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Entropy Generation Minimization
Abstract
This chapter outlines the method of entropy generation minimization or thermodynamic optimization. It determines the thermodynamically optimal size or operating regime of an engineering system, where by optimal means the condition in which the system destroys the least energy while still performing its fundamental engineering function. The thermodynamic optimum is the condition of the most advantageous trade-off between two or more competing irreversibilities. The entropy generation rate of the finite-size control volume is the volume integral of the volumetric entropy generation rate. The mission of the storage device is to temporarily store energy, not energy. The combined effect of the competing irreversibilities is a characteristic of all sensible-heat storage systems. The optimization of power plant models with heat transfer irreversibilities can be pursued either as a power maximization (PM) problem or as an entropy generation minimization (EGM) problem. Hierarchy and specialization are characteristics of the constructal architecture that follows from the constructal law.
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Cited by
Li, Y. F., Xia, G. D., Ma, D. D., Jia, Y. T., & Wang, J. (2016) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2016.03.022 · Cited by 316
Mehta, D. S., Solanki, K., Rathod, M. K., & Banerjee, J. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.03.007 · Cited by 121
Metadata
- Venue
- Advanced Engineering Thermodynamics
- Publisher
- Wiley
- Published
- 2016-09-12
- Pages
- 531-600
- OpenAlex
- W2520901979
- Cited by
- 494
- References
- 0
- Type
- other
- Language
- en
- Open access
- closed
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- Entropy Generation Minimization. (2016). Advanced Engineering Thermodynamics. https://doi.org/10.1002/9781119245964.ch11
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
