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，通过在燃料中掺杂氧化石墨烯和石墨烯量子点，减少与航空航天推进相关的燃气涡轮机模型燃烧室的排放。虽然这种碳纳米材料以前已被用于理解/改善液滴燃烧的特性，但没有研究它们用于减少排放的用途。首先，将实施各种合成程序以产生和表征掺杂氧化石墨烯和量子点在喷气A、柴油和生物柴油中的稳定悬浮液。然后，这些燃料的燃烧化学将使用填充床燃烧室进行研究，该填充床燃烧室将使用温度计和气体分析仪进行监测。其次是更多的工程相关的测试，涉及粒子图像测速和气体涡轮机模型燃烧室中的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沿着纳米材料合成程序和表征数据将用于开发新的机器学习算法，允许预测气体涡轮机模型燃烧室的排放。石墨烯基纳米材料掺杂用于减少排放的实用性允许第一个工业合作伙伴（ZEN石墨烯解决方案）寻求销售掺杂燃料的潜力。机器学习算法将帮助第二个工业合作伙伴（Machinery Analytics）开发软件，以监控工程设备的运行状况。除了工业合作伙伴，拟议的项目涉及一名硕士生，2名博士后和三名教授（来自UBC的Okanagan和温哥华校区）。所有教授将确保人员的公平和包容性的招聘，并在所有通信，不列颠哥伦比亚省的未割让领土，其中拟议的研究将进行承认。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.