Improving Fuel Consumption Prediction for Marine Diesel Engines Using Hierarchical Neural Networks and Pulsating Exhaust Models

Anibal Aguillon Salazar (), Georges Salameh, Pascal Chesse, Nicolas Bulot and Yoann Thevenoux
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Anibal Aguillon Salazar: LHEEA, CNRS, École Centrale Nantes, Nantes Université, UMR 6598, 44000 Nantes, France
Georges Salameh: LHEEA, CNRS, École Centrale Nantes, Nantes Université, UMR 6598, 44000 Nantes, France
Pascal Chesse: LHEEA, CNRS, École Centrale Nantes, Nantes Université, UMR 6598, 44000 Nantes, France
Nicolas Bulot: MAN Energy-Solutions France SAS, 44600 Saint-Nazaire, France
Yoann Thevenoux: MAN Energy-Solutions France SAS, 44600 Saint-Nazaire, France

Energies, 2024, vol. 18, issue 1, 1-26

Abstract: Predicting end-of-process variables in internal combustion engines, such as brake-specific fuel consumption or pollutant emissions, is crucial for engine design decisions. However, errors in common multi-layer-perceptron-based artificial neural network models often match the magnitude of the expected fuel consumption improvements, potentially leading to incorrect decisions. This study introduces a hybrid model where artificial neural networks replace engine block elements, while the 1D gas circuit and turbocharger models are retained. To enhance metamodel accuracy, two modifications are proposed: incorporating a pulsating mass flow rate in the exhaust line to capture pulsating effects missing in mean-value engine models and using a hierarchical arrangement of several multi-layer perceptrons instead of a parallel arrangement. The pulsating mass flow rate approach improves the accuracy of all tested configurations by replicating pulsating effects from a detailed 1D engine model. Meanwhile, the hierarchical arrangement refines predictions of end-of-process variables, such as fuel consumption, by increasing the total layers, with a minimal trade-off in the accuracy of other variables. These findings are validated using a metamodel derived from a calibrated 1D engine model in GT-Suite. The proposed methods are expected to enhance the accuracy of data-driven artificial neural network approaches, contributing to more reliable engine design optimization.

Keywords: diesel engines; neural networks; fuel consumption; metamodel; mean-value engine model; pulse unsteadiness (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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