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To this purpose, a reliable numerical model of ORC turbine flow will be constructed, with focus on suitable thermodynamic and turbulence modeling and on the impact of 3D and unsteady effects. Then, the numerical model will be coupled with an uncertainty quantification algorithm and an optimizer. Initially (see point (1) above), some of the existing robust optimization techniques will be applied to optimize robustly a turbine expander developed in the framework of the TRENENERGY project under realistic operation conditions associated to heat recovery from diesel engines used for power supply of regional trains. The objective is to seek for a compact, highly efficient turbine expander with stable performance under highly variable operating conditions. The choice of the "stability" criteria will also require a specific study, based on a preliminary ANOVA analysis of the turbine performance. The optimized turbine will be constructed and tested in the framework of the project, are feedback from the actual behavior on the test bench will be used to conclude on the more suitable robust optimization strategies and on the possible improvements. Concerning point 2, possible improvements to existing robust optimization techniques, in terms of both accuracy and efficiency, will be investigated. Specifically, hybrid techniques based on the mixed use of sensitivity derivatives and evolutionary algorithms will be taken into account. The improved technique will be applied again to the design of the turbine expander, and compared to the previous results to measure the gains in computational efficiency and performance criteria. [1] Maizza, V., and Maizza, A., 2001. “Unconventional working fluids in Organic Rankine-cycles for waste energy recovery systems”. Appl Therm Eng, 21, pp. 381–390. [2] Hung, T., Shai, T., and Wang, S., 1997. “A review of Organic Rankine Cycles (ORCs) for the recovery of low-grade waste heat”. Energy, 22(7), pp. 661–667. [3] Invernizzi, C., Iora, P., and Silva, P., 2007. “Bottoming micro-Rankine cycles for micro-gas turbines". Appl Therm Eng, 27(1), 100-110. [4] Park G J, Lee T H, Lee K H and Hwang K H. A review of robust design methodologies. Transactions of the KSME A (in Korean), 2004, 28(9): 1368-1383 [5]Fowlkes W Y and Creveling C M, Engineering method for robust product design. Massachusetts: Addison-Wesley, 1995 [6] Duvigneau R., Aerodynamic Shape Optimization with Uncertain Operating Conditions using Metamodels. INRIA Report, march 2007 [7] P. Cinnella, S. Hercus, "Robust optimization of dense gas flows under uncertain operating conditions", Comp Fluids, 39(10), 1893-1908. [8] Z Tang, J Périaux, "Uncertainty based robust optimization method for drag minimization problems in aerodynamics", Volumes 217–220, 1 April 2012, Pages 12–24 [9] P Cinnella, S Hercus, Robust shape optimization of uncertain dense gas flows through a plane turbine cascade, Proceedings of ASME-JSME-KSME Joint Fluids Engineering Conference 2011, Paper AJK2011-05007 [10] Kumar et al., Rubust design using Bayesian Monte Carlo, Int. J. Numer. Meth. Engng 2008; 73:1497–1517PDF Image | Robust optimization of ORC turbine expanders
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