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使用现象学和机器学习模型的组合预测重型燃气发动机的短暂性能
Navid Balazadeh1, Sandeep Munshi2, Mahdi Shahbakhti3
1School of Sustainable Energy Engineering, Simon Fraser University, Surrey, BC, Canada.
开发天然气 (NG) 发动机的预测模型对于减少长途运输中的温室气体 (GHG) 排放至关重要. 这种组合的发动机和机器学习模型准确地预测了NG发动机性能和排放量,有助于脱碳努力.
科学领域:
- * 工程 * 工程师 *
- * 环境科学 环境科学
- * 计算建模 * 计算建模
背景情况:
- * 长途运输严重依赖化石燃料,对温室气体 (GHG) 排放做出了重大贡献.
- *天然气 (NG) 发动机为减少燃料中的碳含量,同时保持发动机效率提供了短期解决方案.
- *准确预测发动机性能和排放量对于评估NG技术在各种应用中至关重要.
研究的目的:
- * 开发和验证一个合的发动机和机器学习模型,用于预测高压直喷NG发动机的性能和排放.
- * 评估模型在预测发动机参数,氧化 (NOx) 和甲 (CH4) 排放在短暂驱动周期中的准确性.
- * 评估天然气发动机在长途运输中减少温室气体的潜力.
主要方法:
- * 适应GT-SUITETM DI-Pulse型号的6NG发动机,采用空气交换系统进行短暂分析.
- *将1D发动机模型与机器学习模型结合起来,以提高CH4排放的预测准确度.
- *使用过渡式发动机动力表数据验证模型,并将其应用于监管和现实世界的驱动周期.
主要成果:
- * 开发的模型准确地预测了稳定运行时不到5%的误差的发动机性能.
- * 在研究的驱动周期中,NOx和CH4排放的累积误差低于10%.
- * 甲 (CH4) 占总温室气体排放量的0.2%至1.4%,取决于驱动周期.
结论:
- * 结合的1D引擎和机器学习模型有效地预测了NG引擎性能和排放量,包括CH4和NOx.
- *这种建模方法对于评估天然气发动机技术及其在长途运输中的温室气体减排潜力是有价值的.
- * 该模型显示了引擎评估研究的巨大潜力,特别是当与新兴技术集成时.
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