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.

The focus was the groundbreaking integration of Hexagon’s Marc nonlinear solver into SimScale’s cloud-native simulation platform, making advanced nonlinear FEA more accessible than ever. Here are the top five takeaways from this insightful discussion.

On-Demand Webinar

If the above highlights caught your interest, there are many more to see. Watch the on-demand Engineering Leaders Series webinar from SimScale on Revolutionizing Advanced Non-linear Simulation using Marc and SimScale integration by clicking the link below.

Watch On Demand

1. The Power of Nonlinear Simulation in Modern Engineering

Real-world engineering challenges often involve nonlinear behavior, from material plasticity to large deformations and complex contact interactions. Traditional linear solvers fall short in these scenarios, which is where Marc’s advanced nonlinear capabilities shine. Industries like automotive, industrial machinery, consumer products, and electronics require highly accurate predictions of structural performance, and the Marc solver is designed to tackle these challenges head-on.

2. Why Bringing Marc to the Cloud is a Game-Changer

SimScale’s cloud-native platform already democratizes simulation by removing the need for expensive hardware and complex software setups. By integrating Marc’s industry-leading nonlinear solver, engineers can now run highly sophisticated simulations directly in their browsers with unlimited scalability and instant collaboration. This means faster results, lower costs, and improved design decision-making at any stage of development.

3. Faster, More Robust Simulation Workflows

Nonlinear simulations can be computationally demanding, often requiring extensive fine-tuning. One of the standout benefits of using Marc on SimScale is its robust contact handling and efficiency. During the webinar, Richard Szöke-Schuller highlighted a benchmark study comparing a plastic push pin simulation:

• With traditional solvers, the simulation took almost 2 hours on 8 cores.
• With Marc on SimScale, the same simulation ran in just 13 minutes: an 80%+ reduction in runtime!

This performance boost means engineers can iterate designs faster than ever, enabling more frequent testing and optimization without sacrificing accuracy.

4. Key Applications: From Automotive to Electronics

With Marc’s nonlinear capabilities now available in the cloud, engineering teams can tackle a broad range of real-world applications scalably and more accessibly, including:

• Automotive fasteners & seals: Optimize plastic rivets and push pins with hyperelastic material models.
• Consumer product drop tests: Simulate impact scenarios to improve durability and safety.
• Electronics & PCB design: Evaluate the structural integrity of connectors, casings, and assembled components under varying loads.
• Industrial machinery: Analyze gasket sealing, elastomer components, and high-load assemblies for long-term reliability.

5. How to Get Early Access to Marc on SimScale

This powerful integration is launching soon, and engineers looking to leverage advanced nonlinear simulation in the cloud can apply for our Early Access Program.
By joining, you’ll gain hands-on experience with Marc on SimScale and help shape the future of cloud-based nonlinear analysis.

Learn more about how you can apply for early access here.

Looking Ahead: The Future of Nonlinear Simulation

As the industry moves toward more complex, high-fidelity simulations, integrating powerful solvers like Marc with cloud-native accessibility will redefine how engineers approach structural analysis. SimScale remains committed to providing cutting-edge simulation tools that are fast, flexible, and accessible without the traditional barriers of desktop-based software.

Stay tuned for more updates, and if you missed the live session, be sure to check out the full webinar recording here!

The post Top 5 Webinar Highlights: Hexagon’s Marc Solver Now on the Cloud appeared first on SimScale.

On-Demand Webinar

If these highlights caught your interest, there are many more to see. Watch the on-demand Simulation Expert Series webinar from SimScale on Rapid Design Simulations for Home Appliances & Consumer Electronics by clicking the link below.

Watch On Demand

1. Accelerate Design with Cloud-Native Simulation

SimScale’s cloud-native approach eliminates the need for heavy computational resources. Engineers can run high-fidelity simulations directly from a browser, anytime, anywhere, even on low-powered devices. The platform supports diverse physics, including structural, thermal, fluid dynamics, and electromagnetic simulations, making it a versatile tool for the entire design process.

2. Optimize Induction Cooktop Designs with Multiphysics Simulations

A live demonstration showcased how SimScale can simulate electromagnetic and thermal behaviors in induction cooktop designs. Key insights included:

• Electromagnetic field optimization to improve energy transfer efficiency and uniform heating
• The ability to analyze variations in coil geometry and air gaps for different cookware materials
• Thermal simulations to assess cooling efficiency within induction hubs, ensuring safe operation

These features help teams fine-tune designs for performance and energy efficiency.

3. Unlock AI-Powered Simulation Predictions

SimScale’s AI capabilities reduce lead times by narrowing down design options, enabling quick identification of optimal configurations. For example, in scenarios requiring the exploration of multiple geometries or material combinations, AI can predict performance trends, cutting down on iterative manual simulations.

4. Seamless Collaboration with Cloud-Native Sharing

SimScale simplifies collaboration with a Google Docs-style sharing model. Engineers and stakeholders can view or edit simulation setups in real time, regardless of location. This promotes faster iterations and minimizes delays in the design process.

5. Thermal Coupling for Improved Product Safety

The webinar highlighted SimScale’s ability to couple electromagnetic and thermal simulations. This is crucial for designs like induction cooktops, where understanding heat distribution and cooling is paramount. SimScale also allows teams to simulate temperature-dependent material properties, ensuring reliability under varying conditions.

Driving Innovation in Home Appliances and Consumer Electronics

The webinar underscored how cloud-native simulation tools like SimScale are reshaping the consumer electronics and home appliance sectors. By enabling engineers to explore designs earlier and more efficiently, teams can accelerate innovation and reduce costs while maintaining high standards of performance and safety.

If you missed the live session, don’t worry! Access the webinar recording here to dive deeper into the capabilities of SimScale for your projects.

For any questions or a live demo tailored to your application, feel free to contact us. Let’s shape the future of design together!

The post Top 5 Webinar Highlights: Rapid Design Simulations for Home Appliances & Consumer Electronics appeared first on SimScale.

Let’s get you up to date with SimScale’s new key features released in Q2 2023.

1. Custom Wind Comfort Criteria/Plots

SimScale already provides today a variety of different Pedestrian Wind Comfort Criteria, like Davenport, Lawson, London LDDC, NEN8100, and more.

Still, this list can never be exhaustive as there are a multitude of locally used and adapted comfort criteria that are either required by local authorities or have proven to be well suited to the specific local conditions.

SimScale enables our users to define their own comfort criteria with custom wind speed ranges and percentage thresholds.

With this new possibility, a range of new comfort criteria can be created. Here are some examples:

• CSTB Wind Comfort Standard
• Auckland Wind Comfort Criterion
• Melbourne Wind Comfort Criterion
• Bristol Wind Comfort Criterion
• Israeli Wind Criteria
• Murakami Wind Comfort Criteria

2. Thermal Resistance Networks for IBM

This feature is a natural extension to the Immersed Boundary solver and is already available for Conjugate Heat Transfer. It provides thermal resistance networks like two-resistor or star resistor models in the simulation setup and allows you to define detailed components like chips or LEDs as customized components. This avoids the necessity for very fine meshes for those often tiny components.

Users can define a thermal resistance network (TRN) by assigning the top surface of a cuboid as a TRN.

Model the chip as a simple cube in a CAD model or replace the detailed 3D model via ‘Simplify’ on SimScale.

3. Multiphase: Surface Tension

With the addition of surface tension, users of the new multiphase module will be able to improve the accuracy of multiphase results for surface tension dominant flows like microgravity sloshing, capillary flows, microfluidics, etc.

4. Ogden Hyperelastic Model

We have added this model to better simulate highly elastic rubber. In the animation below, you can see the movement of two solid parts coming together and separating again. There is a hollow rubber seal between them with significant deformation.

Use Case & Benefits

• Accurately simulate rubbery and biological materials at high strains
• Increasing hyperelastic functionality

5. Cylindrical Hinge Constraint

The Cylindrical hinge constraint boundary condition replicates the behavior of a fixed hinge. The assigned surface is constrained such that only rotational motion around the hinge axis is free.

SimScale can automatically detect the axis of the hinge based on an assigned cylindrical surface, but the boundary condition also allows for a user-defined input.

6. CAD Swap Improvements

When replacing one CAD model with another, it isn’t always clear what worked and what didn’t. With this feature, we add clarity so that users know what was successful and what requires their attention.

7. Parametric Studies

Boundary conditions can now be parametrized to run multiple simulations with a button click. Some examples are:

• Electronics: change inlet flow rates, change the heat load on parts
• AEC: change inlet flow rates to understand the impact on cooling strategies
• Rotating Machinery: change the inlet velocity and rotational velocity and compare designs

8. CAD Extrude Operations

Extrude is similar to move, although it will maintain the same cross-sectional area — often very useful.

This video shows one move operation followed by one extrude operation. Notice how the extrude option maintains the shape of the adjacent surfaces.

9. Distance Measurement

This is a highly requested feature, and I think we have answered nearly all use cases with this first iteration. We now offer the ability to measure the length/area of an entity and also measure the distance between two entities.

Take These New Features for a Spin Yourself

All of these new features are now live and in production on SimScale. They are really just one browser window away from you!

If you wish to try out these new features for yourself and don’t already have a SimScale account, you can easily sign up here for a trial or request a demo below. Please stay tuned for our next quarterly product update webinar and blog.

The post NEW Features: Custom Wind Comfort Criteria, Thermal Resistance Networks, Surface Tension, and More! appeared first on SimScale.

Their purpose? To explain how structural systems behave when they are subjected to dynamic loadings.

Imagine you’re standing on the edge of a freeway, watching cars whizz by, or perhaps looking at a towering skyscraper standing firm against a turbulent wind. The forces and movements you observe are dynamic, constantly changing, imposing loads that challenge these structures.

This article delves deep into understanding the dynamic response, dynamic shock analysis, and their nuances. We will explore the implementation of these analyses in SimScale and how a cloud-native platform enables such FEA simulations. This article also sheds light on the intricate processes of these simulations and the subsequent interpretation of results for optimal system design.

What Is Dynamic Response Analysis?

Dynamic response analysis involves analyzing the behavior of structures under dynamic loading conditions (loads that can change in magnitude, direction, or frequency over time).

Picture a structure under dynamic loads: The load magnitude fluctuates, the direction alternates, and even the frequency evolves with time. Static studies tend to perceive these loads as constant, overlooking essential factors like damping and inertial forces.
However, reality often defies these assumptions. Loads are dynamic, varying with time and frequency.

Dynamic response analysis is designed to address this deficiency by providing a methodology to handle non-constant load conditions. It is typically employed when the frequency of a load exceeds one-third of the basic frequency.

To get a sense of the distinction between static analysis and dynamic analysis, consider the equations used in finite element models:

$$ [K] \vec{u} = \vec{F} \tag{1}$$

$$ [M] \ddot{\vec{u}} + [C] \dot{\vec{u}} + [K] \vec{u} = \vec{F} \tag{2}$$

Where \(\vec{F}\) the load vector, \([K]\) is the global stiffness matrix, \([M]\) is the global mass matrix, \([C]\) is the global damping matrix, \(\vec{u}\) is the displacement vector, \(\dot{\vec{u}}\) is the velocity vector, and \(\ddot{\vec{u}}\) is the acceleration vector.

\([M] \ddot{\vec{u}}\) is the inertial force (i.e., mass times acceleration) and \([C] \dot{\vec{u}}\) represents the damping force (i.e., damping coefficient times velocity). These terms represent the dynamic forces that distinguish dynamic simulations from static simulations.

The computation of this analysis is typically conducted via simulation software, which determines the simulation’s characteristic response by integrating each mode’s contribution to the load.

The value of using dynamic response analysis depends on various aspects of loading:

• How often it changes (load frequency)
• How big it is (load magnitude)
• Which way it’s going (load direction)
• How long it lasts (load duration)
• Where it’s applied (load location)

Dynamic response analysis can be further subdivided into several types of analysis, namely modal analysis, harmonic response analysis, and transient dynamic analysis.

Modal Analysis

Modal analysis is an analysis type that identifies the inherent dynamic properties of a system in order to create a mathematical model, called the modal model, that describes its dynamic behavior using modal data. It helps define the system’s natural characteristics, such as its natural frequency, damping, and mode shapes (mode shapes represent the characteristic displacement pattern of the system).

By studying the frequency and position of a structure, modal analysis enables us to specify when the system would experience resonance, which is the point at which the applied excitation is equal to the system’s natural frequency. This helps make informed design decisions so that phenomena like resonance are avoided.

Harmonic Analysis

Harmonic analysis is a type of dynamic response analysis that simulates the steady-state behavior of solid structures subjected to periodic loads, providing frequency-dependent results. In other words, it studies the response of linear structures under a load varying sinusoidally with time.

Harmonic analysis is particularly useful for evaluating the effects of vibrating forces or linear displacements over a range of frequencies.

Transient Dynamic Analysis

Transient dynamic analysis is a method used to assess the behavior of deformable bodies under conditions where inertial effects play a significant role. It provides time-dependent results, making it particularly useful for evaluating the effects of rapidly applied loads.

What Is Dynamic Shock Analysis?

Dynamic shock analysis specifically focuses on the response of a structure or system to sudden, high-intensity loads or impulses. It aims to assess the behavior and integrity of the structure under extreme loading conditions, such as impact, collision, or explosive forces.
Imagine an extreme scenario – an automotive crash structure colliding, an aircraft experiencing a hard landing, or an electronic device enduring a drop impact.

This is where dynamic shock analysis takes the stage, specializing in understanding how your design would respond to sudden, high-intensity loads.

While dynamic response analysis is a generalist, shock analysis is a specialist, addressing the extraordinary events where high-intensity, rapid-loading events are involved. By doing so, it helps optimize designs for maximum energy absorption and minimum deformation, predicts potential failures for safety enhancement, and even aids in meeting regulatory requirements.

What Is Dynamic Shock Analysis Used for?

Design Optimization

It helps optimize the design of automotive crash structures, ensuring they can absorb maximum impact energy while minimizing deformation and reducing the risk of occupant injury.

Safety and Failure Prediction

It enables the assessment of structures subjected to sudden loads, such as aircraft components during a hard landing, to predict potential failures and improve safety measures accordingly.

Regulatory Compliance

Dynamic shock analysis assists in meeting regulatory requirements, such as testing electronic devices to ensure they can withstand drop impacts within specified limits.

Research and Development

It aids in developing resilient and durable materials for applications like protective gear, where the analysis evaluates their ability to absorb and dissipate impact energy effectively.

FEA for Dynamic Response and Shock Analysis

Imagine being able to simulate the dynamic and shock conditions your design would endure and predict its response – without physical trials. That’s the power of finite element analysis (FEA).

By creating computerized models of structures and applying suitable loads and boundary conditions, you can foresee how these structures would react to dynamic loads and shocks.

The methodology of FEA involves breaking down the structure’s model into thousands of small, interconnected ‘finite elements.’

These elements closely represent the intricate features of the structure, thus enabling accurate calculations of stress, strain, and displacement under dynamic and shock loadings.

To learn more, check out this step-by-step guide to dynamic analysis.

Now, let’s go one step further and introduce SimScale into the equation. This is where your journey toward efficient and accurate solutions begins. SimScale’s Structural Mechanics software is a powerful tool that allows engineers to virtually test and predict the behavior of their designs under dynamic and shock conditions.

Maximize Efficiency with SimScale Simulation

SimScale’s cloud-native platform enables engineers and designers to simulate early in the design process without the hassle of software installation and expensive hardware. It empowers design teams and simulation experts alike to test their designs under various conditions by running multiple simulations simultaneously using the power of the cloud. This minimizes the testing time significantly and enables quicker design optimizations, thus enabling faster innovation. Experience the power of collaboration, innovation, and optimization with SimScale’s cloud simulation, accessible anytime, anywhere. Simply sign up, import your 3D design, and start simulating immediately in your web browser.

Nevertheless, the benefits of SimScale don’t stop at accessibility. It also brings your projects into the collaborative sphere, allowing you to share them with your colleagues and teams. This facilitates rapid design improvement and significantly shortens your workflow.

Take, for instance, TechSAT, a prominent company in the aerospace industry. They use SimScale’s simulation capabilities to optimize and validate the performance of their products. SimScale has significantly reduced TechSAT’s time to develop new products. Here is what other customers have said about SimScale.

If you’re an engineer or a product developer eager to make your design process more efficient and speed up your innovation process, it’s time to take advantage of cloud computing and take the next step towards efficient and accurate engineering solutions with SimScale’s cloud-native platform. Sign up below or request a SimScale demo today.

The post Dynamic Response and Dynamic Shock Analysis in FEA With SimScale appeared first on SimScale.

But that’s not all. In this article, we will explore how simulation can not only help study mechanical phenomena but also enable better-informed decision-making early in the design process. In other words, we will see how engineers can benefit from one particular aspect of simulation that provides them with more accessibility, collaboration opportunities, and efficiency in both time and money.

What is Solid Mechanics?

Solid mechanics is a branch of physical science that focuses on studying the movement and deformation of solid materials under external loads such as forces, displacements, and accelerations. These loads can cause different effects on the materials, such as inertial forces, changes in temperature, chemical reactions, and electromagnetic forces. This field plays a critical role in various engineering disciplines, including aerospace, automotive, civil, mechanical, and materials engineering.

Solid mechanics focuses on understanding the mechanical properties of solid materials and their response to different types of loading. These materials include metals, alloys, composites, polymers, and others. By studying how materials behave under different conditions and in different environments, engineers can gain insights into designing and optimizing structures, components, and systems to ensure their safety, reliability, and performance.

In solid mechanics, there are two fundamental elements:

• The object’s internal resistance that acts to balance the external forces, represented by stress
• The object’s deformation and change in shape as a response to external forces, represented by strain

The relationship between stress and strain is described by Young’s Modulus, which states that strain occurring in a body is proportional to the applied stress as long as the deformation is relatively small – i.e., within the elastic limit of the solid body. This can be visualized in the stress-strain curve shown below.

What is Solid Mechanics Used for?

The importance of solid mechanics lies in its practical applications and contributions to engineering and the industry. The key reasons why solid mechanics is not only practical but crucial for engineers can be categorized as follows:

• Design analysis
• Failure analysis and prevention
• Material selection and optimization
• Structural safety and load-bearing capacity
• Performance optimization and efficiency

Design Analysis

Solid mechanics provides the foundation for designing and analyzing structures and components. By applying principles of solid mechanics, engineers can assess the structural integrity and performance of systems and ensure they meet design requirements and safety standards.

It enables them to predict and understand factors such as stresses, strains, and deformations, which are vital in designing structures that can withstand expected loading conditions and environmental factors.

Failure Analysis and Prevention

Solid mechanics helps engineers investigate and analyze failures in structures or components. By understanding the causes of failure, such as excessive stress, material fatigue, or deformation, engineers can improve design practices, materials selection, and manufacturing processes to prevent failures and enhance the reliability and durability of products.

Material Selection and Optimization

Solid mechanics plays a significant role in material selection and optimization. Engineers need to evaluate the mechanical properties of different materials and assess their suitability for specific applications.

By considering factors such as strength, stiffness, toughness, and fatigue resistance, solid mechanics helps engineers choose the most appropriate materials to meet performance requirements while considering factors such as weight, cost, and manufacturability.

Structural Safety and Load-bearing Capacity

Solid mechanics allows engineers to assess the safety and load-bearing capacity of structures and objects. Through analysis and simulations, engineers can determine the structural stability, response to external forces, and ability to withstand static and dynamic loads.

This knowledge is essential in ensuring the integrity of critical structures, such as bridges, buildings, and aircraft, where failure could have severe consequences.

Performance Optimization and Efficiency

Solid mechanics helps engineers optimize designs to improve performance and efficiency. By analyzing stress distributions, material usage, and structural behavior, engineers can identify areas for improvement, reduce unnecessary material and weight, and optimize designs for enhanced strength, rigidity, or energy efficiency. This optimization process leads to cost savings, improved product performance, and reduced environmental impact.

Using Simulation in Solid Mechanics

Understanding how solid materials behave under different conditions is crucial for a wide range of engineering and design applications. By simulating the behavior of solid materials, engineers and designers can optimize their designs and reduce the need for costly physical prototyping.

Using simulation software, engineers and designers can create virtual models of their designs and analyze their performance under various conditions. They can simulate stresses, strains, and deformations in solid materials.

The example below is a structural analysis of a wheel loader arm. This simulation project enabled the design engineer to study the relative movement between the components and assess the stress performance simultaneously. This assessment was done by calculating the Von Mises stress distribution within the arm. Such an approach almost eliminates the need for physical prototyping in the early stages of the design process.

Finite Element Modeling in Solid Mechanics

Knowing that most engineering cases of solid mechanics are nonlinear by nature, analyzing them with analytical solutions may not be feasible. That’s where numerical modeling comes into play.

To simulate solid mechanics cases and assess the material behavior, engineers use finite element modeling (FEM), a numerical method upon which a simulation technique called Finite Element Analysis (FEA) is based.

FEA involves dividing a complex solid model into a finite number of smaller, interconnected elements to approximate the behavior of the structure. By applying appropriate boundary conditions and material properties, FEA can simulate the response of the structure to different loads, allowing engineers to assess stress, strain, displacement, and deformation patterns.

To further understand the details of FEA, check out our dedicated guide to Finite Element Analysis (FEA).

The FEA software in SimScale, for instance, helps engineers and designers virtually test and predict the behavior of solid bodies. This enables them to solve complex structural engineering problems under static or dynamic loading conditions.

Yet, with all this, you might still be wondering what exactly the single aspect of simulation benefiting engineers today is. Well, it goes beyond the mathematical side of simulation and capitalizes on the integration of another technology: the cloud.

Simulating Faster with SimScale

SimScale combines the capabilities of simulation with the benefits of cloud computing to enable engineers to analyze accurately, collaborate better, and innovate faster.

Using SimScale’s cloud simulation, you can access your simulation projects anytime, anywhere. All you need is a web browser. You simply sign up to SimScale, import your 3D design, and start simulating.

Furthermore, not only are your projects accessible to you, but you can also very easily share them with your colleagues and teams to collaborate on them, improve your designs quickly, and shorten your workflow significantly.

For example, the global engineering and manufacturing company Bühler uses SimScale to enable the collaboration between 15% of its mechanical and process engineers spread across 25 departments in ten business units on four continents.

The post Solid Mechanics Simulation and Analysis with SimScale appeared first on SimScale.

Electronics Enclosure with Snap-Fitting Cover

The model in this case study is an electronics enclosure with a snap-fitting cover. For these types of enclosures, it is very beneficial to conduct a structural analysis early in the design process to optimize the snapping operation. To gather quality design insights, the outputs of interest from the simulations were peak stress regions which are likely to cause permanent deformation and breakage and also snapping kinematics of the snapping operation itself. Performing a trend analysis facilitated the selection of an appropriate snap and support design.

Cloud-Native Simulation Workflow

The simulation workflow in SimScale, which can be repeated and applied to many different use-cases, starts by uploading a solid body CAD geometry to the platform. By using automatic body meshing, the model is quickly ready for simulation. Though the geometry of this case study was relatively uncomplicated, the physics used within the structural analysis is complex. With SimScale, capturing valuable insights from complex simulations is simpler and easier to share within teams and organizations, even with varying levels of simulation expertise. Below, the workflow for a nonlinear static analysis is represented. As SimScale facilitates a cloud-driven design study, users can leverage parallel computation and solve both a higher number of design iterations and more iterations of increased complexity.

Nonlinear Static Analysis in the Cloud

To understand the snapping kinematics, a quick animation can be created when post-processing the results. With the help of the animation, the movement of the casing can be better understood and the regions where the stress value has built up above the yield stress can be identified. This offers an opportunity to further optimize the design by changing the shape or using another material to minimize stress. 

After acquiring the results of the first simulation, the next step was to run a few more iterations. Based on the first design results, changes and alterations could be made within the geometry to converge upon a better design candidate. The first design change enacted in this study was deleting one of the faces, and creating a filet instead of a sharp edge. As the CAD changes are done in Onshape, a cloud-based tool, there is no need to download the file from Onshape and then upload it to SimScale—all can be transferred with cloud integration between two platforms. The previous simulation template can be applied to the new geometry exactly as done in the previous step, requiring no reassessment of the physical constraints or the topological entities. They are already automatically reassociated with the new CAD model. 

A further variable to experiment with in order to optimize design is testing different materials. This is easily done by selecting a new material from the materials library in the simulation setup and assigning this material to the lid. In a similar manner, many different design strategies can be tried and further improved. Once the first simulation setup is completed, iterating on top of that is straightforward and fast, with the power of the parallel computation. 

Electronics Enclosure Design Insights

After performing the first simulation on the design provided by the CAD engineer, the regions above the yield stress were clearly identified. Another interesting point detected was the fact that the support structure underneath the snap is not carrying any stress. As it does not provide added benefits to the structure, designers further assessed its significance in terms of manufacturing. In the second design, the snap is located without support underneath. The same result as with the first design is derived, proving that some cost could be saved in terms of manufacturing by removing the non-beneficial support element. And, in the last design, the shape of the snap is changed slightly, and also the sharp edge is rounded at the bottom part to have a smoother snapping operation.

Even if the above-yield stress observed on the model was reduced, an overall significant impact was not shown. Here, designers might consider material changes, in addition to shape iterations. Apec, Makrolon 8345, and Stanyl TE300 were tested as alternatives for the lid.

Because Makrolon 8345 is very stiff, it created high stresses and was eliminated as a viable option for this design. Stanyl TE300, on the other hand, produced strong results, significantly reducing the yielded areas.

As designers decide on the best shape and material for a model, prototyping or final validation analysis are a natural next step. In this case study, we included a validation analysis scenario. In the validation step, the CAE engineer might prefer to increase the complexity by checking how much deformation they will end up with by using a nonlinear material model. This can be accomplished by uploading a fully detailed stress-strain curve of the experimental testing of the material. Absolute peak stresses, as well as permanent plastic deformations, can be observed and measures taken to ensure that the single snap-fitting operation will be safe. Additionally, a mesh independence study can be conducted on top of the automatic meshing settings assigned by default to validate mesh independence on the results. 

Nonlinear Static Analysis in the Cloud

Engineering simulation in the cloud gives mechanical and structural engineers more detailed insight compared to physical testing, which is critical during the early stages of design exploration. This case study shows how nonlinear static analysis in early-stage design allowed three different snap-support design candidates to be tested, along with material selection. The accessibility of cloud-native engineering simulation enables designers and engineering teams to leverage parallel computation capabilities and achieve faster design cycles and more robust design insights.

The post Nonlinear Static Analysis: Snap-Fit Assembly appeared first on SimScale.

The question on everyone’s mind now is, why does this omnipresent mechanism break so easily on some bodies, and lasts decades in others? How can you, as a designer or an entrepreneur, ensure customer satisfaction by using a better snap-fit design?

The answer lies in engineering simulation, and particularly FEA.

What Engineering Simulation Brings

Product design is a highly complex process, with multiple objectives, requirements, and constraints, all of which need to be satisfied for the final product to be successful. The important factors that the designer needs to keep in mind include aesthetics, functionality, cost-efficiency, durability, and safety. While the aesthetic appeal of a product is not quantifiable, the other design aspects are; and they can be accurately tested.

In the traditional design process, the initial design is derived from best practices and past experience—which limits creativity and leaves little room for radical innovation. Coming up with a truly new, ground-breaking design, without relying on best practices, however, is risky and can lead to poor performance. The only way to ensure the durability of such a product is to perform a high number of design iterations until all the criteria are met. Traditionally, that means a high number of physical prototypes and a time-consuming and expensive physical testing phase.

Of course, physical testing cannot (and should not) be eliminated from the product design process entirely. However, multiple prototype building cycles—which account for the bulk of financial and time costs—can be easily avoided by integrating virtual simulation into the workflow. With computer-aided engineering (CAE), you still have an iterative design process, but the days, weeks or months of physical testing are replaced with hours or sometimes even minutes of a simulation run.

When Should Simulation Be Considered

Employing simulation at the right stage of the product development process is another important decision for the designer.

It is important to keep in mind that the closer to the product launch, the more costly design alterations become, due to the increasing number of dependencies in the design. At some point, implementing minor changes and improvements is simply no longer cost-effective. With simulation, on the other hand, these design changes can be implemented even before the first prototype is built, allowing iteration on the design from the very beginning of the development process. This can potentially result in lower costs, minimized failure risks, lighter design, improved performance and user experience, and increased product lifetime.

So Why Isn’t CAE an Industry Standard Yet?

Despite all the advantages mentioned above, several barriers have prevented more engineers and designers from integrating simulation into their design process. Here’s how SimScale is aiming to change this:

• Accessibility (upfront investment). Traditional software needs to be installed locally and needs a significant amount of computing power. That means highly expensive hardware, that stands idle while the simulations are running. With SimScale, all computations are done in the cloud and require no local installation—just a standard web browser.
• Operating costs. High licensing costs for standard commercial tools put them out of reach for many designers. SimScale starts with a free Community plan with an option to upgrade to an affordable Professional subscription.
• Know-how. Current CAE tools are not too user-friendly and are designed for CAE experts. This expertise gap can be minimized with intuitive UI, large public project templates library, live support chat, and free training material. Any of the public projects can be imported into the workspace, and once can simply exchange the CAD model, reassign the boundary conditions, and run the simulation without having to know too much about simulation upfront. 

Example Study: Smarter Snap-Fit Design Using FEA

The snap-fit design depends on the expected use and life, and can hence vary substantially. Cantilever snap designs are known to be the most common, and the “U” or “L” shape snaps are very popular as well. The design factors to be kept in mind include the shape of the snap, the thickness of the beam, and the ratio of the thickness to the beam length. Optimizing the design of a snap body is a challenging process, and a lot of information regarding the stresses on the body can be obtained from simulations. One can use the information obtained to determine the life cycle of the snap, how much load it can sustain, and under what conditions. This knowledge can be used to determine the expected lifecycle of the part, the different stress concentration regions, and even be used to determine the manufacturing process itself.

Traditionally, engineers have relied on experimentation and physical testing to ensure the reliability of their snap-fits. Yet in addition to being expensive and time-consuming; identifying all stress points through experimentation is highly challenging and the designer risks missing critical information. Simulation simplifies the data analysis necessary to make informed design decisions while saving time and money, and as a result delivering a more reliable product to the market much faster.

In this webinar, we discuss how to make a better snap-fit design. How to ensure that the least possible material is used, and the longest life-cycle achieved, as well as what goes on behind the scenes for ensuring a snap-fit will last forever or break right after the warranty runs out.

Watch Webinar Recording

Find out how an extraordinary engineering marvel can still be improved with cloud-based simulation, and how it can help you become a better designer!

Project Overview

The project used in this webinar session is publicly available in the simulation project library—feel free to copy and modify it: Finite Element Analysis of a Snap-Fit.

Snap-fits come in a wide variety of shapes, sizes, and materials. There is no one-design-suits-all—how well the snap-fit design will perform in real life depends heavily on the intended application. Therefore, in order to approach the problem of optimizing its design, the following design parameters need to be determined:

• Material
• Cross section shape
  • Constant
  • Varying
• Cross section type
  • Cantilever snap joints
  • U-shaped snap joints
  • Torsion snap joint
  • Annular snap joints
• Taper radius

Once all the information is obtained, the simulation setup can proceed. The results can be used to validate the solver for snap-fit design simulations by comparing them to the experimental data presented in the Bayer Material Science paper [1].

After setting up the CAD model, meshing can commence. The mesh defines the accuracy of the result. It should only be fine at the areas of interest: initially, it can be coarse all over, but subsequently, refinements to the areas of interest need to be provided based on the results.

The snap shall be given a displacement boundary condition. The small box against which the body deforms shall be given a fixed boundary condition. Using symmetry conditions allows the decrease of the number of nodes, the increase of the convergence speed, and the reduction of the model size.

Simulation Results

Areas undergoing the highest stress are clearly visible in the simulation results—the snap deforms and snaps into place. This CAD model is already fairly well-designed, with the snap end being tapered, and hence the strain on the final body is not excessive.

Without this taper, however, the strain on the snap would be much higher, causing an earlier breakdown and hence limiting the lifetime of the snap.

For comparison, the initial (points) and the final (solid) positions of the snap body can be seen in the image to the right.

If these results are compared to the ones presented in the paper, it can be seen that the mating force and the maximum deflection of the snap seen in the simulation closely match the experimental values. The minor difference in results can be attributed to the meshing grade and can be fine-tuned later.

Conclusion

Looking at the results of this simulation, it becomes evident that a proper snap-fit design is essential for ensuring low-stress concentration at points of contact, taper points, and at the deflecting face. A snap-fit has significantly more stress near the deflecting face (head) than on the tail of the deflecting snap. Most importantly, it was discovered that, compared to the snap-fit design with a constant cross-section, a tapered snap has lower stress values (can undergo more cycles) and a longer life (increased durability), and uses less material (cost saving).

In this case, we investigated leveraging FEA to design smarter snap-fit mechanisms—but this is just one example of how designers and engineers can apply simulation tools in the product development process. The SimScale Public Projects library has a wide selection of templates simulating various product use conditions across multiple industries, including automotive, aerospace, machinery, electronics, and HVAC.

Explore it by creating a free Community account.

Discover all the simulation features provided by SimScale. Download the document below.

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The post How to Create a Smarter Snap-Fit Design Using FEA appeared first on SimScale.