Nanotechnology-integrated phase change material and nanofluids for solar applications as a potential approach for clean energy strategies: Progress, challenges, and opportunities

Said, Zafar; Sohail, Maham Aslam; Pandey, A.K.; Sharma, Prabhakar; Waqas, Adeel; Chen, Wei‐Hsin; Nguyen, Phuoc Quy Phong; Nguyen, Van Nhanh; Pham, Nguyen Dang Khoa; Nguyen, Xuan Phuong

doi:10.1016/j.jclepro.2023.137736

“…Acquiring geothermal energy involves extracting heat from beneath the Earth's surface. Several worldwide trends fuel the growth of renewable energy sources [58], [59]. In recent years, there have been many studies relating to waste heat recovery, and the waste-to-energy path could also be a potential and promising solution for achieving green energy [60]- [63].…”

Section: Introductionmentioning

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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“…These nanofluids, which are made up of nanoparticles that are dispersed throughout a base fluid, have better thermal characteristics, which makes them potential candidates for enhanced heat transfer applications. Experimentation or simulation is used to gather data on critical factors such as nanoparticle concentration, temperature, fluid flow rate, and thermal conductivity 18 , 19 . However, when data is large and complex-nonlinear, it becomes difficult to model the data and find the correlation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids

Akilu,

Sharma,

Baheta

et al. 2024

Sci Rep

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The study investigates the heat transfer and friction factor properties of ethylene glycol and glycerol-based silicon dioxide nanofluids flowing in a circular tube under continuous heat flux circumstances. This study tackles the important requirement for effective thermal management in areas such as electronics cooling, the automobile industry, and renewable energy systems. Previous research has encountered difficulties in enhancing thermal performance while handling the increased friction factor associated with nanofluids. This study conducted experiments in the Reynolds number range of 1300 to 21,000 with particle volume concentrations of up to 1.0%. Nanofluids exhibited superior heat transfer coefficients and friction factor values than the base liquid values. The highest enhancement in heat transfer was 5.4% and 8.3% for glycerol and ethylene glycol -based silicon dioxide Nanofluid with a relative friction factor penalty of ∼30% and 75%, respectively. To model and predict the complicated, nonlinear experimental data, five machine learning approaches were used: linear regression, random forest, extreme gradient boosting, adaptive boosting, and decision tree. Among them, the decision tree-based model performed well with few errors, while the random forest and extreme gradient boosting models were also highly accurate. The findings indicate that these advanced machine learning models can accurately anticipate the thermal performance of nanofluids, providing a dependable tool for improving their use in a variety of thermal systems. This study's findings help to design more effective cooling solutions and improve the sustainability of energy systems.

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“…Said et al 32 reviewed the newest advances in solar thermal collectors, emphasizing the use of nanofluids as heat transfer fluids to improve the devices' performance. Said et al 33 examined current developments in phase change material nanotechnology with an emphasis on solar energy applications. The thermal properties of a Williamson hybrid nanofluid (MoS2 + ZnO) based on hydraulic fluid flowing across a stretched sheet were examined by Yahya et al 34 .…”

Section: Introductionmentioning

confidence: 99%

Heat transfer analysis of Maxwell tri-hybridized nanofluid through Riga wedge with fuzzy volume fraction

Zulqarnain,

Nadeem,

Siddique

et al. 2023

Sci Rep

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This contribution aims to optimize nonlinear thermal flow for Darcy-Forchheimer Maxwell fuzzy $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} + {\text{Cu }} + {\text{TiO}}_{{2}} {\text{/EO}}} \right)$$ Al 2 O 3 + Cu + TiO 2 /EO tri-hybrid nanofluid flow across a Riga wedge in the context of boundary slip. Three types of nanomaterials, $${\text{Al}}_{{2}} {\text{O}}_{{3}} ,$$ Al 2 O 3 , Cu and $${\text{TiO}}_{2}$$ TiO 2 have been mixed into the basic fluid known as engine oil. Thermal properties with the effects of porous surface and nonlinear convection have been established for the particular combination $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} + {\text{Cu}} + {\text{TiO}}_{{2}} {\text{/EO}}} \right){.}$$ Al 2 O 3 + Cu + TiO 2 /EO . Applying a set of appropriate variables, the set of equations that evaluated the energy and flow equations was transferred to the dimensionless form. For numerical computing, the MATLAB software's bvp4c function is used. The graphical display is used to demonstrate the influence of several influential parameters. It has been observed that flow rate decay with expansion in porosity parameter and nanoparticles volumetric fractions. In contrast, it rises with wedge angle, Grashof numbers, Darcy-Forchheimer, nonlinear Grashof numbers, and Maxwell fluid parameter. Thermal profiles increase with progress in the heat source, nanoparticles volumetric fractions, viscous dissipation, and nonlinear thermal radiation. The percentage increases in drag force for ternary hybrid nanofluid are 13.2 and 8.44 when the Modified Hartmann number takes input in the range $$0.1 \le {\text{Mh}} \le 0.3$$ 0.1 ≤ Mh ≤ 0.3 and wedge angle parameters $$0.1 \le m \le 0.3$$ 0.1 ≤ m ≤ 0.3 . For fuzzy analysis, dimensionless ODEs transformed into fuzzy differential equations and employed symmetrical triangular fuzzy numbers (TFNs). The TFN makes a triangular membership function (M.F.) that describes the fuzziness and comparison. This study compared nanofluids, hybrid nanofluids, and ternary nanofluids through triangular M.F. The boundary layer flow caused by a wedge surface plays a crucial role in heat exchanger systems and geothermal.

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Nanotechnology-integrated phase change material and nanofluids for solar applications as a potential approach for clean energy strategies: Progress, challenges, and opportunities

Cited by 42 publications

References 283 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

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids

Heat transfer analysis of Maxwell tri-hybridized nanofluid through Riga wedge with fuzzy volume fraction

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