Pusat Pengajian Kejuruteraan Aeroangkasa - Tesis

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Validation of plasma probe hardware platform for in-situ measurement and monitoring of atmospheric parameters using sounding rocket
(2024-04-01)
Zulkifli Bin Abdul Aziz
This thesis presents the validation of a plasma probe hardware platform for insitu measurement, accompanied by a real-time communication system. The objective is to create an integrated system that combines a plasma probe hardware platform with a reliable and efficient real-time communication infrastructure. The hardware platform incorporates multiple sensors, including temperature, magnetic, accelerometer, camera, pressure, and GPS, to enhance the understanding of the relationship between in-situ plasma probe measurements and environmental parameters. The real-time communication system ensures seamless data transmission between the platform and the ground station operator during the data collection process, providing valuable realtime access to the collected data. This feature also serves as a redundancy solution in the event of recovery failure, ensuring that data can still be transmitted and monitored even if the platform cannot be recovered. Through this integrated approach, researchers can monitor, analyse, and make informed decisions based on the collected data in real-time, even in challenging recovery scenarios. The validation of the plasma probe hardware platform, coupled with the real-time communication system, advances our capability to study plasma phenomena, offering significant implications for space exploration, atmospheric research, and related scientific endeavours.
Self-supervised learning framework and localization using micro air vehicles for water leak detection
(2024-09-01)
Nurfarah Anisah, Mohd Yussof
Real-time detection and localization of water leakage are crucial for effective water management in smart buildings. Traditional detection technologies based on static sensors frequently entail significant costs for both installation and operation. Utilizing small mobile robots such as Micro Air Vehicles (MAVs) provides a cost-effective and efficient for detecting objects in confined areas. Nevertheless, due to constrained processing power and limited payload capacities, MAVs can only depend on lightweight sensors, such as, tiny thermal sensors, which provide low image resolution and hence reduce detection distances typically within 1𝑚. Therefore, this research presents a Self-Supervised Learning (SSL) framework, where a computer vision algorithm is developed to directly detect water leakage from thermal images, which then is used as supervised output for the training of a deep learning model by using RGB images as input. A pre-trained YOLOv4-tiny model is fine-tuned using 1080 laboratory images. Training test with 50,000 steps and 340 negative images achieves an optimal balance of accuracy, with a detection time of 0.0617 𝑠 and an average precision of 98.97%. In addition, a control strategy that combines the RGB deep learning model and the thermal vision algorithm is shown to allow autonomous MAVs for preliminary predictions of water leakage from further distances and accurately localize the leakage areas when they get closer. To validate the proposed concept, static detection tests were conducted, followed by flight tests in indoor environments. In static tests, the SSL-trained model extends the detection range from 1 𝑚 to 3 𝑚. In real-world flight tests, two scenarios are conducted: three experiments with varying initial positions and six experiments targeting different leak locations. Both static and flight tests confirm the effectiveness of the control strategy and detection algorithm in localizing water leaks in indoor environments. This research advances the sensory capabilities of MAVs equipped with RGB and thermal cameras and extends their detection range of water leakage, thereby mitigating potential damage in large or complex indoor environments.
Power optimization of 2d cylindrical deflector with attached splitters and barrier on savonius turbine through taguchi method
(2024-06-01)
Mohamad Hafizul Fikri, Mahizam
Vertical axis wind turbines (VAWTs) have a vertical rotor axis, designed to generate electricity from wind energy. The primary reason for the low efficiency of Savonius turbines is the negative torque produced by the returning blade. Savonius turbines rotate perpendicular to the wind, relying on drag forces between the advancing and returning blades for efficiency. Thus, the study proposes a novel design of a cylindrical deflector with splitters and a barrier to offset the flow field to the returning blades. This optimization is achieved through a comprehensive approach involving computational fluid dynamics (CFD) simulations and the Taguchi optimization method. CFD is employed to assess the power performance of the turbine, serving as a crucial input for the subsequent Taguchi optimization analysis. The Taguchi method is then utilized to identify the optimal combination of specified characteristics, including the length of the barrier (Ls/D), the barrier attachment angle (α), and the geometric shape of the cylinder deflector with a splitter and a barrier. The tip speed ratio (λ) for this study is fixed at the λ = 1. The simulations and additive model show that the optimal combination involves a cylindrical deflector with dual wake splitters positioned at the top and middle. The deflector is parallel to the flow, and a barrier at its bottom is perpendicular to the flow. The optimal configuration has Ls/D ratio of 0.9 and a barrier attachment angle (α) of 10°. This ideal combination yields a power coefficient of 0.459, indicating a 61% performance increase compared to the 0.284 power coefficient of the double wake splitter deflector.
Stability and tribological performance of dispersed graphene(gr) and aluminium nitride (aln) nanoparticles in mineral oil
(2024-08-01)
Ku Nooryasmin, Ku Wadzer
Additional nanoparticles often agglomerate and are incompatible with base fluids. With time, the mixed nanoparticles would phase-separate from the fluids, losing the benefits of aggregated nanofluids. Thus, this work examines the stability, thermal conductivity (TC), rheology, and tribology of graphene (GR) and alumnium nitride (AlN) in SUNISO 3GS refrigerant lubricant (compressor oil) and PETRONAS SYNTIUM 500 (engine oil). The study determines the best nanolubricant surfactant, the optimum concentration of GR and AlN nanoparticles in SUNISO 3GS and Engine oil 15W-40 nanolubricant, and their rheological and tribological properties. Nanolubricants' stability has been examined by visual observation and UV-vis spectrum intensity. Thermal conductivity has also been measured by changing volume percentage and surfactant presence. The highest percentage difference for TC is 22.58 in comparison with pure compressor oil. Viscosity readings at various temperatures, the ASTM 2270 standard for viscosity index measurement, and flash point were used to estimate rheological parameters. Tribological characteristics were measured using a pin-on disc tribotester to quantify wear rate according to ASTMG99. This study revealed that CTAB is the best surfactant for GR and SPAN80 is the best surfactant for AlN for both compressor oil (CO) and engine oil (EO). Next, the samples with 0.1 vol% with surfactant for both oils have the best stability and highest thermal conductivity. Furthermore, based on rheology performances, GR(0.05)-EO(CT), AlN(0.1)-EO(SP), GR(0.05)-CO(CT) and GR(0.1)-CO have the best performance. Moreover, addition of surfactant proves that it can improve the tribological performance where GR with 0.05 vol% with CTAB for both CO and EO shows the best tribological performance. Lastly, by using pin-on disc tribotester, it can be observed surfactant in nanolubricants reducing the specific wear rate (SWR) and the highest percentage difference is high as 76.37% for nanolubricant.
Study on engine fault diagnosis from knocking and signal uncertainty behaviour
(2024-07-01)
Ajmir, Mohd Saill
Engine knocking is a condition where the engine experiences severe vibration that can cause engine damage. It happens due to pre ignition or spark plug timing too early. Researchers found that knocking occurs cause of substance in the combustion chamber produced auto ignite beyond combustion stroke or power stroke. Apart from that the data indicated the occurrence of knocking after TDC. To identify the occurrence of knocking, a test engine table is used. It is built to simplify the testing process. For the engine control system, a zener diode and a diode circuit assembled with an Arduino UNO are used to replace the original engine control unit. It is to facilitate control of ignition time. The signal from the crank position sensor is used for spark plug ignition control. Using the program set in the Arduino UNO system, it is able to receive the signal from the crank position sensor and send it back to the spark plug system to ignite the fuel in the combustion chamber. The engine control system is tested repeatedly to ensure that it is able to function smoothly. The knock sensor is mounted on the engine block, where it is the most efficient place to get engine knock The bolts used to mount the knock sensor will be in contact with the engine block. The program set on the Arduino UNO will activate five seconds of artificial signals with an interval of Ims. This will result in an indeterminate state of the spark plug signal. Data was acquired using Instrustar Data Acquisition (DAQ) 2 channel. It is connected to a laptop with VirAnalyser software. Properties taken are spark plug timing, flame size and knock signal sensor. The combination of the data can determine the position of the duration of the flame, where normal knock, moderate knock and extreme knock occur. The results obtained, the largest knock identified at 30.221x10'ms, the speed of 4173 rpm produced 5.776 V. This shows that the size of the knock gets stronger with short pre-spark time, while the speed increases.

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