{"id":69679,"date":"2025-10-23T14:25:46","date_gmt":"2025-10-23T18:25:46","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=69679"},"modified":"2026-03-26T06:50:27","modified_gmt":"2026-03-26T10:50:27","slug":"cfd-surface-wrapping","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/cfd-surface-wrapping\/","title":{"rendered":"Rapid CFD surface wrapping: unlock speed and consistency in meshing"},"content":{"rendered":"\n

I love spending my free time reading a nice book, especially with a good cup of coffee in hand. I especially enjoy wandering through bookstores, browsing the shelves, and discovering unexpected reads. During one of these visits, I came across a book that immediately caught my attention: Prediction Machines <\/em>by Ajay Agrawal, Joshua Gans, and Avi Goldfarb.<\/p>\n\n\n\n

Let\u2019s be honest: can you name a person who is not at least a little curious about AI\/ML these days? In their book, the authors explain the \u201csimple economics of AI and describe how the technology is used for point solutions\u201d<\/em>. In one of the chapters of the book, the following stood out to me:<\/p>\n\n\n\n

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With better prediction comes more opportunities to consider the rewards of various actions, in other words, more opportunities for judgment. And that means that better, faster, and cheaper prediction will give us more decisions to make.<\/em><\/p>\n<\/blockquote>\n\n\n\n

While this book focuses on AI and machine learning, this quote resonated with me, as the same idea applies in the world of engineering simulation, particularly in CFD. The ability to make faster, better-informed design decisions is crucial for engineers, regardless of simulation intent. The pressure to innovate and stay competitive only intensifies the need to boost productivity and accelerate simulation workflows.<\/p>\n\n\n\n

Furthermore, the need to create high-fidelity digital twins with increasing complexity has intensified, as engineers want to include as much physical realism and geometric detail as possible. Therefore, fast meshing is a strategic enabler that can bring a competitive advantage, empowering engineers to accelerate product development and get their products to market faster. To support this, we strive for solutions that accelerate CFD workflows, enabling you to quickly evaluate numerous design variants. For meshing, this can involve everything from optimizing individual workflows to Message Passing Interface (MPI) distributed memory parallelization, i.e., the ability to simultaneously execute meshing tasks on multiple CPUs.<\/p>\n\n\n\n

In this blog, we will explore how recent enhancements in Simcenter STAR-CCM+ have increased meshing performance, enabling engineers to increase their simulation throughput and get faster insights.<\/p>\n\n\n\n

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Accelerate geometry cleanup with MPI surface wrapping<\/h2>\n\n\n\n
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One of the key differentiators of Simcenter STAR-CCM+ is the pipelined workflow from CAD to solution that enables engineers to handle complex geometry. Many simulation workflows involve huge CAD assemblies with tens of thousands of parts. These CAD files often include very large assemblies and \u201cdirty\u201d geometry. By \u201cdirty\u201d we mean input geometry that may have holes, intersections, may need defeaturing, contain overlaps, and\/or non-manifold vertices or edges. Manually cleaning and healing such geometry can be a labor-intensive process, potentially taking days or even weeks to complete.<\/p>\n\n\n\n

One of the key tools for preparing the geometry for meshing in Simcenter STAR-CCM+ is the Surface Wrapper. The Surface Wrapper can take any arbitrarily complex CAD or tessellated part and generate a watertight, manifold surface. It works by effectively \u201cshrink-wrapping\u201d a high-quality triangulated surface mesh onto the discretized geometry. One of its key strengths is the ability to accurately preserve geometric features such as sharp edges and corners. In version 2310 of Simcenter STAR-CCM+, we introduced the first MPI-parallelized version of the Surface Wrapper, offering significant performance improvements over the shared-memory parallel version, also known as the \u201cLegacy Wrapper\u201d. In that first version, we demonstrated up to 43% reduction in wrapping time compared to the Legacy Wrapper.<\/p>\n\n\n\n

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\"Parallel
Figure 1: MPI Surface Wrapper Phase 1, 2310 version<\/figcaption><\/figure>\n\n\n\n
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However, this performance increase is not enough for very large cases, and in line with our commitment to continuous improvement, in the Simcenter STAR-CCM+ 2510 version, we are releasing phase two of the MPI Surface Wrapper. Let\u2019s take a look at the performance improvements.<\/p>\n\n\n\n

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\"CFD<\/figure>\n\n\n\n
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As illustrated in Figure 2, the MPI Surface Wrapper in version 2510 delivers up to ~2x faster performance compared to version 2506 (Phase 1 of MPI Surface Wrapper). When compared to the Legacy Wrapper, the speedup reaches up to 5.6x. For a complex geometry, such as the Corvette car, where the total volume mesh count is 93M cells, it takes only 6.5 minutes to wrap the entire geometry on 32 cores (Intel\u00ae Xeon\u00ae Gold 6442Y).<\/p>\n\n\n\n

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\"MPI
Figure 2: MPI Surface Wrapper Phase 2<\/figcaption><\/figure>\n\n\n\n
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What about wrapping a larger case? In the Corvette case, the Surface Wrapper generates 37.4M surface triangles. In comparison, the Maserati Ghibli case, as shown below, is significantly larger, requiring only 9 minutes to wrap with 56.3M triangles and resulting in a final volume mesh of 142M cells.<\/p>\n\n\n\n

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