Neural-Network-Enhanced Air-Standard Thermodynamic Modeling for Spark-Ignition Engines Fueled with Low Heating Value Alternatives

This study presents a computational framework that integrates an air-standard thermodynamic engine model with artificial neural networks to predict the performance of spark-ignition (SI) engines operating with alternative fuels of reduced lower heating value (LHV). A deterministic thermodynamic simulator was developed in Excel, incorporating engine geometric parameters (compression ratio, bore, stroke, displacement), operating speed, and fuel properties, with particular emphasis on LHV as the dominant energetic descriptor. The model computes in-cylinder states, thermal efficiency, indicated mean effective pressure, and power output under idealized air-standard assumptions. To extend predictive capability beyond fixed-parameter analyses, a feedforward neural network was trained using datasets generated from systematic parametric sweeps of engine geometry, speed, and fuel LHV. The neural network captures nonlinear interactions between compression ratio, combustion energy release, and performance indicators, enabling rapid estimation of engine response when substituting conventional gasoline with lower-LHV alternative fuels. Results demonstrate that the hybrid thermodynamic–neural approach accurately predicts trends in efficiency degradation and power reduction associated with decreasing LHV, while identifying compensatory design adjustments, particularly through compression ratio optimization. The methodology provides a low-cost and computationally efficient tool for preliminary evaluation of alternative liquid fuels in SI engines without resorting to complex CFD or experimental campaigns. This work contributes a transparent, reproducible modeling strategy suitable for early-stage engine design studies and fuel screening, supporting sustainable fuel transitions in spark-ignition propulsion systems.