Computational fluid dynamics (CFD) modelling of thermal propulsion system concept performance

低成本运输是人类发展和脱贫的主要驱动力之一，但在内燃机(ICE)车辆中燃烧碳氢燃料会产生各种排放，导致气候变化，并对人类有害。因此，挑战是开发负担得起的动力总成技术，以促进人类发展，同时将对环境和人类健康的影响降至最低。最近的几项研究表明，对未来的车队采取混合技术的方法，包括使用混合动力汽车和内燃机汽车的替代燃料，是以稳健和可持续的方式最快地节省最多碳的方式。因此，为了实现国家和国际气候目标，必须继续研究如何改善冰盖。动力总成设计过程的关键部分是CFD模拟，它提供了快速优化性能、诊断问题和解释观察到的实验结果的机会。内燃机的CFD模型以湍流燃烧固有的复杂多物理现象为特征，跨越非常广泛的空间和时间尺度，需要根据测量数据仔细验证，以便产生可靠的预测。具体来说，本工作回答了两个问题：1.CFD结果和实验测量之间的公平比较是什么？2.CFD模型何时得到充分验证？这项工作开创了尖端机器学习技术的新应用和解释，以从复杂的流体流动数据集中提取特征。特别关注稀疏性促进的动态模式分解，这是一种最新的降维技术，能够将流动的部分与特定频率相关联。因此，定常结构可以从湍流结构中分离出来，这使得可以从实验数据中获得新的见解。特征提取能力对数量幅度的人为减少也是健壮的，这是与推进研究相关的数据集的常见问题。因此，这使得能够为CFD模型构建更公平的验证目标。研究的下一步是使用这些工具对流动中存在的重要物理现象进行更深入的调查，这将突出需要通过CFD捕捉的关键过程以及模型精度中的任何缺陷。该项目属于EPSRC的流体动力学和空气动力学研究领域，是EPSRC繁荣伙伴关系-混合动力系统卓越中心的一部分。合作伙伴包括捷豹路虎、西门子数字工业公司和巴斯大学。

Low-cost transport is one of the leading drivers for human development and routes out of poverty, but burning hydrocarbon fuels in internal combustion engine (ICE) vehicles results in various emissions which contribute to climate change and are toxic to humans. The challenge is therefore to develop affordable powertrain technology that can facilitate human development while minimising impacts on the environment and human health. Several recent studies have shown that a mixed-technology approach to the future fleet, which includes the use of hybrid-electric vehicles and alternative fuels for ICE vehicles, is the fastest way to save the most amount of carbon in a robust and sustainable fashion. Continued research into improving the ICE is therefore imperative for reaching national and international climate targets. A crucial part of the powertrain design process are CFD simulations, which provide opportunities to quickly optimise performance, diagnose problems, and provide explanations for observed experimental results. Characterised by complex multi-physics phenomena inherent in turbulent combustion, and spanning very broad space- and time-scales, CFD models of an ICE need to be carefully validated against measured data in order to produce reliable predictions. In particular, the present work answers two questions:1. What constitutes a fair comparison between CFD results and experimental measurements?2. When has a CFD model been sufficiently validated?This work pioneers a novel use and interpretation of cutting-edge machine learning techniques to extract features from complex fluid flow datasets. Particular focus is placed on sparsity-promoting dynamic mode decomposition, which is a recent dimensionality reduction technique capable of correlating sections of a flow with specific frequencies. Therefore, steady structures can be separated from turbulent structures, which allows new insights to be gained from experimental data. The feature extraction capabilities are also robust against artificial diminishing of quantity magnitudes, which is a common issue with datasets relevant to propulsion research. This therefore enables the construction of fairer validation targets for the CFD models. The next step for the research is to use these tools to conduct a more in-depth investigation into important physical phenomena present in the flows, which will highlight key processes that need to be captured by the CFD as well as highlight any deficiencies in the model accuracy.This project falls within the EPSRC 'Fluid dynamics and aerodynamics' research area, as is part of the EPSRC Prosperity Partnership - Centre of Excellence for Hybrid Propulsion Systems. Collaborators include Jaguar Land Rover, Siemens Digital Industries, and the University of Bath.