The coupling of atmospheric pressure ionization (API) sources like electrospray ionization (ESI) to vacuum based applications like mass spectrometry (MS) or ion beam deposition (IBD) is done by differential pumping, starting with a capillary or pinhole inlet. Because of its low ion transfer efficiency the inlet represents a major bottleneck for these applications. Here we present a nano-ESI vacuum interface optimized to exploit the hydrodynamic drag of the background gas for collimation and the reduction of space charge repulsion. Up to a space charge limit of 40 nA we observe 100% current transmission through a capillary with an inlet and show by MS and IBD experiments that the transmitted ion beams are well defined and free of additional contamination compared to a conventional interface. Based on computational fluid dynamics modelling and ion transport simulations, we show how the specific shape enhances the collimation of the ion cloud. Mass selected ion currents in the nanoampere range available further downstream in high vacuum open many perspectives for the efficient use of electrospray ion beam deposition (ES-IBD) as a surface coating method.

A numerical approach for solving evolutionary partial differential equations in two and three space dimensions on block-based adaptive grids is presented. The numerical discretization is based on high-order, central finite-differences and explicit time integration. Grid refinement and coarsening are triggered by multiresolution analysis, i.e. thresholding of wavelet coefficients, which allow controlling the precision of the adaptive approximation of the solution with respect to uniform grid computations. The implementation of the scheme is fully parallel using MPI with a hybrid data structure. Load balancing relies on space filling curves techniques. Validation tests for 2D advection equations allow to assess the precision and performance of the developed code. Computations of the compressible Navier-Stokes equations for a temporally developing 2D mixing layer illustrate the properties of the code for nonlinear multi-scale problems. The code is open source.

In this work we present a data driven method, used to improve mode-based model order reduction of transport fields with sharp fronts. We assume that the original flow field q(x, t) = f (φ(x, t)) can be reconstructed by a front shape function f and a level set function φ. The level set function is used to generate a local coordinate, which parametrizes the distance to the front. In this way, we are able to embed the local 1D description of the front for complex 2D front dynamics with merging or splitting fronts, while seeking a low rank description of φ. Here, the freedom of choosing φ far away from the front can be used to find a low rank description of φ which accelerates the convergence of q − f (φ n ) , when truncating φ after the nth mode. We demonstrate the ability of this new ansatz for a 2D propagating flame with a moving front.

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