Carbonaceous nanomaterials for improving combustion emissions of a gas turbine model combustor

拟议项目的最终目标是开发知识和技术，允许通过燃料掺杂氧化石墨烯和石墨烯量子点来减少航空航天推进相关燃气轮机模型燃烧器的排放。虽然这些碳质纳米材料已经被用于了解/改善液滴燃烧的特性，但它们在减少排放方面的利用尚未得到研究。首先，将实施各种合成程序，以在喷气机a、柴油和生物柴油中生成和表征掺杂氧化石墨烯和量子点的稳定悬浮液。然后，将使用填充床燃烧器研究这些燃料的燃烧化学性质，并使用温度计和气体分析仪进行监测。随后进行了更多的工程相关测试，包括颗粒图像测速和燃气轮机模型燃烧室中掺杂燃料的喷雾反应流的排放测量。最后，收集的排放和测速数据以及纳米材料合成过程和表征数据将用于开发新的机器学习算法，以预测燃气轮机模型燃烧室的排放。石墨烯基纳米材料掺杂在减少排放方面的效用，使第一个工业合作伙伴（ZEN Graphene Solutions）能够追求掺杂燃料的营销潜力。机器学习算法将帮助第二个工业合作伙伴（Machinery Analytics）开发软件，以监测工程设备的运行状况。除工业合作伙伴外，拟议项目还涉及一名硕士研究生，两名博士后和三名教授（来自UBC的奥肯那根和温哥华校区）。所有教授将确保公平和包容的人员招聘，并在所有通信中，承认不列颠哥伦比亚省的未割让领土，其中拟议的研究将进行。拟议预算总额为331 500美元。工业合作伙伴将以员工工资、原材料以及软件和云计算空间的形式提供111,000美元的实物捐助。

The ultimate goal of the proposed project is to develop knowledge and technology that allows for reducing emissions from an aerospace propulsion-related gas turbine model combustor by fuel doping with graphene oxide and graphene quantum dots. Although such carbonaceous nanomaterials have been previously used for understanding/improving characteristics of droplet combustion, their utilization for decreasing emissions is not investigated. First, a variety of synthesis procedures will be implemented to generate and characterize stable suspensions of the doped graphene oxide and quantum dots in jet A, diesel, and biodiesel. Then, the combustion chemistry of these fuels will be studied using a packed-bed combustor which will be monitored using thermometers and a gas analyzer. This is followed by more engineering relevant tests that involve Particle Image Velocimetry and emission measurements of spray reacting flow of the doped fuel in a gas turbine model combustor. Finally, the collected emission and velocimetry data along with the nanomaterials synthesis procedures and characterization data will be used to develop novel machine learning algorithms that allow for predicting emissions from the gas turbine model combustor. The utility of the doping with the graphene-based nanomaterials for reducing emissions allows the first industrial partner (ZEN Graphene Solutions) to pursue potentials for marketing the doped fuel. The machine learning algorithm will help the development of software by the second industrial partner (Machinery Analytics) that allows monitoring operation health of engineering equipment. In addition to the industrial partners, the proposed project involves one Master's student, 2 postdocs, and three professors (from both Okanagan and Vancouver campuses of UBC). All professors will ensure fair and inclusive recruitment of the personnel, and in all communications, the unceded territories of British Columbia at which the proposed research is to be conducted will be acknowledged. The total proposed budget is $331,500. The industrial partners will contribute $111,000 in-kind contributions in the form of the salaries of staff, raw material, as well as software and cloud computation space.