Turbulence is the leading cause of weather-related aircraft incidents and the underlying cause of many people's fear of air travel. One estimate of turbulence indicates over 63,000 encounters with moderate-or-greater turbulence and 5000 encounters with severe-or-greater turbulence annually. In 34 years, the US reported 883 fatalities associated with turbulence. Turbulence can also damage aircraft, by tearing off winds and engines, as happened in an extreme turbulent event over Colorado in 1992. The economic costs of turbulence are more than just injuries and damage, with flight delays, inspections, repairs, and post-accident investigations also taking their toll. Estimates of the total cost to US carriers alone are nearly $200 million annually. Although the costs of turbulence to UK/EU airlines and over EU airspace are not available, assuming the occurrence of turbulence and the density of air travel are similar to that over the US and that the EU is about the same size as the US, then costs should be comparable. Moreover, climate change is exacerbating the problem. Midlatitude turbulence diagnosed from climate projections increase under increasing atmospheric carbon dioxide, with a doubling or trebling later this century. Thus, the costs of turbulence due to climate change will lead to a substantial increase in turbulent events. Clear-air turbulence, abbreviated as CAT, is turbulence that occurs away from clouds in clear air. CAT is difficult for pilots to detect and for forecasters to predict. One of the reasons that it is difficult to predict is that CAT is believed to have multiple sources and no single forecasting tool works for all of the sources. One suspected source of CAT is the release of hydrodynamic instability, an imbalance between different forces in the atmosphere that lead to large and rapid accelerations of the air. Such accelerations may produce atmospheric phenomena such as roll-type circulations or wave-like motions that result in CAT. Presently, we have an incomplete understanding of how hydrodynamic instability forms, releases, produces turbulence, and returns to stability. In this proposed research, we will look at observations of turbulence from three sources. One is from a vertically pointing radar in Wales that can detect turbulence at the jet stream. A second one is from pilots manually reporting turbulence. A third is from automated instrumentation aboard aircraft. We will use these observations to understand the conditions in which CAT forms and its relationship to hydrodynamic instability. Because these observations are snapshots in time from single measurements, computer model simulations of real and idealised weather phenomena that produce CAT will be critical to determine how the instability forms, how the instability and resulting turbulence evolves, and how the atmosphere returns to balance after the release of the instability. Within the context of the results from the observations, we will construct the life cycle of CAT from its origin, to its growth, to its demise. Given these new insights, we will develop tools for model output (called diagnostics) to quantify the impacts from the release of the instability and evaluate the performance of these diagnostics over North America, the North Atlantic Ocean, and Europe. In this way, improved understanding of the CAT life cycle will lead to better predictions of jet-stream turbulence, as well as reduced costs and injuries to passengers and flight crew.

Congestion at major airports in the UK and across Europe and the rest of the world is a serious and growing problem. Already Heathrow faces problems occasioned by serious congestion for a major part of the day while at Gatwick demand is expected to exceed capacity for 17 hours per day by 2025. According to a Eurocontrol study, planned capacity at the 138 Eurocontrol Statistical Reference Area (ESRA) airports is expected to increase by 41% in total by 2030, with demand exceeding airport capacity by as much as 2.3 million flights (or 11%) in the most-likely forecast growth scenario. The development and deployment of airport capacity is a major societal issue engendering intense public debate in the UK and around the world. Capacity at congested airports is expressed in slots. A slot identifies a time interval on a specific date during which a carrier is permitted to use the airport infrastructure for landing or take-off. Current slot allocation procedures suffer (inter alia) from the following limitations: 1)Simplistic modelling of the objectives and operational/regulatory constraints bearing on the multiple stakeholders involved in (and affected by) the slot allocation process. 2)Insufficient capture of the interactions encountered in airport networks. 3)The use of empirical or ad hoc processes for determining (rather than computing) declared capacity which address neither the uncertainties involved in airport capacity assessment nor the complexity and size of the real-world problem, even at the single-airport level. Consequently, existing approaches to the allocation of airport capacity fail in a number of critical ways to reflect the complexities presented by the real world. This creates allocation inefficiencies which, in turn, result in poor airport capacity utilisation with significant negative impacts on airport revenues, airline operating costs, the level of service offered to passengers and the environment. There is thus a pressing need to meet the major scientific challenge of developing novel mathematical models and solution approaches to transform the airport slot allocation process and its associated outcomes. The programme grant aims to do just that for a single airport and for a network of airports. Mathematical models will be developed and analysed which consider the objectives and requirements of all stakeholders and which take account of a wide range of operational and regulatory constraints. The intrinsic complexity of the proposed programme and its large scale (especially for the case of the network-wide slot allocation) will mean that it will provide an excellent test-bed for the development of new heuristics and hyper heuristics for large scale complex scheduling problems more widely. Algorithms that will be developed and tested by this project will provide essential support for the complex large scale capacity allocation problems that arise in other types of transportation networks, including rail networks. In addition, it could extend to other types of networks that share similar problem structures, such as those in energy and telecommunications. The models and solution techniques developed will underpin the development of novel decision support systems which have the potential to make a major impact on airport operations. The research team has an internationally leading profile in the areas of mathematical modelling, heuristic development, stochastic optimization, airport slot allocation, airport management and performance assessment. It has an excellent track record of research cooperation with all categories of stakeholders. It will cooperate closely with an impressive array of leading industry stakeholders in order to make sure that the work is as cutting edge industrially as it is scientifically.

The upper troposphere in mid-latitudes is the region encompassing altitudes of around 8-12 km, which includes the jet streams, regions of very strong winds, which are closely related to strengths and paths of the mid-latitude depressions. Climate change is expected to change the nature of the upper troposphere at mid-latitudes - climate models indicate that over coming decades, it will warm, the relative humidity will increase and the strength and orientation of the jet stream might change, and the boundary between the troposphere and the overlying stratosphere (the tropopause) will increase in altitude. However, when different climate models are used to predict future climate change, there is a significant spread in the results they produce; the reasons for this spread are not fully understood. Understanding climate change in the mid-latitude upper troposphere is of importance in its own right, but it has a wider economic significance. The cruise altititude of commercial aircraft is in the upper troposphere and flight times can be strongly affected by the wind conditions. Most obviously, the duration of flights between (as an example) London and New York are normally more than an hour faster when going eastbound, as the aircraft attempt to fly in the jet stream and receive an extra "push" - by contrast, westbound flights normally try to avoid the jet stream as this would impede progress. However, day-to-day variations in weather conditions in the north Atlantic mean that flight durations of both eastbound and westbound flights can vary by up to 100 minutes, depending largely on the strength and position of the jet stream. Since fuel use, and hence carbon dioxide emissions, are closely related to the flight duration, there are both economic and climate consequences for this variation. In our recent research we have shown that the weather in the upper troposphere in the North Atlantic can be split into characeristic patterns (5 in winter and 3 in summer) for which the aircraft routes are distinct. In addition we have shown that other climate effects of aircraft emissions (for example, contrails and ozone change resulting from emissions of oxides of nitrogen) very likely vary between these weather patterns. Since aircraft routing is dependent on the weather situation in the upper troposphere, it is natural to ask whether climate change could impact on aircraft routing. There has been much research on the effect of aviation on climate change, but surprisingly little that asks the reverse question: what is the effect of climate change on aviation? Our proposal aims to answer this question, while at the same time improving understanding of upper tropospheric climate change. Since the aviation industry aims to put constraints on its carbon dioxide emissions, the effect of climate change on aviation routing could either assist or work against these aims. We will consider how the routes of individual aircraft may be affected by the changes in the frequency of different weather patterns in the North Atlantic, predicted by a number of different climate models. We will exploit a recent, large and easily available set of simulations of possible future climate change from a range of world-leading climate models that have been produced for the fifth assessment report of the Intergovernmental Panel on Climate Change, which is currently being written. We will assess how well the climate models reproduce the present-day weather patterns in the North Atlantic and then look at how these patterns change for various possible future climates. We will then see how aircraft routing is affected by these weather patterns and compute the impact of this carbon dioxide emissions. We will also investigate the impact of both the climate change and re-routing on the other climate impacts of aviation. We will extend this work to cover the North Pacific, which is expected to show a significant increase in air traffic over coming decades.

As of September 2022, flight numbers in Europe have returned to 88% of the levels seen prior to the global outbreak of Covid-19, and major European hubs such as London Heathrow are again processing more than 1000 runway movements (i.e. landings or take-offs) per day on average. Large volumes of air traffic impose heavy demands on airport infrastructure, with runway capacity being the most critical bottleneck. Demand-capacity imbalances result in flight delays, which not only disrupt airline and passenger itineraries but also have serious financial consequences and environmental impacts. In order to mitigate the risk of flight delays, various types of interventions are possible. "Strategic" interventions are those that are made far in advance of a particular day of operations, before any 'real-time' information (e.g. weather conditions, airline crew shortages) becomes known. These types of interventions typically involve restricting the numbers of arrivals and departures that can be scheduled per hour at an airport. On the other hand, "tactical" interventions are those that are made on a particular day of operations in response to events that unfold in real time. For example, air traffic controllers have knowledge of the latest positions and estimated arrival times of aircraft that are due to arrive in the terminal airspace and can use this information to plan the most efficient sequence of aircraft landings in order to maximise runway throughput rates and reduce expected airborne holding times. In current practice, airport scheduling is carried out via a process known as "slot coordination". Airport schedules are required to comply with airport capacity declarations, which impose limits on hourly numbers of scheduled runway movements. However, even if an airport's schedule is consistent with its capacity declaration, there is no guarantee that the delays seen under that schedule will remain within `acceptable' limits - as, in reality, these delays depend on a range of stochastic factors (e.g. upstream delays, weather conditions) as well as the real-time tactical interventions implemented by air traffic controllers. We propose to develop a new framework for airport schedule optimisation which explicitly models airport delays through a high-fidelity, stochastic and dynamic model of air traffic control and aims to ensure that the final airport schedule results in a relatively low risk of delays exceeding 'acceptable' levels. To elaborate further, our proposed optimisation framework consists of two separate (but related) modules: 1. First, we use a mixed integer linear programming (MILP) model to minimise schedule displacement, which is defined as the total amount of deviation between an airport schedule and an ideal 'baseline' scenario. This MILP formulation includes constraints that restrict the numbers of arrivals and departures that can be scheduled in different time slots. 2. The optimal schedule given by the MILP in Step 1 is regarded as a 'candidate' for the final airport schedule. In this step we use a stochastic, dynamic model of the airport sequencing problem to test whether or not the expected delays under the candidate schedule satisfy a set of delay-based performance criteria, which includes components based on punctuality and fuel emissions. This is a tactical optimisation problem in which aircraft sequencing decisions are made under continuously-evolving random conditions. If the performance criteria are satisfied, then the candidate schedule is accepted as the final schedule and the process is completed. Otherwise, we return to Step 1 and reformulate the constraints of the MILP, making them 'tighter' in order to further restrict the numbers of flights that can be scheduled in particular time slots. This process is repeated iteratively (reformulating the MILP constraints as many times as necessary) until a candidate schedule is found which satisfies the delay-based criteria.

In the last decade, the role of software engineering has changed rapidly and radically. Globalisation and mobility of people and services, pervasive computing, and ubiquitous connectivity through the Internet have disrupted traditional software engineering boundaries and practices. People and services are no longer bound by physical locations. Computational devices are no longer bound to the devices that host them. Communication, in its broadest sense, is no longer bounded in time or place. The Software Engineering & Design (SEAD) group at the Open University (OU) is leading software engineering research in this new reality that requires a paradigm shift in the way software is developed and used. This platform grant will grow and sustain strategic, multi-disciplinary, crosscutting research activities that underpin the advances in software engineering required to build the pervasive and ubiquitous computing systems that will be tightly woven into the fabric of a complex and changing socio-technical world. In addition to sustaining and growing the SEAD group at the OU and supporting its continued collaboration with the Social Psychology research group at the University of Exeter, the SAUSE platform will also enable the group to have lasting impact across several application domains such as healthcare, aviation, policing, and sustainability. The grant will allow the team to enhance the existing partner networks in these areas and to develop impact pathways for their research, going beyond the scope and lifetime of individual research projects.