The cooling circuit is a key subsystem in your car since it primarily ensures the reliability of the engine by limiting and regulating components’ temperature. The coolant temperature also greatly impacts the fuel consumption, the car performance as well as the passenger comfort.
That's why it is crucial to size it right the first time while considering all the subsystems interactions.
In a very early design stage, the characteristics and performance of each individual component are however unknown. A realistic behavior of the system needs to be represented with only a limited set of parameters.
Moreover, when the system design is almost frozen, it needs to be validated together with the associated controller, which requires for the simulation to comply with real-time constraints.
To tackle both challenges, in Simcenter Amesim 17 we have introduced functional cooling system components that will be presented hereafter.
The following figure represents a typical automotive cooling system:
The coolant flows in the circuit thanks to a pump which is entrained by the engine.
Let’s now see how traditionally we represent this system in Simcenter Amesim.
The model consists of 3 main subsystems:
This model enables us to evaluate the oil, engine and coolant temperature evolution during a transient run. As an example, we simulate a warm-up during a NEDC cycle starting from 25°C. The evolution of the different temperatures is the following:
In terms of performance, this reference model is quick to simulate since the CPU time using a variable-step solver is about 14 seconds for 1170 seconds simulated, thus giving a simulation time 80 times faster than real time.
However, the succession of capacitive and resistive elements for the hydraulics generates very small time constants that requires a time step of around 1 nanosecond to ensure the fixed-step solver stability, thus preventing more or less a usage of the model for real-time purposes.
To ensure this real-time compatibility we can work in 2 different ways: we can either apply different reduction techniques to the original model or rely on cooling system functional components.
Let’s take a look at the second option and see how the new cooling loop, oil circuit and oil cooler functional components help to address real-time applications.
The different subsystems of the reference model are now replaced with these components while keeping the same boundary conditions.
How the subsystems' performance is now defined?
From the detailed model, we have, on one hand, the coolant pump characteristics for different rotary speeds, on the other hand, we can generate the Q vs. dP curve characteristic of the coolant circuit for the 2 configurations: the closed and open thermostat.
The intersections at different rpms of pump and circuit characteristic curves enable us to build the 2 curves needed for the functional cooling subsystem component. Note that the interpolation between these 2 curves is done automatically for intermediate thermostat positions.
Regarding the oil circuit, the principle is quite similar: the 2 curves needed to be generated here for 2 extreme oil viscosities.
For the rest, fluid volumes, initial temperatures and radiator performance data are essentially the only required data, thus limiting the number of input parameters:
The oil cooler is using the same data as the original one.
Using the same scenario as the one used for the reference model, we can compare both results:
Temperatures are very similar as well as coolant flow rates.
In terms of CPU time, the model using the functional components is about 10 times faster.
Moreover, this model is now real-time compliant. Switching to a fixed-step solver with a default step of 10ms, we obtain the same results:
This real-time compliance is illustrated by a plot of the ratio between the simulation elapsed time over the simulated time. The maximum value of 1e-3 shown below (i.e. simulation is 1000 times faster than real time) confirms this model capability.
To sum up, Simcenter Amesim enables you to study and optimize the vehicle cooling system at different design stages - from predesign up to controls validation. From a detailed model, you will easily set up and run with a very limited number of parameters a functional, realistic and real-time-ready model.