Wah Yen Tey

Plastic pollution is a pressing issue that has affected not only the oceans and rivers but also the coastal region. While studies have been conducted to determine the pollution in mangrove forests, still little can be done to reduce the pollution issue. The intricate network of mangrove root systems contributes to this issue. This study aimed to create and analyse artificial mangrove root models by using 3D-printed models and Computational Fluid Dynamics (CFD) simulations. The models, with varying porosity levels, were tested for their ability to reduce water flow and capture debris. The results showed that the model with higher porosity (94.5%) exhibited reduced flow attenuation and delayed wake formation compared to the model with lower porosity (75.1%). Tidal wave tank experiments confirmed that higher porosity consistently led to higher debris trapping percentages, regardless of debris mass, size, or type. The increased porosity reduced flow interaction and turbulence, enhancing the effectiveness of debris trapping. These findings contribute to developing more efficient artificial mangrove systems that can effectively mitigate debris transport and accumulation in aquatic environments.

The finite element method (FEM) is a robust and widely applied numerical scheme in the simulation of engineering problems, especially in structural mechanics. However, FEM is not as popular as the finite volume method (FVM) in Computational Fluid Dynamics (CFD), possibly due to its complicated numerical procedures. Indeed, FEM possesses tremendous advantages compared with FVM, particularly in dealing with complex geometry and rendering attractive flexibility to modify the interpolation functions. It is well-known that FEM and FVM differ in mathematical formulation, yet there is a lack of practical comparison between them. Therefore, the paper aims to develop a Galerkin FEM (GFEM) model, investigate its strengths and weaknesses compared with FVM, and discuss the conciliation between FEM and FVM. Our case study focuses on a two-dimensional diffusion problem comprising steady and transient cases, with and without heat generation. Our investigation revealed that GFEM does not possess conservative properties, which might yield spurious heat flux, leading to a 2 – 4% overestimation of the temperature field, depending on the amount of heat generation. Moreover, GFEM incurs approximately 34% higher computational time than FVM. However, FVM can be perceived as a special form of GFEM, and their relations were discussed.

An autonomous car is a one-of-a-kind specimen in today's technology. It is an automatic system in which most of the duties that humans undertake in the car can be done automatically with minimum human supervision for road safety features. Moving automobile detections, on the other hand, are prone to more mistakes and can result in undesirable situations such as minor car wrecks. Moving vehicle identification is now done using high-speed cameras or LiDAR, for example, whereas self-driving cars are produced with deep learning, which requires much larger datasets. As a result, there may be greater space for improvement in the moving vehicle detection model. This research intends to create another moving car recognition model that uses multi-scale feature-based detection to improve the model's accuracy while also determining the maximum speed at which the model can detect moving objects. The recommended methodology was to create a lab-scale model that can be used as a guide for video and image capture on the lab-scale model, as well as the speed of the toy vehicles captured from the Arduino Uno machine before testing the car recognition model. According to the data, Multi-Scale Histogram of Oriented Gradient can recognize more objects than Histogram of Oriented Gradient with higher object identification accuracies and precision.

Modifiers such as fibers, fillers, natural and synthetic polymer extenders, oxidants and anti-oxidants, and anti-stripping agents are added to produce modified asphalt. However, polymers are the most widely utilized modifiers to enhance the function of asphalt mixtures. The objective of this research was to evaluate the mechanical properties and durability of epoxidized natural rubber (ENR)-modified asphalt mix under short- and long-term aging conditions. The physical and rheological characteristics of the base asphalt and ENR-modified asphalt (ENRMA) were tested. In order to evaluate the mechanical properties and durability of the modified mixtures, the resilient modulus of the ENR–asphalt mixtures under unaged, and short- and long-term aging conditions at various temperatures and frequencies was obtained. Furthermore, the resistance to moisture damage of asphalt mixtures was investigated. The findings showed that the stiffness of the ENR–asphalt mixes increased because of the mutual influence of short- and long-term aging on the mixes. In addition, ENR reduced the susceptibility to moisture damage. The stiffness of the mixes was influenced by the temperature and frequencies. By using mathematical modelling via the multivariable power least squares method, it was found that temperature was the dominant factor among all other factors. The results suggested that the durability of asphalt pavements is improved by using ENR.

Cellulose fibers isolated from oil palm empty fruit bunches (OPEFB) have been studied as a potential reinforcement for polyvinyl alcohol (PVA) biocomposite. Analysis of variance (ANOVA) showed that all three parameters—hydrolysis temperature, time and acid concentration, as well as their interactions—significantly affected the yield of cellulose. Moving Least Squares (MLS) and Multivariable Power Least Squares (MPLS) models demonstrated good fitness. The model also proved that acid concentration was the dominant parameter, supported by the Fourier transform infrared spectroscopy (FTIR) analysis. Hydrolysis using 54% acid at 35 °C and 15 min achieved the highest cellulose yield of 80.72%. Cellulose-reinforced PVA biocomposite films demonstrated better mechanical strength, elongation at break, moisture barrier properties, thermal stability and poorer light transmission rate compared to neat PVA due to the high aspect ratio, crystallinity and good compatibility of cellulose fibers. These findings suggested the potential of cellulose fibers-reinforced PVA biocomposite film as water-soluble detergent capsules.

Potential flow formed by doublet flow has been well applied in environmental applications and geothermal designs such as reservoir and fuel injectors. Most of the doublet flow is assumed based on the sink and source with equivalent strength and distance from the origin, forming the well-known Rankine oval structure when a far-field flow is superposed. A semi-analytical method is formulated to systematically investigate the unsymmetrical doublet flow with different strengths of sink and source. The general mathematical expression for unsymmetrical doublet flow is derived analytically before the streamline and the potential line can be visualised via a numerical approach. The results revealed that the doublet flows altered the Rankine oval structure to form aerofoil-like geometry. When the far-field flow interferes with the general Doublet configuration, unique flow structures such as convex, concave, and various wing shapes could be generated. The current study provides new insight on producing aerodynamic curves for the design of bio-inspired structures.

Now a days, the global warming has increased the temperature in the environment that forced the building occupant to get assisting from the air condition to reduce the heat tension inside the building, this could increase the electricity bill amount. The aim of this study is to measure the optimum thickness of Rockwool insulation to experimentally and numerically to reduce the heating load inside the buildings. Two devices have been used through this research, Infrared Thermometer to measure profile temperature of the walls along with VELOCICALC to measure the air temperature and air velocity. Three different layers of Rockwool insulation have been applied on the roof of wooden room. The data present the two layers thickness of Rockwool is the best selection to reduce the heating load inside the room, the differential between outside and inside is 0.9 °C, the Rockwool of one layer reduced only 0.5 °C and the maximum thickness with three layers reduced only 1 °C, which is not much effective compared to the two layers but even more costly. CFD analysis shows agreement with the experimental result. The results shows if the dimensions of a UCSI lecture room is to be considered, then applying Rockwool insulation with a thickness of 100 mm would cost around RM 1520 as a UCSI lecture room is of 8 m width and 9 m length. However, two layers of Rock wool insulation could save around 29.30% of ROI per annum.

The presence of fine particles has reduced the quality of water resources. Unfortunately, the fine particles normally take a longer time to be removed. Realizing this, the present study aims to employ a magnetic adsorbent to assist a fast separation of the fine particles from their solution. Here, micron-sized SiO₂ particles were used as the model fine particles; meanwhile, the magnetic adsorbent was designed by functionalizing iron oxide nanoparticles (IONPs) with chitosan. A process study was done based on the effects of medium pH, IONPs concentration, and contact time. Results implied that both bare IONPs and chitosan functionalized-IONPs (denoted as CF-IONPs) were best in removing SiO₂ at pH 4 owing to the electrostatic attraction force. However, as compared to the bare counterpart, CF-IONPs is more suitable for large scale application as it is effective in a wider pH range, requires lesser adsorbent dosage and shorter adsorption time. Despite that, removal of SiO₂ using both types of adsorbents were found to follow pseudo-second order kinetic. The obtained data was modeled using either the moving least squares (MLS) or multivariable power least squares (MPLS) method. In particular, the indices of each parameter obtained from the MPLS-generated equation indicated that the most dominant parameter that governing the SiO₂ removal by bare IONPs is medium pH, while the one for CF-IONPs is contact time.