Yoshiki Yanagita, Kaishan Feng, Yuko Miyamura, Adi Azriff Basri, Mohammad Zuber, Siti Rohani, Abdul Aziz, Kamarul Arifin Ahmad, Masaaki Tamagawa
{"title":"基于CFD的气道病毒浓度分析的评价","authors":"Yoshiki Yanagita, Kaishan Feng, Yuko Miyamura, Adi Azriff Basri, Mohammad Zuber, Siti Rohani, Abdul Aziz, Kamarul Arifin Ahmad, Masaaki Tamagawa","doi":"10.37934/arnht.13.1.96105","DOIUrl":null,"url":null,"abstract":"Currently, Covid-19 is an epidemic all over the world. When virus directly adhere to mucous membrane of airway by breath, some humans maybe get inflammatory responses by viruses in the first stage of infection. The airway is composed of the nasal cavity, sinuses (Maxillary Sinus, Ethmoid Sinus, Frontal Sinus and Sphenoidal Sinus) and lungs. In the infection stage, the sinuses located in the nasal cavity tend to exhibit particularly high virus concentrations. Therefore, it is important to evaluate quantitatively the areas where viruses are likely to be adhered in the nasal cavity including sinuses. In this study, by CFD including concentration analysis the areas where viruses are likely to be adhered in the nasal cavity are predicted. As for the methods, the nasal cavity was made from 2D-CT image data by Itk-SNAP. For this computation in the nasal cavity continuity equation, Navier-Stokes equation and transport equation are used. And the transport of concentration was computed in the divided 4 parts of nasal cavity. As a result, it was found that the ratio of the concentration to the initial concentration in Ethmoid Sinus is approximately 0.6. It was found that Ethmoid Sinus is the areas where viruses are likely to be adhered and the areas can be predicted by computing the concentration.","PeriodicalId":119773,"journal":{"name":"Journal of Advanced Research in Numerical Heat Transfer","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of Virus Concentration Analysis in the Airway by CFD\",\"authors\":\"Yoshiki Yanagita, Kaishan Feng, Yuko Miyamura, Adi Azriff Basri, Mohammad Zuber, Siti Rohani, Abdul Aziz, Kamarul Arifin Ahmad, Masaaki Tamagawa\",\"doi\":\"10.37934/arnht.13.1.96105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, Covid-19 is an epidemic all over the world. When virus directly adhere to mucous membrane of airway by breath, some humans maybe get inflammatory responses by viruses in the first stage of infection. The airway is composed of the nasal cavity, sinuses (Maxillary Sinus, Ethmoid Sinus, Frontal Sinus and Sphenoidal Sinus) and lungs. In the infection stage, the sinuses located in the nasal cavity tend to exhibit particularly high virus concentrations. Therefore, it is important to evaluate quantitatively the areas where viruses are likely to be adhered in the nasal cavity including sinuses. In this study, by CFD including concentration analysis the areas where viruses are likely to be adhered in the nasal cavity are predicted. As for the methods, the nasal cavity was made from 2D-CT image data by Itk-SNAP. For this computation in the nasal cavity continuity equation, Navier-Stokes equation and transport equation are used. And the transport of concentration was computed in the divided 4 parts of nasal cavity. As a result, it was found that the ratio of the concentration to the initial concentration in Ethmoid Sinus is approximately 0.6. It was found that Ethmoid Sinus is the areas where viruses are likely to be adhered and the areas can be predicted by computing the concentration.\",\"PeriodicalId\":119773,\"journal\":{\"name\":\"Journal of Advanced Research in Numerical Heat Transfer\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Research in Numerical Heat Transfer\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37934/arnht.13.1.96105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Research in Numerical Heat Transfer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37934/arnht.13.1.96105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of Virus Concentration Analysis in the Airway by CFD
Currently, Covid-19 is an epidemic all over the world. When virus directly adhere to mucous membrane of airway by breath, some humans maybe get inflammatory responses by viruses in the first stage of infection. The airway is composed of the nasal cavity, sinuses (Maxillary Sinus, Ethmoid Sinus, Frontal Sinus and Sphenoidal Sinus) and lungs. In the infection stage, the sinuses located in the nasal cavity tend to exhibit particularly high virus concentrations. Therefore, it is important to evaluate quantitatively the areas where viruses are likely to be adhered in the nasal cavity including sinuses. In this study, by CFD including concentration analysis the areas where viruses are likely to be adhered in the nasal cavity are predicted. As for the methods, the nasal cavity was made from 2D-CT image data by Itk-SNAP. For this computation in the nasal cavity continuity equation, Navier-Stokes equation and transport equation are used. And the transport of concentration was computed in the divided 4 parts of nasal cavity. As a result, it was found that the ratio of the concentration to the initial concentration in Ethmoid Sinus is approximately 0.6. It was found that Ethmoid Sinus is the areas where viruses are likely to be adhered and the areas can be predicted by computing the concentration.