Real-Time Prediction of Gas Flow Dynamics in Diesel Engines using a Deep Neural Operator Framework

Understand

We develop a data-driven deep neural operator framework to approximate multiple output states for a diesel engine and generate real-time predictions with reasonable accuracy.

• As emission norms become more stringent, the need for fast and accurate models that enable analysis of system behavior have become an essential requirement for system development.
• The fast transient processes involved in the operation of a combustion engine make it difficult to develop accurate physics-based models for such systems.
• As an alternative to physics based models, we develop an operator-based regression model (DeepONet) to learn the relevant output states for a mean-value gas flow engine model using the engine operating conditions as input variables.