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CFD Simulation Methodology for a Rotary Steam Expansion Piston Engine
Technical Paper
2020-32-2303
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In industrial processes and other power generation processes, large amounts of waste heat are often lost to the environment. The conversion of this thermal energy into mechanical work promises a significant improvement in energy-utilization, the efficiency of the overall system and, consequently, cost-effectiveness. Therefore, the use of a Rankine-Cycle is a well-established technical process. A recent research project has investigated a novel expansion machine to be integrated into such an RC-process.
Primarily, the present work deals with the fluid dynamic simulation of this expander, which is based on the principle of a rotary piston engine. The aim is to develop, analyze and optimize the process and the corresponding components. Hence, a CFD-model had to be built up, which should correspond as closely as possible to the physical engine. However, the special demands on the simulation model turn out to be very challenging for most commercial CFD-tools due to complex rotary kinematics in combination with the port-controlled gas exchange and the unconventional working medium used.
First, the procedure of generating an initial CFD-simulation is examined in detail. Due to the complex processes, leading to high numerical effort in terms of phase transition and large dynamic mesh changes, this model requires several assumptions and simplifications in order to find an optimal balance between computing time and result quality. Thus, some disadvantages, restrictions and difficulties occur, which motivate the development of alternative simulation models. Consequently, some other methods of building up a corresponding model are assessed, being discussed with their associated properties and limitations.
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Lang, M., Zainer, C., Bechter, C., and Kirchberger, R., "CFD Simulation Methodology for a Rotary Steam Expansion Piston Engine," SAE Technical Paper 2020-32-2303, 2020, https://doi.org/10.4271/2020-32-2303.Data Sets - Support Documents
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