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Development of a fully deterministic simulation model for organic Rankine cycle operating under off-design conditions

Authors
Oh, J.Park, Y.Lee, H.
Issue Date
1-2월-2022
Publisher
Elsevier Ltd
Keywords
Experimental validation; Fully deterministic model; Numerical simulation; Off-design conditions; Organic Rankine cycle (ORC)
Citation
Applied Energy, v.307
Indexed
SCIE
SCOPUS
Journal Title
Applied Energy
Volume
307
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137553
DOI
10.1016/j.apenergy.2021.118149
ISSN
0306-2619
Abstract
A fully deterministic simulation model that requires only external boundary conditions as input parameters is newly developed for predicting the off-design performance of an organic Rankine cycle. Accurate prediction of the evaporation and condensation pressures without using any assumptions has been of major concern. In this context, the actual pressure formation characteristics are reflected into the model to identify the high- and low-pressures. The mass balance of the system is realized by applying the proposed passive design of the liquid receiver. The sub-models of each component are integrated into a three-stage solver which iterates under the law of mass and energy conservation. 77 sets of experimental data were collected from a 1 kW scale testbed using R245fa as the working fluid. The developed model is verified by examining the thermodynamic states of the working fluid and the energy balance error of the simulation results is less than 0.3%. The simulation results are compared to the experimental results and are validated within 8% error range. Also, the computational time reduced from 412 h to 93 s after applying the meta-models with only 0.03% difference in thermal efficiency. The effects of various modelling methods are compared to each other which emphasizes the importance of the newly proposed reality-based logics. The simulation model can detect several operational failure scenarios and accurately predict the off-design performance of the system. The developed model is fully predictive without imposing internal assumptions and has the potential to be utilized in various applications without conducting excessive experiments. © 2021 Elsevier Ltd
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