Physics-Guided Graph Partitioning for Multicore Digital Reconstruction of the Engine

Model-based virtual sensing supports onboard engine monitoring and control, but the available computational resources constrain crank-angle-resolved simulation. This study constructs a digital reconstruction engine (DRE) from established single-zone thermodynamic relations and investigates how its coupled calculations can be organized for embedded execution. The framework combines spectral analysis, repeated k-means++ clustering, and physical grouping to construct a three-subgraph implementation on a multicore ARM platform. Across nine steady-state speed–load conditions, pressure reconstruction yields R2 values of 0.951–0.983, pressure RMSEs of 0.54–1.48 bar, and peak-pressure RMSEs of 1.09–3.77 bar. During a 20–80% load increase at 2000 r/min, pressure R2 is 0.969 and RMSE is 0.84 bar. Temperature is compared with a GT-POWER simulation as a model-consistency assessment. For the same three-subgraph program, computation time per four-cylinder cycle decreases from 24.6 ms on one core to 18.1 ms on two cores and 14.3 ms on four cores. A pooled continuous-run assessment of 3200 single-cylinder computations gives P95 = 4.451 ms and P99 = 5.150 ms; 1.34% exceed the 5 ms single-cylinder budget corresponding to 6000 r/min. The results characterize the computational performance and timing variability of the tested implementation.