{"title":"利用动态高阶分解法分析气泡柱的水动力特性","authors":"C. Mendez , F.P Santos , G.G.S. Ferreira","doi":"10.1016/j.jocs.2024.102316","DOIUrl":null,"url":null,"abstract":"<div><p>Bubble columns play a crucial role in a wide range of industries, including chemical and biochemical processes, petrochemicals, and environmental engineering. Understanding the dynamics of bubble columns is essential for optimizing their performance in various applications. This study proposes a data-driven approach for analyzing the dynamics of a two-dimensional bubble column system. We conducted simulations with varying superficial velocities and generated a comprehensive training dataset encompassing the entire velocity and pressure fields to achieve this. We then compared the performance of two approaches, the Fast Fourier Transformation (FFT) and the High-Order Dynamic Mode Decomposition (HODMD), in representing the system’s dynamics. Our findings demonstrate that the conventional FFT approach fails to adequately capture the complex dynamics of the dispersed multiphase flow system. This limitation arises due to the distribution of frequencies along the domain. Conversely, our work highlights the success of the HODMD method in accurately representing the system’s dynamics using only a few arbitrary sampling points within the domain. The implications of this study are significant, as it sheds light on the potential benefits of employing HODMD for analyzing bubble column dynamics. By utilizing this approach, industrial processes can be optimized more effectively across various applications.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hydrodynamic characterization of bubble column using Dynamical High Order Decomposition approach\",\"authors\":\"C. Mendez , F.P Santos , G.G.S. Ferreira\",\"doi\":\"10.1016/j.jocs.2024.102316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Bubble columns play a crucial role in a wide range of industries, including chemical and biochemical processes, petrochemicals, and environmental engineering. Understanding the dynamics of bubble columns is essential for optimizing their performance in various applications. This study proposes a data-driven approach for analyzing the dynamics of a two-dimensional bubble column system. We conducted simulations with varying superficial velocities and generated a comprehensive training dataset encompassing the entire velocity and pressure fields to achieve this. We then compared the performance of two approaches, the Fast Fourier Transformation (FFT) and the High-Order Dynamic Mode Decomposition (HODMD), in representing the system’s dynamics. Our findings demonstrate that the conventional FFT approach fails to adequately capture the complex dynamics of the dispersed multiphase flow system. This limitation arises due to the distribution of frequencies along the domain. Conversely, our work highlights the success of the HODMD method in accurately representing the system’s dynamics using only a few arbitrary sampling points within the domain. The implications of this study are significant, as it sheds light on the potential benefits of employing HODMD for analyzing bubble column dynamics. By utilizing this approach, industrial processes can be optimized more effectively across various applications.</p></div>\",\"PeriodicalId\":48907,\"journal\":{\"name\":\"Journal of Computational Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1877750324001091\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877750324001091","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Hydrodynamic characterization of bubble column using Dynamical High Order Decomposition approach
Bubble columns play a crucial role in a wide range of industries, including chemical and biochemical processes, petrochemicals, and environmental engineering. Understanding the dynamics of bubble columns is essential for optimizing their performance in various applications. This study proposes a data-driven approach for analyzing the dynamics of a two-dimensional bubble column system. We conducted simulations with varying superficial velocities and generated a comprehensive training dataset encompassing the entire velocity and pressure fields to achieve this. We then compared the performance of two approaches, the Fast Fourier Transformation (FFT) and the High-Order Dynamic Mode Decomposition (HODMD), in representing the system’s dynamics. Our findings demonstrate that the conventional FFT approach fails to adequately capture the complex dynamics of the dispersed multiphase flow system. This limitation arises due to the distribution of frequencies along the domain. Conversely, our work highlights the success of the HODMD method in accurately representing the system’s dynamics using only a few arbitrary sampling points within the domain. The implications of this study are significant, as it sheds light on the potential benefits of employing HODMD for analyzing bubble column dynamics. By utilizing this approach, industrial processes can be optimized more effectively across various applications.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).