Research Article

Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study

Volume: 10 Number: 3 October 25, 2026

Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study

Abstract

Accurate prediction of the in-cylinder pressure trace does not necessarily preserve pressure-derived combustion descriptors. This study uses a synthetic spark-ignition benchmark to examine whether explicit supervision of these descriptors changes surrogate accuracy. Six neural-network variants share a 5382-parameter architecture and training budget, with cumulative supervision of pressure derivatives, apparent heat release, peak descriptors, combustion phasing and gross indicated work. The benchmark comprises 960 operating points divided into training, validation, interpolation and simultaneous speed–load extrapolation sets, plus a closure-shift test with altered combustion and wall-heat-transfer relations. At the specified 1° output resolution, validation-selected A4 reduces interpolation CA50 mean absolute error from 0.0938° to 0.0688° and maximum pressure-rise-rate (MPRR) error from 0.0506 to 0.0303 bar/°, corresponding to improvements of 26.6% and 40.1%. Pressure RMSE increases from 0.0980 to 0.1031 bar, and gross IMEP error increases from 0.0296 to 0.0529 bar. A4 retains a lower MPRR error during speed–load extrapolation, with higher pressure and work errors, while all models deteriorate under the closure shift. The 1° MPRR discretisation discrepancy of 0.01702 bar/° is comparable in magnitude to the 0.0203 bar/° model improvement. These results establish a redistribution of approximation error within the prescribed benchmark. The fixed geometry, nominal fuel and simplified single-zone generator, together with the absence of experimental validation, preclude interpreting the reported gains as demonstrated improvements on real engines.

Keywords

References

  1. Heywood JB. Internal combustion engine fundamentals. 2nd ed. New York: McGraw-Hill Education; 2018.
  2. Gatowski JA, Balles EN, Chun KM, Nelson FE, Ekchian JA, Heywood JB. Heat release analysis of engine pressure data. Warrendale (PA): SAE International; 1984. SAE Technical Paper No.: 841359. https://doi.org/10.4271/841359
  3. Payri F, Luján JM, Martín J, Abbad A. Digital signal processing of in-cylinder pressure for combustion diagnosis of internal combustion engines. Mech Syst Signal Process. 2010;24(6):1767-84. https://doi.org/10.1016/j.ymssp.2009.12.011
  4. Aliramezani M, Koch CR, Shahbakhti M. Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: a review and future directions. Prog Energy Combust Sci. 2022;88:100967. https://doi.org/10.1016/j.pecs.2021.100967
  5. Polat S, Solmaz H, Calam A, Yılmaz E. Estimation of the COVIMEP variation in a HCCI engine. Politeknik Derg. 2020;23(3):721-7. https://doi.org/10.2339/politeknik.567865
  6. Huang Q, Xie T, Liu J. Machine learning-assisted reconstruction of in-cylinder pressure in internal combustion engines under unmeasured operating conditions. Energies. 2025;18(19):5235. https://doi.org/10.3390/en18195235
  7. Czarnecki WM, Osindero S, Jaderberg M, Swirszcz G, Pascanu R. Sobolev training for neural networks. In: Guyon I, von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors. Advances in neural information processing systems 30. Red Hook (NY): Curran Associates; 2017. p. 4278-87.
  8. Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys. 2019;378:686-707. https://doi.org/10.1016/j.jcp.2018.10.045

Details

Primary Language

English

Subjects

Automotive Combustion and Fuel Engineering

Journal Section

Research Article

Publication Date

October 25, 2026

Submission Date

September 24, 2026

Acceptance Date

October 25, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

APA
Demirci, O. K. (2026). Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study. International Journal of Automotive Science And Technology, 10(3), 797-810. https://doi.org/10.30939/ijastech..2047265
AMA
1.Demirci OK. Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study. IJASTECH. 2026;10(3):797-810. doi:10.30939/ijastech.2047265
Chicago
Demirci, Oğuz Kürşat. 2026. “Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study”. International Journal of Automotive Science And Technology 10 (3): 797-810. https://doi.org/10.30939/ijastech. 2047265.
EndNote
Demirci OK (October 1, 2026) Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study. International Journal of Automotive Science And Technology 10 3 797–810.
IEEE
[1]O. K. Demirci, “Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study”, IJASTECH, vol. 10, no. 3, pp. 797–810, Oct. 2026, doi: 10.30939/ijastech..2047265.
ISNAD
Demirci, Oğuz Kürşat. “Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study”. International Journal of Automotive Science And Technology 10/3 (October 1, 2026): 797-810. https://doi.org/10.30939/ijastech. 2047265.
JAMA
1.Demirci OK. Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study. IJASTECH. 2026;10:797–810.
MLA
Demirci, Oğuz Kürşat. “Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study”. International Journal of Automotive Science And Technology, vol. 10, no. 3, Oct. 2026, pp. 797-10, doi:10.30939/ijastech. 2047265.
Vancouver
1.Oğuz Kürşat Demirci. Derivative and Combustion Metric Supervised Cylinder Pressure Surrogates for Spark-Ignition Engines: A Synthetic Benchmark Study. IJASTECH. 2026 Oct. 1;10(3):797-810. doi:10.30939/ijastech. 2047265


International Journal of Automotive Science and Technology (IJASTECH) is published by Society of Automotive Engineers Turkey

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