{"id":57925,"date":"2024-06-20T17:12:55","date_gmt":"2024-06-20T21:12:55","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=57925"},"modified":"2026-03-26T06:49:13","modified_gmt":"2026-03-26T10:49:13","slug":"cfd-on-gpu-a-seamless-disruption","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/cfd-on-gpu-a-seamless-disruption\/","title":{"rendered":"CFD on GPU. A seamless disruption with Simcenter STAR-CCM+"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Time flies when you\u2019re having fun<\/h2>\n\n\n\n<p>With the release of Simcenter STAR-CCM+ 2406 on the horizon, this will be our <strong><u>EIGHTH<\/u><\/strong> release and over two years since our original GPU accelerated CFD functionality was introduced.<\/p>\n\n\n\n<p>Back in Simcenter STAR-CCM+ 2022.1, Stamatina Petropoulou introduced our first CFD on GPU with <a href=\"https:\/\/blogs.sw.siemens.com\/simcenter\/gpu-acceleration-for-cfd-simulation\/\">GPU me up Scotty!<\/a>, highlighting the potential cost, time and energy savings of GPUs. The blog also summarizes nicely why GPUs are becoming an ever more important part of the HPC landscape when it comes to CFD. Fundamentally, the introduction of GPUs with sufficient High Bandwidth Memory (HBM) was a game-changer in allowing this hardware to be used for CFD on unstructured meshes.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"GPU enabled acceleration of CFD simulation | Simcenter STAR-CCM+ #SimcenterCFD #SimcenterVehicle\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/1FiDLliDSkc?start=1&#038;feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>In this short video, I took the opportunity to give some insights into how GPUs can help deliver improvements to simulation throughput and how GPUs fit in an increasingly heterogeneous hardware landscape of CPU, ARM and GPU.<\/p>\n\n\n\n<p>However, for those of you who want something more in-depth than my Product Manager <em>spiel<\/em>, I would thoroughly recommend the podcast with Petr Kodl, who leads our CFD on GPU development. He provides excellent detail on why GPUs are suitable for CFD, the challenges involved and some insight into our development philosophy. A great way to spend your lunch break if you ask me!<\/p>\n\n\n\n<p>Since that first release, we have had three main objectives with regards to extending our CFD on GPU offering: add more applications, extend supported GPU types, and continually improve performance. Let\u2019s dive into these in a bit more detail.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"GPU-Accelerated Fluid Dynamics - Petr Kodl | Podcast #121\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/Gr9vxGdlHSs?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>From Auto to Apollo!<\/strong><\/h2>\n\n\n\n<p>Our original GPU release was aimed at transient, automotive aerodynamics and since then the number of applications that can leverage GPUs has exploded! We can look at aeroacoustics, high fidelity CFD on GPU, aerospace aerodynamics, combustion, gas turbine heat transfer and aerodynamics, multi-time scale heat transfer and even hypersonic applications such as the Apollo capsule re-entry.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-video\"><video controls src=\"https:\/\/videos.mentor-cdn.com\/mgc\/videos\/5400\/9352f12e-98f8-40a0-9489-98c2e8af1d5c-en-US-video.mp4\"><\/video><\/figure>\n\n\n\n<p>With every new release of Simcenter STAR-CCM+ you can therefore typically anticipate even more new physics models to be made GPU-native and of course the 2406 release is no different. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">GPU-native solver expansion in Simcenter STAR-CCM+ 2406<\/h2>\n\n\n\n<p>Firstly, you can now leverage Grid Sequencing Initialization (GSI) on GPU. GSI approximates an inviscid solution to the flow field which can provide a better initial condition and faster convergence of CFD on GPU using the coupled flow solver. This is commonly leveraged for example in steady-state automotive applications.<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" data-id=\"57979\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1-1024x576.png\" alt=\"\" class=\"wp-image-57979\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1-1024x576.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1-600x337.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1-768x432.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1-1536x863.png 1536w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1-395x222.png 395w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1-900x506.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture1-1.png 1884w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"734\" data-id=\"57980\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture2-1024x734.png\" alt=\"\" class=\"wp-image-57980\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture2-1024x734.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture2-600x430.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture2-768x551.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture2-900x645.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Picture2.png 1476w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<figcaption class=\"blocks-gallery-caption wp-element-caption\">Case 1: client production case \u2013 157M cells; Case 2: client production case \u2013 215M cells. v2402 and v2406 have been compared by running Case1 and Case 2 on 8 NVIDIA A100 cards (80Gb).<\/figcaption><\/figure>\n\n\n\n<p>Looking at two industrial automotive examples, it can be observed that performing GSI directly on GPU can save up to 17% of total simulation time, with the GSI process itself 2.5x faster. A nice result that rounds out GPU-native functionality for steady-state vehicle aerodynamics.<\/p>\n\n\n\n<p>Secondly,&nbsp; moving away from vehicle aerodynamics to vehicle thermal management (VTM) then&nbsp; another significant milestone in GPU-native functionality has been reached. In Simcenter STAR-CCM+ 2406, the Surface-to-Surface (S2S) radiation model is now&nbsp; available. This is an essential piece of the simulation puzzle when modelling conjugate heat transfer and VTM . Alongside other pieces introduced in previous releases (Coupled \/ segregated solvers, Multi-part solids, Mapped Interfaces and more), GPUs can now be used for VTM.&nbsp; Taking as an example the GM Corvette VTM case, we can compare the normalized time per iteration when using 128 CPU cores against 4 GPU cards. In this case the 4 GPUs can deliver up to 4.5x speed up whilst maintaining excellent consistency in results (look at the exhaust manifold seen below).<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"815\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic2-1024x815.png\" alt=\"Comparison of CFD Results run on CPU vs GPU\" class=\"wp-image-57954\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic2-1024x815.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic2-600x478.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic2-768x611.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic2-900x716.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic2.png 1279w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"733\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic3-1024x733.png\" alt=\"Runtime reduction from 128 cores CPU to 4 and 8 GPUs\" class=\"wp-image-57955\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic3-1024x733.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic3-600x430.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic3-768x550.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic3-900x645.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic3.png 1491w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>More hardware, more options<\/strong>!<\/h2>\n\n\n\n<p>Whilst we work at pace to extend our GPU-native solvers, simultaneously more and more GPU cards enter the market. The rate at which data center and workstation GPUs are being released is quite astonishing. And with every new generation, performance is improving with respect to GPU memory and memory bandwidth. We ensure that Simcenter STAR-CCM+ is able to support new generations of GPU cards and have continuously expanded the range of available GPUs since our initial release:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"590\" height=\"248\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic4.png\" alt=\"\" class=\"wp-image-57957\"\/><\/figure><\/div>\n\n\n<p>We recently undertook a collaboration with NVIDIA and Mercedes-Benz to showcase the potential of <a href=\"https:\/\/www.nvidia.com\/en-us\/on-demand\/session\/gtc24-s62159\/\" target=\"_blank\" rel=\"noreferrer noopener\">GPU-accelerated computing for automotive applications<\/a>. In that study we were able to highlight the link between improved GPU hardware and Simcenter STAR-CCM+ performance:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"517\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic5-1024x517.png\" alt=\"Accelerated Aerodynamics: Hardware generational comparison \u2013 single node 115M cells\" class=\"wp-image-57959\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic5-1024x517.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic5-600x303.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic5-768x388.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic5-1536x776.png 1536w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic5-900x455.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic5.png 1960w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<p>Unsurprisingly, more memory bandwidth is giving more performance!<\/p>\n\n\n\n<p>I also recently <a href=\"https:\/\/www.hpcwire.com\/2024\/06\/10\/siemens-taps-amd-instinct-gpus-to-expand-high-performance-hardware-options-for-simcenter-star-ccm\/?utm_source=HPCwire+Newsletter&amp;utm_medium=email&amp;utm_campaign=&amp;utm_term=1805D0491578H7V&amp;oly_enc_id=1805D0491578H7V\" target=\"_blank\" rel=\"noreferrer noopener\">spoke to AMD<\/a> about our efforts to allow Simcenter STAR-CCM+ to leverage AMD GPUs. In Simcenter STAR-CCM+ 2406 we continue to expand our hardware options with compatibility for the AMD Instinct\u2122 MI300 series and the AMD Radeon\u2122 Pro W7000 series.<\/p>\n\n\n\n<p>We are looking forward to continuing close collaboration with GPU vendors to ensure optimal performance on the next generations of GPU such as <a href=\"https:\/\/nvidianews.nvidia.com\/news\/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing\" target=\"_blank\" rel=\"noreferrer noopener\">NVIDIA Blackwell<\/a> and <a href=\"https:\/\/www.amd.com\/en\/newsroom\/press-releases\/2024-6-2-amd-accelerates-pace-of-data-center-ai-innovation-.html\" target=\"_blank\" rel=\"noreferrer noopener\">AMD Instinct MI350<\/a>.<\/p>\n\n\n\n<p>Of course, Simcenter STAR-CCM+ is a general purpose tool that is designed to run across the full hardware spectrum, CPU (x86 \/ ARM), GPU (AMD \/ NVIDIA), on-premise or cloud. Why not explore the <a href=\"https:\/\/blogs.sw.siemens.com\/simcenter\/an-engineers-guide-to-the-cfd-hardware-galaxy\/\" target=\"_blank\" rel=\"noreferrer noopener\">CFD Hardware Galaxy<\/a> to learn more?<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Go faster, go further!<\/h2>\n\n\n\n<p>So, in the last two years, it can be observed that we have expanded greatly in terms of GPU-native physics and the range of GPUs themselves that can be leveraged. But another cherry on top of this is continued GPU performance improvements from the Simcenter STAR-CCM+ side as well.<\/p>\n\n\n\n<p>These performance improvements aim at ensuring there are no bottlenecks of CPU-GPU data migration during the simulation process, for example when monitoring transient data or key engineering parameters to judge convergence. Since our original implementation of GPU-native solving, we have striven to ensure the full simulation process can be performed on GPU and continue to improve in this area. Alongside this, our team has continually optimized GPU performance. For example, we made the Coupled Flow solver up to 10% faster on GPUs in 2406 and have more improvements for cases using Coupled Energy in the pipeline.<\/p>\n\n\n\n<p>Putting this together with hardware improvements, we can see a combined software\/hardware speed on GPU of up to 4x since our original release!<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"547\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6-1024x547.png\" alt=\"Combined solver and hardware performance improvements 2022-2024\" class=\"wp-image-57960\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6-1024x547.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6-600x321.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6-768x411.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6-1536x821.png 1536w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6-2048x1095.png 2048w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6-900x481.png 900w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\">Simcenter STAR-CCM+ on GPUs enters industrial adoption <\/h2>\n\n\n\n<p>You don\u2019t have to take my word for it regarding the potential benefits of GPU to accelerate the design process. At our <a href=\"https:\/\/www.linkedin.com\/posts\/simonjfischer_realize-live-europe-2024-cfd-highlights-activity-7205888272844144642-Gwo_?utm_source=share&amp;utm_medium=member_desktop\" target=\"_blank\" rel=\"noreferrer noopener\">recent Realize Live Europe event we had not one, not two, but three automotive OEMs presenting the benefits of moving their workflows to GPUs<\/a>. &nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-video\"><video controls poster=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/EQE_wCPtot.png\" src=\"https:\/\/videos.mentor-cdn.com\/mgc\/videos\/5400\/9a09faed-3be2-435b-a7d9-aec2182a4487-en-US-video.mp4\"><\/video><\/figure>\n\n\n\n<p>Another nice example of CFD on GPU used in  aeroacoustics (in collaboration with Stellantis) can be found <a href=\"https:\/\/blogs.sw.siemens.com\/simcenter\/hvac-flow-induced-noise\/\" target=\"_blank\" rel=\"noreferrer noopener\">here<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-video\"><video controls src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2023\/12\/emile_video-1-1-2.mp4\"><\/video><\/figure>\n\n\n\n<p>And it\u2019s not just the automotive sector that is benefitting, for example <a href=\"https:\/\/blogs.nvidia.com\/blog\/trek-bicycle-tour-de-france-gpus\/#:~:text=The%20Simcenter%20STAR%2DCCM%2B%20simulations,core%20CPU%2Dbased%20HPC%20server.\" target=\"_blank\" rel=\"noreferrer noopener\">Trek Bicycle discussed their usage of GPUs with Simcenter STAR-CCM+<\/a> and in the world of combustion, we collaborated with Siemens Energy to show the benefits of GPUs for <a href=\"https:\/\/asme-turboexpo.secure-platform.com\/a\/solicitations\/223\/sessiongallery\/15424\/application\/122041\" target=\"_blank\" rel=\"noreferrer noopener\">design of industrial combustion systems<\/a>. <\/p>\n\n\n\n<figure class=\"wp-block-video\"><video controls poster=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/Trek-Madone_CFD-672x340-1.png\" src=\"https:\/\/videos.mentor-cdn.com\/mgc\/videos\/5400\/9dd375c7-9018-4188-b830-220f0492bf7e-en-US-video.mp4\"><\/video><\/figure>\n\n\n\n<p>If that\u2019s not enough, how about this presentation from DEME leveraging CPU and GPU to enhance <a href=\"https:\/\/www.youtube.com\/watch?v=NAGioYfCV7c&amp;t=24s\" target=\"_blank\" rel=\"noreferrer noopener\">sustainable marine solutions and operations<\/a>. My final pointer is to a webinar hosted by Vlaams Supercomputer Centrum (VSC) where I discussed the potential of GPUs, as well as some practical tips for <a href=\"https:\/\/www.youtube.com\/watch?v=W0PVI46j-oA\" target=\"_blank\" rel=\"noreferrer noopener\">getting started<\/a> (I promise this is the last link to one of my own presentations!).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">CFD on GPUs &#8211; a seamless disruption in a whirlwind two years<\/h2>\n\n\n\n<p>So, there you have it, in a whirlwind two years since the first release, we have truly pushed the boundaries of GPU-accelerated CFD. Personally, I\u2019m excited to see how much further we go in the next two years. There\u2019s no doubt in my mind that the role of GPUs for CFD will continue to disrupt traditional workflows, and Simcenter STAR-CCM+ will be there to facilitate a seamless transition.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Don\u2019t wait \u2013 <\/strong>start your first CFD on GPU today!<\/h2>\n\n\n\n<p>So, it\u2019s never been a better time to try GPUs and unlock the benefits of GPU-accelerated CFD. With Simcenter STAR-CCM+, a single Power Session Plus licence allows you to run on <strong><u>unlimited CPUs or&nbsp;GPUs. <\/u><\/strong>Unlike other CAE codes, this means no tiered pricing and no cost variation depending on GPU type.<\/p>\n\n\n\n<p>For all the new GPU capabilities, download Simcenter STAR-CCM+ 2406, out since July 3.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button aligncenter is-style-fill is-style-primary-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/blogs.sw.siemens.com\/simcenter\/simcenter-star-ccm-2406-released\/\" target=\"_blank\" rel=\"noreferrer noopener\">Explore more exciting new features in Simcenter STAR-CCM+ 2406<\/a><\/div>\n<\/div>\n\n\n\n<a href=\"https:\/\/www.g2.com\/products\/simcenter-star-ccm\/reviews?utm_source=review-widget\" title=\"Read reviews of Simcenter STAR-CCM+ on G2\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"full-width\" style=\"max-width: 200px\" alt=\"Read Simcenter STAR-CCM+ reviews on G2\" src=\"https:\/\/www.g2.com\/products\/simcenter-star-ccm\/widgets\/stars?color=gray&amp;type=read\" \/><\/a><script>(function(a,b,c,d){window.fetch(\"https:\/\/www.g2.com\/products\/simcenter-star-ccm\/rating_schema.json\").then(e=>e.json()).then(f=>{c=a.createElement(b);c.type=\"application\/ld+json\";c.text=JSON.stringify(f);d=a.getElementsByTagName(b)[0];d.parentNode.insertBefore(c,d);});})(document,\"script\");<\/script>\n","protected":false},"excerpt":{"rendered":"<p>With the release of Simcenter STAR-CCM+ 2406, this will be our EIGHTH release since we introduced GPU accelerated CFD. Check out how far we&#8217;ve come<\/p>\n","protected":false},"author":86706,"featured_media":57967,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1,179],"tags":[63787,242],"industry":[],"product":[513],"coauthors":[39668],"class_list":["post-57925","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-product-updates","tag-cfd-on-gpu","tag-computational-fluid-dynamics-cfd","product-simcenter-star-ccm"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2024\/06\/pic6.png","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/57925","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/users\/86706"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/comments?post=57925"}],"version-history":[{"count":5,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/57925\/revisions"}],"predecessor-version":[{"id":70398,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/57925\/revisions\/70398"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media\/57967"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media?parent=57925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/categories?post=57925"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/tags?post=57925"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/industry?post=57925"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/product?post=57925"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/coauthors?post=57925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}