The drive to improve internal combustion engines has led to efficiency objectives that exceed the capability of conventional combustion strategies. As a result, advanced combustion modes are more attractive for production. These advanced combustion strategies typically add sensors, actuators, and degrees of freedom to the combustion process. Spark-assisted compression ignition (SACI) is an efficient production-viable advanced combustion strategy characterized by spark-ignited flame propagation that triggers autoignition in the remaining unburned gas. Modeling this complex combustion process for control demands a careful selection of model structure to maximize predictive accuracy within computational constraints. This work comprehensively evaluates a physics-based and a data-driven model. The physics-based model produces a burn duration by computing laminar flame speed as a function of test point conditions. The crank-angle domain is intentionally excluded to reduce computational expense. The data-driven model is an artificial neural network (ANN). The candidate models are compared to a one-dimensional engine model validated to experimental SACI engine data. Though both models capture the trends in burn rates, the ANN model has a root-mean square error (RMSE) of 1.4 CAD, significantly lower than the 10.4 CAD RMSE of the physics-based model. The exclusion of the crank-angle domain results in insufficient detail for the physics-based model, while the ANN can tolerate this exclusion.

改进内燃发动机的努力已导致效率目标超过了常规燃烧策略的能力。结果，先进的燃烧模式对生产更具吸引力。这些先进的燃烧策略通常会在燃烧过程中增加传感器，执行器和自由度。火花辅助压缩点火（SACI）是一种高效可行的先进燃烧策略，其特征在于火花点火的火焰传播会触发剩余未燃烧气体的自燃。对这种复杂的燃烧过程进行建模以进行控制，需要仔细选择模型结构，以在计算限制内最大化预测精度。这项工作全面评估了基于物理学和数据驱动的模型。基于物理学的模型通过计算作为测试点条件的函数的层流火焰速度来产生燃烧持续时间。故意排除曲柄角域以减少计算费用。数据驱动模型是人工神经网络（ANN）。将候选模型与通过实验SACI引擎数据验证的一维引擎模型进行比较。尽管两个模型都记录了燃烧速率的趋势，但ANN模型的均方根误差（RMSE）为1.4 CAD，大大低于基于物理模型的10.4 CAD RMSE。曲柄角域的排除导致基于物理模型的细节不足，而ANN可以容忍这种排除。数据驱动模型是人工神经网络（ANN）。将候选模型与通过实验SACI引擎数据验证的一维引擎模型进行比较。尽管两个模型都记录了燃烧速率的趋势，但ANN模型的均方根误差（RMSE）为1.4 CAD，大大低于基于物理模型的10.4 CAD RMSE。曲柄角域的排除导致基于物理模型的细节不足，而ANN可以容忍这种排除。数据驱动模型是人工神经网络（ANN）。将候选模型与通过实验SACI引擎数据验证的一维引擎模型进行比较。尽管两个模型都记录了燃烧速率的趋势，但ANN模型的均方根误差（RMSE）为1.4 CAD，大大低于基于物理模型的10.4 CAD RMSE。曲柄角域的排除导致基于物理模型的细节不足，而ANN可以容忍这种排除。