Thermal Analysis with SOLIDWORKS Flow Simulation
SOLIDWORKS Flow Simulation is a powerful, general-purpose CFD package integrated directly into the SOLIDWORKS CAD environment. Because it is a general-purpose fluid dynamics analysis package, Flow Simulation can analyze a wide variety of problems, including: aerodynamic and hydrodynamic problems such as pump and propeller design, head loss in piping systems and coefficient of drag calculations for vehicles.
One of the most common applications of Flow Simulation today though is thermal analysis for predicting cooling performance of electronics and other heat generating components. The ability to simulate heat conduction combined with convective heat transfer generated by airflow over heatsinks and chip packages offers a high degree of confidence in temperatures predicted, especially when compared to traditional hand calculations or FEA-based thermal analysis where assumptions about airflow must be input in the form of convection coefficients.
This article will examine the use cases of SOLIDWORKS Flow Simulation as it relates to thermal analysis, with a specific focus on predicting the performance of electronics cooling systems.
Background & Terminology
Flow Simulation is a computational fluid dynamics (CFD) analysis package using the finite volume method. The computational domain is broken up into a Cartesian mesh, a grid-like mesh made up of box-shaped cells, which will be discussed later in this article.
Key parameters of interest are tracked during the solution by the creation of user-defined goals. In the case of steady-state analysis, the convergence of goals is tracked and utilized as a stopping criteria for the solver. In other words, the solver continues to iterate until the values of the goals flatten off, indicating that the system has reached steady-state equilibrium. The appropriate definition of goals is thereby crucial to ensuring accuracy and reasonable computation time.
Steady-state and transient calculations can be performed. By default, new projects are treated as steady-state. Transient analysis is enabled via the time-dependent checkbox in the Project Wizard, which iterates over physical time steps and stores results over the time history of the solution, at the cost of extended solution time. Transient analysis makes it possible to input curves for conditions such as heat sources to represent duty cycle, or analyze problems which may be constantly fluctuating and have no steady-state solution at all.
Analysis can be internal or external. Internal analyses represent closed wall systems such as electronics enclosures, or a piping system or manifold. External analyses represent a larger computational domain, such as a room full of air around the product to be analyzed.
Mechanisms of Heat Transfer
By default when creating a new project, Flow Simulation simulates heat transfer in fluids and performs a steady-state analysis.
The Project Wizard allows selection of various physical effects at the time of project creation. Enabling the option for heat conduction in solids will open up a variety of new options in the project, namely: the ability to define materialswith thermal conductivity properties, heat sourceswith their own temperatures or heat generation rates and goals that track the temperature and thermal properties of various solids.
Figure 1. Flow Simulation Project Wizard.
The ability to simulate heat transfer in fluids is maintained, so heat generating solids will automatically convect heat away to the surrounding fluid.
Enabling the radiation option in the Project Wizard allows definition of emissivity and performs simulation of radiative heat transfer.
Heat removal in the high-powered electronics manufactured today is typically accomplished by air or liquid cooling. As the thermal performance of such systems is often dominated by conduction and convection, radiative heat transfer is often assumed to be minor and neglected in the simulation to reduce solution time.
Radiation becomes crucial between components with very high temperatures, or those operating in near-vacuum conditions. Applications where radiative heat transfer is critical include: design of light bulbs and lamps, heating elements and furnace equipment, and spacecraft and satellites.
For the special case of problems that are dominated by conductive heat transfer and/or radiation, Flow Simulation has an option for “Heat conduction in solids only” which completely disables the fluid flow calculations—effectively removing the “flow” in Flow Simulation. This option is appropriate for drastically speeding up calculation of systems that operate in a vacuum.
Example 1: Natural Convection Analysis
The amplifier pictured in Figure 2 below is the subject of a natural convection thermal analysis, using an external analysis project type, as well as the options for heat conduction in solids and gravity (appropriately oriented). Solid materials with appropriate thermal conductivity are defined and heat sources are applied to any heat generating components.
As the option for Gravity is enabled in the Project Wizard, the heating of the surrounding fluid will cause convective currents to form as the heated fluid rises due to its lighter density and heavier cooler fluid descends to take its place—a process known as natural convection.
Figure 2. Example of Natural Convection Analysis of Amplifier.
Aside from setting up goals to track the temperatures of the solids of interest in the study, not much else is required from the user to quickly establish baseline results. Accuracy of the simulation can be improved by refining the mesh and establishing thermal contact resistances, as well as some specific considerations unique to external analyses.
Conjugate Heat Transfer
The convection coefficient, or “h” value, varies over a wide range based on fluid flow and geometry. It is calculated as an output from the Flow Simulation and can be extracted as a results parameter. This makes a CFD-based thermal analysis capable of solving coupled or “conjugate” solid/fluid heat transfer, which is a much more reliable tool for predicting cooling performance than hand calculations or analysis performed in thermal FEA, which require inputting a best guess at an h-value.
As a thought exercise, consider the case of a thermal FEA study for a heatsink. Without a way to accurately predict changes in h-value due to geometry, the results of the study will indicate that more fins on a heatsink is always superior due to the increased surface area they present. In reality, there will be an optimal point in terms of fin density that, once exceeded, will begin to impede the surrounding fluid flow and reduce the effective convection coefficient.
Thermal FEA will also fall short if the orientation of a heatsink is changed with respect to gravity direction, which can drastically affect thermal behavior for passive cooled systems.
SOLIDWORKS Flow Simulation is able to accurately predict these behaviors and allows for optimizing heat sink design, as well as predicting performance in alternate orientations.
Special Considerations for Natural Convection
Two considerations of note can affect results accuracy for natural convection problems and other external analyses. First is representing the geometry in its appropriate orientation and positioning. If a device is to be mounted flat on a table, the Gravity direction in the project must be oriented appropriately. Additionally, if the device is being mounted flat to some piece of equipment, the flow restriction from this should be modeled in.
The geometry used for analysis should match as closely as possible the geometry used in physical testing or production implementation, which may necessitate modeling a blocking surface in CAD.
The impact of such geometry is visible in the flow trajectories of Figure 3 below.
Figure 3. Flow Trajectories With and Without Blocking Surface.
The second consideration for external analyses is the sizing of the computational domain. Figure 4 below compares three different computational domain sizes.
Figure 4. Sizing of Computational Domain.
There is no exact rule for predicting adequate computational domain size. Rules of thumb can be found in the literature and typically will vary based on the velocities of fluid flow present in the analysis. Too small of a computational domain may negatively impact results, while an oversized computational domain will needlessly extend solution time.
Much like mesh cell refinement, a best practice would be to conduct a virtual experiment by cloning (duplicating) the Flow Simulation projects and iterating on computational domain size until an adequate balance of accuracy and solve time is determined. Such an experiment can determine guidelines that can be used for similar geometries and analyses moving forward.
Rapid iterations can be performed via the parametric study functionality, which is discussed later in the article.
A step-by-step tutorial covering set up of a simple natural convection problem and discussing these special considerations can be found in this video.
Example 2: Internal Analysis with Forced Convection
Electronics enclosures are often best represented by an internal analysis. Setup of an internal analysis requires capping off any openings of the enclosure. Flow Simulation offers a Lid Creation tool that can help speed up the process of creating lids to cap off the geometry. In order to conduct an internal analysis, the software must extract a “watertight” fluid body from the enclosed space.
Results from an internal analysis of a rackmount server are visible in Figure 5 below.
Figure 5. Internal Flow of Rackmount Server.
If it ends up being too difficult to create a watertight body, a fallback method is to approach the project as an external analysis. In this scenario, it could be thought of as a a pseudo-internal analysis within a slightly larger computational domain, as visible in Figure 6 below.
Figure 6. External Approach for an Enclosure.
Representing an enclosure within an external analysis in this way has a couple additional benefits: it can predict air leakage through any openings, as well as predict the convective cooling of the entire enclosure to the environment, with the trade-off of additional solve time required.
Forced convection from cooling fans is represented by definition of fan conditions. A handful of fan definitions are included with Flow Simulation by default, but additional representations of cooling fans (and even water pumps) can be user-defined by inputting a static-pressure curve. They can be placed as inlet or outlet fans at the edge of the computational domain, or internal fans in the middle of an enclsoure.
Figure 7 below shows a static pressure curve as input in the Flow Simulation Engineering Database, using data from the manufacturer’s specification sheet for a fan.
Figure 7. Fan Curve Definition.
The optional add-on module for SOLIDWORKS Flow Simulation known as the “Electronics Cooling” module greatly expands the built-in library of manufacturer’s fan definitions, as well as adding a variety of other useful features for electronics analysis such as enhanced materials for PCBs and IC packages, additional heating models such as two-resistor components, Joule heating and native support for heat pipes.
Aside from preparing the geometry for internal analysis and defining fans, the bulk of the setup work for this type of problem lies in defining the appropriate solid materials and heat sources. There are productivity tools that can help, such as importing setup conditions from child components and propagating definitions across all assembly instances.
Example 3: Liquid Cooling with Fluid Subdomain
It is increasingly common for high powered equipment such as computers in machine learning workloads to utilize liquid cooling. Liquid cooling may be used as a complete cooling solution, or in tandem with air cooling. An example analysis setup for combined liquid and air cooling is visible in Figure 8 below.
Figure 8. Open-Loop Liquid Cooling with Fluid Subdomain.
This liquid cooling within an air-cooled internal analysis is accomplished via a fluid subdomain. Fluid subdomains allow separating distinct fluid regions and applying unique conditions such as inlet temperature and flow rate to the distinct region. This process allows for simulation and prediction of liquid-air heat exchangers and radiators.
SOLIDWORKS Flow Simulation projects update automatically with changes to the CAD geometry. Another capability is to manually “clone” or duplicate projects to test and store the results of such variations.
Anytime there are many iterations required, such as when attempting to optimize a geometry, users can take advantage of the in-built parametric study functionality, which allows creating a virtual design of experiments (DoE). Such a DoE setup for optimization of the CPU cooling waterblock is visible in Figure 9 below.
Figure 9. Waterblock Optimization with Parametric Study.
Parametric studies allow varying multiple parameters (in this case, the thickness and number of fins), tracking results parameters and optionally calculating an optimal design point.
Meshing Technology in Flow Simulation
The Cartesian or grid-shaped mesh that SOLIDWORKS Flow Simulation utilizes is relatively unique among analysis tools. This meshing technology brings with it some distinct advantages, such as ease of generating the mesh and precise control over how much detail is included in the analysis.
Compared to a tetrahedral-based mesh which must start by resolving the solid geometry, the approach used in Flow Simulation begins by subdividing the computational domain. This means that it’s possible to force a coarser mesh density, which will effectively ignore tiny details in the model without actually performing extensive geometry simplification. This ability to “look past” tiny solid features means that Flow Simulation has a much easier time generating meshes for complex geometries.
This also comes with some additional responsibility for the user to ensure that areas of interest are being adequately resolved by the mesh. While the default mesh settings are often a good starting point to establish a baseline, manual adjustment of the global refinement settings, as well as a few carefully placed Local Mesh refinements, can go a long way toward ensuring accuracy of the solution.
Figure 10 below shows an example of mesh refinement around a CPU heatsink using a local mesh control, and a second lesser tier of local mesh refinement defined around the RAM modules.
Figure 10. Mesh Refinement around Heatsink.
Color-coded mesh refinement plots help the user to identify levels of refinement and the areas to which they apply throughout the meshing process.
This article presented the case that thermal analysis involving convection is best analyzed using a tool capable of natively solving conjugate heat transfer, such as the CFD-based approach presented by SOLIDWORKS Flow Simulation. Predicting thermal performance in this way prevents the need to estimate convection coefficients, as would be required to perform hand-calculations or an FEA-based thermal approach.
Several examples were showcased detailing setup of natural convection and forced convection problems for air-cooled electronics, as well as how combined liquid-to-air cooling can be analyzed.
Use of a CAD-integrated CFD tool also allows for rapidly analyzing geometry variations either through manual duplication of projects, or by conducting a virtual design of experiments utilizing the parametric study functionality in Flow Simulation. Lastly, a robust set of meshing defaults should make it easy to establish baseline analyses with limited geometry preparation required by the user.
Advances in software multithreading, combined with the rise of affordable many-core CPUs, has also drastically lowered the solution time requirements for CFD analysis on modern hardware—making it a viable tool to incorporate early on and throughout the design process.
To learn more, check out the whitepaper Design Through Analysis: Today’s Designers Greatly Benefit from Simulation-Driven Product Development.