Fang Yenn Teo's Lab
Institution: University of Nottingham, Malaysia Campus
Department: Department of Civil Engineering
About the lab
Featured projects (4)
To explore the application of SUDS as highlighted in Manual Saliran Mesra Alam or MSMA
Featured research (17)
Climate change have led to extreme weather events such as higher rainfall frequency that can cause severe flooding. In Malaysia, there is an increasing trend on extreme rainfall events and short temporal rainfall, particularly during the inter-monsoon season. In order to protect private properties and public premises from flooding, Mobile Flood Wall Barrier (MFWB) has been found to be more suitable as it is less costly, easier to deploy and does not require large space. Buildings such as factories and commercial shops that have larger entrances, they would require heavy-duty type of MFWB as compared to those for residential buildings. Heavy-duty MFWB has a better ability to withstand higher hydrostatic pressure from floodwater, hence suitable for public premises and buildings in industrial and commercial areas. In this paper, various types of heavy-duty MFWB and their application will be presented and discussed. The standards for testing MFWB products presented in this paper are summarised. Some existing testing requirements are also presented. Based on the review, the mobility characteristic indicated that the heavy MFWB can be installed temporarily to prevent flooding and be removed easily to ensure no interruption to the daily activities after flood events. There are many potential advantages for flood protection, in particular, it serves as the way forward for Malaysia.
Watercourses and roadways commonly intersect in their layout at many locations through bridges, drainages, and fords. During heavy rain events, watercourses may overflow causing serious disturbance toward traffic movement. Under such circumstances, attempting to drive through these intersections can be extremely dangerous. Therefore, understanding the responses of the vehicles moving through floodwaters is of utmost importance. Between 1967 and 2021, several studies have been published investigating the stability of static flooded vehicles. However, studies on the stability of vehicles in the movement are not sufficient at which only few experimental studies were published. Herein, for the very first time numerical simulations were conducted to investigate the hydrodynamic forces on a full‐scale medium‐size passenger vehicle moving perpendicular to the incoming floodwaters. Sliding and floating instability modes were observed by detecting the position of the vehicle centre of mass at each time step. Further, horizontal (FH) and vertical (FV) forces were measured and plotted against the governing flow parameters. Finally, it was observed that the critical flow depth was 0.38 m, while the minimum depth×velocity$$ depth\times velocity $$ threshold function was 0.39 m2/s, for the tested vehicle. Later, a comparison between simulation outcomes and previously published experimental work was performed and a good agreement was observed.
Seawater reverse osmosis (SWRO) system is popular in most islands and coastal areas around the world with limited fresh water resources. A SWRO system was designed to fulfil 50% of the population water demand in Cape Town, South Africa, which can lead up to 383,353 m3/day in the next 15 years. The total dissolved solid (TDS) within the product (i.e. drinking water) was lowered to 600 ppm by referring to standard guideline stated by the World Health Organization (WHO). Design flowrate was set at 766,704 m3/day and the parameters such as feed, permeate, concentrate flowrate, number of vessels, number of membrane elements, number of stages, seawater chemical composition, temperature, pressure, and system configuration were considered in this study. Reverse Osmosis System Analysis (ROSA) 2017 software was integrated the design with simulation to further understand how different parameters affect one another in SWRO system. The TDS standard value of 600 ppm in the products were 73.76 ppm and 181.13 ppm after further optimisation. The efficiency of the SWRO system recovery rate was 75.87% and specific energy consumption (SEC) was 6.18 kwh/m3, greater than the previous value of 50% and 9.7 kwh/m3 in terms of recovery percentage and SEC respectively. The findings indicate that a lower amount of feed and energy is needed to achieve the desired production value. Hence, it is also resulting in major savings in terms of operating cost for SWRO system.
During extreme flood events, various debris like floating vehicles can block the bridges in urban rivers and floodplains. Blockage of vehicles can influence the floodwater hydrodynamics and potentially on the flood risk implications. Such obstructions often raise upstream water levels with back water effects, causing more water to be redirected into nearby metropolitan areas. This study attempts at evaluating artificial neural network (ANN) model in predicting the variations in floodwater depths and velocities along the channel centreline based on the changes in flowrate and distances from the inlet. The floodwater depth and velocity variations were obtained for three different types of bridges at specified sites along the channel centreline with three incoming discharges. A multilayer feedforward neural network (FFNN) model was used to investigate the effects of discharge (Q) and distance, on depth variation rate (D) or velocity (V). Additionally, a comparison study was done between 2 input 1 output and 2 input 2 output i.e. single output (depth variation rate (D) or velocity (V) versus multi-output depth variation rate (D) and velocity (V) for all the three models of bridges that are blocked by vehicles. The study has applied 12 training algorithms (TA) in the ANN modelling to optimize the TA that is most suitable for the dataset of three different bridges. The optimization is based on the performance criterion namely regression (R), mean squared error (MSE), mean absolute error (MAE), mean absolute percentage (MAPE), accuracy and coefficient of determinant (R ² ). Bayesian regularization backpropagation (BR) training algorithm gives a highest accuracy when compared in all three bridges. The scenario 2 input 2 output gave greatest accuracy results compared to 2 input 1 output. The findings showed a reliable estimation of significant impacts on the flow propagations and the hydrodynamic processes along rivers and floodplains. This study can help the decision makers in effective river and floodplain management practices.
- Department of Civil Engineering
About Fang Yenn Teo
- Fang Yenn Teo currently works at the Department of Civil Engineering, the University of Nottingham, Malaysia Campus. Fang Yenn does research on Hydro-Environmental Engineering, Flood Risk Modelling, and Water Resources Management.
Kian Wah Liew
Selam Solomon Gebreselassie
Wong Jun Lim