Intelligent modelling in Computational Rheology: introduction of Machine Learning approaches and exploitation

计算流变学中的智能建模：机器学习方法的介绍和开发

• 批准号: 2745296
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2745296
• 关键词: Intelligent; modelling; Computational; Rheology; introduction

In recent years, a vast array of industrial disciplines dealing with complex fluid systems have become eager for numerical methods and approaches which can provide quantitative numerical results to enhance industrial planning and to minimise the expenses resulting from trial-and-error procedures and attempts to gather empirical data. Companies in various engineering fields from oil and gas and bioengineering, up to food production, either utilise or create products possessing complex rheological characteristics such as viscoelasticity, and their behaviours deviate significantly from the simple and well-understood characteristics of Newtonian fluids such as water. Their peculiar flow behaviours arise from fundamentally different microstructures, resulting in complex mechanical properties.When performing numerical simulations to approximate real fluid flows it is important to quantify precisely the underlying physical mechanisms for predicting as accurate as possible the real-world behaviour. Achieving a full resolution of the resulting dynamics often requires very expensive computational procedures that demand time. An additional complexity is induced when the problems under investigation are in the field of Computational Rheology (CR) where non-Newtonian viscoelastic fluids are considered. In contrast to the "simple" fluids that obey Newton's law of viscosity, the models employed for investigating these complex fluids require additional equations that need to be solved and exhibit strongly non-linear responses, which enormously increase the computational demands. The majority of models employed in CR originate from kinetic theories and series of closures have been proposed for deriving closed-form equivalents for use in Computational Fluid Dynamics (CFD). Furthermore, to approximate experimental responses, most of these models introduce physical variables which are typically treated as constants and are responsible for controlling the rheological properties of the simulated fluids. All the above, apply drastic simplifications to the rich dynamics that are imposed by the complex fluid microstructure and thus, when are employed in CFD they cannot produce accurate results from a quantitative perspective. Attempts made to increase the accuracy of the closed-form models, led to a greater complexity, where new, non-linear, constitutive relationships are proposed which introduce additional parameters that need to be controlled. Results are encouraging in terms of accuracy, where for a good range of parameters the experimental measurements are more closely approximated, but still are far from real observations and responses.This PhD project is aiming to develop further novel numerical models and procedures, attempting to increase the accuracy and predictivity of CFD calculations when investigating flows of non-Newtonian fluid systems and provide new results and insights of behaviours met in complex fluid flows. Modifications will be applied to the promising closed form of the Adaptive Length Scale model and Machine Learning approaches will be incorporated in CR which are the future in CFD applications and numerical modelling in fluid mechanics, aiming to shape new routes in fluid flow simulations.

近年来，处理复杂流体系统的大量工业学科已经变得渴望数值方法和途径，这些方法和途径可以提供定量的数值结果，以增强工业规划，并最大限度地减少试错程序和尝试收集经验数据所产生的费用。从石油天然气和生物工程到食品生产的各个工程领域的公司，要么利用或创造具有复杂流变特性（如粘弹性）的产品，其行为明显偏离牛顿流体（如水）的简单和众所周知的特性。它们独特的流动行为源于根本不同的微观结构，从而产生复杂的力学性能。当执行数值模拟以逼近真实的流体流动时，精确量化潜在的物理机制对于尽可能准确地预测真实世界的行为非常重要。实现所得到的动态的全分辨率通常需要非常昂贵的计算程序，需要时间。一个额外的复杂性是诱导时，正在调查的问题是在计算流变学（CR）领域，其中考虑非牛顿粘弹性流体。与遵循牛顿粘度定律的“简单”流体相反，用于研究这些复杂流体的模型需要求解额外的方程，并表现出强烈的非线性响应，这极大地增加了计算需求。CR中采用的大多数模型都来自动力学理论，并且已经提出了一系列闭合，用于推导计算流体动力学（CFD）中使用的闭合形式等效物。此外，为了近似实验响应，大多数这些模型引入物理变量，这些物理变量通常被视为常数，并负责控制模拟流体的流变特性。所有上述内容都对复杂流体微观结构施加的丰富动力学进行了大幅简化，因此，当在CFD中使用时，它们无法从定量角度产生准确的结果。试图增加的封闭形式的模型的准确性，导致了更大的复杂性，其中提出了新的，非线性的，本构关系，引入额外的参数，需要控制。结果在准确性方面令人鼓舞，在很大的参数范围内，实验测量值更接近近似，但仍然与真实的观察和响应相去甚远。该博士项目旨在开发进一步的新型数值模型和程序，试图提高计算流体力学计算的准确性和预测性时，调查流动的非-牛顿流体系统，并提供了新的结果和复杂的流体流动中遇到的行为的见解。修改将应用于自适应长度尺度模型的有前途的封闭形式，机器学习方法将被纳入CR中，这是CFD应用和流体力学数值建模的未来，旨在塑造流体流动模拟的新路线。