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New Concepts FOR Organic Rankine Cycle Power Systems

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New Concepts FOR Organic Rankine Cycle Power Systems ( new-concepts-for-organic-rankine-cycle-power-systems )

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Chapter 5 1.01 1 0.99 0.98 0.97 40 50 60 70 80 time [s] Reference model This work Figure 5.6: Dynamic validation results, normalized frequency vs time. Comparison between the reference model provided by the gas turbine manufacturer and the model developed in the present chapter. 0.0206 and a rise time of 5.5 s, while the model presented here gives a normalized frequency drop of 0.0202 and a rise time of 6.0 s. Based on this results, it is possible to conclude that the gas turbine dynamic model developed in the present chapter is able to reproduce both the steady-state and the dynamics of the gas turbine with reasonable accuracy, over the entire range of loads encountered during real operation. The model of the ORC system is composed of software objects taken from a library that was developed in order to model a 150 kW ORC system using toluene as the working fluid, and suc- cessfully validated for transient operation against experimental data [24]. The developed models are therefore deemed reliable, considering the similarity of the application at hand with the one presented in the cited reference. Furthermore, it has been verified that the on-design and off-design steady-state operating points predicted by the ORC system model are consistent with those com- puted by the design tool described in ยง5.4.1. 5.4.4 The DYNDES Tool The DYNDES computer tool couples steady state and the dynamic software models in order to provide an integrated program for the optimal design of power generation systems, including dy- namic criteria. The two computer programs are interfaced by means of shared files and command scripts. More in detail, the results of the multi-objective design optimization is saved in an ap- propriate file, then the dynamic simulation program is run in command-line mode to: i) extract information from the design results file (e.g. the optimal design data relative to the geometry of the once-through boiler), ii) convert such data into parameters and inputs for the dynamic models, iii) run the simulations, and iv) save quantities of interest for further post-processing. Figure 5.7 shows the flowchart of the DYNDES tool. 134 freq. [-]

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