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  1. A method to predict sub-filter shear-induced velocities on a liquid-gas phase interface for use in a dual scale LES model is presented. The method reconstructs the sub-filter velocity field in the vicinity of the interface by introducing a vortex sheet at the interface. The vortex sheet is transported by an unsplit geometric volume and surface area advection scheme with a Piece- wise Linear Interface Construction (PLIC) representation of the material interface. At each step and desired location the shear-induced velocities can be calculated by integrating the vortex sheet and other relevant quantities over the liquid-gas surface with the sub-grid velocity recon- struction limited to a small number of cells near the phase interface. The vortex sheet method is tested and compared against prior literature.
  2. A method to predict sub-filter shear-induced velocities on a liquid-gas phase interface for use in a dual scale LES model is presented. The method reconstructs the sub-filter velocity field in the vicinity of the interface by introducing a vortex sheet at the interface. The vortex sheet is transported by an unsplit geometric volume and surface area advection scheme with a Piecewise Linear Interface Construction (PLIC) representation of the material interface. At each step and desired location the shear-induced velocities can be calculated by integrating the vortex sheet and other relevant quantities over the liquid-gas interface with the sub-grid velocity reconstruction limited to a small number of cells near the phase interface. The vortex sheet method is tested and compared against prior literature.
  3. A method to compute sub-filter velocities due to shear induced instabilities on a liquid-gas interface for use in a dual scale LES-DNS model is presented. The method reconstructs the sub-filter velocity field as the sum of a prescribed base velocity profile and a perturbation velocity field determined by the Orr-Sommerfeld equations. The base velocity profile is approximated as an error function appropriately scaled with flow parameters, and the perturbation velocity field is computed by solving the Orr-Sommerfeld equations with appropriate boundary and interface conditions. The perturbation velocities of the Orr-Sommerfeld equations are expanded into Chebyshev polynomials to create a linear eigenvalue problem as outlined by Schmid and Henningson (2001). Finally the eigenvalue problem is solved using a standard linear algebra package and used to evaluate the perturbation velocities. The Chebyshev method is tested under a variety of flow parameters and initial interface disturbances. Results are presented and compared against prior literature and asymptotic solutions.
  4. Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
    Free, publicly-accessible full text available December 1, 2023
  5. Abstract The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
    Free, publicly-accessible full text available December 1, 2023
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