Improvement of Transonic Low Pressure Steam Turbine using High Performance Computation.
Degree: 2019, ETH Zürich
Steam turbines hold the largest share of electricity production worldwide. In order to meet growing energy demands, steam turbine designers strive for increased power output, improved efficiency, and longer operational lifespan. Modern steam turbine designs employ large exit annulus area with long blades of up to 60 inches in the last stage of a low-pressure steam turbine in order to achieve these objectives. Increase in last stage blade height introduces supersonic flows in the tip span of the last stage rotor inlet. Such flows are subject to high unsteadiness induced by shock waves. In addition, condensation, droplet formation and unsteady blade loads in the rotor transonic tip region introduce a very complicated flow for numerical computations. Therefore, optimization of blade stacking in the last stage of a transonic low-pressure steam turbine is one of the most delicate and time-consuming parts of the design process. This requires design modifications including blade sweep, lean or twist. The experiments for scaled geometries are nevertheless very expensive and designers have to rely on time accurate computational fluid dynamics. The accuracy of computations is extremely critical in order to guide optimization algorithms and designers to the most viable design. The time-accurate computations are an order of magnitude more expensive than steady state. During design optimization, detailed geometrical features are excluded in order to achieve realistic computational runtime at the cost of accuracy. The geometrical features mostly excluded are hub-tip cavities, seals, part span snubbers, full span shroud, blade count modification or exclusion of upstream or downstream stages. Full-scale multi-stage model with all-inclusive geometrical features results in very large meshes of up to one billion mesh nodes. The proposed meshes must have matching block interfaces throughout the mesh in order to keep a second-order accuracy in space and time posing additional requirements of a very fine mesh in order to resolve high blade twist, shroud connections, and cavities-seals in the flow path. In the case of low-pressure steam turbines, the steam transition from superheated to condense in penultimate and last stages. This necessitates the inclusion of steam modeling at the least for the prediction of wetness as well as numerical accuracy. This results in additional computational resource requirements posing a very challenging computing problem. The recent widespread use of modern general-purpose graphics processing units (GPUs) for scientific computing provides a possibility to scale time-accurate computational fluid dynamic solvers for such challenging problems. A steady decline in graphics processing unit costs and at the same time improvements in throughput and onboard memory gradually allow hybrid high performance computing cluster as a viable option for the design engineering process. The key objective of this work is to improve the aerodynamic efficiency of a modern low-pressure steam turbine, using carefully tailored…
Advisors/Committee Members: Abhari, Reza S., Jenny, Patrick.
Subjects/Keywords: Low pressure steam turbine; CFD; HPC; Turbomachinery; GPU computing; Stator stacking; STEAM TURBINES (HEAT ENGINES); Unsteady aerodynamics; Transonic flows; SHOCK WAVES (FLUID DYNAMICS); Forward curved sweep; Stator twist; stator–rotor interaction
to Zotero / EndNote / Reference
APA (6th Edition):
Raheem, A. (2019). Improvement of Transonic Low Pressure Steam Turbine using High Performance Computation. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/331142
Chicago Manual of Style (16th Edition):
Raheem, Asad. “Improvement of Transonic Low Pressure Steam Turbine using High Performance Computation.” 2019. Doctoral Dissertation, ETH Zürich. Accessed September 22, 2019.
MLA Handbook (7th Edition):
Raheem, Asad. “Improvement of Transonic Low Pressure Steam Turbine using High Performance Computation.” 2019. Web. 22 Sep 2019.
Raheem A. Improvement of Transonic Low Pressure Steam Turbine using High Performance Computation. [Internet] [Doctoral dissertation]. ETH Zürich; 2019. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/20.500.11850/331142.
Council of Science Editors:
Raheem A. Improvement of Transonic Low Pressure Steam Turbine using High Performance Computation. [Doctoral Dissertation]. ETH Zürich; 2019. Available from: http://hdl.handle.net/20.500.11850/331142