Lim Eng Aik

Simulating Evacuations with Obstacles Using a Modified Dynamic Cellular Automata Model

A modified dynamic cellular automata model is proposed to simulate the evacuation of occupants from a room with obstacles. The model takes into account some factors that play an important role in an evacuation process, such as human emotions and crowd density around the exits. It also incorporates people’s ability to select a less congested exit route, a factor that is rarely investigated. The simulation and experimental results show that modifications to the exits provide reasonable improvement to evacuation time, after taking into account the fact that people will tend to select exit routes based on the distance to the exits and the crowd density around the exits. In addition, the model is applied to simulations of classroom and restaurant evacuation. Results obtained with the proposed model are compared with those of several existing models. The outcome of the comparison demonstrates that it performs better than existing models.

Intelligent Exit-Selection Behaviors during a Room Evacuation

A modified version of the existing cellular automata (CA) model is proposed to simulate an evacuation procedure in a classroom with and without obstacles. Based on the numerous literature on the implementation of CA in modeling evacuation motions, it is notable that most of the published studies do not take into account the pedestrians ability to select the exit route in their models. To resolve these issues, we develop a CA model incorporating a probabilistic neural network for determining the decision-making ability of the pedestrians, and simulate an exit-selection phenomenon in the simulation. Intelligent exit-selection behavior is observed in our model. From the simulation results, it is observed that occupants tend to select the exit closest to them when the density is low, but if the density is high they will go to an alternative exit so as to avoid a long wait. This reflects the fact that occupants may not fully utilize multiple exits during evacuation. The improvement in our proposed model is valuable for further study and for upgrading the safety aspects of building designs.

Real-Time People Counting System using Curve Analysis Method

A new people-counting system is described in this paper. The proposed system showed its effectiveness and flexibility to count the number of people in a certain image and performing real-time people counting. A Curve Analysis method is developed for the crowd monitoring system to count the objects. Promising results were obtained and the evaluation on their performance indicates the proposed Curve Analysis method counting method provides good results.

Optimization of Tesla turbine using Computational Fluid Dynamics approach

A Tesla turbine is developed in order to utilize the potential energy within household water supply and convert to electricity energy without significant head loss. Pressure within the water supply having higher potential energy compared to the energy needed to reach the reservoir tank. This extra potential energy can be utilized and convert to useful energy before it being waste after reach reservoir tank. The development of Tesla turbine is carried out to determine disc size, disk gap and number of disc base on theoretical calculation of Tesla turbine. Optimization is done by using Computational Fluid Dynamics (CFD) software package. Actual performance analysis for prototype based on RPM and torque also conducted. After the optimization, we observed that the Tesla turbine design yields torque of 0.0330N.m with an efficiency of 10.7% .

Response Surface Analysis of Crowd Dynamics during Tawaf

A refined cellular automata model is applied to simulate the crowd movement of Muslim pilgrims performing the Tawaf ritual within the Al-Haram Mosque in Mecca. The results from the simulation are obtained and the influence of the predictor variables of the evacuation process (pedestrian flow and Tawaf duration) on the responses (pedestrian density, average walking speed, and cumulative evacuee) is investigated using response surface methodology (RSM). The average results from the experiments with an rms error less than 0.5 are obtained from the RSM. Its performance indicates that the RSM possesses excellent predictive ability for the model evacuation study, because both the experimental and the predicted values agree well with the results obtained in this study.

Static hole error analysis within laminar and turbulent regime using CFD approach

This project is regarding conduct static hole error analysis on Venturi Tube using CFD approach in order to having better visualization of this phenomenon. Based on the dimensionless analysis, relevant parameters to static hole error are identified and the effect of ratio of pressure tap depth to pipe diameter range 0.4, 1.0, 1.5, 2.0, 2.5 and 3.0 and ratio of pressure tap diameter to pipe diameter range 0.02, 0.04, 0.06 and 0.08 were simulated in both laminar and turbulent flow regime. The results were compared with journal papers results for validation with error of 1.86%. Besides that, results from static hole error analysis shows both hole diameter and depth of hole can influence the static pressure measurement.

Computational fluid dynamics analysis of shelland-double concentric-tube heat exchanger

A shell-and-double concentric-tube heat exchanger is a new invention in heat transfer devices that is used for transfer of internal thermal energy between three fluids at different temperatures. The structure of the heat exchanger for this project is made of a shell which encloses sixty-six double concentric tubes. There are three inlets and three outlets for all the fluids. The typical shell-and-tube heat exchanger is not ideal in terms of its size. This contributes to an increased cost of manufacturing and installation, and on top of that, consumes a lot of space. This project is conducted using Computational Fluid Dynamics software package of EFD.Lab. At the end, the result in terms of the length of the heat exchanger required to achieve the desired outlet fluid temperature is compared with Kern method. It is found that all results have close agreement with each other, with acceptable range of percentage differences and errors.

An incremental clustering algorithm based on Mahalanobis distance

Classical fuzzy c-means clustering algorithm is insufficient to cluster non-spherical or elliptical distributed datasets. The paper replaces classical fuzzy c-means clustering euclidean distance with Mahalanobis distance. It applies Mahalanobis distance to incremental learning for its merits. A Mahalanobis distance based fuzzy incremental clustering learning algorithm is proposed. Experimental results show the algorithm is an effective remedy for the defect in fuzzy c-means algorithm but also increase training accuracy.

Tesla turbine for energy conversion: An automotive application

This project is to design and fabricate the Tesla turbine with gas as working fluid and looking at the potential of installation a Tesla turbine onto the car in order to utilize those energy which originally generated from vehicles engine and loss to environment due to drag force, and convert partial back to electrical energy and store within battery which hopefully can be used in hybrid vehicles or electrical vehicles for small energy consumption applications. The optimum design is determined based on the study parameters such as distance between disks and total contact area between working fluid and disks. In addition, the Tesla turbines torque is analyzed using Computational Fluid Dynamics (CFD) Software and found that the best distance between disks is 0.001 m while the most suitable contact area between working fluid and disks is 0.099 m2. At the end of the project, it can be observed that the torque has increased by 29.6 % while the head loss has a significant drop of 26.9 %.

Effects of traffic speed level on frustration index in cellular automata traffic simulation

Traffic system shows a very complex and stochastic features, but it seems to be simulated successfully by cellular automata (CA) models. Various models have been developed in order to study and understand traffic behavior and network traffic situations. In this study, the relationship for speed and frustration index is simulated by CA under multi-class user traffic conditions. Acceleration rule, slow down probability, traffic density, maximal and minimal speeds are main effects, which determine driving behavior. According to the results, it is found that either the mixture of the average speed-frustration index or density-frustration index of multi-class traffic is the linear combination of users involving in traffic flow. Therefore, to gain a better understanding on multi-class user traffic is better to simulate it directly instead of estimating it by using the combination of each user.