Accelerating RFQ/RFP responses: Why the rush?

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.

But are we experiencing the same in mechanical engineering?

Our State of Engineering AI survey suggests that only 3% of hardware engineering companies are seeing comparable ‘significant’ gains from adopting AI in their workflows. Why such a small number? One of the most commonly cited reasons was the inflexibility of legacy software.

Luckily, there is a whole raft of new AI tools for mechanical engineers emerging, covering the whole product lifecycle. Let’s dive in.

1. Ideation: Beyond the Blank Page

The conceptual design phase sets the foundation for an entire project. Traditionally, this relies on an engineer’s experience and intuition to sketch out a few potential solutions. AI, specifically through generative design, challenges this paradigm.

Generative Design

Generative design tools use algorithms to explore thousands of potential design solutions based on a set of constraints you define. You input the non-negotiable parameters—functional requirements, material properties, manufacturing methods, and performance criteria—and the AI generates a massive number of high-performing options. Your role shifts from being a generator of a few ideas to a curator of many.

Autodesk Fusion 360

Autodesk’s platform uses cloud-based machine learning to automatically generate and rank design solutions. As an example, check out this case study with General Motors, which used generative design to redesign a seat bracket. The AI-generated result was a single, organic-shaped part that was 40% lighter and 20% stronger than the original component, which had previously consisted of eight separate parts.

nTop 

This tool excels in creating highly complex, performance-critical components. For instance, Cobra Aero used nTop to redesign a drone engine cylinder. Instead of traditional cooling fins, the software generated an intricate internal lattice structure. This AI-driven design significantly reduced weight while improving thermal performance—a result that would be nearly impossible to arrive at through traditional design methods.

2. Drafting, Design and Review: The Intelligent Drawing Board

Once a concept is chosen, it must be translated into a detailed CAD model. AI is now being embedded directly into CAD software to serve as an intelligent co-pilot, automating repetitive tasks and streamlining the design process.

AI-Assisted CAD

This category includes AI features that automate common tasks like dimensioning, applying constraints, design reviews and compliance checks. These tools learn from your design habits and company standards to make intelligent suggestions, reducing errors and saving significant time.

Onshape AI Advisor

This tool acts as an intelligent assistant directly within the cloud-native CAD environment. An engineer can ask natural-language questions (e.g., “How do I create a variable-pitch helix?”). The AI provides step-by-step recommendations, troubleshooting help, and best practices drawn from Onshape’s training materials, effectively accelerating the learning curve and day-to-day workflow. This cloud-native approach is also critical for AI-driven simulation, as it allows parametric CAD models to connect directly to analysis tools like SimScale, enabling the rapid, automated iteration that AI requires.

Shapr3D

Shapr3D bypasses the hours typically spent in dedicated rendering software with its embedded AI Visualization. By simply positioning a model and typing a text prompt (e.g., “modern kitchen counter”), engineers can generate context-aware renders in seconds. The tool uses your specific CAD geometry as a guide, allowing for rapid concept validation and “mood boarding.” Features like Variation Intensity let you control the AI’s creative freedom, enabling you to present polished, stakeholder-ready visuals instantly without leaving the design environment.

Intelligent Design Review (ReviewOps)

CoLab Software

While tools like Onshape and Shapr3D streamline CAD creation, CoLab addresses the bottleneck of reviewing it. Traditionally, critical feedback gets lost in “data graveyards” of static screenshots, email threads, and PowerPoint decks. CoLab’s AutoReview utilizes AI to automate the administrative heavy lifting of this process, scanning models against your specific design standards before a human ever looks at them.

This acts as an intelligent “pre-flight check,” automatically flagging routine errors like missing hole callouts or GD&T non-compliance. By filtering out this administrative noise, the AI allows senior engineers to focus their expertise on complex problem-solving rather than checklist verification. This effectively closes the loop between design intent and manufacturing reality, ensuring faster, higher-quality feedback cycles.

3. Simulation and Optimization: No More Waiting

For many engineering teams, this is the most painful part of the product development lifecycle. Traditional simulation (CAE) is a well-known bottleneck, but it’s actually two problems. The first is simulation cycle time: the hours or days you wait for a complex computation to run. The second, and often bigger, problem is the simulation lead time: the days or weeks spent by specialists manually setting up physics, preparing geometry, and re-meshing every new design variant. This ‘test-and-wait’ workflow means engineers can only analyze a handful of designs. Engineering AI is the solution to this entire bottleneck, tackling both problems at once to achieve one goal: no more waiting.

Engineering AI (Solving the Lead Time Bottleneck)

This category of AI acts as an intelligent co-pilot to automate the complex, multi-step setup process. Unlike a traditional macro or script which follows a rigid list of commands, Engineering AI uses Large Language Models (LLMs) to reason through the physics of your model.

SimScale Engineering AI

SimScale has introduced an agentic AI assistant that resides directly in the simulation platform. It transforms how engineers interact with simulation through three core capabilities:

1. Democratization for Novices: Traditionally, simulation required years of specialized training. Engineering AI lowers this barrier by guiding novice users step-by-step. It can diagnose missing inputs, suggest appropriate settings based on the geometry, and flag potential errors before a simulation is run. This turns the platform into a mentor, helping junior engineers get to valid results faster.
2. Promoting Best Practices: For larger organizations, consistency is key. Engineering AI can be configured to enforce company-specific “Gold Standard” settings. The agent ensures that every simulation—whether run by an expert in Germany or a novice in the US—adheres to the same quality standards and methodologies, reducing the risk of human error.
3. Reasoning and Adapting: Unlike rigid scripts, the agent uses reasoning to navigate deviations. If a geometry changes slightly or a parameter is missing, the agent evaluates the context to adapt its approach rather than failing.

Physics AI (Solving the Computation Bottleneck)

This is the surrogate model. It’s a lightweight, data-driven approximation of a high-fidelity simulation. An AI model learns the complex, non-linear relationships between inputs (geometry, boundary conditions) and outputs (performance, stress, temperature). Once trained, this ‘Physics AI’ model provides near-instant predictions, solving the computation time bottleneck.

SimScale’s Integrated AI Platform

SimScale has integrated this technology directly into its workflow. This approach was highlighted in a real-world case study by Convion, a part of HD Hyundai, who needed to optimize a complex hydrogen ejector pump.

The Challenge: Armin Narimanzadeh, Senior Thermofluids Expert at Convion, faced a multi-objective optimization problem. The design was complex, and using traditional CFD-driven optimization to find the best-performing design would have taken months.

The AI Solution: Using SimScale, Armin’s team first generated a training dataset by running a number of simulations. This data was used to train a reusable Physics AI model within the SimScale platform. The results were transformative. The team now has an AI model that can generate a new, optimized design in under an hour.

This “months to hours” transformation is a perfect example of AI’s power. It was made possible by a fully cloud-native toolchain: a parametric model built in Onshape was connected via an API to SimScale, allowing the AI to automatically test hundreds of variants to find the optimal design. The Engineering AI component automates this workflow, while the Physics AI component provides the instant predictions.

Learn more about the full process by watching the AI Engineering Bootcamp webinar (Session 1) on-demand.

4. Deployment: From Smart Manufacturing to Live Maintenance

AI’s role doesn’t stop when the design is finalized. It extends into the manufacturing process and the operational life of the product (deployment). Increasingly, smart manufacturing systems 

Smart Manufacturing

AI is streamlining the path from a 3D digital model to a physical part. CAM software is using AI to automate the complex and error-prone task of CNC programming, which is a major bottleneck due to a shortage of skilled machinists.

Siemens NX CAM

NX CAM uses AI for “Feature-Based Machining”. It automatically analyzes a 3D model and recognizes geometric features like holes, pockets, and slots. It then suggests the most suitable machining operations and sequences, even learning from a programmer’s past choices to improve future suggestions.

Real-time Digital Twins

Once a product is deployed, AI can help you operate it more effectively by building a real-time digital twin. A Physics AI model fed with boundary conditions from a live system can provide crucial insight into function and health, including ‘virtual sensing’ of metrics which might be impossible to directly measure. Machine learning can also be used to predict when it will fail before it happens, based on historical operational data.

Physical AI

Connect live sensor data streams from your operational assets or system-level models directly to Physics AI surrogates via SimScale’s open API. This creates a closed loop where real-world conditions continuously inform and update your digital twin for maximum accuracy.

By combining live data and hardware-in-the-loop (HiL) setups with AI models, you move from reactive maintenance to predictive operations. This allows you to forecast performance degradation and potential failures before they occur, preventing costly downtime and improving asset reliability.

5. The Next Frontier: Multi-Agent Workflows

While individual AI tools are powerful, the future of engineering automation lies in multi-agent systems. Imagine a “digital engineering team” where specialized AI agents collaborate to execute complex, cross-functional tasks without constant human hand-holding.

In a multi-agent workflow, different agents act as specialists—one might be an expert in reading requirements, another in CAD generation, and another in physics simulation. They communicate with each other to complete an objective that spans multiple software platforms.

Synera

Synera, a leading platform for engineering process automation, is pioneering the use of connected AI agents. In the recent AI Engineering Bootcamp (Session 3), Ram Seetharaman (Head of AI at Synera) demonstrated a live multi-agent workflow that orchestrates the entire design-simulate-iterate loop.

In this example, a “Manager Agent” in Synera interprets requirements and delegates tasks. It triggers a “Geometry Agent” to modify a design logic, which then hands off the new geometry to a “Simulation Agent” (powered by SimScale) to validate performance. The results are fed back to the Manager, which decides whether to iterate further or finalize the design.

By chaining these agents together, you eliminate the coordination bottlenecks that often stall projects for days or weeks, allowing for continuous, “always-on” engineering operations.

The Engineer’s Future: AI as an Amplifier

Across all these stages, the theme is the same: AI is an amplifier for engineering expertise. It tackles the repetitive, time-consuming, and data-heavy tasks that slow down innovation. This allows you, the engineer, to focus on what you do best: problem-solving, creativity, and making the critical decisions that lead to breakthrough products. The companies and engineers who embrace these tools today will be the ones shaping the future of the industry.

The post AI Tools for Mechanical Engineers: Transforming Your Workflow appeared first on SimScale.

“The moment you push performance limits, heat becomes the enemy that never sleeps.”

Alexander Fischer

Co-founder & Product Manager, SimScale

Electric vehicles depend on compact thermal architectures that keep batteries and power electronics within a narrow operating windows. AI accelerators concentrate extraordinary wattage into small footprints. Industrial automation, renewable energy hardware and medical technology all follow the same pattern.

They raise performance expectations while shrinking available space. This creates a new reality in which cold plate design becomes a strategic engineering function rather than a late stage add on. Teams that recognize this shift early gain more performance, more reliability and more control over how their products evolve.

The Practical Challenges Facing Design Teams

Engineering teams face real constraints. They must balance:

• manufacturability,
• pressure drop,
• integration,
• weight targets,
• and routing!

You often work within tight envelopes while trying to handle rising heat flux. Parametric CAD can slow the process because feature trees resist change and complex channels break easily when edited. Conservative geometry becomes the default. This is risky as thermal loads continue to rise across industries. Cold plate cooling demands broader concept exploration, faster iteration and clearer structure throughout the development process.

A High Level View of the Cold Plate Design Workflow Step by Step

A typical cold plate project moves through several major steps from concept to validated geometry.

• It begins with requirement gathering where engineers define heat flux levels, target temperatures, available space, allowable pressure drop, material constraints and manufacturing options.
• Next comes the architectural exploration where macro level decisions such as cooling method, channel layout, inlet and outlet placement and flow balance strategies are evaluated.
• Concept modeling follows with early geometry that tests feasibility and identifies potential performance issues.
• Detailed design development then refines internal channels, surface area enhancements, flow paths and structural supports.
• In parallel, system level integration ensures correct fit and interaction with electronics, enclosures and the larger cooling loop.
• The final stages focus on simulation driven optimization, design for manufacturability and preparation for prototyping.

High performance applications cycle through these steps rapidly as iteration speed becomes a core advantage.

How Implicit Modeling Transforms the Design Phase

Implicit modeling fits directly into this workflow and accelerates it significantly. Traditional parametric CAD relies on sketches, constraints and feature trees. Implicit modeling uses continuous mathematical fields to define form.

Complex shapes become easy to create and sturdy during modification. Families of designs can be generated quickly without model failures. Smooth blends are inherent. Microchannels, graded thicknesses, TPMS surfaces or lattice supported walls appear without manual surfacing.

This matters because cold plate cooling often benefits from organic or highly detailed internal geometry that explicit modeling tools struggle to express.

Why Advanced Cooling Geometry Matters Now

This shift aligns perfectly with the pressure placed on modern hardware. EV power electronics keep increasing in output while packaging shrinks. AI hardware demands targeted thermal strategies that match component level heat flux. Data centers monitor every watt because cooling efficiency now affects operating cost directly. Aerospace, hydrogen systems and compact industrial machinery all follow similar trends. They require high performance cooling solutions that combine low weight, high efficiency and manufacturable complexity.

Cold plate design sits at this intersection because it enables direct heat removal and supports structurally complex yet lightweight geometries.

The Impact of Simulation and AI Assisted Optimization

When advanced modeling is paired with CAE simulation or AI driven physics prediction, the later stages of the workflow become dramatically more effective. Engineers can apply cold plate topology optimization to reshape channels for uniform thermal behavior. Microchannel networks can align with localized heat flux. TPMS or lattice structures can increase surface area while keeping weight low. Iteration becomes flexible and exploration becomes normal rather than exceptional. Cold plates evolve into highly tuned components tailored to the exact demands of each device.

Key Insights

1. Microchannel cold plates deliver high surface area for extreme heat flux handling
2. TPMS and lattice structures enable lightweight internal geometries with strong manufacturability profiles
3. Implicit modeling and topology optimization accelerates every design stage and supports shapes that parametric tools struggle to represent
4. Simulation driven workflows improve accuracy and bridge the gap between concept and validated performance
5. Cold plate design has become a strategic differentiator for any product facing rising thermal loads

Cold plates are no longer secondary components. They enable the future of mobility, computing and energy systems and they reward engineering teams that prioritize them early in development.

The post Cold Plate Cooling Design 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.

From maximizing efficiency in a jet engine’s turbine to ensuring the reliable, vibration-free operation of a simple industrial pump, the rotating component is where performance and reliability are won or lost.

What is rotating equipment?

Simply put, rotating equipment is any machinery that relies on rotational motion to perform its function. Unlike static equipment (like pipes or storage tanks), these devices are designed to convert energy into mechanical motion, often for moving fluids or generating power.

Common examples span a wide range of industries:

• Pumps (Centrifugal, Axial)
• Turbines (Gas, Steam, Wind)
• Compressors (Centrifugal, Axial)
• Fans and Blowers
• Motors and Generators
• Propellers and Impellers

What is the difference between static and rotating equipment?

The main difference between static and rotating equipment is movement.

• Static equipment (like pipes, tanks, or heat exchangers) is stationary and has no moving parts.
• Rotating equipment (like pumps, turbines, or motors) uses spinning components to actively create motion or generate power.

Rotating equipment types in depth

Let’s have a look at the different types of rotating equipment in a bit more depth.

Pumps (Centrifugal, Axial)

Pumps stand as the workhorses of industry, hydraulic machines designed to transport fluids by converting rotational kinetic energy into hydrodynamic energy. The most common types, centrifugal pumps, use a rotating impeller to fling fluid outwards, while axial pumps use a propeller-style mechanism to move fluid along the shaft’s axis. From water supply systems to chemical processing, their applications are endless. But how can one optimize their performance and prevent damaging issues like cavitation? This is where modern engineering simulation steps in, allowing engineers to digitally visualize flow patterns and pressure zones to achieve design excellence before a physical prototype is ever built.

Turbines (Gas, Steam, Wind)

Turbines are critical in our pursuit of energy, acting as sophisticated engines that extract energy from a fluid flow and convert it into useful rotational work, most often to power a generator. Whether it’s a gas turbine using hot combustion gases, a steam turbine driven by high-pressure steam, or a wind turbine capturing the kinetic energy of the air, their core principle is to harness natural forces. The design of their complex blades is paramount to performance. Cloud-native simulation platforms offer a robust framework for modeling these intricate fluid-structure interactions, helping engineers optimize blade geometry to maximize efficiency and reliability.

Compressors (Centrifugal, Axial)

Compressors are vital machines whose primary function is to increase the pressure of a gas by reducing its volume. Like pumps, they are commonly found in centrifugal (radial) and axial flow configurations, each suited for different pressure ratios and flow rates. These devices are the heart of everything from jet engines and industrial refrigeration to gas pipeline transport. Achieving high efficiency and a stable operating range is a key design challenge. Using computational fluid dynamics (CFD), engineers can meticulously simulate the high-speed, complex flow through blade passages to optimize designs and minimize energy losses.

Fans and Blowers

While often grouped with compressors, fans and blowers are specifically designed to move large volumes of air or gas, typically at a much lower pressure differential. Their function is essential for ventilation in buildings (HVAC), cooling electronics, or supplying combustion air in industrial furnaces. The main challenge in their design is to achieve the required flow rate while minimizing power consumption and acoustic noise. Engineering simulation plays a key role here, allowing designers to analyze airflow, test blade profiles, and visualize turbulence to create quieter and more efficient systems.

Motors and Generators

These two devices are the cornerstone of our electrified world, managing the conversion between mechanical and electrical energy. A motor takes electrical energy and converts it into mechanical rotation to drive a pump, fan, or compressor. A generator does the exact opposite, taking rotational energy from a turbine and converting it into electrical energy for the grid. Optimizing their design requires a deep understanding of electromagnetics and thermal management. Multiphysics simulation is crucial, enabling engineers to analyze magnetic fields, predict heat buildup, and ensure the structural integrity of these fundamental machines.

Propellers and Impellers

At the very heart of nearly all turbomachinery, you will find a rotating component designed to interact with a fluid: the propeller or the impeller. An impeller, such as one in a centrifugal pump, is designed to transfer energy to the fluid, increasing its velocity and pressure. A propeller, used for propulsion or in a turbine, interacts with the fluid to create thrust or extract work. The specific geometry of these components—their blade curvature, angle, and number—is the single most critical factor in the machine’s overall performance. Their design is a perfect use case for simulation, which allows for detailed flow analysis to maximize efficiency and minimize wear.

Rotating equipment in our public projects

Here is a range of rotating equipment that has been simulated with SimScale by our community.

Rotating equipment challenges

The continuous operation of rotating equipment is fundamental to global infrastructure, from power generation to manufacturing. Efficiency and reliability failures carry a massive cost. For instance, even a one percent drop in efficiency across a fleet of industrial pumps or compressors translates into millions of dollars in wasted energy annually. Any component failure driven by vibration or fatigue can trigger unscheduled downtime, which instantly halts production or utility service.

Beyond these operational costs, the industry is now facing unprecedented pressure driven by global Net-Zero targets and evolving sustainability mandates. This shift requires nothing less than a revolution in design, pushing engineers to develop machines that achieve record-breaking efficiencies while incorporating new low-carbon technologies, such as advanced compressors for hydrogen or lighter, more powerful electric motors. Therefore, optimizing these machines isn’t merely about achieving peak performance; it is a business-critical requirement for safeguarding financial viability and ensuring operational continuity in a sustainable future.

Some examples of government mandated Minimum Energy Performance Standards (MEPS) are given below.

Simulating rotating equipment with SimScale

Engineering simulation provides the vital advantage needed to tackle the complex challenges of rotating equipment proactively. By modeling real-world physics in a virtual environment, designers can rapidly explore changes and predict performance before a prototype is ever built, leading to significant cost reduction, increased reliability and lifespan, and accelerated time to market.

At SimScale, we are committed to providing accessible, high-performance simulation tools right in your web browser. For rotating equipment, our platform offers a powerful, validated suite of simulation types across fluid dynamics, structural mechanics, and electromagnetics.

Computational Fluid Dynamics (CFD)

To master the fluid flow within your turbomachinery, SimScale’s online CFD simulation capabilities offer two primary approaches. For initial design exploration and quickly mapping basic performance curves (like pump curves), the Multiple Reference Frame (MRF) method provides a computationally fast, steady-state approximation. However, for maximum fidelity in predicting complex phenomena—such as rotor-stator interactions, pressure pulsations, or cavitation—the Transient Analysis utilizing the highly accurate Sliding Mesh technique is essential. Furthermore, you can accelerate your entire development cycle by using Parametric Studies to automatically run dozens of variations (different speeds, flow rates, geometries) in parallel, efficiently generating a full performance map.

Read how Hazleton Pumps generated performance curves 100x faster with SimScale.

Finite Element Analysis (FEA)

Structural integrity and vibration avoidance are paramount for rotating equipment reliability. SimScale’s FEA tools focus on guaranteeing these factors. By conducting Rotational Modal Analysis, engineers can compute natural frequencies while accounting for rotational effects (centrifugal forces), which is crucial for identifying and avoiding critical speeds that lead to destructive resonance. The results are used to generate Campbell Diagrams for definitive vibration diagnosis. For detailed assessment, Harmonic and Transient Analysis allows designers to predict dynamic stresses resulting from forces like unbalance, ensuring the component’s fatigue life is sufficient.

Learn more about Rotational Modal Analysis and Campbell Diagrams on the SimScale Blog.

Electromagnetic (EM) Simulations

Electric motors and generators are a critical subset of rotating equipment, and their efficiency is governed by electromagnetics. SimScale’s EM solvers accurately calculate crucial machine characteristics like magnetic fields, flux, and torque output, which is necessary for meeting strict IE4/IE5 efficiency standards. To capture real-world performance accurately, these results can be integrated into Multiphysics Coupling workflows, linking electrical losses (heat) to a thermal analysis, which then feeds into a structural analysis to assess the impact of temperature on material stresses

Learn more about Multiphysics Simulation in SimScale.

Why SimScale for Your Rotating Equipment Design?

SimScale’s commitment to the rotating machinery sector is all about making advanced physics accessible. Our cloud-native platform is specifically engineered to overcome the common barriers of traditional CAE:

1. Speed and Parallelism: Run complex transient and parametric studies in parallel, reducing the turnaround time from days to hours.
2. Accessibility: No heavy software installation or specialized hardware is needed. Access powerful HPC from your web browser, anywhere.
3. Ease of Use: An intuitive interface and validated workflows, including specialized rotating machinery meshing, allow design engineers to conduct simulations without needing deep simulation expertise.

By leveraging SimScale, your team can Simulate Early, Simulate More, and Simulate Now—leading to faster design cycles, highly optimized products that meet global efficiency mandates, and maximum operational reliability for all your rotating equipment.

FAQs

The post What is Rotating Equipment? 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.

Sounds interesting?

Let’s take a closer look at how it all works.

What is a solenoid?

A solenoid, a coil of wire, is an electromechanical device that uses electromagnetism to produce controlled motion. As an electric current passes through the wire coil, magnetic field that can move a ferrous armature is generated.

What is the function of a solenoid?

This controlled motion of a solenoid can open or close valves to control fluid flow in hydraulic and pneumatic systems, engage locks, activate switches – depending on the application.

Solenoids are widely used as they can provide precise motion control using electricity alone, without a need for complex mechanical linkages.

Parts of a Solenoid

Here is a breakdown of the key components that work together to generate and utilize a magnetic field for mechanical action.

How does a solenoid work step-by-step?

To truly understand how a solenoid works, it helps to look inside and observe what takes place the moment electricity is applied.

Below is a step-by-step explanation of the entire process – starting with the initial flow of current and ending with the resulting mechanical motion:

1. Electrical current energizes the coil (solenoid activation) : Once voltage is applied to the solenoid, electrical current starts flowing through the copper winding. This flow of electricity creates a magnetic field around the coil, a process explained by Ampère’s Law. How strong this magnetic field gets depends on factors such as: the number of turns in the winding, how strong the current is, and the magnetic permeability of the core material.
2. Magnetic field strengthens and focuses in the core: Next, the stationary core – usually made of something like soft iron – channels and intensifies the magnetic flux created by the coil. This process creates a powerful magnetic circuit between the core and the plunger (also known as the armature). At this point, the magnetic energy is concentrated and ready to push the plunger into motion.
3. The plunger is pulled in: Now the magnetic force comes into play, pulling the plunger toward the coil’s center. This is how electromagnetic energy is converted into linear mechanical motion. Depending on how the solenoid is built, the plunger either moves in (pull-type) or pushes out (push-type). That movement is what performs the work – whether it’s flipping a switch, opening a valve, or locking something into place.
4. Power off – the spring takes over: As soon as the power is cut, current stops flowing and the magnetic field fades away. Without that force holding the plunger in place, the return spring takes over and pushes the plunger back to its ‘resting’ position. This mechanism ensures fail-safe operation and resets the solenoid for its next activation.

Types of Solenoids

Without realizing it, solenoids are actually used every day for a variety of purposes – quietly powering a wide range of devices.

Their adaptability in size and strength makes them suitable for everything from small gadgets to heavy-duty machines. Different jobs call for different traits – like how fast they respond, how much energy they use or how they move – so there are many types of solenoids, each built to handle specific tasks.

Solenoid types can be broken down as follows.

Based on function and design

• Linear solenoids: These produce a linear, in-and-out motion, most commonly seen in push/pull applications.
• Push/pull (or monostable): The armature moves in or out when the coil is energized and returns to its original position when the power is removed, often with the help of a spring.
• Latching (or bistable): These require a pulse of energy to move to an “on” or “off” state, and they stay in that position without continuous power.
• Proportional: The position of the plunger is proportional to the amount of power supplied to the coil.
• Rotary solenoids: These create a rotational motion instead of linear movement.
• Solenoid valves: These control the flow of fluids or gases by using a solenoid to open or close a valve.
• Direct-acting: The solenoid directly opens or closes the valve, and this can be done with or without pressure acting on the valve.
• Pilot-operated (or indirect-acting): These use the fluid pressure as a pilot force to help operate the valve.

Based on electrical type and frame design

• AC solenoids: Solenoids designed to run on alternating current, often using a laminated frame to prevent buzzing.
• DC solenoids: Solenoids designed to run on direct current.
• C-Frame solenoids: These have a C-shaped frame around the coil and are popular in many DC applications.
• D-Frame solenoids: These have a two-piece, D-shaped frame and are commonly used in industrial applications.

Solenoid Applications

Compact, efficient, and remarkably versatile – solenoids play a quiet but crucial role in powering modern technology.

Whether in automotive, manufacturing equipment or medical devices, their ability to deliver precise motion makes them indispensable to today’s engineering solutions. Let’s explore some of the most common and important solenoid applications.

Design & Simulation of Solenoids

Designing a well-functioning solenoid involves carefully balancing several interdependent factors – including magnetic strength, actuation speed, heat buildup, and in certain cases, fluid behavior. The key design challenge is to ensure the solenoid generates sufficient electromagnetic force to move the plunger reliably, all while avoiding overheating or performance drops under real-world conditions.

Since solenoids operate through interconnected physical processes, their design requires consideration of multiple physics. The flow of electric current produces a magnetic field, which in turn drives motion and can cause heat generation. In valve-related applications, this motion further influences fluid pressure and flow.

Accurately modeling these various physical phenomena requires a combination of electromagnetic, thermal and fluid dynamics simulations.

With SimScale’s cloud-based multiple physics platform, engineers can simulate and refine every aspect of solenoid behavior in a single workspace – from observing magnetic field distribution to assessing thermal performance and analyzing internal fluid flow. This holistic simulation approach speeds up development, cuts down on physical prototyping and ensures consistent performance across a wide range of use cases.

Solenoids in our projects

Here are some amazing SimScale projects simulating solenoids.

FAQs

The post How do Solenoids Work appeared first on SimScale.