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reformulate the non-linear APS plant model in FORTRAN where there is no intrinsic capability for handling such a problem. In addition, property data is not readily accessible like it is in EES, furthering the challenge. To make implementation of the APS plant model tractable in TRNSYS a linear regression was performed to correlate model outputs and inputs. The correlation parameters are the temperature and flow rate of the heat transfer fluid and the minimum working fluid temperature (condensing temperature). Heat transfer fluid temperature and mass flow rate will vary based on weather and solar field performance. Minimum working fluid temperature will vary based on ambient conditions as well as the cooling technique employed. The outputs of interest are the net power produced by the cycle, as well as temperature of the heat transfer fluid as it returns to the solar field. Net power produced by the power cycle is correlated in the following manner: & W = a + a ⋅ T + a ⋅ T 2 + a ⋅ m& + a ⋅ m& 2 + a ⋅ T 6 WF,min 7 HTF,in HTF 8 HTF,in WF,min 9 HTF WF,min where a0-a9 are determined using the linear regression capability built into EES [Klein, 2006]. NET 0 1 HTF,in 2 HTF,in 3 HTF 4 HTF 5 WF,min + a ⋅ T 2 + a ⋅ T ⋅ m& + a ⋅ T ⋅ T + a ⋅ m& ⋅ T [3.1.36] Table 3.2 coefficients for net power correlation 41 coefficient a0 a1 a2 a3 a4 a5 a6 a7 a8 a9 value -1321 -3.401 0.008 -61.97 -1.507 152.75 -3.219 0.641 -0.037 -0.468 Figure 3.17 shows the output of equation [3.1.36] for different values of heat transfer fluid temperature, flow rate and a minimum working fluid temperature fixed to the design value. There is no perceivable error in between the full model and the linear regression (R2=99.9).PDF Image | Organic Rankine Cycle Solar-Thermal Powerplants
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