Research Papers: Gas Turbines: Cycle Innovations

Physics-Based Dynamic Models of Three SOFC/GT Emulator Test Rigs

[+] Author and Article Information
Iacopo Rossi

Department of Mechanical Engineering,
University of Genoa,
Via Montallegro, 1,
Genova 16145, Italy
e-mail: iacopo.rossi@edu.unige.it

Alberto Traverso

Department of Mechanical Engineering,
University of Genoa,
Via Montallegro, 1,
Genova 16145, Italy
e-mail: alberto.traverso@unige.it

Martina Hohloch

German Aerospace Center (DLR),
Institute of Combustion Technology,
Pfaffenwaldring 38,
Stuttgart 70569, Germany
e-mail: martina.hohloch@dlr.de

Andreas Huber

German Aerospace Center (DLR),
Institute of Combustion Technology,
Pfaffenwaldring 38,
Stuttgart 70569, Germany
e-mail: andreas.huber@dlr.de

David Tucker

National Energy Technology Laboratory (NETL),
U.S. Department of Energy,
3610 Collins Ferry Road,
Morgantown, WV 26507
e-mail: david.tucker@netl.doe.gov

Contributed by the Cycle Innovations Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 10, 2017; final manuscript received August 11, 2017; published online December 6, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 140(5), 051702 (Dec 06, 2017) (10 pages) Paper No: GTP-17-1329; doi: 10.1115/1.4038152 History: Received July 10, 2017; Revised August 11, 2017

This paper presents the development, implementation, and validation of a simplified dynamic modeling approach to describe solid oxide fuel cell gas turbine (SOFC/GT) hybrid systems (HSs) in three real emulator test rigs installed at University of Genoa (Italy), German Aerospace Center (DLR, Germany), and National Energy Technology Laboratory (NETL, USA), respectively. The proposed modeling approach is based on an experience-based simplification of the physical problem to reduce model computational efforts with minimal expense of accuracy. Traditional high fidelity dynamic modeling requires specialized skills and significant computational resources. This innovative approach, on the other hand, can be easily adapted to different plant configurations, predicting the most relevant dynamic phenomena with a reduced number of states: such a feature will allow, in the near future, the model deployment for monitoring purposes or advanced control scheme applications (e.g., model predictive control). The three target systems are briefly introduced and dynamic situations analyzed for model tuning, first, and validation, then. Relevance is given to peculiar transients where the model shows its reliability and its weakness. Assumptions introduced during model definition for the three different test rigs are discussed and compared. The model captured significant dynamic behavior in all analyzed systems (in particular those regarding the GT) and showed influence of signal noise on some of the SOFC computed outputs.

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Fig. 1

Reference plant and layout

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Fig. 2

Input to the model in GC configuration

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Fig. 3

Power generated in GC configuration

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Fig. 4

Shaft speed in GC configuration

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Fig. 5

COT results for GC configuration

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Fig. 6

Reference plant and layout: (a) bleed-air valve, (b) bleed-air path, (c) cold-air valve, (d) cold-air path, (e) rev. bypass valve, (f) interface, (g) bypass valve cluster, (h) bypass path, (i) hot-air valve cluster, (j) hot-air path, (k) pressure vessel, and (l) preheater

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Fig. 10

Reference plant and layout

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Fig. 11

Input to the model for a cold air steps series

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Fig. 12

(a) Recuperator outlet temperature and (b) air mass flow incoming to the vessel

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Fig. 13

(a) Temperature of the gas leaving the stack and (b) solid average temperature

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Fig. 14

(a) Cell voltage and (b) fuel utilization factor

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Fig. 15

Errors recap of the three models, starting from Model01 down to Model03

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Fig. 7

Relative losses as function of nondimensional turbine speed

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Fig. 8

Recuperator outlet temperatures as a function of shaft speed

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Fig. 9

Vessel outlet temperatures as a function of turbine speed




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