nTop

We are thrilled to announce the acquisition of cloudfluid a German company specializing in computational fluid dynamics (CFD) software. The addition of high-speed CFD capabilities directly into nTop’s computational design software will enable engineers to iterate on designs in near real-time and create breakthrough products. The GPU-native solver technology from cloudfluid eliminates the need for complex conformal meshes, making CFD practical for rapid design iterations. This expansion enhances applications in aerospace, defense, and turbomachinery, and addresses machine learning's data challenges by generating high-quality simulation data for digital twins and design optimization. Read the full press release to learn more. https://hubs.ly/Q037msBM0 #nTop #cloudfluid #CFD #engineering

Day 2: Advancing the Future of Aircraft Design ✈️ Aerospace engineers at OEMs — this week is about solving your real-world design challenges. Yesterday (Day 1) was all about establishing the foundation: 🔨 - Defining goals for the week - Getting closer to customer needs - Mapping aircraft design workflows and identifying bottlenecks - Highlighting where intelligent tools can drive meaningful ROI Today, we’re shifting from insight to action: 💥 - Beginning early integration of fuselage and wing designs - Reviewing wing block concepts - Splitting into focused breakout groups to ideate and demo parametric wing models We’re already uncovering key opportunities in: 💡 - Collaborative, cross-functional design - Streamlining OML development processes - Balancing performance and iteration speed in early-stage modeling At the end of the workshop, we’ll be compiling everything we’ve learned into a focused summary — a toolkit of practical insights for engineers working at the intersection of design, performance, and product development. 👍 If that sounds valuable, let us know by liking this post. We’ll make sure you get it when it’s ready. Thank you for joining us today, Blake Courter! #AerospaceEngineering #AircraftDesign #EngineeringInnovation #OML #FuselageDesign #WingDesign #PerformanceEngineering

Don't miss out on tomorrow's webinar, Intro to nTop. 🙌 Brian Sather, Director of Product Marketing and Guenael Morvan, Senior Application Engineer, will show you the power of computational design to help you accelerate your engineering products. April 24th at 12pm ET / 9am PT Register now: https://hubs.li/Q03jgdmV0 #nTop #computationaldesign #webinar

Advanced Aircraft Design Workshop Kicks Off Today in NYC 🛩️ A few of us from nTop are on the ground in New York this week, joining a hands-on workshop focused on one thing: solving real aircraft design challenges, together. 🤝 Over the next few days, we’ll be deep in sessions on OML shaping, fuselage structure, wing performance, and prototyping — the core building blocks of modern aircraft. We’re here to work alongside engineers facing the same pressures we hear about every day: - Fast-moving programs with no time for rebuild errors - Structural designs that can’t wait on siloed simulation - Prototypes that need to reflect performance from the start Workshops like this help us stay sharp. They connect our team directly to the field — not just to understand what’s hard, but to help shape what’s next. We’ll be learning, listening, and building — and we’re excited to bring those insights back into the platform. 👍 Because staying close to the work is how we move it forward. Thank you George B Irving and Jan Vandenbrande for sharing their expertise on aircraft designs in today's kickoff sessions. 🙌 #AircraftDesign #AerospaceEngineering #nTop #EngineeringInPractice #ComputationalDesign #NYCWorkshop

📏 3cm → 67cm → 130cm We took our nTop designed #drone geometry, and printed it in three sizes. All 3 were sliced with Aibuild, all needing minimal setup time. How was this possible? The machines are already pre-configured in our platform. Meaning no friction when switching between the different #printers, #materials and #scales. #Scalable manufacturing in one platform. Luminary Cloud | Generative Machine Company #additivemanufacturing #software #3dprinting #design #3dprinting #3dprint #lfam #desktop #robotics #innovation #manufacturing #technology

Discover the power of computational design to help accelerate your engineering projects. Learn how you can build and run computational models to explore your feasible design space, perform rapid refinement cycles with simulation in the loop, and reuse high-performing design strategies across product part families. Join us in this webinar, Intro to nTop. April 24th at 12pm ET / 9am PT Register here: https://lnkd.in/eEPWdfVU #nTop #computationaldesign #webinar

Every engineer wants real-time physics simulations. Machine learning might be the way to get there. Right now, geometry modeling is fast (instant, even.) But physics simulations are still relatively slow. Cloud computing has helped, but it doesn’t close the gap. If we want true real-time inverse design, we need a different approach, and that’s where ML comes in. Slow simulations kill iteration speed. If we can shrink the gap between modeling and physics results, engineers can explore more designs, optimize faster, and build better products. We’ve already seen this in action. In a recent AI-powered design project, we ran large-scale cloud simulations to generate the necessary dataset, trained an ML model, and inserted it into the design loop. The result? A process that once took hours (or days) became near-instantaneous. Of course, ML isn’t magic. It needs data—and that’s where most engineering teams struggle. CAD data has long been well-organized, but simulation data? Not so much. Many teams generate vast amounts of it, only to discard most of it, keeping only the final results. The industries making the most progress (like automotive) invest in structured data collection. A decade ago, companies set aside budgets to experiment with additive manufacturing. Today, they’re doing the same with ML—learning, testing, and figuring out how it fits into their workflows. ML isn’t a silver bullet, but it’s a lever that can fundamentally shift engineering workflows. The teams that figure out how to apply it now will lead the next design era.

Iterating between modeling and simulation to optimize a design is time-intensive — especially when objectives and constraints keep changing. nTop’s parametric modeling allows engineers to drastically reduce this cycle time by enabling real-time geometry generation. However, slow integrations with CFD and other simulation tools can still be a big bottleneck. Naturally, we asked ourselves: Could AI help? Starting with design intent, could an AI-enabled loop optimize a design space and provide performance feedback? -- The answer was yes. With nTop and Luminary Cloud, we built a parametric model and ran headless CFD simulations to generate a machine learning dataset—all in just a few hours. We then trained a neural network using NVIDIA’s PhysicsNeMo framework and integrated it into an optimization algorithm to solve the inverse design problem. Once the optimal design was identified, we used NVIDIA DoMINO to visualize full-field aerodynamics in near real time. This workflow wasn’t just fast—it’s reusable. The first inverse design required a few hours of data collection and training. But every new design task now takes only seconds—no black boxes, no guesswork. Could this be valuable for your designs?

DMG MORI AKZ FDS Adapter Powered by Materialise Magics and nTop Implicit Designs 🙌 At RAPID + TCT, we had the chance to see DMG Mori's AKZ FDS adapter, which was redesigned using nTop for additive manufacturing, with the help of Additive Intelligence. Through our partnership with Materialise, they were able to export the part, which would have been 1.62 GB in mesh size, as a 66.5 MB implicit file from nTop. This file was then directly imported into Magics in just seconds. 🤝 This collaboration showcases the power and efficiency of combining nTop's implicit design capabilities with Materialise's advanced software solutions. 🚀 Learn more about our partnership with Materialise here: https://hubs.li/Q03hrNXt0 #nTop #computationaldesign #implicit #additivemanufacturing

Closing out the week flexing this workflow Mandy Rosengren built — it’s a parametric cold plate modeled totally in nTop to test out our enhanced curves and profiles. These go live this upcoming Monday, 4/14 in v5.20 — Excited to see how cloudfluid’s LBM solver calculates the performance…and then, who will be the first to train up a surrogate model for real-time predictions on a design space like this? Also, there are a few impressive performance enhancements that go live in v5.20 too.