Abstract
Thermal energy storage plays an important role in the energy management and has got great attention for many decades; stratification is a key parameter to be responsible for the performance of the stratified thermal energy storage tank. In this paper detailed study of modelling techniques used to analyse thermal energy storage has been conducted. The division of literature has been made as numerical, analytical, and artificial neural network-based. Numerical modelling being very physical based and required more specific software’s tools remain costly and computationally very complex at the same time it provides the detailed insights into the system, analytical model provide the exact solutions but need some assumptions which make the system unrealistic in some cases but is easy and flexible in terms of computational requirements, ANN though recently used modelling technique is a black box model which merely needs the data rather than any physical based complex calculations is attracting the scientific community.
Highlights
Energy storage technology is getting more attention day by day due to increasing need of the energy conservation
This paper review research conducted in modelling of stratified thermal energy storage tanks and categories them as numerical, analytical and neural network based
Stratified thermal energy storage plays an important role in filling the gap between the demand and supply, to measure the performance of the TES tank is very important
Summary
Energy storage technology is getting more attention day by day due to increasing need of the energy conservation. Stratification can be damaged or destratification in the tank can mostly be occur by four reasons i.e the heat loss to the environment, heat loss to the adjacent layers of the water, axial wall conduction and the mixing in the inlet jet of the water. These losses can be reduced by proper design of the tank and the diffusers used for the supply of the inlet jet. This paper review research conducted in modelling of stratified thermal energy storage tanks and categories them as numerical, analytical and neural network based
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