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CHAPTER 6: Three-Dimensional CFD Optimization of the Dual-Stage Organic RIT of the complete dual-stage organic RIT was performed to create the full performance maps. Such maps are beneficial to determine the performance of the ORC under a range of off-design operating conditions. 6.2. Methodology for three-dimensional CFD optimization Quantifying the influence of design variables on the performance of the turbine is an exhaustive procedure. ANSYS provides a so-called Design ExplorationTM module that consists of Design of Experiments (DOE), Meta-models (surrogate models) and optimization algorithms in order to replace the lengthy, and time-consuming process of trial-and-error in the search for the optimum design. Generally speaking, optimization methods attempt to determine the design variables (Xi) that maximize or minimize an objective function (OFl) as below: ππΉ =ππΉ(π(π),π), π=1π‘ππ, π=1π‘ππ Equation6-1 ππππ Where N is the number of design parameters and M is the number of objective functions. Moreover, U(Xi) is the solution of the flow equations R(U(Xi),Xi)=0 and subject to nA performance and nG geometrical constrains as following (Van den Braembussche 2008): π΄π(π(ππ),ππ)β€0, π=1,ππ΄ Equation6-2 πΊπ(ππ)β€0, π=1,ππΊ Equation6-3 The design variables are the parameters used to define the blade geometry while the objective function is the turbine performance. The goal of 3-D CFD optimization is to modify the blade geometry to maximize the turbine performance by minimizing the passage losses (i.e. entropy generation) and reducing the flow non-uniformity (i.e. the secondary flows). Such numerical optimization comprises of three main stages as following: 217 | P a g ePDF Image | SMALL-SCALE RADIAL INFLOW TURBINE FOR WHR ORC
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