Multi-level Reinforcement Learning for flow control

流量控制的多级强化学习

• 批准号: EP/V048899/1
• 负责人: Ahmed Elsheikh
• 金额: $ 25.79万
• 依托单位: Heriot-Watt University
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV048899%2F1
• 关键词: Multi; level; Reinforcement; Learning; flow

Flow control is the process of targeted manipulation of fluid flow fields to accomplish a prescribed objective (e.g. reduce drag). Flow control uses information from the flow (provided by sensors) to adapt to incoming perturbations and adjust to changing flow conditions. General flow control is a largely unsolved mathematical problem appearing in many industries, including automotive, aerospace and environmental subsurface flow problems. The missing ingredient for turning flow control into a practical tool is the development of general flow control algorithms that can handle the following: (a) uncertainties in the system perturbations (e.g. the speed and direction of the perturbation), (b) uncertainties in the flow model parameters, (c) sparsity of the observations (i.e. partial and noisy observations) (d) modelling errors due to discretization and parameter upscaling.In this proposal, Reinforcement Learning (RL) algorithms will be utilized to learn general flow control polices using reliable simulated flow environments. From an application point of view, the developed mathematical techniques address flow control in two applications: (a) increasing energy efficiency in transportation trucks by flow control of incompressible Navier-Stokes flow past an obstacle and (b) safe and efficient storage of anthropogenic carbon dioxide (CO2) in deep geological formations using flow control in a Darcy-type subsurface flow. For the first application, road freight transportation accounts for approximately 5% of the UK's carbon footprint and flow control to reduce the aerodynamic drag could significantly improve the fuel efficiency, for example a 15% reduction in drag is equivalent to about 5% in fuel savings. For the CO2 storage application, the produced CO2 by human activities, for example from a power stations or an energy-intensive industries, could be injected into deep saline aquifers as a possible mitigation strategy to reduce anthropogenic emissions of carbon dioxide into the atmosphere. The control of injection strategies in the subsurface storage sites, given the inherent uncertainties in the subsurface properties, would minimize the risk of leakage while maximising the storage capacity.

流量控制是对流体流场有针对性操纵以实现规定目标的过程（例如减少阻力）。流控制使用流量（由传感器提供）的信息适应传入的扰动并适应不断变化的流量条件。一般流量控制是许多行业中出现的一个未解决的数学问题，包括汽车，航空航天和环境地下流动问题。 The missing ingredient for turning flow control into a practical tool is the development of general flow control algorithms that can handle the following: (a) uncertainties in the system perturbations (e.g. the speed and direction of the perturbation), (b) uncertainties in the flow model parameters, (c) sparsity of the observations (i.e. partial and noisy observations) (d) modelling errors due to discretization and parameter在此提案中，将利用可靠的模拟流环境来学习通用流控制策略。从应用的角度来看，开发的数学技术解决了两个应用中的流量控制：（a）通过不可压缩的Navier-Stokes的流量控制运输卡车的能源效率超过了障碍物，并且（b）在Darcy-darcy-sispurface flow Flow Flow Flow Flow Flow Flow Controntion中，人为二氧化碳（CO2）安全有效地存储人为的人为碳（CO2）。对于第一个应用程序，道路运输运输约占英国碳足迹和流量控制以减少空气动力阻力的5％，可以显着提高燃油效率，例如，降低15％的阻力相当于节省燃料的5％。对于CO2存储应用，可以将人类活动产生的CO2（例如电站或能源密集型行业）注入深盐水含水层，是一种可能减少二氧化碳碳化物排放到大气中的缓解策略。鉴于地下属性中固有的不确定性，对地下存储站点中的注射策略的控制将最大程度地降低泄漏的风险，同时最大程度地提高存储容量。

项目成果

Stochastic optimal well control in subsurface reservoirs using reinforcement learning

• DOI: 10.1016/j.engappai.2022.105106
• 发表时间: 2022-07
• 期刊: ArXiv
• 作者: A. Dixit;A. Elsheikh

Robust Well-Production Control Using Surrogate Assisted Reinforcement Learning

使用替代辅助强化学习的鲁棒油井生产控制

• DOI: 10.3997/2214-4609.202244101
• 发表时间: 2022
• 作者: Dixit A

Robust Optimal Well Control using an Adaptive Multigrid Reinforcement Learning Framework

• DOI: 10.1007/s11004-022-10033-x
• 发表时间: 2022-11-04
• 期刊: MATHEMATICAL GEOSCIENCES
• 影响因子: 2.6
• 作者: Dixit, Atish;Elsheikh, Ahmed H.
• 通讯作者: Elsheikh, Ahmed H.

Gym-preCICE: Reinforcement learning environments for active flow control

Gym-preCICE：用于主动流量控制的强化学习环境

• DOI: 10.1016/j.softx.2023.101446
• 发表时间: 2023
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: Shams M