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The PREON® technology is the result of many years of research with one goal: Finding a new approach for fluid simulation that allows for physically accurate simulations in unprecedented resolutions. With PREON® at its core, PreonLab allows simulations using large time steps without compromising on simulation quality. Moving geometries are handled completely without re-meshing. Finally, PREON® employs highly efficient data structures and full parallelization using a state-of-the-art hybrid parallel programming implementation which gets the best performance out of your desktop PC or your high performance cluster. PREON® means less computation time and faster answers.
Computational efficiency offers no benefit if the final simulation does not match reality. This is why we have invested a lot of effort to validate PREON® in cooperation with our partners. Visit our showcase for more information.
Handles any Geometry Gracefully
With PreonLab, there is no need for preliminary meshing of geometries. There are no prerequisites for the involved geometries or their complexity. The fluid simulation can be performed with volumetric and non-volumetric objects. Even overlapping geometries with holes do not pose a problem when working with PreonLab. Therefore, PreonLab significantly reduces the initial effort for the simulation setup.
Another key advantage of our mesh-free solver is the efficient handling of moving or animated geometries – highly dynamic scenes usually simulate at a similar speed compared to static ones. Object kinematics can be either simulated via the built-in rigid body solver, scripted via the Python interface or animated using the keyframe dialog which also supports different import formats from external tools. Besides scripting kinematics, the Python interface can be used to vary any parameter and to access simulation statistics. This enables you to automate the process, further reducing your manual effort.
Gain Insight
PreonLab empowers the user to get the most out of the simulation data with integrated plotting and a large variety of different sensors. This includes the projection of fluid attributes on planes and meshes, force analysis, fluid volume analysis, pathline analysis, wettings analysis, and more.
All sensors can operate efficiently as a post-process without the need to resimulate. For instance, you may notice after simulating that the fluid reached an unexpected region on your geometry. With PreonLab, you can now analyze the flow of the fluid in that region using a wetting and a pathline sensor without running the simulation again.
Short Turnaround Times
PreonLab is an all-in-one tool that features scene setup (pre-processing), simulation and analysis of results (post-processing). We have always set highest focus on usability and computational efficiency in order to solve engineering problems as fast as possible. The unique, intuitive user interface offers a quick start and shortens the learning period dramatically. Novices can usually start using PreonLab on their productive cases within two days.
The PREON® core comprises a blazing fast particle solver. Its high performance is based on a combination of scientific break-throughs in numerical discretization, data structures and parallel programming, developed by our R&D team. The state-of-the-art hybrid parallel programming implementation minimizes the communication overhead and waiting cycles between CPU cores and cluster nodes to achieve highest scalability on shared memory systems as well as on high performance clusters. For more information, check out the MPI Benchmark documents (Water Wading / Raining).
Advanced Visualization
With PreonLab, you can directly render the surface of volumetric particle data without the need to generate a mesh. This allows you to create beautiful videos from your simulations very quickly that are perfect to communicate your findings to others. PreonLab also supports physically based refractions and reflections, resulting in spectacular and realistic renderings. Checkout our showcase for videos generated with PreonLab.
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