Thermal network

Introduction

CFD simulations often require a trade-off between accuracy and computational speed. Increasing the accuracy of a simulation necessitates capturing more intricate details, which inevitably extends the computation time—sometimes requiring multiple hours or even days for highly complex cases. However, simplified approaches exist to bypass these lengthy calculations and obtain rapid results. The thermal network is one such approach. It must be made clear from the beginning that this thermal network approach is specifically relevant for correlation-based cases, serving as a rapid estimation tool prior to or instead of full simulations.

Thermal network estimation

Once a case is fully set up on the platform and ready for processing, it can be submitted as a 'Correlation based estimation'. Upon submission, a thermal network representing the case is constructed and simulated.

  • Standard Design Cases: A thermal network is generated for each tested heatsink type.
  • Simulation Cases: A thermal network is generated prior to the start of the full CFD computation, providing a quick initial impression of the thermal behavior.

The estimation utilizes correlation-based techniques to provide rough estimates of the thermal resistance values at each connection (either between two adjacent regions or between a region and a boundary). Using these thermal resistances, the expected temperatures and heat fluxes are derived via integrated equations, resulting in a computationally inexpensive process.

This option requires only 1 credit and delivers results within an hour at most, making it an highly efficient trade-off between accuracy and resource expenditure.

Once the thermal network is published, it can be viewed in the Thermal network tab. For standard designs, selecting a different iteration number will dynamically update the corresponding thermal network display.

Thermal network example

Diagram features

The thermal network diagram utilizes various symbols to represent different physical entities. The following list explains these representations:

  • Solid squares: Regions.
  • Solid ovals: Thermal boundaries.
  • Dashed diamonds: Interfaces between two adjacent regions. These always appear in pairs (representing the heat transfer from Region 1 to Region 2, and vice versa).
  • Dashed circles: Fluid boundaries.

Color Coding and Visualization

The node representing the entity with the highest temperature is colored dark red, while the node with the lowest temperature is colored dark blue. All intermediate temperatures are assigned gradient shades of red or blue.

Entities are connected by directed arrows indicating the direction of heat flow, providing an immediate visual overview of the general thermal path resulting from the case setup. For fluid boundaries, these arrows always point from the inlet(s) toward the fluid region(s) and onward to the outlet(s).

Data Interaction

Hovering over a node displays the estimated mean temperature of the corresponding region in a steady-state regime. It should be kept in mind that this is a rough estimation and may deviate somewhat from the temperatures derived from a full CFD analysis. Estimated velocities, pressures, and mass flow rates (where applicable) can also be viewed. Hovering over the connecting arrows displays the estimated heat flux and thermal resistance values.

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Note

The entire thermal network or independent nodes can be dragged and zoomed, allowing the diagram to be optimally arranged for convenience. This is particularly useful for cases containing a large number of entities.

Thermal network after CFD analysis

Although the thermal path and the connections between regions are accurately established in the correlation-based estimation mode, obtaining the exact values for temperatures or thermal resistances requires a full CFD simulation.

At the end of a full simulation, a revised thermal network diagram is displayed with updated values. The mean temperatures, heat fluxes, and resistance values in this updated diagram are derived directly from the fully converged CFD simulation, rather than being estimated based on correlations. The diagram can still be manipulated and arranged as previously described.

How to use thermal networks efficiently

The utility of the correlation-based thermal network feature can be demonstrated using a simplified example. Consider a geometry consisting of two adjacent regions ('Box1' and 'Box2') that touch each other.

  • 'Box1' is heated on one side by a boundary condition named 'Wall1'.
  • One boundary of 'Box2' is maintained at a constant temperature of 20°C (293.15 K) via a boundary condition named 'Wall2'.
  • All free-floating edges of both regions are perfectly insulated.
Example geometry

In a steady-state regime, the heat will flow directly from 'Wall1' to 'Wall2'. This allows the case to be simplified into a straightforward thermal path.

Thermal network for example geometry
  1. First Attempt: An estimation is performed using a heat input of Q=5WQ=5W. If the resulting correlation-based estimation indicates a mean temperature for 'Box1' that significantly exceeds the desired target, it immediately proves the heat input is too high.
  2. Second Attempt: A subsequent estimation using Q=3WQ=3W might yield an estimated mean temperature of 300.46 K, which is too low.
  3. Conclusion: Through these rapid estimations, it becomes evident that 'Wall1' should be heated with a total power of approximately Q=4WQ=4W to achieve the 303.15 K target in 'Box1'. A full CFD simulation can then be confidently submitted using this optimized boundary condition.
Let us perform this with a heat input of five Watt (Q˙=5W)(\dot Q=5W). From the correlation based estimation, this will result in the following mean temperature for 'Box1':
Mean temperature 'Box1'
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Note

As the complexity of a setup increases, it becomes more difficult for the correlation-based estimation to remain perfectly aligned with the actual converged temperature values. However, the quick estimation mode provides highly valuable preliminary data rapidly and at minimal cost.


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