nTop, the leader in computational design software for high-performance engineering, has acquired cloudfluid, a German company specializing in computational fluid dynamics (CFD) software. With the addition of cloudfluid to its technology stack, nTop provides the most advanced and complete platform for computational design, revolutionizing how engineers design high-performance products.
CFD typically involves complex meshing and lengthy run times, making it impractical to use in rapid design iterations. Cloudfluid addresses this challenge with its GPU-native solver technology, which accurately predicts fluid flow without the difficulty of creating complex conformal meshes. Coupled with nTop’s industry-leading implicit geometry kernel, engineers can now iterate on designs in near real-time to create breakthrough products.
"We are hyper-focused on building software that helps engineers go from requirements to design as fast as the latest computing processors allow—that’s the power of computational design," said Brad Rothenberg, CEO of nTop. "One of the biggest bottlenecks has always been solving the physics—it takes time to mesh and converge on a solution. cloudfluid solves this by integrating directly with our implicit modeling core, bringing CFD into the iterative computational design loop. With the acquisition of cloudfluid, we’re not only expanding our capabilities in CFD—we’re also strengthening our internal expertise to accelerate future integrations with other best-in-class tools.”
The integration of cloudfluid’s high-speed CFD with nTop’s computational design platform expands applications in aerospace, defense, and turbomachinery, where fluid dynamics are crucial. Engineers can now explore complex geometries and optimize designs faster, advancing propulsion, aerodynamics, and thermal management systems. At the same time, these technologies address machine learning's data challenges, where curated simulation data is often lacking. This integration enhances decision-making, accelerates innovation, and improves manufacturing efficiency by enabling the cost-effective generation of high-quality simulation data for training predictive models in digital twins and design optimization.
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