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Mohammed Suleman Aldlemy
Department of Mechanical Engineering, Collage of Mechanical Engineering Technology, Benghazi, Libya

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Journal article
Published: 31 July 2021 in Nanomaterials
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Numerical studies were performed to estimate the heat transfer and hydrodynamic properties of a forced convection turbulent flow using three-dimensional horizontal concentric annuli. This paper applied the standard k–ε turbulence model for the flow range 1 × 104 ≤ Re ≥ 24 × 103. A wide range of parameters like different nanomaterials (Al2O3, CuO, SiO2 and ZnO), different particle nanoshapes (spherical, cylindrical, blades, platelets and bricks), different heat flux ratio (HFR) (0, 0.5, 1 and 2) and different aspect ratios (AR) (1.5, 2, 2.5 and 3) were examined. Also, the effect of inner cylinder rotation was discussed. An experiment was conducted out using a field-emission scanning electron microscope (FE-SEM) to characterize metallic oxides in spherical morphologies. Nano-platelet particles showed the best enhancements in heat transfer properties, followed by nano-cylinders, nano-bricks, nano-blades, and nano-spheres. The maximum heat transfer enhancement was found in SiO2, followed by ZnO, CuO, and Al2O3, in that order. Meanwhile, the effect of the HFR parameter was insignificant. At Re = 24,000, the inner wall rotation enhanced the heat transfer about 47.94%, 43.03%, 42.06% and 39.79% for SiO2, ZnO, CuO and Al2O3, respectively. Moreover, the AR of 2.5 presented the higher heat transfer improvement followed by 3, 2, and 1.5.

ACS Style

Omer A. Alawi; Ali H. Abdelrazek; Mohammed Suleman Aldlemy; Waqar Ahmed; Omar A. Hussein; Sukaina Tuama Ghafel; Khaled Mohamed Khedher; Miklas Scholz; Zaher Mundher Yaseen. Heat Transfer and Hydrodynamic Properties Using Different Metal-Oxide Nanostructures in Horizontal Concentric Annular Tube: An Optimization Study. Nanomaterials 2021, 11, 1979 .

AMA Style

Omer A. Alawi, Ali H. Abdelrazek, Mohammed Suleman Aldlemy, Waqar Ahmed, Omar A. Hussein, Sukaina Tuama Ghafel, Khaled Mohamed Khedher, Miklas Scholz, Zaher Mundher Yaseen. Heat Transfer and Hydrodynamic Properties Using Different Metal-Oxide Nanostructures in Horizontal Concentric Annular Tube: An Optimization Study. Nanomaterials. 2021; 11 (8):1979.

Chicago/Turabian Style

Omer A. Alawi; Ali H. Abdelrazek; Mohammed Suleman Aldlemy; Waqar Ahmed; Omar A. Hussein; Sukaina Tuama Ghafel; Khaled Mohamed Khedher; Miklas Scholz; Zaher Mundher Yaseen. 2021. "Heat Transfer and Hydrodynamic Properties Using Different Metal-Oxide Nanostructures in Horizontal Concentric Annular Tube: An Optimization Study." Nanomaterials 11, no. 8: 1979.

Research article
Published: 08 March 2021 in Complexity
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Accurate and reliable prediction of Perfobond Rib Shear Strength Connector (PRSC) is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression (SVR), artificial neural network (ANN), and M5 tree model. In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model.

ACS Style

Jamal Abdulrazzaq Khalaf; Abeer A. Majeed; Mohammed Suleman Aldlemy; Zainab Hasan Ali; Ahmed W. Al Zand; S. Adarsh; Aissa Bouaissi; Mohammed Majeed Hameed; Zaher Mundher Yaseen. Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction. Complexity 2021, 2021, 1 -21.

AMA Style

Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed, Zaher Mundher Yaseen. Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction. Complexity. 2021; 2021 ():1-21.

Chicago/Turabian Style

Jamal Abdulrazzaq Khalaf; Abeer A. Majeed; Mohammed Suleman Aldlemy; Zainab Hasan Ali; Ahmed W. Al Zand; S. Adarsh; Aissa Bouaissi; Mohammed Majeed Hameed; Zaher Mundher Yaseen. 2021. "Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction." Complexity 2021, no. : 1-21.

Original article
Published: 09 August 2020 in Engineering with Computers
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The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R2) during the testing phase (R2 = 0.95), whereas the other comparable models generated relatively lower values of R2 ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

ACS Style

Guangnan Zhang; Zainab Hasan Ali; Mohammed Suleman Aldlemy; Mohamed H. Mussa; Sinan Q. Salih; Mohammed Majeed Hameed; Zainab S. Al-Khafaji; Zaher Mundher Yaseen. Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model. Engineering with Computers 2020, 1 -14.

AMA Style

Guangnan Zhang, Zainab Hasan Ali, Mohammed Suleman Aldlemy, Mohamed H. Mussa, Sinan Q. Salih, Mohammed Majeed Hameed, Zainab S. Al-Khafaji, Zaher Mundher Yaseen. Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model. Engineering with Computers. 2020; ():1-14.

Chicago/Turabian Style

Guangnan Zhang; Zainab Hasan Ali; Mohammed Suleman Aldlemy; Mohamed H. Mussa; Sinan Q. Salih; Mohammed Majeed Hameed; Zainab S. Al-Khafaji; Zaher Mundher Yaseen. 2020. "Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model." Engineering with Computers , no. : 1-14.

Journal article
Published: 30 May 2020 in Applied Sciences
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High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.

ACS Style

Ahmad Sharafati; Masoud Haghbin; Mohammed Suleman Aldlemy; Mohamed H. Mussa; Ahmed W. Al Zand; Mumtaz Ali; Suraj Kumar Bhagat; Nadhir Al-Ansari; Zaher Mundher Yaseen. Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction. Applied Sciences 2020, 10, 3811 .

AMA Style

Ahmad Sharafati, Masoud Haghbin, Mohammed Suleman Aldlemy, Mohamed H. Mussa, Ahmed W. Al Zand, Mumtaz Ali, Suraj Kumar Bhagat, Nadhir Al-Ansari, Zaher Mundher Yaseen. Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction. Applied Sciences. 2020; 10 (11):3811.

Chicago/Turabian Style

Ahmad Sharafati; Masoud Haghbin; Mohammed Suleman Aldlemy; Mohamed H. Mussa; Ahmed W. Al Zand; Mumtaz Ali; Suraj Kumar Bhagat; Nadhir Al-Ansari; Zaher Mundher Yaseen. 2020. "Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction." Applied Sciences 10, no. 11: 3811.

Journal article
Published: 26 May 2020 in Energy Reports
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The effectiveness of a flat-plate solar collector was studied by using SiO2, Al2O3, Graphene, and graphene nanoplatelets nanofluids with distilled water as the working fluids. The energy efficiency was theoretically compared using MATLAB programming. The prepared carbon and metallic oxides nanomaterials were structurally and morphologically characterized via field emission scanning electron microscope. The study was conducted under different operating conditions such as different volume fractions (0.25%, 0.5%, 0.75% and 1%), fluid mass flow rate (0.0085, 0.017, and 0.0255 kg/s), input temperatures (30, 40, and 50 °C), and solar irradiance (500, 750, and 1000 W/m2). Nanofluids showed better thermophysical properties compared to standard working fluids. With the addition of the nanofluids SiO2, Al2O3, Gr and GNPs to the FPSC the highest efficiency of 64.45%, 67.03%, 72.45%, and 76.56% respectively was reached. The results suggested that nanofluids made from carbon nanostructures and metallic oxides can be used in solar collectors to increase the parameters of heat absorbed/loss compared to water only usage.

ACS Style

Suhong Liu; Haitham Abdulmohsin Afan; Mohammed Suleman Aldlemy; Nadhir Al-Ansari; Zaher Mundher Yaseen. Energy analysis using carbon and metallic oxides-based nanomaterials inside a solar collector. Energy Reports 2020, 6, 1373 -1381.

AMA Style

Suhong Liu, Haitham Abdulmohsin Afan, Mohammed Suleman Aldlemy, Nadhir Al-Ansari, Zaher Mundher Yaseen. Energy analysis using carbon and metallic oxides-based nanomaterials inside a solar collector. Energy Reports. 2020; 6 ():1373-1381.

Chicago/Turabian Style

Suhong Liu; Haitham Abdulmohsin Afan; Mohammed Suleman Aldlemy; Nadhir Al-Ansari; Zaher Mundher Yaseen. 2020. "Energy analysis using carbon and metallic oxides-based nanomaterials inside a solar collector." Energy Reports 6, no. : 1373-1381.

Review
Published: 24 February 2020 in Sustainability
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Dam and powerhouse operation sustainability is a major concern from the hydraulic engineering perspective. Powerhouse operation is one of the main sources of vibrations in the dam structure and hydropower plant; thus, the evaluation of turbine performance at different water pressures is important for determining the sustainability of the dam body. Draft tube turbines run under high pressure and suffer from connection problems, such as vibrations and pressure fluctuation. Reducing the pressure fluctuation and minimizing the principal stress caused by undesired components of water in the draft tube turbine are ongoing problems that must be resolved. Here, we conducted a comprehensive review of studies performed on dams, powerhouses, and turbine vibration, focusing on the vibration of two turbine units: Kaplan and Francis turbine units. The survey covered several aspects of dam types (e.g., rock and concrete dams), powerhouse analysis, turbine vibrations, and the relationship between dam and hydropower plant sustainability and operation. The current review covers the related research on the fluid mechanism in turbine units of hydropower plants, providing a perspective on better control of vibrations. Thus, the risks and failures can be better managed and reduced, which in turn will reduce hydropower plant operation costs and simultaneously increase the economical sustainability. Several research gaps were found, and the literature was assessed to provide more insightful details on the studies surveyed. Numerous future research directions are recommended.

ACS Style

Zaher Mundher Yaseen; Ameen Mohammed Salih Ameen; Mohammed Suleman Aldlemy; Ameen Mohammed Salih; Haitham Abdulmohsin Afan; Senlin Zhu; Ahmed Mohammed Sami Al-Janabi; Nadhir Al-Ansari; Tiyasha Tiyasha; Hai Tao. State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations. Sustainability 2020, 12, 1676 .

AMA Style

Zaher Mundher Yaseen, Ameen Mohammed Salih Ameen, Mohammed Suleman Aldlemy, Ameen Mohammed Salih, Haitham Abdulmohsin Afan, Senlin Zhu, Ahmed Mohammed Sami Al-Janabi, Nadhir Al-Ansari, Tiyasha Tiyasha, Hai Tao. State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations. Sustainability. 2020; 12 (4):1676.

Chicago/Turabian Style

Zaher Mundher Yaseen; Ameen Mohammed Salih Ameen; Mohammed Suleman Aldlemy; Ameen Mohammed Salih; Haitham Abdulmohsin Afan; Senlin Zhu; Ahmed Mohammed Sami Al-Janabi; Nadhir Al-Ansari; Tiyasha Tiyasha; Hai Tao. 2020. "State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations." Sustainability 12, no. 4: 1676.