PhD Studentship: LiMET: CFD-LiDAR Methane EsTimation. Department of Engineering, UQ-Exeter Institute PhD Studentship (Funded) for January 2027 Entry

Project Description

Methane is a potent greenhouse gas with a global warming potential significantly greater than carbon dioxide over short time horizons. Accurate quantification of methane emissions from diffuse and complex sources—such as energy infrastructure, landfills, and urban environments—remains a major scientific and regulatory challenge. This is particularly important in environmental impact assessment for primary extractive industries; disused boreholes particularly from hydrocarbon prospecting are a major and unquantified source of methane emissions, as are the various processing plants involved in refining hydrocarbons. Emerging optical sensing technologies, including quantum gas LiDAR, offer the ability to detect methane concentrations remotely with unprecedented sensitivity and spatial coverage. However, translating such measurements into robust emission fluxes remains difficult due to atmospheric transport effects, complex flow environments, and significant uncertainty.  

This PhD project will develop an integrated framework combining computational fluid dynamics (CFD) with quantum gas LiDAR measurements to improve the quantification of methane emissions. The central research question is: how can physics-based flow modelling be coupled with advanced optical sensing to infer methane emission rates accurately and with quantified uncertainty? 

The project will pursue three core objectives. First, it will develop high-fidelity CFD models of methane dispersion in representative environments, including idealised test cases and realistic geometries relevant to energy and environmental monitoring. These models will resolve the interaction between atmospheric flow, turbulence, and gas transport, providing a physically grounded description of plume evolution. Second, the project will integrate CFD predictions with quantum gas LiDAR measurements, using simulated and experimental data to relate observed concentration fields to underlying emission sources. This will include the development of inversion or data-assimilation approaches that exploit the totality of data from both measurement and modelling. Third, the project will quantify uncertainty arising from flow variability, measurement noise, and model assumptions, providing confidence bounds on inferred emission rates.  

Methodologically, the research will combine CFD simulations,  surrogate modelling, and inverse techniques to enable efficient interpretation of LiDAR data. The project will make use of high-performance computing facilities to support large-scale simulations and ensemble-based uncertainty quantification. A key innovation of the project is the use of LiDAR-derived spatial information to automate the construction of CFD simulation domains. Drawing on perception pipelines widely used in LiDAR-based autonomous systems such as self-driving cars, raw LiDAR data can be processed to identify and segment relevant objects (e.g. buildings, infrastructure, terrain) and convert them into simplified geometric representations suitable for CFD. This approach removes the need for manual geometry definition, enabling rapid and objective generation of flow domains directly from measurement data.  

The expected deliverables include: 

• validated CFD models of methane dispersion tailored to LiDAR-based sensing
• a coupled modelling–measurement framework for emission rate inference; and
• quantitative assessment of uncertainty in methane emission estimates. 

The project will contribute new knowledge at the interface of computational fluid dynamics, atmospheric sensing, and environmental monitoring, and will provide tools directly relevant to improving methane emission inventories and mitigation strategies.

Contact

Questions about this project should be directed to Professor Gavin Tabor at G.R.Tabor@exeter.ac.uk

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