Sounds interesting?

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

What is a solenoid?

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

What is the function of a solenoid?

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

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

Parts of a Solenoid

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

How does a solenoid work step-by-step?

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

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

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

Types of Solenoids

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

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

Solenoid types can be broken down as follows.

Based on function and design

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

Based on electrical type and frame design

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

Solenoid Applications

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

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

Design & Simulation of Solenoids

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

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

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

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

Solenoids in our projects

Here are some amazing SimScale projects simulating solenoids.

FAQs

The post How do Solenoids Work appeared first on SimScale.

Yet, as component complexity increases and demand for efficiency grows, traditional methods of designing and optimizing induction hardening processes often fall short. 

In this article, we explore how modern simulation technologies, especially cloud-native tools, are revolutionizing the way engineers approach induction hardening as a non contact heating process , making it faster, more accurate, and significantly more accessible.

If you’re already familiar with induction hardening click here to jump to the juicy part on how to optimise it with simulation. 

What Is Induction Hardening?

Induction hardening is a surface heat treatment process that enhances the surface hardness and durability of metal components – utilized in industries where durability is key.

The process involves heating the metal’s surface using electromagnetic induction and then cooling it down quickly (quenching). This rapid heating and cooling changes the metal’s surface structure, increasing its wear resistance while maintaining a tough and ductile core.

Stages of Induction Hardening

It’s useful to understand how induction hardening works as a sequence of processes. Each stage from heating to quenching affects the final quality and performance of the treated component.

Induction Heating

First, the metal part is placed inside a induction coil, and an alternating current is passed through the coil. This creates an alternating magnetic field that induces eddy currents (loops of electrical current) on the metal’s surface, rapidly heating it to the transformation temperature.

Quenching

Next comes the cooling. The heated metal surface is quickly cooled using water, oil, or a special polymer solution (quenchant), with immediate quenching being crucial . This rapid cooling, causes surface hardening which transforms the surface layer into a hard, crystalline structure called martensite.

Tempering / Heat Treatment (if needed):

Sometimes, the newly hardened surface can be a bit too brittle, so tempering helps by gently reheating and cooling the part. This step softens it just enough to keep it durable without cracking under stress.

Hardness Level Control

The hardness achieved depends on a multitude of factors such as the mechanical properties of the material.

• coil design
• heating frequency
• quenching speed

The aim is to create a hardened surface layer while preserving the material’s core properties.

Advantages of Induction Hardening

Induction hardening offers several engineering and production benefits that make it an attractive option across many industries. Here are some of the key advantages:

• Localized Heating: Induction hardening offers precise control due to its reliance on electrical energy rather than combustion. This enables selective hardening of specific component areas while preserving the original properties of the surrounding material – ideal for achieving targeted enhancements without compromising overall integrity
• Enhanced Wear Resistance: The hardened surface significantly improves resistance to wear and fatigue.
• Minimal Distortion: Rapid heating and cooling reduce the risk of part distortion compared to conventional heat treatments.
• Energy Efficiency: The process is rapid and energy efficient, making it cost-effective.
• Automation Capability: You can integrate induction hardening into automation lines with ease, keeping production consistent and fast.

Disadvantages of Induction Hardening

While induction hardening offers numerous advantages, it is not without limitations. This section outlines some of the key drawbacks and constraints associated with the process.

• Material Limitations: Not all metals are suitable; typically, medium to high-carbon steels are required for effective hardening. Lower carbon materials generally don’t produce enough surface hardening.
• Setup Costs: The equipment can be expensive, making it less appealing for small-scale processes.
• Geometry Restrictions: Complex shapes are difficult to heat evenly – leading to uneven hardening.
• Risk of Cracking: Without due oversight, the rapid heating and cooling can introduce thermal stresses, which might cause cracks on the surface.

Challenges in Induction Hardening Processes

This section delves into the key technical and actionable operational challenges that engineers face when applying induction hardening in real-world manufacturing environments.

1. Material Suitability: Ensure that the selected material is compatible with induction hardening. Medium to high-carbon steels are optimal due to their capacity to form martensite upon rapid cooling. Evaluate material composition early to avoid performance shortfalls.
2. Complex Geometries: Use simulation tools to validate coil and part design before production. Optimizing coil shape ensures uniform heating and minimizes defects such as hot spots and uneven hardening.
3. Frequency and Power Density Optimization: Apply simulation to fine-tune induction parameters. Choosing the correct frequency and power settings helps achieve desired case depths while avoiding overheating or insufficient hardening.
4. Cooling and Quenching Control: Design a quenching process tailored to your component geometry. Proper control over cooling media, rates, and flow uniformity prevents thermal stress and distortion.
5. Managing Equipment Investment: For smaller operations, consider cloud-based simulation platforms to test and refine induction strategies before investing in physical equipment—reducing trial-and-error and capital risk.

The Role of Simulation in Induction Hardening

Induction hardening is a complex process that requires precise control over multiple physical phenomena, including electromagnetic fields, heat transfer, and material behavior.

Achieving consistent hardness while minimizing thermal stresses causes significant challenges, especially when dealing with complex geometries that are common for manufacturing applications.

“Traditional trial-and-error methods are often inefficient, as they require multiple physical prototypes and time-consuming adjustments to coil configurations, inductor geometry, and quenching parameters. No need to even mention high costs that come with this.”

Simulation provides an efficient and accurate approach to tackling these challenges in the induction heating process . By creating virtual models of the hardening setup, engineers can visualize temperature distribution, predict hardness profiles, and fine-tune process parameters before any physical testing takes place. This process not only saves time and costs but also allows for testing various design configurations without the risk of damaging real components.

When applied to localized solutions like hardening crankshafts, gear teeth, camshafts, and other critical components, simulation offers the flexibility to experiment with different coil designs and material properties. This makes it possible to optimize heating efficiency and uniformity for a wide range of parts, from simple shafts to complex assemblies.

One of the most powerful aspects of cloud-native simulation software is its ability to integrate multiple physics into a single environment, including induction hardening equipment . Platforms like SimScale enable engineers to evaluate electromagnetic heating, thermal stresses, and cooling strategies, providing a comprehensive understanding of how design choices impact performance and durability. As a cloud-native tool, SimScale also makes high-fidelity simulations accessible without the need for expensive on-premises hardware, empowering engineers to innovate faster and more efficiently.

Revolutionizing Induction Hardening with SimScale: Scalable, Collaborative, and User-Friendly

Traditionally, engineers have relied on complex, on-premises simulation tools or outsourced analysis to specialists, leading to lengthy workflows and high costs. However, SimScale is changing the game by offering a cloud-native, user-friendly solution that brings electromagnetic simulation to the earliest stages of design, making it accessible to engineers, designers and manufacturing teams.

Cloud Scalability for Rapid Iteration

SimScale’s cloud-based 3D electromagnetic solver lets engineers test and iterate multiple induction hardening designs in parallel—directly in the browser. No need for costly hardware or complex setup. Its scalable infrastructure supports multiple simulations at once, accelerating validation and decision-making.

Cost and Time Efficiency

By unifying AI-powered multiphysics (electromagnetic, thermal, structural, and flow) in one platform, SimScale reduces the need for physical prototypes. This cuts costs, shortens iteration cycles, and delivers reliable results faster.

User-Friendly Interface

SimScale is intuitive and easy to navigate, even for users new to electromagnetic simulation. The clean interface simplifies setup, so engineers can focus on optimization, not software.

Early Design Integration

Simulation with SimScale starts at the concept phase, which can also include flame hardening considerations . Engineers can evaluate coil efficiency, hardening depth, and quenching strategies early—avoiding expensive redesigns later.

Seamless Collaboration

SimScale enables real-time collaboration across teams through the browser. Everyone can view and comment on simulation results, improving alignment and speeding up decisions.

Empowering Engineering Teams

SimScale brings high-end simulation directly to engineers—no need for external experts. Its AI-enhanced, cloud-native platform enables smaller teams to work independently and innovate confidently.

Start Simulating Today

SimScale’s cloud-native platform makes it easier than ever to start analyzing induction hardening processes. You can test designs, optimize coil configurations, and explore the impacts right from your browser. There’s no upfront investment or installation—just sign up, upload your model, and begin your analysis.

The post Induction Hardening: From Basics to Optimization appeared first on SimScale.

Electromagnetic simulation (EM simulation) plays an important role in adapting solenoid design. By providing deep insight into magnetic field distribution, coil efficiency, electromagnetic force generation, and thermal behavior, the simulation allows engineers to refine the solenoid performance before the physical prototype.

This article will explore the different types of solenoids, their design principles, and how cloud-native multiple physics simulation can improve development processes.

Introduction to Solenoids

A solenoid is a device that consists of a housing, a moving plunger (armature), and a coil winding. A magnetic field surrounds the coil when an electrical current is applied, drawing the plunger in. A solenoid, to put it simply, transforms electrical energy into mechanical work.

Solenoid Design Principles

Electromagnetic Design Principles

• Coil Design and Specifications: The solenoid coil is the central component. Usually, copper wire is twisted around a core to form it. The strength of the magnetic field and power consumption is influenced by the wire gauge and the number of turns. A well-defined solenoid coil specification ensures optimal performance. Key factors include:
  • Electrical Properties: Resistance, inductance, and capacitance must be optimized for efficiency.
  • Material Selection: Copper is commonly used for winding due to its conductivity.
  • Coil Winding Techniques: Layering techniques impact performance and thermal behavior.
  • Manufacturing Considerations: Space availability, cost constraints, and production lead times dictate coil design feasibility.
• Core Material: To strengthen the magnetic field, ferromagnetic elements such as iron are utilized for the core. The performance and saturation point of the solenoid are influenced by the material selection. Evaluating the advantages and disadvantages of each material ensures the best fit for application-specific needs. Common materials include:
  • Amorphous and Nano-Crystalline Materials: Offer high permeability and low core losses.
  • Neodymium: Provides high magnetic saturation for strong field generation.
  • Copper Clad Steel: Balances cost-effectiveness with performance.
• Magnetic Circuit: Effective force creation requires magnetic circuit optimization. Taking into account the air gap, which influences the force-stroke characteristics, is part of this.
• Saturation: The “knee” of the B-H curve, where maximal domain alignment happens with the least amount of current, should be the target of design. For solenoid design, this is regarded as the optimal point.

Thermal Design Principles

• Heat Dissipation: Resistive losses in the coil cause solenoids to produce heat. To avoid overheating, proper thermal management is vital.
• Temperature Rise: Until thermal stabilization is achieved, the coil temperature rises. The resistance of the coil and, consequently, the current and magnetic force are impacted by this temperature increase.
• Insulation Class: It is critical to choose the right insulation materials depending on the anticipated operation temperatures. This choice is guided by the IEC’s thermal classes, such as Class B or H.
• Cooling Techniques: Additional cooling techniques like heat sinks or water cooling can be required for high-power applications.

Challenges in Solenoid Design

The challenges in solenoid electromagnetic design must balance practical limitations with performance optimization. Designers must ensure longevity in a variety of climatic situations while navigating size and weight constraints, particularly in consumer electronics and automotive applications. There is ongoing pressure on businesses to cut expenses, speed up development cycles, and satisfy a variety of customized requirements. To be competitive in the market, designers also need to adhere to legal requirements, maximize performance indicators like efficiency and response speed, and consistently innovate. Advanced design methods and a thorough comprehension of electromagnetic principles and particular application needs are necessary to meet these complex problems.

Operational Challenges

• Power Efficiency: Reducing energy consumption without compromising performance.
• Response Time Optimization: Enhancing speed while maintaining precision.
• Durability and Reliability: Ensuring solenoids operate efficiently under extreme conditions.

Engineering and Manufacturing Challenges

• Size and Weight Limitations: Particularly relevant in consumer electronics and automotive applications.
• Environmental Conditions: Temperature, humidity, and vibration impact long-term performance and reliability.
• Regulatory Compliance: Meeting efficiency, safety, and performance standards is crucial.
• Manufacturing Constraints: Factors like production costs, material sourcing, and lead times influence design choices.

Simulation for Solenoid Design and Modeling

Traditional solenoid designs often depend on iterative prototyping and physical testing processes that can be expensive and time-consuming. However, cloud-native 3D electromagnetic simulation enables engineers to rapidly explore a huge design space, adapting solenoid geometry, materials, and coil configurations much before the physical tests begin.

With real-time computational insight, design teams can evaluate the impact of parameters such as electromagnetic force, electromagnetic losses, and thermal behavior under various operating conditions.

Benefits of Cloud-Native EM Simulation

• Rapid Design Iterations: Engineers can test multiple solenoid configurations quickly.
• Comprehensive Multiphysics Analysis: Evaluates electromagnetic forces, losses, and thermal behavior under various operating conditions.
• Optimized Performance: Identifies energy-efficient and high-reliability designs before production.

SimScale’s cloud-native simulation platform empowers engineers with real-time computational insights, allowing them to:

• Evaluate electromagnetic field distribution and coil efficiency.
• Analyze force-stroke characteristics for improved response time.
• Predict and mitigate thermal issues with advanced thermal simulation tools.

With SimScale, solenoid design engineers can make data-driven decisions, significantly reducing development time and improving overall solenoid efficiency and reliability.

By following the linked tutorial below, you can learn how to run an electromagnetics simulation on a Linear Pushing Solenoid using SimScale, where the objective is to achieve the linear pushing force of the solenoid.

Tutorial: Electromagnetics Simulation on a Linear Pushing Solenoid

The post Solenoid Design and Modeling with Cloud-Native Simulation appeared first on SimScale.

Switched Reluctance Motor (SRM) is a type of electric motor characterized by its simple and robust design. As its name suggests, it operates based on reluctance torque rather than electromagnetic torque.

The term “switched” refers to the motor’s reliance on power-switching transistors for its operation. It consists of a stator with multiple salient poles equipped with coils and a rotor made of ferromagnetic material with no windings or permanent magnets. This offers several significant advantages, particularly in terms of mechanical simplicity, durability, cost, and performance.

The term “reluctance” refers to the tendency of magnetic flux to follow the path of least resistance, just as electric current follows the path of least resistance. Ferromagnetic materials (like the iron in the SRM rotor) have high permeability, allowing the magnetic flux to pass through them easily compared to the air gap and other non-magnetic materials, thereby presenting low reluctance.

As the magnetic field is established, the rotor is attracted to a position where the magnetic circuit’s reluctance is minimized. This optimal position occurs when the rotor poles are aligned with the energized stator poles, as this alignment shortens the magnetic path and maximizes the cross-sectional area through which the magnetic flux can pass.

Due to their unique advantages, such as simplicity, robustness, cost-effectiveness, and high efficiency, SRMs are gaining importance across various industries. From automotive to home appliances, renewable energy, and medical devices, SRMs are becoming increasingly prominent. To meet the growing electrification demands in the automotive industry, particularly in EVs, SRMs offer a compelling alternative without relying on permanent magnets.

Advantages of SRMs in the Automotive Industry

The electric motor requires a high torque-to-power ratio, a characteristic typically fulfilled by permanent magnet motors. However, these magnets can be costly due to the rare earth materials involved, such as neodymium, dysprosium, and samarium.

To mitigate motor costs while maintaining performance, the SRM emerges as a favorable option for EV drives, particularly for long power range applications beyond the base speed. SRM drives offer several advantages for EVs, including:

• The absence of permanent magnets
• High fault tolerance
• Straightforward construction
• High efficiency during high-speed operations

Due to their more straightforward design compared to other motors, SRMs have fewer moving parts like brushes or permanent magnets. This simplicity enhances their ability to tolerate faults, making them more resilient to wear and tear, mechanical failures, and various operating conditions commonly faced in EVs.

Additionally, the straightforward construction of SRMs leads to easier manufacturing processes and fewer potential points of failure. This simplicity enhances reliability and reduces maintenance needs throughout the EV’s lifespan. For example, SRMs are increasingly used in electric vehicle propulsion systems due to their high efficiency, robustness, and ability to operate at high speeds, significantly enhancing vehicle performance and range.

In hybrid vehicles, SRMs contribute to better fuel economy and lower emissions through their efficient operation and reliable performance. SRMs can also power various auxiliary systems such as power steering, cooling pumps, and electric superchargers, providing reliable and efficient performance.

Challenges of Switched Reluctance Motors

Despite their numerous advantages, SRMs also face several challenges and limitations, particularly in automotive applications, such as:

• Torque Generation Mechanisms: This phenomenon, known as the torque ripple, can lead to vibrations, heightened noise levels, and possible mechanical stresses, often necessitating supplementary control measures for mitigation.
• Efficiency at High Speeds: SRMs may experience reduced efficiency at high speeds due to increased eddy current losses and electromagnetic effects.
• Saturation Effects: Saturation effects can limit the operating range of SRMs, particularly in applications requiring high torque or high-speed operation.

Switched Reluctance Motor Simulation with SimScale

Utilizing cloud-native electromagnetics simulation offers a solution to address these challenges by providing insights into torque generation mechanisms, torque ripple effects, and motor efficiency across diverse operating conditions.

Optimization of Motor Components

• Stator and Rotor Geometries: Cloud-based simulation can model the magnetic fields and electromagnetic forces and help identify designs that maximize magnetic efficiency and torque while minimizing losses.
• Magnetic Material Selection: By simulating various magnetic material assignments, engineers can select those with the best performance characteristics, such as high permeability and low hysteresis losses.
• Optimal Winding Layout: Simulating different winding configurations helps in determining the optimal layout for maximizing torque and efficiency.
• Eddy Current Analysis: Time-Harmonic Magnetics simulations can model the effects of varying magnetic fields to observe the rise of eddy currents and their impact on efficiency.
• Loss Minimization: By understanding how eddy currents form and interact with the motor components, designs can be optimized to minimize these losses, often through material selection and geometric design tweaks.
• Frequency Effects: Simulating different operating frequencies helps in understanding their impact on eddy currents and overall motor efficiency, guiding the design of motors that operate efficiently across a range of conditions.

To learn more about cloud-native electromagnetics simulation, check out our Magnetostatics and Time-Harmonic Magnetics documentations.

Future Trends and Opportunities for SRMs

Emerging trends in SRM technology are driving innovation across industries, resulting in motor solutions that are more efficient, reliable, and environmentally sustainable. The future of the SRMs market looks promising, with emerging trends that focus on sustainability and environmental impact, advanced control and sensing technologies, high-performance materials and design optimization, and more.

These emerging trends are indeed shaping the future of automotive engineering. Here are some examples:

• By accelerating the adoption of EVs, SRMs contribute to reducing greenhouse gas emissions and decreasing dependence on fossil fuels. This aligns with global efforts to combat climate change and promote sustainable transportation solutions.
• With advanced control and sensing technologies, real-time monitoring and diagnostics enable predictive maintenance, reducing downtime and ensuring vehicle reliability and safety.
• Advancements in material science and design optimization result in higher motor efficiency, increased power density, and improved thermal management capabilities, ultimately enhancing vehicle performance and range.
• Modular motor designs and scalable platforms offer flexibility to adapt motor configurations to meet specific vehicle needs, simplifying manufacturing and lowering development costs.

Start your Switched Reluctance Motor simulation in the cloud now by clicking the “Start Simulating” button below.

The post Switched Reluctance Motor (SRM): Overview & Simulation appeared first on SimScale.

Case Study: Electric Vehicle Battery

In this case study, battery design is investigated both in terms of thermal management and mechanical performance. This project requires that we assess a battery pack assembly, which contains a couple of cooling plates, pouch cells, insulator pads, and gap filler material. 

Geometry & Problem

For this project, we used a 3D solid body geometry. Any tool can be used to generate such a solid body model and then can be uploaded to SimScale. 

Simulation Model

After CAD import, simulations require meshing. In this case study, we used automatic meshing.

Physics

In this case study, we conducted a thermomechanical analysis, which allows us to bring thermal stresses into a conduction-based simulation.

Solve

Our cloud-native solution gives us the ability to identify the temperature hot spots as well as the peak mechanical stresses and deformations to understand how our module is performing both thermally, as well as mechanically.

EV Battery Pack Gap Fillers: Project Scope

The model is a lithium-ion battery pack for electric vehicles that consists of four modules. We have two water cooling plates on the top and bottom of the battery module with the gap filler material separating the cooling plates from the pouch cells and the casing itself. 

Gap fillers are critical for thermal management for a number of reasons:

• They provide electric insulation
• They provide vibration damping 
• They have the ability to conduct the heat across the interface between the battery packs and the cooling plates

We want to make sure we utilize the maximum cooling capacity of our cooling plates by selecting the most efficient gap filler material for the model. Additionally, we want to focus not only on optimizing the gap filler selection but also on determining the best thickness of the gap filler.

Gap Filler Materials

The first step of the study is to simulations with three different gap filler materials, each with the same thickness. Each material has a different conductivity value. We want to investigate the effect of using different gap-filling materials, in terms of the maximum cell temperatures that are observed inside the pouch cells. Additionally, we want to detect the amount of mechanical swelling that can take place due to the temperature in the pouch cells. Increased conductivity results in reduced maximum cell temperature, however, change in conductivity had a minor effect on our study, since the dominant impact on reducing temperature was provided by the water cooling plates. Even these minor effects are valuable to note in the early-design stage, however, as the material selection impacts cost in manufacturing.

If we have the optimal selection of gap filler material, we can select the most efficient or least energy-intensive cooling apparatus on the cooling side of the battery cells. Also, the pouch cell principal strain decreases, which is an indication of the actual volumetric swelling of those cells. We can see that we have a higher peak temperature on the left-hand side where we have the gap filler material with low conductivity; when we go over to a high conductivity material, we have both lower temperatures and more uniform temperature distribution as well, critical for battery design. We want to make sure that our cooling capacity is being utilized across the whole module uniformly to avoid cold spots and hot spots. In another cutting plane on the gap filler materials, we see the same trend. 

Gap Filler Thickness

We can use SimScale to optimize the thickness of the battery pack gap filler material, as well. Simulation can help us understand the bearing that gap filler thickness has on the actual temperature distributions and thus predict and manage the mechanical thermal strains within the system. As we increase the thickness of the gap filler material, we see the reverse effect. With a thicker gap filler material, we have a higher pouch cell temperature. That is because we are reducing the effectiveness of the water cooling plates. We are trying to keep the gap filler material as thin as possible, but while still allowing the gap filler material to serve for vibration damping and insulation, as well. Simulation gives engineers the ability to navigate this balancing act. With thermomechanical analysis, we can investigate these physical behaviors and understand what is causing the hot spots, swelling, etc. and test the best way to mitigate them.

Mechanical Performance

Finally, we can assess the mechanical performance from the same simulation. Looking at the lowest conductivity gap filler material set at the lowest thickness, we can see the actual mechanical stress in the steel casing around the battery model itself. Here, we observe maximum mechanical stress to the point of yielding at that joint section. So, in those regions where we have significant stress, we could expect to see some damage and plastic deformation. This asks us to consider resizing the casing thickness or maybe use a more ductile material for the casing, in order to reduce those stresses and increase the lifetime of the product. If we want to go into further detail, beyond a linear static analysis that identifies the areas above yield stress, we can convert this into a non-linear simulation. This will allow us to see the absolute maximum stresses that we would experience during the thermal cycling of the battery module. The same goes for the total strain in the pouch cell. We have roughly 1 to 2 percent of total strain magnitude in the pouch cells due to thermal behavior.

Maintaining battery packs within a specific temperature range is essential for ensuring the performance of the product and the safety of users. With cloud-native engineering simulation, you have the ability to run multiple design iterations in parallel means that simulating a battery pack module from CAD preparation to post-processing can be performed with fast turnaround times.

The post EV Battery Pack Gap Fillers: A Thermomechanical Simulation Study 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.

Below, we cover two case studies that demonstrate what we’ve learned from over two years of rapid ventilation design assessments. These techniques enable architects and engineers to quickly and accurately predict air movement and air quality, determine HVAC sizing, and assess ventilation strategies.

Case Study 1: Classroom Ventilation Strategy 

In this case study, the aim was to assess the heating and ventilation strategy of a classroom and to ensure that the design complied with the Passivhaus environmental and energy metrics. In various HVAC systems, CFD can be used to determine which parameters will have the largest impact in relation to improving indoor air quality and living conditions. Some of these parameters within this case were U-values for different parts of the room (due to the different materials being used), radiation sources to represent solar gains, occupants, and additional insights the team wanted, such as the quality of air.

Once the base design was simulated, the architects faced two challenges: Firstly, due to the inlet, the air was drafted on top of the occupants in a rough manner, which would create a significant amount of thermal comfort disturbance. And secondly, existing air within the classroom was observed to be of low quality with high CO2 levels. Adjustments would be required to make the design more efficient, with increased air quality and better thermal comfort, but without additional energy loss.

In order to solve these issues, architects came up with different designs to improve the air supply strategies and validate cases. They ran different configurations in parallel to converge on the optimal solution. For each of the design variations, the team compared temperature distribution within the space and assessed air quality by means of CO2 concentration inside the room.

In the first design change of the base case, different HVAC inlet configurations were tested. By utilizing the guide vanes and having a high wall diffuser, inlet air was pushed towards the ceiling. As the air hit the wall it allowed for circulation inside the room and solved the first issue of occupant discomfort.

In the second design change of the base case, the designers focused on solving the low air quality issue inside the room. In order to reduce the amount of CO2, the high wall diffuser setup was combined with various window opening scenarios to measure the impact on ventilation and indoor air quality. In the end, the combination of a top-hung window opening and a high wall diffuser setup was found to increase air quality and reduce CO2 by improving the flow pattern within the room. This scenario satisfied both air quality and thermal comfort criteria.

Case Study 2: External Wind Conditions and Ventilation

The goal of this case study was to assess the effect of external wind conditions on ventilation strategies. By taking into account wind conditions, designers can ensure their study is as close to real-world scenarios as possible. The goal here was to assess ventilation in every room of an apartment building when external wind impacts are considered.

The process to derive the internal conditions for this particular case was started by modeling the building together with its surroundings in a Pedestrian Wind Comfort (PWC) analysis. With a PWC analysis, we are interested in assessing the wind comfort criteria for various wind directions. For this internal case study, the interest was in one particular direction so that it could be applied directly to the building. We focused specifically on observing how the wind from the southwest direction would affect the third floor of the building. Pressure tappings were applied from the external wind study to the windows and, to simulate a life-like scenario as much as possible, internal door leakages were also considered within the setup.

As the base design was simulated, engineers focused on answering two main questions. First, whether the natural ventilation of one window open was sufficient for the building or if forced ventilation was needed. And secondly, if CFD could help in deciding the correct ventilation strategy.

After getting the results of the base case setup, different scenarios with wider window openings were simulated to compare the mean age of air within the room. By increasing the opening angle of the window, a one-sided improvement in the ventilation was observed. But after some duration, no matter how much the window was opened there were no more significant changes in the ventilation inside the building.

Mean age of air plots can give us a good understanding of where the air is stagnant and where there is a good amount of ventilation inside of a building. As the plots were checked, a pattern was observed. Rooms on the corners and the kitchen were well ventilated, whereas the rooms in the middle were not well ventilated.

In light of the results obtained from the  CFD analysis, it became easier to focus on potential reasons for poor ventilation in some rooms and solutions on how to improve it. For example, even though rooms six and seven looked very similar to each other, the conditions of airflow and ventilation were not the same. This was due to prevailing wind conditions.

The animation visualizing the wind results shows a large amount of wind coming from the right-hand side and hitting the side of the building where the kitchen is located. This helped with both ventilation in the kitchen and with the cross-ventilation, as a high degree crossflow was entering from the window and then going out of the other side. On the other side of the building, the air was creating a recirculation zone within rooms five, seven, and eight. This was causing single-sided ventilation with air entering and leaving if the other window was also open. This explained the reason why even though rooms six and seven were similar in structure, room six was not as well ventilated. Room six lay in a region where the recirculation ended and also the interaction between the wind effects from the surrounding buildings started. There was a dead zone created in front of the building and that caused insufficient air to pass through.

In the end, CFD studies determined that natural convection was not sufficient for all rooms of this building. In most cases, opening the windows increased the ventilation, but for the corridor and some of the rooms, like room six, additional forced ventilation is needed to satisfy the air quality standards.

Cloud-Native Simulation for Ventilation Strategies

As shown in these two case studies, CFD analysis is necessary for understanding and predicting the effectiveness of natural and forced ventilation.  As a next step, CFD analysis can even inform design decisions on the best sizing for HVAC equipment for a particular building or room. This not only helps avoid undersizing or oversizing HVAC equipment but also ensures proper ventilation, thermal comfort, and indoor air quality while optimizing designs for less energy loss. 

Cloud-native CFD analysis enables engineers to solve for internal and external flows, study indoor and outdoor thermal comfort, and scale HVAC device-level simulation results from room-level to building-level and beyond.

The post Ventilation Strategies: Tested with Simulation appeared first on SimScale.

With the growing interest in electric vehicles, competition in the market—and the resulting demand on product performance—have drastically increased. The need to quickly improve highly-efficient electric car components requires a tool that allows engineers to simulate multiphysics early, reduce the need for physical prototyping and costly late-stage design changes. With cloud-native simulation, engineers have access to testing many scenarios in a simpler workflow with shorter turnaround times. In this article, we explore a multiphysics investigation of an EV inverter. Three different physics simulations are performed including a pressure drop analysis, a Conjugate Heat Transfer (CHT) analysis to validate that all components operate in a safe temperature range, and a vibration study—three different physics, one web application, simulated in under 30 minutes. 

EV Inverter: One Model, Three Physics Simulated

To achieve better thermal performance and structural integrity of an EV inverter three simulations are run using the same EV inverter model. Each simulation defines a different analysis but follows the same workflow. Depending on the placement of this equipment, different physical factors need to be taken into account. Oftentimes they are set in vehicles, hence vibrations become a concern. The size of the pump is also essential to ensure the pressure drop across the flow channel is minimized. And lastly, as different electronics components are mounted and operated simultaneously within the EV inverter, temperature and power should be safely managed, as well, to ensure liquid within the device is kept cool. 

Case Study 1: Thermal Management

The first simulation was a thermal management analysis of the module by using SimScale’s CHT solver. This study was used to ensure that all components operate in a safe temperature range. By performing a full-fledged CHT analysis, convective cooling through the flow channel was simulated, followed by conduction cooling within the solid. Temperature distribution both within the fluid and solid parts were of concern, so the CAD model was used together with the flow volume during the CHT simulation. As expected, the electronics components had high temperatures. By visualizing the streamlines through the water channel, the temperature distribution within the flow was also observed. The goal of running the CHT analysis was to assess the quantitative results on the critical components of the EV inverter and determine whether it was operating under safe conditions. If not, the analysis would inform design changes to ensure an effective and safe operation by keeping the temperature within certain limits.

Case Study 2: Pressure Drop Analysis

The second simulation was an incompressible pressure drop analysis to predict the fluid flow inside the flow channel and reduce the overall pressure drop. In order to run pressure drop analysis, the flow channel should be isolated as a separate part. SimScale provides all tools for an end-to-end simulation platform. Prior to setting up the physics of the simulation, the flow volume was extracted by using SimScale’s CAD environment. To assess the pressure drop across the channel an incompressible CFD analysis was performed. Based on the results, the critical points could be determined and changes in the design could be enacted. With the changes in the design, new simulations are run to achieve a much more efficient overall product performance. 

Case Study 3: Vibration Analysis

The third and last simulation was a vibration analysis using SimScale’s frequency analysis tool. This simulation helps to identify the first 20 eigenfrequencies and eigenmodes to prevent unwanted vibrations. The resulting eigenfrequencies and eigenmodes would help the designer evaluate the overall rigidity of the model, and assess whether there might be eigenmodes affecting the operation of the device. Knowing that this piece of equipment would be mounted on a vehicle, eigenfrequency analysis would be needed to ensure that vibrations are not triggered by the operating conditions of the device. As the simulation is run, the physical deformation of the device can be visualized by stepping through each of the eigenfrequencies. The resulting frequencies and modes are dependent on the geometry and material distribution of the structure. Therefore, by performing vibration analysis for different geometry alternatives, the best design can be picked. Thus, unwanted vibrations can be prevented. 

Digital Prototype of an EV Inverter

Traditional product development processes require lab testing and physical prototyping, both of which often reveal design flaws that necessitate further redesign. It’s a multi-step process that can stretch the development schedule indefinitely. By adopting digital prototyping, users can eliminate the unnecessary loops from the design optimization process at an early stage, cutting both time and costs of development as a result.

The introduction of cloud-native multiphysics simulation into the R&D process has empowered engineers to accurately and efficiently simulate hundreds of simulations, including a wide range of complex physical phenomena. As SimScale was born in the cloud, it’s scalable by nature, meaning computational power can scale up or down depending on project demand and multiple design options can be explored across multiple teams. This gives the design team an edge over traditional CAE tools. Providing designers and engineers access to multiphysics simulation allows teams all over the world to push the boundaries of design.

Get a deep dive in this on-demand demo, where we walk you through simulating the three different physics discussed in our EV inverter case:

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The post Multiphysics Simulation of an EV Inverter appeared first on SimScale.

Case Study: Optimizing Design for Improved Performance

In this project, SimScale, Onshape, and Datadvance collaborated to optimize the geometry of an insulated-gate bipolar transistor (IGBT) cooling plate design. The objective of this study was to minimize the average surface temperature for maximum thermodynamic efficiency while also minimizing the pressure drop or drag power on the cooling fluid in order to increase the fluid dynamic efficiency of the cooling plate. The project serves as a model for how shape optimization can be used in conjunction with thermo-fluid dynamics to optimize the design.

The cooling plate consists of a milled aluminum block with vertical pins to increase the surface area between the cooling fluid and the IGBT hot surface. The geometric variables were selected to ensure that any final design could be manufactured using approximately the same tooling and manufacturing methods:

• Pin diameter: 2 mm – 6 mm
• Pin row offset: 0 – 0.5 row width
• Pin rows in X: 6, 7 or 8
• Pin rows in Y: 3, 4 or 5

The flow rate was varied in an achievable range for the cooling pump:

• Flow rate: 1.2 l/min – 3 l/min 

The IGBT geometry was modeled in Onshape and the geometric variables were parameterized so that they could be driven by pSeven, Datadvance’s optimization software through the Onshape Python Application Programming Interface (API).

If a single design is investigated, the workflow begins by generating the geometry in Onshape, with a specified parameter set. For that specific design, Computational Fluid Dynamics (CFD) simulation is conducted in the cloud using  SimScale. As relevant quantitative results are extracted from the SimScale simulation, the next step is to set up a surrogate-based optimization in pSeven. The parameters which need to be altered to minimize temperature and pressure drop, hence optimizing the design, can be intelligently decided. Instead of following this procedure one by one for each parameter variation, a feedback loop for the CAD parameters between the optimized design obtained from pSeven and Onshape within this project was established. The API was used to automate the parameter variation and provide programmatic access to preprocessing, simulating, and postprocessing.

Cloud-Native Design Space Exploration

API gives analysts the ability to automate the workflows, explore parameter spaces automatically, and optimize the shape by connecting to the CAD authoring and optimization tools. Within this parameterization study, the API connected the geometric optimization tool to Onshape’s cloud-based design tool and to SimScale’s cloud-native multiphysics simulation engine. pSeven Enterprise was used to build and run the optimization workflow. Each time a particular geometry and simulation parameters were sent to the API, SimScale simulated the model and returned outputs for that instance, including temperature distribution, pressure, and efficiency. Data was then fed into the pSeven surrogate optimization model. As the workflow is scalable, many hundreds of geometric scenarios can be modeled. The physics outputs for each scenario were tracked using a Pareto front to converge on the local minima (the optimized solution) where the average surface temperature and the pressure drop were the key parameters.

The results of the workflow showed that the highest pin density, with pin offset, gave the minimum surface temperature. However, the minimum pressure drop was obtained with the lowest diameter pins and no pin offset. With this set of results, and a maximum surface temperature design target, the design team would be able to select the design that would have the greatest pump efficiency while achieving the required cooling properties.

“Serverless” Design Studies

This project demonstrated how end-to-end design space exploration can be fully performed by using only cloud-native components. By removing the problems caused by the complexity of setup and maintenance of the required software and hardware stack, full design space exploration can be achieved, even in a  ‘serverless’ workflow. Cloud-native components also enable the use of optimization in scenarios and organizations, where previously it was technically and economically not feasible, creating a fully accessible, fully realized design space exploration in new applications.

Learn how connecting Datadvance’s cloud-native low code platform pSeven Enterprise to SimScale’s multiphysics cloud simulation engine using API allows a drastic speedup of simulation and optimization procedures in this on-demand webinar:

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The post Design Space Exploration and Performance Optimization in the Cloud appeared first on SimScale.