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    Optimization of an Organic Ranking Cycle Radial Turbine Using a Reduced-Order Model Coupled With Computational Fluid Dynamics

    Source: Journal of Engineering for Gas Turbines and Power:;2022:;volume( 144 ):;issue: 010::page 101012
    Author:
    Alvarez-Regueiro, Eva;Yang, Bijie;Barrera-Medrano, Esperanza;Martinez-Botas, Ricardo;Rajoo, Srithar
    DOI: 10.1115/1.4055359
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents the geometry optimization of a single stage radial turbine for an organic ranking cycle (ORC) system operating over a pressure ratio of 9. The specific fluid used in this investigation is R1233zd (E), but the methodology applies to other organic fluids as well. The ORC system is used to recover excess waste heat from the operation of an offshore oil and gas platform in the gulf of Thailand and its conditions will be replicated at pilot plant level. The geometry is optimized for the highest total-to-static efficiency using nongradient based algorithms to allow for wide design space. Firstly, a one-dimensional meanline geometry is optimized, which is followed by a computational fluid dynamics (cfd) optimization in three-dimensional using a parameterized model. cfd is used to validate and calibrate the meanline model as well as to understand the flow and the sensitivity of the design parameters not captured by the low-order model. Moreover, the flow field of the successful designs is analyzed by cfd to identify the main flow structures that explain the difference in performance among the designs. The nonideal gas thermophysical properties of R1233zd (E) are calculated using equations of state to account for the nonideal gas behavior.
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      Optimization of an Organic Ranking Cycle Radial Turbine Using a Reduced-Order Model Coupled With Computational Fluid Dynamics

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    contributor authorAlvarez-Regueiro, Eva;Yang, Bijie;Barrera-Medrano, Esperanza;Martinez-Botas, Ricardo;Rajoo, Srithar
    date accessioned2022-12-27T23:11:18Z
    date available2022-12-27T23:11:18Z
    date copyright9/12/2022 12:00:00 AM
    date issued2022
    identifier issn0742-4795
    identifier othergtp_144_10_101012.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288058
    description abstractThis paper presents the geometry optimization of a single stage radial turbine for an organic ranking cycle (ORC) system operating over a pressure ratio of 9. The specific fluid used in this investigation is R1233zd (E), but the methodology applies to other organic fluids as well. The ORC system is used to recover excess waste heat from the operation of an offshore oil and gas platform in the gulf of Thailand and its conditions will be replicated at pilot plant level. The geometry is optimized for the highest total-to-static efficiency using nongradient based algorithms to allow for wide design space. Firstly, a one-dimensional meanline geometry is optimized, which is followed by a computational fluid dynamics (cfd) optimization in three-dimensional using a parameterized model. cfd is used to validate and calibrate the meanline model as well as to understand the flow and the sensitivity of the design parameters not captured by the low-order model. Moreover, the flow field of the successful designs is analyzed by cfd to identify the main flow structures that explain the difference in performance among the designs. The nonideal gas thermophysical properties of R1233zd (E) are calculated using equations of state to account for the nonideal gas behavior.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleOptimization of an Organic Ranking Cycle Radial Turbine Using a Reduced-Order Model Coupled With Computational Fluid Dynamics
    typeJournal Paper
    journal volume144
    journal issue10
    journal titleJournal of Engineering for Gas Turbines and Power
    identifier doi10.1115/1.4055359
    journal fristpage101012
    journal lastpage101012_10
    page10
    treeJournal of Engineering for Gas Turbines and Power:;2022:;volume( 144 ):;issue: 010
    contenttypeFulltext
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