Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Abstract

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

“…From the result it is revealed that the COF of GFRPA66 with 35 wt.% is low as compared with GFRPA66 with 30 wt.% reinforcements, since it has better transfer layer formation, increased adhesion of PA66, and low abrasion by glass fiber with less temperature between the contact surfaces. Also the elastic modulus and ultimate strength of glass fiber improve as the weight of glass fiber increases [ 70 , 71 , 72 , 73 ].…”

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder

“…Since GP and SVM-Schotastic model performed the best among the other models for FS and CS for this dataset, sensitivity analysis was carried out on it by changing the input combination and taking out one input parameter at a time, as shown in Table 9 and Table 10 . Statistical assessment metrics such as CC, MAE, and RMSE were used to assess each model’s performance [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ]. Table 9 and Table 10 , demonstrates that the number of curing days followed by CA, C, w and MP is critical in predicting the flexural and compressive strength of a concrete mix.…”

“…Table 9 and Table 10 , demonstrates that the number of curing days followed by CA, C, w and MP is critical in predicting the flexural and compressive strength of a concrete mix. Due to the pozzolanic reactions, concrete recovers 60% of its strength after 7 days of curing and increases by 99% after 28 days, resulting in a low CC value after removing the aforementioned characteristic [ 76 , 77 , 78 , 79 ]. The pozzolanic reaction is a slow process, and as the curing period lengthens, the amount of gel produced in the mix increases, resulting in greater strength [ 65 ].…”

Applications of Thermoelectric Generators To Improve Catalytic-Assisted Hydrogen Production Efficiency: Future Directions

“…Global warming as a consequence of burning fossil fuels has encouraged researchers to explore, design, and optimize alternative renewable energy technologies. − The establishment of sustainable energy sectors aims to decrease greenhouse gas (GHG) emissions and thus reduce global warming to well below 2 °C to align with the goals set out in the Kyoto Protocol and the Paris Agreement . In this approach, thermoelectric generators (TEGs) have shown promising capabilities in recovering the low-grade waste energy. − This technology is an effective method employed to take full advantage of using wasted thermal energy to improve the efficiency of clean energy sources, such as solar and geothermal.…”