October 2025 Entry

TITLE: Model and atmospheric sensors' fusion for optimal aviation climate impact reduction with scientific machine learning

DEPARTMENT(S): Department of Aeronautics

SUPERVISORS:  Prof. Luca Magri and Dr. Sebastian Eastham

Aviation has significant non-CO2 climate impacts due in part to the formation of contrails. Since contrails only form in specific conditions it should be possible to reroute to avoid their formation, but it is not yet possible to accurately predict those conditions. Although contrail sensors are being developed, these will still have limited accuracy and must be interpreted as part of an imperfect forecasting system.

This project aims to understand the opportunities and limitations of novel sensors in an optimization framework. The goal is to develop an efficient, robust optimization algorithm with scientific machine learning, which can estimate the effectiveness of different re-routing strategies given uncertainties in sensor and model accuracy. Simulators of sensors will need to be created, including a parameterization of accuracy as a function of (e.g.) time of day, location, altitude, and temperature. These will then need to be integrated into a forecasting model which can assimilate the (artificial) sensor data and provide forecasts of contrail-forming regions at different levels of confidence. An aircraft performance model will be needed to predict contrail formation impacts. Finally, different routing strategies will be optimized to find the maximum benefit given different levels of sensor accuracy and given different scales of sensor deployment.

TITLE: Quasi-linear approximation of the aircraft wake

DEPARTMENT(S): Department of Aeronautics

SUPERVISORS:  Prof. Yongyun Hwang and Dr. Kostas Steiros

The formation of contrail cirrus in aircraft exhausts is currently understood to contribute as much to climate change as the CO2 emitted from fossil fuel combustion. Unfortunately, the semi-empirical global models used in contrail prediction are with large uncertainties, partly because they do not capture the complex flow instabilities that appear in the turbulent aircraft wake, whose physics largely determine the expansion and fragmentation of the contrail plume. To model such instabilities, one currently needs to resort to high-fidelity simulations, which are too expensive to be used in contrail prediction protocols.

This project will investigate whether the instabilities that develop in the far wake of aircraft and simplified bluff bodies can be modelled using modelling based on the quasi-linear approximation of the Navier-Stokes equations. Previous work has demonstrated that this theoretical approach can predict the instabilities that develop in turbulent pipe-flow, at a fraction of the cost of high fidelity simulations. The goal of the project will be to apply this approach to turbulent wakes. The project will involve both experiment, where an extensive data-set of turbulent wakes will be acquired from wind tunnels, and mathematical modelling where the measured wake instabilities will be studied using the quasilinear approach.

PROJECT 5

TITLE: Infosymbiotics for sustainable aviation: on non-CO2 emissions

DEPARTMENT(S): Department of Aeronautics

SUPERVISORS:  Prof. Laura Mainini and Dr. Sebastian Eastham

Different energy vectors and alternative technologies are being explored to enable a cleaner air transportation network. However several questions remain about the true environmental impacts of those solutions across space and time scales, and how impacts are changed by design and operational decisions. This problem is often treated in a linear fashion: quantify impact, estimate sensitivity to some upstream choices, and adapt. Such static framings cannot adapt or properly exploit capabilities such as live reporting, rapid atmospheric simulation, Earth observing systems, and digital twins. This project will instead address impact characterization and decision tasks as symbiotic problems: accordingly, aircraft and fleets are conceived as multi-sensing systems whose measurements can inform operational decisions at scale and actively mitigate impacts. Infosymbiotics approaches will be advanced to integrate adaptive models and data acquisition as mutually informed dynamic systems; methods will be developed at the intersection of scientific computing and machine learning for decision-oriented sensing, data acquisition/assimilation, uncertainty reduction, aerospace engineering, and Earth system science. The project will show how aircraft design and operations choices on timescales of years can enable a downstream feedback loop, with shorter-term decisions made in a data assimilation framework which allows and empirically verifies a reduction in environmental impact.

TITLE: Actionable SAF Pathways: multi-criteria assessment of production and impact on aviation

DEPARTMENT(S): Centre for Environmental Policy and Department of Aeronautics

SUPERVISORS:  Prof. Niall Mac Dowell and Prof. Laura Mainini

There are multiple pathways to producing sustainable aviation fuel (SAF), each presenting their own advantages and criticalities from a technology, economic, environmental, and policy perspective. However, open questions remain about the impact of specific choices of fuel alternatives on airport logistics (storage, handling, refuelling) as well as on the design and operation of aircraft and fleets. Within this context, the purpose of this PhD project is to perform a comparative analysis of the different SAF pathways through the lens of impact on aircraft design, operations, and logistics. The project will build up on a system perspective and multidisciplinary modelling and simulation frameworks will be developed to capture the implications of fuel alternatives/choices at scale. Computational search methods and multisource learning schemes will be advanced to ease the formal and algorithmic tractability of those large-scale comparative assessments, which could also account for technology and availability uncertainties associated with different SAF pathways.

TITLE: Ιnteraction of contrails from SAF and H2 combustion with aircraft trailing vortices: High fidelity simulations and reduced order modelling.

DEPARTMENT(S): Department of Mechanical Engineering and Department of Aeronautics

SUPERVISORS:  Prof. George Papadakis and Dr. Stelios Rigopoulos

Contrails are clouds formed behind aircraft due to ice nucleating on particles at the engine exhaust and have been identified as the greatest impact of aviation on climate change. Sustainable Aviation Fuels (SAF) and hydrogen (H2) have been proposed as alternatives to the standard jet fuel to reduce CO2 emissions and there is an urgent need to understand better the mechanisms of contrail formation from these fuels. The proposed PhD research will build upon the work that will be carried out in the recently funded NERC project “Contrails from SAF and H2 combustion; from lab experiments to global mitigation policy”. In the proposed PhD project, we will focus on the vortex regime, which is the interaction of the exhaust jet with the aircraft trailing vortices. We will perform high resolution Large Eddy Simulations and develop parameterised reduced order models (ROMs) of the interaction process for different ambient conditions (humidity, temperature etc). The long-term objective is to provide accurate contrail parametrisations that can be included in global weather and aviation system models to reduce current uncertainty and guide global mitigation efforts (these aspects are studied by our NERC project collaborators).