Construction of Digital Fuel Center and Application of Genetic Algorithms

WU Chuting; WANG Hongxin; Oskar Haidn

doi:10.12405/j.issn.2097-1486.2023.02.002

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Construction of Digital Fuel Center and Application of Genetic Algorithms

更新时间：2024-03-29

- Construction of Digital Fuel Center and Application of Genetic Algorithms
- Emerging Science and Technology(Nat.Sci.Ed.) Vol. 2, Issue 2, Pages: 126-134(2023)
- 作者机构：
  
  1.国家开放大学，北京 100039
  2.慕尼黑工业大学，慕尼黑德国 85748
- 作者简介：
- 基金信息：
- DOI：10.12405/j.issn.2097-1486.2023.02.002
  CLC： TM621
- Published：25 June 2023，
  
  Received：02 April 2023，
  
  Revised：05 May 2023，
扫描看全文
吴楚婷,王洪信,Oskar Haidn.数字化燃料中心建设及遗传算法的应用[J].新兴科学和技术趋势,2023,2(2):126-134.

WU Chuting,WANG Hongxin,Haidn Oskar.Construction of Digital Fuel Center and Application of Genetic Algorithms[J].Emerging Science and Technology,2023,2(2):126-134.
吴楚婷,王洪信,Oskar Haidn.数字化燃料中心建设及遗传算法的应用[J].新兴科学和技术趋势,2023,2(2):126-134. DOI： 10.12405/j.issn.2097-1486.2023.02.002.

WU Chuting,WANG Hongxin,Haidn Oskar.Construction of Digital Fuel Center and Application of Genetic Algorithms[J].Emerging Science and Technology,2023,2(2):126-134. DOI： 10.12405/j.issn.2097-1486.2023.02.002.

摘要

随着计算机技术的发展，燃烧科学和工程正朝着数字化、自动化和高效化方向发展。为了紧跟科学进展和需求，慕尼黑工业大学正在建设数字化燃料中心，以用于可再生燃料/能源、新型高效环保燃料和燃烧机械、航空航天推进技术的研究和开发。本文以甲醇燃烧化学动力学模型的优化为例，展示了实验和模型参数的数字化和人工智能优化算法的应用。本研究基于遗传算法建立了对燃烧化学反应动力学的自动优化优化工具，并将其用于甲醇燃烧的化学动力学模型优化。已优化了包括40个重要反应的反应速率常数和第三体效率以及反应模型的选定物种在内的54个参数。用于模型验证的实验数据包括激波管测得的点火延迟时间和在喷射搅拌反应器和平推流反应器测得的浓度分布。甲醇氧化的数值仿真使用开源工具Cantera进行，对点火延迟时间和浓度的模拟数据使用遗传算法进行优化。与初始模型的计算结果相比，优化模型对实验目标的预测能力得到了显著的提高，本研究所开发的遗传算法工具已被证明能够用于优化详细化学动力学模型。

Abstract

With the development of computer technology， the field of combustion science and engineering is advancing towards digitization， automation， and efficiency. In order to keep pace with scientific advancements and demands， the Technical University of Munich is establishing a digital fuel center for research and development in renewable fuels/energy， novel high-efficiency environmentally friendly fuels， combustion machinery， and aerospace propulsion technology. This article takes the optimization of the methanol combustion chemical kinetics model as an example to demonstrate the application of digitalization and artificial intelligence optimization algorithms for experimental and model parameters. This work presents an automatic optimization tool using a genetic algorithm for the chemical kinetic model of methanol combustion. 54 parameters including reaction rate constants and third body efficiencies for 40 important reactions and selected species of the reaction model have been optimized. Experimental data for model validation include： ignition delay times measured in shock tubes and concentration profiles measured in plug flow reactors. Homogeneous modeling of the methanol oxidation was conducted with the open-source tool Cantera in Python and the simulated ignition delay times and concentrations were optimized using the genetic algorithm programed in Python. The prediction ability of the optimized model has been improved compared with the initial model based on the estimated reaction rate constants. The developed genetic algorithm tool was proved to be able to optimize the detailed chemical kinetic model.

关键词

燃烧化学动力学遗传算法甲醇

Keywords

MethanolCombustionChemical kinetic modelGenetic algorithm

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