Enhancing Solar Energy Conversion Efficiency: Thermophysical Property Predicting of MXene/Graphene Hybrid Nanofluids via Bayesian-Optimized Artificial Neural Networks

Results in Engineering, Год журнала: 2024, Номер 24, С. 102858 - 102858

Опубликована: Сен. 7, 2024

Язык: Английский

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Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models

Fuel, Год журнала: 2024, Номер 374, С. 132431 - 132431

Опубликована: Июль 8, 2024

Язык: Английский

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Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 31, 2024

Abstract Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications building materials, textiles, cooling systems. This study focuses on accurately predicting the dynamic viscosity, critical thermophysical property, of suspensions MPCMs MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) GPR are analyzed separately classified into three groups based their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), marine predators (MPA), employed to optimize HPs. Optimizing four most significant (covariance function, basis standardization, sigma) within first group any algorithms resulted excellent outcomes. All achieved reasonable R-value (0.9983), demonstrating effectiveness this context. The second explored impact including additional, moderate-significant HPs, such as fit method, predict method optimizer. While resulting models showed some improvement over group, PSO-based model exhibited noteworthy enhancement, achieving higher (0.99834). Finally, third was examine potential interactions between all twelve comprehensive approach, employing GA, yielded an optimized with highest level target compliance, reflected by impressive 0.999224. developed cost-effective efficient solution reduce laboratory costs for various systems, from TES management.

Язык: Английский

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A novel CFD framework for frost-free domestic refrigerators using fan performance curve and radiator model

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103947 - 103947

Опубликована: Янв. 1, 2025

Язык: Английский

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Numerical investigation on the thermal performance of perforated and non-perforated twisted fins at different twisting angles

Results in Engineering, Год журнала: 2024, Номер 23, С. 102332 - 102332

Опубликована: Июнь 15, 2024

The present study numerically investigated the effect of variations in twisting angle and perimeter diamond-shaped perforation on heat transfer twisted fin sinks. showed a possible configuration sink design, aiming to improve hydrothermal performance factor (HTPF). ANSYS/FLUENT computational fluid dynamics software was used perform simulations, Reynolds-averaged Navier–Stokes-based k−ε turbulence model used. Results revealed that maximum 46 % enhancement Nusselt number 25 increase (HTPF) could be attained with 540° comparison conventional cylindrical fins. Furthermore, variation at resulted 28 value 36 HTPF fins no perforations. Therefore, this is recommended as optimal configuration.

Язык: Английский

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Comparative investigation on heat transfer augmentation in a liquid cooling plate for rectangular Li-ion battery thermal management

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102913 - 102913

Опубликована: Сен. 1, 2024

Язык: Английский

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Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 27, 2024

Abstract Optimization of thermophysical properties (TPPs) MXene-based nanofluids is essential to increase the performance hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a approach optimize TPPs Ionanofluids. The input variables are MXene mass fraction (MF) temperature. optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), conductivity (TC). In proposed approach, powerful group method data handling (GMDH)-type ANN technique used model in terms variables. obtained models integrated into multi-objective particle swarm (MOPSO) exchange (MOTEO) algorithms, forming three-objective problem. final step, TOPSIS technique, one well-known multi-criteria decision-making (MCDM) approaches, employed identify desirable Pareto points. Modeling results showed that developed for TC, DV, SHC demonstrate strong by R-values 0.9984, 0.9985, 0.9987, respectively. outputs MOPSO revealed points dispersed broad range MFs (0-0.4%). However, temperature these optimal was found be constrained within narrow near maximum value (75 °C). scenarios where TC precedes other objectives, recommended utilizing an MF over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt ranging from 0.15 0.17%. Also, becomes primary concern, advised base fluid without any additive.

Язык: Английский

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Accurate prediction of the rheological behavior of MWCNT-Al2O3/water-ethylene glycol nanofluid with metaheuristic-optimized machine learning models

International Journal of Thermal Sciences, Год журнала: 2025, Номер 211, С. 109691 - 109691

Опубликована: Янв. 13, 2025

Язык: Английский

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Synergizing Neural Networks with Multi-Objective Thermal Exchange Optimization and PROMETHEE Decision-Making to Improve PCM-based Photovoltaic Thermal Systems

Case Studies in Thermal Engineering, Год журнала: 2025, Номер unknown, С. 105851 - 105851

Опубликована: Фев. 1, 2025

Язык: Английский

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Artificial intelligence aided microwave coagulation therapy: Analysis of heat transfer to tumor tissue via hybrid modeling

Case Studies in Thermal Engineering, Год журнала: 2025, Номер unknown, С. 105927 - 105927

Опубликована: Фев. 1, 2025

Язык: Английский

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