CNN-based temperature dynamics approximation for burning rooms

The chaotic and dynamic nature of heat transfer results is either time-consuming or inaccurate predictions of the temperature field in simulations. In particular, the simulation of burning buildings is complex and at the same time the key enabler in saving lives and keeping property damage to a minimum. Deep Learning Neural Networks are a possibility to speed up the simulation process and make real-time predictions of temperature behavior or even replace the simulation process as a whole in the long run. This paper proposes a novel procedure to create a convolutional neural network-based method CNN1D3D, that is capable of approximating the behavior of temperature in burning rooms. The data used for training is created with time intensive, numerical simulations. CNN1D3D's architecture consists of an convolution-based temporal and spatial encoding as well of a transposed convolution based decoder, that creates temperature predictions in real-time. The work shows a possibility for a distinct feature extraction for temporal and spatial features. It shows how solutions generated by simulations based on differential equations hold implicitly the necessary information needed for the method to recreate the context of the data set. This forms a basis for the abstraction onto further fluid dynamics applications. This work has several real-world applications and forms the basis for future rescue route calculations. The solution can be generalized on other applications with similar data structures. It provides the opportunity to capture the complexity of interdependencies and correlations in the field of fluid dynamics.

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CNN-based temperature dynamics approximation for burning rooms