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

热推进系统概念性能的计算流体动力学 (CFD) 建模

• 批准号: 2773138
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2773138
• 关键词: Computational; fluid; dynamics; CFD; modelling

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.

低成本运输是人类发展和摆脱贫困的主要驱动力之一，但内燃机车辆燃烧碳氢化合物燃料会导致各种排放，导致气候变化，对人类有毒。因此，我们面临的挑战是开发负担得起的动力总成技术，以促进人类发展，同时最大限度地减少对环境和人类健康的影响。最近的几项研究表明，未来车队的混合技术方法，包括使用混合动力电动汽车和ICE车辆的替代燃料，是以稳健和可持续的方式节省最多碳的最快方法。因此，为了实现国家和国际气候目标，必须继续研究改进ICE。动力系统设计过程的一个关键部分是CFD模拟，它提供了快速优化性能、诊断问题并为观察到的实验结果提供解释的机会。内燃机的CFD模型具有湍流燃烧中固有的复杂多物理现象的特征，并且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。特别是，目前的工作回答了两个问题：1。什么是CFD结果和实验测量之间的公平比较？2.什么时候CFD模型得到了充分验证？这项工作开创了尖端机器学习技术的新用途和解释，以从复杂的流体流动数据集中提取特征。特别关注的是稀疏促进动态模式分解，这是一个最近的降维技术，能够相关的特定频率的流段。因此，稳定结构可以从湍流结构中分离出来，这使得从实验数据中获得新的见解。特征提取能力也是强大的，对人工减少的数量大小，这是一个共同的问题与数据集相关的推进研究。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捕获的关键过程，并突出模型精度中的任何缺陷。该项目福尔斯EPSRC的“流体动力学和空气动力学”研究领域，作为EPSRC繁荣伙伴关系的一部分-混合推进系统卓越中心。合作者包括捷豹路虎，西门子数字工业和巴斯大学。