Hello, I am Alberto Soto from Siemens Digital Industries Software.
Today, I am going to show how easily you can create a neural network, representing an air conditioning loop.
Our final goal will be to perform thermal and energy analysis, faster.
Let’s start from a detailed model of the system.
It includes the air-conditioning loop, the cabin and the temperature controller, which pilots the compressor.
To train the neural network, a batch is prepared with various driving cycles and ambient temperatures.
As such, a comprehensive set of operating points are generated to train the network.
The inputs and outputs of the network are added to the watch variables and the batch runs are simulated.
With such a complex model, the simulation takes a few minutes.
Now, we can launch the Neural Network Builder from the App space.
Let’s create a project and import simulation results.
Batch results and variables can be renamed for easier manipulation.
2 driving cycles are selected to train the network, the 3 others will be used for validation.
The network inputs are: engine and ambient temperature,
vehicle and compressor speed, and the compressor command.
Outputs are: fresh air temperature and humidity, and the compressor power.
Next, the network structure is defined.
Here we use 3 hidden layers with 2 dense and one dynamic RNN-layer, to capture transient effects.
After looking at the dynamics of the cooled air in the original model, a one second sample time is chosen.
Everything is defined, and the training can now begin.
Behind the scenes, an optimization algorithm identifies the network parameters from the training sets.
The tool offers the possibility to either stop or extend the training.
Let’s stop after 2000 epochs and save this model.
The generated model can now be evaluated for both the training and validation scenarios.
The tool provides fidelity metrics that clearly show how good the network is.
In addition, detailed metrics and plots for each output and scenario are also available.
This neural network can now be exported to Simcenter Amesim, with a few clicks, by using the export assistant.
Additionally, existing ONNX neural networks can be imported into Simcenter Amesim
thanks to the Neural Network Import tool.
We can now close the Builder and go back to our model.
Let’s replace the detailed model of the air-conditioning loop by its equivalent neural network
and perform the final closed-loop validation by running the same batch.
Results are as good as expected, while simulation time dropped by a factor of a hundred or more,
when compared to the original model.
In this example, we demonstrated how to simplify a complex physical model
into a fixed-step, computationally fast model by using Simcenter Amesim’s Neural Network Builder.
This approach can be replicated for various types of models that must be reduced.
It is also a valid approach for protecting the intellectual property of a model.
Thank you for watching.
For more information, please visit our website.