Researcher: Osama Sabir and Tuan Yusof Tuan Ya
Title: Numerical Simulation of Fluid Flow based on 2 Stage Pressure Velocity Correction

A two stage pressure-velocity correction approach for immersed boundary method is proposed. The model is illustrated the interaction between incompressible viscous fluid and immersed bodies in three dimensional domain. A second correction step is added to the pressure value and the velocity vectors using Simplified Marker and Cell method (SMAC). This new scheme is applied on staggered grid using implicit finite difference methods in order to achieve second order accuracy. The algorithm is validated in comparison with a bench mark fluid solid structure case of laminar flow around a cylinder. The drag and lift coefficients are chosen to be the fundamental element to authenticate the new algorithm. Adding new stage of second pressure corrections did not affect the computations cost comparing with single correction stage methods. The preliminary results show there is a strong statistical correlation between Reynolds number and the error in pressure values. The stability of the developing method remain at the same level with other immersed boundary methods. Associated with conventional numerical optimization methods, the proposed approach achieve an acceptable degree of convergence rate per iteration and confirm decent performance.

Researcher: Montaser Osman
Title: Dynamics of Mooring line systems considering mooring, water, soil interactions: Experimental studies vs numerical predictions

Mooring lines, which are essential components of floating offshore platforms, are used to anchor the platform to the seabed. Quasi-static analysis ignores the effect of line dynamics which, in some situations, may prove to be significant element in the dynamic analysis of a moored offshore vessel, particularly in ultra deep water. Coupled analysis, which simultaneously solves the dynamics of the platform and the mooring system, can handle such a vessel/mooring/riser coupling including all the dynamic effects. However, such analysis may become quite expensive. This project aims to develop an efficient hybrid criteria for the mooring line analysis, which considers the dynamic effect as well as the static effect.

Researcher: Said Jadid Abdulkader, Vivian Yong Suet Peng, Nordin Zakaria
Title: Neural Network Model for Metocean Data Analysis

Metocean time-series data is generally classified as highly chaotic thus making the analysis process tedious. The main aim of forecasting Metocean data is to obtain an effective solution for offshore engineering projects, such analysis of environmental conditions is vital to the choices made during planning and operational stage which must be efficient and robust. This paper presents an empirical analysis of Metocean time-series using a hybrid neural network model by performing multi-step-ahead forecasts. The proposed hybrid model is trained using a gauss approximated Bayesian regulation algorithm. Performance analysis based on error metrics shows that proposed hybrid model provides better multi-step-ahead forecasts as in comparison to previously used models.

Researcher: Paras Q. Memon, Vivian Yong Suet Peng, William Pao
Title: Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network

Reservoir simulation provides information about the behaviour of a reservoir in various production and injection conditions. Reservoir simulator is used to predict the future behaviour and performance of a reservoir field. However, the heterogeneity of reservoir and uncertainty in the reservoir field cause some obstacles in selecting the best calculation of oil, water and gas components that lead to the production system in oil and gas. This paper presents a dynamic well Surrogate Reservoir Model (SRM) to predict reservoir bottom-hole flowing pressure by varying the production rate constraint of a well. The proposed SRM adopted Radial Basis Neural Network to predict the bottom-hole flowing pressure of well based on the output data extracted from a numerical simulation model in a considerable amount of time with production constraint values. It is found that the dynamic SRM is capable to generate the promising results in a shorter time as compared to the conventional reservoir model

Researcher: Abdul Latiff Yussiff, Vivian Yong Suet Peng
Title: Detecting People Using Histogram of Oriented Gradients: A Step towards Abnormal Human Activity Detection

Human activity understanding is a branch of research in computer vision that has attracted a lot of attention for decades. Accurate identification of humans in video surveillance is fundamental prerequisite towards activities' understanding. Little or no research has been conducted for human detection in financial endpoint premises specifically Automatic Teller Machine (ATM) sceneries. The video surveillance settings have some unique features compared to others applications: static and non-uniform background, low resolution images, and lack of initial background model. The Histogram of oriented gradient technique was used to locate people in each frame of the surveillance video.

Researcher: Djamalladine Mahamat Pierre, Nordin Zakaria
Title: Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage policies addresses a common limitation of the traditional genetic algorithm when optimizing complex combinatorial problems. The limitation, in question, is the inability of the traditional genetic algorithm to perform local optimization. A series of tests based on the Solomon benchmark instances show the level of competitiveness of the newly introduced crossover operator.

Researcher: Ferozkhan Safiyullah, Shaharin Anwar Sulaiman, Nordin Zakaria
Title: Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM). The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

Researcher: Shakirah Mohd Taib
Title: Analysis of Time Series Representation in Weather Prediction

Most of the weather time series dataset were collected through sequential observations. The weather time series use large space and computationally expensive due to various complex predictors. A number of algorithms have been adopted in the development of weather analysis and weather forecasting model. The performance of the model can be influenced by many factors including the representation of weather data. This study compares the representation and analysis of weather time series data in the weather forecasting development process.