Abstract
Thermal stratification is a technique for maintaining separate layers of fluid having different temperatures. It plays a significant role in creating a large thermal gradient which in turn helps in storing more thermal energy in a solar thermal energy storage system. This paper investigates the effect of storage tank variables in terms of aspect ratio, equivalent diameter and its relationship with average stratification coefficient by varying them to different ranges to propose the optimized models.
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References
Twidell J, Weir T (2015) Renewable energy resources. Routledge, London, UK
Sarbu I, Sebarchievici C (2016) Solar heating and cooling: fundamentals, experiments and applications. Elsevier, Oxford, UK
Saito A (2002) Recent advances in research on cold thermal energy storage. Int J Refrig 25:177–189
Hasnain SM (1998) Review on sustainable thermal energy storage technologies, part II: cool thermal storage. Energy Convers Manage 39:1139–1153
Elhab BR, Sopian K, Mat S, Lim C, Sulaiman MY, Ruslan MH (2012) Optimizing tilt angles and orientations of solar panels for Kuala Lumpur, Malaysia. Science
Kalra J, Raghav G, Nagpal M (2016) Parametric Study of Satratification in packed bed sensible heat, solar energy storage system. Appl Solar Energy 52(4):259–264
Fatema N et al (2021) Intelligent data-analytics for condition monitoring: smart grid applications. Elsevier, 268 pp. ISBN: 9780323855112
Aggarwal S et al (2020) Meta heuristic and evolutionary computation: algorithms and applications. Springer Nature, Berlin, 949 pp. https://doi.org/10.1007/978-981-15-7571-6. ISBN 978-981-15-7571-6
Yadav AK et al (2020) Soft computing in condition monitoring and diagnostics of electrical and mechanical systems. Springer Nature, Berlin, 496 pp. https://doi.org/10.1007/978-981-15-1532-3. ISBN 978-981-15-1532-3
Smriti S et al (2019) Applications of artificial intelligence techniques in engineering, vol 1. Springer Nature, 643 pp. https://doi.org/10.1007/978-981-13-1819-1. ISBN 978-981-13-1819-1
Gopal et al (2021) Digital transformation through advances in artificial intelligence and machine learning. J Intell Fuzzy Syst. Pre-press 1–8. https://doi.org/10.3233/JIFS-189787
Jafar A et al (2021) AI and machine learning paradigms for health monitoring system: intelligent data analytics. Springer Nature, Berlin, 496 pp. https://doi.org/10.1007/978-981-33-4412-9. ISBN 978-981-33-4412-9
Smriti S et al (2018) Special issue on intelligent tools and techniques for signals, machines and automation. J Intell Fuzzy Syst 35(5):4895–4899. https://doi.org/10.3233/JIFS-169773
Kumar A, Shukla SK (2015) A review on thermal energy storage unit for solar thermal power plant applications. Energy Procedia 74:462–469
Xu B, Li P, Chan C (2015) Application of phase change materials for thermal energy storage in concentrated solar thermal power plants: a review to recent developments. Appl Energy 160:286–307
Zhang H, Baeyens J, Cáceres G, Degrève J, Lv Y (2016) Thermal energy storage: recent developments and practical aspects. Prog Energy Combust Sci 53:1–40
Cárdenas B, León N (2013) High temperature latent heat thermal energy storage: phase change materials, design considerations and performance enhance-ment techniques. Renew Sustain Energy Rev 27:724–737
Liu M, Saman W, Bruno F (2012) Review on storage materials and thermal performance enhancement techniques for high temperature phase change thermal storage systems. Renew Sustain Energy Rev 16:2118–2132
Brumleve TD (1974) Sensible heat storage in liquids, solar energy technology division. Sandia Laboratories Report SLL-73-0263. Livermore, CA 94550, United States
Wu L, Bannerot RB (1987) Experimental study of the effect of water extraction on thermal stratification
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Kalra, J., Pant, R., Negi, P., kumar, V., Pant, S., Tiwari, S. (2022). Design Optimization of Solar Thermal Energy Storage Tank: Using the Stratification Coefficient. In: Tomar, A., Malik, H., Kumar, P., Iqbal, A. (eds) Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-16-2354-7_5
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DOI: https://doi.org/10.1007/978-981-16-2354-7_5
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