giecli / axialopt

A Meanline Model for the Design and Optimization of Axial Turbines

AxialOpt

AxialOpt is a code for the preliminary design and optimization of axial turbines. The output of AxialOpt can be used in system-level analyses (such as a power cycle optimization) to estimate the efficiency or footprint of axial turbines for a given set of thermodynamic specifications. In addition, the information provided by AxialOpt can be used as the starting point for the aerodynamic design of the turbine blades using more advanced flow models based on CFD.

The models and optimization methodology of AxialOpt are documented in a peer-reviewed, open-access publication and the source code is also stored in a Zenodo repository.

Features

• The axial turbine model is composed of three sub-models that are used as building blocks:
  1. A meanline model to describe the flow in each cascade
  2. An empirical loss model to evaluate the entropy generation in each cascade
  3. A one-dimensional flow diffuser model to compute the exit kinetic energy recovery
• The model is formulated for axial turbines with any number of stages
• The model is formulated to use arbitrary equations of state to compute thermodynamic properties:
  1. The current version uses the REFPROP v9.1 fluid library
  2. Other fluid libraries and look-up tables may be implemented in the future
• The loss model is formulated in a general way to use:
  1. Any set of empirical correlations to compute the losses:
     1. Ainley and Mathieson (implemented)
     2. Dunhan and Came (implemented)
     3. Kacker and Okapuu(implemented)
     4. Craig and Cox (will be implemented soon)
     5. Other loss model contributions are welcome
  2. Different definitions for the loss coefficient:
     1. Stagnation pressure loss coefficient
     2. Enthalpy loss coefficient (also known as kinetic energy loss coefficient)
     3. Entropy loss coefficient
• The preliminary design is formulated as a constrained optimization problem
  1. This allows explore the design space in a systematic way and account for technical constraints
  2. It is straighforward to modify the objective function and constraints depending on the problem
  3. There are several gradient-based algorithms available to solve the optimization problem, including:
     1. Sequential Quadratic Programming (SQP)
     2. Interior Point (IP)
• The output of AxialOpt can be saved as:
  1. Data files:
     1. Summary of the solution of the optimization problem
     2. Geometry of each cascade
     3. Thermodynamic properties at each station
     4. Velocity triangles of each stage
  2. Figures:
     1. T-s and h-s diagrams of the expansion
     2. Velocity triangles of each stage
     3. Axial-radial and axial-tangential views of the turbine
     4. Breakdown of the losses within the turbine

Note: AxialOpt is not suitable to estimate the performance of an existing design under different conditions. The extension of the code to compute the performance at off-design conditions is underway.

Requisites

AxialOpt was implemented in MATLAB R2018a and requires a REFPROP v9.1 installation. The folder link_refprop_matlab contains instructions about how to link REFPROP with MATLAB.

AxialOpt has the option to use the export_fig library to produce publication-quality figures. Using this library requires ghostcript and pdftops. See the installation instructions in the original repository.

Examples

The folder AxialOpt_examples contains five examples commented in detail to get started with the code:

• A supercritical Carbon dioxide turbine
• A organic Rankine cycle (ORC) turbine using R125 as working fluid
• A organic Rankine cycle (ORC) turbine using hexane as working fluid
• An industrial gas turbine
• A high-pressure steam turbine

These examples show the capabilities AxialOpt and they can be used as a template to start your own projects.

License

AxialOpt is licensed under the terms of the MIT license. See the license file for more information.

Contact information

AxialOpt was developed by PhD candidate Roberto Agromayor and Associate Professor Lars O. Nord at the Process and Power Research Group of the Norwegian University of Science and Technology (NTNU)

Please, drop us an email to [email protected] if you have questions about the code or you have a bug to report. We would also love to hear about your experiences with AxialOpt in general.

How to cite AxialOpt?

If you want to cite AxialOpt in a scientific publication, please refer to the Zenodo repository listed in the references below.
DOI: https://doi.org/10.5281/zenodo.2635586

References

R. Agromayor and L. O. Nord, Preliminary Design and Optimization of Axial Turbines Accounting for Diffuser Performance, International Journal of Turbomachinery, Propulsion and Power (submitted).

R. Agromayor, and L. O. Nord, AxialOpt - A Meanline Model for the Design and Optimization of Axial Turbines, Zenodo repository.

E. W. Lemmon, M. L. Huber, and M. O. McLinden, NIST Standard Reference Database 23: Reference Fluid Thermodynamic and Transport Properties (REFPROP) version 9.1, National Institute of Standards and Technology, 2013.

Ainley, D.G.; Mathieson, C.R. A Method of Performance Estimation for Axial-Flow Turbines. Aeronautical Research Council Reports and Memoranda 1951, 2974, 1–30.

Dunham, J.; Came, P.M. Improvements to the Ainley-Mathieson Method of Turbine Performance Prediction. Journal of Engineering for Power 1970, 92, 252–256.

Kacker, S.C.; Okapuu, U. A Mean Line Prediction Method for Axial Flow Turbine Efficiency. Journal of Engineering for Power 1982, 104, 111–119.

Craig, H.R.M.; Cox, H.J.A. Performance Estimation of Axial Flow Turbines. Proceedings of the Institution of Mechanical Engineers 1970, 185, 407–424.