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Working Fluid Design for Organic Rankine Cycle

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Working Fluid Design for Organic Rankine Cycle ( working-fluid-design-organic-rankine-cycle )

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4-3 Analysis of the simulated optimized ORC Turbogenerator 41 The relative error between the pure component parameters of this work and FluidProp is less than 4% which is reasonable. The coefficients of ideal gas heat capacity differ in their magnitude but the overall heat capacity calculated from both the coefficients show a variation of only 1-5% for the given temperature range. Figure 4-1 illustrates the comparison of the specific heat capacity at constant pressure from this work and FluidProp for MDM. This variation in the specific heat capacity is acceptable as the values of siloxanes available in literature have an uncertainty of about 5% [60]. The difference in the specific heat is reflected onto the output power which has a relative error of around 3% when compared to the power output obtained using the default fluid MDM available in FluidProp. Based on the system and the thermodynamic model, the results of the optimization was obtained which has been presented in the following section. 4-3 Analysis of the simulated optimized ORC Turbogenerator A design of experiments procedure was implemented in order to generate the required in- put population for the genetic algorithm. The following section analyses the results of this procedure. 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 Iterations [−] Figure 4-2: Fitness entropy during each iteration in Latin Hypercube Sampling 4-3-1 Analysis of design of experiments The Optimal Latin Hypercube Sampling procedure, described in the previous chapter, gen- erates a set of initial sample points based on the entropy criteria. Figure 4-2 illustrates the minimization of this entropy over 100 iterations. The sample points are then generated such that the fitness entropy of the set of optimization variables is equal to the minimum entropy after 100 iterations. Figure 4-3 illustrates the result of this sampling procedure. From the figure, it is seen that the value of the optimization variables are spread over the whole domain Master of Science Thesis Akshay Hattiangadi Fitness Entropy [−]

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