On-Demand Webinar

Want the full picture? Watch the on-demand webinar on democratizing advanced nonlinear simulation with Marc and AI.

Watch On Demand

1. The Simulation Bottleneck Is Breaking Down

If you’ve ever waited days for a simulation expert to re-run your updated design, you know the traditional workflow is broken. Designers run simplified linear analyses in their CAD tools, then hand off to specialists for the real nonlinear studies — and what follows is a slow loop of emails, PDFs, and meetings while you wait for each iteration.

SimScale collapses this. On a collaborative, cloud-native platform, both you and your simulation experts work on the same project. Template simulations designed by specialists can be reused and adapted for new designs, while AI agents provide guidance and catch errors. You get faster iterations without sacrificing the depth that nonlinear analysis demands.

2. The Real World Is Nonlinear — And Marc Is Built for It

If you’re relying solely on linear analysis, you’re working with an approximation. The real world doesn’t behave linearly — materials yield, creep, and undergo stress relaxation. Rubber seals stretch and recover. Thermoplastics soften permanently under repeated loading. Parts come into and out of contact, slide, stick, and separate. And loading sequences matter — the same final load can produce entirely different outcomes depending on the path taken to get there.

Marc, the world’s first commercial nonlinear FEA code (since 1971), was purpose-built for these challenges. With over 30 analysis classes, it offers fully coupled structural, thermal, and electromechanical solutions alongside advanced material models — from hyperelastic rubber to viscoplastic materials with damage, creep, and permanent softening. What makes it robust? Automatic contact detection that tracks evolving conditions as parts deform, automatic remeshing when elements distort severely, and intelligent load stepping that adapts as the physics demands. These are exactly the capabilities you need for complex contact scenarios and large-deformation problems.

3. Complex Simulations, Surprisingly Simple Setup

In a live demo, Richard set up a nonlinear rubber bushing simulation from scratch — hyperelastic materials, self-contact with friction — in just minutes, directly in the browser. Import your CAD, let automatic contact detection handle the assembly, assign a Mooney-Rivlin hyperelastic model, define your loads, and run. Everything executes on the cloud with up to 192 cores on a single license.

He also demonstrated sequential loading: applying a 500 N force first, then locking it in place and applying a 45° rotation — configured through simple load steps in the UI. This kind of multi-step scenario is critical for realistic component assessment, and Marc handled the path-dependent behavior seamlessly.

Need to iterate? Duplicate the simulation, tweak the CAD, and re-run — boundary conditions automatically reassociate to the new geometry. Multiple variants run in parallel without touching your local machine. And the built-in Engineering AI agent even caught a unit error (Pascals instead of megapascals) before the simulation launched.

4. AI Agents Automate Your Entire Simulation Workflow

Imagine giving an AI agent a 78-component gearbox assembly and a single instruction. That’s exactly what the webinar demonstrated. A custom “gearbox assessment” agent autonomously identified every component, assigned materials, defined bolt preloads to internal specifications, applied gear loads and fixtures, and launched the simulation — no manual setup required.

A second example went further: a bike frame design workflow where the agent ingested an RFQ document and a CAD file, set up the required load cases, ran them, compared results against specification limits, and recommended design changes when the initial design fell short. You define the agent once with your organization’s best practices, material libraries, and specs — then it handles the rest, consistently, every time.

5. Physics AI Unlocks the Nonlinear Design Space

Here’s a challenge you may not have considered: the design space for nonlinear problems grows exponentially. Material parameters, contact friction, geometric imperfections, loading sequences — the permutations multiply fast. And because nonlinear analysis is path-dependent, changing a parameter even slightly doesn’t just shift results — it can change the entire system evolution.

This is where Physics AI complements Marc. By training surrogate models on a relatively small number of high-fidelity Marc simulations, you can predict outcomes across thousands of design variations almost instantly. The surrogate models learn the nonlinear response surfaces, letting you explore the full design space rather than guessing at isolated points. And since everything is integrated on SimScale, you can switch between surrogate predictions and full Marc validations at any point — speed when you need it, fidelity when it matters.

What’s Next: Marc on SimScale Roadmap

The session previewed what’s coming: coupled thermomechanical analysis by end of Q1 2026, temperature-dependent materials in Q2, dynamics analysis in Q3, and adaptive mesh refinement later in the year.

Watch Now

Don’t miss out on the full experience and deeper insights into how SimScale’s latest features can transform your engineering workflow. Watch the complete webinar on-demand to see these tools in action and understand how they can be applied to your specific challenges. Click here to access the webinar recording and start accelerating your design process today!

The post Webinar Highlights – Democratizing Advanced Nonlinear Simulation with Marc and AI appeared first on SimScale.

It used to be the case that proposals were gathered (in a leisurely manner) and then a selection made according to the customer’s preferences for quality or cost. But now there is another consideration – bid speed. 

Accelerating RFQ/RFP responses: Why the rush?

In many fast-moving and competitive industries – let’s take the automotive industry as an example – the timeframe for an RFQ has shrunk significantly. This is driven by the need to get to market sooner because of a fast moving technology backdrop. In the case of the car industry, this is driven by electrification and battery technology.

Traditional RFQ processes often stretch over several days or even weeks, involving multiple handoffs between engineering, simulation and commercial teams. Each step, from interpreting requirements to running simulations and coordinating design updates, is typically done manually and across disconnected tools.

To get your competitive and de-risked bid over the line first, all that up-front engineering work still needs to happen. Just now it has to be much, much faster.

Beat your competition with agentic AI

Imagine if your business could respond to RFQs in a matter of hours, rather than days or weeks? It would win you more bids, but what would it take?

Interestingly, while few organizations have fully embraced AI in their engineering workflows, the gap isn’t usually due to technical constraints. More often, it’s the result of legacy systems, limited access to data or internal resistance to change. The reality is that effective automation is already achievable by guiding AI with familiar engineering inputs, like geometry, materials, loads and boundary conditions, and allowing it to manage repetitive tasks such as simulation setup, execution and iterative design updates.

RFQ automation workflow 

At a high level, the workflow follows six steps:

1. Upload RFQ documents and CAD geometry
   
2. Extract requirements using AI
   
3. Automatically prepare and run simulations
   
4. Evaluate results against requirements
   
5. Apply design improvements and re-simulate
   
6. Generate a final report that could be customized for the customer
   

To see how this works in practice, explore the interactive demo below. It walks through the same RFQ automation workflow described above, showing step by step how an RFQ progresses from document and CAD intake to fully validated results – quickly, autonomously and with engineers in the loop. 

Keeping engineers in the loop

Although the RFQ automation workflow operates from start to finish with minimal manual effort, it’s intentionally designed to avoid becoming a black box. One common myth about automation is that it sidelines human judgment. In practice, the most effective systems are those that involve engineers exactly where their expertise has the greatest impact.

This aligns with the growing shift toward human-in-the-loop AI, where intelligent agents take care of repetitive, structured tasks, while engineers retain control. At every stage, engineers can:

• Review extracted requirements
• Track simulation progress
• Assess CAD modifications
• And examine detailed simulation outputs

The workflow remains fully transparent and flexible – it can be paused, adjusted or investigated at any time.

Business impact of RFQ automation

Implementing end-to-end RFQ automation delivers measurable business value that extends well beyond simple productivity improvements. It fundamentally changes how quickly teams can respond to customer requests, how efficiently engineering resources are used and how reliably high-quality proposals are generated.

Key business advantages include:

• Major time savings: RFQ turnaround is cut from days or weeks to just hours, supporting faster decisions and increasing the likelihood of winning new business
• Eliminated engineering bottlenecks: Routine setup and analysis work is handled by the system, allowing engineers to concentrate on strategic design and validation tasks
• Accelerated customer engagement: Shorter response times enable teams to interact earlier and with more confidence during the sales process
  
• Higher-quality proposals: Consistent, simulation-driven insights and optimized designs help produce more accurate and competitive quotes

Conclusion

End-to-end RFQ automation is transforming the way engineering teams handle customer requests. What used to involve multiple tools, time-consuming handoffs and weeks of manual effort can now be executed through a single, integrated workflow, from RFQ intake and requirement extraction to simulation, design refinement and final report generation. By combining AI-driven intelligence with automated analysis and optimization, teams can respond faster, scale effectively and deliver consistently high-quality, engineering-validated proposals – all without losing visibility or control.

If you’d like to discuss how RFQ automation could fit into your own engineering workflows, feel free to get in touch with our team.

The post RFQ Response Automation – Speed is Survival appeared first on SimScale.

This Autumn, we are excited to release a suite of new features focused on automation, solver robustness, and expanded physics capabilities. From the introduction of our new Engineering AI Agent to major upgrades in our FEA and CFD solvers, this update is designed to shorten your simulation lead time and accelerate innovation.

Here is an overview of the key updates from the Autumn 2025 release.

On-Demand Webinar

If the highlights caught your interest, there are many more to see. Watch the on-demand Simulation Expert Series webinar from SimScale on how real-time simulation with AI is driving faster design cycles and superior products by clicking the link below.

Watch On Demand

SimScale AI: Agentic Automation

We are taking a significant leap forward in how users interact with simulation through our combined AI technologies: Engineering AI and Physics AI.

• Workbench Agent (Beta): The first step in our Engineering AI roadmap, the Workbench Agent acts as an intelligent assistant within the platform. It can help validate setups, answer simulation queries using documentation, and even automate workflows by reusing setups from previous projects.
• Physics AI Integration: Engineering AI can now leverage Physics AI to perform rapid design optimizations, allowing you to predict results instantly before validating the final design with a traditional solver.

To experience the power of Engineering AI and Physics AI together, click through the demo below. If you would like to take part in our beta program, contact your support team.

Ray – AI Chat Support:

Available to all users, Ray is our 24/7 AI support assistant. Ray can visually diagnose error messages from screenshots and provide instant troubleshooting steps, ensuring you are never stuck waiting for a resolution.

Ray is designed to be a SimScale expert. It has memorized our entire documentation and is trained on simulation best practices, as well as the most common issues and workflow questions you are likely to encounter.

Ray helps to get the information you need as quickly as possible, however your dedicated human engineer is still here for complex, deep-dive project support.

Computational Fluid Dynamics (CFD)

This quarter brings significant quality-of-life improvements and feature parity to our CFD solvers, particularly for rotating machinery and thermal management.

• Variable Time Steps (Multi-purpose): You can now define variable time steps for transient simulations. This allows users to start with coarser steps to establish flow stability and switch to finer steps for high-accuracy resolution, significantly optimizing runtime without compromising quality.
• Run Continuation for CHT (IBM): Users running Immersed Boundary Method (IBM) Conjugate Heat Transfer simulations can now continue transient runs or steady-state simulations from where they left off. This saves valuable computing resources by eliminating the need to restart long simulations from zero.
• Periodic Boundary Conditions: Efficiently simulate complex, repetitive structures (like heat exchangers) by modeling only a single unit cell or subset, reducing model size and computation time.

Finite Element Analysis (FEA)

Following the integration of the Hexagon Marc solver earlier this year, we have released a massive set of new capabilities to handle complex nonlinear structural problems.

• Load Steps: You can now define multiple load steps within a single simulation run. This is essential for manufacturing processes like pipe bending or rubber seal compression, where constraints and loads change sequentially during the event.
• Automatic Contact Detection and Glued Contact Tolerance: For large assemblies with 10+ parts, SimScale now automatically detects and glues contact pairs. This automation drastically reduces setup time, allowing you to focus only on the specific contacts that require manual definition.
• Contact Forces and Pressure: Get deeper insights into nonlinear simulations involving contact by reporting on forces, stresses, contact gap/state and contact pressure.
• Advanced Material Models:
  • Marlow Hyperelastic Model: Drive hyperelastic material behavior directly from test data without complex parameter fitting.
  • Viscoplastic Materials: Accurately model creep, relaxation, and damage in polymers under cyclic loading.
• Remote Displacement & Force: Apply distributed forces or rigid body motions (including rotations) via a central pilot point using new RBE2/RBE3 connectors.
• Symmetry Constraints: We have introduced a plane-based symmetry condition, allowing you to model only a half or quarter of your geometry to save computational effort while maintaining full-model accuracy.

Electromagnetics

Our electromagnetic analysis capabilities continue to grow, with a focus on high-frequency efficiency and power electronics.

• Litz Wire Modeling: You can now model complex bundles of insulated wire strands as a single equivalent geometry. This preserves key electrical and thermal characteristics while avoiding the computational heavy lifting of modeling every individual strand.
• Time-Periodic Acceleration: For transient magnetic simulations driven by periodic excitations (e.g., PWM or sinusoidal waveforms), this feature accelerates the solver to reach steady-state results in a fraction of the time.

General Platform Enhancements

We have also introduced several non-physics updates to improve the overall user experience and workflow efficiency.

• CAD Section View: Inspect the internal details of complex 3D CAD models by slicing them along a cutting plane directly in the pre-processor.
• Cancel CAD Operations: You now have the ability to stop long-running CAD operations midway, giving you better control over your preparation workflow.
• Automatic Extrusion Meshing: The Standard Mesher now automatically detects the optimal direction for extrusion (longest or shortest dimension), ensuring higher quality meshes for elongated bodies and thin plates.
• Pedestrian Wind Comfort (PWC) Cutting Planes: Post-processing for PWC analysis now supports cutting planes, allowing you to visualize comfort plots hidden by complex geometry like canopies or overhangs.

Watch Now

Don’t miss out on the full experience and deeper insights into how SimScale’s latest features can transform your engineering workflow. Watch the complete webinar on-demand to see these tools in action and understand how they can be applied to your specific challenges. Click here to access the webinar recording and start accelerating your design process today!

The post Webinar Highlights – SimScale Autumn 2025 Product Updates appeared first on SimScale.

For decades, the language of design has been based on surfaces, sketches, and constraints.

Engineers have grown used to constructing geometry one step at a time – extruding, sweeping, or filleting to build models layer by layer.

But what happens when geometry is no longer described by a sequence of operations, but instead by mathematical fields and equations?

That’s the shift implicit geometry modeling brings. It’s not a new CAD feature or another incremental tool. It’s a fundamentally different way of thinking about how objects are created, changed, and optimized.

Even better, implicit modeling unlocks powerful opportunities to integrate with AI algorithms, enabling advanced shape and topology optimization.

The changing landscape of design

Every product engineer knows the trade-off between creativity and control. Traditional parametric CAD excels at precision, repeatability, and manufacturability—but struggles with complexity and adaptability. The moment a design needs to evolve beyond its original constraints, the model often breaks. Surfaces fail to regenerate. Feature trees become tangled. Performance and hardware requirements add to the challenge. The geometry, instead of serving creativity, starts limiting it.

At the same time, AI simulation-driven design and optimization are moving to the center of product development. Engineers want to explore hundreds of design iterations, automatically test performance, and converge on the best possible shape.

Traditional CAD, built around static geometry, simply can’t keep up. Implicit modeling offers an answer.

What is implicit modeling?

Implicit geometry modeling represents 3D shapes using mathematical functions rather than explicit surface definitions.

Instead of describing a solid by its boundaries (as in B-Rep or mesh-based systems), an implicit model defines a region of space where a function equals zero—the so-called implicit surface. This allows for smooth, continuous transitions, blending, and deformation at any scale without worrying about topology or feature dependencies.

In practice, this means you can modify or combine complex geometries – like lattices, organic forms, or porous structures – using simple operations. Shapes can be added, subtracted, or morphed together using equations instead of manual CAD features. The result is a workflow that is more robust, more flexible, and dramatically faster when exploring non-traditional geometries.

Implicit vs. traditional modeling

To understand the impact, consider a typical CAD-based workflow.

1. You start with sketches
2. define constraints
3. extrude features
4. and trim surfaces.

Every change requires the system to recalculate dependencies. It’s precise, but fragile.

Now imagine instead defining the same geometry as a mathematical field. You can modify it globally – smooth transitions, blend regions, or adjust material density – without breaking any relationships.

This difference has huge implications for design automation and optimization. Implicit models can directly interface with algorithms that search, test, and evolve geometry automatically. They’re also inherently compatible with lattice generation, topology optimization, and generative design tools. Instead of trying to force simulation-ready meshes out of rigid CAD structures, implicit models create analysis-ready geometries by default.

Why now?

Several trends are converging to make implicit modeling more relevant than ever.

First, manufacturing is changing. Additive processes – such as metal 3D printing or high-resolution polymer fabrication—allow the production of complex, non-linear geometries that traditional CAD was never built to handle.

Second, computational power has caught up. With cloud-based platforms and GPU acceleration, implicit models can be calculated, visualized, and simulated in real time.

And third, simulation-driven design and AI-based optimization are entering everyday workflows. Engineers no longer design once and simulate later; they design through simulation. Implicit geometry provides the missing foundation for this level of integration.

Real engineering impact

Implicit modeling isn’t just about generating futuristic shapes—it’s about solving real engineering challenges. Lightweighting, for instance, becomes more than removing material; it becomes a question of continuously varying density to match structural or thermal demands. Fluid flow optimization can be achieved by smoothly adjusting surfaces for better aerodynamics or cooling. Complex lattices can be embedded into structural components without manual feature management.

In fields like aerospace, medical devices, or consumer products, this approach means faster iteration, fewer redesigns, and products that are both lighter and stronger. The design space expands, while the time-to-simulation and time-to-market shrink.

The power of integration: Implicit + Simulation + AI

The real magic happens when implicit modeling connects directly with CAE simulation or AI-driven physics prediction.

Optimizing within the implicit space opens up massive opportunities. The feedback loop tightens. Designers no longer need to spend cumbersome work to rebuild and simplify models for each test or optimization cycle – changes are immediately updated in the field representation.

Powerful simulation technologies using meshless or quasi-meshless Cartesian techniques allow to directly evaluate the design without manual user input.

This synergy unlocks new frontiers for generative design, topology optimization, and AI-assisted shape exploration. Imagine defining not just a component, but an entire system, where materials, structures, and flows are optimized together, automatically, based on real physics.

The essential takeaways

Here’s what makes implicit modeling a quiet revolution in design engineering:

• Continuous geometry control – Modify shapes smoothly without constraint rebuilds or topology breaks
• Seamless integration with simulation – Connects directly with CAE and generative optimization workflows
• Ready for AI and automation – Enables algorithmic exploration and machine learning in design space
• Scalable complexity – Handle intricate lattices and organic structures efficiently
• Accelerated innovation – Iterate faster with fewer modeling bottlenecks and simulation-ready output

Looking ahead

Implicit modeling challenges a long-held belief in engineering: that geometry must be built piece by piece. As more tools adopt implicit representations, designers and engineers will find themselves working less with constraints and more with possibilities. Instead of fighting the model, they’ll collaborate with it—shaping, simulating, and refining in one continuous loop.

For design engineers, this is not just an efficiency gain; it’s a creative shift. It’s the ability to think in systems, not sketches. To design performance into geometry, rather than fitting geometry to performance. And as implicit modeling merges with AI-driven design, we’re seeing the emergence of a new era of computational creativity.

Ready to see how implicit modeling connects with advanced simulation? Explore the partnership here.

The post Implicit Modeling appeared first on SimScale.

Eliminating Bottlenecks

The session brought together three distinct perspectives on how to operationalize AI in production environments: Ram Seetharaman (Head of AI, Synera) on Agentic AI, Matthias Bauer (Director of Software Development, Autodesk / Founder, NAVASTO) on Physics AI, and David Heiny (CEO, SimScale) on the cloud-native infrastructure that binds them together.
The consensus? The industry is moving past the “chatbot” phase. We are entering an era where AI Agents orchestrate complex tools to automate busy work, and Physics AI provides instant feedback loops, allowing engineers to traverse design spaces at unprecedented speed.

Key Takeaways:

1. Agentic AI is the “Digital Engineer,” Physics AI is the “Calculator”

The session clarified the distinction between the two critical types of AI. Agentic AI (using LLMs) acts like a digital employee—reasoning, planning, and orchestrating tools to handle complex processes like RFQ responses. Physics AI (using GNNs) acts as an ultra-fast solver, providing instant performance predictions to accelerate the design iterations that the agents (or humans) generate.

2. Integration is the Multiplier (The “Electric Motor” Analogy)

Matthias argued that simply swapping a solver for an AI model isn’t enough. He compared it to the industrial revolution: replacing a steam engine with an electric motor didn’t yield efficiency gains until factories were redesigned around the new power source. Similarly, AI only delivers ROI when deep-integrated into the tools engineers already use (like CAD), rather than sitting in a silo.

3. Trust Comes from Traceability, Not Blind Faith

A major barrier to AI adoption is the “black box” problem. The panel emphasized that trust is built through auditability. For Agentic AI, this means viewing the “chain of thought”—seeing exactly which tools the agent used and why. For Physics AI, it means statistical validation and “traffic light” confidence scores that tell an engineer when a prediction is reliable and when to fall back to traditional simulation.

4. The “Junior Engineer” Model

AThe most practical way to deploy AI today is to treat it as a “junior engineer.” It can autonomously handle tedious tasks (like meshing, setup, or initial design sweeps) and present 80% complete work for expert review. This keeps humans in the loop for critical engineering judgments while removing the bottleneck of manual execution.

Watch the full webinar recording below. And if this seems interesting, be sure to check out the rest of the series!

The post Webinar Highlights: AI-Native Engineering Workflows appeared first on SimScale.

Eliminating Bottlenecks

The discussion broke down the two primary bottlenecks in engineering: simulation lead time (setup) and simulation cycle time (computation), and explored the AI technology that lets you transform engineering processes by effectively eliminating them.

We heard some great insights from Brian Sather from nTop, explaining the importance of a robust geometry pipeline for effective design exploration. Jon Wilde described SimScale’s approach to tackling the bottlenecks in simulation workflows that unlock the full potential of AI-driven engineering.

Key Takeaways:

1. Solving This Needs a Two-Pronged Solution

Physics AI (using GNNs/PINNs) learns physics to deliver instant predictions, crushing the computation bottleneck. Engineering AI (using LLMs) understands user intent to automate and orchestrate entire multi-step processes, crushing the setup and lead time bottleneck.

2. To Scale AI, You Must Solve Data Generation

One of the most significant challenges in scaling AI is assimilating or generating training data. Here, the robustness and speed of geometry generation is key, and traditional CAD models can struggle. We looked at how “computational design” tools can algorithmically generate thousands of valid design variants, creating the synthetic data needed to train a reliable Physics AI model.

3. A Connected Toolchain Is Critical

Eliminating process bottlenecks is only possible with a seamlessly connected toolchain with limited sprawl. The session demonstrated how to build tight, AI-driven optimization loops involving nTop’s implicit models that can be read directly by SimScale, eliminating manual prep work and ensuring a robust transfer from geometry to simulation.

4. AI Agents Are the New “UI” for Democratization

A live demo of SimScale’s Engineering AI agent showed how non-experts can now drive complex simulation much faster. By using natural language (e.g. “optimize this heat sink for me”), a user can trigger an agent to orchestrate CAD, simulation, and optimization in the background. This moves simulation from a specialist-only tool to a capability accessible to the entire organization.

Watch the full webinar recording below. And if this seems interesting, be sure to register for the rest of the series!

The post Webinar Highlights: Scaling AI-Powered Simulation Across Teams appeared first on SimScale.

An AI Masterclass – How to Fit Months into Hours

The highlight of the session was a real-world case study from Armin Narimanzadeh, Senior Thermofluids Expert at Convon (part of HD Hyundai). Armin shared his first-hand experience of using SimScale’s AI-powered simulation to optimize a hydrogen ejector pump, building a reusable Physics AI model that produces instant performance predictions for new designs.

This transformative approach reduced a design optimization process that previously took months down to under an hour, enabling rapid iteration and data-driven decision-making.

The discussion, featuring insights from Mike LaFleche of PTC and Steve Lainé of SimScale, explored the crucial role of a cloud-native ecosystem in making these workflows possible and how to overcome common blockers like data availability and trust in AI.

Key Takeaways:

1. AI is an Amplifier, Not a Replacement for Expertise

A recurring theme was that AI serves as a powerful tool to amplify your engineering expertise. Armin emphasized that while the AI model delivered incredible speed, his engineering expertise was still crucial to guide the optimization, validate the final results against CFD, and make the final design decisions. The goal is to empower experts, not replace them.

2. The “Months to Hours” Transformation is Real

The most powerful takeaway was the quantifiable impact on the product development cycle. Having invested in the initial model training and data generation, Armin’s team now has a reusable AI model that can generate a new, optimized design for their ejector in under an hour. This is a game-changing acceleration that directly impacts business agility.

3. A Cloud-Native Ecosystem was Key

This level of automation and speed is only possible when the entire toolchain is cloud-native. The seamless, API-driven connection between a parametric model in Onshape and the simulation in SimScale was essential for automatically generating and testing hundreds of design variants to firstly map the design space and then to explore and optimize within.

4. You Can Start Now, Even Without Perfect Data

Armin carefully tested different training data sets to find the dataset ‘sweet spot’ – how much data was needed to build an accurate model. He found that the number of samples needed was not as large as originally expected, allowing him to refine his approach for future projects.

Watch the full webinar recording below. And if this seems interesting, be sure to register for the rest of the series!

The post Webinar Highlights: Kickstarting Engineering AI in Manufacturing appeared first on SimScale.

How to Calculate the Pipe Flow Rate

Our calculator determines the flow rate based on the principle of the continuity equation. It’s a straightforward calculation that multiplies the pipe’s internal area by the speed of the fluid flowing through it.

The Flow Rate Equation

The calculator uses the standard formula for volumetric flow rate:

$$Q = A \times v$$

Where:

• \(Q\) is the Volumetric Flow Rate
• \(A\) is the Cross-sectional Area of the pipe
• \(v\) is the Flow Velocity

The cross-sectional area \(A\) is calculated from the given Pipe Inner Diameter (\(D\)) using the formula for the area of a circle, $$A = \frac{\pi D^2}{4}$$. The calculator automatically converts all your inputs into a consistent set of SI units (meters, seconds) before performing the calculation to ensure an accurate result, which is then converted to your desired output unit.

Input Parameters

• Pipe Inner Diameter (D): This is the internal width of the pipe, which defines the space available for the fluid to flow. Common units like millimeters (mm), centimeters (cm), meters (m), inches (in), and feet (ft) are available.
• Flow Velocity (v): This is the average speed at which the fluid is moving through the pipe. It can be entered in various units, such as meters per second (m/s) or feet per minute (ft/min).

Frequently Asked Questions

The post Pipe Flow Calculator appeared first on SimScale.

How to Calculate the Lift Coefficient

Our calculator determines the lift coefficient based on the fundamental lift equation. Here’s a breakdown of the inputs and the formula used.

The Lift Equation

The calculator uses the standard formula for the lift coefficient, which is derived by rearranging the lift equation:

$$C_L = \frac{L}{\frac{1}{2} \rho v^2 A}$$

Where:

• L is the Lift Force
• ϱ(rho) is the Fluid Density¹
• v is the Flow Velocity
• A is the Reference Area

The calculator automatically converts all your inputs into standard SI units (Newtons, kg/m³, m/s, m²) before performing the calculation to ensure a correct, dimensionless result.

Input Parameters

Fluid Density (ρ): The mass of the fluid per unit volume. The calculator includes presets for common fluids like air and water at standard conditions. You can also select "Other" to input a custom density value in either kg/m³ or slug/ft³.

Lift Force (L): This is the component of the aerodynamic force that is perpendicular to the direction of the oncoming flow. It's the force that "lifts" an object, like an airplane wing.

Flow Velocity (v): The speed of the fluid relative to the object (or the object's speed relative to the fluid).

Reference Area (A): This is a characteristic area of the object, typically the planform area (top-down view) of a wing or hydrofoil. For a simple rectangular wing, it would be the chord length multiplied by the wingspan.

Frequently Asked Questions

The post Lift Coefficient Calculator appeared first on SimScale.

How to Calculate Reynolds Number

Our calculator is designed to be flexible and user-friendly, accommodating various scenarios you might encounter. Here’s a breakdown of its features and the calculations we carry out in order to determine the results.

1. Flow Type (Internal vs. External):

• Internal Flow (e.g. through a pipe or duct): Select this if your fluid is confined within a boundary. For these cases, the characteristic length (L) in the Reynolds number formula is typically the diameter for circular pipes or the hydraulic diameter for non-circular ducts.
• External Flow (e.g. over a flat plate, around a sphere): Choose this when the fluid flows around an object. Here, the characteristic length (L) is a dimension of the object, such as the length of a plate or the diameter of a sphere. The Reynolds number for external flow often dictates where boundary layers transition from laminar to turbulent.

2. Fluid Properties (Kinematic vs. Dynamic Viscosity & Density):

The Reynolds Number can be calculated using either kinematic or dynamic viscosity. Our calculator allows you to choose based on the data you have:

• Kinematic Viscosity (ν): This option uses the formula Re = (v x L) / ν. Kinematic viscosity already accounts for the fluid’s density and is often provided for common fluids. Units are typically m²/s or cSt (centistokes).
• Dynamic Viscosity (μ) & Density (ρ): If you have dynamic viscosity and density separately, select this. The calculator will use the formula Re = (ρ x v z L) / μ. Dynamic viscosity (also known as absolute viscosity) represents a fluid’s resistance to shear flow. Units are typically Pa·s (Pascal-seconds) or cP (centipoise).
  • Need to convert? Remember, kinematic viscosity (ν) can be calculated from dynamic viscosity (μ) and density (ρ) using the relationship: ν = μ / ρ.

3. Input Parameters:

• Fluid Velocity (v): The average speed of the fluid.
• Pipe Diameter (D) / Characteristic Length (L):
  • For Internal, Circular Flow: Enter the pipe’s diameter.
  • For Internal, Rectangular Flow: You’ll input the duct’s width and height. The calculator will automatically calculate the Hydraulic Diameter (D_h) using the formula D_h = (4 x Area) / Perimeter. This equivalent diameter is used as the characteristic length for non-circular ducts.
  • For External Flow: Enter the characteristic length relevant to the geometry of the object (e.g., length of a plate, diameter of a cylinder).
• Kinematic Viscosity (ν) or Dynamic Viscosity (μ) and Density (ρ): Input these values based on your “Fluid Properties” selection.

4. Units:

We’ve included common units for all inputs, and the calculator will handle the conversions to ensure accurate results in SI units internally. Just select the unit you’re working with for each parameter.

Frequently Asked Questions

The post Reynolds Number Calculator appeared first on SimScale.