Projects
Projects: Projects for Investigator |
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| Reference Number | EP/V048899/1 | |
| Title | Multi-level Reinforcement Learning for flow control | |
| Status | Completed | |
| Energy Categories | Energy Efficiency(Transport) 10%; Not Energy Related 90%; |
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| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Pure Mathematics) 40%; PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 35%; PHYSICAL SCIENCES AND MATHEMATICS (Statistics and Operational Research) 25%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Dr AH Elsheikh Sch of Energy, Geosci, Infrast & Society Heriot-Watt University |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 April 2021 | |
| End Date | 30 September 2023 | |
| Duration | 30 months | |
| Total Grant Value | £202,415 | |
| Industrial Sectors | No relevance to Underpinning Sectors | |
| Region | Scotland | |
| Programme | NC : Maths | |
| Investigators | Principal Investigator | Dr AH Elsheikh , Sch of Energy, Geosci, Infrast & Society, Heriot-Watt University (100.000%) |
| Web Site | ||
| Objectives | ||
| Abstract | 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 | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 05/10/21 | |
