用机器学习与模拟结合，提升生物质气化预测精度与效率
A novel biomass fluidized bed gasification model coupled with machine learning and CFD simulation
- 融合机器学习与计算流体动力学构建耦合模型
- 基于实验与高保真模拟数据训练反应动力学代理模型
- 实现反应速率与组分演化的实时更新，适合能源转化研究者
提出一种基于机器学习与计算流体动力学（CFD）的生物质流化床气化耦合模型，旨在提升复杂热化学反应过程的预测精度与计算效率。通过整合实验数据与高保真模拟结果，构建高质量数据集，训练用于描述反应动力学特性的代理模型，并嵌入到CFD框架中，实现反应速率与组分演化的实时更新。
原文摘要 · Abstract (English)
A coupling model of biomass fluidized bed gasification based on machine learning and computational fluid dynamics is proposed to improve the prediction accuracy and computational efficiency of complex thermochemical reaction process. By constructing a high-quality data set based on experimental data and high fidelity simulation results, the agent model used to describe the characteristics of reaction kinetics was trained and embedded into the computational fluid dynamics (CFD) framework to realize the real-time update of reaction rate and composition evolution.
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