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.
nTop is an engineering tool designed for rapid iteration, utilizing advanced computing to generate and simulate high-fidelity designs at scale quickly. With the acquisition of cloudfluid, nTop enables engineers to seamlessly converge on a valid design using high-speed CFD. Engineers can run thousands of CFD simulations overnight by integrating nTop’s powerful geometry engine with cloudfluid’s Lattice-Boltzmann fluid solver. This is crucial for training AI models that further accelerate design decisions. The cloudfluid acquisition brings nTop one step closer to real-time, automatic requirements-to-design workflows, allowing engineers to iterate at the speed of compute.
"We are hyper-focused on building software that helps engineers go from requirements to design as fast as the latest 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, apply boundary conditions, and converge on a solution. cloudfluid solves this by integrating directly with our implicit modeling core, bringing CFD into the iterative computational design loop. This unlocks new applications for nTop's use in aerospace, defense, and turbomachinery design."
Current meshing and solve times can’t keep up with the pace of design, optimization, and machine learning, creating bottlenecks in product development. The cloudfluid acquisition allows engineers to modify designs in real time using nTop's industry-leading geometry kernel and enlist cloudfluid to verify fluid and thermal performance in minutes. By combining these technologies with machine learning, engineering teams can generate high-fidelity performance data to train predictive models, significantly accelerating design exploration to achieve breakthrough products and compress the product development lifecycle.
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