Wolfram Blog
Anneli Mossberg

Model and Simulate Cooling Circuits with the SmartCooling Library

June 9, 2015 — Anneli Mossberg

Explore the contents of this article with a free Wolfram SystemModeler trial.The SystemModeler Library Store, launched with the release of Wolfram SystemModeler 4, is continually growing with free and purchasable libraries developed by both Wolfram and third parties. One of our commercial newcomers is SmartCooling, a Modelica library developed by the Austrian Institute of Technology (AIT) that is used for modeling and simulating cooling circuits. When I was asked to present this library on our blog, my first thought was, “Who better to demonstrate the ideas of SmartCooling than the people who actually developed it?” So I asked Thomas Bäuml, one of the creators of SmartCooling, to help answer some of my questions regarding the principles behind the library and its applications.

SystemModeler and SmartCooling array

Thomas, could you please explain what SmartCooling is and what it can be used for?

The SmartCooling library is developed for the simulation of cooling circuits with mechanically and electrically operated auxiliaries. Like in a real workshop—but in a virtual environment—the library offers all the components you need to build your own cooling applications for testing, studying, or performing comprehensive experiments.

It contains a variety of components, such as cooling fans, heat exchangers, and valves, that can be used to create cooling circuits of basically any degree of complexity. But like in real life, selecting which components to use is crucial, and you have to think about how they interact with each other, and what their physical properties are. Of course, the physical data that you put into your models is also very important. Usually, data for SmartCooling models can be taken directly from ready-to-use specification sheets—for example, performance, temperature ranges, operating conditions, speed, etc.—or it is gained from measurements of a real cooling system. The models of the SmartCooling library can be parametrized by entering this data in input fields.

SmartCooling library can be parametrized by entering this data in input fields

With SmartCooling, it becomes a lot faster and a lot more efficient to design, simulate, and dimension cooling concepts for, for example, automotive applications.

An example of a typical automotive application is to use SmartCooling to investigate new cooling concepts in hybrid electric vehicles (HEV) and their impact on energy consumption. By using modeling and simulation, it can be shown that energy can be saved when substituting the water pump of an internal combustion engine (ICE) by an electrically operated water pump. This is because a mechanically operated water pump (mechanically powered by the ICE) is fixed to the speeds of the ICE, and is not working in its optimal operation area. With an electrically operated water pump, it is possible to control the speed of the water pump independently from the speed of the ICE. This results in better cooling of the ICE and an increase in efficiency, since the electric water pump can be operated in its optimum operating area. The impact of saving energy following this approach was the subject of the scientific paper “Optimization of a Cooling Circuit with a Parameterized Water Pump Model” (5th International Modelica Conference, Vienna, Austria).

Also, the SmartCooling library lets you choose between different levels of abstraction in its applications. This means that you can choose to use simplified models or more detailed models where the physical description is more extensive. These usually contain additional parameters and boundary conditions. Being able to choose a level of abstraction that fits your purpose, I think, is a great advantage. Simplified models help to save computing time, for example by using scalability (being able to transform small structures to large structures by using scaling factors), whereas detailed models allow you to more deeply investigate system behavior and phenomena. In contrast to scaling, each model is then considered individually.

What was your motivation for developing this library?

The reason why the SmartCooling library was developed came from praxis. At our business unit at AIT, the unit for Electric Drive Technologies (EDT), the focus of our work is on automotive applications. We especially target alternative vehicle concepts, like hybrid vehicles, pure electric vehicles, etc.

When you model an entire vehicle, you also have to take thermal aspects, such as thermal management and cooling, into account. The Modelica Standard Library (MSL) offers a lot of basic models and tools to build up applications for the automotive section, but sometimes it is rather unclear which components from which sublibrary of the MSL to use, and if they are appropriate for your particular application. This makes working in this domain difficult. As a result, we developed the SmartCooling library to allow us to model thermodynamics in an easy-to-use manner that is focused on automotive applications.

Could you give our readers an example of a great SmartCooling application?

Yes, of course!

A very good and realistic use case is that you need to evaluate a model of an electrically operated water pump. Let’s say it’s for the automotive application mentioned earlier, that the mechanically operated water pump of the cooling circuit in a conventional ICE-driven car is to be replaced by an electrically operated one.

The evaluation of models is necessary in order to get realistic simulation results. That also means that the models need to be well parametrized with realistic data. Much of the physical data can be taken from specification sheets, or gained from easily accessible measurement data, but you often find yourself in the situation where the determination of some parameter is difficult, and more detailed measurements have to be done. In this example, a real test bench for the electrically operated water pump is set up.

Water pump

To measure the pressure difference and flow, the power consumption, and the hydraulic efficiency, sensors have been set up in the test bench. It wouldn’t be possible to do that in the real ICE system in the car because the space there is restricted. Another important reason for using a test bench is that the measurements are not restricted to just one characteristic curve, which would be the case in the real system due to the cooling circuit of the car.

Below is a model of the test bench circuit in SystemModeler, modeled with components from the SmartCooling library. The valve component creates a pressure drop in the circuit. While the water pump is driven at a certain constant speed, equal to that of the test bench, the friction losses can be adjusted by the valve. The circuit is investigated for different applied rotational speeds, ranging from 2,000 to 7,000 rpm.

Model of test bench built with SmartCooling components

The model is easily built with drag and drop, using the components from the SmartCooling library. The library itself functions much like a virtual lab area providing the right tools and equipment. The figure below shows exactly which components were used in the test bench model:

Structure of SmartCooling library

The remaining components, such as the electric machine and the sensors, can be found in the Modelica Standard Library:

Other MSL components used for the example

When the model has been fully assembled, the water pump, the pipeline systems, and the electric machine are all parametrized with data obtained from the laboratory test bench.

A very good way of evaluating the test bench model is by using the Wolfram Language. The Wolfram Language makes it easy to run parameter sweeps of the geometrical and mechanical parameters in order to visualize the simulation results as a series of curves. These curves can then be compared with the measured data from the real test bench. Being able to run parameter sweeps in this manner makes the evaluation and validation process a lot simpler, and it leads to a better understanding of how certain values affect the behavior of the model.

Here is a parametric plot of the pressure increase (dp) over the volume flow (Vflow) of the water pump. The plot was generated with WSMLink by varying the water pump speed from 2,000 to 7,000 rpm.

Characteristic curves of the water pump

So in this test bench example, we used the SmartCooling library to investigate state-of-the-art approaches and innovative system design for cooling architectures. The findings from the SmartCooling model supported the theory that the cooling circuit for a conventional ICE-driven car can be improved if the mechanically operated water pump is replaced by an electrically operated one. This is a great advantage, since a more efficient cooling of the ICE helps to save both energy (less fuel consumption) and money.

The validity of the simulation results, and further details on the evaluation process, are covered in the paper “Optimization of a Cooling Circuit with a Parameterized Water Pump Model.”

Learn more

I’d like to thank Thomas for providing us with this fascinating demonstration of the SmartCooling library and its applications. If you, or perhaps your company, would like to try modeling cooling circuits with SmartCooling, it is available for purchase in the SystemModeler Library Store. From the website you can also download a free trial of SystemModeler. For more SmartCooling examples, check out Battery Stack: Modeling a Cooling Circuit and Cylinder Cooling in our collection of SystemModeler industry examples, or visit our online Documentation Center, which hosts the full SmartCooling library documentation.

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