Muhammad Izham Ismail
Thumbnail Image
Preferred name
Muhammad Izham Ismail
Official Name
Muhammad Izham, Ismail
Alternative Name
Ismail, Muhammad Izham
Ismail, M. I.
Main Affiliation
Scopus Author ID
57212349161
Researcher ID
GAQ-6580-2022

Research Output

Filters

1
5
Reset filters

Settings
Now showing 1 - 5 of 5
  • Publication
    Statistical Analysis on The Near-Wake Region of RANS Turbulence Closure Models for Vertical Axis Tidal Turbine
    ( 2023-01-01)
    Rahim M.W.A.
    ;
    Anas Abdul Rahman
    ;
    Abdul-Rahman A.
    ;
    Muhammad Izham Ismail
    ;
    Mohd Shukry Abdul Majid
    ;
    Nasrul Amri Mohd Amin
    The flow field in the near wake region (up to six turbine diameters downstream) of a tidal current turbine is strongly driven by the combined wake of the device support structure and the rotor. Accurate characterisation of the near-wake region is important, but it is dominated by highly turbulent, slow-moving fluid. At present, limited number of researches has been undertaken into the characterisation of the near-wake region for a Vertical Axis Tidal Turbine (VATT) device using the Reynolds Averaged Navier Stokes (RANS) model in the shallow water environment of Malaysia. This paper presents a comprehensive statistical analysis using the Mean Absolute Error (MEA), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the near-wake region for shallow water application by comparing numerical solutions (i.e., different types of RANS turbulence models using Ansys Fluent) with published experimental data. Seven RANS turbulence models with a single VATT, represented by using a cylindrical object, were employed in the preliminary study. The statistical analysis performed in this study is essential in exploring and giving a detailed understanding on the most suitable RANS turbulence model to be improved, specifically on its near-wake region. In this study, the near wake region is defined as D ≤ 6, where D is the device diameter. The analysis shows that the RANS numerical solutions are unable to accurately replicate the near-wake region based on large statistical errors computed. The average RMSE of near-wake region at z/D = [2, 3, 4, 6] are 0.5864, 0.4127, 0.4344 and 0.3577 while the average RMSE at far-wake region z/D = [8, 12] are 0.2269 and 0.1590, where z is the distance from the cylindrical object along the length of domain. The statistical error values are found to decrease with increasing downstream distance from a cylindrical object. Notably, the standard k–ε and realizable k–ε models are the two best turbulent models representing the near-wake region in RANS modelling, yielding the lowest statistical errors (RMSE at z/D = [2, 3, 4, 6] are 0.5666, 0.4020, 0.4113 and 0.3455) among the tested parameters.
  • Publication
    A first principles study of Palladium-based full Heusler ferromagnetic Pd2MnSb compound
    ( 2023)
    Zeshan Zada
    ;
    Abdul Ahad Khan
    ;
    Ali H. Reshak
    ;
    Abdul Munam Khan
    ;
    Shakeel S.
    ;
    Dania Ali
    ;
    Muhammad Ismail
    ;
    Muhammad Mahyiddin Ramli
  • Publication
    Statistical analysis on the near-wake region of RANS Turbulence Closure Models for Vertical Axis Tidal Turbine
    ( 2023-01-01)
    Muhammad Wafiuddin Abd Rahim
    ;
    Anas Abdul Rahman
    ;
    Ayu Abdul-Rahman
    ;
    Muhammad Izham Ismail
    ;
    Mohd Shukry Abdul Majid
    ;
    Nasrul Amri Mohd Amin
    The flow field in the near wake region (up to six turbine diameters downstream) of a tidal current turbine is strongly driven by the combined wake of the device support structure and the rotor. Accurate characterisation of the near-wake region is important, but it is dominated by highly turbulent, slow-moving fluid. At present, limited number of research has been undertaken into the characterisation of the near-wake region for a Vertical Axis Tidal Turbine (VATT) device using the Reynolds Averaged Navier Stokes (RANS) model in the shallow water environment of Malaysia. This paper presents a comprehensive statistical analysis using the Mean Absolute Error (MEA), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the near-wake region for shallow water application by comparing numerical solutions (i.e., different types of RANS turbulence models using Ansys Fluent) with published experimental data. Seven RANS turbulence models with a single VATT, represented by using a cylindrical object, were employed in the preliminary study. The statistical analysis performed in this study is essential in exploring and giving a detailed understanding on the most suitable RANS turbulence model to be improved, specifically on its near-wake region. In this study, the near wake region is defined as D ≤ 6, where D is the device diameter. The analysis shows that the RANS numerical solutions are unable to accurately replicate the near-wake region based on large statistical errors computed. The average RMSE of near-wake region at z/D = [2, 3, 4, 6] are 0.5864, 0.4127, 0.4344 and 0.3577 while the average RMSE at far-wake region z/D = [8, 12] are 0.2269 and 0.1590, where z is the distance from the cylindrical object along the length of domain. The statistical error values are found to decrease with increasing downstream distance from a cylindrical object. Notably, the standard k–ε and realizable k–ε models are the two best turbulent models representing the near-wake region in RANS modelling, yielding the lowest statistical errors (RMSE at z/D = [2, 3, 4, 6] are 0.5666, 0.4020, 0.4113 and 0.3455) among the tested parameters
  • Publication
    Classification of Multiple Visual Field Defects using Deep Learning
    ( 2021-03-01)
    Masyitah Abu
    ;
    Nik Adilah Hanin Zahri
    ;
    Amir A.
    ;
    Muhammad Izham Ismail
    ;
    Latifah Munirah Kamarudin
    ;
    Nishizaki H.
    In this work, a custom deep learning method is proposed to develop a detection of visual fields defects which are the markers for serious optic pathway disease. Convolutional Neural Networks (CNN) is a deep learning method that is mostly used in images processing. Therefore, a custom 10 layers of CNN algorithm is built to detect the visual field defect. In this work, 1200 visual field defect images acquired from the Humphrey Field Analyzer 24-2 collected from Google Image have been used to classify 6 types of visual field defect. The defect patterns are including defects at central scotoma, right/left/upper/lower quadratopia, right/left hemianopia, vision tunnel, superior/inferior field defect and normal as baseline. The custom designed CNN is trained to discriminate between defect patterns in visual field images. In the proposed method, a mechanism of pre-processing is included to improve the classification of visual field defects. Then, the 6 visual field defect patterns are detected using a convolutional neural network. The dataset is evaluated using 5-fold cross-validation. The results of this work have shown that the proposed algorithm achieved a high classification rate with 96%. As comparison, traditional machine learning Support Vector Machine (SVM) and Classical Neural Network (NN) is chose and obtained classification rate at 74.54% and 90.72%.
      1
  • Publication
    Statistical analysis on the near-wake region of RANS turbulence closure models for vertical axis tidal turbine
    ( 2022)
    Muhammad Wafiuddin Abd Rahim
    ;
    Anas Abdul Rahman
    ;
    Ayu Abdul-Rahman
    ;
    Muhammad Izham Ismail
    ;
    Mohd Shukry Abdul Majid
    ;
    Nasrul Amri Mohd Amin
    The flow field in the near wake region (up to six turbine diameters downstream) of a tidal current turbine is strongly driven by the combined wake of the device support structure and the rotor. Accurate characterisation of the near-wake region is important, but it is dominated by highly turbulent, slow-moving fluid. At present, limited number of research has been undertaken into the characterisation of the near-wake region for a Vertical Axis Tidal Turbine (VATT) device using the Reynolds Averaged Navier Stokes (RANS) model in the shallow water environment of Malaysia. This paper presents a comprehensive statistical analysis using the Mean Absolute Error (MEA), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the near-wake region for shallow water application by comparing numerical solutions (i.e., different types of RANS turbulence models using Ansys Fluent) with published experimental data. Seven RANS turbulence models with a single VATT, represented by using a cylindrical object, were employed in the preliminary study. The statistical analysis performed in this study is essential in exploring and giving a detailed understanding on the most suitable RANS turbulence model to be improved, specifically on its near-wake region. In this study, the near wake region is defined as D ≤ 6, where D is the device diameter. The analysis shows that the RANS numerical solutions are unable to accurately replicate the near-wake region based on large statistical errors computed. The average RMSE of near-wake region at z/D = [2, 3, 4, 6] are 0.5864, 0.4127, 0.4344 and 0.3577 while the average RMSE at far-wake region z/D = [8, 12] are 0.2269 and 0.1590, where z is the distance from the cylindrical object along the length of domain. The statistical error values are found to decrease with increasing downstream distance from a cylindrical object. Notably, the standard k–ε and realizable k–ε models are the two best turbulent models representing the near-wake region in RANS modelling, yielding the lowest statistical errors (RMSE at z/D = [2, 3, 4, 6] are 0.5666, 0.4020, 0.4113 and 0.3455) among the tested parameters
      1  9