Quantifying the Effect of Kinetic Uncertainties on NO Predictions at Engine-Relevant Pressures in Premixed Methane–Air Flames

Author:

Durocher Antoine1,Bourque Gilles2,Bergthorson Jeffrey M.1

Affiliation:

1. Alternative Fuels Laboratory (AFL), Department of Mechanical Engineering, McGill University, Montréal, QC H3A 0C3, Canada

2. Combustion Senior Key Expert SiemensEnergy Canada Limited, Montréal, QC H9P 1A5, Canada; Alternative Fuels Laboratory (AFL),Department of Mechanical Engineering, McGill University, Montréal, QC H3A 0C3, Canada

Abstract

Abstract Accurate and robust thermochemical models are required to identify future low-NOx technologies that can meet the increasingly stringent emissions regulations in the gas turbine industry. These mechanisms are generally optimized and validated for specific ranges of operating conditions, which result in an abundance of models offering accurate nominal solutions over different parameter ranges. Under atmospheric conditions, and for methane combustion, relatively good agreement between models and experiments is currently observed. At engine-relevant pressures, however, a large variability in predictions is obtained as the models are often used outside their validation region. The high levels of uncertainty found in chemical kinetic rates enable such discrepancies between models, even if the reactions are within recommended rate values. This work investigates the effect of such kinetic uncertainties in NO predictions by propagating the uncertainties of 30 reactions that are both uncertain and important to NO formation, through the combustion model at engine-relevant pressures. Understanding the uncertainty sources in model predictions and their effect on emissions at these pressures is key in developing accurate thermochemical models to design future combustion chambers with any confidence. Lean adiabatic, freely propagating, laminar flames are therefore chosen to study the effect of parametric kinetic uncertainties. A nonintrusive, level 2, nested sparse-grid approach is used to obtain accurate surrogate models to quantify NO prediction intervals at various pressures. The forward analysis is carried up to 32 atm to quantify the uncertainty in emissions predictions to pressures relevant to the gas turbine community, which reveals that the NO prediction uncertainty decreases with pressure. After performing a reaction pathway analysis (RPA), this reduction is attributed to the decreasing contribution of the prompt-NO pathway to total emissions, as the peak CH concentration and the CH layer thickness decrease with pressure. In the studied lean condition, the contribution of the pressure-dependent N2O production route increases rapidly up to 10 atm before stabilizing toward engine-relevant pressures. The uncertain prediction ranges provide insight into the accuracy and precision of simulations at high pressures and warrant further research to constrain the uncertainty limits of kinetic rates to capture NO concentrations with confidence in early design phases.

Funder

Natural Sciences and Engineering Research Council of Canada

Fonds de Recherche du Québec - Nature et Technologies

Siemens Energy Canada Limited

Publisher

ASME International

Subject

Mechanical Engineering,Energy Engineering and Power Technology,Aerospace Engineering,Fuel Technology,Nuclear Energy and Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3