Peter Selmeczy | Blog | SimScale

Pipe Flow Calculator

Peter Selmeczy — Fri, 17 Oct 2025 08:27:47 +0000

Use this Pipe Flow Rate Calculator to find the Volumetric Flow Rate ($Q$) of a fluid moving through a pipe.

How to Use

Enter the pipe dimensions and fluid velocity for your scenario.

Select the corresponding units for each value.

Click Calculate Flow Rate to get the Volumetric Flow Rate $Q$.

Pipe Flow Rate Calculator

Pipe Inner Diameter

Flow Velocity

Calculated Flow Rate

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

What is Volumetric Flow Rate?

The Volumetric Flow Rate $Q$ is the volume of fluid that passes through a specific point in a system per unit of time. Think of it as the answer to the question, “How much fluid is moving through this pipe?” It’s typically measured in units like cubic meters per hour (m³/h), liters per second (L/s), or US Gallons Per Minute (GPM)

Why is Pipe Flow Rate important?

Calculating the flow rate is crucial for the proper design and operation of countless systems.
Civil Engineering & Plumbing: It’s used to size pipes for residential and municipal water supply, ensuring adequate pressure and flow to fixtures. It’s also vital for designing storm drains and wastewater systems.
HVAC Systems: Engineers use it to determine the required flow of air in ductwork or water/coolant in heating and cooling systems to efficiently manage building climates.
Process & Chemical Engineering: In industrial plants, it’s essential for controlling the movement of liquids and gases, ensuring reactions happen correctly and safely.
Agriculture: Flow rate calculations are fundamental to designing irrigation systems that deliver the right amount of water to crops without waste.

What factors influence the Flow Rate?

Based on the formula $Q = A \times v$, the two direct factors you input into the calculator determine the flow rate:
Pipe Inner Diameter (D): This has the most significant impact. Because the area is proportional to the square of the diameter ($A \propto D^2$), even a small increase in diameter leads to a much larger increase in flow rate, assuming velocity stays the same. Doubling the diameter increases the potential flow rate by a factor of four.
Flow Velocity (v): This relationship is linear. If you double the velocity of the fluid, you double the volumetric flow rate. In real-world systems, velocity is determined by factors like pump pressure and friction losses from the pipe’s length and roughness.

The post Pipe Flow Calculator appeared first on SimScale.

Lift Coefficient Calculator

Peter Selmeczy — Fri, 17 Oct 2025 08:00:50 +0000

Use this Lift Coefficient Calculator to find the dimensionless Lift Coefficient (C_L) for an object moving through a fluid. The result is a critical value in aerodynamics and hydrodynamics for analyzing the performance of wings, hydrofoils, and other lifting surfaces.

How to Use

Enter the aerodynamic forces and fluid properties for your scenario.

Select the corresponding units for each value.

Click Calculate to get the Lift Coefficient (C_L).

Lift Coefficient Calculator

Lift Force (L)

Flow Velocity (v)

Reference Area (A)

Fluid Density (ρ)

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

What is the Lift Coefficient $C_L$?

The Lift Coefficient $C_L$ is a dimensionless number that relates the lift generated by a lifting body to the fluid density around the body, the fluid velocity, and an associated reference area. It's a way to normalize the complex relationship between an object's shape, its orientation (angle of attack), and the amount of lift it produces. A higher $C_L$ means more lift is generated for a given area and velocity.

Why is the Lift Coefficient important?

The lift coefficient is essential for designing and analyzing anything that needs to generate lift.
Aerospace Engineering: It's used to design aircraft wings to ensure they can generate enough lift to overcome gravity for takeoff, cruise, and landing.
Automotive Design: Race car designers use wings and spoilers to generate negative lift (downforce) to increase traction. The $C_L$ helps quantify this downforce.
Naval Architecture: It's critical for designing hydrofoils, which are underwater wings that lift a boat's hull out of the water to reduce drag and increase speed.
Wind Turbines: The blades of a wind turbine are essentially rotating wings. Their $C_L$ determines how efficiently they can capture energy from the wind.

What factors influence the Lift Coefficient?

While our calculator computes the $C_L$ from a given lift force, it's important to know what physical factors determine the coefficient itself:
Airfoil Shape: The cross-sectional shape of the wing is the most significant factor. Thicker, more curved (cambered) airfoils generally produce a higher lift coefficient.
Angle of Attack (α): This is the angle between the object's reference line (e.g., the wing's chord line) and the oncoming flow. As the angle of attack increases, the lift coefficient increases, up to a certain point.
Stall: If the angle of attack becomes too high, the airflow can separate from the top surface of the wing. This causes a sudden and dramatic drop in the lift coefficient, a dangerous condition known as a stall.

Is the Lift Coefficient constant?

No. Unlike a material property, the lift coefficient is not a fixed value for an object. It changes primarily with the angle of attack. Engineers often use plots of $C_L$ versus angle of attack to characterize the performance of an airfoil. The calculator helps you determine the $C_L$ for a specific flight condition (i.e., a specific amount of lift being generated at a certain speed and altitude).

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Reynolds Number Calculator

Peter Selmeczy — Fri, 17 Oct 2025 03:30:19 +0000

Use this Reynolds number calculator to find the Reynolds Number (Re) for a given scenario. The result helps predict if a fluid’s flow is laminar (smooth), transitional, or turbulent (chaotic).

How to Use

Select the Flow Type and Fluid Properties

Enter the required values and their corresponding units.

Click Calculate to get the Reynolds number and the flow regime.

Reynolds Number Calculator

Flow Type

Internal (Pipe/Duct) External (Over a Body)

Fluid Properties

Kinematic Viscosity Dynamic Viscosity & Density

Fluid Velocity (v)

Duct Shape

Circular Rectangular

Pipe Diameter (D)

Kinematic Viscosity (ν)

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

What is the Reynolds number (Re)?

The Reynolds number is a dimensionless quantity used in fluid mechanics to predict flow patterns. It represents the ratio of inertial forces (a fluid’s tendency to keep moving) to viscous forces (a fluid’s internal friction or “stickiness”).

Why is knowing the flow regime (laminar vs. turbulent) important?

The flow regime has major real-world consequences:
Pressure & Energy: Turbulent flow dissipates energy faster and causes a significantly higher pressure drop in pipes, requiring more powerful pumps.
Drag: Flow over a car or airplane wing creates drag. The type of flow in the boundary layer determines the amount of drag.
Heat Transfer: Turbulent flow transfers heat much more effectively than laminar flow, which is critical in designing heat exchangers or cooling systems.
Mixing: If you need to mix chemicals, turbulent flow is far more effective

What’s the difference between kinematic and dynamic viscosity?

This is a common point of confusion.
Dynamic Viscosity (μ): This is the fluid’s fundamental resistance to flow. Think of it as the fluid’s absolute “thickness” or internal friction. Its common units are Pa·s or cP.
Kinematic Viscosity (ν): This is the dynamic viscosity divided by the fluid’s density (ν = μ/ρ). It describes how easily a fluid flows under the force of gravity. Its common units are m²/s or cSt.

My pipe isn’t circular. How do I calculate the characteristic length?

For non-circular pipes or ducts (like triangles or ovals), you need to use the Hydraulic Diameter (D_h) as the characteristic length. The general formula is:
D_h = (4 x Cross-Sectional Area) / Wetted Perimeter
Our calculator automatically computes this for rectangular ducts, but you can use this formula to find the hydraulic diameter for any shape and use it in calculations.

Are the transition values (e.g., Re ≈ 2300) always exact?

No, they are rules of thumb, not strict physical laws. The transition from laminar to turbulent flow can be influenced by a multitude of factors.

Does the Reynolds number apply to gases too?

Yes. Gases like air, nitrogen, and steam are also fluids. The Reynolds number is used in exactly the same way to predict whether the flow of a gas is laminar or turbulent, which is essential for aerodynamics and HVAC design.

The post Reynolds Number Calculator appeared first on SimScale.

Webinar Highlights: Unlock Magnetic Pulse Welding Simulation

Peter Selmeczy — Fri, 26 Sep 2025 08:04:57 +0000

In our recent webinar, our application engineering team dove deep into the transformative realm of electromagnetic simulations, specifically focusing on the advanced technique of magnetic pulse welding.

As pioneers of cutting-edge, cloud-native simulation solutions, SimScale is dedicated to democratizing access to sophisticated engineering tools, allowing teams to accelerate design decision-making and optimize performance across various industries. This session unpacked how SimScale’s platform leverages AI-enhanced simulations to streamline and enhance the electromagnetic welding process, making it more accessible and efficient for engineering professionals.

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. Effortlessly Analyze Core Electromagnetics

Tackle computationally heavy transient simulations with ease using SimScale’s cloud-native platform. The webinar shows how on-demand computing power removes hardware limitations , allowing you to accurately analyze magnetic field distribution and eddy current density from your web browser. An integrated AI assistant can also guide you through the setup process, making complex analysis more accessible.

2. Accurately Predict Welding Forces

Learn how to predict the critical Lorentz forces that create the weld without melting the materials. The webinar demonstrates how SimScale’s high-fidelity results provide the insight needed to assess the effectiveness of the magnetic impulse. You can then instantly share these results with your team using a simple URL link, streamlining review and decision-making.

3. Manage Thermal Effects with Confidence

The high-intensity current pulse generates significant Joule heating, even in a microsecond-long event. Discover how to run a coupled thermal-magnetic analysis to visualize these heating effects and ensure temperatures don’t compromise material properties. If you need assistance, the platform’s integrated support chat provides expert advice in minutes.

4. Rapidly Optimize Your Design

See how the cloud enables rapid design optimization by running a limitless number of parallel simulations to explore a wide design space. The webinar shows how to easily compare different coil geometries, materials, and air gaps to improve weld consistency. This ability to iterate quickly allows your team to innovate faster and reduce the risk of failure.

5. Democratize Simulation for the Entire Team

SimScale is built to make simulation accessible to both experts and beginners on your team. The webinar explains how this approach helps break down knowledge silos and avoid bottlenecks common with traditional simulation tools. By using pre-validated simulation templates, designers and engineers can confidently run their own analyses, fostering a more collaborative and efficient workflow.

Conclusion

This SimScale webinar illuminated the profound impacts of integrating cloud-native, AI-enhanced simulation tools in addressing complex engineering challenges like magnetic pulse welding. The insights presented underscore how SimScale is at the forefront of the technological revolution in engineering, providing solutions that are not only powerful and comprehensive but also accessible and conducive to collaborative innovation.

Watch Now

For a deeper dive into how SimScale’s groundbreaking features can significantly benefit your projects, watch the full on-demand webinar recording. Discover firsthand the detailed demonstrations and expert discussions that will equip you with the knowledge to leverage magnetic pulse welding and other advanced simulations for your applications. Click here to access the full session and start transforming your engineering workflow today.

The post Webinar Highlights: Unlock Magnetic Pulse Welding Simulation appeared first on SimScale.

Webinar Insights: Valve Design and Flow Control

Peter Selmeczy — Wed, 17 Sep 2025 08:18:03 +0000

In the rapidly evolving field of engineering, the ability to quickly and accurately predict simulation outcomes is paramount. This necessity becomes even more significant when dealing with complex systems such as valve design and flow control. SimScale’s recent webinar, spearheaded by AI and engineering simulation experts, delved into the transformative capabilities of AI-powered design and optimization for these systems. Leveraging SimScale’s cloud-native platform, the session showcased how engineers could drastically reduce simulation times and enhance decision-making, thereby accelerating design cycles and fostering innovation.

On-Demand Webinar

Watch On Demand

1. Simplifying Complex Simulations with Physics AI

Often, engineers face the daunting challenge of lengthy computation times that can extend from hours to days, hindering rapid conceptual testing and development. SimScale’s Physics AI comes as a revolutionary solution, enabling the prediction of simulation outcomes instantaneously. This integration within the SimScale platform means that engineers no longer need to endure lengthy wait times for results, thereby accelerating the entire design process. This capability is especially beneficial in scenarios where multiple iterations are necessary, as it allows for a substantial increase in experiments conducted within a much shorter timeframe.

2. Built-in Data Management and Model Training

Before you can benefit from the huge speedup offered by Physics AI, you first need to build a model from a dataset. In fact, this can be one of the most time consuming aspects of model training, if simulation data is scattered across different devices and systems, or buried in organizational siloes. SimScale’s built-in data management keeps all of your simulation data in the cloud, ready to use for AI model training, at all times. It means that engineers using SimScale can decide at any point to build a Physics AI model from their simulation data, and directly use the integrated AI infrastructure to do so in just a few mouse clicks,

3. Collaborative and Accessible Cloud-Native Platform

Collaboration in engineering projects, particularly when involving multiple stakeholders, can be cumbersome if not facilitated by the right tools. SimScale’s cloud-native platform excels in making collaboration simple and effective. Multiple users can view and edit the same simulation project simultaneously, regardless of their physical location. This aspect is crucial for cross-functional teams working on complex projects as it ensures that all team members have real-time access to the latest project developments, enhancing both communication and output quality.

4. Comprehensive Coverage and Integration of Broad Physics Disciplines

A unique advantage of using SimScale is its comprehensive capability across various physics disciplines, including flow, thermal, structural, and electromagnetics, all within a unified user experience. This holistic approach allows engineers with expertise in one area to easily transition to others, fostering a versatile skill set and offering a broader perspective on multi-physics problems. The platform’s ability to handle these diverse disciplines underpins its versatility and appeal across different engineering sectors.

5. Scalability and High-Performance Computing (HPC) Capabilities

The need for scalability in simulations cannot be overstated, especially for enterprises handling large-scale projects. SimScale’s platform is designed to scale effortlessly with automatic HPC provisioning that requires no manual intervention from the user. This feature means that engineers can run multiple simulations concurrently, reducing the time taken to arrive at optimal solutions and greatly increasing the throughput of design explorations.

Conclusion

Today’s webinar highlighted SimScale’s continuous commitment to revolutionizing the engineering simulation landscape through innovative AI integrations and cloud-native technologies. The platform’s advanced features, such as Physics AI and Engineering AI, not only simplify and speed up the simulation process but also democratize access to advanced engineering capabilities, enabling engineers to make timely and informed decisions.

Watch Now

To gain a deeper understanding of how SimScale can transform your engineering workflow, we encourage you to watch the full webinar. The session is packed with insightful demonstrations and expert discussions tailored to help you leverage AI in your simulation projects effectively. Access the on-demand recording here and start transforming your engineering challenges into opportunities today.

The post Webinar Insights: Valve Design and Flow Control appeared first on SimScale.

Webinar Highlights: AI Agents in Engineering

Peter Selmeczy — Fri, 12 Sep 2025 11:30:56 +0000

The engineering sector is undergoing a significant transformation driven by Artificial Intelligence. While the industry has been slower to adopt AI due to complex engineering data management requirements and proprietary systems, this is rapidly changing. AI agents are evolving from a concept into practical tools that automate workflows, accelerate innovation, and redefine what’s possible in product development.

Our recent webinar, “The Rise of AI Agents in Engineering,” featuring SimScale’s CEO David Heiny and Application Engineering Manager Dr. Steve Lainé, explored this exciting frontier. Here are five key takeaways from the discussion.

On-Demand Webinar

If the highlights caught your interest, there are many more to see. Watch the on-demand Engineering Leaders 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. Agentic AI is a Leap Beyond Traditional Automation

For decades, engineers have relied on rigid scripts and macros for automation. While useful, these tools are often brittle, difficult to maintain, and only viable for highly repetitive workflows where the upfront cost is justified.

Agentic AI is different.

Instead of following a fixed script, an AI agent can:

Interpret Intent: An engineer can state a high-level goal, and the agent can interpret it to determine the necessary actions.
Reason and Adapt: The agent uses reasoning to navigate deviations from a standard process, handling unexpected variables that would break a traditional script.
Leverage Context: It learns from past simulations and organizational best practices to make intelligent decisions, such as applying the correct materials and boundary conditions without explicit, step-by-step instructions.

This flexible, intelligent approach makes automation more powerful and applicable to a wider range of engineering challenges.

2. AI Agents Eliminate Manual, Repetitive Work

A significant portion of an engineer’s time is spent on low-level tasks rather than creative problem-solving and innovation. AI agents are designed to take over this manual work, freeing up engineering teams to focus on high-value activities.

In a live demo, we showed how an AI agent could set up three different simulations in just minutes; a manifold stress analysis, an inverter NVH analysis, and a valve CV assessment. The agent autonomously:

Created the required analysis type in the platform.
Assigned materials based on past projects and internal data.
Applied relevant forces, pressures, and other boundary conditions.
Launched the simulation to run in the cloud.

By automating these manual setup processes, engineers can get critical performance feedback in minutes or hours instead of weeks, directly addressing the bottleneck of simulation lead time.

3. AI Agents Can Unlock Rapid Design Exploration

The webinar highlighted how SimScale’s unique combination of predictive Physics AI and agentic Engineering AI can work together to dramatically speed up innovation:

Engineering AI (Agentic AI): This system automates the manual work of setting up and managing simulations.
Physics AI: This system uses deep learning to accelerate the computational work, predicting simulation outcomes in seconds instead of hours.

When combined, these two systems create a powerful framework for design space exploration. An Engineering AI agent can autonomously generate and test hundreds of design variations, with each one being evaluated almost instantly by a Physics AI model. An example showed this in action, where a centrifugal pump was optimized by evaluating 400 different designs in approximately five minutes. A task that would take hours or even days using traditional solvers and programmatic automation.

4. Trust is Built Through Transparency, Not Black Boxes

A primary concern with AI in engineering is whether its output can be trusted. SimScale’s approach addresses this by making the AI’s process fully inspectable, not a “black box”.

Engineers can review, and even discuss, every step the agent takes:

every material it assigns,
every boundary condition it creates,
and every setting it chooses.

This transparency allows for complete oversight and can agents can operate in a fully automatic or human-supervised manner as desired. Furthermore, teams can implement “guardrails” and provide instructions based on their specific best practices, ensuring the agent operates within established organizational standards for quality and accuracy.

5. The Future is Collaborative, Agent-to-Agent Workflows

In this webinar we showcasing the ‘art of the possible’ in terms of interaction between a human engineer and a single AI agent, the natural first step in embracing this technology. Agentic AI in engineering also opens up a rich set of possibilities for even greater transformation: a collaborative ecosystem where specialized AI agents interact with each other.

Imagine a workflow where:

A CAD agent generates a new design based on system-level requirements.
It automatically passes the design to a simulation agent (like SimScale’s) for performance validation.
The results are then sent to a DFM (Design for Manufacturing) agent to check for manufacturability.

This seamless agent-to-agent communication, managed by an orchestration platform, will further break down silos and accelerate the entire product development lifecycle, allowing engineers to operate at a higher, more strategic level.

Watch Now

Experience the full potential of AI in engineering by watching our on-demand webinar. Delve into detailed demonstrations and discussions to understand how you can leverage SimScale’s AI capabilities in your projects. Watch the full webinar here.

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

Webinar Highlights – SimScale Summer 2025 Product Updates

Peter Selmeczy — Fri, 05 Sep 2025 11:20:55 +0000

In the fast-paced world of engineering, staying at the forefront of technology is key. The latest SimScale Summer 2025 Product Update is here to empower you, by dramatically accelerating your design processes.

Our mission is to democratize simulation, and these updates, powered by cloud-native solutions and AI, are our next step forward.

Below you’ll find the top five updates or you can watch the on-demand webinar to get the most insights into what’s happening behind the scenes.

On-Demand Webinar

Watch On Demand

1. Supercharge Your Workflow with SimScale AI

Imagine slashing your simulation times from hours to mere seconds. We’re making this a reality with two major additions to SimScale AI:

Physics AI: Use AI-driven surrogate models to predict outcomes based on existing simulation data, allowing for near-instantaneous performance feedback. Now also available to use with the Multi-purpose solver, using NVIDIA PhysicsNeMo. Make faster, data-driven design decisions and gain a competitive edge.
Foundation Models: We are introducing foundation models to further streamline your simulation setup, making the process more intuitive and efficient than ever before.

2. Experience a Smarter, More Intuitive Platform

We’ve rolled out several improvements to enhance your user experience and streamline your workflow:

Recent Projects: A dedicated page to help you quickly find and access your latest work.
Analytics Dashboard: Gain deeper insights into your projects and simulation usage with our new, comprehensive analytics tools.

3. Dive Deeper with Enhanced Simulation Capabilities

We’ve pushed the boundaries of our core simulation tools to help you tackle more complex challenges with greater ease and accuracy:

CFD: Several updated including the release of our latest integration with nTop for native import of implicit geometry models for flow and thermal analysis as well as adding turbulence modeling options for CHT analysis and physics modeling and meshing enhancements for Multi-purpose analysis.
FEA: Tackle highly nonlinear problems (large displacements, contact, hyperelasticity) with greater speed and stability using the renowned Marc solver from Hexagon. (watch the recent webinar here for more information)
Electromagnetics: Get localized insights from simulation results with the addition of probe points in the post-processor

4. Pre/Post Processing Enhancements

Visualize your simulations more efficiently with these new features:

5. Seamless, Instant Access to Innovation

One of the biggest hurdles in traditional engineering software is the delay in accessing new features. Because SimScale is cloud-native, these updates are available to you the moment they’re released. There’s no downtime and no installation required, ensuring you always have the latest tools at your fingertips.

Conclusion

The latest SimScale webinar demonstrated our commitment to pushing the boundaries of simulation technology. By continuously enhancing our platform with innovative features like Physics AI, implicit modeling, and advanced probing tools, we ensure that our customers can achieve optimal design outcomes faster and more reliably than ever before.

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 Summer 2025 Product Updates appeared first on SimScale.

Y+ Calculator

Peter Selmeczy — Thu, 04 Sep 2025 07:09:32 +0000

This Y+ calculator computes the required wall spacing to achieve a desired Y+ using flat-plate boundary layer theory.

Wall Spacing (Δs) Calculator

Calculate the required first layer thickness for a desired Y+ value.

Freestream Velocity (U_∞) m/s

Freestream Density (ρ) kg/m³

Dynamic Viscosity (μ) kg/m·s

Reference Length (L) m

Desired Y⁺

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

What is Y+ (Y-Plus)?

Y+ is a non-dimensional distance from the wall to the first mesh node, crucial for turbulence modeling in CFD. It determines how the boundary layer is resolved.

Why is the first cell height important in CFD?

The first cell height, or wall distance, determines the Y+ value. An appropriate Y+ is essential for accurately capturing fluid behavior near walls, which directly impacts the simulation’s overall accuracy.

The post Y+ Calculator appeared first on SimScale.

Webinar Highlights – Turn Past Engineering Data Into Future Engineering Decisions

Peter Selmeczy — Wed, 03 Sep 2025 13:00:41 +0000

The ever-increasing pace of innovation demands rapid advancements in engineering design and simulation capabilities.

However, unlocking the full potential of AI in engineering isn’t just about sophisticated algorithms; it’s fundamentally about data and infrastructure. Many organizations struggle with disparate data volumes, inconsistent folder structures, and scattered spreadsheets, creating a chaotic environment that hinders AI adoption. To truly leverage the transformative power of AI simulation, a robust, centralized data foundation is not just beneficial—it’s essential.

At SimScale, we address this challenge head-on. Our cloud-native platform provides the necessary infrastructure to turn your past engineering data into a strategic asset for future decisions.

In a recent webinar, our experts explored the critical link between data, AI, and engineering, demonstrating how SimScale empowers you to build a successful AI adoption plan – have a look at they 5 key highlights below.

On-Demand Webinar

Watch On Demand

1. The data foundation for Physics AI

The need for speed in design validation is more critical than ever, but traditional simulation processes often create bottlenecks. SimScale’s Physics AI dramatically reduces simulation time from hours to seconds, enabling instant physics predictions. This allows engineers to perform multiple iterations quickly, fostering broader design exploration and faster convergence on optimal solutions.

However, the power of Physics AI is unlocked by high-quality engineering data management. This is where SimScale’s cloud-native architecture becomes a game-changer. By centralizing and structuring your simulation data, our platform ensures it is AI-ready. This means you can retrospectively create Physics AI models from simulations that were not originally intended for this purpose, turning your existing data into a valuable resource for future innovation.

2. From data to decisions: A practical example

To illustrate how SimScale transforms data into actionable insights, we’ve created a standalone video demonstrating the end-to-end process. This video showcases how a complex valve simulation can be simplified and accelerated using our AI-powered tools, providing a clear, real-world example of the benefits discussed in the webinar.

3. Optimizing data utilization for AI integration

A critical aspect covered in the webinar is the importance of engineering data management for effective AI deployment. SimScale’s cloud-native platform facilitates the centralization and organization of simulation data, which is essential for training and refining AI models. By ensuring data quality and relevance, SimScale enables engineers to leverage AI more effectively, leading to more accurate and reliable simulation results.

4. Enhancing simulation accuracy with Agentic AI

SimScale’s introduction of Agentic AI, or Engineering AI, represents a significant leap in intelligent simulation environments. This AI form can perform tasks autonomously, learning from each interaction and continuously improving its understanding of complex engineering problems. Engaging with Agentic AI allows engineers to offload routine simulation tasks and data analysis, ensuring that even the most subtle design optimizations are considered, leading to improved product performance and reliability.

5. Streamlining workflows with Engineering AI

The integration of Engineering AI goes beyond individual simulations, facilitating a more holistic approach to engineering workflows. Unlike Physics AI, which focuses on speeding up specific simulation tasks, Engineering AI acts as an intelligent assistant that aids in managing and automating broader aspects of engineering projects. By handling repetitive tasks and providing smart suggestions, it enables engineers to focus more on critical design decisions and innovation. This AI-driven approach ensures that designs not only meet the technical requirements but are also optimized for performance, cost, and manufacturability.

Conclusion: AI in Engineering represents a paradigm shift

The integration of AI within SimScale represents a paradigm shift in how engineering simulations are performed and utilized. By embracing AI technologies like Physics AI and Engineering AI, engineers can not only accelerate their workflows but also achieve higher precision and efficiency in their designs. This revolution in simulation technology is not just about speed but about enabling smarter, data-driven decision-making that propels innovation forward.

Watch Now

To dive deeper into how SimScale is harnessing the power of AI to transform engineering simulations, we invite you to watch the full on-demand webinar. Explore detailed demonstrations and discussions by our experts and learn how these technologies can be applied to your specific engineering challenges. Watch the full webinar here and see first-hand how AI is reshaping the future of engineering.

The post Webinar Highlights – Turn Past Engineering Data Into Future Engineering Decisions appeared first on SimScale.

AI for Engineering Design and Manufacturing

Peter Selmeczy — Thu, 28 Aug 2025 23:25:26 +0000

For today’s engineering leaders, the operating environment has become a crucible of competing pressures – pun fully intended!

The market demands unprecedented speed, pushing for shorter design cycles and faster time-to-market. Simultaneously, products are becoming exponentially more complex, integrating sophisticated electronics, software, and new materials.

The need to optimize product performance is more critical than ever to meet these increasing demands.

Add to this the intense pressure to drive down costs all while meeting stringent sustainability and regulatory targets that have become a prerequisite for market access.

This is the engineer’s dilemma: a delicate, high-stakes balancing act between speed, cost, and sustainability.

Traditional, sequential engineering workflows and legacy software tools, which have served the industry for decades, are proving insufficient to navigate this new reality.

The strategic response cannot be another incremental tool; it must be a systemic shift toward a more integrated, intelligent, and data-centric ecosystem.

The engineering design process itself is being transformed, as AI-driven design integrates with ideation, creation, validation, and manufacturing to deliver innovative solutions more efficiently.

This is where Artificial Intelligence (AI) transitions from a futuristic buzzword to a present-day strategic imperative. By embedding AI into the core of the design process, organizations can move beyond making difficult trade-offs and begin to achieve multiple objectives at once, creating a powerful and sustainable competitive advantage.

The Modern AI Toolkit: From Augmenting Insight to Generating Innovation

To deploy AI effectively, it’s crucial to understand its distinct capabilities.

For engineering leaders, the AI toolkit can be broadly divided into two primary functions:

Predictive AI, which uses data -often analyzed by machine learning algorithms—to forecast future outcomes, and
Generative AI, which acts as a creative partner to autonomously generate novel design solutions, guided by specific design requirements to ensure fit, form, and function.

Predictive AI: From Reactive Fixes to Proactive Strategy

Predictive AI is about moving your organization from a reactive to a proactive posture. Its core function is to analyze vast datasets, including historical data, to predict outcomes faster than traditional methods, enabling proactive problem-solving and risk mitigation. By leveraging historical data, predictive AI systems train and validate predictive models that can forecast equipment failures and optimize operations. In the industrial sector, this has several high-impact applications, most notably in predictive maintenance. By analyzing real-time data from equipment, AI can anticipate component failures before they occur, minimizing costly unplanned downtime.

The business impact is not theoretical; it’s measured in millions of dollars saved.

Predictive AI enables project managers to make more informed decisions by providing accurate forecasts and actionable insights.

Operational Continuity at Methanex: When faced with a potentially high-risk leak in one of its methanol production plants, the company’s reliability engineering team and project managers used cloud-native simulation to analyze the fluid dynamics and validate a new containment component. They designed, simulated, and verified a solution within a single business day, preventing a forced outage of the unit and saving an estimated $3.5 million in production losses.

Read the Case Study

Downtime Reduction at Nalco Water: In another high-stakes scenario, Nalco Water used CFD simulation to urgently diagnose and fix a faulty water nozzle that was causing significant downtime at a large paper mill. By optimizing the nozzle design, the company achieved a 70% reduction in unplanned downtime, translating to an annual saving of $10 million for their client.

Read the Case Study

We have been using SimScale for FEA, CFD, and thermal analysis of our methanol production plants and components. We had a potentially high-risk situation with a leak in one of our plants and were able to use SimScale to turn around a verified solution within one business day. Without SimScale, we would have had to force an outage of the unit, costing time and significant production losses.

— Reliability Engineer at Methanex

Generative AI Models: Your Partner in Design Exploration

While predictive AI uses past and present engineering data to predict future outcomes, generative AI is a revolutionary force that changes how that future is created. It represents a paradigm shift in the way engineers interact with software and the capabilities of the workflows they make use of.

Powered by deep generative models, Engineering AI enables engineers and designers to define engineering design problems using higher level descriptions and have the AI autonomously explore the entire design space to generate a range of optimized designs.

Natural Language: A Force for Democratization

Using Gen AI powered agents to communicate with engineering software lessens the learning curve for new users of a software tool, allowing them to communicate intent and the engineering parameters and constraints, letting an agentic AI do the rest.

Guiding the Application of Company Best Practices

Gen AI can address the challenge of maintaining simulation quality across distributed teams. It acts as a digital mentor that reinforces company best practices and prevents common errors during workflows. By learning from successes and mistakes, the AI drives continuous improvement and consolidates organizational expertise for users of all experience levels.

Collaborating With Other Agents

The potential of Generative AI is greatest when agents collaborate with each other. We recently demonstrated a proof-of-concept where specialized agents from SimScale and Generative Engineering worked together to autonomously explore and iterate on designs. This form of digital teamwork will transform tool interoperability, leading to seamless, agent-powered engineering workflows in the near future.

Automating RFQ Responses

A key future application for this technology is automating RFQ (Request for Quotation) responses. Engineering agents will be able to automatically interpret customer requirements, run simulations, and validate a design proposal against performance criteria. This offers significant value to organizations handling high volumes of RFQs by reducing manual work, accelerating response times, and freeing engineers for more creative tasks.

Accelerating design exploration and generation

With predictive AI delivering near-instantaneous design evaluation, and agentic AI massively accelerating and automating workflows, we start to see a transformation of New Product Development (NPD) programs:

Define the Problem: The engineer inputs the high-level parameters and constraints. This includes performance requirements (e.g., load conditions, stiffness), material options, cost targets, cost goals, and, crucially, the available manufacturing methods (e.g., 5-axis CNC machining, casting, additive manufacturing).
Generate Solutions: The AI algorithm then explores all possible permutations, using existing designs and previous designs as foundational input data for generative models. It generates hundreds or even thousands of novel design options that satisfy all the defined constraints. The resulting geometries are often “organic” or “skeletal,” mimicking nature’s efficiency by placing material only where it is structurally necessary. These models optimize designs for both performance and cost-effective outcomes.
Explore and Select: The software presents the array of solutions, often on a trade-off plot (e.g., cost vs. mass). The engineer can then explore the different options, analyze the trade-offs, and select the optimal design that best aligns with the project’s strategic goals. This process involves solving engineering design problems and specifically addressing complex engineering design problems to ensure the selected solution meets all requirements.

By exploring the design space using complex simulations, generative AI enables rapid evaluation of a wide range of possibilities, accelerating the path to innovative solutions.

This approach leads to parts that are not just innovative but also holistically optimized.

The case study from General Motors powerfully illustrates this. In a collaboration with Autodesk, GM engineers used generative design to reimagine a common seatbelt bracket. The AI produced over 150 design alternatives. The final selected design consolidated what was originally an eight-component assembly into a single part that was 40% lighter and 20% stronger than the original.

This has become the quintessential example of generative design’s power to simultaneously reduce complexity, cut weight, and improve performance, benefiting mechanical engineers and advancing the field of mechanical engineering.

In summary, generative AI is transforming product design & development by enabling engineers and designers to optimize designs for performance, cost, and manufacturability, while leveraging previous and existing designs, advanced simulations, and real-time feedback to solve complex engineering design problems in a cost-effective and innovative manner.

The Accelerator: How AI Driven Simulation is Supercharging Engineering

For decades, engineering simulation has been an indispensable tool, but its reliance on computationally intensive numerical methods has often relegated it to a late-stage validation role, performed by a small number of specialists.

This creates a critical bottleneck, severely limiting the number of design iterations a team can explore and stifling innovation.

AI is shattering this paradigm, transforming simulation from a bottleneck into a real-time design exploration engine. Instead of solving complex physics equations from scratch for every new design, AI models are trained on data from past simulations to learn the intricate relationships between a design’s geometry and its performance.

Once trained, these models can make nearly instantaneous predictions for new designs, significantly improving efficiency and operational efficiency.

This dramatic acceleration “democratizes” simulation, making it an accessible, interactive tool for design engineers, not just CAE specialists. AI-driven tools help reduce errors and save valuable time by automating complex analysis and streamlining workflows. The business impact is profound, enabling a level of design iteration that was previously unimaginable and directly contributing to enhanced product quality.

Comprehensive Design Exploration at Bühler Group: The industrial equipment leader needed to optimize a new food processing technology. By leveraging a cloud-native simulation platform, their teams were able to evaluate 60 distinct design variants in just two weeks.

Read the Case Study

Radical Time Reduction at Hazleton Pumps: The global pump supplier traditionally relied on building and testing physical prototypes—a slow and expensive process. By adopting early-stage simulation, they achieved a staggering 99% reduction in their development timeline, compressing cycles that took months into mere days and saving time at every stage.

Read the Case Study

Simulation (SimScale) drastically changed our R&D landscape regarding time (99.9% quicker), cost (no HPC and data storage), and simulation accuracy… It allows us to complete development cycles within days instead of months, giving us a massive advantage compared to our competition. I would say that this (software) is not evolutionary but rather disruptive. Engineer, Hazleton Pumps

The Cloud-Native Imperative: The Foundation for AI at Scale

The transformative potential of AI-powered design and simulation cannot be fully realized within the constraints of legacy, on-premise computing infrastructure. A cloud-native platform is the essential foundation required to enable this new engineering paradigm.

On-Demand Scalability: AI model training and large-scale generative design studies require immense computational power. Cloud platforms provide this massive scalability on demand, allowing teams to access supercomputing-level resources when needed and pay only for what they use, eliminating the need for costly in-house HPC hardware.
Centralized Data: AI models are only as good as the data they are trained on. Cloud-native platforms act as a centralized hub for all critical engineering data—CAD, CAE, test data, technical documentation, and operational feedback—breaking down the information silos that cripple agility and providing the fuel for the AI engine. With all this data in one place, advanced data analytics can be applied to monitor processes, optimize operations, and drive better decision-making.
Seamless Collaboration: The fast, iterative workflows enabled by AI demand a new level of teamwork. Cloud platforms with built-in, collaboration features are designed for this purpose, allowing globally dispersed teams to work together and concurrently on the same models, share results in real-time, and benefit from real time feedback during the product development process.

The Final Piece: Future-Proofing Your Engineering Process and Your Team

The convergence of AI and cloud-native simulation is more than a technological upgrade; it’s a strategic inflection point that will define the next generation of industry leaders. The evidence clearly shows that “companies investing in cloud-native simulation today are investing in their ability to compete tomorrow.”

This investment is not about acquiring a static tool. It is about plugging into a platform for continuous, compounding improvement. The data feedback loops, where real-world operational data informs the next generation of designs, create a virtuous cycle. AI-driven systems can automate repetitive tasks, freeing up engineering teams to focus on innovation and strategic problem-solving. Additionally, AI helps reduce waste in production processes, supporting greater efficiency and sustainability. Early adopters will not just gain a one-time advantage; they will build an accelerating competitive lead that becomes increasingly difficult for followers to close.

For engineering leaders, the path forward is clear. The future of manufacturing will be defined by intelligence, agility, and sustainability. Embracing an AI-driven, cloud-native design platform, in collaboration with a human team, is the most critical strategic step you can take to ensure your organization is not just keeping up with the pace of change, but driving it. Looking ahead, the integration of advanced technologies like computer vision will further expand the potential of engineering applications, enabling even greater precision and efficiency.

A new global study of 300 engineering leaders reveals a widening gap between AI expectation and execution in engineering workflows, and the lessons from top performers who are closing it.

Read the State of Engineering AI

The post AI for Engineering Design and Manufacturing appeared first on SimScale.