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32 Model ORC applications, availability, and moderate cost. They are also considered as non-ozone- depleting in the stratosphere, and have negligible global warming potential due to their short atmospheric lifetimes thus complying to the environmental performance limits [52, 53]. In addition, they have good lubricating properties; thus allowing for lubrication of the turbine shaft by means of the working fluid itself, greatly simplifying the overall system [21]. Thus by limiting to the class of siloxanes, most of the fluid performance measures have been accounted for and hence can be excluded from the objective function. This allows the net output power to be defined as the single objective function within the scope of this work. 3-6 Design of Experiments Design of Experiments is a general term to denote any method available for setting parameter values in a certain domain of interest forming a set of experiments or sample points [54]. The primary goal of such an exercise is to extract the maximum amount of unbiased information about the objective function from as few observations as possible [55]. There are several meth- ods to generate a set of sample points such as Random Allocation, Optimal Latin Hypercube design, Optimal Latin Square design, Cubic Face Centered design, Box-Behnken, etc. In this work, the Optimal Latin Hypercube design procedure has been chosen to generate inputs for the optimization algorithm. The sample points are created using a two level pseudo random Table 3-7: Parameters for the Optimal Latin Hypercube allocation Tuning Parameter Number of Points Seed Base Generator Distribution Point Combiner Maximum Iterations Maximum No Improvement Iterations Optimization Algorithm Value 200 1000 Centered Point Uniform Entropy 100 15 Iterated Local Search process wherein each dimension is randomly sampled according to a predefined distribution in the first level and then recombined to form the final design in the second level. The recom- bination of points in the second pseudo level undergoes an optimization process in order to spread the points as evenly as possible within the design space based on the entropy criteria. A centered point generator with a uniform distribution is used to generate the first pseudo level of points. An entropy point combiner is used to recombine the points with an objective to minimize the entropy of system [54]. The entropy of the system is computed as: S = âln(det(C)) (3-14) where C is the correlation matrix of the N sample points defined as: cij = exp {â2 (xi â xj )} (3-15) The entropy point combiner operates on all the points of the system at the same time and tries to minimize the correlation matrix using an optimization algorithm called the Iterated Local Search [54]. Figure 3-14 presents a pictorial representation of the iterated local search algorithm. Akshay Hattiangadi Master of Science ThesisPDF Image | Working Fluid Design for Organic Rankine Cycle
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