A novel method for in-cylinder pressure prediction using the engine instantaneous crankshaft torque

Abstract

Current practices of condition assessment in large marine engines are largely based on the measurement of cylinder pressure using external kits, which poses challenges due to sensors synchronisation and durability issues, as well as the inability to perform continuous monitoring. For addressing these challenges, this study aims at developing a novel method to solve the inverse problem of predicting the pressure variations in all engine cylinders, by using the Instantaneous Crankshaft Torque (ICT) measurement for large internal combustion engines. This method is developed by considering the Initial Value Problem (IVP) technique along with the integration of a direct crankshaft dynamics model incorporating the sensitivity parameters and stability criteria calculation based on the Lyapunov Exponent (LE) as well as a state-of-the-art Nonmonotone Self-Adaptive Levenberg-Marquardt (NSALMN) optimisation algorithm. The method is tested for a number of case studies using different combustion models based on the Weibe and sigmoid functions, as well as for healthy, degraded and faulty engine conditions. The derived results demonstrate adequate accuracy exhibiting a maximum error of 0.3% in the prediction of the mean peak in-cylinder pressure. The analysis of the calculated sensitivity parameters resulted in the identification of the parameters that significantly impact the solution, thus providing improved insights for selecting the developed method settings. The developed method renders the continuous and non-intrusive in-cylinder pressures monitoring feasible, by using a permanently installed shaft power metre sensor with higher sample rates.

Health assessment framework of marine engines enabled by digital twins

“…Future research can focus on the combination of this framework with the novel in-cylinder pressure prediction approach using the engine instantaneous torque previously developed by the authors. 19 This is expected to provide a significantly larger field of in-cylinder pressure data, by deploying only a low-cost torque metre sensor. Therefore, it can allow for timely assessing the current engine conditions, expanding the overall health assessment process presented within this framework.…”

“…In addition, crankshaft dynamics models were utilised in few applications for large diesel engines to derive cylinder performance information, which is subsequently used to perform diagnostics. 18,19 However, despite the strong link between the engine mechanical and thermodynamic performance, thermodynamics models offer a more direct approach in gaining insight into the engine operation and performing health assessment. Furthermore, by considering the most recent model formulations and computational advancements, thermodynamics models have demonstrated their effectiveness in developing fast-running digital twins of marine engines with predictive capabilities.…”

Comparative Analysis of Data-Driven Models for Marine Engine In-Cylinder Pressure Prediction

“…The main particulars of this engine are presented in Table 1. This digital twin was calibrated based on the framework developed by Tsitsilonis et al [17], which involves the determination of the combustion and friction mean effective pressure parameters for the reference operating point. Subsequently, the calibration determines the values of the Woschni-Anisits combustion model constants by considering all the remaining shop tests' operating points.…”

“…Table 2 lists the obtained percentage errors for the engine brake's specific fuel consumption and maximum in-cylinder pressure. The detailed discussion of the validation and verification process at healthy and limited faulty conditions is provided in [17][18][19]. The validated digital twin is employed to generate datasets (in-cylinder pressure profiles) considering the complete engine operating envelope.…”

Decision support methods for sustainable ship energy systems: A state-of-the-art review

“…This increasing need for data gathering and analysis could be supported by the rapid development of big data acquisition and analysis. In addition, with the digitalisation advancements, there is rapid progress in real-time monitoring of different performance parameters, such as temperature, speed, emissions [163] as well as the shaft instantaneous torque [164]. These inputs could have a significant impact on the decision support methods and consequently, on the autonomous ships design [165].…”