2023
DOI: 10.1016/j.egyr.2023.09.044
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Optimisation of thermal energy storage systems incorporated with phase change materials for sustainable energy supply: A systematic review

Flavio Odoi-Yorke,
Richard Opoku,
Francis Davis
et al.
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Cited by 11 publications
(5 citation statements)
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“…Melting upon heating and crystallization upon cooling was observed only for the first two heating / cooling steps. After the third melting, the sample supercooled as can be seen from cooling scan (3). During the fourth heating scan, cold crystallization of the metastable supercooled liquid was observed at around 40 °C which was followed by melting above 50 °C.…”
Section: Resultsmentioning
confidence: 87%
See 2 more Smart Citations
“…Melting upon heating and crystallization upon cooling was observed only for the first two heating / cooling steps. After the third melting, the sample supercooled as can be seen from cooling scan (3). During the fourth heating scan, cold crystallization of the metastable supercooled liquid was observed at around 40 °C which was followed by melting above 50 °C.…”
Section: Resultsmentioning
confidence: 87%
“…An additional Adafruit Feather M0 acts as the master microcontroller and controls the six microcontrollers responsible for the individual heating modules. Within the master microcontroller, a temperature program is defined in terms of four stages including (1) dwelling at a base temperature for a specified period of time, (2) heating at a defined heating rate, (3) dwelling at the maximum temperature for a specified period of time and (4) cooling back to the base temperature at a defined cooling rate. These four stages constitute a cycle and can be repeated until the total number of specified cycles is reached.…”
Section: Design Of the Heatmaster Instrumentmentioning
confidence: 99%
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“…Recent strides in ML, particularly through deep learning and reinforcement learning, have demonstrated substantial success in predicting PCM thermophysical behaviors in varying conditions, thus surpassing the limitations of traditional selection methods. The process involves training ML models on extensive datasets, including PCM properties and performance metrics across different scenarios, enriched with computational fluid dynamics (CFD) and finite element analysis (FEA) simulations [102]. These models, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel at detecting intricate patterns and correlations, accurately forecasting PCM responses [103].…”
Section: Optimizing Pcm Selection and Configuration With MLmentioning
confidence: 99%
“…Simulation methods have emerged as indispensable instruments for optimizing the performance of PCM-based systems and gaining a deeper understanding of their operation [11]. These simulation techniques enable scientists and engineers to model and analyze the intricate heat transfer and phase change phenomena that occur in PCM systems.…”
Section: Introductionmentioning
confidence: 99%