Characterisation of Knock in Direct Injection Hydrogen Internal Combustion Engines

内燃机(ICE)一直是为运输、采矿和建筑行业的机械提供动力的“银弹”。然而，随着现有和即将出台的二氧化碳排放法规的出台，该行业正在探索将氢气作为碳中性替代燃料的可行性--著名的例子包括宝马、丰田、雅马哈和JCB。目前氢燃烧研究的重点是通过使用直接喷射燃料策略来实现高刹车热效率(大于或等于45%)，同时保持较低的NOx排放水平。这提高了充气效率，并允许与港口燃油喷射相比，更精确地控制异常燃烧事件。然而，诸如燃烧不规则性、专用于氢气操作的涡轮增压器设计、换热和喷射策略优化等主题仍未得到充分研究。这项研究项目的主要目标是通过建立计算成本低廉的爆震模型并与实验数据进行验证，来改进一维模拟环境-GT-Power中最先进的氢冰爆震模型。目前，GT开发了一个快速运行的商用爆震模型，该模型基于由Keromnes等人开发的动力学机制用氢气点火延迟时间训练的神经网络。(2013年)。该型号可提供约与H2/02/NOx动力学机制相比，模拟速度快16倍。然而，它受到几个限制-(1)它只基于燃烧室压力、温度和再循环的废气，但没有考虑到工作流体未燃烧区域中存在不同的爆震诱导物种，如NOx分子和自由基；(2)它基于现已过时的动力学机制，目前最先进的机制包括Konnov(2019)、Polimi(2020)、Kovacs等。(2020)和Sun等人。(2022)；(3)与以前为汽油机开发的爆震模型相比，该模型不能用基于Arrhenius方程的数学方程来量化。因此，目前这个项目的目标是提供使用当前最先进的动力学机制创建的新爆震模型。这些模型将基于神经网络，类似于GT专有的爆震模型，但也考虑了爆震引起的一氧化二氮的存在，以及Arrhenius方程，该方程在以前特定于汽油的模型的基础上进行扩展，包括再循环废气和NOx浓度的术语。已经确定了以下行动：1.对敲入冰的主题进行文献综述，重点是氢冰和动力学机制的应用。评估创建基于神经网络的模型和基于Arrhenius方程的模型的途径。3.开发基于神经网络的模型，并利用详细的动力学机制探索其预测精度，并使用经验数据进行验证。4.开发了基于Arrhenius方程的爆震模型，并与详细的化学动力学和实验数据进行了比较。本项目的潜在好处是显著改进了一维内燃机模拟的计算时间，同时保持了最先进的爆震模型的爆震预测精度。这最终将导致发动机热效率的提高和校准时间的减少，因为在发动机测试之前可以更好地预测爆震限制的火花提前。

The internal combustion engine (ICE) has been the 'silver bullet' in powering machinery for the transportation, mining and construction industries. However, with existing and upcoming regulations on CO2 emissions, the industry is exploring the viability of fuelling ICEs with hydrogen as a carbon neutral alternative - notable examples include BMW, Toyota, Yamaha and JCB. Current hydrogen combustion research focuses on achieving high brake thermal efficiency (greater or equal 45%) while keeping NOx emissions levels low by utilising direct injection fuelling strategies. This results in increased volumetric efficiency and allows for a more precise control of abnormal combustion events compared to port fuel injection. Nevertheless, topics such as combustion irregularities, turbocharger design for hydrogen-specific operation, heat transfer and injection strategy optimisation remain underresearched. The main goal of this research project is to improve the state-of-the-art knock modelling for hydrogen ICEs in a 1D simulation environment - namely GT-Power, by creating computationally inexpensive knock models and validating them against experimental data. At present, there is a single fast-running commercially available knock model developed by GT, which is based on a neural network trained with hydrogen ignition delay times using the kinetics mechanism developed by Keromnes et al. (2013). This model offers approx. 16 times quicker simulations compared to a H2/02/NOx kinetics mechanism. However, it suffers from several limitations - (1) it is based only on the chamber pressure, temperature and recirculated exhaust gases, but does not account for the presence of different knock-inducing species such as NOx molecules and radicals in the unburned zone of the working fluid; (2) It is based on a now-outdated kinetics mechanism, current state-of-the-art mechanisms include Konnov (2019), Polimi (2020), Kovacs et al. (2020) and Sun et al. (2022) ; (3) in comparison to previous knock models developed for gasoline engines, this model cannot be quantified using mathematical equations based on the Arrhenius equation. Therefore, at present this project aims to deliver new knock models created using the current state-of-the-art kinetics mechanisms. These will be based on neural networks, similar to GT's proprietary knock model, but also accounting for the presence of knock-inducing nitrous oxides, as well as on the Arrhenius equation, which expands on previous gasoline-specific models and includes terms for the recirculated exhaust gas and NOx concentrations. The following actions have been identified: 1. Conduct a literature review on the topic of knock in ICEs with a focus on hydrogen ICEs and applications of kinetics mechanisms.2. Assess the pathways to creating neural network-based models and Arrhenius equation-based models. 3. Develop neural network-based models and explore their predictive accuracy against simulations using the detailed kinetics mechanism and validate them using empirical data. 4. Develop Arrhenius equation-based knock models and compare against detailed chemical kinetics and experimental data.The potential benefits of this project are significant improvements to the computational time of 1D ICE simulations, while retaining the knock prediction accuracy of the state-of-the-art knock models. This will ultimately lead to increased engine thermal efficiency and reduced calibration times, as the knock-limited spark advance can be better predicted prior to engine testing.