Fuel cells are one of the cleanest and most effective technologies for producing electricity [1]. They generate electricity through an electrochemical process that combines hydrogen and oxygen, expelling water and heat as byproducts. There are several uses for fuel cells, including powering homes, automobiles, and even spacecraft.

Harnessing the full potential of a hydrogen fuel cell requires a thorough understanding of how it operates and how its performance can be optimized. To that purpose, engineers are designing effective and long-lasting fuel cell systems using fuel cell simulation and modeling phenomena like heat transfer, fluid flow, structural integrity, and electrochemical processes that take place within the fuel cell structure.

This article aims to explore the intricacies of hydrogen fuel cell simulation and modeling and the ways we can leverage them to enhance the design and performance of hydrogen fuel cells.

How Does a Hydrogen Fuel Cell Work?

A hydrogen fuel cell is essentially an electrochemical device that produces electricity by utilizing the chemical energy generated during the interaction between hydrogen and an oxidizing substance, usually oxygen [2]. The main benefit of hydrogen fuel cells is their clean emissions, which are a drastic environmental advantage over the CO2 emissions produced by conventional energy sources. They only include heat and water as byproducts.

A fuel cell has an anode and a cathode (the electrodes) separated by an electrolyte. At the anode, Hydrogen is divided into protons and electrons.

Only protons may flow through the electrolyte, which forces the electrons to move along an external circuit and generate electrical energy. The electrons and protons interact with oxygen at the cathode to produce water.

Types of Fuel Cells

There are various types of fuel cells, primarily characterized by the electrolyte type they employ, which influences their operational properties and appropriate applications. Some of the most popular types are Polymer Electrolyte Membrane Fuel Cells, Alkaline Fuel Cells, and Phosphoric Acid Fuel Cells. Other types worth mentioning are Direct Methanol Fuel Cells, Molten Carbonate Fuel Cells, Solid Oxide Fuel Cells, and Reversible Fuel Cells [4].

Polymer Electrolyte Membrane Fuel Cells (PEMFC)

Solid polymer is used as the electrolyte in polymer electrolyte membrane fuel cells (PEM), also referred to as proton exchange membrane fuel cells. Because of their low operating temperature (about 80 °C), they can start quickly and sustain less wear. PEM fuel cells normally employ a platinum or platinum alloy as a catalyst and only need hydrogen, oxygen, and water to function. They are renowned for being both compact and having a high power density [4]. The PEMFC is one of the most promising fuel cell types, especially for use in vehicles. They are appropriate for quick start-up and shut-down cycles.

Alkaline Fuel Cells (AFC)

The electrolyte in these cells is an aqueous solution of sodium hydroxide or potassium hydroxide. AFCs typically function at temperatures below 100 °C. They are primarily powered by hydrogen gas and oxygen, though under some circumstances, other materials like zinc and aluminum may also be employed. Applications like the American space shuttle orbiters have made use of AFCs [6].

Phosphoric Acid Fuel Cells (PAFC)

These fuel cells have an efficiency of roughly 40–50% and operate between 150–200 °C. The phosphoric acid is the electrolyte. At high temperatures, they can tolerate fuel impurities, but at lower temperatures, they can be damaged by carbon monoxide. Hospitals, lodging facilities, workplaces, airports, and educational institutions use PAFCs [8].

Simulating and Modeling Fuel Cells

Simulation and modeling of fuel cells permit the analysis of complicated phenomena prior to actual implementation, saving time and money.

By enabling engineers to visualize multiple complex phenomena (like heat transfer, fluid flow, and structural integrity) within the cells, fuel cell simulation and modeling support the design and development of efficient and long-lasting fuel cell systems.

Simulation software like SimScale can help perform measurements and analyses that would be otherwise difficult to do in situ. SimScale is an all-in-one, cloud-native simulation software across CFD, FEA, and Thermal Analysis that enables users to conduct their analyses directly in their browser.

Engineers can simulate and optimize hydrogen fuel cell designs with SimScale, minimizing the need for costly physical prototypes. It offers crucial insights into the design and operation of fuel cells, including how different operating and environmental parameters affect performance. For instance, engineers can investigate various hydrogen fuel cell topologies and evaluate design trade-offs to ensure optimal performance and reliability of the fuel cell.

Thermal Analysis of Hydrogen Fuel Cell

Heat management poses a significant challenge in operating hydrogen fuel cells because it directly affects cell performance and efficiency, particularly in air-cooled PEM fuel cells [8]. If the heat generated by the fuel cell’s electrochemical process is not effectively controlled, it may harm its components or worsen its performance. Therefore, it is essential to have a thorough understanding of the thermal behavior and heat sources within the fuel cell system [10].

PEM fuel cell thermal management requires effective heat removal and a uniform temperature distribution. The latter is made even more challenging by low-temperature generated heat and limited exchange regions, particularly in mobile applications [11].

A thermal analysis tool can address these issues quickly and help predict airflow, temperature distribution, and heat transfer, owing to its accessibility, scalability, and capacity to run multiple simultaneous thermal simulations in parallel. The stack, the anode and cathode gas supply subsystems, and the tail gas exhaustion subsystem can all be included in the simulation’s overall model of the PEM fuel cell system.

In the example below, a multiphysics simulation of a cooling plate for a hydrogen fuel cell was run in SimScale. Different design iterations were studied using thermal analysis and flow analysis to identify the best design for optimal cooling. To further explore and analyze this application, you can simply copy the project by clicking on the “Copy Project” button and run your own simulations with your desired parameters.

Case Studies of Hydrogen Fuel Cell Simulation

Hydrogen fuel cell simulations have been effectively used in a number of industries, emphasizing their critical importance in the advancement of clean energy. The automotive industry serves as a prime illustration of this, with engineers creating fuel cell systems for automobiles.

So, how does the hydrogen fuel cell work in this context? PEM fuel cells provide an alternative to fossil fuels by converting chemical energy from hydrogen into electrical energy. However, building these systems for real-world applications necessitates a thorough examination of a wide range of factors, from fluid dynamics to heat management.

In a noteworthy work, PEM fuel cells with metal foam were simulated to enhance overall cell performance [13]. The simulation showed that the use of metal foam leads to a more uniform distribution of reactant gas and temperature, improving fuel cell performance.

Another successful application involves marine power systems. For efficient scaling and feasibility studies, which are crucial for correct sizing in maritime applications, several PEM fuel cells were simulated to understand the PEM fuel cell behavior at different sizes and configurations in power systems [14].

SimScale provides various industries, including the automotive and energy industries, with simulation and analysis tools that enable testing and optimization of fuel cell designs early in the design process. With its cloud-native multiphysics capabilities, engineers can leverage SimScale’s powerful solvers and ease of use to run multiple simulations in parallel directly in their web browser without having to worry about expensive hardware. This enables collaboration across the product design cycle by simply sharing the simulation project with team members with a simple click of a button.

Future of Hydrogen as Fuel

The technology for hydrogen fuel cells has a promising and ever-expanding future. Recent advancements include enhancing PEM fuel cell performance with novel materials like metal foam and improving temperature distribution and reactant gas flow [13].

Using software like SimScale, researchers, engineers, and designers all over the world are utilizing fuel cell simulation to innovate faster, optimize their designs early in the design process, and boost their R&D efforts. According to current trends, hydrogen fuel cell designs should be optimized for particular uses where thorough simulations are essential for feasibility studies and appropriate scaling [14].

A majority of energy and utility companies are expected to invest in low-carbon hydrogen initiatives by 2030 [15], reflecting the industry’s particular interest in hydrogen. As a result, hydrogen has the potential to influence a number of industries and lead to a cleaner and more sustainable future.

By 2030, the value of the hydrogen economy could reach $500 billion. Achieving commercial feasibility and ensuring effective storage and production are two issues that have hindered its implementation so far [16]. Additionally, continued research is needed to improve heat transfer and decrease energy waste in PEM fuel cells [17].

Nevertheless, these difficulties also bring about new possibilities. The integration of cloud-based simulation software like SimScale into the design workflow allows for more extensive testing and development of solutions in a cost-effective manner. The use of hydrogen as a fuel has enormous promise as the world shifts to cleaner energy.

Hydrogen fuel cells have enormous promise for the development of a sustainable future. As we have seen, these cells use an eco-friendly electrochemical method to produce energy, with the only waste being heat and water.

Engineers can use platforms like SimScale’s fuel cell simulation and modeling tools to develop and tune these cells for best performance and longevity.

What does the future hold for hydrogen fuel cells? Only time will tell. With the help of advanced simulation and modeling tools like SimScale, engineers and designers can contribute to the development of hydrogen fuel cells toward a greener, more sustainable future.

The post Hydrogen Fuel Cell: Simulation & Modeling 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.

Transient Conjugate Heat Transfer

Transient simulations capture changes over time, where no steady state really exists.

• Components heating or cooling over time
• Fans failing — how long does it take until critical temperatures are reached?

Individual Color Settings for Model Parts

It is now possible to change color settings independently for each part of the simulated model. This improves rendering by allowing users to customize their scene and enhance results visualization over the original model.

Wall Roughness Factor for Subsonic Simulations

It is now possible to add the wall roughness factor for more accurate modeling of wall boundary conditions in Subsonic simulations. For applications such as rotating machinery and flow valves, it is important to model surface roughness in order to accurately assess its influence on flow conditions and calculate pressure rise/drop.

This functionality allows analyzing complex model cases such as:

• Erosion of surfaces due to cavitation or particulate matter 
• Accumulation of particles (debris) on the surfaces 
• Inherent roughness of the material used 
• Manufacturing processes e.g., 3D printing can create uneven surfaces

Stress-strain Mapped from Company Material Library into SimScale

It is now possible to directly use a stress-strain curve from your company material library for structural simulations. This means faster simulation setup times and reduced input errors. Users will no longer need to characterize elastoplastic material behavior and nonlinear materials with data tables and can directly utilize the material behavior available in their company library.

The added value of this functionality is especially useful for:

• Analyzing elastoplastic material behavior of mechanical components
• Simulating fasteners where the stress-strain curve is mapped vs temperature

Thin Section Mesh Refinement for Structural Analysis

Maximize accuracy and solution efficiency by modeling low-thickness parts with second-order hexahedral and prismatic elements using the new ‘thin section mesh refinement‘.

Physical Contact Monitoring

During nonlinear contact simulations, you are now provided with contact monitoring plots keeping you in the loop on contact penetration and solution convergence, giving you confidence and control over your result outputs.

Conformal Meshing for Heat Transfer Analyses

This is the first step in the direction of conformal meshing for all structural analysis which will bring gains in terms of bonded and thermal contact accuracy as well as dramatically improved solution performance thanks to the merging of nodes at contact surfaces.

Non-linear Static Analysis Stability: Automatic Boundary Condition Ramping

Increasing the automation and robustness of nonlinear static analyses. This feature will ramp up constant loads in the background if necessary for a stable solution.

Highlight Minimum and Maximum Values

Highlight the minimum and maximum values for a given result quantity (within Statistics). This is an extremely valuable way for engineers to quickly and precisely view simulation results.

It is especially useful for:

• Locating the maximum stress within a structure to predict the factor of safety
• Identifying the highest displacements within assemblies

Clear Vibration Result Presentation

A number of post-processing improvements have been released to provide intuitive and clear default post-processing for vibration analyses.

• Frequency analysis deformations are now scaled for a perfect fit within the viewer
• Magnitude and phase are now set as default for all result fields in harmonic analysis, providing physically meaningful visualization of complex quantities
• Absolute motion results can now be visualized for harmonic analysis with base excitation allowing direct comparison with physical test data

Improved Robustness for PWC and Incompressible (LBM) via Manual Velocity Scaling

The LBM solver on SimScale used for the Incompressible (LBM) and Pedestrian Wind Comfort analysis, pacefish®, is using an explicit time stepping to solve the transient flow analysis. For some cases where we experience locally very high velocities in automotive aerodynamics or urban wind simulations, the local LBM velocities can surpass the stability cliff of 0.5, leading to divergence. For such cases, we enable an option in the simulation control to manually adjust the velocity scaling factor to a value lower than the default value of 0.1.

Aerodynamic Roughness in PWC and LBM

This feature provides two new input options for Aerodynamic Roughness:

1. Enable the direct definition of “Aerodynamic Roughness”, e.g., z₀= 0.5m to represent a Suburban exposure or z₀ = 1m for an Urban Exposure
2. Alternatively, using an automatic definition “from Exposure”. Here the selected surfaces will get the respective aerodynamic roughness from the exposure category for each individual wind direction assigned

Natural Convection Boundary Condition for Natural Ventilation Cases

Connect outdoor wind simulations with internal, natural convection studies. The natural convection boundary condition takes into account the facade pressure conditions influenced by outdoor wind and surrounding buildings, improving the accuracy of indoor CFD analyses.

The workflow for the natural ventilation boundary condition consists of the following steps:

• Run a Pedestrian Wind Comfort (PWC) study on the building of interest and its surroundings
• Apply the facade pressure results from the previous simulation as reference pressure for the natural ventilation BC in a consecutive indoor analysis

Dashboard Folders and Spaces

• You can now organize your projects into folders
• Companies can also create spaces that give access to limited groups of users

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: Wall Roughness Factor, Contact Monitoring, Conformal Meshing, Dashboard Improvements, and More! appeared first on SimScale.

As a cloud-native application, SimScale is able to continuously release new features and perform regular product maintenance on the fly. We realize that it’s often difficult to keep up with the latest news so this blog provides you with an opportunity to get up to date with all of the main new features released in Q3 2022. Enjoy!

Rotational Modal Analysis

Rotational modal analyses allow users to simulate rotating shafts and take into account centrifugal forces, stiffness, and damping effects. This is important for rotating machinery applications (turbomachinery or electric machines). Users are able to export the modal analysis results to construct a Campbell Diagram to understand a component/system’s response spectrum.

• Simulates rotating shafts taking into account centrifugal forces 
• Includes gyroscopic stiffness (and gyroscopic damping) effects

Use Case & Benefits

• Rotating machinery engineers looking to perform vibration analysis, modal surveys, and frequency analyses
• Accurate calculation of eigenmodes for rotating machinery
• The ability to produce Campbell Diagrams

Sweep Meshing for Structural

Sweep meshing is now available for structural analysis simulations. This feature generates a prismatic mesh that sweeps between two surfaces. This feature is useful for reducing the mesh count and speeding up simulations, which saves time.

• Generates a prismatic mesh, swept from a source to a target surface
• Define element thickness and number of elements along the sweep direction
• Optionally define the absolute size of the mesh on the source and target faces
• Supports multiple source/target end face pairs

Use Case & Benefits

• Applications needing to efficiently mesh high aspect ratio shapes
• Improves meshing efficiency and solution accuracy

Nonlinear Stability

Several enhancements to our structural nonlinear settings have been released to improve simulation success rate/robustness with smart default numerics. Highlights include: 

• User-friendly contact stiffness control, ensuring valid penalty coefficient selection for node-to-surface nonlinear contact based on contacting materials
• Reactive nonlinear solution control. This adds additional iterations, where needed, to improve simulation convergence and success

Use Case & Benefits

• Smarter default settings make nonlinear simulation even more stable and accessible

Fan Modeling

Model fans with a simple volume (momentum source), rather than needing to model the fan in detail. This is much faster than a detailed approach and a fan curve can still be applied. Fans are used extensively for active cooling in the electronics and EV/HEV industries.

• Fan Momentum Source: Allows modeling of internal fans as a momentum source that is embedded within the model
• Fan Curves: Allows modeling of fans based on fan curves (flow rate vs pressure drop). Fan curve tables can be created via table input or uploaded from a CSV file
• 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 of the enclosure

Use Case & Benefits

• Engineers who are interested in flow simulations using active fan cooling where the fan geometry is not part of the design optimization process
• Users can input fan curves from the manufacturer’s specification
• Predict the operating point of the fan

Real Gas Model

The Real Gas Model is important for accurately simulating “real” fluids that have physical and thermodynamic properties that vary as a function of temperature and pressure.

This significant new feature allows SimScale to accurately model compressors, the behavior of supercooled liquids, or the flow of steam in a steam turbine.

• Density and Conductivity are expressed as a function of Pressure and Temperature
• Users are able to input thermodynamic properties as a table, or upload a CSV file

Use Case & Benefits

• Turbomachinery and flow control simulation applications that need to model real gas effects observed at very low temperatures or very high pressures e.g., compressor modeling, supercooled liquid simulation, CO2 capture
• Allows engineers to include realistic fluid properties in their simulations, including experimental data that helps tune the simulation results to be more reliable

Parametric Studies

Perform parametric studies on various settings. These include the most common CFD and FEA inputs, such as velocity, pressure, temperature, power and momentum source values, centrifugal forces, and more. SimScale will run all of these studies for you in parallel.

Use Case & Benefits

• Any users who wish to parameterize boundary conditions. Examples include:
  • Electronics cooling: Change inlet flow rates or heat loads on a part to understand the impact on cooling strategies
  • Flow Control: Compare the performance of a valve with different inlet velocities
  • Turbomachinery: Set up various rotational speeds and compare results
  • AEC – HVAC: Change inlet flow rates to understand the impact on cooling efficiency
• This automated process adds value to SimScale by enabling users to quickly compare designs and understand which is the optimum

Heat Flux, Heat Transfer Coefficient, and Nusselt Number

SimScale users can now plot and measure heat transfer, a highly requested feature for applications like heat exchangers, electronics enclosures, and building design. Three results are available:

• Heat flux
• Heat transfer coefficient (HTC) 
• Nusselt number

Use Case & Benefits

• AEC users that want to compute the total heat loss through walls/facade/windows — especially useful for Thermal Bridging Calculations
• Electronics designers who need to understand the flow of heat through their assembly
• Cooling or heating systems, often incorporating heat exchangers and heat sinks

Age of Air

AEC simulation users can now calculate & visualize the Local Mean Age of Air (of any fluid) or Mean Residence Time (in seconds). Understanding the mean age of air and air exchange rates is critical when designing ventilation systems in buildings, especially for natural or mixed ventilation applications where engineers need to demonstrate that a design complies with regulatory requirements.

 Use Case & Benefits

• Designers of indoor ventilation strategies where Mean Age of Air and Air Exchange rates are common design goals and even needed for compliance — in schools or factories for example
• Users can easily assess and compare age of air across different design strategies

Porous Media

This porous media feature allows the simulation of partially permeable materials. It was developed to make it easier and faster to model lattices, grids, or perforations that would normally be represented as 3D solids but are challenging to mesh due to the level of detail present. This feature can also be used to model dense filter materials.

Use Case & Benefits

• Reduces the effort needed to simplify CAD geometry and reduces meshing effort & mesh size, in turn reducing simulation time. Overall a much easier process for users.
• The exact flow through these regions is usually not important, but the correct modeling of pressure drop and how it globally affects the flow is.

Take These New Features for a Spin Yourself

All of these new features are now live on Simscale and 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: Rotational Modal Analysis, Real Gasses, Fan Modeling, Parametric Studies, and More! 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.

This article presents a design and simulation study of battery cold plate technology for electric vehicles. Engineering simulation is used to perform a fully-coupled conjugate heat transfer analysis of a cold plate for dynamic thermal management. Furthermore, using an advanced Subsonic CFD solver, a design study is performed for evaluating pressure-flow characteristics across the cold plate flow channel. Parallel simulations in the cloud are used for scenario analysis both for geometric variants and multiple coolant flow rates. In this sample case, our simulation workflows show users how to set up and run a complete heat transfer and flow analysis of a cold plate, including pressure drop and temperature at various coolant flow rates. Engineers can follow this example to learn how to quickly complete a parametric design study in SimScale and answer key design questions. 

Reaching Optimum Battery Reliability with CFD and Heat Transfer

SimScale has many analysis types available depending on the application. In this example, two analysis types have been used to analyze battery cold plate optimization for electric vehicle batteries. The Subsonic analysis type in SimScale produces an automated and robust hexahedral cell mesh, using the body-fitted Cartesian meshing technique that significantly reduces mesh generation times by an order of magnitude. The highly parallelized meshing algorithm gives a higher quality mesh requiring much fewer cells to attain comparable accuracy to traditional discretization schemes. This leads to faster convergence and hence, faster simulations.

The Subsonic analysis type is used to simulate both incompressible and compressible flow, with turbulence modeled using the RANS equations and the k-epsilon turbulence model. A powerful feature of this analysis type is the built-in parametric capability for defining velocity inlet boundary conditions. At the simulation setup stage, users can define multiple inlet flow rates at once, that are then simulated simultaneously. The conjugate heat transfer (CHT) analysis type in SimScale enables heat transfer analysis between solid and fluid domains. Typical applications of CHT analysis type include analysis of heat exchangers, cooling of electronic equipment and electronics enclosures, and LED luminaire design. A fluid/solid mesh is required for a CHT simulation with clear definitions of the fluid and solid interfaces, also referred to as contacts. The mesh is automatically generated in SimScale, with local refinement available for advanced users. In the case of the battery cold plate, the subsonic analysis is used to simulate coolant flow through the cold plate channel to ascertain optimal flow rates with regard to pressure drop. The CHT solver is used to demonstrate heat exchanger effectiveness and thermal performance.

Improving Battery Cold Plate Design

The purpose of this study is to maximize the dynamic thermal management of a new battery cold plate design, used to cool electric vehicle battery packs. We will consider two versions of a cold plate heat exchanger. The original design (V1) is a common component widely used in industry and has a single serpentine cooling channel. The design is known to cause hotspots that lead to battery longevity issues. A thermal engineer has come up with a new design (V2) that uses a labyrinth cooling channel with a much-increased surface area for thermal energy exchange. This is an early-stage proof of concept design that requires simulations to validate its predicted performance and compare it with the original version. 

Using SimScale, an engineer can quickly and accurately assess both designs. Both battery cold plate designs have one inlet and one outlet. A velocity inlet is used to define the mass flow rate of the coolant (water) which is the primary heat removal mechanism for extracting heat from the batteries. The battery cold plate is assigned Aluminum from the SimScale materials library, water for the coolant, and a power source is applied to represent heat generated by the battery. When importing the V2 CAD file, associativity between cad files is applied automatically in SimScale, maintaining naming conventions for parts/faces from the V1 CAD model. This means that when swapping CAD files for comparative studies, users don’t have to reassign boundary conditions, mesh settings, or result control outputs, making comparing two or more CAD variants of a single product much faster. 

The heat transfer analysis shows that despite a larger contact surface area in V2, thermal results are much worse compared to the original V1 design, as V2 exhibits higher temperatures across the battery cold plate. The V2 design needs further simulation and optimization. To help resolve these counterintuitive results, the design engineer analyzes the flow in isolation.

Results of the Subsonic fluid flow analysis that takes just four minutes to solve, yields further insight. The flow in V1 maintains a steady flow of 3.5 m/s throughout the single channel. In the V2 design, highly varied coolant velocity is seen throughout the labyrinth channels. In many areas, almost stagnant flow is observed in the areas of peak temperature, hence the cooling channels are not effectively removing heat from those areas which leads to poorer thermal performance. We can use the fluid flow analysis to look purely at flow velocity to better understand pressure drop across the cold plate. The converged pressure drop across any two points can be extracted from SimScale using the results control menu. In this case, for a constant coolant flow rate of 0.13 kg/s, V1 has a 292 KPa pressure drop between inlet and outlet and, V2 is ten times less at 22.7 KPa. This might have some advantages if the thermal performance of V2 was comparable to V1, such as less pump power and hence energy needed to operate the pump. First, we need to try and increase its thermal performance, however. Since we have enough pressure drop to play with, we might try and increase the coolant flow through V2.

Using the parametric inputs in the Subsonic analysis type, we can specify multiple flow rates in the inlet boundary condition to run in parallel. Simscale will recognize that we have a parameterized study setup and all runs (all flow rates) are simulated in parallel in the cloud on SimScale servers. This frees up your local PC/laptop and does not consume machine resources. SimScale will send users an email notification when all the runs have been completed successfully. 

A higher mass flow rate of 0.3 kg/s gives increased flow velocities in V2 and improved thermal performance. The pressure drop across the cooling plate has increased considerably but is still 60% lower than the original V1 design. The new V2 cooling plate design has achieved a lower average temperature, although less uniform, with a higher flow rate but a much lower pressure drop than V1. Additional design iterations would be necessary to improve the temperature distribution and battery reliability further in V2. Engineers looking to optimize this battery cooling plate can parametrize the CAD model, coolant flow rate, and inlet temperatures for more detailed design studies. Swapping materials is also simple by using the extensive SimScale materials library.

Heat Transfer Simulation in the Cloud

Access to simulation in the early product design stages is essential to achieving the required thermal performance of battery cold plate designs, all while reducing physical prototyping costs. With the cloud-native SimScale platform engineers and designers can harness advanced solvers that account for coupled analyses of conduction, convection, and radiation to deliver accurate results across a variety of problem domains and scales. 

Watch our on-demand demo, Cooling Performance Optimization of Electric Vehicle Batteries, to see how you can quickly complete a parametric design study and run a complete thermal analysis of a cooling plate including pressure drop and temperature at various coolant flow rates:

The post Simulating and Optimizing an Electric Vehicle Battery Cold Plate 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.