To increase performance of air-to-air missile guidance, a novel guidance law is developed using reinforcement learning methods. This guidance law is based on behavior obtained from optimal control methods and subsequently aims to approximate its performance. The study compares the developed guidance law to a traditional guidance law and optimal solutions. It is established that the novel guidance law outperforms the implemented traditional guidance law in terms of range and time of flight to target. Generated trajectories mimic behavior of optimal control and a large fraction of optimal performance is achieved in terms of range (~90%) and time of flight (~95%).

Travelling by air has become commonplace, and more and more people can afford themselves to fly. In twelve years time, the amount of people that were carried by airlines doubled to four billion passengers in 2017. At Amsterdam Airport Schiphol, the main gateway into the Netherlands and also a large hub for connecting flights, saw a growth of 20 million passengers in four years time. The rise in air travel also has its downsides. Not only the boundaries of airport and airspace capacity are run into, noise annoyance and pollution caused by aircraft are also everyday's business. The growth in aviation and population will inevitably lead to conflicts. Several strategies have been proposed in literature and by legislators to address this conflict. Advanced aerodynamics, aircraft engines and alternative fuels can be considered as long term solutions for this, but changing operational procedures of aircraft and airport is considered as a good solution in terms of costs and time-wise. Most research on changing operational procedures has shown significant results, however only consider a single operation while these are of influence on one another. The integration of several operational procedures into a single problem has been recognized as one of the future research topics. In this research an integrated optimization framework is proposed that can simultaneously optimize aircraft and airport operations by allocating routes, flight procedures and runways. The framework is able to handle fuel usage, noise annoyance and capacity. Amsterdam Airport Schiphol will be used as case study airport, and a real flight departure schedule is used as reference. The framework consists of two main steps. In the first step, flights are groups according to their aircraft type, destination and departure time, and are subsequently distributed over the available runways, routes and departure procedures. This distribution is performed with a \acrshort{NSGA}-II algorithm that is capable of handling non-linear, multi-objective optimization problems. In the second step, the flight distribution is apportioned over the flights in the schedule, while adhering the separation requirements. In this way it is verified if the required capacity is available, by adhering the separation at departure, a possible crossing point and the terminal point between consecutive flights. The second step is implemented with a \acrshort{ILP} algorithm. Results from the case study has shown that significant gains can be obtained in terms of noise annoyance. While requiring little extra fuel, up to 4\%, the noise annoyance can be lowered with 31\%. Not much variance is possible for flights that operate during the night hours, and so all gains are obtained on flights in the daytime. Especially the amount of people that are highly annoyed can be lowered of up to almost 50\%. A further analysis of the noise contours shows that the framework is able to shift noise in such a way that the noise levels drop below the threshold values of the noise equivalence criteria. In other words, the noise contours can decrease in size and with that the amount of people annoyed is lowered. These results are obtained by shifting flights from runway 18L to runway 24, which is more noise friendly, and by using \acrshort{NADP}2 more frequently compared to \acrshort{NADP}1. The model is also able to make a distinction between destinations and aircraft type, and puts heavy aircraft more frequently on shorter flight legs in order to minimize the fuel usage and the exposure to noise of residents. An examination of the entire solution set in terms of the separation requirements indicates that most of the found results are feasible. By shifting more flights to a single runway, the required capacity is at stake and adding extra flights with delay will cause an exponential growth in total required delay.
Using the integrated optimization framework leverages the potential of aircraft specific capabilities by allocating the right runway, route and procedure to each flight in such a way that an optimum noise contour is formed. Most gains can be obtained for the number of people annoyed and disturbed, and as these noise equivalence criteria are directly related to the well-being of residents around the airport and their opinion on the operation of the airport, this can only be considered as beneficial. The available capacity of a runway is deployed to alleviate noise annoyance by residents. The framework proposed in this research can be improved at several components in future research. The addition of arrivals, extra routes and procedures would extend the applicability and value of the framework. Another improvement that can be made is the incorporation of the second step into the first step. This would eliminate infeasible solutions inside the solution set.

Nowadays an increasing number of offshore wind farms (OWF) is getting built. Several disciplines are considered into this subject. The manual and sequential design approach used so far is not sufficient to guarantee optimized systems because interactions between the different components are disregarded. Therefore, engineering companies are looking for a multidisciplinary, modular, user-friendly and easy tool to be applied during the preliminary design assessment. This should take into consideration almost every crucial aspect of OWF design. Moreover, it should be able to perform some overall layout optimization, i.e. providing the best turbine positioning w.r.t. the energy yield, the costs, the cables, the installation and the O&M, ensuring some good constraint-handling techniques and guaranteeing a sufficient degree of robustness, precision, flexibility and speed. Therefore, the concept of MDAO (Multisciplinary Design Analysis and Optimization) workflow has been introduced. With respect to the common sequential design procedure, which optimizes the wind farm components separately, the MDAO analysis helps the user deal with the interactions between different design areas whilst automating the design process. The result is a design where all the parts are jointly optimized.Within the MDAO domain, two components are identified. The former is called analysis block and it comprehends all the modular tools which refer to a specific physical/economic discipline; the latter is the driver, i.e. an optimizing procedure which calls the analysis block in each iteration.This thesis focuses mostly on the analysis of the driver. In particular, the first goal is to identify the best optimizing strategy to be adopted, thanks to several quantitative assessment criteria. The second research goal is studying to what extent the different design areas - aerodynamics, support structure, cable topology etc. - affect the result of the optimization. The third research objective is to see whether the chosen optimizing procedures are able to deal with more complex design situations, such as more constraints or longer design vectors. In the end, an additional analysis on the effect of neighbouring wind farms on the layout optimization has been done, in order to see whether the optimization should consider the effect of those wind farms (disturbed wind conditions) or whether the results from the undisturbed case can be still valuable if re-computed in disturbed conditions.

In the year 2050, global anthropogenic radiative forcing from aircraft emissions are projected to increase significantly. Recent studies have considered climate optimized flight trajectories to be a promising measure to mitigate non-CO2 emissions’ environmental impact, which is highly sensitive to locus and time of emissions. Estimating the maximum mitigation potential from these trajectories requires accounting of air traffic regulations. As designing regulated climate optimal trajectories necessitates solving a hybrid optimal control system with unknown mode sequence and associated switching times, there is a need to build an efficient and systematic control technique. In this thesis, a bi-level optimal control algorithm is proposed for designing climate optimal cruise trajectories, the lower level calculates the optimal switching times and control
inputs of a fixed mode sequence and, the upper level updates the mode sequence with mode insertion which lower the cost locally. The problem for trajectory optimization is formulated here as a hybrid optimal control problem with a switched system and with a variable mode sequence, where step-climb and descent modes are included in the mode sequence. Optimal Control problems for minimizing operating cost and climate cost with fictitious climate cost functions (CCF), varying with altitude, are solved to study the performance of the algorithm. The algorithm is implemented within the Trajectory Optimization Module (TOM) by building a bi-level framework. The framework was validated by solving the operating cost optimal control problem. The maximum error between the cost reduction estimated by the algorithm and the actual cost reduction was found to be less than 15%. With high probability it can be stated that the bi-level framework is able to calculate an optimal mode sequence as the framework allow for zero entry modes in the mode sequence i.e. modes of zero duration. Although, careful consideration is required while selecting a mode for insertion as the framework is highly dependent on the sequence of the set of modes.

Despite a satisfactory performance of the bi-level optimal control technique there are few challenges which limits the scope of this technique. The maximum error was found to increase for optimal control problems with AirClim CCFs. The dependence of the AirClim CCFs on position of the aircraft influences the locus of the trajectory at each flight level. Because of this the the trajectories calculated in each iteration of the framework are found to be inconsistent. A flight trajectory guided by waypoints is proposed as a solution for future studies to handle the inconsistency between trajectories. As future studies are expected to focus on finding optimal mode definitions for designing climate optimal trajectories, the bi-level optimal control algorithm can act as an intermediary tool with which the researchers can systematically investigate cost benefits along the trajectories.

Tilt-rotor aircraft combine the helicopter’s benefit of VTOL capability and the flight performance that turboprops possess on range, speed and endurance. Tilt-rotor aircraft have the potential to decrease airport congestion and average flight delay in passenger transportation, while benefits are numerous in i.e. search and rescue, disaster relief and military application. Time, money and risk can be significantly reduced through the application of flight trajectory optimization and assessment prior to flight, as this can accurately simulate flight and its related performance limits. A numerical three-dimensional pointmass model for a tilt-rotor type aircraft has been derived to fill the gaps that currently exist in tilt-rotor modelling and the understanding of their flight mechanics. After validation, the model is applied to optimize integral flight trajectories using optimal control theory in GPOPS. It was concluded that the derived model is valid under its assumptions and limitations. From the tilt-rotor flight behaviour it was assessed that specific nacelle tilting behaviour could be observed, as the tilt-rotor can exploit its unique rotor tilting capability. Since the power required is the driving factor in most flight optimization, the nacelle angle is driven by the aircraft’s velocity and altitude. This model can be used in theoretical flight trajectory optimization studies. The model can be adapted to account for specific requirements and aircraft types.

Preface In the wake of the economical crisis of 2008, the shipping industry changed from a very profitable industry to a struggling one, aiming to optimize vessel operations in order to survive. Theories regarding route optimization based on weather and oceanic currents exist, but only few reliable industrial applications can be found. This, together with nowadays global environmental concerns, is where the roots of this project, aiming at developing a route optimization tool for seagoing vessels, based on real-life vessel data, short-term weather forecasts and oceanic currents and monthly averaged sailing conditions can be found. Due to the huge amount of fuel burned by seagoing vessels, achieving just a fraction of fuel savings already results in a significant reduction of global greenhouse gas production. The concept of weather routing is not new, but the results that can be achieved by using it are not widely documented. In order to quantify this, a weather route optimization tool has been developed. The availability of detailed hindcast datasets made it possible to incorporate monthly averaged sailing conditions in the optimization process, influencing the decision on which route to take when weather forecasts are not available anymore. Analysis of different ratios between forecasts, monthly averaged conditions and taking the shortest path to describe the sailing environment, led to the conclusion that forecasts are more reliable than sailing according to the monthly averages or taking the shortest path, as long as these forecasts are available. When no forecasts are available, using monthly averages as reference environment is favored over taking the shortest path. While evaluating randomly selected routes, it became clear that the usage of these monthly averaged sailing conditions can reduce the fuel consumption by 0.59 %, where the total effect of applying weather route optimization is found to be approximately 3.18 %. Due to the limited number of simulations performed and the unstructured nature of the data distribution, the 95 % confidence interval of the expected fuel savings ranges from 2.54 % to 4.02 %. When assuming the achieved savings are approximating reality, application of weather route optimization on the entire CMB fleet, containing close to 100 vessels, leads to a CO2 emission reduction almost 80 thousand metric tonnes per year. This is the result of a fuel consumption reduction of 23.5 thousand tons, which would roughly saves 9.35 million US Dollar in bunker costs.

The most common operational measure in aviation to reduce the effect of environmental noise pollution is noise abatement procedures. Noise abatement procedures are recommended flying techniques based on noise optimal trajectories, which aim at reducing the noise impact on local communities as much as possible. Atmospheric conditions influence sound propagation, however the noise abatement procedures are often fixed and independent of weather conditions.

This research has been set up in order to analyse whether the implementation of weather-adaptive noise abatement procedures result in different noise optimal trajectories in different weather conditions. The research objective is to assess the influence of weather on optimised noise abatement departure procedures for one fly over, by developing an optimisation tool that allows for the synthesis of weather adaptive noise abatement procedures within the terminal manoeuvring area.

To analyse the influence of different atmospheric conditions on the noise optimal trajectories in various weather conditions, a sound propagation model has been developed with the inclusion of variable air temperature and lapse rate, relative humidity, wind profile and ground surface type. Together with an aircraft performance model enhanced with varied atmospheric conditions and a noise impact model, the noise optimal trajectories in different weather conditions are obtained using optimal control theory in GPOPS. The model can be adapted to various requirements and aircraft types.

The resulting trajectories are used to evaluate whether weather-adaptive noise abatement procedures should be implemented or not and to evaluate the performance of optimal control theory in this framework.

Condensation trails, or contrails in short, are the white lines that can often be seen trailing high-altitude jet aircraft. Due to their interference with the local energy balance of the atmosphere they contribute to anthropogenic climate change. Research has shown strategies with great contrail mitigation potential at relatively small fuel and/or time cost in free flight. This thesis attempts to quantify contrail mitigation potential in practical and realistic scenario by introducing flight planning as tool for mitigation. A tool was developed that plans and simulates flights from the Netherlands to several destinations in North America. From the results it is clear that at least 50% of contrails can be mitigated at less than 2% additional fuel through flight planning. The results have confirmed the hypothesis that large shares of contrails can be mitigates against a few percent additional fuel consumption and flight time.

A Hybrid Optimal Control (HOC) framework which can be used in conjunction with existing optimal control software is presented. By using hybrid optimal control theory, trajectory optimization problems for systems that are both discrete time and continuous time from a mathematical perspective can be solved. A case study on multi-aircraft formation flying for civil aviation is performed which demonstrates the capabilities of the designed method. Whilst previous research has dealt with either high accuracy trajectories for small formation flying problems or low accuracy modelling of very large formation flying trajectories, in this thesis HOC is used to achieve a high accuracy optimal trajectory for formations of three aircraft and larger. Given the number of flights that are performed every day, the impact of saving fuel just by optimizing their trajectories using formation flying can be significant on a global scale, both environmentally and economically.

Electric low-thrust propulsion has nowadays found wide application in space dynamics as it entails considerable savings in spacecraft propellant mass, thanks to the very high specific impulse that this kind of engine is able to generate. However, continuous thrust opens new extensive sets of feasible trajectories, and optimization algorithms are needed to mine the admissible search space and find optimal transfers. Practical methods to solve complex optimal control problems as low-thrust trajectory optimization typically involve high computational times, the major bottleneck of these techniques. The objective of this work is the development of a novel multiple-shooting optimization tool, employing a variational approach for quick derivative computation, and the assessment of its performance against a variety of test cases. Indeed, after a first analysis of practical optimization methods, the derivative estimation by finite-difference approximations has been found as the major contributor to the computational burden, and the propagation of the variational dynamics has been selected as an accurate approach to speed-up their computation. The theory of variational dynamics for multiple-shooting application has been analyzed in detail, and further developed for what concerns the second-order equations. After practical considerations on the method implementation (scaling procedures, sparsity patterns, et cetera) and its interface with WORHP, the selected non-linear programming solver, the tool has been applied to a broad range of test cases, spanning from elementary problems to practical applications. For what concerns the latter cases, two complex problems were analyzed and optimized: a CubeSat rendezvous departing from Earth-Moon L2 and arriving at asteroid 2000SG334, resulting in a propellant mass convenient trajectory suitable for asteroid reconnaissance well before a proposed NASA manned mission in 2069; The Kessler Run, i.e. the 9th Global Trajectory Optimization Competition (GTOC9), in which the developed tool, employed as last step of the optimization cascade of Strathclyde++ team, managed to make the solution constraint-feasible, valid and further mass-optimal.

The goal is to present a class II aircraft design that will meet
the requirements set for the design of the Next-Generation Airlift Military Support Aircraft.