Modeling of PCM melting: Analysis of discrepancy between numerical and experimental results and energy storage performance

Soni, Vikram; Kumar, Arvind; Jain, V.K.

doi:10.1016/j.energy.2018.02.097

Cited by 62 publications

(15 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The fourth and fifth term on the right‐hand side of “ x ” and “ y ” momentum equations, respectively, is the Darcy source term . The constant C depicts the reflects the solidification morphology . There is a substantial literature which discusses the effect of the mushy constant on the accuracy of enthalpy‐porosity technique and the numerical predictions .…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The latent heat content of each cell is updated for predicting of the liquid fraction. Therefore, an iterative updating scheme, as detailed in references is employed and as follows:

{(Δ H_{P})}_{n + 1} = {(Δ H_{P})}_{n} + ({}, \frac{a_{normalp}}{a_{normalp}^{0}} λ ([], {(h_{p})}_{n} - G^{- 1} {(Δ H_{P})}_{n}))

…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Using Equations , the iterative updating technique for solid fraction f s update is as follows,

{(f_{s})}_{n + 1} = {(f_{s})}_{n} + ({}, \frac{a_{normalp}}{a_{normalp}^{0}} λ \frac{c_{normalp}}{L} ([], {(T_{p})}_{n} - {(T_{f})}_{n}))

where λ is a relaxation factor, and in simulations its value is taken as 0.5 …”

Section: Mathematical Modelmentioning

confidence: 99%

“…These latent heat‐based TES systems are being studied extensively for the phase change characteristic and energy storage. This is mainly due to the reason that the latent heat‐based PCMs have a high energy density . Further, such TES systems can store the available/surplus energy as a combination of sensible and latent heat, and can be used later when needed .…”

Section: Introductionmentioning

confidence: 99%

“…This is mainly due to the reason that the latent heat-based PCMs have a high energy density. [4][5][6] Further, such TES systems can store the available/surplus energy as a combination of sensible and latent heat, and can be used later when needed. 7,8 The establishment of any such energy storage system requires a detailed analysis of the associated physical phenomenon in the PCM.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Real‐time experimental study and numerical simulation of phase change material during the discharge stage: Thermo‐fluidic behavior, solidification morphology, and energy content

Soni

Kumar

et al. 2019

Energy Storage

Self Cite

View full text Add to dashboard Cite

This article experimentally and numerically investigates solidification behavior of a phase change material during the discharge stage. Particle image velocimetry technique is used to measure the real‐time flow field. The visualization and measurement of solidification characteristics (solidifying interface, mushy zone, solidifying morphology—columnar, and equiaxed dendrites, etc.) are carried out using a high‐resolution camera. Transient temperature at strategic locations is measured using instrumented thermocouples. Experimental results observe multiple rotating vortices and thermal plumes due to Rayleigh‐Bénard convection currents. An interesting phenomenon of detachment of dendrite flakes from the developing mushy zone is observed, which provides several nucleation sites for the initiation of the solidification and leads to heat transfer enhancement. Numerical simulations are performed using a one‐domain continuum model. A comprehensive description of the local and the global scale behavior of solidified fraction, thermal field, and flow field is provided, and their role on energy discharge is established. The energy discharge rate from the system is quantitatively computed using thermal performance indicators, such as latent energy, total (sensible + latent) extracted specific energy, and specific power.

show abstract

Section: Mathematical Modelmentioning

confidence: 99%

“…The latent heat content of each cell is updated for predicting of the liquid fraction. Therefore, an iterative updating scheme, as detailed in references is employed and as follows:

{(Δ H_{P})}_{n + 1} = {(Δ H_{P})}_{n} + ({}, \frac{a_{normalp}}{a_{normalp}^{0}} λ ([], {(h_{p})}_{n} - G^{- 1} {(Δ H_{P})}_{n}))

…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Using Equations , the iterative updating technique for solid fraction f s update is as follows,

{(f_{s})}_{n + 1} = {(f_{s})}_{n} + ({}, \frac{a_{normalp}}{a_{normalp}^{0}} λ \frac{c_{normalp}}{L} ([], {(T_{p})}_{n} - {(T_{f})}_{n}))

where λ is a relaxation factor, and in simulations its value is taken as 0.5 …”

Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real‐time experimental study and numerical simulation of phase change material during the discharge stage: Thermo‐fluidic behavior, solidification morphology, and energy content

Soni

Kumar

et al. 2019

Energy Storage

Self Cite

View full text Add to dashboard Cite

show abstract

Numerical and experimental studies of packed bed thermal energy storage system based on a novel transient energy model

Ma¹,

Fan

Shao

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

Packed bed thermal energy storage (PBTES) is an essential means to solve the temporal difference and continuity between energy supply and utilization in the fields of concentrating solar power, compressed‐air energy storage, and waste heat recovery. In this paper, to solve the imperfection and inaccuracy of the current energy model, a precise transient two‐dimensional multiequation model is developed. The model comprehensively considers thermal dispersion, wall effects, thermophysical property changes, and other factors. Experiments are performed to compare and validate the simulation results, and the simulation error is −4.5% to 1.9%, achieving excellent simulation accuracy. Also, the wall effect on the PBTES is observed, and the gas‐solid temperature fields exhibit an evident nonuniform oscillation distribution in the radial direction. The effects of storage tank length, thermophysical properties, particle diameter, and gas‐solid initial temperature on the heat release rate are discussed in detail. Results indicated that decreasing the tank length, increasing the gas‐solid temperature difference, and particle materials with high thermal diffusivities can increase the heat release rate, and a clear linear change was found. Particularly because a larger range of particle diameter variation is considered, the heat release rate is shown to vary nonlinearly with particle diameter, and the selection of the optimal diameter must comprehensively consider the effects of the heat capacity and the heat transfer rate. This study provides a theoretical basis for the design of thermal energy storage systems.

show abstract

Numerical prediction of the solidification and melting of encapsulated nano‐enhanced phase change materials

Cofré‐Toledo,

Muñoz‐Cuevas,

Jofré‐Severino

et al. 2023

Energy Storage

View full text Add to dashboard Cite

The thermal properties of Octadecane vary due to the addition of copper oxide (CuO) nanoparticles. The synthesis of two nano‐enhanced phase change material (NEPCM) required the implementation of the two‐step method, using Octadecane as the base phase change material and CuO nanoparticles at 2.5 and 5.0 wt%. The experimental characterization determined the specific heat capacity, and thermal conductivity of the NEPCMs solid phase, including phase change enthalpy and temperature. These experimental results were then utilized for computational simulation of the thermal charging (solidification) and discharging (melting) processes of NEPCMs within a spherical enclosure, employing the ANSYS/Fluent software. The incorporation of CuO nanoparticles led to an increase in thermal conductivity while causing a decrease in specific heat capacity, enthalpy, and phase change temperature of Octadecane. The computational results revealed a reduction in the melting period and an improvement in the solidification of both NEPCMs. In terms of melting, the convective heat transfer coefficient increased by approximately 27.4% and 3.2% for the NEPCM at 2.5 and 5.0 wt% CuO, respectively. During the solidification process, the overall heat transfer coefficient experienced a significant increase of 43.8% and 59.8% for the NEPCM at 2.5 and 5.0 wt%, respectively.

show abstract

Modeling of PCM melting: Analysis of discrepancy between numerical and experimental results and energy storage performance

Cited by 62 publications

References 25 publications

Real‐time experimental study and numerical simulation of phase change material during the discharge stage: Thermo‐fluidic behavior, solidification morphology, and energy content

Real‐time experimental study and numerical simulation of phase change material during the discharge stage: Thermo‐fluidic behavior, solidification morphology, and energy content

Numerical and experimental studies of packed bed thermal energy storage system based on a novel transient energy model

Numerical prediction of the solidification and melting of encapsulated nano‐enhanced phase change materials

Contact Info

Product

Resources

About