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Published by Nurul Adilah, 2019-08-07 00:10:43

ENGINEERING COMPUTATION GROUP

ENGINEERING COMPUTATION GROUP

STAFF

DR. TUAN YUSOF TUAN YA - LEADER
DR. VIVIAN YONG SUET PENG
DR. NORDIN ZAKARIA
DR. SAID JADID ABDULKADIR

RESEARCHED AND ABSTRACTS

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

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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.


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