A lot of studies have been done on Acoustic Emission (AE) covering a wide range of materials and applications. Within a structure that is under loading, AE has proven to be useful in determining the type, location and accumulation of damage within a material. The general goal is to use AE for inservice monitoring of structurally loaded parts. However, more research is necessary before the method can be employed in real-time applications. The objective of this project is to contribute to the development of algorithms that classify failure modes real time in composites using AE. Composite material specimens are loaded under tension while recording AE, after which the signals that are recorded are used to create an independent damage mode signature. This leads to the ability to classify damage modes per time increment. Mechanical tests are performed on specimens while recording AE signals. Carbon fiber reinforced polymer specimen with unidirectional 90∘layup are loaded in tension up to failure to create a fingerprint of the matrix cracking damage mechanism. The same material with a crossply layup is loaded in tension under quasi static and fatigue loading. The fingerprint is then used to separate signals coming from this type of damage and other damage mechanisms. Next to the basic parameters of these signals,
additional features are generated by processing the waveform of each signal. The wavelet transform is used to find frequency related features that characterize each signal. Machine learning algorithms are developed and used to cluster and classify the AE Signals. Overall, the real-time clustering algorithms have proven to be successful in clustering and classifying incoming AE signals in an efficient way. The system was able to correlate incoming data to matrix cracking data, within a very limited time frame. With room for optimization the proposed methods seem fit to be applied in real-time applications. Before this can be done however, more testing and validation of these methods is required. The most valuable addition to this study would be a proper validation of the clustering results. Regarding improvement of the system, the main bottleneck of the proposed algorithms is loading large datafiles. This can be solved by reading data in batches, but requires further development of the algorithms.

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.