A Support-Vector Machine Model to Predict the Dynamic Performance of a Heavy-Duty Natural Gas Spark Ignition Engine 2021-01-0529
Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets. As a result, the established correlative model can be used to replace more complex, time-consuming engine models with acceptable predictive capability, therefore accelerating future engine development and optimization work.
Citation: Liu, J., Huang, Q., Ulishney, C., and Dumitrescu, C., "A Support-Vector Machine Model to Predict the Dynamic Performance of a Heavy-Duty Natural Gas Spark Ignition Engine," SAE Technical Paper 2021-01-0529, 2021, https://doi.org/10.4271/2021-01-0529. Download Citation
Author(s):
Jinlong Liu, Qiao Huang, Christopher Ulishney, Cosmin Dumitrescu
Affiliated:
West Virginia University
Pages: 9
Event:
SAE WCX Digital Summit
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Spark ignition engines
Computational fluid dynamics
Natural gas
Machine learning
Engines
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