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
Reset filters

Settings
Now showing 1 - 8 of 8
Thumbnail Image
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.

View
Thumbnail Image
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

View
Thumbnail Image
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%.

View
Thumbnail Image
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

View
No Thumbnail Available
Publication

CFD Simulation on an Improvised Ice Cream Container

2023 , Mohd Tasyrif Abdul Rahman , Nursyazwani Abdul Aziz , Nasrul Amri Mohd Amin , Mohd Ijmal Kamil Patriot , Mohd Sofwan Mohd Resali , Azizul Mohamad , Muhammad Izham Ismail , Mohd Asrul Md Saad

A temperature-sensitive product such as ice-cream may cause the industry to face several challenges throughout the production, storage, packaging, and distribution processes. With the purpose to improve the performance of an ice cream container that acts as cold storage during the delivery process, the integration of a tube-type phase change material (PCM) thermal storage system was studied. In this work, a Computational Fluid Dynamic (CFD) method was used to model and analysed eight designs of phase change thermal storage systems incorporated within the ice-cream container. The tube type PCM was modeled, with and without the conducting pins, aiming to maximise the heat exchange within the system. To obtain a proper design, parametric studies on the number of pins and its diameter were further analysed. For all simulations, the initial time for freezing simulation was set to 2℃, assuming the PCM was fully in a liquid state with the ice mass fraction was set to 0. With that, the PCM average temperature and the total mass fraction was observed and analysed. From the results, the ice mass fraction percentage of the systems was observed to increase with the increasing number of pins. Model with (the maximum) 40 pins has improved ice mass fraction for at least 67.58% when compared to the configuration without pin. Also, the average temperature of PCM for model with maximum pins, was observed to be 37.14% lower when compared to the configuration without pins and less pin numbers. Nevertheless, although the presence of pins has proven to enhance the heat exchange within the system, the percentage of ice formation was considered to be low and the average temperature was still as high as 0.66℃ after 12 hrs of freezing process. This indicating that a proper design of TES is inevitably needed, in order to maximise both heat exchanges and PCM storage ability within the system. © 2023, Semarak Ilmu Publishing. All rights reserved.

View
Thumbnail Image
Publication

Analysis of the effectiveness of Metaheuristic methods on Bayesian optimization in the classification of visual field defects

2023 , Masyitah Abu , Nik Adilah Hanin Zahri , Fumiyo Fukumoto , Amiza Amir , Muhammad Izham Ismail , Yoshimi Suzuki , Azhany Yaakub

Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer learning models to improve the model’s performance significantly. In BO, the acquisition functions direct the hyperparameter space exploration during the optimization. However, the computational cost of evaluating the acquisition function and updating the surrogate model can become prohibitively expensive due to increasing dimensionality, making it more challenging to achieve the global optimum, particularly in image classification tasks. Therefore, this study investigates and analyses the effect of incorporating metaheuristic methods into BO to improve the performance of acquisition functions in transfer learning. By incorporating four different metaheuristic methods, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) Optimization, Harris Hawks Optimization, and Sailfish Optimization (SFO), the performance of acquisition function, Expected Improvement (EI), was observed in the VGGNet models for visual field defect multi-class classification. Other than EI, comparative observations were also conducted using different acquisition functions, such as Probability Improvement (PI), Upper Confidence Bound (UCB), and Lower Confidence Bound (LCB). The analysis demonstrates that SFO significantly enhanced BO optimization by increasing mean accuracy by 9.6% for VGG-16 and 27.54% for VGG-19. As a result, the best validation accuracy obtained for VGG-16 and VGG-19 is 98.6% and 98.34%, respectively.

View
Thumbnail Image
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

View
No Thumbnail Available
Publication

Tidal energy in Malaysia: an overview of potentials, device suitability, issues and outlook

2023 , M.W. Abd Rahim , Anas Abdul Rahman , Muhammad Izham Ismail , Nasrul Amri Mohd Amin

Malaysia is heavily dependent on non-renewable energy sources for electricity generation, particularly fossil fuels such as coal, oil, and gas. However, the alarming increase in CO2 emissions and the depletion of fossil fuel reserves have given rise to imminent challenges in meeting the strong demand for electricity in Malaysia. Thus, this paper explores various types of tidal stream devices that have been experimentally developed for electricity generation and are well established, with a specific focus on potential devices to be implemented in the shallow water environment of Malaysia. These devices are chosen based on the average Malaysian tidal stream velocity, which is approximately 1.0 ms−1, and the average Malaysian water depth, which is approximately 30 m. The selection of the appropriate device is based on six fundamental criteria: (i) power density, (ii) scalability, (iii) durability, (iv) maintainability, (v) economic potential, and (vi) potential issues. Moreover, previous research and development (R&D) studies on tidal streams in Malaysia are taken into consideration in order to identify the most suitable device. Based on the review, it is concluded that the vertical axis tidal turbine (VATT) is the most suitable device for utilisation in the shallow water environment of Malaysia.

View