Electromagnetics (EM) simulation is an advanced technique to investigate the performance of electronic devices and systems virtually, minimizing the need for expensive and time-intensive legacy physical prototyping. With cloud-native simulation capabilities, engineers can go even further and eliminate their reliance on expensive hardware and complex installations of software by simply running all their simulations in parallel directly in their favorite web browser — no installation required. This not only accelerates the design cycle but also enables engineers to innovate faster, collaborate more easily in real time, and apply multiple physics simulations all in one place.

A Deeper Look into SimScale’s Electromagnetics Simulation

Electromagnetic fields play a pivotal role in countless technological innovations, from motors and transformers to medical devices and beyond. That’s why it’s crucial for engineers and designers to have access to state-of-the-art analysis tools that enable them to explore, understand, and optimize electromagnetic phenomena with unprecedented precision.EM systems often present challenges of different scales, particularly when it comes to frequency ranges. In our first roll-out, SimScale is offering low-frequency electromagnetics analysis capabilities with a dedicated magnetostatics solver powered by our partner, EMWorks. This will enable various low-frequency applications, such as linear actuators, sensors, and motors.

The SimScale EM solver enables engineers to visualize and analyze various electromagnetic parameters in magnetostatics, including:

• Magnetic flux density
• Magnetic field strength
• Current density
• Linear and non-linear magnetic permeability
• B-H curves
• Permanent magnets
• Inductance matrix
• Coil resistance
• Forces and torques

Thanks to the power of cloud computing, engineers can run as many simulations as needed at the same time and iterate on their designs following the results of their simulations to reach the optimal design.

Simulate Magnetostatics in SimScale

Magnetostatics is a model that describes magnetic fields when currents are temporally constant (stationary) or approximately constant. It has numerous applications in engineering and science that can be used in a wide variety of industries, including automotive, aerospace, consumer products, healthcare, electronics, and more. Of these applications, one can utilize the magnetostatics analysis type to answer various design questions on:

• DC machines
• Electromagnetic brakes and clutches
• Magnetic levitation devices
• MEMS
• Motors and generators
• Permanent magnet motors
• Relays
• Sensors
• Solenoids

In SimScale, engineers can simulate various low-frequency electromagnetics by simply using the electromagnetics solver, as shown in the figure below.

Electromagnetics Simulation Examples in SimScale

Switched Reluctance Motor (SRM)

Switched Reluctance Motors (SRMs) are distinct electric motors operating on the principle of variable magnetic reluctance. Yet, they do suffer from the presence of torque ripples, which result from the abrupt switching of currents during motor operation. These lead to vibrations, noise, and undesirable mechanical stresses.

With SimScale’s electromagnetics solver, engineers can run magnetostatics simulations that provide a comprehensive understanding of the torque generation mechanisms, torque ripple effects, and efficiency of the motor under different operating conditions.

Electromagnetic-Toothed Brake

The electromagnetic-toothed brake is a sophisticated braking mechanism that operates through the manipulation of magnetic forces to control its engagement and disengagement. It shares structural similarities with the conventional power-on brake, but it boasts a distinct advantage in terms of static torque, which stems from the interlocking teeth between the driving and driven components. By incorporating these teeth into its design, the toothed brake achieves a notably higher torque capacity compared to devices of similar size, thus offering precise and efficient control of motion. When the coil is energized (power-on), the toothed brake engages to provide effective braking, making it a valuable tool for halting the rotation of a load when electrical power is applied.

In the image below, we provide a visual representation of electromagnetics simulation results in SimScale, illustrating the magnetic toothed brake in action. The image showcases both the engaged and disengaged states of the brake.

Linear Solenoid (Actuator)

Linear solenoids are electromagnetic devices that generate linear push or pull motion using magnetic fields. By adjusting the number of coil turns, material properties of the parts, or the applied current through the solenoid, engineers can optimize the stroke length of a linear direct-pushing solenoid. In other words, by controlling the magnetic field, engineers can tailor the solenoid’s stroke to suit specific application requirements, such as valves, locks, actuators, and other linear-motion devices.

More Electromagnetics Simulations Coming Soon

Low-frequency electromagnetics is just the beginning for SimScale. In the near future, we plan to introduce additional modules that will enable simulations of AC magnetics, transient magnetics, electrostatics, AC electrics, and high-frequency applications at last.

All these modules contribute to the multiphysics capabilities that SimScale provides, enabling engineers to run all the necessary simulations and analyses to ensure proper testing and validation before the need for any physical prototyping.

SimScale’s EM simulation software is the new kid on the block, but it is a game changer in terms of minimizing go-to-market time and costs for electromagnetic products.

In our effort to enable engineering organizations to deploy simulation broadly while maintaining central control over simulation knowledge and usage, SimScale is integrating electromagnetics into its comprehensive suite of simulation tools, delivered via a single consistent GUI and API.

The post Low-Frequency Electromagnetics Simulation — Now in Your Browser appeared first on SimScale.

Recent advancements have profoundly shifted the legacy product development workflow. Cloud computing has allowed engineers to collaborate in real-time, access advanced simulation capabilities earlier in the design process, obviate costly physical prototyping, and avoid expensive hardware costs. The virtually unlimited computing power and scalability of the cloud mean that deploying these capabilities across an entire distributed engineering organization is now considered a preferred strategy. 

Additional benefits of cloud-native simulation include access to 3D parametric scenarios analyses without any impedance to time and computing resources. The value added is the ability to fully explore design space and disqualify poor design candidates earlier in the development process. Application programming interfaces (APIs) further amplify the toolkit available to electronics designers and enable third-party CAD, analysis, optimization, and parametric design tools to talk to each other.

However, common bottlenecks to simulation have been CAD preparation and the numerical discretization of that model (meshing). Both consume time and manual intervention. The advent of advanced physics solvers and novel meshing techniques, such as the immersed boundary method, means that engineers spend less time making their CAD models simulation-ready and more time on insight-driven design. Skipping the time-intensive CAD preparation also opens up the possibility of doing simulations very early when some components are still in the draft stage and comparing many variants that otherwise would have required repeated CAD simplification efforts.

Immersed Boundary Method in SimScale

The Immersed boundary method is based on a cartesian grid, in which the geometry gets immersed. It is resilient to geometrical details and does not require CAD simplification, even for very complex models. Salient features of this approach include:

• Automatic defeaturing of small geometrical details
• Mesh refinements are physics-based rather than geometry-based
• Perfect hexahedral meshes
• Highly flexible mesh sizing from very coarse to very fine for all levels of CAD complexity

The Challenge with Complex CAD Models

Engineers who want to improve their thermal design by running 3D simulations with conventional tools based on so-called body-fitted meshing are forced to pick the lesser evil of simplifying their CAD model heavily or requiring huge computational resources to simulate.

In any case, they will waste valuable time. Either they spend hours of their limited working time on tedious CAD cleanup and simplification to reduce the model complexity and required computing resources, or they need to put an unreasonable amount thereof to solve the model. While the second approach allows the engineer to continue working on other topics during the calculation, it likely still requires some level of initial CAD cleanup even to get a successful mesh. Additionally, it blocks any advances on their current design for the time the simulation is running, not even speaking about the required hardware costs.

Immersed Boundary Method to the Rescue

The Immersed Boundary method addresses the core of this dilemma. It completely removes the CAD preparation or reduces it to a few minutes at most. At the same time, the physics-driven meshing avoids high mesh resolutions on detailed CAD features that are insignificant to the system’s thermal behavior. Yet, it resolves physically relevant regions like power sources or flow channels to the level the user requires. This level might differ significantly based on the current simulation intent.

Early in the design process, the engineer might be more interested in qualitative insights into the thermal management concepts he is experimenting with. Those simulations often only require coarse mesh resolutions. Body-fitted approaches do not allow this design space as the geometric details always lead to large mesh sizes (see Figure 2 above).

Later in the process, the focus shifts towards quantitative results, such as maximum temperatures on critical components. In order to provide the required accuracy, the mesh resolution required by body-fitted and IBM meshing will be closer. The CAD preparation time, of course, remains as saved time, and the engineer benefits from the underlying solving based on the same high-fidelity finite volume implementation in both cases.

The Main Benefits for Engineers

Summarizing the main benefits for the engineer, immersed boundary method enables you to:

• Simulate early in the design phase when parts of the system are in the concept phase
• Simulate the detailed original model without the need for CAD preparation
• Run extensive design of experiment studies
• Derive accurate critical temperatures during the validation phase

The graphic below shows how an important result quantity e.g. the junction temperature of a chip changes with higher mesh resolutions i.e. higher computational costs for body-fitted and IBM-based simulations.

So what’s the catch? Well, there is no catch. The Immersed boundary method is a perfect fit for thermal engineers dealing by default with complex systems and looking for optimization insights throughout the design process.

Suppose an engineer is looking for a very accurate representation of the flow boundary layer, for example, when designing fan blades or something similar. In that case, it usually makes more sense to capture the physical effect with mesh boundary layers and prepare the CAD for a conventional body-fitted simulation.

A Case Study of an Electric Vehicle Battery Pack

We have shown a case of an electric vehicle battery pack. The design is an example of an air-cooled lithium-ion battery model for an FSAE electric race car, which is utilized as the accumulator to power the car. Effective thermal management of the battery pack is essential to ensure the reliable and safe operation of the battery cells and hence the car.

When the rest of the vehicle design is constantly changing to meet performance objectives, a continuous update in the battery pack is also required to align with these evolving design parameters. Throughout each design iteration, key questions arise regarding the amount of heat generated within the batteries for relevant duty cycles and the necessary air flow rate to maintain the battery pack within its designated temperature range.

Finding accurate answers to these questions using a robust electronics cooling simulation not only increases the confidence in the design but also makes it possible to get those answers faster, even when the model is geometrically very detailed. The provided example demonstrates the significant advantage of the immersed boundary method in handling geometrical details without the need for CAD simplification. As a result, valuable insights can be swiftly obtained from each design iteration, enabling a more efficient and thorough evaluation of the design without compromising accuracy or intricate geometric details.

Comparing Meshing Methods

In this battery pack example, we attempted to simulate both the original and simplified versions of the CAD model using Conjugate Heat Transfer analyses with body-fitted and Immersed Boundary Method solvers. The main advantage of using the cartesian mesh-based Immersed Boundary Method is that it allows us to overcome the inability to mesh the original model using a body-fitted mesh. By immersing the geometry into the cartesian grid, the cartesian mesh automatically defeatures small geometrical details.

Whereas to prepare the model for CHT analysis, we need to invest time in CAD cleaning. This involves removing small details that do not directly impact the problem’s physics and achieving an efficient mesh size without unnecessary refinements. The time devoted to cleaning the small geometric details for the body-fitted mesh, in this case, ~ 2.5 hours, can be instead well-spent on design iterations when using the immersed boundary method.

Benefits of Using the Immersed Boundary Analysis in SimScale

Based on the results of the two different methods, the benefits of using IBM can be summarized as follows:

• Automatic handling of small details: With IBM, the geometry is ready for simulation without the need for CAD cleaning, even when the original model contains numerous small details. In contrast, CHT relies on a body-fitted mesh, and if the unsimplified original model is not simulation-ready, additional effort must be spent on CAD cleaning.
• Computational efficiency: By manually cleaning the small details in the geometry, the model becomes ready for CHT. Even when this is the case, IBM requires significantly fewer computational resources than CHT. With IBM, the mesh size is reduced by 72%, computational resources are reduced by 61%, and the runtime is reduced by 62%. Moreover, the results of the two analysis methods are almost identical (0.002% difference), demonstrating the accuracy of using IBM for design iterations while efficiently utilizing resources.
• Effective design exploration: Even when the original model is simulated using IBM, the computational resources required are still much less compared to the simplified model simulated with CHT. The mesh size of the original model with IBM is reduced by 70% compared to the simplified model with CHT, while both the computational resources and the runtime are reduced by 44%. This highlights that without wasting time cleaning the model, multiple design iterations can be tested without the overhead of fine mesh regions around insignificant details. Consequently, the time required for users to discover the best design is significantly shortened.

The post Simplify Your Thermal Simulation With Immersed Boundary Method 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.

In this blog post, we want to get you up to date with all of the new key features released in Q1 2023. Let’s dive in!

1. Improved Wind Data for PWC Analysis
2. Humidity Modeling
3. Realizable Turbulence Model
4. Solids included in solar radiation
5. Joule Heating
6. Immersed Boundary Method (IBM) external flow domain flexibility
7. Simplify/heal bodies with Surface Wrapping
8. Multiphase
9. Relative Velocity
10. Boundary condition Visualization Inside 3D Viewer
11. Export result statistics to CSV
12. Teams and Permissions
13. Bilinear elastoplastic material model
14. “Max over Phase” von Mises stress result field for harmonic analysis

1. Improved Wind Data for PWC Analysis

Improved accuracy and global coverage with the new ERA5T dataset, which replaces the previous NEMS30 dataset. Also, the new modal is provided via our connected wind data service partner, meteoblue, and is the most accurate dataset available for wind data. Additionally, we now have the ability to derive seasonal wind roses from the new dataset, which will be coming in future months.

2. Humidity Modeling

Humidity plays a big part in thermal comfort analyses, and SimScale can now account for it.
Humidity modeling can be hugely important to internal thermal comfort studies for identifying where condensation might occur as well as for analyzing indoor environments where tightly controlled humidity levels are critical, such as concert halls, storage facilities, or indoor farming.

3. Realizable Turbulence Model

For urban wind applications, this turbulence model is declared as the preferred one by several best practice guidelines (COST Action 732) as well as wind engineering guidelines, such as the City of London (CoL) Wind Microclimate Guidelines (ref), if a steady-state CFD simulation is run.
The realizable k-epsilon model is now available for the Incompressible analysis type on SimScale within the top-level analysis type settings.

Use Case & Benefits

Urban pollutant dispersion analysis using the Incompressible analysis type on SimScale for enhanced result accuracy compared to the standard k-epsilon turbulence model.

4. Solids included in solar radiation

It is now possible to model solar loads in CHT analyses with models that have both fluids and solids included.

Use Case & Benefits

• Solar radiation can play a large factor in thermal comfort, and the ability to model it with solids included increases the overall simulation accuracy.
• Solid walls at the boundaries of the flow region don’t need to be modeled with specific boundary conditions defining the conductivity and material thickness but can instead be assigned the specific material, and their thermal properties will be correctly accounted for
• Solids inside the fluid domain simply need the correct material assigned.

The current limitation is that the solids in a CHT analysis with solar load can not be semi-transparent, so windows and facade glazings need to be modeled with an appropriate boundary condition.

5. Joule Heating

Q1 sees the release of Joule heating. This works with Direct Current (DC) applications and is integrated into the CHT and IBM analysis types. Applying Joule Heating to a part, or parts will cause them to heat up realistically.

6. Immersed Boundary Method (IBM) external flow domain flexibility

IBM (Immersed Boundary Method) now allows for different external flow domain positions. This means that we can position the test unit on the floor, wall, ceiling, or suspended in the middle.

Use Case & Benefits

Useful for:

• Lighting, as it can be positioned anywhere
• All electronic assemblies as they are often designed with multiple orientations and installation positions in mind

7. Simplify/heal bodies with Surface Wrapping

Parts are sometimes too complex to work with and so can be simplified with Simscale’s CAD tools. This can currently be found under ‘surface wrap’.

• Faulty models can cause meshing problems, and fixing/simplifying parts in advance should avoid this
• Sheet bodies can be difficult to mesh – wrapping them and forming a solid can solve this too

8. Multiphase

One of our highest-requested features is now in production!

This feature introduces a proprietary multiphase capability within SimScale, with industry-validated methods for high accuracy and fast simulation turnaround for rotating equipment, hydraulics, and industrial equipment simulations.

Benefits

• Volume-of-Fluid algorithm with proprietary high-order reconstruction scheme that captures sharp interfaces well
• Comprehensive physics, including heat transfer and surface tension
• Handling of realistic fluid and material properties
• Binary tree-based meshing and automatic local time stepping for proven stability for complicated geometries

Use-Cases

• All types of turbomachinery, rotating equipment & flow control simulations
• Hydraulic engineering / AEC applications (reservoir, dam gate, etc.)
• Industrial mixers, aeration tanks, tank filling simulations
• Marine applications (static ship hydrodynamics, propulsion systems)

9. Relative Velocity

For correct visualization of rotating equipment flow simulations, it is important to show velocity relative to the rotating blades.

Visualizing the relative velocity field in a turbomachine is crucial as it gives designers insight into the nature of flow within the machine. It is used for creating velocity triangles, which help designers estimate the early-stage performance of the rotating geometry.

Highly demanded by our turbomachinery customers, SimScale will now compute and render relative velocity fields through the rotating regions. This field can be visualized as streamlines, vectors, contours, iso-surfaces, or iso-volumes, and will provide our users greater insight into the flow around rotating components.

10. Boundary condition Visualization Inside 3D Viewer

Boundary conditions are now shown inside SimScale! This has been a long time coming, with (believe it or not) years of effort to prepare everything in the background. Now that it is live, we will continue to iterate on it. If you are actively using SimScale, you will see this evolve over the next quarters.

11. Export result statistics to CSV

The ‘Statistics’ panel can now export all of the data points into a CSV file for external processing. This can be hugely useful with models that contain multiple fluid channels like the one shown below.

Use Cases

• Large organizations that need to control access to content internally
• Small organizations that need to organize content more efficiently

Benefits

• Granular access control
• Intuitive data segregation
• Control of sharing of Team content
• All self-managed

12. Teams and Permissions

Members of teams can now have varying levels of access to content contained within a team, and administrators can manage these settings in their dashboard.

Each member can have view, copy, or edit permissions of the teams they belong to. Each team also includes a setting to control with whom content can be shared (to/from a team):

• No sharing
• Share within the Team
• Share with the organization
• Share with anyone

13. Bilinear elastoplastic material model

Users can now apply bi-linear material behavior via a dedicated interface. Young’s Modulus, Yield stress, Ultimate stress, and strain are simple to define. This change improves non-linear simulation robustness.

14. “Max over Phase” von Mises stress result field for harmonic analysis

Von Mises stress is now available for each frequency in harmonic analyses. This means that as well as identifying resonant frequencies, engineers can also ensure that the structure remains within maximum stress safety limits.

Take These New Features for a Spin Yourself 

All of these new features are now live 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 then you can easily sign up here for a trial. Please stay tuned for our next quarterly product update.

The post NEW Features: Multiphase, Joule Heating, Humidity Modeling, Boundary Condition Visualization, and More! appeared first on SimScale.

Joule heating is an important phenomenon to capture in the design of many power electronics products and components. It is the physical effect of current passing through an electrical conductor and converting to thermal energy, causing heating. Increasing temperatures in the conductor material can impact the overall efficiency of the components or even be harnessed.

SimScale has launched new features that enable engineers to perform Joule heating simulations on the platform using an easy-to-use interface with powerful and automated post-processing features.

Simulating Joule heating is necessary for numerous industry applications where resistive heating is a common artifact, whether that is intentional or unintentional. For intentional usages such as electric heaters and soldering irons, Joule heating analysis is necessary to optimize the heat output of the device.

More commonly, however, the increase in temperature from converting electrical energy into thermal is an unwanted effect that could decrease the overall efficiency of components. Examples include busbars and wiring in power electronics, where the efficiency drops with the inverse of increasing temperature.

A similar effect is observed in batteries that have an ideal operating temperature range. Above this, the battery performance and lifetime begin to degrade. Other common components like fuse blocks and resistors are also impacted by Joule heating.

Joule Heating Analysis in SimScale

Simplified approaches to Joule heating analysis included adding dissipated power as a power source on the electronic components. The dissipated power was based on hand calculations, approximated, and could not robustly handle situations where the current density was not uniformly distributed, including:

• Varying electrical resistivities in parallel
• Different cross-section-sized components in serial alignment 
• Contact resistances, for example, soldering connections

With the new features introduced in SimScale, users can now explicitly define the key Joule heating parameters, variables and output key metrics to base design decisions on. 

• Analysis type: users can toggle on Joule Heating when setting up a Conjugate heat transfer (CHTv2 or IBM) analysis type in SimScale.
• Materials: when defining material properties, choose the isotropic or orthotropic conductor option. The materials can be imported from the library or added to the database and can be shared among projects and teams. 
• Boundary conditions: in the boundary conditions dialog box, users can specify the current flow direction and electric potential (see images below).
• Outputs: include current density, electric potential, and Joule heat generation.

Joule Heating Simulation Setup

A case of an electrical inverter used in race cars is used to demonstrate the new Joule heating features in SimScale. The image below shows the 3D geometry of an inverter that is liquid-cooled using a water and glycol mix with a flow of 3 L/Min.

The model contains various MOSFETS and capacitors with electrical load and current of up to 70 Amps RMS continuous load. The twelve MOSFETS for 6-phase AC current supply are each modeled with 18.5 W applied to them.

We have used the materials database in SimScale to apply conducting materials and coolants that can be parameterized to evaluate material properties if needed.

New options in the simulation setup dialog boxes are used to specify Joule heating simulation particulars:

1. Specifying Joule heating analysis

2. Material properties for Joule Heating simulation

3. Adding boundary conditions for Joule Heating simulation

Visualising Joule Heating Simulation Results

The Electric Potential (voltage) gives insights into the voltage drop across electrical components and wires within the electric circuit and the expected voltage drop overall in case of a current drain-driven simulation. Derived from the electric potential field, the electric current density (A/m²) provides essential information about the electric current flow in the whole circuit and if current density spikes occur, e.g., in small cross sections or at sharp corners.

Those often lead to high thermal losses as the dissipated power depends on the electric current by the power of two. This information can be used to thicken the cross sections at critical spots or change the overall current path by rounding off unfavorable edges.

The Joule Heat Generation (W/m³) result field comes in handy when judging the heat flux that needs to be considered when designing the thermal management solution for the system. Even if the Joule heating contribution is not the main thermal load in the system, it can harm the overall performance or reliability of the product if local heat flux spikes happen distant or shielded from the main cooling solution, e.g., a liquid cooling plate or a fan.

Using the statistical tools in the Post-Processor, one can extract both the distributed heat load as well as the integrated total power loss on a part of the model.

Power Electronics Simulation in SimScale

Joule heating is essential or relevant to the design of a variety of applications. Next to the inverter use case, those include other components of the electric powertrain in electric vehicles such as the battery pack or electric motors. It is also the most important aspect of electric resistor thermal considerations. In the following, we present industry-relevant examples.

Resistors

Resistors are used to protect other components in an electric circuit with high voltages or current pulses using highly resistive materials, ideally in a compact structure. While the potential drop and the resulting heat conversion are therefore intended, the heat can still be damaging to the resistor or the surrounding system. Cooling solutions include mostly mounted heat sinks but can also involve active air or liquid cooling.

The power resistor model has four separate resistive conductor circuits and is a device that has been used in the automotive industry in the past extensively. This particular model was used in Jaguar oldtimers and ensures the electronic control unit (ECU) for fuel injection does not overload from high current spikes.

In order to open the injectors, the full 12V potential is connected and provides the required high opening current of 22.6 A. After that, the required current is much lower and the components can not withstand the high current load for long. Hence the ~6 Ohm resistor circuits are added to the circuit with a switch and reduce the current below 2A. The component is mounted at a sheet metal component next to the engine and therefore must only rely on natural convection cooling.

Electric Vehicle Battery

The battery module case is from an electric vehicle from the Formula SAE (Society of Automotive Engineers) race cars. Formula SAE is a series of international competitions in which university teams compete to design and manufacture the best-performing race cars and simulation is extensively used by academic teams to optimize their race car designs and components.

The battery module uses forced convection air cooling from fans for thermal management and has aluminum busbars. It has 100 lithium-ion cells in a 10S10P arrangement (10 cells in series, 10 cells in parallel).

The Joule heating analysis is needed to predict heat gain from current flowing through the battery components and test optimal cooling strategies. Analyzing the electric potential drop and current density is additionally helpful in order to avoid current spikes at sharp corners or thin sections and aim for a uniform load across the pack. In this case, a 1C scenario is simulated with 40 Amps drained from the module.

Fuse Blocks

The following fuse block case is widely used by automotive Original Equipment Manufacturers (OEMs). Fuses operate under a small potential difference, allowing current to flow within the fuse. As long as the current remains within safe limits, the fuse functions normally.

However, fuses are designed to serve as intentional weak links in electrical circuits, such that they sacrifice themselves by failing at their weakest points to protect expensive or sensitive equipment from high current values. The failure of a fuse occurs due to heat generated by current flow at its weakest points, which may result in local melting around those regions.

To simulate the operation of a fuse and be able to observe the temperature rise around the weak regions, a transient conjugate heat transfer analysis is used with the potential difference between the two ends of the fuse being set. In this scenario, a potential drop of 0.2V was applied to the fuse with a resistance of only a few milliOhm resulting in a huge overload current with the highest current density around the weak point.

The post SimScale Launches Joule Heating Simulation to Accelerate Innovation in Power Electronics appeared first on SimScale.

Nearly all lithium batteries are required to comply with the UN 38.3 international transportation standard. Ever wondered why you cannot have your laptop or any electronic equipment stored in your check-in baggage while flying? Well, that’s because batteries are sensitive to low-pressure conditions due to potential leakage which may cause a fire. Batteries are also sensitive to shocks and vibration. Batteries are also sensitive to…well the list will keep going but no worries, we have the UN 38.3 standard that helps us mitigate those risks.

Under-pressurized situations are definitely not anything pleasant so let’s leave it for now and just focus on how simulation in the cloud can help us design better batteries from the vibration perspective. SimScale can be used to understand and improve battery product design to comply with vibration test standards relating to the transport of dangerous goods. SimScale helps designers and engineers create reliable battery systems by virtually replicating multiple vibration tests within their web browser. This article touches upon a virtual shaker table test of an electric vehicle (EV) battery module and its housing, trying to replicate the UN 38.3 T3 standard using SimScale.

Simulation Strategy and Setup Within SimScale

According to the UN 38.3 T3 requirements, a battery module should show no leakage, venting, rupture, or fire under a sinusoidal vibration test spanning from 7 Hz to 200 Hz. Key test details are summarized below:

• Sinusoidal vibration waveform
• Tests are to be performed in all X, Y, and Z directions
• Test range from 7 Hz up to 200 Hz
  • 7 Hz – 18 Hz: 1 G acceleration applied
  • 18 Hz – 50 Hz: Peak acceleration gradually raised to 8G at 50 Hz
  • 50 Hz – 200 Hz: Peak acceleration maintained at 8G until 200 Hz

Leveraging the SimScale platform, designers and engineers can perform structural analyses which will help them ensure that stresses on their product will not exceed certain limits and that deformation magnitudes will not entail any clearance issues. Let’s look a bit more in detail at how this process may look like and what a reasonable simulation strategy can be.

Using simulation, we would be interested to examine whether or not there are “resonance” modes for our components within the test frequency range (7 Hz to 200 Hz). If such modes exist, we would then be interested to see the response of our structure under the required loading conditions (acceleration 1G to 8G). The above strategy will help us identify whether or not there is a risk of resonant behavior and if yes, where the peak stresses and deformations are located. Knowing the above, we can then take corrective action through design changes to improve the performance of our battery product before having to go to the actual test bench in order to achieve compliance with the UN 38.3 standard.

CAD Upload

All simulations start from a CAD model. SimScale is CAD agnostic, meaning that it can handle almost any CAD model in its native format as well as neutral files. We can import our models into CAD mode, inspect them, do any modifications necessary, and then proceed with the simulation setup.

Setup

Since our CAD is ready, we are good to continue with the next steps. Setting up a simulation was never easier. Simply by picking the desired analysis type we get a simulation tree prepopulated in our workspace. All we have to do is define a few essential pieces of information for the simulation to run.

Modal Analysis

The first step is to conduct a Modal Analysis. This will help us identify any modes laying below 200 Hz. As the geometry is imported into the simulation tree, all bonded contacts are automatically identified. Special care should be taken in material definition. We can either pick materials from our prepopulated default material library or create our own customized materials and save them in our user materials for future use. The final step would be to fix our model, in this case at the holes located in the battery’s outer shell, and request the number of modes we need to calculate. Before hitting solve, we can choose to go with the robust automated meshing of SimScale or maybe add a couple of manual controls, such as Sweep Meshing which will help build higher quality elements reducing the overall cell count.

Running the frequency analysis for the battery module leads to no significant modes within the frequency range of interest. The first mode is at 233 Hz. On the other hand, the housing seems a bit more prone, as at least 3 resonance frequencies exist (72 Hz, 80 Hz, and 138 Hz). It looks like there is a higher chance that the housing might experience resonance during the vibration test.

Harmonic Analysis

To perform a Harmonic Analysis we follow a very similar procedure. The materials and boundary conditions definition are identical to the Modal Analysis. However, we now have to also include a Base Excitation covering the desired frequency range according to the UN 38.3 T3 requirements. This can be input in a table format to depict the actual test requirements.

The Harmonic Analysis should be performed in all three directions (X, Y, and Z). This is made extremely easy by taking advantage of the templated simulation approach offered by SimScale and since SimScale runs in the cloud, all three directions can be simulated in parallel.

Results

The Modal Analysis provided us with valuable information about the potential frequencies that are worth investigating. More specifically, the EV battery module has no eigenmodes within the test range, with the first eigenmode being at 233 Hz. The housing, though, has at least three eigenmodes below 200 Hz. The results can be nicely summarized and exported in table format.

Now, we are ready to examine the structural response of our components by checking the Harmonic Analysis results. Despite not having any eigenmode within the test range, with the closest one being at 233 Hz, it still might be worth having a look at the excitation results of the EV battery module at 200 Hz.

A maximum displacement of 1.8 mm is not worrying as it is not going to cause any clearance issues given the model tolerances. On the other hand, we can see some quite high peak stresses being developed near the mounting holes of the module. While these are within the material structural limits it may be worth having a deeper look to see whether those are real, or it may be a product of numerical errors, often called stress singularities.

Moving on with the housing, things get a little bit more “dangerous” since now there are significantly higher deformations and stresses present.

The structure is highly sensitive to z-axis excitation. Checking the displacement at 144 Hz, we get large portions of the model, especially in the center baffle, being deformed by more than 4 mm. Given the small overall size of the battery housing, 4 mm of deformation, may definitely increase the chances of structural damage within the battery components due to clearance issues. In addition to that, peak stress appears to be well above the yield point of aluminum (casing material), especially around the corner mounting holes. This is extremely worrying and we, as designers, should definitely take action and try to improve/stiffen the housing design before conducting any actual vibration tests.

SimScale Simulation Design Insights

SimScale has been helpful in simulating Modal and Harmonic analyses on two different designs related to an EV battery module. Battery modules need to meet certain requirements/pass certain tests before being released to the market. We have seen how we can successfully implement a structural analysis simulation strategy within SimScale and be able to extract valuable insights about our designs. These and many more simulation capabilities are available through your browser and SimScale platform.

Be sure to watch our on-demand webinar if you would like to know more about how engineers and designers can leverage cloud computing using SimScale to build successful and reliable products:

The post Transporting Dangerous Goods: Vibration Analysis of EV Batteries appeared first on SimScale.

Forced convection cooling is an essential component of modern electronics where high power density devices and enclosures generate heat that needs dissipating. In many cases, natural ventilation-based heat rejection is not always enough, and fans must be used. Fan performance can be complex and based on several variables that need accurate representation in any modeling and analysis. Fan manufacturers provide a fan curve that describes the relationship between static pressure, power demand, speed, and efficiency values per-flow rate. This information is essential for cooling purposes, so we need to model the fan curve when possible. The pressure flow characteristics described by manufacturer fan curves dictate how the volume flow from a fan is affected by pressure drop. Most fan manufacturers provide this data based on standardized testing according to international standards.

Engineers evaluating different fan types and sizes in their device designs must be able to account for the fan curve and trust that it is a true reflection of how the fan might perform in real-world conditions. Creating a digital twin of the fan curve is an excellent way to simulate its performance. The SimScale platform is a cloud-native engineering simulation tool for general purpose flow, thermal and structural analysis. A typical application is the analysis of electronics enclosures for thermal management, specifically, scenario testing multiple cooling strategies. Fan behavior can be simulated in several ways in SimScale. A boundary condition where the fan inlet is placed can have a direct mass flow applied to it as a velocity inlet with appropriate fluid material properties and ambient temperature. Alternatively, a momentum source can be defined to represent the flow from a fan, and, most recently, engineers can now upload fan-curve data in a tabular format directly into the SimScale platform and use it as a boundary condition.

Thermal Performance of a Raspberry Pi Computer

To demonstrate some of the fan modeling features in SimScale, we have simulated a Raspberry Pi computer using a publicly available 3D model of the device taken from GrabCAD. A conjugate heat transfer simulation with forced convection (fan) is used to model the computer using standard 30 mm fans for cooling. We have used manufacturer data for the fan performance, which comes in four different model types; each has a fan-curve providing data on volumetric flow rate and pressure drop (static) at ambient conditions. We have taken the 3D model and simplified it using the CADmode editing features in SimScale. The study is not interested in geometric changes to the enclosure, only in fan and cooling performance.

We can directly upload the manufacturer fan curve data and derive the fan operating points explicitly using simulation. We want to show how the manufacturer’s data can be applied to the former. We can extract the fan specification sheet data into a spreadsheet to upload into SimScale and perform a complete thermal analysis of a Raspberry Pi. In the latter case, we can conduct a flow rate study using simulation to derive the fan operating point, e.g. at what flow rate can we generate the given pressure drop and derive a system resistance curve. Using the parallel simulation capabilities in SimScale, we can run multiple operating points simultaneously and begin to generate the fan curve for evaluating how efficient these fans are for cooling purposes and whether they adequately cool the computer chips in the enclosure. This approach is instrumental when the full fan curve data is not available or to assess in-situ fan performance in the system or device (the manufacturer data is for a simple test setup).

A simplified evaluation might look like this:

• Use the original CAD model and a fan curve as the baseline case
• Derive operating points from simulation
• Derivation of system resistance curve from flow rate study
• Cooling efficiency comparison with different fan models
• Compare to directly uploading manufacturer data

We have used the conjugate heat transfer (CHT) analysis type in SimScale. The CHT analysis type allows for the simulation of heat transfer between solid and fluid domains by exchanging thermal energy at the interfaces between them. Typical applications of this analysis type include heat exchangers, cooling of electronic equipment, and general-purpose cooling and heating systems. A multi-region mesh is required for a CHT simulation to have a clear definition of the interfaces in the computational domain. With the interfaces adequately defined, this is automatically taken care of in SimScale and, in this case, generates a five million cell mesh. For the simulation setup – fan inlet and pressure outlet boundary conditions are used, the air is used for the flow region, and several materials are specified for the chips and electronic components, including Copper, PCBs, Silicon for chips, and Aluminum for heat sinks. Power values represent the CPU (3 watts) and more minor chips (0.25 watts). The air inlet is ambient at 19.85 ℃, and a CSV file is used to upload the fan curve for later use. 

Thermal Simulation of Electronics Cooling

We can visualize heat removal on the chips by looking at surface area average temperatures. The images show up to 392 K on the CPU (max). SimScale will also extract point-specific data and pressure drop across inlets and outlets. Fan inlets show a 4.78 pascal (0.5 mmAq) pressure difference at a flow rate of 7.55e-4 m3/s, and this matches the fan curve data sheet for the baseline model (L). The fan outlets are at 0 gauge pressure as intended. We can easily switch the fan curve data to simulate a more robust fan (H) for comparison. Doing this shows us a 10 pascal (1 mmAq) pressure drop, and the two are compared in the image below. By doing this analysis, we can start generating a system curve for the enclosure (Raspberry Pi) derived from simulating various fan operating points (Orange line in the image below). Running ten simulations of different operating points in parallel, we generated the entire system curve for the Raspberry Pi in two hours. Chip cooling performance based on comparing the two fans is also shown, with the more substantial model H fan better at removing heat.

Fans as Momentum Sources

In some cases, modeling the fan as a momentum source might be needed. Momentum sources can be used to simulate fans, ventilators, propellers, and other similar fluid acceleration devices without having to model the exact geometry of the device. For instance, users might want to model a fan whose dimensions and output velocity are known. With this feature, it is possible to assign average velocities or fan curves to volumes of interest. An external flow domain is needed here to act as the air supply for the CAD opening where the fan would be, and a momentum source is used to define the flow behavior. Users can upload the fan curve, decide which direction the flow is going, and add a geometry primitive cylinder as the source. The external flow domain is used because flow enters the fan as it would do in a reality where the fan inlet is the interface of internal and external boundaries.

Summary 

Simulation is now considered essential to optimizing electronic product design and performance. Using multiple methods to represent complex fan performance, engineers can quickly evaluate the cooling impact of fan types, and using the parallel simulation capabilities in SimScale, a more comprehensive range of scenarios can be evaluated. In summary:

• Fan curves: allows modeling of fans based on fan curves (flow rate to pressure drop relation)
• Fan boundary condition: users can specify a fan inlet or fan outlet as a boundary condition to model fans that are placed at the edge/outside of the enclosure domain.
• Fan Momentum Source: allows modeling of internal fans as a momentum source that are embedded within the model.

Furthermore, in the same platform, engineers can perform virtual shaker table tests, structural analysis, and more specialist fluid flow analysis using the same CAD model and simulation environment. 

To learn more, watch the fan modeling webinar below:

The post Electronics Cooling Using Fans appeared first on SimScale.

Simulation is an essential part of designing thermal management in electronics. It allows engineers to evaluate their designs by quantifying the temperature distribution throughout the device depending on the materials, geometry, air or coolant flow characteristics, and the power consumption of the components. Simulating electronics cooling has a unique difficulty that does not occur with other thermal simulations. Electronic components have many little parts and details such as components, pins, chip markings, plate-through holes, etc. We can see this when considering a typical printed circuit board (PCB) assembly shown in the image below.

These components are too small to influence the heat transfer and flow behavior. It is possible to include them in the simulation, however, this often leads to low-quality meshes or very resource-intensive simulations which often fail. Therefore, a typical step in electronics cooling simulation is to defeature the CAD model so that it can be meshed easily. Unfortunately, defeaturing and CAD cleanup are tedious and extremely time-consuming tasks. SimScale offers an analysis method capable of dealing with complicated or unclean CAD models.

Using the Immersed Boundary Method for Electronics Cooling

The Immersed Boundary Method (IBM) is a new analysis type available on the cloud-native simulation platform SimScale. It allows for the simulation of heat transfer between solid and fluid domains by exchanging thermal energy at the interfaces between them. While in standard CFD and FE simulations the domain is discretized using body-fitted meshes, IBM uses Cartesian meshes where the elements are aligned parallel to the Cartesian directions. The geometry of the simulated device is immersed into the Cartesian mesh. The mesh is resilient to geometrical details and does not require CAD simplification even for very complex models. A visual example of a Cartesian mesh used in IBM can be seen in the image below.

By using the IBM solver in SimScale, an engineer can completely skip the painstaking CAD-cleanup phase of the simulation preparation which can often take several hours. This is a massive gain in time and effort and just another way in which SimScale enables engineering innovation by making simulation technically and economically accessible at any scale.

Use Case: Thermal Performance of Alternative Designs of an LED Lamp

To showcase the new IBM solver, we will consider the heat transfer behavior of the LED lamp shown in the image below. The lamp is designed as a work lamp for industrial applications where the presence of dust may be an issue. Therefore, the lamp is watertight thanks to the gasket which can be seen in the image in light blue. The lamp relies on passive heat sinks and natural convection for cooling.

The image below shows the two design alternatives that shall be considered here. The subtle difference between the two lies in the thickness of the base plate of the heat sink. The two designs are otherwise identical.

Simulation Set Up

One of the advantages of SimScale is how easy it is to set up a simulation. IBM underlines this by removing the necessity to clean up the CAD. In this case, for example, we have small gaps around the fasteners and gaskets as in the image below. These details are typical of manufacture-ready and also early-stage design CAD. These are normally things that would have to be defeatured to avoid low-quality body-fitted meshes, however, the IBM solver easily deals with these kinds of details. 

CAD associativity is a new feature in SimScale which allows engineers to design and simulate iteratively. When switching between different geometries as in this case, all materials, boundary and initial conditions, and energy sources are reassigned automatically. This makes it very efficient to carry out comparative or parametric simulations. Currently, CAD associativity is possible with Onshape® and Solidworks®, for which SimScale also offers plugins for the direct transfer of CAD files from within the respective CAD packages.

Completing the setup requires only a couple of steps: 

1. Material assignment: it is possible to define orthotropic thermal conductivities to capture the properties of PCB
2. Bounding box: a large enough cuboid is defined around the model to capture the thermal plume forming above the lamp
3. Boundary/initial conditions: it is not necessary to define boundary conditions in the IBM solver when simulating external flow. The walls of the bounding box are considered to be open
4. Heat sources: each LED dissipates 3 W of thermal energy
5. Mesh: the beauty of a Cartesian mesh is its simplicity; therefore, its definition is also straightforward. You can usually leave the default settings and mainly work with refinements in case adjustments are needed.
6. Click Run! 
7. Switch Geometry
8. Click Run!

SimScale is completely cloud-native. This means that the simulation does not run locally on your computer but on remote servers. It is possible to run an unlimited number of simulations in parallel. Since for every simulation a new virtual machine is created, one simulation takes the same time as ten simulations. So it is possible to consider twenty different designs and get results in just over an hour, which is approximately the duration of the simulation presented here. Furthermore, there is also no need to install or maintain any software or hardware since SimScale runs entirely in your browser. 

Results

In the chart above, we can see the temperature values of the LED chips for the two designs. The first design has significantly warmer chips. Apparently, the thicker heat sink traps too much heat, while the thinner heat sink is more efficient in transferring heat to the thermal plume. 

Moreover, we can also have a look at further results to carry out a sanity check on our simulation. In this next plot, we visualize the thermal plume forming above the lamp. Typically the flow velocity of the thermal plume in natural convection cases is in the order of 0.5 m/s to 1.5 m/s, which is also what we see here.

As the lamp may be installed inside a wall or cupboard it is important to know both the temperature on the surfaces of the lamp and the temperature of the air around the lamp. There are often safety specs that define a minimum distance to the wall if the air around the lamp is too high. To make sure we are fulfilling these specs, we can look at the temperature distribution on the surfaces of the lamp and also in the air around the lamp. Both plots can be found in the two images below. In the latter of the two plots, we are showing an isosurface corresponding to 35 °C. The volume inside this surface will be hotter than 35 °C and the temperature outside this surface is below 35 °C.

Leveraging the Cloud for Electronics Cooling

SimScale enables engineers to innovate more quickly by making simulation more accessible. The IBM solver makes CAD clean-up obsolete which is an extremely time-consuming (and very annoying) exercise. Additionally, CAD associativity allows for rapid swaps of geometries without having to redo the simulation setup. Coupled with the unlimited and parallelized computing power of the cloud, an engineer using SimScale can innovate and iterate much more quickly.

Learn more about SimScale’s state-of-the-art IBM solver for fast, easy-to-use, and accurate modeling of thermal management in electronics in this on-demand webinar:

The post This Might Get Hot! Thermal Simulation of High Power Density Electronics               appeared first on SimScale.

Case Study: Enclosed LED Heatsink

The electronic device in question is an enclosed LED heatsink with a metal core PCB as shown below.

Using the Conjugate Heat Transfer (CHT) simulation type in SimScale a full thermal analysis was performed with a focus on radiative heat loss contribution.

In order to better understand the influence of radiation on the proposed LED heat sink, multiple design considerations were investigated:

• Radiation: Disabled vs Enabled – To understand if we should consider radiation in further design iterations
• Surface finish: Anodized aluminum vs radiative paint – To understand the influence of increasing the emissivity of the heat sink surfaces
• Heat sink design: ‘Dense’ vs ‘Sparse’ heat sink  – To understand the influence of increasing the total surface area of the heat sink on the overall heat transfer.
• Cooling strategy: Natural vs forced convection – To understand the overall contribution of radiation in both cooling scenarios.

The comparisons were done by varying one parameter at a time as illustrated in the figure below.

Simulation Setup

•  A steady-state Conjugate Heat Transfer (CHTv2) simulation is used to model convection, conduction, and radiation
•   The flow volume that encloses the LED CAD module can be created using our CAD mode feature, which is used to create two flow regions as follows:
  • Box: To emulate the setting of a natural convection scenario where the device sits open to the environment. The flow is allowed to enter and exit freely through the boundaries of the box.
  • Cylinder: To emulate the effect of a forced convection flow. Where a flow rate of 0.04887 m³/s was applied to the inlet face.
• The materials used:
  •   PCB: Aluminum (emissivity = 0.3)
  •   LED chip: Silicon (emissivity = 0.9)
  •   Heat sink:  Anodized Aluminum (emissivity = 0.7)
  •  Aluminum with radiative paint (emissivity = 0.95)
• The ambient temperature is 19.85 °C
• The total power dissipated by the LED chip equals 10 W.
• The standard mesher with automatic settings was used.
• The average time to complete one of the particular design iterations mentioned above is between two to three hours. However, because SimScale allows all simulations to be run in parallel with cloud computing, this now becomes the total time needed to run all design scenarios.

Simulation Results

The chart below compares the results obtained from the base setup with its design variations.

• The left axis provides the maximum LED temperature in °C.
• The right axis provides the contribution of radiation to the overall heat loss in the system.
• The bottom axis shows the design variations simulated.

Radiation Enabled vs Disabled

The first design variant stems from asking, “Do I need to include thermal radiation effects in my further design iterations?”

The answer is yes. If we compare the results obtained from the base setup with the results from the simulation without radiation, we can clearly see that the maximum LED temperature obtained with radiation activated was around 46 °C while in the case without radiation was 53 °C. That’s about 27% increase in temperature when radiation effects were not included in the base design, and a sufficient indication that thermal radiation effects should be considered. Moreover, it can be seen that in the base setup radiation contributed close to 30% of the total heat loss in the system.

Surface Finish: Anodized vs Radiative Paint

Using Anodized Aluminum as a material for the heat sink contributed to lower radiative heat loss when compared to its radiative paint finish counterpart. Subsequently, this resulted in a higher LED temperature with a value of around 47 °C.

Heatsink Design: ‘Dense’ vs ‘Sparse’ Heatsink Fins

Interestingly, the maximum LED temperature obtained for the design with a dense heat sink proved to be higher than the design with a sparse heat sink; 53 °C in comparison to the 46 °C of the base setup. This is simply due to the low velocities that are usually encountered in natural convection which results in insufficient flow penetrating the gaps between the fins and hence not providing adequate cooling.

On the other hand, the radiative heat loss contribution was almost 5% higher than in the base design.

Cooling Strategy: Natural vs Forced Convection

In the forced convection case the maximum LED temperature recorded was significantly lower than the other design variants with a natural convection cooling strategy. In this case, 25 °C in comparison to 46 °C with natural convection. Moreover, the contribution of radiation to the overall heat loss is about 2% which is minimal.

Simulating Radiation Heat Transfer for Optimized Thermal Management

Designers have opportunities at an early design stage to optimize the performance of electronic products by investigating multiple factors like thermal management, materials, geometries, and environmental conditions. With thermal management playing such an important role in electronics design it is crucial to investigate the contribution of the three fundamental heat transfer methods to the overall heat loss of the system and it is especially important to consider radiation heat transfer effects. Using CFD thermal simulation numerically replicates working conditions, materials, and heat thermal transfer methods on a 3D model so that the design engineers can make informed decisions.

This on-demand webinar introduces our radiation heat transfer simulation features for electronics cooling. Watch and learn how to efficiently mesh the model, set up the boundary conditions including gravity, enable radiation, and post-process and visualize the simulation results:

The post Simulating Radiation Heat Transfer appeared first on SimScale.

Cloud-Native Engineering Simulation for Thermal Analysis

SimScale offers multiple analysis types for engineering simulation including conjugate heat transfer (CHT) capabilities coupling solid and fluid domains and also leverages automated parallel computation capabilities in the cloud. These capabilities give engineers and designers the benefits of:

• Faster design cycles and electronics performance insights
• A full exploration of multiple design iterations in parallel
• Reduced costs and time investment in the costly prototyping phases
• In-platform CAD editing for streamlined simulation workflows

All electronics devices generate heat that must be managed to avoid overheating and component failure. The SimScale platform can be used to simulate and optimize multiple electronics enclosure cooling strategies and common features, including:

• Natural and forced convection
• Air and liquid cooling 
• Fan modeling
• Anisotropic materials (PCB)
• Thin layer resistances
• Power networks

The conjugate heat transfer (CHT) v2.0 analysis type in SimScale enables heat transfer analysis between solid and fluid domains by transferring energy (thermal) at the interfaces (contacts) between them. This means that the model must contain at least one fluid and solid region. In most cases, a CAD geometry will not have a fluid domain assigned as default. The SimScale CAD mode provides a flow volume extraction tool to do this. Typical applications of CHT analysis type include analysis of heat exchangers, cooling of electronic equipment and electronics enclosures, LED luminaire design, and similar cooling and heating systems. The upgraded version of the CHT analysis type in SimScale (CHT v2.0) is more stable and provides faster convergence as the energy equation is strongly coupled between the solid and fluid regions. With the upgrades, both incompressible and compressible flows can now be modeled including the impact of radiation heat transfer. Both fluid and solid domain mesh are required for a CHT simulation with clear definitions of the fluid and solid interfaces or contacts. With these interfaces properly defined, the mesh is automatically taken care of in SimScale.

Avoid Electronics Enclosure Overheating with Active Cooling

In the example of the forced convection cooling case, the key design considerations that are explored include the velocity and temperature distributions across electronic components and their dependence on fan (forced convection) inlet speeds. Multiple heat sink designs can also be simulated in parallel for comparison.

The case in question is the heat transfer analysis (cooling) of densely packaged electronics inside an enclosure. The simulation setup is as follows:

• A conjugate heat transfer (CHT) simulation is used to model conduction in solids and forced convection (air).
• There are two inlet fans (simulated) providing forced convection into the electronics enclosure. Two velocity inlet boundary conditions where the fans would be on one end, give a ventilation rate of 0.014 m³/s per fan at ambient conditions (20℃). The other end of the enclosure has openings using a pressure outlet boundary condition. 
• The fan flow rate is constant. For more detailed analyses, a custom fan performance curve can also be uploaded into the SimScale platform.  
• Materials including silicon, tin, copper, aluminum, and polylactic acid (PLA) have been assigned to the electronics enclosure, boards, and components using the extensive material library. Custom materials can also be defined.  
• Heat transfer coefficients (HTC) are applied to the walls.
• Electronics components receive electrical power and generate heat (e.g. resistive losses). The heat load from each component can be defined by assigning an absolute power source in watts. Various thermal loads and power sources from IC chips, resistors, etc. have been defined totaling 100 Watts with the single largest load being the main CPU at 40 Watts.

Simulating Heat Transfer for Better Electronics Design

The simulation takes approximately 30 minutes to run. Because of the high-density power electronics in a confined space, we observe high temperatures on some of the components because not enough airflow is reaching those parts. The SimScale platform allows users to easily alter the flow rate, reposition the supply fans, or employ a combination of the two.

A design study on the heat sink, using various materials, or altering the number and spacing of fins, can also be undertaken at this stage. Importantly, when users set up and run these alternative designs, running all of them together simultaneously would still only take 40 minutes, saving considerable time when compared to sequential runs. Simulating heat transfer analysis at the early stages of design can give designers more options to finalize a design before spending money on costly prototyping. 

Evaluate the electronics enclosure cooling design and learn more about simulating forced convection cooling from a  web browser, in our on-demand webinar, Thermal Analysis of an Electronics Enclosure: Forced Convection Simulation.

The post Electronics Enclosure Cooling: Forced Convection Simulation appeared first on SimScale.