Siemens Energy, headquartered in Munich, Germany, is one of the world’s leading energy companies operating at the forefront of energy transition for a more sustainable future. Its product portfolio and services cover almost the entire energy value chain, supporting companies and countries to reduce emissions across the energy landscape for a more reliable, affordable and sustainable energy system.
Siemens Energy uses Additive Manufacturing where traditional technologies prove unable to meet the extraordinary performance that they require. By exploiting the additional design space that Additive Manufacturing opens up to them, the team around Markus Lempke is pushing the limits of thermal performance of heat exchangers and other products. As additive manufacturing costs are higher, maximizing performance is absolutely critical and requires an efficient optimization process that only computational design practices can deliver.
Siemens Energy uses implicit CAD modelling with nTop for the geometrical design. This technology enables them to create complex geometries like TPMS or lattice structures easily, iterating rapidly through different design variations.
More traditional CAD modelling technologies, such as parametric BRep modelling, do not scale well and cannot provide a sufficiently robust workflow to generate the parts and flow regions required for CFD simulation. Instead, a field-driven design approach aligns perfectly with these requirements.
Importing intricate TPMS and lattice geometries into a typical simulation workflow introduces further challenges because the CAD geometry must have a surface mesh or tessellation applied before a volume mesh can be generated. Creating this faceted geometry
balloons the file size and slows down the workflow. For this heat-exchanger example, the triangulated geometry weighs in at about 6 GB and takes several hours just to generate and import.
SimScale uses nTop’s native library ‘nTop Core’, handling visualization, meshing, and simulation directly on the implicit model. This direct hand-off cuts import times from hours to seconds, removing geometry approximation (and all its reliability and performance headaches) from the workflow entirely.
Optimizing heat exchangers requires the Siemens Energy team to balance competing objectives: achieving maximum heat transfer for minimum pressure loss. A variety of parameters like TPMS cell type, TPMS cell sizing and wall thickness, surface roughness and design of inlets and outlets can play a major role in determining the aerothermal performance. Fast iterations and parallel evaluation of dozens of design variations are key to finding the best solution.
SimScale’s fully Cartesian Conjugate Heat Transfer solver based on the Immersed Boundary Method enables the evaluation of the heat transfer efficiency and pressure drop simultaneously, the two main performance indicators in this design challenge. Pairing this technology with the native nTop core support allows SimScale to match nTop’s design robustness and speed. This empowers Siemens Energy to quickly iterate through design options. Using ‘nTop Automate, and SimScale’s public API, these validations can also be executed headless and be included in programmatic optimization loops.
Due to the geometrical and physical complexity, mesh sizes of 50 to 150 million cells are expected. 192 core instances were chosen to run the CHT simulations with SimScale’s cloud native simulation platform.
Implicit modeling and direct simulations on implicit geometry is a real step change in speed and robustness of optimization workflows and necessary to unlock the real potential of additive manufacturing.
Markus Lempke
Computational Designer at Siemens Energy
The integration of SimScale into Siemens Energy’s design workflow has yielded several critical benefits:
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