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Updated: Jun 12, 2025

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从高速柴油发动机中预测短暂NO_x排放的半经验物理模型的开发

Abdullah Bajwa¹, Gongyi Zou¹, Fengyu Zhong¹

¹Department of Engineering Science, University of Oxford, Oxford, UK.

International journal of engine research

|September 26, 2024

概括

一个新的基于物理学的模型准确地预测了柴油发动机在短暂运行期间的氧化 (NOx) 排放. 这种基于气压力峰值的模型优于实时排放监测的数据驱动方法.

科学领域:

内部燃烧发动机内部燃烧发动机
环境科学环境科学
计算流体动力学的流体动力学.

背景情况:

越来越严格的排放法规要求对柴油发动机的发动机氧化物 (NOx) 排放进行先进的控制.
虽然数据驱动模型在稳定状态NOx预测方面表现出色,但它们在过渡运行和超越训练数据的推断方面遇到了困难.
捕捉循环在内变化的基于物理的模型为暂时的NOx建模提供了一个有希望的替代方案.

研究的目的:

开发和验证基于循环峰值气压力对柴油发动机的半实证NOx预测模型.
评估模型在短暂操作条件下的性能,并将其与数据驱动的人工神经网络模型进行比较.

主要方法:

开发了一个基于周期性峰值压力的半经验模型.
该模型使用在短暂发动机运行期间的高速NO和NO2排放测量进行校准.
模型的短暂性能被评估并与先前开发的人工神经网络模型进行了比较.

主要成果:

与数据驱动模型相比,开发的基于物理的模型表现出优越的瞬态性能.
该模型在短暂条件下实现了更好的动态响应和较低的预测误差 (<10%).
证实了气压力峰值和循环NOx排放之间的强烈相关性.

结论:

基于物理的周期性峰值压力模型对柴油发动机中短暂的NOx排放的预测是有效的.
这些模型显示了作为实时排放监测虚拟传感器的潜力.
这些发现支持将这些模型整合到未来的物理引导神经网络开发中,以加强排放控制.

关键词:

氧化物 (NOx) 是一种有机物.一个半经验性的NOx模型.人工神经网络的人工神经网络扭转角度解决的NOx可以解决.柴油柴油柴油是什么内部燃烧发动机的内部燃烧发动机.这是一个短暂的过渡.

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