Towards predictive combustion kinetic models: Progress in model analysis and informative experiments
Towards predictive combustion kinetic models: Progress in model analysis and informative experiments
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迈向预测燃烧动力学模型：模型分析和信息实验的进展
DOI：
10.1016/j.proci.2020.11.002
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发表时间：
2020-12
影响因子：
3.4
通讯作者：
Bin Yang
中科院分区：
文献类型：
--
作者：
Bin Yang
燃烧化学研究的关键任务之一是为实际燃料建立准确、鲁棒的燃烧动力学模型。一个准确和强大的动力学模型产生的预测是高度一致的实验测量在广泛的操作条件下，预测的不确定性是可以接受的。各种强大的诊断技术产生的可靠的实验数据继续发挥重要作用，在这种模式的发展。本文综述了基于同步加速器的组分测量在燃烧系统中的贡献、模型验证、模型结构开发和模型参数优化。特别强调的是最近报道的战略信息和可靠的实验数据生成，包括燃烧动力学模型输入参数的评估，计算成本降低模型分析，基于模型分析的实验设计，实验数据处理和减少误差。特别是基于主动子空间的方法（ASSM）可以降低燃烧动力学模型的维数，而基于人工神经网络的替代模型（ANN-HDMR和ANN-MCMC）可以显著降低计算成本。基于全局灵敏度的实验设计方法，包括灵敏度熵和替代模型相似性（SMS）can.guide动力学信息丰富的实验数据生成。基于模型分析的实验误差校正和实验目标特征提取可以提高实验数据质量。一个计算框架（OptEx），使实验数据与机制开发，实验设计和模型优化的整合，提供了一种新的手段，更有效地开发可靠的动力学模型。
One of the key tasks of combustion chemistry research is to develop accurate and robust combustion kinetic models for practical fuels. An accurate and robust kinetic model yields predictions that are highly consistent with experimental measurements over a wide range of operating conditions, with prediction uncertainties that are acceptable. Reliable experimental data generated by various powerful diagnostic techniques continue to play an essential role in the development of such models. This review focuses on the contributions of synchrotron-based species measurements in combustion systems, on model validation, model structure development, and model parameter optimization. Special emphasis is placed on recently reported strategies for informative and reliable experimental data generation, including combustion kinetic model input parameter evaluation, computational cost reduction for model analysis, model-analysis-based experimental design, experimental data treatment and error reduction. Particularly, the active-subspace-based method (ASSM).can reduce the dimensionality of combustion kinetic models and the aritificial-neural-network-based surrogates (ANN-HDMR and ANN-MCMC) can reduce the computational cost significantly. Global-sensitivity-based experimental design methods including sensitivity entropy and surrogate model similarity (SMS) can.guide kinetics-information-enriched experimental data generation. Model-analysis-based calibration for experimental errors and feature extraction of experimental targets can improve the experimental data quality. A computational framework (OptEx) enabling the integration of experimental data with mechanism development, experimental design and model optimization, provides a new means to develop reliable kinetic models more efficiently and effectively.
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