Optimising Thermal Management Circuits

This blog post theme was motivated by the increasing trend and interest in thermal management of systems, not only of vehicles but buildings and other applications requiring such systems.

The ability to thermally manage a system to allow it to reach and maintain ideal operating temperatures is a complex balance of compromises. On one side you are striving to maintain the comfort of occupants, for example in a home or in a vehicle cabin, whilst on the other side you are trying to reduce the energy consumption of such thermal management and keep the system costs under control.

One such application, we at Claytex have worked on for clients in the past few years, has been thermal management of EVs (Electric Vehicles) and HEVs (Hybrid-Electric Vehicles). In order to keep costs down, the clients asked us whether it was possible to optimise the geometric characteristics of the liquid cooling ducting to achieve specific flow rates throughout the pipe system. The objective was to achieve the target flow rates without having to install expensive partialising valves or indeed additional pumps.

I will show a much simplified example of how we can use the “Optimization” library to achieve such objectives by tuning the pipe diameters. The library is available for both DYMOLA standalone and DBM (Dymola Behavior Modelling) within the 3DEXPERIENCE platform.

Figure 1: Fluid dynamics model of a parallel path system. Flow direction is from source to sink.
Figure 1: Fluid dynamics model of a parallel path system. Flow direction is from source to sink.

In the model shown in Figure 1, the design objective is to achieve a flow of 6e-3 kg/s through pipe2 and 1e-2 kg/s through pipe3 without having to include flow restrictors or valves in the system. The optimisation of pipe2 and pipe3 diameters will allow us to achieve such flows without having to perform parameter sweeps or even try and manually perform this operation. We will of course set limits for the maximum and minimum pipe internal diameters to cater for packaging requirements and pipe availability.

Figure 2: Optimization library package browser screenshot.
Figure 2: Optimization library package browser screenshot.

Within the Optimization library there are several optimization tasks we can choose from. In this case we are going to call a function within the ModelOptimization package.

Figure 3: Choosing the model containing the parameters to optimize.
Figure 3: Choosing the model containing the parameters to optimize.

In Figure 3, the model containing the parameters to optimize is chosen. The model is then automatically translated so that the user can choose the parameters to optimize (Figure 4) and the criteria that calculates the error between the target and the current value (in our case for the mass flow rates – Figure 5).

Figure 4: Choosing the parameters to optimize, the start values (first iteration) and the bounds.
Figure 4: Choosing the parameters to optimize, the start values (first iteration) and the bounds.
Figure 5: Choosing the criteria to minimise, in our case the error between the target and the measured mass flow rate. The demand column can be used to apply weights to each criteria.

Figure 5: Choosing the criteria to minimise, in our case the error between the target and the measured mass flow rate. The demand column can be used to apply weights to each criteria.
Figure 6: Setting the optimisation method and other optimization related parameters.
Figure 6: Setting the optimisation method and other optimization related parameters.
Figure 7: Setting the simulation parameters.
Figure 7: Setting the simulation parameters.

In relation to Figure 7, because the model actually initialises in steady state and there are no transients, the error at initialisation will therefore suffice for the optimization purposes. This means we can simulate using a stop time of 0 second (instead of the default which is 1s).

While the function runs, the command window of Dymola will be updated with the optimisation information, for example the current parameter values it is using. Any plots will also update so we can monitor the convergence in real time. After the function has run, the best values for the tuner parameters will be written to the command line and an html report output into the current working directory.

Figure 8: Example of the information that the optimization function outputs in the Dymola command line and DBM scripting window.
Figure 8: Example of the information that the optimization function outputs in the Dymola command line and DBM scripting window.

At this point we can choose to save the function call for future use. Like the report, the function call is saved in the current working directory. We get to choose also whether to update the tuner parameter start values in the saved function call with the optimised ones for further optimization runs.

The Optimization library can be used to optimise any model parameters to improve both steady state and transient performance of systems. In our case, it allows us to design fluid systems that have reduced component complexity and therefore reduced weight, cost and comissioning times.

Do not hesitate to contact us for any further information and demonstrations on how to make use of the library for your specific applications and models.

Written by: Alessandro Picarelli – Engineering Director

Please get in touch if you have any questions or have got a topic in mind that you would like us to write about. You can submit your questions / topics via: Tech Blog Questions / Topic Suggestion.

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