“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.

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.

The question is no longer if you should adopt AI in your engineering workflows, but how to do it effectively and at scale. Across industries, engineering leaders are experimenting with AI pilots, but many are finding it difficult to move from isolated experiments to production-scale adoption. The result is often a collection of stalled initiatives and an uncertain return on investment.

For many engineering teams, the core challenge is the lack of a clear blueprint – what does success look like? Unlike other business functions, engineering has unique data and workflow complexities. Your proprietary design data is complex, multimodal, and not easily digestible by the large, general-purpose AI models that have captured the public imagination. A “one-size-fits-all” strategy simply doesn’t work.

A successful AI strategy in engineering isn’t a case of finding one algorithm or system to do everything. The key is to identify points in your workflow where it can add the most value – in other words so you can tackle the most significant bottlenecks in your product development cycle. This is the path from experimentation to transformation.

Two Bottlenecks, Two AIs

The product development process is a race against time. The goal is to shorten the loop from the moment a design is created to the moment its performance is fully understood. This delay is caused by two fundamental bottlenecks:

1. Simulation Lead Time: This is the manual effort and human waiting time. It includes the handoffs between teams, CAD preparation, meshing, and the entire simulation setup process. It’s the time spent preparing to get an answer.
2. Simulation Cycle Time: This is the raw compute time. It’s the hours—or even days—your high-fidelity solvers need to run to deliver a single, accurate result. It’s the time spent waiting for an answer.

To truly accelerate innovation, you need to attack both bottlenecks simultaneously. SimScale does this with a two-pronged AI strategy, deploying a purpose-built AI to solve each problem:

• Engineering AI: An autonomous agent designed to eliminate Simulation Lead Time by automating the entire workflow from setup to execution.
• Physics AI: A predictive system designed to eliminate Simulation Cycle Time by providing instant performance insights without running a full simulation.

Engineering AI in Action: Your New Autonomous Teammate

Consider the following engineering challenge: designing a cold plate for cooling electronics. The goal is to find the right trade-off between thermal performance (cooling) and pressure drop (efficiency).

Usually, an engineer would manually or programmatically set up a simulation for each design iteration—a repetitive and time-consuming process. Engineering AI transforms this. It acts as an autonomous agent that can perceive the simulation context (the geometry, the physics) and execute the entire workflow based on simple instructions. It becomes a new kind of teammate, handling the mundane tasks so your experts can focus on analysis and innovation.

The payoff here isn’t just about speed; it’s about liberating your most valuable engineering talent. By automating the setup, you empower your team to focus on the high-value work of interpreting results and driving design decisions.

Physics AI in Action: Real-Time Insight

Even with a fully automated setup, complex physics can take hours to compute. This inherent delay makes rapid, comprehensive design space exploration impossible.

This is where Physics AI comes in. By training on the results of previous high-fidelity simulations, it learns the physics of your design. It can then infer the performance of a new design variant in seconds, without ever running a full solver. This transforms the workflow from a slow, iterative process into an interactive design session.

As noted in our recent webinar with Ian Pegler from NVIDIA, this capability finally makes comprehensive design space exploration feasible for complex problems. It’s the difference between testing a handful of ideas and exploring thousands.

The Blueprint: How Engineering and Physics AI Work Together

The true revolution happens when these two systems work in concert. They are symbiotic, creating a workflow that is both automated and instantaneous. This is a day in the life of the modern, agent-augmented engineering team:

1. An engineer wishes to kick off a project to optimize a design (let’s say it’s a hydraulic manifold). Instead of digging out CAD files and simulation templates, they instead brief a customized Engineering AI agent in SimScale to perform the study, based on some simple instructions.
2. As part of the study, the AI agent chooses to make use of a relevant pre-trained Physics AI model which can immediately run inferences to provide a real-time prediction of each design’s thermal and hydraulic performance. The engineer, supervising the process, can interactively explore dozens of variations in minutes.
3. The human engineer and Engineering AI discuss the foremost design candidates, selecting a promising variant which demonstrates the desired characteristics spanning performance, cost and manufacturability.
4. Then it is back to the Engineering AI agent to autonomously set up and run a full, high-fidelity simulation in the background for final validation, while the engineer is already moving on to the next creative task.

The total time from a design change to a fully validated performance insight drops from days or hours to minutes. This isn’t a minor improvement; it’s a fundamental acceleration of the entire innovation cycle.

With expert knowledge captured in the AI agent, the whole workflow described above is now accessible to less expert, junior engineers. It is this democratization that allows engineering teams to scale and move faster.

Your Starting Point

Look at your own organization and ask: which bottleneck is slowing you down the most? Is it the manual lead time spent on setup and preparation, or the computational cycle time spent waiting for solvers?

Answering that question will reveal your starting point for a scalable AI strategy that delivers tangible results. If you would like to discuss your strategy with one of our experts, get in touch today.

Catch up on the webinar

Learn more about deploying AI in engineering with SimScale and NVIDIA as part of engineering.com’s Digital Transformation Week

Watch On Demand

The post A Day in the Life of Engineering AI appeared first on SimScale.

How this Y+ calculator works

This calculator works using the Schlichting and Gersten method and you can read more about the technical details of Y Plus and it’s calculation in this superb SimScale forum post.

Frequently Asked Questions

The post Y+ Calculator appeared first on SimScale.

STEM Racing challenges students to design, analyze, and manufacture a miniature Formula 1 car, which is raced on a 20-meter track. Beyond racing, the competition emphasizes key skills such as teamwork, project management, and entrepreneurial thinking.

Design Challenges

To compete successfully, the team needed a fast car—something only achievable through aerodynamic optimization. While physical track testing offers accurate results, manufacturing prototypes was both costly and time-consuming. To overcome these limitations, the team turned to SimScale’s online CFD platform.

Before adopting CAE, they faced several challenges:

• No visual feedback to guide design changes
• Inability to calculate drag and lift forces
• Slow and expensive trial-and-error with physical prototypes

With SimScale, the team gained access to:

• Accurate and fast cloud-based simulations
• A student-friendly, browser-based interface—no installation required
• A wealth of tutorials and learning resources that made onboarding easy

SimScale proved to be a powerful and accessible tool that allowed us to efficiently evaluate and enhance our car’s aerodynamic performance.

– Team Lightning

How SimScale Simulations Led to Success

The team imported their F1 car model into SimScale and created a flow volume to simulate a wind tunnel. Using an incompressible steady-state setup, they assigned air as the working fluid and defined key boundary conditions, including a 20 m/s velocity inlet, pressure outlet, and no-slip walls on the car’s surfaces. A hex-dominant mesh with surface and region refinements, as well as inflation layers, was used to ensure accuracy. A forces and moments control was added to track drag and lift throughout the simulation.

Over the course of development, the team ran 12 CFD simulations, each focusing on refining aerodynamic components such as the nose, side pods, and diffuser. Simulations averaged 25–30 minutes each, with SimScale automatically allocating cores based on mesh complexity. The final mesh contained approximately 1.4 million nodes, using the hex-dominant automatic meshing algorithm at a medium-to-fine fineness level.

SimScale provided the team with detailed results, including velocity planes, pressure distribution, and force coefficients. Particle traces revealed vortex behavior and flow separation, while wall shear stress and velocity contours guided further surface optimizations. The clear visual outputs and reliable data enabled efficient, data-driven design improvements throughout their project.

The team plans to use simulation results to refine key aerodynamic elements such as the nose, side pods, and diffuser, while also conducting FEA to evaluate structural strength. By virtually testing each component before physical production, they aim to ensure a more precise and efficient development process, reducing reliance on trial-and-error prototyping.

SimScale has been a game-changer in our F1 in Schools journey. Their support has been instrumental in helping us develop and refine our car. The cloud-based platform gave us the freedom to run high-quality simulations without hardware limitations, delivering accurate and reliable results throughout the process.

– Team Statement

The post Student Success Story: Team Lightning Demons appeared first on SimScale.

Design Challenges

The team aimed to implement numerous geometry optimizations to reduce drag but initially faced challenges in identifying the most aerodynamically effective solutions. To address this, they utilized SimScale’s CFD tools to simulate changes in drag force resulting from modifications to various parts of their car. Their CAD models were created in Onshape, and thanks to SimScale’s direct integration with Onshape, the team was able to import their car bodies seamlessly—greatly improving workflow efficiency compared to other CAE software.

In addition to CFD, the team also leveraged SimScale’s FEA suite to optimize components such as the wheels and the wing support structure, ensuring these parts were both lightweight and structurally sound.

The integration of several CAE tools within a single streamlined user interface makes SimScale, compared to other options, very easy and convenient to use.

– Team Zephyros

How SimScale Simulations Led to Success

To set up their CFD simulations, the team imported their car geometry from Onshape into SimScale. They created an external flow volume, applied boundary conditions including a pressure outlet and moving wall to simulate ground and wheel motion, and selected an incompressible steady-state analysis using the k-omega SST turbulence model. A region refinement was added around the car to improve mesh resolution, and force/moment controls were used to track aerodynamic performance.

For FEA simulations, the team imported their wheel geometry, applied appropriate boundary conditions (a 60N vertical load and fixed support), and increased mesh quality to improve accuracy. These simulations were key to optimizing weight and structural integrity.

One major challenge was fine-tuning the car’s body design for aerodynamic efficiency. After researching natural streamlined shapes, the team experimented with a concave front-end profile inspired by penguins, which research showed had a lower drag coefficient than traditional teardrop shapes. Simulations confirmed their hypothesis: the concave body produced less drag (0.354N vs. 0.362N), leading the team to adopt this optimized design. The pressure visualization tools in SimScale were especially helpful in guiding this decision

Using just a few core hours, the team achieved an excellent CFD convergence with acceptable range of residual values showing an improvement over the method used in the previous season despite lower computational cost. Over the course of the project, they ran 165 simulations across both CFD and FEA. The standard meshing algorithm with a fineness level of 5 proved most effective, with region refinements providing sufficient accuracy.

SimScale proved to be an invaluable tool throughout our development process, offering both efficiency and ease of use. We’re excited to continue this partnership as we head into the World Finals.

– Team Zephyros

The post Student Success Story: Team Zephyros appeared first on SimScale.

The session featured insights from our co-founder and Product Manager, Alex Fischer, alongside Nantoo’s CEO and Founder, Beatrice Sileno , and Chief R&D Engineer, Andrea Taurino. They walked us through Nantoo’s journey of developing a multi-action system for leaf collection and how simulation was the key to their success.

For those who missed it, here are our top five highlights from the session.

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

1. The Challenge: From a Vision to a Scalable Product

Nantoo set out to solve a common and frustrating problem: the inefficient and messy process of collecting autumn leaves. Their solution is an ambitious integrated system that not only vacuums and shreds leaves into a compostable bag but also functions as a blower and supports various accessories for total outdoor cleaning.

However, turning this brilliant idea into a scalable product proved to be a massive challenge. Beatrice Sileno, Nantoo’s CEO, shared their initial struggles, stating, “getting from vision to reality has been far tougher and more frustrating than I ever imagined and every prototype came with a high price tag, long delays and a constant echo: this is impossible”. This frustration with physical prototyping led them to reimagine their design process, and they discovered a paradigm shift with SimScale.

Key Takeaway:

Evaluate your physical prototyping process for bottlenecks; if costs and delays are high, adopting a digital twin approach early can de-risk development and prevent frustrating setbacks.

2. The Solution: A 3-Phase Digital Twin Strategy

Andrea Taurino, Nantoo’s Chief R&D Engineer, detailed the company’s shift to a “digital twin” methodology, breaking down the complex design process into a manageable three-phase strategy. Instead of relying on costly physical prototypes, Nantoo embraced simulation to systematically optimize their product. The first phase focused on optimizing the core of the system, the impeller, using a “virtual wind tunnel” approach within SimScale to meet performance and low-power consumption targets. Once the impeller was optimized, the second phase shifted to the airflow within the complete machine to achieve perfect suction, blowing, and the cyclonic effect needed to keep leaves in the bag. Finally, the third phase used SimScale to develop and analyze various accessories, such as an electric broom and a flexible pipe, ensuring they integrated perfectly with the main unit.

Key Takeaway:

For complex product designs, break the project into manageable phases by first simulating and optimizing critical components in isolation before analyzing the complete system’s performance.

3. The Method: Smart Optimization with Taguchi

To avoid endless trial-and-error simulations, Nantoo employed the Taguchi method, a powerful statistical approach for design optimization. Andrea explained how they defined key control factors for the impeller—such as inner/outer diameters, blade shape, and twist—and used SimScale to analyze the design cases. This systematic approach required a significant number of simulations. For the impeller alone, Nantoo ran 13 iterations of the Taguchi method, totaling 247 simulations.

It would have definitely been impossible to do so many simulations cost effectively and rapidly without SimScale.

Andrea Taurino

The results were astounding: impeller efficiency in their test setup skyrocketed from an initial 20% to a remarkable 90%. This entire data-driven strategy was completed in just six months.

Key Takeaway:

Instead of manual trial-and-error, use statistical methods like the Taguchi approach combined with cloud computing to efficiently explore a vast design space and achieve significant performance gains.

4. The Future is Now: An Introduction to Physics AI

Building on the theme of rapid iteration, our co-founder Alex Fischer introduced our platform’s strategy for Physics AI. He explained that while traditional simulation has revolutionized engineering, Physics AI takes it a step further by dramatically cutting down the time to get results.

It works by feeding simulation results to a graph neural network, which is then trained to provide physics predictions in seconds. Alex demonstrated how AI models can be trained using two primary methods: running a targeted set of simulations on new, synthetic data specifically to train a model, or leveraging valuable existing data from past simulation projects to accelerate future designs.

Key Takeaway:

Leverage Physics AI to get near-instant feedback on design changes, making it feasible to run extensive optimization studies and test more ideas in a fraction of the time.

5. The Demo: Predicting Performance in Seconds

The highlight for many was the live demo, where Alex showed Physics AI in action. Using an AI model trained on synthetic leaf blower data, he predicted the performance of a completely new design variation in a matter of seconds—a process that would take about an hour with a traditional CFD simulation. Even more impressively, he used Nantoo’s own past simulation data to train a custom AI model on the fly. This model then accurately predicted the pressure field on an unseen impeller design, demonstrating how companies can build valuable, proprietary AI models from their existing engineering work. This capability allows engineers to use AI for rapid iteration to find the best design quickly, and then use a small number of traditional simulations for final validation.

Key Takeaway:

Treat your historical simulation data as a valuable asset; it can be used to train custom, proprietary AI models that accelerate future product development and build upon your team’s past work. Because Nantoo’s data on the SimScale platform was already organized and in the cloud it was ready to use for AI training with no additional work needed.

Final Thoughts

This webinar provided a compelling look at how cloud-native simulation empowers innovative companies like Nantoo to build better products faster. The integration of Physics AI promises to further accelerate this process, turning extensive simulation data into an invaluable asset for instant design feedback.

To get all the details and see the live demonstrations for yourself, be sure to watch the full webinar on demand!

The post Webinar Highlights: Optimizing Home Appliance Design with Cloud-Native Simulation and Physics AI appeared first on SimScale.

An agentic Engineering AI is in active development at SimScale, integrated directly into our simulation platform, and we have customers using it today in alpha testing. This agent is set to make simulation even more accessible, intelligent, and productive, regardless of your experience level.

In this article, we dive into five agentic workflows that we see Engineering AI unlocking, either today or in the near future. The technology is maturing fast and we are excited about what’s coming next!

1. Helping Novice Users Get to a Working Simulation Sooner

Making simulation approachable for new users has always been at the core of SimScale’s mission. Engineering AI agents turbocharge this democratization. Rather than relying on static documentation or guides, an agent understands your CAD model and context in real time. It can proactively guide you step-by-step toward a viable setup—automatically diagnosing missing inputs, suggesting best practices, and flagging potential challenges long before you hit “run.” This transforms onboarding from a knowledge hurdle into a guided, confidence-building experience.

2. Accelerating Model Setup With Intelligent Automation

Setting up a simulation is often time-consuming, particularly when it comes to assigning boundary conditions, materials, or physics models. SimScale’s Engineering AI leverages both geometric information and vast simulation knowledge to streamline this process. The agent recognizes context from your geometry and model setup, recommends or auto-applies suitable settings, and raises issues only when human judgment is needed. The result: faster, more consistent model preparation with less context switching or uncertainty—bringing value to novices and seasoned analysts alike.

3. Guiding the Application of Company Best Practices

Scaling simulation quality and consistency across a distributed team remains an industry pain point. Engineering AI can actively reinforce organizational best practices. It serves as a digital mentor, surfacing internal guidelines and guarding against common pitfalls as users progress through a workflow. Over time, such agents learn from recurring mistakes or successes, driving incremental improvement and consolidating hard-won expertise—no matter the user’s prior experience.

4. Collaborating With Other Agents

Some of the most exciting potential of Engineering AI agents emerges through collaboration—between agents, not just humans. Multiple specialized agents, each with domain-specific reasoning, can work together across complex engineering challenges. Recently, during a joint webinar with Generative Engineering, we demonstrated a proof-of-concept agent-to-agent workflow. Here, SimScale and Generative Engineering agents jointly orchestrated a design space exploration, translating broad goals into practical analyses and autonomously iterating on concepts in the background. This kind of digital teamwork could transform the status quo of tool interoperability and ultimately deliver seamless, agent-powered multi-tool engineering workflows in the near future.

5. Automating RFQ Responses

One high-impact future application of the workflow demonstrated above is automating RFQ (Request for Quotation) responses. Mature engineering agents will soon be able to ingest customer requirements, map them to relevant design specs, set up and run appropriate simulations, and validate that the proposal meets performance criteria—all automatically. For organizations handling large volumes of RFQs or routine design studies, this unlocks significant value: reducing manual workload, speeding up response times, and freeing engineers to focus on high-impact, creative tasks.

Webinar: Is Your Organization Ready for AI?

Looking Ahead

Engineering AI agents represent a step change for simulation-driven design. They reduce ramp-up times, accelerate core workflows, promote best practices, enable multi-agent collaboration, and automate entire processes. At SimScale, we’re building these capabilities directly into our platform so that more organizations and engineering teams can benefit from agentic workflows.

If you’re interested in what AI-accelerated engineering could do for your team, get in touch with us below and be part of this new era of digital engineering.

The post What can Engineering AI do for you? Here are 5 agentic workflows set to transform productivity appeared first on SimScale.