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用物理编码的机器学习来预测柴油发动机中铁纳米催化剂和丰富生物柴油的性能和排放

Nguyen Van Minh^1,2, Ravikumar Jayabal³, Lionus Leo G M⁴

¹Institute of Research and Development, Duy Tan University Da Nang 550000 Vietnam.

|February 26, 2026

概括

废食用油生物柴油 (WCOB) 与NiFe2O4纳米催化剂和 (H2) 丰富混合,显著提高柴油发动机效率并减少排放. 一个物理编码的机器学习模型准确地预测了这些协同效应,以实现可持续的发动机运行.

科学领域:

燃烧科学与工程燃烧科学与工程
催化和纳米材料的使用
发动机性能和排放情况

背景情况:

在柴油发动机中对可持续燃料的需求不断增长.
需要先进的燃烧分析和性能预测.
研究废油生物柴油 (WCOB) 作为可行的替代燃料.

研究的目的:

评估WCOB,NiFe2O4纳米催化剂和H2丰富对柴油发动机在气中的过程,性能和排放的综合影响.
开发和验证一个物理编码的多任务机器学习 (PE-MTMML) 模型,用于准确的热力学预测.
评估优化燃油效率和减少污染物的协同效应.

主要方法:

使用WCOB混合物与NiFe2O4 (50-150 ppm) 和H2丰富 (5 LPM) 的单四冲程柴油发动机在每分钟1500转的实验测试.
使用单区域模型,测量气内的压力,以推导出峰值压力和热释放率 (HRR).
开发一个包含热力学互惠,催化氧化和不确定性约束的PE-MTMML模型.

主要成果:

与柴油相比,NiFe2O4纳米粒子增强了催化氧化和预混合燃烧,高达7.8%的峰值压力和14%的HRR.
在WCOB+NiFe2O4+H2混合物中,制动热效率 (BTE) 提高了29.2%,制动特定能耗 (BSEC) 降低了22.3%.
烟雾不透明度 (10.9%),CO (20%) 和HC (25%) 的显著降低,NOx (7.5%) 的轻微增加. 该PE-MTMML模型显示出高预测准确度 (R2 = 0.9993).

结论:

WCOB,NiFe2O4纳米催化和H2丰富的协同组合为柴油发动机提供了高效,低污染的燃料解决方案.
PE-MTMML模型作为可靠的数字双胞胎,以最小的实验数据优化发动机性能,支持可持续能源转型.
这种方法符合循环经济原则,并有助于开发可持续的,碳中和的柴油发动机技术.

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