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RESEARCH PRODUCT

Adaptive discontinuous evolution Galerkin method for dry atmospheric flow

Andreas MüllerFrancis X. GiraldoMaria Lukacova-medvidovaVolkmar WirthLeonid Yelash

subject

Backward differentiation formulasteady statesPhysics and Astronomy (miscellaneous)Wave propagationdry atmospheric convectionlarge time stepsystems of hyperbolic balance lawssymbols.namesakeDiscontinuous Galerkin methodApplied mathematicsevolution Galerkin schemesGalerkin methodMathematicssemi-implicit approximationNumerical AnalysisAdaptive mesh refinementApplied MathematicsEuler equationsRiemann solverComputer Science ApplicationsEuler equationsComputational MathematicsNonlinear systemClassical mechanicsModeling and Simulationsymbols

description

We present a new adaptive genuinely multidimensional method within the framework of the discontinuous Galerkin method. The discontinuous evolution Galerkin (DEG) method couples a discontinuous Galerkin formulation with approximate evolution operators. The latter are constructed using the bicharacteristics of multidimensional hyperbolic systems, such that all of the infinitely many directions of wave propagation are considered explicitly. In order to take into account multiscale phenomena that typically appear in atmospheric flows nonlinear fluxes are split into a linear part governing the acoustic and gravitational waves and a nonlinear part that models advection. Time integration is realized by the IMEX type approximation using the semi-implicit second-order backward differentiation formula (BDF2). Moreover in order to approximate efficiently small scale phenomena, adaptive mesh refinement using the space filling curves via the AMATOS function library is employed. Four standard meteorological test cases are used to validate the new discontinuous evolution Galerkin method for dry atmospheric convection. Comparisons with the Rusanov flux, a standard one-dimensional approximate Riemann solver used for the flux integration, demonstrate better stability and accuracy, as well as the reliability of the new multidimensional DEG method. German Research Foundation DFG Center of Computational Sciences in Mainz National Science Foundation ONR LU 1470/2-2 (DFG) LU 1470/2-3 (DFG) SPP 1276 (DFG) grant 1216700 (NSF) grant PE-0602435N (ONR)

https://doi.org/10.1016/j.jcp.2014.02.034