| Waldmann, Moritz; Grosch, Alice; Witzler, Christian; Lehner, Matthias; Benda, Odo; Koch, Walter; Vogt, Klaus; Kohn, Christopher; Schröder, Wolfgang; Göbbert, Jens Henrik; Lintermann, Andreas An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology Artikel In: Medical & Biological Engineering & Computing , 2021. @article{Waldmann2021,
title = {An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology},
author = {Waldmann, Moritz and Grosch, Alice and Witzler, Christian and Lehner, Matthias and Benda, Odo and Koch, Walter and
Vogt, Klaus and Kohn, Christopher and Schröder, Wolfgang and Göbbert, Jens Henrik and Lintermann, Andreas },
editor = {Springer },
url = {https://link.springer.com/content/pdf/10.1007/s11517-021-02446-3.pdf},
doi = {10.1007/s11517-021-02446-3},
year = {2021},
date = {2021-12-23},
urldate = {2021-12-23},
journal = {Medical & Biological Engineering & Computing },
abstract = {Physics-based analyses have the potential to consolidate and substantiate medical diagnoses in rhinology. Such methods are frequently subject to intense investigations in research. However, they are not used in clinical applications, yet. One issue preventing their direct integration is that these methods are commonly developed as isolated solutions which do not consider the whole chain of data processing from initial medical to higher valued data. This manuscript presents a workflow that incorporates the whole data processing pipeline based on a Jupyter environment. Therefore, medical image data are fully automatically pre-processed by machine learning algorithms. The resulting geometries employed for the simulations on high-performance computing systems reach an accuracy of up to 99.5% compared to manually segmented geometries. Additionally, the user is enabled to upload and visualize 4-phase rhinomanometry data. Subsequent analysis and visualization of the simulation outcome extend the results of standardized diagnostic methods by a physically sound interpretation. Along with a detailed presentation of the methodologies, the capabilities of the workflow are demonstrated by evaluating an exemplary medical case. The pipeline output is compared to 4-phase rhinomanometry data. The comparison underlines the functionality of the pipeline. However, it also illustrates the influence of mucosa swelling on the simulation.},
keywords = {Computational Fluid Dynamics, High performance computing, Machine Learning, Rhinology},
pubstate = {published},
tppubtype = {article}
}
Physics-based analyses have the potential to consolidate and substantiate medical diagnoses in rhinology. Such methods are frequently subject to intense investigations in research. However, they are not used in clinical applications, yet. One issue preventing their direct integration is that these methods are commonly developed as isolated solutions which do not consider the whole chain of data processing from initial medical to higher valued data. This manuscript presents a workflow that incorporates the whole data processing pipeline based on a Jupyter environment. Therefore, medical image data are fully automatically pre-processed by machine learning algorithms. The resulting geometries employed for the simulations on high-performance computing systems reach an accuracy of up to 99.5% compared to manually segmented geometries. Additionally, the user is enabled to upload and visualize 4-phase rhinomanometry data. Subsequent analysis and visualization of the simulation outcome extend the results of standardized diagnostic methods by a physically sound interpretation. Along with a detailed presentation of the methodologies, the capabilities of the workflow are demonstrated by evaluating an exemplary medical case. The pipeline output is compared to 4-phase rhinomanometry data. The comparison underlines the functionality of the pipeline. However, it also illustrates the influence of mucosa swelling on the simulation. |
| Lintermann, Andreas; Schröder, Wolfgang Lattice–Boltzmann simulations for complex geometries on high-performance computers Artikel In: CEAS Aeronautical Journal, 2020, ISBN: 1869-5582. @article{Lintermann2020c,
title = {Lattice–Boltzmann simulations for complex geometries on high-performance computers},
author = {Lintermann, Andreas and Schröder, Wolfgang },
url = {http://link.springer.com/10.1007/s13272-020-00450-1},
doi = {10.1007/s13272-020-00450-1},
isbn = {1869-5582},
year = {2020},
date = {2020-05-13},
journal = {CEAS Aeronautical Journal},
abstract = {Complex geometries pose multiple challenges to the field of computational fluid dynamics. Grid generation for intricate objects is often difficult and requires accurate and scalable geometrical methods to generate meshes for large-scale computations. Such simulations, furthermore, presume optimized scalability on high-performance computers to solve high-dimensional physical problems in an adequate time. Accurate boundary treatment for complex shapes is another issue and influences parallel load-balance. In addition, large serial geometries prevent efficient computations due to their increased memory footprint, which leads to reduced memory availability for computations. In this paper, a framework is presented that is able to address the aforementioned problems. Hierarchical Cartesian boundary-refined meshes for complex geometries are obtained by a massively parallel grid generator. In this process, the geometry is parallelized for efficient computation. Simulations on large-scale meshes are performed by a high-scaling lattice–Boltzmann method using the second-order accurate interpolated bounce-back boundary conditions for no-slip walls. The method employs Hilbert decompositioning for parallel distribution and is hybrid MPI/OpenMP parallelized. The parallel geometry allows to speed up the pre-processing of the solver and massively reduces the local memory footprint. The efficiency of the computational framework, the application of which to, e.g., subsonic aerodynamic problems is straightforward, is shown by simulating clearly different flow problems such as the flow in the human airways, in gas diffusion layers of fuel cells, and around an airplane landing gear configuration},
keywords = {Airway, Computational Fluid Dynamics, High performance computing, Large-Scale Simulation Data, Lattice-Boltzmann method},
pubstate = {published},
tppubtype = {article}
}
Complex geometries pose multiple challenges to the field of computational fluid dynamics. Grid generation for intricate objects is often difficult and requires accurate and scalable geometrical methods to generate meshes for large-scale computations. Such simulations, furthermore, presume optimized scalability on high-performance computers to solve high-dimensional physical problems in an adequate time. Accurate boundary treatment for complex shapes is another issue and influences parallel load-balance. In addition, large serial geometries prevent efficient computations due to their increased memory footprint, which leads to reduced memory availability for computations. In this paper, a framework is presented that is able to address the aforementioned problems. Hierarchical Cartesian boundary-refined meshes for complex geometries are obtained by a massively parallel grid generator. In this process, the geometry is parallelized for efficient computation. Simulations on large-scale meshes are performed by a high-scaling lattice–Boltzmann method using the second-order accurate interpolated bounce-back boundary conditions for no-slip walls. The method employs Hilbert decompositioning for parallel distribution and is hybrid MPI/OpenMP parallelized. The parallel geometry allows to speed up the pre-processing of the solver and massively reduces the local memory footprint. The efficiency of the computational framework, the application of which to, e.g., subsonic aerodynamic problems is straightforward, is shown by simulating clearly different flow problems such as the flow in the human airways, in gas diffusion layers of fuel cells, and around an airplane landing gear configuration |
| Lintermann, Andreas; Schröder, Wolfgang A Hierarchical Numerical Journey through the Nasal Cavity: From Nose-Like Models to Real Anatomies Artikel In: Flow, Turbulence and Combustion, 2017, ISSN: 1386-6184. @article{Lintermann2017FTaC,
title = {A Hierarchical Numerical Journey through the Nasal Cavity: From Nose-Like Models to Real Anatomies},
author = {Lintermann, Andreas and Schröder, Wolfgang},
editor = {Springer Netherlands},
url = {http://rhinodiagnost.eu/wp-content/uploads/2017/12/paper_FTAC_SI_health_Lintermann.pdf, A Hierarchical Numerical Journey through the Nasal Cavity: From Nose-Like Models to Real Anatomies},
doi = {10.1007/s10494-017-9876-0},
issn = {1386-6184},
year = {2017},
date = {2017-12-20},
issuetitle = {special issue "CFD in Health"},
journal = {Flow, Turbulence and Combustion},
abstract = {The immense increase of computational power in the past decades led to an evolution of numerical simulations in all kind of engineering applications. New developments in medical technologies in rhinology employ computational fluid dynamics methods to explore pathologies from a fluid-mechanics point of view. Such methods have grown mature and are about to enter daily clinical use to support doctors in decision making. In light of the importance of effective respiration on patient comfort and health care costs, individualized simulations ultimately have the potential to revolutionize medical diagnosis, drug delivery, and surgery planning. The present article reviews experiments, simulations, and algorithmic approaches developed at RWTH Aachen University that have evolved from fundamental physical analyses using nose-like models to patient-individual analyses based on realistic anatomies and high resolution computations in hierarchical manner.},
keywords = {Digital particle image velocimetry, Finite volume method, High performance computing, Lattice-Boltzmann method, Nasal cavity flows},
pubstate = {published},
tppubtype = {article}
}
The immense increase of computational power in the past decades led to an evolution of numerical simulations in all kind of engineering applications. New developments in medical technologies in rhinology employ computational fluid dynamics methods to explore pathologies from a fluid-mechanics point of view. Such methods have grown mature and are about to enter daily clinical use to support doctors in decision making. In light of the importance of effective respiration on patient comfort and health care costs, individualized simulations ultimately have the potential to revolutionize medical diagnosis, drug delivery, and surgery planning. The present article reviews experiments, simulations, and algorithmic approaches developed at RWTH Aachen University that have evolved from fundamental physical analyses using nose-like models to patient-individual analyses based on realistic anatomies and high resolution computations in hierarchical manner. |
| Göbbert, Jens Henrik Flow predictions for your nose Artikel In: Exascale-Newsletter, Bd. 3, S. 3, 2017. @article{Göbbert2017exa,
title = {Flow predictions for your nose},
author = {Göbbert, Jens Henrik},
editor = {Forschungszentrum Jülich GmbH},
url = {http://exascale-news.de/en/2017/index/#!/Flow-Predictions-for-Your-Nose, Flow predictions for your nose (Englische Version online)
http://rhinodiagnost.eu/wp-content/uploads/2017/11/exascale_nl_03_2017.pdf, Strömungsvorhersage für die Nase (Deutsche Version)
},
year = {2017},
date = {2017-11-09},
urldate = {2017-11-09},
journal = {Exascale-Newsletter},
volume = {3},
pages = {3},
institution = {Forschungszentrum Jülich GmbH},
keywords = {Computational Fluid Dynamics, High performance computing, Höchstleistungsrechner, Medizin, Nasal respiration, Strömungssimulation},
pubstate = {published},
tppubtype = {article}
}
|