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.

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.

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.

This article describes the structural assessment and vibration analysis of an electric motor support bracket to calculate and verify if its critical response frequencies lie within the intended operating range. In addition to the vibration analysis, we show engineers how to perform a load analysis on the electric motor shaft under an applied torque. The design goal of this simulation study is to optimize the electric motor support bracket to ensure that its eigenfrequencies remain outside of the motors’ operating speeds, avoiding possible part damage, bolt loosening, and reduction in clearances between parts and unwanted noise. In many cases, there is a danger that resonance effects might excite vibrations in the support bracket or other components. Meticulously analyzing this uncertainty is needed to reduce the risk of unwanted issues and, simulation at the early stages provides this guarantee. Geometry changes are made to mitigate dangerous vibration at shaft speeds close to the calculated first eigenfrequencies of the support bracket. Note, that the motor in question has a fixed rotational speed. Vibration analysis is even more important when variable speed drive motors are used for example and more in-depth studies are needed in those cases. We can also perform a quick safety factor check of the motor shaft under an applied torque to represent real-world loads and ensure a minimum safety factor of two is validated under normal operating conditions. A factor of two is a good starting point as it means the stresses are half of the yield stress of the shaft material (mild steel), which is getting close to the material endurance and performance limits that affect fatigue and component life. The entire engineering simulation workflow is implemented via a web browser and performed in the cloud.

Fast and Accurate Structural Simulation

The FEA capabilities in SimScale enable engineers to virtually test and predict the behavior of structures and components, and to solve complex engineering problems subjected to static and dynamic loading conditions. SimScale uses scalable numerical methods that can calculate physical variables otherwise very challenging due to complex loading, geometries, or material properties. Another structural analysis software feature, modal (frequency) analysis can help determine the eigenfrequencies (eigenvalues) and eigenmodes (mode shapes) of a structure due to vibration. The results are important parameters to understand and simulate structures and products that are subject to unsteady conditions. The resulting frequencies and deformation modes are dependent on the geometry and material distribution of the structure, with or without the displacement constraints. A static analysis type allows time-invariant calculation of displacements, stresses, and strains in one or multiple solid bodies and the results are a consequence of the applied constraints and loads. SimScale has integrated the Code_Aster solver into the platform for frequency and static analysis simulations. A much broader study can use a multidisciplinary physics approach to simulate and optimize the loading, modal, thermal, and rotating aspects of the electric motor. In this example, we will focus on the vibration analysis of the electric motor support bracket and load stresses on the motor shaft.

Vibration Analysis of an Electric Motor Support Bracket

A simple workflow is required to complete the analysis for both the support bracket and shaft.

Import CAD – Users can import many types of common CAD file formats and use CAD connectors with tools such as Onshape, Solidworks, AutoCAD, and more. The feature CAD mode in SimScale allows users to perform basic CAD operations for editing without leaving the platform. The motor geometry is imported from Onshape. The number of faces increases with support bracket geometry modifications with a total of six bracket CAD variants defined. The motor shaft is also a single volume. 

Simulation set up & mesh for the support bracket – The bracket has a frequency analysis type selected in SimScale. The mesh is generated automatically with 23.5K cells and the element sizing is set to automatic which takes the geometry fineness into account when deciding size. In this case, a moderately fine mesh has been chosen (the user has manual control over mesh settings). A point mass boundary condition is applied to represent the mass/moments of inertia of the whole motor eliminating the need to include the fully detailed motor geometry in the simulation. Our goal is to find the first eigenmode of the support bracket that needs the least energy and is thus the easiest mode to excite. An eigenfrequency plot is generated automatically.  

Simulation set up and mesh for the shaft  – The motor shaft has a static (linear) analysis applied to it and from the materials library, we have selected mild steel as the primary material with linear elastic behavior. In a static analysis, we can define constraints and loads. The shaft has fixed support connected to the motor and the main shaft can be deformed due to remote load forces that represent applied torque using moments around the center of rotation. Deformable here means that the shaft is allowed to deform without applying additional stiffness. Undeformable would mean the shaft was a rigid entity which is not what we want. A centrifugal force is applied to the whole volume of the shaft and a rotational speed is set in radians/second. Automatic mesh settings are again used and a second-order tetrahedral mesh is generated for the shaft with 170K cells.

Post-processing – The SimScale platform’s field results allow us to visualize the deformed shape for each mode showing the displacement magnitude in meters. Statistical data is available to further interrogate the eigenmode number, eigenfrequency, Modal Effective Mass (MEM), Normalized Modal Effective Mass, and cumulative Normalized Modal Effective Mass (CNME). Multiple plots are pre-populated showing the eigenfrequency plot, modal effective mass, and accumulated normalized modal effective mass. From the static analysis, we can evaluate standard outputs such as Von Mises and Cauchy stresses, displacement, strain, and a new output variable that is the safety factor.

Design Insights

Bracket Vibration Analysis

The support bracket vibration assessment shows us that the first eigenfrequency (21.8 Hz) of the original bracket (CAD 1) design is identified as being dangerous as it is too close to the shaft rotation speed equivalent in Hz (19 Hz), causing dangerous resonance effects and possibly leading to severe deformation. This could easily excite the first eigenmode just by running the motor and the excitation could cause resonance in the bracket itself —the displacement would move the motor beyond operating parameters and could cause damage. Although the first eigenmode is the most important to reconcile, different eigenmodes cause more displacement and deformation. They can all be visualized in SimScale. Geometry optimization by defining several CAD variants (1-6) of the support bracket can be simulated in parallel to achieve successful inflation of the first eigenfrequency value, away from the shaft speed. The full results are shown in the table below:

Shaft Safety Factor

The shaft structural integrity is simulated under real-world operating conditions, ensuring that the factor of safety did not drop below 2. Under two loading conditions with a nominal torque of 0.5 Nm and 10 Nm respectively we found the following:

1. Load Case 1 – Nominal Torque of 0.5 Nm
   1. Results show a peak stress value 4.01 MPa.
   2. Results show a minimum factor of safety of 62.35, achieving the design goal.
2. Load Case 2 – Maximum Torque of 10 Nm
   1. Results show a peak stress value 80.19 MPa. 
   2. Results show a minimum factor of safety of 3.118, achieving the design goal.

Engineers must embed structural simulation in the cloud early in their product development processes to ensure that performance of critical design parameters is met using sound engineering analysis. Using parametric analysis, engineers can increase their confidence that the final product will operate and function within expected limits.

The post Vibration Analysis of an Electric Motor Bracket 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.

Today, the company develops high-performance electric drivetrain and battery systems for many of the world’s largest automotive manufacturers. Its customers include Aston Martin, Porsche, Koenigsegg, Cupra, and many more.

Rimac, known for its electric hypercars, is working on highly efficient battery solutions for the most demanding projects and conditions in the automotive industry. In this article, we explore Rimac Automobili‘s approach to simulation and learn how they leverage cloud-native engineering simulation within their battery pack team design and beyond.

Multiphysics Simulation and Product Development at Rimac

SimScale is used by several different departments responsible for designing components for battery packs and for testing the structural integrity of components. Currently, Rimac’s design engineers count on SimScale as a tool in their stack that augments their simulation capabilities. Battery System Engineer Antonio Radenić shares that the team’s main interest lies in heat transfer phenomena. They perform thermal simulations to determine the thermal gradients and hot spot areas they can expect on various concepts and designs.

The cooling performance assessed in the project shown below includes the maximum temperatures reached by the battery pack cells, as well as the battery gradient, which is essential for optimal battery performance. The parameter studies included a variation of the inlet flow velocity as well as the thermal conductivity of the electrical insulator.

These types of studies assure Rimac designers reliability, expected lifespan, and safety of their product. Conducting them with cloud-native engineering simulation means the turn-around time of investigating multiple design iterations is drastically reduced.

As SimScale was born in the cloud, it’s scalable by nature. This gives the design team an edge over traditional CAE tools. Computational power can scale up or down depending on project demand and multiple design options can be explored across multiple teams within Rimac, as cloud-native tools have sharing and collaboration features baked in.

Rimac is interested in exploring the full physical spectrum of the battery packs, which includes testing the structural integrity of their designs. With simulation, they can determine the type of stresses they might expect in their structures and cooling elements and the type of loads that might occur with applied pressure inside various geometries.

With electronics design, investigating thermal aspects is often at the center of the product development process. But Rimac’s work offers many areas of investigation for which multiphysics simulation can be used. A battery pack drop test analysis, for example, represents an opportunity to investigate and improve product safety.

CFD analysis only begins to scratch the surface of the type of design space exploration available to engineers using multiphysics simulation in the cloud. Access to cloud computing enables users to kick off simulations in parallel, meaning in the same amount of time that traditional CAE might require for one simulation run, designers at Rimac can explore the specifics of multiple designs, quickly iterate from there and make fast design decisions.

Simulating Designs for Electric Hypercars 

The introduction of SimScale into the R&D process of Rimac Automobili has empowered them to accurately and efficiently simulate hundreds of virtual battery pack models, including a wide range of complex real-life hypercar driving cycles and scenarios. In the future, they plan to expand the use of SimScale between and across different departments to fully avail themselves of the benefits of a SaaS engineering tool in a highly competitive automotive market. Providing their design team with access to multiphysics simulation allows Rimac to optimize at the top of their field, pushing the boundaries of design with the next generation of electric hypercars. 

Watch our on-demand webinar with Rimac Automobili, where presenters showcase a Design of Experiments (DoE) study seeking to increase battery life while maintaining an optimal operating temperature inside the battery module:

Watch On-Demand

The post Cloud-Native Simulation for the Next Generation of Electric Hypercars appeared first on SimScale.

CAD & Geometry

In electronics cooling design, it is not uncommon to spend significant time preparing CAD models for simulation. Detailed production-ready models in their original state are likely to be unsuitable for most simulation tools, burdening simulation engineers with the tedious process of de-featuring geometry, simplifying parts, closing small gaps in the model, and fixing faulty geometry. And, in the case of geometry studies consisting of multiple variants of one base model, all this work often needs to be repeated. Traditional CAD tools in simulation software are, in many cases, inefficient or ill-equipped for such tasks, as they are primarily optimized for designing models from scratch, not simplifying existing models. The CAD mode feature available in the SimScale platform offers a set of purpose-built CAD editing and simplification tools. For example, the Flow Volume Extraction operation alleviates the engineer from laboriously isolating a flow volume as required in other pre-processing systems. Unlike traditional CAD systems, CAD mode focuses on a core set of simple, intuitive, but also versatile tools that are ideal in making CAD models simulation-ready.

This short demo video illustrates the power of CAD mode:

Electronics Design Performance

A common challenge in early-stage design is the lack of ability to quickly investigate and simulate design performance. Engineers want the capability to receive design feedback in an iterative manner using fast and accurate thermal and flow analyses applied to their designs. A lack of accessible engineering simulation at the early design stages can significantly reduce product performance. With SimScale’s full-stack solution, users can easily go from CAD import and edit to auto-meshing the model and post-processing in a few minutes, all from a web browser.

Cost Reduction

Simulation tools that utilize cloud computing eliminate much of the costs associated with traditional simulation. They not only require no hardware installation or space for a workstation on-premise but are inherently scalable—enabling users to run multiple design iterations in parallel and deploy additional licenses organization-wide. The costs of traditional simulation stacks usually add up to big up-front investments before proving the added value of all the acquired hardware. With that also comes an enormous amount of responsibility in terms of maintaining the systems and a considerable know-how barrier requiring teams to conduct specialized training. Integrating cloud-native simulation into thermal management design also holds the potential for reducing enormous costs in the product development process. The more quickly a design study converges on its final version, the less costly it is. With simulation tools, designers can identify a problem before money is spent on producing physical prototypes. This is why Raycore Lights uses SimScale to shorten the design and prototype lifecycle of their products. In one example, Raycore ran a design study with eight simulations, each simulation taking five hours to complete.

Using legacy software, these would have been completed sequentially over a forty-hour period. With parallel simulation capabilities, all eight simulations were run concurrently, yielding a total simulation time of five hours.

Product Performance and Lifespan

In the absence of the right cooling strategy, the lifespan of components can decrease and the respective electronic device will operate at a suboptimal level. Failure to effectively cool electronics can lead to loss of efficiency and even system malfunction. Therefore, high-performance microchips and electronic devices of all shapes, sizes, and levels of functionality need innovative thermal design techniques to maintain design operating conditions and remain within peak performance range. The life of LED systems, for example, shortens significantly when exposed to prolonged heat.

Following standardized physical testing procedures is expensive, time-consuming, and becomes prohibitive for each LED design. Using thermal simulation means that engineers can quickly evaluate the thermal performance of multiple LED designs at many operating points and use the results to predict component performance and lifespan.

Electronics Design with Simulation

A fully cloud-native simulation platform allows engineers to simulate and analyze high-fidelity models with complex physics by making High-Performance Computing (HPC) accessible, giving unprecedented accuracy in results, efficiency in design collaboration, and versatility in the vast range of electronics cooling applications that can be solved. Engineering managers rely on the accessibility of SimScale to provide their team with immediate access to electronics cooling digital prototyping, early in the design stage, throughout the entire R&D cycle, and across the entire enterprise.

Learn more in our whitepaper: Electronics Cooling Multiphysics Simulation

Download Whitepaper

The post Electronics Design Challenges: Solved with Simulation appeared first on SimScale.

Cloud-based simulation equips engineers with the ability to test multiple scenarios and validate designs without the time-consuming and costly practice of prototyping for each iteration. This article shows you how and presents a case study from Forwiz System, who used thermal simulation to further optimize a CPU cooling system, winning their client improved product performance at less cost.

What is a CPU Cooling System?

A CPU cooling system effectively moves heat away from sensitive (and expensive) component parts through the use of heat pipes and heat sinks. Heat pipes are rapid heat-transferring devices with high thermal conductivity, sometimes up to 100 times more effective at conducting heat than typical metals.

Heat pipes transfer heat generated by the CPU towards the heat sink. The functionality of the heat sink is to increase the contact area with air to accelerate the final dissipation of heat via natural or forced convection to the surrounding environment. For the effective performance of the CPU cooling, each component needs to be optimized.

How to Improve CPU Cooling with Simulation 

By nature, the design process for electronics cooling is very iterative. CPU cooling systems entail many parameters, and each plays a critical role. In their journey towards optimization, engineers are presented with a variety of strategies to experiment with including adjusting the number of heat pipes or changing their diameter, increasing the number of heat sink fins or adjusting their thicknesses, use of surface treatment, radiative paint for optimal heat loss via thermal radiation or testing out different materials. 

As engineers are striving for the optimal solution across these multiple parameters, virtual testing before prototyping represents a huge cost and time-saving opportunity. A traditional design workflow for a CPU cooling system involves testing designs against their expected results, often a target temperature.

Electronics design that does not achieve the required cooling performance during thermal analysis requires a second or third iteration which means manufacturing more than one prototype. Manufacturing, shipping, and prepping the prototype for testing all contribute to time delays and increase the number of steps in the process, and thus, the number of possibilities for issues. With simulation, engineers have access to testing many scenarios in a simpler workflow with shorter turnaround times. A design tested and optimized for simulation can then be moved along to the final stage of the process, i.e., prototype testing, and the cost and time investment associated with it need only occur once. 

Cloud-based simulation takes this even further. With computing power off-loaded to servers, high-fidelity engineering simulation is made accessible, regardless of an engineer’s hardware capacity. Cloud-based simulation platforms, like SimScale, make it possible to run all simulations in parallel, driving down the design process from weeks to hours. Rapid iterations in-house eliminate the need for external simulation consultants, which provides cost savings, as EUROpack A/S. found when integrating SimScale into their workflow.

Case Study: Forwiz System

Forwiz System, an IT services company, received a request from a client to improve the cooling of the CPU inside their 2U servers. The cooling needs of their CPU chip, which had many cores, were not being met by the CPU coolers readily available on the market. In fact, when the chips were fully operational the CPU temperature was easily exceeding 90 degrees Celsius. This restricted their CPU from being fully operational. 

As the position and arrangement of different components installed inside their server were fixed, Forwiz had to work within the existing system to take on the challenge. 

To achieve better cooling performance they first increased the width of the upper part of the heat sink, taking advantage of previously unused, surrounding space. Then, they added more heat pipes to the newly increased size and lastly, they applied a special paint that has high-emissivity for additional thermal radiation.

Results from their initial experiment show that the structural change significantly increased performance when compared to the existing “benchmark” cooler design. The effect of the radiation paint also contributed to a drop in temperature, but not significantly which is important to note because that requires extra cost and manufacturing process.

The changes to the CPU cooling system made by Forwiz dropped the operating temperature from 90 degrees C down to 80 degrees C, within five degrees from the target temperature at which the client’s CPU chips could be fully operational. With cloud-based simulation, the engineering team was able to further optimize and deliver the target temperature for their client.

Forwiz used SimScale’s CHT solver to test the geometry of heat pipes and the number of fins within the CPU cooler and to validate the effectiveness of their previous cooling strategies.

With more than 100 simulations run, the results not only showed that their original structural changes contributed to a 15-degree C decrease in temperature but revealed that the high-emissivity paint could account for another 4 degrees C. The new heat sink geometry, optimized via simulation, reduced the temperature by another 5 degrees C. With the newly proposed CPU cooling system, the final temperature reached was around 77-76 degrees C, achieving the target temperature set by their client. 

For Forwiz, simulation enabled the engineering team to reach optimal cooling performance and facilitate the full operating capacity of their client’s CPUs. Simulation in the cloud is a powerful tool for designers seeking optimal thermal management in a quick and highly iterative manner.

To learn more about electronics cooling and how engineers can optimize the thermal management of their designs with SimScale, check out more resources here: 

• The Evolution of Evaluating Thermal Management: SimScale Customer Spotlight
• Thermal Management: Lighting, Battery, and Enclosure Cooling Projects
• Conjugate Heat Transfer Simulation: Best Practices in SimScale

The post How to Optimize a CPU Cooling System with Simulation appeared first on SimScale.