Browsing by Author "Subramanian, Nithya"

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    Development of tail rotor power analysis model with feasibility study of electrical tail rotor.
    (2016-09) Subramanian, Nithya; Lawson, Craig
    In recent years, there has been significant work undertaken by the aviation industry to increase the overall rotorcraft performance, and eventually, eliminate leak prone hydraulic fluids and to reduce CO₂ emissions. Even though a mechanical-gearbox-driven tail rotor has been extensively used in several applications, it comes at the expense of high life cost of the gearbox and shaft gear mechanism. This thesis concentrates on the developing a model to analyse the power requirement for the tail rotor drive and feasibility investigation of an electrical tail rotor to substitute the shaft geared system and the conventional tail rotor power transmission gearbox. A case study is conducted on the Sikosky UH-60A rotorcraft to assess the conventional tail rotor power requirement and Electrical systems. A mathematical model based on Rankine Froude’s momentum theory is created to analyse the power required to drive the anti-torque system, which could be adapted to any conventional drive train (with the main rotor and a tail rotor) rotorcraft. A mission profile and trajectory are created and implemented into Excel based mathematical model. The challenges in implementing electrical drivetrain (electrical generation, energy conversion and electric transmission) are briefly discussed in this thesis. Electrical load analysis database is generated to find the electrical load of the generator for the entire flight phases and utilised to up-scaled the generator to compensate the new load from Electrical tail rotor. The electrical powertrain system is designed with a Brushless DC motor attached to the tail rotor and the generator and the battery for redundancy purposes. The research thesis develops an understanding of current electric motor and battery technology to create a novel design of electric tail drive that increases the reliability of the helicopter system.
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    Fault diagnosis in aircraft fuel system components with machine learning algorithms
    (2022-01) Subramanian, Nithya; Starr, Andrew; Perinpanayagam, Suresh
    There is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario. The CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components. The aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure, the fuel in the aircraft will become unusable/unavailable to reach the destination. It is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner. This thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is similar to Boeing-777. The data is collected is processed through Machine learning Techniques and the results are compared