Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535636
A. Diker
The brain tumor classification and detection are significant problems in computer-assisted diagnosis (CAD) for medical applications. In this study, the performance comparison of pre-trained deep learning models which are AlexNet, GoogleNet ,and ResNet-18 for the classification of brain MRI images was made. The performances of these models are compared with each other. Experimental results show that the AlexNet model achieves the highest accuracy at 96%. It is followed by the GoogleNet and ResNet-18 model with an accuracy of 90.66% and 88% respectively.
{"title":"A Performance Comparison of Pre-trained Deep Learning Models to Classify Brain Tumor","authors":"A. Diker","doi":"10.1109/EUROCON52738.2021.9535636","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535636","url":null,"abstract":"The brain tumor classification and detection are significant problems in computer-assisted diagnosis (CAD) for medical applications. In this study, the performance comparison of pre-trained deep learning models which are AlexNet, GoogleNet ,and ResNet-18 for the classification of brain MRI images was made. The performances of these models are compared with each other. Experimental results show that the AlexNet model achieves the highest accuracy at 96%. It is followed by the GoogleNet and ResNet-18 model with an accuracy of 90.66% and 88% respectively.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134540318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535564
A. Khan, S. Zubair, Samreen Khan
Detection and prediction of Alzheimer's Disease (AD) conversion from the stage of Mild Cognitive Impairment (MCI) have remained a challenging task. Regression analysis is a method that sorts those essential features/biomarkers that have a strong impact on the overall prediction. This study centres on delivering an individualized regression analysis of cognitively normal and MCI converts over the twenty independent biomarkers that leverage clinical data. Out of the 1713 male and female subjects, 768 female subjects were studied to investigate the prevalence of AD and MCI, those diagnosed with AD and MCI and their associated risk factors. The study data were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Twenty potential clinical features were included; comprised of a combination of demographic, cerebral spinal fluid, cognitive, diffusion tensor imaging, electroencephalography, genetic, magnetic resonance imaging, and positron emission tomography testing variables. The regression analysis metrics R-squared, F-statistic, Omnibus, Durbin-Watson, Coefficient and Standard error, were used to evaluate the model. Our results showed that cognitive assessment metrics were highly significant among the other testing biomarkers. Additionally, we determined the significance of each clinical variable. Our performed analysis could impact the clinical setting as a means to further develop a machine learning model in predicting the conversion of MCI to AD or to detect principle subjects for clinical trials.
{"title":"An Epidemiological-based Regression Analysis of Alzheimer’s disease and Mild Cognitive Impairment Converts in the Female Population","authors":"A. Khan, S. Zubair, Samreen Khan","doi":"10.1109/EUROCON52738.2021.9535564","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535564","url":null,"abstract":"Detection and prediction of Alzheimer's Disease (AD) conversion from the stage of Mild Cognitive Impairment (MCI) have remained a challenging task. Regression analysis is a method that sorts those essential features/biomarkers that have a strong impact on the overall prediction. This study centres on delivering an individualized regression analysis of cognitively normal and MCI converts over the twenty independent biomarkers that leverage clinical data. Out of the 1713 male and female subjects, 768 female subjects were studied to investigate the prevalence of AD and MCI, those diagnosed with AD and MCI and their associated risk factors. The study data were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Twenty potential clinical features were included; comprised of a combination of demographic, cerebral spinal fluid, cognitive, diffusion tensor imaging, electroencephalography, genetic, magnetic resonance imaging, and positron emission tomography testing variables. The regression analysis metrics R-squared, F-statistic, Omnibus, Durbin-Watson, Coefficient and Standard error, were used to evaluate the model. Our results showed that cognitive assessment metrics were highly significant among the other testing biomarkers. Additionally, we determined the significance of each clinical variable. Our performed analysis could impact the clinical setting as a means to further develop a machine learning model in predicting the conversion of MCI to AD or to detect principle subjects for clinical trials.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132871637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535618
Laice Neves de Oliveira, F. Freitas, A. P. Oliveira
This paper presents approaches to the calculation of the solution of the load flow problem in ill-conditioned systems based on Newton’s Continuous philosophy. Two approaches to the Heun-King-Werner method are proposed with the objective of obtaining a better computational cost from the reduction of the number of calculations and factorizations of the Jacobian matrices. The methods performance were compared in terms of convergence with the standard Newton-Raphson and the original Heun-King-Werner method. Experiments carried out in ill-conditioned power system models demonstrated that the proposed approaches can significantly reduce the computational cost employed by the original Heun-King-Werner method.
{"title":"Heun-King-Werner Method’s based approaches for the solution of the Load Flow Problem","authors":"Laice Neves de Oliveira, F. Freitas, A. P. Oliveira","doi":"10.1109/EUROCON52738.2021.9535618","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535618","url":null,"abstract":"This paper presents approaches to the calculation of the solution of the load flow problem in ill-conditioned systems based on Newton’s Continuous philosophy. Two approaches to the Heun-King-Werner method are proposed with the objective of obtaining a better computational cost from the reduction of the number of calculations and factorizations of the Jacobian matrices. The methods performance were compared in terms of convergence with the standard Newton-Raphson and the original Heun-King-Werner method. Experiments carried out in ill-conditioned power system models demonstrated that the proposed approaches can significantly reduce the computational cost employed by the original Heun-King-Werner method.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128190073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535640
A. Neftissov, A. Biloshchytskyi, A. Novozhilov, A. Kislov
This paper presents a theoretical justification for the possibility of determining the current of a single-phase earth fault in an indirect way, as well as a method for determining the line capacitance. An experiment was carried out to measure the current of a single-phase earth fault through a capacitive shunt. Comparison of calculated data and measured results is presented.
{"title":"Method for indirect measurement of the phase capacitance of a distribution substation and the single-phase earth fault current","authors":"A. Neftissov, A. Biloshchytskyi, A. Novozhilov, A. Kislov","doi":"10.1109/EUROCON52738.2021.9535640","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535640","url":null,"abstract":"This paper presents a theoretical justification for the possibility of determining the current of a single-phase earth fault in an indirect way, as well as a method for determining the line capacitance. An experiment was carried out to measure the current of a single-phase earth fault through a capacitive shunt. Comparison of calculated data and measured results is presented.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535643
B. Pryymak, S. Korol, M. Ostroverkhov
This paper proposes a variant of solving the problem of synthesis of a digital following system of the welding robot when using a visual sensor as the meter of the relative position of the torch and the parts being welded. A discrete mathematical model of the controlled plant was derived. Structural and parametric synthesis of the control algorithm was performed, which includes a state observer and a modal combined regulator of welding robot link position. The performance of the proposed digital following system of welding robot link was studied.
{"title":"Design of a Digital Following System of Welding Robot With a Visual Sensor","authors":"B. Pryymak, S. Korol, M. Ostroverkhov","doi":"10.1109/EUROCON52738.2021.9535643","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535643","url":null,"abstract":"This paper proposes a variant of solving the problem of synthesis of a digital following system of the welding robot when using a visual sensor as the meter of the relative position of the torch and the parts being welded. A discrete mathematical model of the controlled plant was derived. Structural and parametric synthesis of the control algorithm was performed, which includes a state observer and a modal combined regulator of welding robot link position. The performance of the proposed digital following system of welding robot link was studied.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115698247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535585
A. Aydin, Mehmet Umut Salur, I. Aydin
Due to the rapid development of the railway industry, the task of checking the fit and defects of rails has become of high importance. The train tracks, which are kilometers long, are obtained with hours of video recording. It is almost impossible to examine the images obtained by one or more human eyes. Even if factors that may affect people (such as discomfort, fatigue) are ignored, we can easily state that the time required for the completion of damage assessment will take weeks or months. During the period of investigation, the condition of serious damage may worsen and undesirable results may occur. Therefore, it will save time and cost if the flaws on the rails are made by a deep learning model instead of being made by humans. At the same time, safety in rail transport will be ensured. We propose a high-performance fine-tuning convolutional neural network model that can be improved with negligible losses by using image data to detect defects that occur depending on time or impact on the rail surfaces they use for the transportation of trains. In our study, a two-step approach is followed. In the first stage, we get cropped images focused on the train tracks instead of the rail image captured with a large area. In the second stage, various convolutional neural network models were applied using the cropped images and the classification was provided. While our model continues to work with high success, it works with increasing parameters that accelerate training, such as batch size, and it works very little or even without any loss of success. Experimental results show that our model is better than previous studies.
{"title":"Fine-Tuning Convolutional Neural Network Based Railway Damage Detection","authors":"A. Aydin, Mehmet Umut Salur, I. Aydin","doi":"10.1109/EUROCON52738.2021.9535585","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535585","url":null,"abstract":"Due to the rapid development of the railway industry, the task of checking the fit and defects of rails has become of high importance. The train tracks, which are kilometers long, are obtained with hours of video recording. It is almost impossible to examine the images obtained by one or more human eyes. Even if factors that may affect people (such as discomfort, fatigue) are ignored, we can easily state that the time required for the completion of damage assessment will take weeks or months. During the period of investigation, the condition of serious damage may worsen and undesirable results may occur. Therefore, it will save time and cost if the flaws on the rails are made by a deep learning model instead of being made by humans. At the same time, safety in rail transport will be ensured. We propose a high-performance fine-tuning convolutional neural network model that can be improved with negligible losses by using image data to detect defects that occur depending on time or impact on the rail surfaces they use for the transportation of trains. In our study, a two-step approach is followed. In the first stage, we get cropped images focused on the train tracks instead of the rail image captured with a large area. In the second stage, various convolutional neural network models were applied using the cropped images and the classification was provided. While our model continues to work with high success, it works with increasing parameters that accelerate training, such as batch size, and it works very little or even without any loss of success. Experimental results show that our model is better than previous studies.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126640215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535558
I. Reva, O. Bialobrzheskyi, O. Todorov, M. Bezzub
Transformers in today's network are increasingly affected by distribution generation. Due to significant fluctuations in source primary energy flow arise idling mode in transformer, increasing loss of saturated magnetic circuit. In this regard, the transformer condition monitoring problems arise and are solve, taking into account its power distribution, especially in damages case. Existing studies in most cases are based on the power distribution analysis, or its harmonic components for normal operating condition. The rules for power distribution determining process of three-phase transformer with inter-turn fault in the windings group of one of the phases is proposed. An experimental study the three-phase transformer idling mode was performed on the laboratory equipment. Developed the primary sequence of the conducting an experiment for research and monitoring of three-phase transformer energy performance in idle mode with a damaged winding. The method was used for this purpose - the power harmonic componets distribution analysis of three-phase transformer provided that the determination location of the damage is by infrared measurement. It is noted that the power of harmonics current and voltage, significantly different from the power harmonics in each of the phases transformer and between phases in general. This allows to apply previously known criteria for evaluating the power distortion character as transformer damage indicators. The results of the study can be used as components for a hybrid monitoring and expert system in SmartGrid.
{"title":"The Power Components Distribution Investigating in Idle Mode of Three-Phase Transformer with Damage Winding","authors":"I. Reva, O. Bialobrzheskyi, O. Todorov, M. Bezzub","doi":"10.1109/EUROCON52738.2021.9535558","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535558","url":null,"abstract":"Transformers in today's network are increasingly affected by distribution generation. Due to significant fluctuations in source primary energy flow arise idling mode in transformer, increasing loss of saturated magnetic circuit. In this regard, the transformer condition monitoring problems arise and are solve, taking into account its power distribution, especially in damages case. Existing studies in most cases are based on the power distribution analysis, or its harmonic components for normal operating condition. The rules for power distribution determining process of three-phase transformer with inter-turn fault in the windings group of one of the phases is proposed. An experimental study the three-phase transformer idling mode was performed on the laboratory equipment. Developed the primary sequence of the conducting an experiment for research and monitoring of three-phase transformer energy performance in idle mode with a damaged winding. The method was used for this purpose - the power harmonic componets distribution analysis of three-phase transformer provided that the determination location of the damage is by infrared measurement. It is noted that the power of harmonics current and voltage, significantly different from the power harmonics in each of the phases transformer and between phases in general. This allows to apply previously known criteria for evaluating the power distortion character as transformer damage indicators. The results of the study can be used as components for a hybrid monitoring and expert system in SmartGrid.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"42 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116129278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535568
V. Pires, D. Foito, A. Cordeiro, J. F. Silva
This paper is focused on the control system for photovoltaic (PV) generators using three-phase triple inverters. These inverters allow for a distributed PV system, and at the same time, they provide AC multilevel operation. The proposed controller is designed to allow the operation of the inverter in a way that it can extend support to the grid with ancillary services. One of the ancillary services that the three-phase triple inverter can support is the injection of unbalanced currents in order to eliminate or attenuate grid load unbalances. Therefore, the proposed topology will be extended to accommodate four wires. The capability of the system to support the grid with ancillary services, especially the compensation of unbalanced loads, the four-wire multilevel topology operation and the proposed controller will be verified through several simulation tests.
{"title":"Control of PV Distributed Systems Based on Three-Phase Triple Inverters to Support Grids with Unbalanced Loads","authors":"V. Pires, D. Foito, A. Cordeiro, J. F. Silva","doi":"10.1109/EUROCON52738.2021.9535568","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535568","url":null,"abstract":"This paper is focused on the control system for photovoltaic (PV) generators using three-phase triple inverters. These inverters allow for a distributed PV system, and at the same time, they provide AC multilevel operation. The proposed controller is designed to allow the operation of the inverter in a way that it can extend support to the grid with ancillary services. One of the ancillary services that the three-phase triple inverter can support is the injection of unbalanced currents in order to eliminate or attenuate grid load unbalances. Therefore, the proposed topology will be extended to accommodate four wires. The capability of the system to support the grid with ancillary services, especially the compensation of unbalanced loads, the four-wire multilevel topology operation and the proposed controller will be verified through several simulation tests.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129617331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535544
V. Zhukovskyy, S. Shatnyi, N. Zhukovska, A. Sverstiuk
It is offered the information system of recognition of cartographic images of soil massifs and classification of landscape areas by types of soil massifs using the neural network. Here was described approaches to architecture design, teaching methods, data preparation for teaching, training and neural network testing. The functional scheme of the neural network is developed, which consists of the input, hidden and output layer, collecting and processing of data, and training algorithm. The analysis of efficiency, speed and accuracy of work of a neural network as a part of information technology is carried out.
{"title":"Neural Network Clustering Technology for Cartographic Images Recognition","authors":"V. Zhukovskyy, S. Shatnyi, N. Zhukovska, A. Sverstiuk","doi":"10.1109/EUROCON52738.2021.9535544","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535544","url":null,"abstract":"It is offered the information system of recognition of cartographic images of soil massifs and classification of landscape areas by types of soil massifs using the neural network. Here was described approaches to architecture design, teaching methods, data preparation for teaching, training and neural network testing. The functional scheme of the neural network is developed, which consists of the input, hidden and output layer, collecting and processing of data, and training algorithm. The analysis of efficiency, speed and accuracy of work of a neural network as a part of information technology is carried out.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131574091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1109/EUROCON52738.2021.9535570
V. Ionescu
This numerical study investigated the mixing efficiency and pressure drop characteristics of water-based liquid flow through five different serpentine-type microchannel structures with a square cross-section, for Reynolds numbers Re between 0.5 and 40. The cost of mixing was also evaluated here in terms of pressure drop and input power required to transport the fluid through the channel. The passive micromixer models were characterized by a constant hydraulic diameter of 200 μm, with the height H and pitch Pi of the microchannel modified at the values from 0.6 to 1 mm and 1.2 to 2 mm, respectively, maintaining a constant Pi/H ratio of 2. At Re = 2, Re = 10 and Re =30, the mixing efficiency for the model with Pi = 2 mm and H = 1 mm was 1.27, 1.1 and 1.26 times the mixing indexes of the model with Pi = 1.2 mm and H = 0.6 mm, respectively. This enhanced mixing quality was obtained with the sacrifice of a lower mixing index to pressure drop ratio (1.18, 1.32 and 1.13 times lower) and a higher mixing cost in terms of input power (1.18, 1.49 and 1.15 times higher).
{"title":"Numerical Investigation of the Mixing Efficiency in Square-Wave Passive Micromixers for Microfluidic Device Applications","authors":"V. Ionescu","doi":"10.1109/EUROCON52738.2021.9535570","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535570","url":null,"abstract":"This numerical study investigated the mixing efficiency and pressure drop characteristics of water-based liquid flow through five different serpentine-type microchannel structures with a square cross-section, for Reynolds numbers Re between 0.5 and 40. The cost of mixing was also evaluated here in terms of pressure drop and input power required to transport the fluid through the channel. The passive micromixer models were characterized by a constant hydraulic diameter of 200 μm, with the height H and pitch Pi of the microchannel modified at the values from 0.6 to 1 mm and 1.2 to 2 mm, respectively, maintaining a constant Pi/H ratio of 2. At Re = 2, Re = 10 and Re =30, the mixing efficiency for the model with Pi = 2 mm and H = 1 mm was 1.27, 1.1 and 1.26 times the mixing indexes of the model with Pi = 1.2 mm and H = 0.6 mm, respectively. This enhanced mixing quality was obtained with the sacrifice of a lower mixing index to pressure drop ratio (1.18, 1.32 and 1.13 times lower) and a higher mixing cost in terms of input power (1.18, 1.49 and 1.15 times higher).","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124697480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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