20120828
Consistently replacing prototypes or physical experiments by computations has been pursued within the Engineering and Applied Science community in the last decades, to a large extent due to the impressive computer power available nowadays. But the use of computer simulations as an effective tool still faces conceptual and technical challenges. Chief among these is the reliability of the predictions regarding the real physical response of the systems. Computational analysis in general is intended to provide reliable predictions of particular events, which are used as the basis for crucial decisions. In that sense, Uncertainty Quantification (UQ), which is a critical element in experimental exploration, has been considered recently an important conceptual basis for improving the reliability and wide acceptance of computer simulation predictive capacity.
In recent years there has been significant progress in quantifying and modeling the effect of input uncertainties in the response of partial differential equations (PDEs). The presence of uncertainties is incorporated by transforming the PDEs representing the system into a set of stochastic PDEs (SPDEs). The spectral representation of stochastic dimension has led to Generalized Polynomial Chaos Expansion, which has shown its utility and efficiency in different domains. As a drawback, this approach requires significant recoding which seems not very attractive especially when legacy codes are to be used. In order not to face that type of barrier, collocation methods has been developed which also relies on an explicit representation of the stochastic dimension but retrieve the decoupled nature and nonintrusive implementation of sampling methods like Montecarlo Method.
Some theoretical results involving the convergence of collocation methods have been developed for elliptic linear problems, so the performance of such methods outside this realm is still to be proved, which already been done for some nonlinear problems. Here we apply the collocation method to fluidstructures interaction problems. The first example deals with FluidStructure Interaction (FSI) in the context of VortexInduced Vibrations (VIV), which is central to the design of risers and floating structures in offshore engineering. Here, the VIV phenomenon is described by a simple model, often used by engineers in the initial design stages, that, despite its simplicity, is capable of tracking important aspects of the dynamics. A longterm response is a key ingredient for understanding fatigue failure mechanisms of structures. As the longterm statistics lead to a great amount of correlated data, these can be obtained and handled with the help of our enabling computational infrastructure.
The second example is devoted to a critical assessment of Large Eddy Simulation (LES) models. The inherent complexity of turbulent flows demands the use of refined grids in time and space for representing the multiscale character of the involved phenomena, specially the dissipation mechanisms. The use of direct numerical simulation (DNS) often leads to prohibitive computational costs that scale with the Reynolds number, in that case, inversely proportional to the smallest scale to be captured within the simulation. Despite some recent improvement on DNS schemes, in many practical engineering applications, LES models, which rely on adding extra dissipation, are often used. Here, a sensitivity analysis with respect to a LES parameter, similar to the one proposed in , is pursued in a benchmark problem. In this example, emphasis is placed on UQ performed within a stabilized finite element high performance computing code coordinated by a scientific workflow.
Category: CE SeminarTechnische Universität Darmstadt
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