Neural-network prediction of methanol-additive spray penetration and cone angle across flash-boiling and non-flashing regimes

“Abstract

Methanol and its oxygenated blends are credible low-carbon fuels for direct-injection engines, yet classical correlations predict their spray behaviour poorly across practical pressures. Penetration sets the risk of wall impingement and cone angle governs air entrainment, so the two must be predicted together. High-speed Schlieren imaging in a constant-volume vessel recorded sprays of pure methanol and of blends with methyl tert‑butyl ether, 2-ethylhexyl nitrate or acetone at 1, 5 and 10% by volume, at injection pressures of 2 to 10 MPa and ambient pressures of 4 to 200 kPa, spanning flash-boiling and non-flashing regimes. Averaging five repeats per condition gave 4320 time-resolved observations across 144 operating conditions. A multi-output backpropagation neural network predicted both descriptors simultaneously and was evaluated on entire withheld spray conditions rather than individual frames. On 21 unseen conditions the backpropagation model reached coefficients of determination of 0.994 for penetration and 0.989 for cone angle, with root-mean-square errors of 2.00 mm and 1.82 degrees, at or below measurement repeatability. A calibrated Hiroyasu-Arai correlation remained useful under non-flashing conditions but lost most of its skill under flash boiling, where the backpropagation model did not. Gradient-boosted trees predicted cone angle marginally better, although only the backpropagation model describes both descriptors at once. Optimising its weights with a genetic algorithm gave no gain in accuracy. Penetration was governed by injection and ambient pressure, cone angle by additive composition. Withholding an entire ambient pressure level or additive concentration drove the coefficient of determination below zero, defining the model’s practical limits.”

 

The full article is accessible via:

https://doi.org/10.1016/j.egyai.2026.100893

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