HPC-based uncertainty quantification for fluidstructure coupling in medical engineering

In recent decades biomedical studies with living probands (in vivo) and artificial experiments (in vitro) have been complemented more and more by computation and simulation (in silico). In silico techniques for medical engineering can give for example enhanced information for the diagnosis and risk stratification of cardiovascular disease, one of the most occurring causes of death in the developed countries. Other use cases for in silico methods are given by virtual prototyping and the simulation of possible surgery outcomes. High reliability is a requirement for cardiovascular diagnosis and risk stratification methods especially with surgical decision-making. Given uncertainties in the input data of a simulation, this implies a necessity to quantify the uncertainties in simulation results. Uncertainties can be propagated within a numerical simulation by methods of Uncertainty Quantification (UQ)

Uncertainty Quantification for Fluid-Structure Interaction: Application to Aortic Biomechanics

Diseases of the cardiovascular system count to the most common causes of death in the developed countries. There are many open research questions with respect to a better understanding for example of the physiology of the heart and the main arteries or to the determination of the factors for aneurysm or stenosis development of the aorta. Furthermore, on a daily basis, a heart surgeon has to estimate the probability of success for different treatment scenarios as opposed to no intervention. In recent decades, methods of investigation with living probands (in vivo) and artificial experiments (in vitro) have been complemented more and more by computational methods and simulation (in silico). In particular, numerical simulations have the capability to enhance medical imaging modalities with additional information. However, to date, the biomechanical simulation of aortic blood flow given an uncertain data situation represents a major challenge. So far, mostly deterministic models have been used, Yet, measurement data for the configuration of a simulation is subject to measurement inaccuracies. For the choice of model parameters, which are non-measurable in a living body, often imprecise information is available only. In this work, novel development steps for a numerical framework are presented aiming for the simulation and evaluation of aortic biomechanics using methods of Uncertainty Quantification (UQ). The work includes the modelling of the aortic biomechanics as a fluid-structure interaction (FSI) problem with uncertain parameters. By means of a subject-specific workflow, the simulation of different probands, phantoms and, ultimately, patients is enabled. For the solution of the complex partial differential system of equations, they are discretised with the finite element method (FEM) and a novel, parallelly efficient and problem-specific solver is developed. To verify the numerical framework implemented in the course of this work, a novel analytically solvable benchmark for UQ-FSI problems is proposed. Furthermore, the numerical framework is validated by means of a prototypical aortic phantom experiment. Finally, the UQ-FSI simulation enables the evaluation of a stress overload probability. This novel parameter is exemplarily evaluated by means of the simulation of a human aortic bow. Therewith, this work represents a new contribution to aspects of the development of simulation methods for the investigation of aortic biomechanics

Forschungsdatenmanagement: Workshop

Dieser Foliensatz dient der Unterstützung von Lehrveranstaltungen im Bereich des Forschungsdatenmanagements. Gemäß der Creative Commons Lizenz CC-BY-SA können sie spezifisch zugeschnitten und weiterverwendet werden. Die Autoren hoffen so einen Beitrag zur Verbesserung der Weiterbildung in diesem sich stark entwickelnden Bereich zu leisten. Entstanden ist der Foliensatz aus dem baden-württembergischen Verbundprojekt „bwFDM-Info“ und dem Projekt „Community-spezifische Forschungsdatenpublikation“ des Kompetenzzentrums Forschungsdaten an der Universität Heidelberg, gefördert durch das Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg

Öffentlicher Abschlussbericht von bwFDM-Communities - Wissenschaftliches Datenmanagement an den Universitäten Baden-Württembergs

Im Projekt bwFDM‐Communities wurde Kontakt zu allen wissenschaftlichen Communities aufgebaut, um deren Bedarf an Diensten, Infrastruktur und Unterstützung beim Umgang mit Forschungsdaten an den Universitäten des Landes Baden‐Württemberg zu erfassen. Ziel war es, eine Grundlage für den nachhaltigen Ausbau von Expertise und Know‐How im Forschungsdatenmanagement an allen universitären Rechenzentren, Bibliotheken und anderen Wissenschaftseinrichtungen (z.B. Sonderforschungsbereiche, GESIS, ...) Baden‐Württembergs zu legen, um den wissenschaftlichen Communities langfristig ein Umfeld bieten zu können, in denen sie die neuen Herausforderungen des digitalen Wissenswettbewerbs annehmen können

Proceedings of the 4th bwHPC Symposium

The bwHPC Symposium 2017 took place on October 4th, 2017, Alte Aula, Tübingen. It focused on the presentation of scientific computing projects as well as on the progress and the success stories of the bwHPC realization concept. The event offered a unique opportunity to engage in an active dialogue between scientific users, operators of bwHPC sites, and the bwHPC support team

Das Informationsportal zum Forschungsdatenmanagement: forschungsdaten.info

Das Informationsportal zum Forschungsdatenmanagement: forschungsdaten.inf