Multiscale Simulation of Rarefied Gas Flow for Engineering Design

用于工程设计的稀薄气体流动的多尺度模拟

• 批准号: EP/V012002/1
• 负责人: Matthew Borg
• 金额: $ 57.24万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV012002%2F1
• 关键词: Multiscale; Simulation; Rarefied; Gas; Flow

Microprocessors chips are in most devices we interact with in our daily lives. From mobile devices, TVs, cars, fridges, petrol station pumps, servers that power the web and social media infrastructure --- the list is endless. Microprocessors have been doubling in power roughly every two years following Moore's law, which has been enabled by making the features of the chips smaller, fitting more transistors per unit area and driving the entire consumer electronics market worth more than £1 trillion per year. In order to continue to satisfy the industrial and societal demand that drives Moore's law, there are some fluid dynamics modelling challenges that we need to overcome. The next-generation of photolithography machines that need to manufacture smaller, faster microprocessor chips and the new devices required to supercool the high-performance chips during operation can be enabled by understanding and predicting accurately how gases behave at the micro/nanoscales, or in vacuum-like conditions. In these multiscale flow problems, the fluid dynamics is often unintuitive and all equations we normally turn to for modelling and designing engineering flow problems, such as flow around aircraft and ships using Navier-Stokes equations, are no longer valid here, because the gas is no longer in local thermodynamic equilibrium, on which these classical equations are formulated. The direct simulation Monte Carlo (DSMC), is the state-of-the-art software for modelling these non-equilibrium gas flows. It is a stochastic particle method with large numerical stability and can resolve the molecular nature of gases in three dimensional geometries. However, because it is a particle method, it requires a voracious computational cost to produce engineering solutions of scales that matter to industry. DSMC also performs poorly if those flows are at low speed, due to the inherent thermal noise in the particles blocking the measurable signals.In this project, we propose developing a new multiscale method, one which combines DSMC with computationally cheaper models such as those used in Computational Fluid Dynamics (CFD). We will produce a step change in simulation efficiency and accuracy by connecting DSMC and CFD solvers using surrogate modelling and Bayesian inference.With strong backing from our industrial partners, we will turn the outcome of this project into a free open-source computational solver released in the UK's OpenFOAM software that is validated with experimental data. The industrial focus of this project will be on processor-chip manufacturing, chip thermal management and electrospray technologies, but the underlying method is general to new directions in other research and industrial areas.

微处理器芯片存在于我们日常生活中与之交互的大多数设备中。从移动设备、电视、汽车、冰箱、加油站加油泵、为网络供电的服务器和社交媒体基础设施-清单上有无数个。根据摩尔定律，微处理器的处理能力大约每两年翻一番，这是通过使芯片的特征变得更小，在单位面积上安装更多晶体管，并推动整个消费电子市场每年价值超过1万亿GB来实现的。为了继续满足推动摩尔定律的工业和社会需求，我们需要克服一些流体动力学建模方面的挑战。下一代光刻机需要制造更小、更快的微处理器芯片，以及在运行期间过冷高性能芯片所需的新设备，可以通过准确了解和预测气体在微米/纳米级或类似真空的条件下的行为来实现。在这些多尺度流动问题中，流体动力学往往是不直观的，我们通常用来模拟和设计工程流动问题的所有方程，如使用Navier-Stokes方程的飞机和船舶周围的流动，在这里不再有效，因为气体不再处于局部热力学平衡，这些经典方程是在局部热力学平衡的基础上建立的。直接模拟蒙特卡罗(DSMC)是模拟这些非平衡气体流动的最先进的软件。它是一种具有很大数值稳定性的随机粒子方法，可以在三维几何空间中求解气体的分子性质。然而，由于它是一种粒子方法，它需要贪婪的计算成本来产生对工业重要的大规模工程解决方案。在这个项目中，我们建议开发一种新的多尺度方法，将其与计算流体动力学(CFD)中使用的计算成本较低的模型相结合。我们将通过使用代理建模和贝叶斯推理连接DSMC和CFD求解器，使模拟效率和精度发生阶段性变化。在我们工业合作伙伴的大力支持下，我们将把这个项目的结果转化为一个免费的开源计算求解器，在英国的OpenFOAM软件中发布，并通过实验数据进行验证。该项目的工业重点将是处理器芯片制造、芯片热管理和电喷雾技术，但其基本方法一般适用于其他研究和工业领域的新方向。

项目成果

Inertio-thermal vapour bubble growth

惯性热蒸汽气泡生长

• DOI: 10.1017/jfm.2022.734
• 发表时间: 2022
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Sullivan P

The role of surface wettability on the growth of vapour bubbles

• DOI: 10.1016/j.ijheatmasstransfer.2023.124657
• 发表时间: 2023-12
• 期刊: International Journal of Heat and Mass Transfer
• 影响因子: 5.2
• 作者: Patrick Sullivan;Duncan Dockar;Ryan Enright;M. Borg;R. Pillai

Impact of surface physisorption on gas scattering dynamics

表面物理吸附对气体散射动力学的影响

• DOI: 10.1017/jfm.2023.496
• 发表时间: 2023
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Chen Y

Methane scattering on porous kerogen surfaces and its impact on mesopore transport in shale

• DOI: 10.1016/j.fuel.2022.123259
• 发表时间: 2022-05
• 期刊: Fuel
• 影响因子: 7.4
• 作者: Yichong Chen;Jun Li;S. Datta;Stephanie Y. Docherty;L. Gibelli;M. Borg

Self-diffusivity of dense confined fluids

• DOI: 10.1063/5.0059712
• 发表时间: 2021-08-01
• 期刊: PHYSICS OF FLUIDS
• 影响因子: 4.6
• 作者: Corral-Casas, Carlos;Gibelli, Livio;Zhang, Yonghao
• 通讯作者: Zhang, Yonghao