Non-linear system identification of a latent heat thermal energy storage system. (April 2018)

• Record Type: Journal Article
• Authors: Ghani, F.; Waser, R.; O'Donovan, T.S.; Schuetz, P.; Zaglio, M.; Wortischek, J.
• Is Part Of: Applied thermal engineering. Volume 134(2018)
• Page Start: 585
• Page End: 593
• Publication Date: 2018-04
• Subjects: Dynamic neural network -- Latent heat storage -- Phase change material -- Modelling -- Thermal characterisation; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2018.02.035
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A computationally efficient method to characterise a latent heat store is proposed. Method based on a dynamic neural network. Experimental data collected to compile training data. Neural network tested against experimental data for testing. Results demonstrate that a dynamic neural network is suitable for this application. Abstract: Latent heat storage systems utilising phase change materials have potential to offer several advantages over sensible heat storage, including higher energy storage densities and thermal modulation. Despite these advantages, only a few commercialised products incorporating this technology exist due to several engineering challenges. One problem is how to model this technology in a computationally efficient manner which allows simulating this technology with variable heat sources such as solar thermal and heat pump systems and assess their long-term system performance. In this study, the application of a dynamic neural network for this purpose is investigated, where a Layered Digital Dynamic Neural (LDDN) type network is trained using experimental data to approximate the outlet fluid temperature of a latent heat storage system given inlet fluid temperature and mass flow rate. To acquire the training data necessary for the neural network, an experimental apparatus was designed, built and operated under laboratory conditions. Twenty experiments were conducted to obtain training data where the latent heat storage system was charged to different operating temperatures ranging from 25 to 70 °C. The mass flow rate of the heat exchanger fluid flowing through the heat exchanger was also varied: 0.045 and 0.05 kg/s such that the flow of heat exchanger fluid remained turbulent. These data were then presented to the network for training and optimisation of the network architecture using the Bayesian Regularization training algorithm. It was found, that the LDDN type architecture was suitable to characterise the thermal operational behaviour of a latent heat storage system with good accuracy and with little computational effort once trained. Based on an energy analysis, the neural network response predicted the quantity of energy stored and discharged with approximately 5% and 7% accuracy respectively when presented with data not used during the training process. These results indicate that a dynamic neural network may be a computationally efficient method to model the non-linear operational characteristics of a latent heat storage system. It may therefore be implemented within a simulation environment such as TRNSYS or Simulink.