Computational Meshing for CFD Simulations

Lintermann, Andreas

doi:10.1007/978-981-15-6716-2_6

Cited by 16 publications

(6 citation statements)

References 47 publications

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“…Computational mesh (or grid) refers to the spatial discretization required for the numerical solution of the governing equations of CFD. Rodriguez [ 94 ] presented best practice guidelines in establishing computational meshes for CFD simulations, and Lintermann [ 102 ] gave an introduction to the creation of computational meshes for the nasal cavity, in particular. There are three main aspects to consider when establishing a computational mesh for CFD simulations, namely, the mesh type, quality, and size.…”

Section: Part I—computational Rhinologymentioning

confidence: 99%

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Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Johnsen

2024

Bioengineering

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Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts—an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

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Section: Part I—computational Rhinologymentioning

confidence: 99%

“…The Lattice–Boltzmann (LB) method is an alternative to the Navier–Stokes-based CFD and is mentioned here for completeness due to its suitability for fluid dynamics simulations in complex geometries. One of the method’s main strengths is its scalability on high-performance computers [ 102 ].…”

Section: Part I—computational Rhinologymentioning

confidence: 99%

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Johnsen

2024

Bioengineering

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“…A high-quality mesh typically adheres to two key criteria: minimal skewness, implying minimal changes in the area between adjoining elements, and an appropriate aspect ratio, ensuring sufficient resolution in information-dense regions. As Lintermann [14] point out, element shape, defined by skewness and aspect ratio, is the primary determinant of mesh quality.…”

Section: Evaluating Mesh Qualitymentioning

confidence: 99%

“…Standard values of the mesh quality parameters for aspect ratio and skewness Parameter Standard Value Aspect ratio [14] 0.2 -5.0 Skewness [15] equilateral Excellent good fair poor bad degenerate 0 0 -0.25 0.25 -0.50 0.50 -0.75 0.75 -0.90 0.90 -<1 1…”

Section: Tablementioning

confidence: 99%

Stretched Meshing in a 2D Computational Domain with Elbow Edges for CFD Applications

Gancang Saroja,

Sugeng Rianto,

Abdurrouf

et al. 2024

ARFMTS

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Mesh generation is critical for obtaining accurate and detailed solutions to mesh-based numerical problems, particularly when capturing specific information within a designated area of the domain. While structured meshes offer consistency, their uniform resolution limits their ability to achieve this. Mesh stretching offers a solution by introducing non-uniform element sizes based on an analytical relationship while preserving the original mesh structure. The objective of this study is to create a hybrid mesh model that leverages the strengths of both structured and stretched meshes. A 2D rectangle with elbow edges serves as the domain. To address the requirements of CFD applications, the domain is refined by increasing element density near the boundaries and corners. Skewness, aspect ratio, and element quality are then assessed to determine the overall mesh quality. The results demonstrate that stretching the structured mesh produced a mesh with quality that meets CFD domain standards.

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“…Computational mesh (or grid) refers to the spatial discretization required for the numerical solution of the governing equations of CFD. Rodriguez [94] presented best practice guidelines in establishing computational meshes for CFD simulations, and Lintermann [102] gave an introduction to the creation of computational meshes for the nasal cavity, in particular. There are three main aspects to consider when establishing a computational mesh for CFD simulations, namely, the mesh type, quality, and size.…”

Section: Computational Meshesmentioning

confidence: 99%

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Johnsen

2024

Preprint

View full text Add to dashboard Cite

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in e.g. the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. The paper consists of three distinct parts – an introduction to and review of the field of computational rhinology, a review of published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of the paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

show abstract

Computational Meshing for CFD Simulations

Cited by 16 publications

References 47 publications

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Stretched Meshing in a 2D Computational Domain with Elbow Edges for CFD Applications

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

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