawen; Wang, Jun; Jiang, Boyan; Li, Zhiang; Xiao, Qianhao; Wu, Lanyong; Xie, Bochao

doi:10.3390/pr10040753

Cited by 13 publications

(11 citation statements)

References 26 publications

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“…The key model in this paper(Gang neuron) This paper used DD of Gang neuron. Study using Gang neurons [55]:"Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades"(see Fig. 39)…”

Section: A Polynomial Convolution and Dendrite In This Papermentioning

confidence: 99%

It may be time to improve the neuron of artificial neural network

Liu¹

2023

Preprint

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Artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence in recent years. The neuron of ANNs was designed by the stereotypical knowledge of biological neurons 70 years ago. Artificial Neuron is expressed as f(wx+b) or f(WX). This design does not consider dendrites’ information processing capacity. However, some recent studies show that biological dendrites participate in the pre-calculation of input data. Concretely, biological dendrites play a role in extracting the interaction information among inputs (features). Therefore, it may be time to improve the neuron of ANNs. In this study, some dendritic modules with excellent properties are proposed and added to artificial neurons to form new neurons named Gang neurons. E.g., The dendrite function can be expressed as Wi,i-1Ai-1 ○ A0|1|2|...|i-1 . The generalized new neuron can be expressed as f(W(Wi,i-1Ai-1 ○ A0|1|2|...|i-1)).The simplified new neuron be expressed as f(∑(WA ○ X)). After improving the neurons, many networks can be tried. This paper shows some basic architecture for reference in the future. Up to now, others and the author have applied Gang neurons to various fields, and Gang neurons show excellent performance in the corresponding fields. Interesting things: (1) The computational complexity of dendrite modules (Wi,i-1Ai-1 ○ Ai-1) connected in series is far lower than Horner's method. Will this speed up the calculation of basic functions in computers? (2) The range of sight of animals has a gradient, but the convolution layer does not have this characteristic. This paper proposes receptive fields with a gradient. (3) The networks using Gang neurons can delete Fully-connected Layer. In other words, the parameters in Fully-connected Layers are assigned to a single neuron, which reduces the parameters of a network for the same mapping capacity. (4) ResDD(ResDD modules+One Linear module) can replace the current ANNs’ Neurons. ResDD has controllable precision for better generalization capability. Gang neuron code is available at https://github.com/liugang1234567/Gang-neuron.

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Section: A Polynomial Convolution and Dendrite In This Papermentioning

confidence: 99%

It may be time to improve the neuron of artificial neural network

Liu¹

2023

Preprint

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“…Ding et al [29] introduced a blade design methodology using standard-form rational quadratic Bezier curves to define blade angles and bending angles. They employed the Dendrite Net neural network model to predict fan pneumatic performance and applied a non-dominated sorting genetic algorithm for global optimization.…”

Section: State Of Art Studymentioning

confidence: 99%

“…In certain applications involving the use of axial flow fans with liquids, the risk of cavitations, phenomenon where local pressure drops below the fluid's vapor pressure needs to be addressed through design considerations. Selecting appropriate materials for fan components is imperative, especially in environments with corrosive or high-temperature conditions, as material choices directly impact durability and maintenance needs [27]- [29]. Vibration is another challenge, as axial flow fans can experience vibration issues leading to noise and reduced lifespan.…”

Section: Summary Of Research Gapsmentioning

confidence: 99%

Hybrid optimal deep learning with IoT based smart based monitoring and maintenance system for axial flow fan using feature optimization

Andreica,

Offenberg

2023

J. Adv. Zool.

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Axial flow fan is a mechanical fan that generates airflow in the same direction as its rotational axis. These fans find widespread use in various applications, including ventilation, cooling, and air circulation across industrial, commercial, and residential settings. However, designing these fans can be challenging in the fan manufacturing industry due to the need to accommodate diverse operating conditions. This complexity arises from the fact that multiple design parameters significantly influence fan performance, requiring careful consideration and optimization to ensure efficient operation across various scenarios. In this paper, we present a technique for monitoring and maintenance of axial flow fans using hybrid optimal deep learning with IoT system. Our method leverages the pre-trained U-Net architecture to extract hidden features effectively from the dataset. Furthermore, we introduce an improved triple tree-seed optimization (IT2SO) algorithm for feature optimization, which identifies the most optimal features among the extracted ones. To make informed decisions about axial flow fan process monitoring, we propose the deep boosted hybrid learning (DBHL) technique as the decision model to maintain the proper operation. To validate the effectiveness of proposed IT2SO+DBHL technique, we have conducted experiments using the air movement and control association international (AMCA) dataset. The results demonstrate the superior performance of our monitoring approach compared to existing techniques across various evaluation measures.

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“…For single-stage blades, to improve the performance of a low-pressure axial flow fan, Ding et al [ 6 ] adopted bending and twisting laws constructed by Bézier curves. After the optimization with a non-dominated sorting genetic algorithm, the spanwise load distribution of the fan blade became more uniform and led to a less adverse pressure gradient.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Radial Velocity Distribution on the Loss Generation of a Contra-Rotating Fan in a Ventilation System

Jia

Zhang

Guo

et al. 2023

Entropy

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Quantification of the loss generation of ducted contra-rotating fan (CRF) blades is difficult to achieve, since there are no guide vanes between rotors. A blade design program was established to investigate the relationship between radial velocity distribution and incurred loss. Numerical and experimental techniques were used to confirm the optimal configuration’s overall performance. The relationship between loss and velocity distribution under the impact of spanwise load distribution was confirmed by the entropy contour from various perspectives. The appropriate radial velocity distribution can improve the operating efficiency of a CRF by reducing the entropy around the annulus under design and near-stall conditions. This regularity could provide some strategies in the design of contra-rotating blades.

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Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades

Cited by 13 publications

References 26 publications

It may be time to improve the neuron of artificial neural network

It may be time to improve the neuron of artificial neural network

Hybrid optimal deep learning with IoT based smart based monitoring and maintenance system for axial flow fan using feature optimization

Effect of the Radial Velocity Distribution on the Loss Generation of a Contra-Rotating Fan in a Ventilation System

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