{"title":"基于 NURBS 的高效等距分析,适用于有平流和无平流的耦合非线性扩散-反应方程","authors":"Ilham Asmouh, Alexander Ostermann","doi":"10.1016/j.jocs.2024.102434","DOIUrl":null,"url":null,"abstract":"<div><p>Nonlinear diffusion–reaction systems model a multitude of physical phenomena. A common situation is biological development modeling where such systems have been widely used to study spatiotemporal phenomena in cell biology. Systems of coupled diffusion–reaction equations are usually subject to some complicated features directly related to their multiphysics nature. Moreover, the presence of advection is source of numerical instabilities, in general, and adds another challenge to these systems. In this study, we propose a NURBS-based isogeometric analysis (IgA) combined with a second-order Strang operator splitting to deal with the multiphysics nature of the problem. The advection part is treated in a semi-Lagrangian framework and the resulting diffusion–reaction equations are then solved using an efficient time-stepping algorithm based on operator splitting. The accuracy of the method is studied by means of a advection–diffusion–reaction system with analytical solution. To further examine the performance of the new method on geometries more general than rectangles (e.g., L-shaped domains and parts of annuli), the well-known Schnakenberg–Turing problem is considered with and without advection. Finally, a Gray–Scott system on a circular domain is also presented. The results obtained demonstrate the efficiency of our new algorithm to accurately reproduce the solution in the presence of complex patterns on more complicated geometries. Moreover, the new method clarifies the effect of geometry on Turing patterns.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"83 ","pages":"Article 102434"},"PeriodicalIF":3.1000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324002278/pdfft?md5=08cba31da5697d13b5d5ac89f8b16f40&pid=1-s2.0-S1877750324002278-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Highly efficient NURBS-based isogeometric analysis for coupled nonlinear diffusion–reaction equations with and without advection\",\"authors\":\"Ilham Asmouh, Alexander Ostermann\",\"doi\":\"10.1016/j.jocs.2024.102434\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Nonlinear diffusion–reaction systems model a multitude of physical phenomena. A common situation is biological development modeling where such systems have been widely used to study spatiotemporal phenomena in cell biology. Systems of coupled diffusion–reaction equations are usually subject to some complicated features directly related to their multiphysics nature. Moreover, the presence of advection is source of numerical instabilities, in general, and adds another challenge to these systems. In this study, we propose a NURBS-based isogeometric analysis (IgA) combined with a second-order Strang operator splitting to deal with the multiphysics nature of the problem. The advection part is treated in a semi-Lagrangian framework and the resulting diffusion–reaction equations are then solved using an efficient time-stepping algorithm based on operator splitting. The accuracy of the method is studied by means of a advection–diffusion–reaction system with analytical solution. To further examine the performance of the new method on geometries more general than rectangles (e.g., L-shaped domains and parts of annuli), the well-known Schnakenberg–Turing problem is considered with and without advection. Finally, a Gray–Scott system on a circular domain is also presented. The results obtained demonstrate the efficiency of our new algorithm to accurately reproduce the solution in the presence of complex patterns on more complicated geometries. Moreover, the new method clarifies the effect of geometry on Turing patterns.</p></div>\",\"PeriodicalId\":48907,\"journal\":{\"name\":\"Journal of Computational Science\",\"volume\":\"83 \",\"pages\":\"Article 102434\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1877750324002278/pdfft?md5=08cba31da5697d13b5d5ac89f8b16f40&pid=1-s2.0-S1877750324002278-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1877750324002278\",\"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/S1877750324002278","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}
引用次数: 0
摘要
非线性扩散反应系统可以模拟多种物理现象。一个常见的情况是生物发育建模,这类系统被广泛用于研究细胞生物学中的时空现象。耦合扩散-反应方程系统通常具有一些与其多物理特性直接相关的复杂特征。此外,平流的存在通常是数值不稳定性的来源,也给这些系统增加了另一个挑战。在本研究中,我们提出了一种基于 NURBS 的等几何分析 (IgA),并结合二阶斯特朗算子拆分来处理问题的多物理特性。平流部分在半拉格朗日框架下处理,然后使用基于算子拆分的高效时间步进算法求解扩散-反应方程。通过分析求解的平流-扩散-反应系统,研究了该方法的准确性。为了进一步检验新方法在比矩形更一般的几何图形(如 L 形域和环形的一部分)上的性能,研究了有无平流的著名 Schnakenberg-Turing 问题。最后,还介绍了圆形域上的格雷-斯科特系统。所获得的结果表明,我们的新算法能够在更复杂的几何图形上准确地重现复杂图案的解。此外,新方法还阐明了几何图形对图灵模式的影响。
Highly efficient NURBS-based isogeometric analysis for coupled nonlinear diffusion–reaction equations with and without advection
Nonlinear diffusion–reaction systems model a multitude of physical phenomena. A common situation is biological development modeling where such systems have been widely used to study spatiotemporal phenomena in cell biology. Systems of coupled diffusion–reaction equations are usually subject to some complicated features directly related to their multiphysics nature. Moreover, the presence of advection is source of numerical instabilities, in general, and adds another challenge to these systems. In this study, we propose a NURBS-based isogeometric analysis (IgA) combined with a second-order Strang operator splitting to deal with the multiphysics nature of the problem. The advection part is treated in a semi-Lagrangian framework and the resulting diffusion–reaction equations are then solved using an efficient time-stepping algorithm based on operator splitting. The accuracy of the method is studied by means of a advection–diffusion–reaction system with analytical solution. To further examine the performance of the new method on geometries more general than rectangles (e.g., L-shaped domains and parts of annuli), the well-known Schnakenberg–Turing problem is considered with and without advection. Finally, a Gray–Scott system on a circular domain is also presented. The results obtained demonstrate the efficiency of our new algorithm to accurately reproduce the solution in the presence of complex patterns on more complicated geometries. Moreover, the new method clarifies the effect of geometry on Turing patterns.
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).