Optimisation robuste de turbines pour les cycles organiques de Rankine (ORC)

Abstract : In recent years, the Organic Rankine Cycle (ORC) technology has received great interest from the scientific and technical community because of its capability to recover energy from low-grade heat sources. In some applications, as the Waste Heat Recovery (WHR), ORC plants need to be as compact as possible because of geometrical and weight constraints. Recently, these issues have been studied in order to promote the ORC technology for Internal Combustion Engine (ICE) applications. The idea to recover this residual energy is not new and the 1970s energy crisis encouraged the development of feasible ORC small-scale plants (1-10 kWe). Due to the molecular complexity of the working fluids, strong real gas effects have to be taken into account because of the high pressures and densities, if compared to an ideal gas. In these conditions the fluid is known as dense gas. Dense gases are defined as single phase vapors, characterized by complex molecules and moderate to large molecular weights. The role of dense gas dynamics in transonic internal flows has been widely studied for its importance in turbomachinery applications involved in low-grade energy exploitation, such as the ORC. Recently, the attention has been focused on axial turbines, which minimize the system size, if compared with radial solutions at the same pressure ratios and enthalpy drops. In this work, a novel design methodology for supersonic ORC axial impulse turbine stages is proposed. It consists in a fast, accurate two-dimensional design which is carried out for the mean-line stator and rotor blade rows of a turbine stage by means of a method of characteristic (MOC) extended to a generic equation of state. The viscous effects are taken into account by introducing a proper turbulent compressible boundary layer correction to the inviscid design obtained with MOC. Since proposed heat sources for ORC turbines typically include variable energy sources such as WHR from industrial processes or automotive applications, as a result, to improve the feasibility of this technology, the resistance to variable input conditions is taken into account. The numerical optimization under uncertainties is called Robust Optimization (RO) and it overcomes the limitation of deterministic optimization that neglects the effect of uncertainties in design variables and/or design parameters. To measure the robustness of a new design, statistics such as mean and variance (or standard deviation) of a response are calculated in the RO process. In this work, the MOC design of supersonic ORC nozzle blade vanes is used to create a baseline injector shape. Subsequently, this is optimized through a RO loop. The stochastic optimizer is based on a Bayesian Kriging model of the system response to the uncertain parameters, used to approximate statistics of the uncertain system output, coupled to a multi-objective non-dominated sorting genetic algorithm (NSGA). An optimal shape that maximizes the mean and minimizes the variance of the expander isentropic efficiency is searched. The isentropic efficiency is evaluated by means of RANS (Reynolds Average Navier-Stokes) simulations of the injector. The fluid thermodynamic behavior is modelled by means of the well-known Peng-Robinson-Stryjek-Vera equation of state. The blade shape is parametrized by means of a Free Form Deformation approach. In order to speed-up the RO process, an additional Kriging model is built to approximate the multi-objective fitness function and an adaptive infill strategy based on the Multi Objective Expected Improvement for the individuals is proposed in order to improve the surrogate accuracy at each generation of the NSGA. The robustly optimized ORC expander shape is compared to the results provided by the MOC baseline shape and the injector designed by means of a standard deterministic optimizer.
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Elio Antonio Bufi. Optimisation robuste de turbines pour les cycles organiques de Rankine (ORC). Mécanique des fluides [physics.class-ph]. Ecole nationale supérieure d'arts et métiers - ENSAM; Politecnico di Bari. Dipartimento di Ingegneria Meccanica e Gestionale (Italia), 2016. Français. ⟨NNT : 2016ENAM0070⟩. ⟨tel-01535746⟩

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