Applied geometry optimization of a novel 3D-printed wet-scrubber nozzel with Lattice Boltzmann methods

  • Chair:

    FILTECH 2022, The Filtration Event

  • Place:

    Cologne, Germany

  • Date:


  • Author:

    F. Reinke, M. Novosel, J. Meyer, A. Dittler

  • In the course of progress in metal cutting processes (grinding, brushing, polishing), increasingly fine metal particles are produced. The separation of these particles is becoming more complex. Particularly in the case of submicron particles (mainly PM1 and PM2.5 fraction), downstream filter elements are required, which are associated with an additional pressure loss of the overall system. For this reason, new technologies with high particle separation efficiency and minimal energy input are required. One possibility can be wet-separation processes with innovative 3D-printed nozzle geometries for dispersion of the washing liquid. Since the dispersed washing fluid captures the particles, a fundamental understanding of the prevailing flow conditions is necessary. Here, numerical flow simulations can support the development in many steps of the product development cycle. Enabling pre-manufacturing studies for various designs, represents one major benefit of such an approach. Due to the degrees of freedom in terms of the geometry shapes by using rapid prototyping technology, the simulation is able to assist with the selection of design choices by predicting relevant physical properties. On the one hand, it therefore expands the overall range of possible design studies and on the other hand, reduces the amount of physical testing and premature manufacturing steps e.g. printing. This results in a reduction of time and cost in the whole development cycle.

    In this contribution, an applied numerical geometry optimization with several design studies of an innovative 3D-printed wet-scrubber nozzle is presented. The applied open source tool for numerical flow simulations is OpenLB, which provides an implementation of lattice Boltzmann methods (LBM). Due to its highly efficient parallel algorithm, the LBM has a high potential in simulating turbulent flows with moderate computation times.