CFD Analysis of Heat Transfer Enhancement in a Flat-Plate Solar Collector with Different Geometric Variations in the Superficial Section

Quitiaquez, William; Estupiñán-Campos, José; Nieto-Londoño, César; Isaza-Roldán, C.A.; Quitiaquez, Patricio; Toapanta-Ramos, Fernando

doi:10.18517/ijaseit.11.5.15288

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…By analyzing the patterns of sunshine, the topography, and other geographical aspects, machine learning algorithms are able to provide recommendations for the optimal locations and angles for the deployment of solar panels in order to maximize energy capture [165], [166]. When this process is optimized, not only does it increase the efficiency of solar farms, but it also ensures that smaller, dispersed installations such as rooftop panels make a significant contribution to the production of electricity [167], [168]. An additional benefit of machine learning is that it assists with the maintenance and operation of solar power plants [169].…”

Section: A Ai and ML Techniques In Renewable Energy Forecasting 1) So...mentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

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Section: A Ai and ML Techniques In Renewable Energy Forecasting 1) So...mentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…This underscores the urgency of transitioning to cleaner energy sources to diminish dependence on fossil fuels, especially for vital requirements like heating water. Solar energy offers a sustainable solution to decrease conventional energy consumption, cut costs, and mitigate greenhouse gas emissions [1][2][3]. Despite the promising strides in solar energy applications, challenges persist, including intermittency, lower thermal efficiency compared to conventional sources, and temporal imbalances [4].…”

Section: Introductionmentioning

confidence: 99%

“…1 PCM is not specified/disclosed. 2 HTF is no specified/disclosed. The literature review underscores the widespread utilization of rectangular fins for HTE across various applications.…”

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confidence: 99%

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Heat Transfer Enhancements Assessment in Hot Water Generation with Phase Change Materials (PCMs): A Review

Berrocal,

Blandon Rodriguez,

Ortega Del Rosario

et al. 2024

Energies

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The utilization of phase change materials (PCMs) in solar water heating systems (SWHS) has undergone notable advancements, driven by a rising demand for systems delivering superior performance and efficiency. Extensive research suggests that enhancing heat transfer (HTE) in storage systems is crucial for achieving these improvements. This review employs a bibliometric analysis to track the evolution of HTE methods within this field. While current literature underscores the necessity for further exploration into hot water generation applications, several methodologies exhibit significant promise. Particularly, strategies such as fins, encapsulation, and porous media emerge as prominent HTE techniques, alongside nanofluids, which hold the potential for augmenting solar water heating systems. This review also identifies numerous unexplored techniques awaiting investigation, aiming to pave new paths in research and application within the field of hot water generation. It highlights methods that could be used independently or alongside predominantly used techniques.

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CFD Analysis of Heat Transfer Enhancement in a Flat-Plate Solar Collector with Different Geometric Variations in the Superficial Section

Cited by 2 publications

References 18 publications

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Heat Transfer Enhancements Assessment in Hot Water Generation with Phase Change Materials (PCMs): A Review

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