Browsing by Author "Maleki, Hamid"

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Citation Count: 0
Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks
(Elsevier, 2024) Jasim, Dheyaa J.; Rajab, Husam; Alizadeh, As'ad; Sharma, Kamal; Ahmed, Mohsen; Kassim, Murizah; Maleki, Hamid
Accurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leveraging Bayesian optimization to refine architectural and training hyperparameters, including hidden layers, neurons, activation functions, standardization, and regularization terms. A comparative analysis of Bayesian acquisition functions-the probability of improvement (POI), lower confidence bound (LCB), expected improvement (EI), expected improvement plus (EIP), expected improvement per second plus (EIPSP), and expected improvement per second (EIPS)-demonstrated that the POI-MLPNN achieves the most accurate results, as evidenced by the lowest MAPE of 1.0923 % and exceptional consistency with an R-value of 0.99811. The EI-MLPNN and EIP-MLPNN models recorded the same outputs. The EI/EIP-MLPNN (R = 0.99668) model excels in consistency over LCB-MLPNN (R = 0.99529) and EIPSP-MLPNN (R = 0.99667). The optimized models offer a reliable, cost-efficient alternate for experimental and computational TPP analyses. Leveraging insights from these models enables better control over nanofluid TPPs in solar systems, enhancing energy conversion efficiency.
Citation Count: 2
A novel insight into the design of perforated-finned heat sinks based on a hybrid procedure: Computational fluid dynamics, machine learning, multi-objective optimization, and multi-criteria decision-making
(Pergamon-elsevier Science Ltd, 2024) Abdollahi, Seyyed Amirreza; Alenezi, Anwur; Alizadeh, As 'ad; Jasim, Dheyaa J.; Ahmed, Mohsen; Fezaa, Laith H. A.; Maleki, Hamid
The optimal design of heat sinks presents a challenge for engineers. Using longitudinal perforations is an innovative technique employed in the design of parallel finned heat sinks that can be applied to various equipment. This technique leads to the simultaneous improvement of the heat transfer rate, pressure drop, and weight of heat sinks. The size (phi) and shape of the perforations alongside the Reynolds number are considered design variables. The results obtained from machine learning showed that the combinatorial algorithm is more reliable in modeling various objectives compared to the GMDH neural networks. The Pareto fronts generated by the NSGA-II algorithm indicated that >75% of the optimal points in the perforated-finned heat sinks (PFHSs) with square perforations had a phi >= 0 .6. The reason for this superiority is the geometric compatibility between the square perforations and rectangular fins. This compatibility enables the possibility of enlarging the perforations, resulting in improvements in essential parameters like heat dissipation, drag force, and overall heat sink volume. Various scenarios for weighting objectives in the multi-criteria decision-making (MCDM) process revealed that square-based PFHSs with Reynolds numbers around 39,900 in a wide range of perforation sizes could be applied as optimal design in real-world applications.
Citation Count: 1
Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making
(Elsevier Science Sa, 2024) Zhang, Tao; Salahshour, Soheıl; Sajadi, S. Mohammad; Jasim, Dheyaa J.; Nasajpour-Esfahani, Navid; Maleki, Hamid; Baghaei, Sh.
The rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their thermophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanofluids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparticle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques, TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al2O3-based nanofluid, around 90 % of the optimal points were within 40-50 degrees C. Conversely, for nanofluids containing CeO2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO2/ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications.