Optimal fuel blends for ammonia fuelled thermal propulsion systems

可再生、无碳燃料以及优化的燃烧系统最近引起了发动机研究的极大关注，以进一步减少运输过程中的标准污染物和温室气体(GHGs)的排放。虽然目前正在向电力和电化学能源系统分配大量投资，但对于电池能否在中期内提供具有成本效益的国家能源安全，仍存在重大疑问。此外，运输部门产生的温室气体排放量约占全球温室气体排放量的10%，用“零排放”替代品取代内燃机在减少全球温室气体排放方面的潜力有限。由于大约80%的有用能源，如热能、推进能和电力是通过燃烧过程产生的，由于燃料的高能量密度和存储能力，燃烧在几十年内仍将是许多交通和发电应用的技术、经济和生态上最好的解决方案。英国目前的长期排放目标是到2050年比1990年减少100%的温室气体排放。满足英国未来的碳预算，将需要应对无碳能源系统这一重大挑战。氨(NH3)被认为是最有发展前景的氢能载体之一，将在电力行业和交通运输的脱碳过程中发挥重要作用。氨是无碳的，没有直接的温室气体影响，可以用完全无碳的工艺从可再生能源合成。氨的最大优势是其高能量密度(与化石燃料相当)，这使其成为一种有效的燃料和能量存储选择。研究表明，要使氨成为内燃机的可行燃料，需要将其与其他燃料(如氢气)混合作为燃烧促进剂，因为氨的火焰速度较低，抗自动点火能力强。燃气轮机是使用氨的高效率候选者，有可能降低每千瓦时的生产成本，同时为发电和推进系统提供清洁、绿色的能源。虽然最近的研究表明，氨/氢混合燃料可以在燃气轮机中以低排放和高效率的方式高效燃烧，但它们需要在燃料组成的选择和先进喷射策略的开发方面进行优化研究，以在保持高热效率的同时达到可接受的NOx水平。拟议项目的目的是开发一种具有计算成本效益的数值工具，用于系统地对燃料混合和燃烧系统进行联合优化，并研究如何通过在氨中添加气体燃料(例如甲烷、氢气和合成气)来满足相互冲突的要求，从而使发动机能够稳定可靠地运行，提高热效率并将NOx排放降至最低。这项计算研究需要开发一种燃料混合物的简化反应机理，以进一步减少发动机相关条件下的反应数量。虽然计算流体动力学(CFD)模拟与反应机理相结合可以高精度地捕捉复杂现象，但它们的计算成本很高，因此对于优化研究来说效率不高。因此，将开发一个可靠的、全面的、化学成分简化的零维唯象模型，用于发动机燃用氨基燃料混合燃料的燃烧模拟。将开发一个与0D模型耦合的遗传算法(GA)优化模型，以同时优化燃料组成和发动机输入参数(例如，燃油喷射策略、进气条件、当量比)。最后，在加的夫大学燃气轮机研究中心(GTRC)的相关发动机实验中，将对最佳混合燃料和优化的发动机参数的性能进行实验研究。

Renewable, carbon-free fuels as well as optimised combustion systems have recently drawn a lot of attention in engine research to further reduce emissions of criteria pollutants and greenhouse gases (GHGs) in transport. Whilst significant investment is being currently allocated to electric and Electrochemical energy systems, there remains significant doubts as to whether batteries will provide cost-effective national energy security over medium-duration periods. Moreover, the transport sector produces about 10% of the world's GHG emissions and replacing combustion engines with "zero emission" alternatives has only a limited potential in reducing global GHG emissions. Since around 80% of useful energy such as heat, propulsion energy and electricity is produced via combustion processes and due to the high energy density and storability of fuels, combustion will remain for several decade the technologically, economically and ecologically best solution for many applications in transport and power generation.The UK's long-term emissions target is currently for a 100% reduction in GHG emissions by 2050 compared to 1990. Meeting future UK carbon budgets will require the grand challenge of carbon-free energy systems to be addressed. Ammonia (NH3) has been identified as one of the most promising hydrogen energy carriers, and will play an important role during the process of decarbonisation of power sector and transport. Ammonia is carbon-free, has no direct GHG effect, and can be synthesised with an entirely carbon-free process from renewable power sources. The greatest advantage of ammonia is its high energy density (comparable to that of fossil fuels), which makes it an effective fuel and energy storage option. Studies have shown that to make ammonia a viable fuel in combustion engines, it needs to be mixed with other fuels (e.g., hydrogen) as combustion promoters due to ammonia's low flame speed and high resistance to auto-ignition.Gas turbines are high-efficiency candidates for use of ammonia and have the potential to reduce the cost per kWh produced whilst providing clean, green energy for power generation and propulsion systems. Although, recent studies have shown that ammonia/hydrogen blends could be burned efficiently with low emissions and high efficiencies in gas turbines, they require optimisation study in terms of choice of fuel composition and development of advanced injection strategies to achieve acceptable NOx levels while maintaining high thermal efficiencies.The aim of the proposed project is to develop a computationally cost-effective numerical tool for Co-Optimisation of fuel blend and combustion system in a systematic way, and to examine how the conflicting requirements can be met by adding gaseous fuels (e.g., methane, hydrogen and syngas) to ammonia so that engine can be operated stably and reliably with improved thermal efficiency and minimal NOx emissions. This computational study requires development of a reduced reaction mechanism for fuel blends with the goal of further reduction in the number of reactions under engine relevant conditions. While computational fluid dynamics (CFD) simulations in combination with reaction mechanisms can capture complex phenomena with high accuracy, they have high computational cost and, therefore, are not efficient for the optimisation studies. Thus, a reliable and comprehensive 0D phenomenological model with reduced chemistry will be developed for combustion modelling of the engine fuelled with ammonia-based fuel blends. A genetic algorithm (GA) optimisation model coupled to the 0D model will be developed to simultaneously optimise fuel composition and engine input parameters(e.g., fuel injection strategy, inlet conditions, equivalence ratio). Finally, the performance of the optimal blend and optimised engine parameters will be experimentally studied in the relevant engine experiments at Gas Turbine Research Centre (GTRC), in Cardiff University.