During the last decade, organic Rankine cycle (ORC) turbogenerators have become very attractive for the exploitation of low-temperature heat sources in the small to medium power range. Organic Rankine cycles usually operate in thermodynamic regions characterized by high pressure ratios and strong real-gas effects in the flow expansion, therefore requiring a nonstandard turbomachinery design. In this context, due to the lack of experience, a promising approach for the design can be based on the intensive use of computational fluid dynamics (CFD) and optimization procedures to investigate a wide range of possible configurations. In this work, an advanced global optimization strategy is coupled with a state-of-the-art CFD solver in order to assist in the design of ORC turbines. In particular, a metamodel assisted genetic algorithm, based on the so-called 'off-line trained' metamodel technique, has been employed. The numerical solutions of the two-dimensional (2D) Euler equations are computed with the in-house built code zFlow. The working fluid is toluene, whose thermodynamic properties are evaluated by an accurate equation of state, available in FluidProp. The computational grids created during the optimization process have been generated through a fully automated 2D unstructured mesh algorithm based on the advancing-Delaunnay strategy. The capability of this procedure is demonstrated by improving the design of an existing one-stage impulse radial turbine, where a strong shock appears in the stator channel due to the high expansion ratio. The goal of the optimization is to minimize the total pressure losses and to obtain a uniform axisymmetric stream at the stator discharge section, in terms of both the velocity magnitude and direction of the flow
An adjoint-based shape optimization approach for supersonic turbine cascades is proposed\ud for application to organic Rankine cycle (ORC) turbines. The algorithm is based\ud on an inviscid discrete adjoint method and encompasses a fast look-up table (LuT)\ud approach to accurately deal with real-gas flows. The turbine geometry is defined by\ud adopting state-of-the-art parameterization techniques (NURBS), enabling to handle both\ud global and local control of the shape of interest. A preconditioned steepest descent\ud method has been chosen as gradient-based optimization algorithm to efficiently search\ud for the nearest minimum. The potential of the optimization approach is first verified by\ud application on the redesign of an existing converging–diverging turbine nozzle operating\ud in thermodynamic regions characterized by relevant real-gas effects. A significant\ud efficiency improvement and a more uniform flow at the blade outlet section are achieved,\ud with expected beneficial effects on the aerodynamics of the downstream rotor. The optimized\ud configuration is also assessed by means of high-fidelity turbulent simulations,\ud which point out the capability of the present inviscid approach in optimizing supersonic\ud turbine cascades with very limited computational burdens. Finally, the newly developed\ud real-gas adjoint method is compared against adjoints based on ideal equations of state\ud on the same design problem. Results show that the performance gain obtained by a fully\ud real-gas optimization strategy is by far higher than that achieved with simplified\ud approaches in case of ORC turbines. This proves the relevance of including accurate\ud thermodynamic models in all steps of ORC turbine desig
There is a growing interest in organic Rankine cycle turbogenerators because of their ability to efficiently utilize external heat sources at low-to-medium temperature in the small-to-medium power range. High-temperature organic Rankine cycle turbines typically operate at very high pressure ratio and expand the organic working fluid in the dense-vapour thermodynamic region, thus requiring computational fluid dynamics solvers coupled with accurate thermodynamic models for their performance assessment and design. In this article we present a steady-state three-dimensional viscous computational fluid dynamics study of the Tri-O-Gen organic Rankine cycle radial turbine, including the radial nozzle, the rotor and the diffuser. The turbine operates with toluene as the working fluid, whose accurate thermophysical properties are obtained with a look-up table approach. Based on the three-dimensional simulation results, together with a two-dimensional fluid dynamic optimisation procedure documented elsewhere, an improved nozzle geometry is designed, manufactured and experimentally tested. Measurements show it delivers 5 kWe or 4% more net power output, as well as improved off-design performance.
This work proposes an automated strategy for the preliminary design of turbomachinery, based on the application of a throughflow code and of a highly flexible and efficient optimization strategy. The code solves for the circumferentially-averaged flow equations, including the effects of aerodynamic and friction forces and of blade thickness; the outcome of the code is the flow distribution on the meridional surface. The fluid-dynamic solver is coupled with the optimization tool in order to determine the “optimal” mean flow surface, as a result of a multiobjective optimization procedure, in which nonconcurrent goals are simultaneously considered. A global optimization strategy is applied, based on the combination of a Genetic Algorithm with a metamodel to tackle the computational cost of the process. The optimization method is applied to a low speed axial compressor, for which the optimization goals are the minimization of aerodynamic loss and discharge kinetic energy at the exit of the stage, as well as the uniformity of work exchange along the blade span. The method proves to match all the objectives, providing a clear improvement with respect to classical and well-established design methods. The optimization provided by the automated design is finally assessed by high-fidelity calculations performed with a fully three-dimensional CFD code on both the baseline and optimized machine configurations. Improvements are confirmed for all the goals specified in the optimization strategy, resulting in a more efficient machine.
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