A Fast-Reduced Model for an Innovative Latent Thermal Energy Storage for Direct Integration in Heat Pumps

Palomba, Valeria; Frazzica, Andrea

doi:10.3390/app11198972

Cited by 2 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, for both lab-scale and full-scale prototypes, the results under optimal operating conditions are considered and, since the scope of the investigation is to compare the effect of scaling up of the system, the variation of the capacity of the heat pump is not relevant nor significantly affects the outcomes of the analysis. A predictive mathematical model for the system, including the effect of the variation of the layout and heat pump capacity on system performance, are instead presented in [33]. The discharging process started with most of the PCM in a solid state and it was heated up by the HTF that entered the storage module at constant temperature of around 12 • C. The discharging process finished when most of the PCM was in a liquid state.…”

Section: Experimental Methodologymentioning

confidence: 99%

A New Methodological Approach for the Evaluation of Scaling Up a Latent Storage Module for Integration in Heat Pumps

et al. 2021

Self Cite

View full text Add to dashboard Cite

A clear gap was identified in the literature regarding the in-depth evaluation of scaling up thermal energy storage components. To cover such a gap, a new methodological approach was developed and applied to a novel latent thermal energy storage module. The purpose of this paper is to identify some key aspects to be considered when scaling up the module from lab-scale to full-scale using different performance indicators calculated in both charge and discharge. Different normalization methods were applied to allow an appropriate comparison of the results at both scales. As a result of the scaling up, the theoretical energy storage capacity increases by 52% and 145%, the average charging power increases by 21% and 94%, while the average discharging power decreases by 16% but increases by 36% when mass and volume normalization methods are used, respectively. When normalization by the surface area of heat transfer is used, all of the above performance indicators decrease, especially the average discharging power, which decreases by 49%. Moreover, energy performance in charge and discharge decreases by 17% and 15%, respectively. However, efficiencies related to charging, discharging, and round-trip processes are practically not affected by the scaling up.

show abstract

Section: Experimental Methodologymentioning

confidence: 99%

A New Methodological Approach for the Evaluation of Scaling Up a Latent Storage Module for Integration in Heat Pumps

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, this requires prior evaluation of the usefulness of the neural models for describing the thermal energy storage phase of the thermal accumulator. Many authors have made successful attempts to develop neural models to describe the charging phase [26], which have proved useful, for example, in the process of identifying uneven air flows through the deposit. The basis for building the original neural models originated from the results of a natural experiment, in which the subject was a rock bed thermal storage system working in a horizontal setting and designed to work with a solar collector.…”

Section: Storage Phase Of the Rock Bed Thermal Storagementioning

confidence: 99%

Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage

2021

View full text Add to dashboard Cite

Thermal energy stored within a rock bed thermal storage system, which is mostly used in agriculture, can be identified during the storage phase using mathematical models based on heat transfer, which concerns batteries running in a vertical setting. However, this requires the conversion of differential equations into algebraic equations, as well as knowledge about the initial and boundary conditions. Furthermore, a lack of information or incomplete information about the initial conditions makes it difficult or impossible to evaluate the volume of stored energy, or can cause significant errors during evaluation. Such situations occur in systems equipped with a rock battery, in which solar collectors act as source of energy. Considering the above, as well as the lack of a model for batteries in a vertical setting, we identified the need for research into the storage phase of rock bed thermal storage systems, working in a horizontal setting, and generating MLP-type neural models. Among these models, MLP 4-7-1 turned out to be the best both in terms of the values of regression statistics and possibilities of generalization. According to the authors, artificial neural models depicting temperature changeability in storage phase will be helpful in the development of a new methodology that can predict the heat volume in rock bed thermal storage systems.

show abstract

A Fast-Reduced Model for an Innovative Latent Thermal Energy Storage for Direct Integration in Heat Pumps

Cited by 2 publications

References 35 publications

A New Methodological Approach for the Evaluation of Scaling Up a Latent Storage Module for Integration in Heat Pumps

A New Methodological Approach for the Evaluation of Scaling Up a Latent Storage Module for Integration in Heat Pumps

Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage

Contact Info

Product

Resources

About