基于智能算法的柴油/天然气双燃料发动机的性能和排放特性预测和验证
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Updated: Jul 28, 2025

A Rapid Method for Modeling a Variable Cycle Engine
Published on: August 13, 2019
Jialong Li1, Taoming Wan1, Haozhong Huang1
1College of Mechanical Engineering, Guangxi University, Nanning 530004, China.
一个新的高斯过程回归和反神经网络 (GPR-FNN) 算法准确地预测了双燃料柴油/天然气发动机的性能和排放. 这种智能算法显示出高精度,为未来的发动机研究提供了洞察力.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 智能算法在各种应用中变得越来越重要,包括发动机性能分析.
- 预测双燃料发动机复杂的性能和排放特性对于优化至关重要.
研究的目的:
- 提出和评估一个合高斯过程回归和反神经网络 (GPR-FNN) 算法.
- 预测重型柴油/天然气双燃料发动机的关键性能指标和排放.
主要方法:
- 使用发动机转速,扭矩,天然气替代率,柴油注入压力和计时作为输入的GPR-FNN模型的开发.
- 预测50%的热量释放,燃料消耗,热效率,CO,CO2,UHC,NOx和烟尘排放.
- 对模型与实验数据的验证.
主要成果:
- 对于所有输出参数,GPR-FNN模型实现了高预测准确度.
- 对于所有预测的输出,回归相关系数超过0.99.
- 平均绝对百分比误差低于5.9%,直线图证实了模型的精度.
结论:
- 拟议的GPR-FNN算法在预测柴油/天然气双燃料发动机性能和排放方面表现出极高的准确性.
- 这项研究为优化双燃料发动机研究提供了一种新的计算方法.
- 这些发现可以指导开发更高效,更清洁的替代燃料发动机.
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