Deep learning for both broadband prediction of the radiated emission from heatsinks and heatsink optimization

Başyiğit, İbrahim Bahadır; Genç, Abdullah; Doğan, Habib; Şenel, Fatih Ahmet; Helhel, Selçuk

doi:10.1016/j.jestch.2021.01.006

Cited by 11 publications

(9 citation statements)

References 29 publications

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“…One can find quite many papers describing studies of the influence of heat sink geometries on their thermal performance [5][6][7][14][15][16][19][20][21][22][24][25][26][28][29][30][31][32][33][34][35][36][37][38][42][43][44]47,49,58,63]. Kumar et al performed an analysis of the fluid flow and thermal behavior of aircooled microchannel heat sinks.…”

Section: Heat Sinks In Engineeringmentioning

confidence: 99%

“…Heat sinks have found particular use in electronics [9][10][11]15,21,26,[34][35][36][37][47][48][49][50][51][52]58]. However, they are also used in various fields of industry, including the cooling of photovoltaic systems [16], battery thermal management systems [17], platform inertial navigation systems [18] in space engineering applications [19], the cooling of semiconductor chips [22], in air dryers [26], lighting systems [8], and thermoelectric generators [59].…”

Section: Introductionmentioning

confidence: 99%

“…An examples of commonly used heatsinks are presented in the Figure 1. Several papers have been so far published that address the optimization of cooling systems, both experimentally [7][8][9][10][11][12]16,19,[26][27][28][29][30][31][32][33][34][38][39][40][41]43,54,56,58,62] and analytically or based on simulations [4][5][6][7][8][9][13][14][15][16][17][18][19][20][22][23][24][25][26][28][29][30][31]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Influence of Geometry, Surface Texture, and Cooling Method on the Efficiency of Heat Dissipation through the Heat Sink—A Review

Grochalski,

Rukat,

Jakubek

et al. 2023

Materials

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The performance of a heat sink is significantly influenced by the type of cooling used: passive or active (forced), the shape of the heat sink, and the material from which it is made. This paper presents a review of the literature on the influence of geometry and surface parameters on effective heat transfer in heat sinks. The results of simulation studies for three different heat sink fin geometries and cooling types are presented. Furthermore, the influence of the surface texture of the heat sink fins on the heat transfer efficiency was determined. It was shown that the best performance in terms of geometries was that of a wave fin heat sink. When the surface texture was analyzed, it was found that an increase in the amplitude values of the texture decreases the heat dissipation efficiency in the case of active cooling, while for passive cooling, an increase in these parameters has a beneficial effect and increases the effective heat transfer to the surroundings. The cooling method was found to be the most important factor affecting heat dissipation efficiency. Forced airflow results in more efficient heat transfer from the heat sink fins to the surroundings.

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Section: Heat Sinks In Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Geometry, Surface Texture, and Cooling Method on the Efficiency of Heat Dissipation through the Heat Sink—A Review

Grochalski,

Rukat,

Jakubek

et al. 2023

Materials

View full text Add to dashboard Cite

show abstract

“…Te researchers presented a deep learning approach based on a convolutional neural network combined with far-feld wave data generated from a nearfeld resonant metal body at microwave frequencies for subwavelength imaging in the far-feld [23]. In other studies, researchers used near-feld scanning microscopy or an equivalent set of elemental dipoles methods associated with genetic algorithms [24], convolutional neural networks [25,26], hierarchical attention-based deep neural networks [27], extreme gradient boosting method [28], or strategies based on artifcial neural networks and optimizer algorithm [29,30]. We use the transformer model to capture the relationship on feature maps to establish the correlations between two multivariate data series.…”

Section: Related Workmentioning

confidence: 99%

Field Deviation in Radiated Emission Measurement in Anechoic Chamber for Frequencies up to 60 GHz and Deep Learning-Based Correction

Shi

Yan

Yang

et al. 2022

International Journal of Antennas and Propagation

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The measurement of radiated emission (RE) in an anechoic chamber becomes very challenging at high frequencies, up to 60 GHz, because the scanning plane of the receiver is in measurement standard deviation from the actual wavefront. As a result, the RE intensity of the devices may be underestimated, resulting in electromagnetic interference. The deviation between the electric field at the far-field vertical scanning point and the actual wavefront is researched. Then, in an anechoic chamber, a hybrid deep learning amendment model of convolutional neural network (CNN) and transformer is proposed to correct the RE measurement at a 3 m distance. The results indicate that the correction is reliable, with an average error of 6.35% for a 3 m distance in a semianechoic chamber and less than 4.83% for other test scenarios. The proposed method provides a promising solution for RE measurement at a millimeter wave band in an anechoic chamber.

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“…Recently, it is possible to see deep neural networks applications in many different fields from health to education [37][38][39][40]. There are different models such as trained and pre-trained in the literature [41][42][43]. It is frequently preferred in image processing applications, especially due to its success in feature extraction.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Disease Detection From Cassava Leaf Images With Deep Learning Methods in Web Environment

Metlek

2021

International Journal of 3D Printing Technologies and Digital Industry

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In this article, it is aimed to classify healthy and four different plant diseases from Cassava plant leaf images. For this purpose, the "Cassava-Leaf-Disease-Classification" data set, which is an up-to-date and difficult data set published in 2020, was used. The used data set includes a total of 21,397 images consisting of healthy and four different diseases. In the study, in the MATLAB environment, the images were first subjected to the Chan-Vese (CV) Segmentation method and the area of interest was determined. Features were extracted with the ResNet 50 and MobileNetV2 deep learning architectures from the detected areas. Extracted features are classified by Support Vector Machine and K-Nearest Neighbor algorithms. The images are divided into two as training and testing according to the K-fold 5 value. The average highest success rates in training and test data were achieved by using the ResNet50 architecture and SVM classifier together, as a result of segmentation. As a result of training and testing processes, 85.4% and 84.4% success rates were obtained, respectively. At the end of the test process of the study, a trained network was obtained according to ResNet50, where the highest success rates were obtained, and MobileNetV2, another deep learning architecture used in the study. It has been compiled with MATLAB Builder NE in order to run these two networks in the web environment. Finally, it has been made available to manufacturers with a web-based embedded interface. Thus, a deep learning-based decision support system has been developed that can be easily used by all manufacturers in the web environment.

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Deep learning for both broadband prediction of the radiated emission from heatsinks and heatsink optimization

Cited by 11 publications

References 29 publications

The Influence of Geometry, Surface Texture, and Cooling Method on the Efficiency of Heat Dissipation through the Heat Sink—A Review

The Influence of Geometry, Surface Texture, and Cooling Method on the Efficiency of Heat Dissipation through the Heat Sink—A Review

Field Deviation in Radiated Emission Measurement in Anechoic Chamber for Frequencies up to 60 GHz and Deep Learning-Based Correction

Disease Detection From Cassava Leaf Images With Deep Learning Methods in Web Environment

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