Scalable Data Driven Models for Control of Multi-Fuel Compression Ignition Engine

Modeling the combustion characteristics in a multi-fuel compression ignition engine, under varying operating conditions is a challenging problem. Physics-based models can be developed but tend to be quite extensive or limited to certain operating conditions. To achieve reliable combustion under these conditions, the number of actuators required can be high, further increasing the complexity of the model and in turn the difficulty of control design based on it. To simplify the control, feedforward (FF) control is developed by inverting steady state models, built based on data collected at various operating points. Use of data driven models for capturing these steady state characteristics has gained a lot of attraction in the recent years due to the available computational resources and ease of model development. For developing the FF control, data-driven models are inverted by numerically searching for desired control inputs. The time taken for this inversion grows with the complexity of the model and the complexity of the model increases with the number of operating conditions and actuators. In this paper, use of scalable Gaussian process (GP) methods for building computationally efficient models to reduce the time taken for generating FF maps is proposed. The performance of these models and control design is validated using computational fluid dynamics (CFD) and experimental data.