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Fluid flows are characterized by uncertain inputs such as random
initial data, material and flux coefficients, and boundary
conditions. The current volume addresses the pertinent issue of
efficiently computing the flow uncertainty, given this initial
randomness. It collects seven original review articles that cover
improved versions of the Monte Carlo method (the so-called
multi-level Monte Carlo method (MLMC)), moment-based stochastic
Galerkin methods and modified versions of the stochastic
collocation methods that use adaptive stencil selection of the
ENO-WENO type in both physical and stochastic space. The methods
are also complemented by concrete applications such as flows around
aerofoils and rockets, problems of aeroelasticity (fluid-structure
interactions), and shallow water flows for propagating water waves.
The wealth of numerical examples provide evidence on the
suitability of each proposed method as well as comparisons of
different approaches.
Fluid flows are characterized by uncertain inputs such as random
initial data, material and flux coefficients, and boundary
conditions. The current volume addresses the pertinent issue of
efficiently computing the flow uncertainty, given this initial
randomness. It collects seven original review articles that cover
improved versions of the Monte Carlo method (the so-called
multi-level Monte Carlo method (MLMC)), moment-based stochastic
Galerkin methods and modified versions of the stochastic
collocation methods that use adaptive stencil selection of the
ENO-WENO type in both physical and stochastic space. The methods
are also complemented by concrete applications such as flows around
aerofoils and rockets, problems of aeroelasticity (fluid-structure
interactions), and shallow water flows for propagating water waves.
The wealth of numerical examples provide evidence on the
suitability of each proposed method as well as comparisons of
different approaches.
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