Experimental and theoretical study for suitability of hybrid nano enhanced phase change material for thermal energy storage applications

Kalbande, Vednath P.; Fating, Ganesh; Mohan, Man; Rambhad, Kishor S.; Sinha, Agnivesh Kumar

doi:10.1016/j.est.2022.104431

Cited by 49 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rapid development of nanotechnology has made it possible to create hybrid nanofluids (HNFs), which are composed of two or more distinct NPs and may be able to outperform the low TC of PCMs. Vednath et al [21] examined the suitability of hybrid NePCMs (HNePCM) for applications involving TES. Arshad et al [22,23] examined the TPCs of nano and HNePCMs using CuO, Al 2 O 3 , TiO 2 , MWCNTs, and GnPs as metallic NPs.…”

Section: Doi: 101002/ente202301398mentioning

confidence: 99%

Designing Next‐Generation Thermal Energy Storage Systems with Nanoparticle‐Based Hybrid Phase Change Materials: A Computational Analysis

Chavan,

Venkateswarlu,

Kim

et al. 2024

Energy Tech

View full text Add to dashboard Cite

The disparity between the supply and demand for thermal energy has encouraged scientists to develop effective thermal energy storage (TES) technologies. In this regard, hybrid nano‐enhanced phase‐change materials (HNePCMs) are integrated into a square enclosure for TES system analysis. Several HNePCMs are formulated with different highly conductive nanoparticles in varying proportions. For the numerical study, three HNePCMs, namely HNePCM‐1, HNePCM‐2, and HNePCM‐3, are employed to investigate melting characteristics and energy‐storage capacity analysis. The outcomes of the analysis are determined by examining several variables, such as mass fraction, enthalpy, and temperature, concerning the melting and heat‐transfer processes. In the results, it is indicated that HNePCM‐1_10% yields the best thermal performance as compared to the other HNePCM_8% and HNePCM_5% case studies. Furthermore, the melting time is shortened for HNePCM‐1_10%, HNePCM‐2_10%, and HNePCM‐3_10% by 20%, 10%, and 5%, respectively. The enthalpy of HNePCM‐1 exhibits greater enhancement compared to HNePCM‐2 and HNePCM‐3, respectively. The enhancement in energy‐storage capacity for HNePCM‐1_10%, HNePCM‐2_10%, and HNePCM‐3_10% is 18.69%, 16.66%, and 6.85% higher than that of the base material. Thus, HNePCMs are demonstrated to be more efficient materials and are emerging as potential materials to augment the performance of TES applications.

show abstract

Section: Doi: 101002/ente202301398mentioning

confidence: 99%

Designing Next‐Generation Thermal Energy Storage Systems with Nanoparticle‐Based Hybrid Phase Change Materials: A Computational Analysis

Chavan,

Venkateswarlu,

Kim

et al. 2024

Energy Tech

View full text Add to dashboard Cite

show abstract

“…Integration of PCM with solar energy systems represents a promising approach for enhancing energy efficiency, improving energy storage capacity, and advancing the adoption of renewable energy technologies. Kalbande et al 19 evaluated a hybrid system using nano‐enhanced PCM with a parabolic dish solar collector to store solar energy via PCM. Comparison with various nano‐enhanced PCMs and pure paraffin wax revealed higher thermal conductivity and faster phase transformation in the nano‐enhanced PCM, where the CuO‐ multi‐walled carbon nanotube‐based system demonstrated the greatest latent heat storage capacity.…”

Section: Introductionmentioning

confidence: 99%

A methodical approach for the design of thermal energy storage systems in buildings: An eight‐step methodology

Rahnama,

Khatibi,

Maccarini

et al. 2024

Energy Storage

View full text Add to dashboard Cite

Recent research focuses on optimal design of thermal energy storage (TES) systems for various plants and processes, using advanced optimization techniques. There is a wide range of TES technologies for diverse thermal applications, each with unique technical and economic characteristics. Matching an application with the most suitable TES system remains challenging. This study proposes an eight‐step design methodology guiding the process from describing the thermal process to defining the most appropriate TES based on constraints and requirements. The steps include specifying the thermal process, system design parameters, storage characteristics, integration parameters, key performance indicators, optimization method, tools, and design robustness. Seven already‐designed TES systems are evaluated to assess the methodology's effectiveness, where the design procedures have been adapted to the proposed steps. Case studies involve various applications with both sensible and latent TES systems, demonstrating the applicability of the proposed design procedure. A significant diversity exists among the design cases regarding the design objective, input, design, and output parameters. Nevertheless, the design procedure in each case can be deconstructed into the outlined design steps. The last design step has been excluded from all case studies due to insufficient information regarding the robustness of the design process. The paper demonstrates how a methodical approach can be applied to examine the TES design and the integration. The design steps proposed in this study can serve as a foundation for developing a more systematic approach for designing TES systems in future works, resulting in simplifying the design process.

show abstract

“…Subsequently, significant research evolved toward understanding the thermophysical properties of PCMs. Reports in the last few years have demonstrated the continuous development of the thermophysical properties of PCMs [2][3][4][5]. Thermal energy-storage systems, which predominantly depend on solar energy, the limited availability of solar energy during daylight hours presents a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation and Artificial Neural Network-Based Prediction of Thermal Conductivity of Metal Oxide-Enhanced Organic Phase-Change Materials

Mohan,

Dewangan,

Rao

et al. 2024

International Journal of Energy Research

Self Cite

View full text Add to dashboard Cite

This research article presents a comprehensive study on the prediction of thermal conductivity (TC) as a primary outcome for an artificial neural network (ANN) model in the context of nanoenhanced phase change materials (NEPCMs). To improve predictive accuracy and to reduce variation within the NEPCM dataset, a targeted dataset was employed, consisting exclusively of NEPCMs synthesized using paraffin wax (PW) and metal oxide nanoparticles. Unlike existing empirical models that predict TC of NEPCM without simultaneously considering multiple factors influencing it, this study integrates multiple factors, providing a more accurate prediction of NEPCM thermal conductivity. Additionally, the study explores the impact of synthesis parameters on the thermal performance of NEPCMs, focusing on the examination of factors such as the melting temperature of pure phase change material (PCM), nanoparticle size, and NEPCM composition. The thermal characterizations demonstrate outstanding thermophysical properties in NEPCMs, particularly in terms of thermal conductivity, phase change enthalpy, and thermal stability compared to their respective base PCM. An ANN TC prediction model demonstrates exceptional correlation (>99%) with reported NEPCMs, providing a reliable tool for TC forecasting in similar NEPCM categories. The backpropagation ANN model predicts NEPCM TC with a mean squared error (MSE) of 0.031124 within eight epochs. The dataset used exhibits high fit values, with R-values of 0.99825, 0.99208, and 0.9824 for training, validation, and testing, respectively. These values closely match experimentally determined TC, with less than 4% error.

show abstract

Experimental and theoretical study for suitability of hybrid nano enhanced phase change material for thermal energy storage applications

Cited by 49 publications

References 31 publications

Designing Next‐Generation Thermal Energy Storage Systems with Nanoparticle‐Based Hybrid Phase Change Materials: A Computational Analysis

Designing Next‐Generation Thermal Energy Storage Systems with Nanoparticle‐Based Hybrid Phase Change Materials: A Computational Analysis

A methodical approach for the design of thermal energy storage systems in buildings: An eight‐step methodology

Experimental Investigation and Artificial Neural Network-Based Prediction of Thermal Conductivity of Metal Oxide-Enhanced Organic Phase-Change Materials

Contact Info

Product

Resources

About