The AerGo is a recreational ultralight water-aircraft, designed to be transportable by bike and operated without a license. This report outlines its concept, characteristics, and feasibility.

The AerGo features a biwing design with swept wings, closed by vertical plates at the tips. It has a constant wing chord of 0.79 m and a wingspan of 12 m. The aircraft is powered by two six-bladed propellers mounted on the top wing and driven by electric engines. The lower wing is attached to a buoyant hull, stabilized by side floats. A paddle is used for taxiing and docking on water, as the aircraft is designed exclusively for water take-off and landing. The pilot sits inside the hull and controls pitch by shifting weight on a swing. Roll control is achieved through wing warping, and yaw by rudders. The total empty mass is 44.7 kg. A multipurpose trailer system facilitates storage, transport, and deployment. The AerGo is designed for single-person assembly and operation and is safe to fly for over 270 days per year.

A market analysis of ultralight aircraft reveals a European fleet of over 25,000, with 500 in the Netherlands. While most ultralights are custom-built, successful models like the Woopyfly and Lazair have seen large-scale production. The AerGo’s mobility, ease of use, and hydrodynamic capabilities position it competitively. A conservative estimate projects annual sales of 20 units, with 25% for the Dutch market and 75% for Europe. Expansion to North America is a future opportunity.

The design is driven by weight and energy efficiency, with batteries forming a significant part of the empty weight. The primary energy requirement comes from cruise flight, optimized through airfoil selection. The NACA 6415 airfoil, chosen for its high lift coefficient at zero angle of attack, minimizes drag and maximizes cruise efficiency. The hydrodynamic model, based on DSDS data, estimates a take-off speed of 9.9 kg and a 170 m take-off distance. The computed climb rate is 1.43 m/s, reaching a cruise altitude of 150 m in 105 seconds.

The engine and battery design prioritize take-off power while ensuring a minimum flight time of 60 minutes. With a cruise speed of 15 m/s, the AerGo has a range of 40 km. The pilot’s longitudinal position, optimized at 1.6 m, ensures stability. The upper wing has a positive stagger of 0.45 m forward, creating a seesaw effect that smooths weight-shift control. Roll is controlled by wing warping, and yaw by rudders placed at the wing tips. A 15° wing sweep enhances rudder effectiveness, allowing safe operation on a single engine.

The aircraft’s lightweight structure consists of a skin-on-frame design, with a nylon-covered carbon fiber wing weighing 11.3 kg and a dacron-covered carbon fiber hull at 4 kg. Noise reduction is achieved with six-blade propellers, keeping levels below 40 dB at 100 m distance. Approximately 74% of the aircraft is recyclable, with a carbon footprint of 800 kg CO2 per unit.

At an estimated price of €20,000 per unit and annual sales of 20, the break-even point is 9.5 years, with a projected return on investment of €700,000 after 12 years. Further analysis is required on cost budgeting, structural impact resistance, and user assembly instructions to ensure feasibility.

Ever since the attacks on the World Trade Center, airport security has been a topic of interest. The United States was caught by surprise and the attacks triggered a renewed interest in aviation security. It was well recognized that airport security was one step behind on intelligent attackers and this created the need to developed better risk assessment methods.

Unfortunately, none of the risk assessment methods developed has the possibility to quantify the vulnerabilities in an airport checkpoint. One of the main challenges in developing a method which can do this, is to find a technique that can account for the complexity of the airport environment from which the vulnerabilities emerge. Furthermore empirical research has shown that security operators do not necessarily follow protocol, but behave as autonomous agents which regularly bend or break the rules.

A method which can potentially identify vulnerabilities in such a complex environment is agent based modeling. This modeling technique has proven to be very powerful in modeling complex systems that emerge from the behaviour of autonomous agents. Some work has been done in this area, but until now this technique has not been used to quantify vulnerabilities in an airport checkpoint. Therefore, the aim of this project is to develop an agent based model of an airport checkpoint, quantify the vulnerabilities emerging from this checkpoint and analyze the effect of employee behaviour on these vulnerabilities. To do this, the behaviour of the security operators is modeled using models that are rooted in behavioural psychology and have strong empirical backing. The employees decision making is modeled using Decision Field Theory and the employee performance is modeled using the Functional State Model.

With the model developed, a set of experiments is performed to calibrate the model and analyze the vulnerabilities. These experiments result in the quantification of vulnerabilities for a predefined set of threat scenarios.

The analysis of these threat scenarios shows that employee behaviour mainly impact threats scenarios in which a weapon is hidden in the carry-on baggage. The reason that security operators have a large influence on the outcome of this screening process is that it is the process in which employees have to perform multiple activities and make multiple decisions. It is found that the vulnerabilities in this screening process are mainly dependent on the speed/accuracy trade-off as made by the employees. The perceived risk of the detected prohibited items plays a limited role, since most prohibited items are only seen as a small risk. The performance of the employees played a less important role on the outcome of the screening process and differences in performance are mainly caused by the personality type of the agents. The personality type that put more effort into a task, outperformed the other agents. The skill level of the operators however, did not significantly effect the outcome the simulation. This suggests that the effort an operator puts in is more important than the skill level of the operator.

Finally it is found that it is beneficial to minimize the number of steps in the screening process. This benefits overall performance, since adding steps to the screening process means adding opportunities to make mistakes.