Solidification of nano-enhanced PCM-porous composites in a cylindrical cold thermal energy storage enclosure

Afsharpanah, Farhad; Izadi, Masoud; Hamedani, Farzam Akbarzadeh; Ajarostaghi, Seyed Soheil Mousavi; Yaïci, Wahiba

doi:10.1016/j.csite.2022.102421

Cited by 46 publications

(6 citation statements)

References 30 publications

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“…It was observed that adding 3.2% of hybrid nanoparticles can decrease total melting time by 18.6%. Afsharpanah et al [30] performed a comparison of different carbon-based nanoparticles in cold energy storage system for air cooling purposes in buildings. Additives of graphene nanoparticles and single-walled and multi-walled carbon nanotubes (SWCNT and MWCNT) were evaluated in this unit in comparison to metal-oxide nanoparticles in an effort to increase the discharging rate.…”

Section: Introductionmentioning

confidence: 99%

Improving the charging rate and energy recovery of triplex tube heat storage-based pcm with mono/hybrid nanoparticles

Sadiq,

Aljabair,

Karamallah

2024

ETJ

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The heat transfer performance of energy storage was improved.• Hybrid nano-PCM was the most efficient enhancer.• An optimal 1.6% volume fraction balanced performance and economics. • 1.6% hybrid nano-PCM reduced overall melting time by 16.8%.Latent thermal energy storage systems are widely utilized to match the inequality between heat supply and demand. Despite these systems' wide range of uses in various applications, their high potential is limited by the slow charging rate. The target of this study is to augment the thermal performance of paraffin-based on triplex tube heat storage by dispersion of two different types of conductive mono nanoparticles (Al2O3, CuO) and hybrid Nano additives of various volume fractions (0.4, 0.8, 1.6, 3.2%) into paraffin wax. The experimental work involves measurements and preparation of the considered Nano-PCM. The enthalpy porosity model and finite volume method simulated the melting process. The study also investigated the temperature and liquid fraction variations in the axial, radial, and angular directions throughout melting to aid in predicting heat transfer in the storage throughout the phase transition process of PCM. Results revealed that including 1.6% of hybrid nanoparticles in PCM can increase the stored energy by 5.97%. The results also indicated that the hybrid nano-PCM exhibits the best phase transition rate and energy recovery for all volume fractions compared to mononano-PCM. At 1.6% volume fraction, the storage efficiency can be improved up to 76.8%, 75.5%, and 73.63% for hybrid nano-PCM, Al2O3-PCM, and CuO-PCM, respectively.

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Section: Introductionmentioning

confidence: 99%

Improving the charging rate and energy recovery of triplex tube heat storage-based pcm with mono/hybrid nanoparticles

Sadiq,

Aljabair,

Karamallah

2024

ETJ

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“…Several finite element simulation studies [9][10][11][12][13][14][15][16][17][18] have addressed the influence of geometries on LHTES performance. The discharging thermal cycles of thermal energy storage with NaNO 3 /KNO 3 -PCM in an AISI 321 tube were studied by Zhang et al [9].…”

Section: Introductionmentioning

confidence: 99%

“…The thermal properties of a PU-PCM composite were experimentally and numerically examined by Purohit and Sistla [17], and the study output indicated nucleation and crystallization domination of salt hydrate. Afsharpanah et al [18][19][20] focused on several enhancement methods, such as porous foams, fins, and nanomaterials. A copper foam enhancement technique [18] increased the phase change rate by 92.5%.…”

Section: Introductionmentioning

confidence: 99%

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A Numerical Parametric Study of a Double-Pipe LHTES Unit with PCM Encapsulated in the Annular Space

et al. 2022

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Latent heat thermal energy storage (LHTES) with Phase Change Materials (PCM) represents an interesting option for Thermal Energy Storage (TES) applications in a wide temperature range. A tubular encapsulation model of an LHTES with PCM was developed, and the calculated data were analyzed. In addition, a parametric analysis for the preferable system geometry is presented. Organic paraffin RT18 with a melting point of 18 °C was utilized as PCM for different geometries of LHTES, and the addition of internal and external fins and their influence on LHTES thermal conductivity was investigated. One-step heat exchange from outdoor air to PCM and from PCM to water characterizes the LHTES system in solidification and melting processes, respectively. A 2D axisymmetric model was developed using Comsol Multiphysics 6.0. The LHTES unit performance with PCM organic paraffin RT18 encapsulated in electrospun fiber matrices was analyzed. The study results show that longer internal fins shorten the melting and solidification time. The phase change process was accelerated by 31.12% in the charging compared to the case with no fins of the same external tube diameter. Direct contact of PCM electrospun fiber matrix with 23 °C water showed instant melting, and the phase change process was accelerated by 99.97% in the discharging cycle.

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“…To deal with this situation, the use of load-leveling techniques based in energy storage devices is a common and useful solution [10]. Some of the most common energy storage appliances are the compressed-air energy storage [11], the potential hydro storage [12], the use of super capacitors [13], super magnetic storage systems [14], batteries [15], and thermal energy storage systems [16,17]. All these systems consider that there are some periods over the day where the energy production exceeds the energy consumption; thus, the surplus energy can be stored and used later when the energy production is lower than the energy demand.…”

Section: Introductionmentioning

confidence: 99%

Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques

Perez-Anaya,

Elvira-Ortiz,

Osornio-Rios

et al. 2022

Electronics

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The use of renewable energies is increasing around the world in order to deal with the environmental and economic problems related with conventional generation. In this sense, photovoltaic generation is one of the most promising technologies because of the high availability of sunlight, the easiness of maintenance, and the reduction in the costs of installation and production. However, photovoltaic panels are elements that must be located outside in order to receive the sun radiation and transform it into electricity. Therefore, they are exposed to the weather conditions and many environmental factors that can negatively affect the output delivered by the system. One of the most common issues related to the outside location is the dust accumulation in the surface of the panels. The dust particles obstruct the passage of the sunlight, reducing the efficiency of the generation process and making the system prone to experimental long-term faults. Thus, it is necessary to develop techniques that allow us to assess the level of dust accumulation in the panel surface in order to schedule a proper maintenance and avoid losses associated with the reduction of the delivered power and unexpected faults. In this work, we propose a methodology that uses a machine learning approach to estimate different levels of dust accumulation in photovoltaic panels. The developed method takes the voltage, current, temperature, and sun radiance as inputs to perform a statistical feature extraction that describes the behavior of the photovoltaic system under different dust conditions. In order to retain only the relevant information, a genetic algorithm works along with the principal component analysis technique to perform an optimal feature selection. Next, the linear discrimination analysis is carried out using the optimized dataset to reduce the problem dimensionality, and a multi-layer perceptron neural network is implemented as a classifier for discriminating among three different conditions: clean surface, slight dust accumulation, and severe dust accumulation. The proposed methodology is implemented using real signals from a photovoltaic installation, proving to be effective not only to determine if a dust accumulation condition is present but also when maintenance actions must be performed. Moreover, the results demonstrate that the accuracy of the proposed method is always above 94%.

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Solidification of nano-enhanced PCM-porous composites in a cylindrical cold thermal energy storage enclosure

Cited by 46 publications

References 30 publications

Improving the charging rate and energy recovery of triplex tube heat storage-based pcm with mono/hybrid nanoparticles

Improving the charging rate and energy recovery of triplex tube heat storage-based pcm with mono/hybrid nanoparticles

A Numerical Parametric Study of a Double-Pipe LHTES Unit with PCM Encapsulated in the Annular Space

Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques

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