Machine-learnt design guidelines for axial vanes in Supersonic turbines operating with non-ideal compressible flows

Machine-learnt design guidelines for axial vanes in Supersonic turbines operating with non-ideal compressible flows

Jhamb, Shubham (TU Delft Aerospace Engineering)

Pini, Matteo (mentor)
Anand, Nitish (graduation committee)

Delft University of Technology

Aerospace Engineering

2020-03-31

The scientific community is consistently focused on identifying new sources of energy, which can reduce the consequences of climate change and depleting natural resources. Organic Rankine Cycle (ORC) based power systems have been touted as one of the promising technologies of extracting thermal energy from waste-heat and renewable sources such as geothermal, solar radiation and biomass, to name a few. Use of an organic fluid makes it theoretically possible to design a compact, high volumetric flow turbine that can extract energy from sources with low temperature head. However, realization of this turbine becomes challenging because of their unsteady nature which stems from high expansion ratio across the cascade and low speed of sound in organic fluids. The expansion of fluid in these turbines occur in the dense gas supersonic regime where ideal gas assumptions do not hold and the flow is characterized by shock waves and expansion fans. The success of ORC systems thus depends on how well these effects are modeled into the design stage. Stator vanes have a major contribution to the stage losses in these high-expansion low reaction turbines. In current literature, there exists no co-relation that takes into account dense gas effects for the preliminary design of these stator vanes. Thus the objective of this thesis is to study the trend of stator losses with the variation of geometric de- sign variables and inlet conditions, subsequently to qualitatively and quantitatively assess the performance of the blade and finally to propose new design correlations for the organic fluid Toluene. To achieve this objec- tive, the stator design variables affecting 2D loss mechanisms are identified and a design space is constrained. It was hypothesized that the total losses are minimum for an optimum vane design corresponding to a unique value of post-expansion ratio. A semi-automated analysis framework was made, that assessed the fluid dy- namic performance of vanes with varying post-expansion ratio, which upon analysis backed the hypothesis successfully. Further, the trend behavior of this optimum post-expansion ratio with design variables such as stator blade angle, total-static expansion ratio, nozzle solidity and fluid non-ideality is investigated and val- ues of post-expansion ratio corresponding to optimum design vanes are obtained. These discrete optimum points are interpolated to obtain a continuous second order function which is the proposed co-relation that can be used for the preliminary design of a stator vane in an ORC turbine.

Machine-learning
non-ideal compressible fluid dynamics
Axial stator
Turbomachinery

http://resolver.tudelft.nl/uuid:ca7980f0-ab62-4223-8ce0-789ac3c7859e

Student theses

master thesis

© 2020 Shubham Jhamb