探索人工神经网络,以解决乱反应流中的复杂碳化合物化学
Jian An1, Fei Qin2, Jian Zhang1
1Institute for Aero Engine, Tsinghua University, Beijing 100084, China.
Fundamental research
|June 27, 2024
概括
人工神经网络 (ANN) 代表了发动机模拟的复杂碳化合物化学. 这种方法显著降低了节能和减少排放的计算成本.
科学领域:
- 燃烧科学是一种科学.
- 计算流体动力学 计算流体动力学
- 人工智能的人工智能是人工智能.
背景情况:
- 全球变暖需要节能和减少发动机燃烧器的排放.
- 高保真计算流体动力学 (CFD) 对于发动机设计至关重要,但受到复杂的碳化合物化学的限制.
- 燃烧的预测建模需要精确地表示复杂的化学动力学.
研究的目的:
- 在CFD模拟中开发一种有效的方法来表示复杂的碳化合物化学.
- 为了减少与燃烧建模中的详细化学动力学相关的计算负担.
- 为了使现代发动机燃烧器的基于模拟的设计优化.
主要方法:
- 使用人工神经网络 (ANN) 来表示复杂的化学动力学的新方法.
- 通过拉丁式超立方采样 (LHS) 生成的全面热化学样本数据来培训ANN.
- 采用双层ANN模型:自组织地图 (SOM) 和反向传播神经网络 (BPNN).
主要成果:
- 基于ANN的模型准确地代表了30种甲化学机制.
- 对未预混合 (DLR_A) 和部分预混合 (Flame D) 流火焰的模拟验证了ANN模型的适用性.
- 在不牺牲准确度的情况下,实现了大约两个数量级的计算成本降低.
结论:
- 拟议的基于ANN的化学动力学方法为燃烧模拟提供了显著的效率提升.
- 该方法在不同的火焰类型和复杂的碳化合物燃料中显示了广泛的适用性.
- 这种方法具有很大的潜力,可以在发动机燃烧器中推进基于模拟的设计优化.
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