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TurboFlow

TurboFlow: Meanline Modelling of Axial Turbines - Published in JOSS (2025)

https://github.com/turbo-sim/turboflow

Science Score: 93.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in JOSS metadata
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Scientific Fields

Computer Science Computer Science - 40% confidence
Last synced: 4 months ago · JSON representation

Repository

Meanline model for performance prediction and preliminary design of turbomachinery

Basic Info
  • Host: GitHub
  • Owner: turbo-sim
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 493 MB
Statistics
  • Stars: 25
  • Watchers: 2
  • Forks: 8
  • Open Issues: 12
  • Releases: 1
Created almost 2 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License

README.md

TurboFlow: Meanline Modelling of Axial Turbines

TurboFlow is a Python package for mean-line modelling of axial turbines. It aims to offer flexible and reliable simulations for both performance prediction and design optimization, and should present a valuable resource for engineers and researchers working in the field of turbomachinery.

PyPI Documentation

Core features

  • Performance Prediction: Accurately predict the performance of axial turbines based on various input parameters.
  • Design Optimization: Optimize preliminary axial turbine design to achieve optimal performance metrics.
  • Equation-oriented problem formulation: Equation-oriented problem formulation for performance analysis and design optimization.
  • Model consistency: The model is consistent for both performance prediction and design optimization.
  • Efficient solution: The model adopts gradient-based root-finding and optimization solver
  • Real gas fluid property analysis: Use CoolProp to determine thermohpysical properties.
  • Flexible model: The model offers options for submodels for loss, deviation and choking calculations
  • General geometry: Geometrical variables are defined to cover a wide range of designs, including multistage configurations.
  • Easy-to-use: Intuitive and easy setup of input parameters for rapid development and analysis.
  • Extensive Documentation: https://turbo-sim.github.io/turboflow/

Quick Installation Guide

This guide will walk you through the process of installing Turboflow via pip. To isolate the Turboflow installation and avoid conflicts with other Python packages, it is recommended to create a dedicated Conda virtual environment.

  1. Install Miniconda if you don't have it already.

  2. Open a terminal or command prompt and create a new virtual environment named turboflow_env with Python 3.11: conda create --name turboflow_env python=3.11

  3. Activate the newly created virtual environment: conda activate turboflow_env

  4. Install Turboflow using pip within the activated virtual environment: pip install turboflow

  5. Verify the installation by running the following command in your terminal: python -c "import turboflow; turboflow.print_package_info()"

If the installation was successful, you should see the Turboflow banner and package information displayed in the console output.

Congratulations! You have now successfully installed Turboflow in its own Conda virtual environment using pip. You're ready to start using Turboflow in your Python projects.

Owner

  • Name: turbo-sim
  • Login: turbo-sim
  • Kind: organization

JOSS Publication

TurboFlow: Meanline Modelling of Axial Turbines
Published
July 09, 2025
DOI
Volume 10, Issue 111, Page 7588
Authors
Lasse B. Anderson ORCID
Department of Energy and Process Engineering, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
Roberto Agromayor ORCID
Department of Civil and Mechanical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Fredrik Haglind ORCID
Department of Civil and Mechanical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Lars O. Nord ORCID
Department of Energy and Process Engineering, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
Editor
Philip Cardiff ORCID
Tags
python turbomachinery meanline modeling design optimization performance analysis
View PDF Review Thread Software Archive

GitHub Events

Total
  • Issues event: 23
  • Watch event: 22
  • Issue comment event: 26
  • Push event: 127
  • Pull request event: 2
  • Fork event: 6
  • Create event: 7
Last Year
  • Issues event: 23
  • Watch event: 22
  • Issue comment event: 26
  • Push event: 127
  • Pull request event: 2
  • Fork event: 6
  • Create event: 7

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 18
  • Total pull requests: 6
  • Average time to close issues: 27 days
  • Average time to close pull requests: 11 days
  • Total issue authors: 4
  • Total pull request authors: 3
  • Average comments per issue: 1.61
  • Average comments per pull request: 0.33
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 18
  • Pull requests: 4
  • Average time to close issues: 27 days
  • Average time to close pull requests: 7 days
  • Issue authors: 4
  • Pull request authors: 2
  • Average comments per issue: 1.61
  • Average comments per pull request: 0.0
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • RoberAgro (14)
  • SrinivasDiwanji (2)
  • lasseband (1)
  • ediemiglio (1)
Pull Request Authors
  • philipcardiff (2)
  • kyleniemeyer (2)
  • danielskatz (2)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 26 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 14
  • Total maintainers: 2
pypi.org: turboflow

A Python tool for meanline analysis and optimization of turbomachinery.

  • Versions: 14
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 26 Last month
Rankings
Dependent packages count: 10.9%
Average: 36.0%
Dependent repos count: 61.2%
Maintainers (2)
RoberAgro lasseba
Last synced: 4 months ago

Dependencies

.github/workflows/ci.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • conda-incubator/setup-miniconda v2 composite
.github/workflows/deploy_docs.yaml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • conda-incubator/setup-miniconda v2 composite
  • peaceiris/actions-gh-pages v3 composite
.github/workflows/generate_regression_data_ubuntu.yaml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • actions/upload-artifact v3 composite
  • conda-incubator/setup-miniconda v2 composite
.github/workflows/draft-pdf.yml actions
  • actions/checkout v4 composite
  • actions/upload-artifact v3 composite
  • openjournals/openjournals-draft-action master composite
docs/requirements.txt pypi
  • Sphinx ==6.2.1
  • numpydoc ==1.6.0
  • pydata-sphinx-theme ==0.14.1
  • sphinx-book-theme ==1.0.1
  • sphinx-rtd-theme ==1.3.0
  • sphinxcontrib-applehelp ==1.0.7
  • sphinxcontrib-bibtex ==2.5.0
  • sphinxcontrib-devhelp ==1.0.5
  • sphinxcontrib-htmlhelp ==2.0.4
  • sphinxcontrib-jquery ==4.1
  • sphinxcontrib-jsmath ==1.0.1
  • sphinxcontrib-qthelp ==1.0.6
  • sphinxcontrib-serializinghtml ==1.1.9
poetry.lock pypi
  • accessible-pygments 0.0.5
  • alabaster 0.7.16
  • annotated-types 0.7.0
  • asttokens 2.4.1
  • babel 2.15.0
  • beautifulsoup4 4.12.3
  • bibtexparser 1.4.1
  • black 24.4.2
  • bump2version 1.0.1
  • certifi 2024.2.2
  • charset-normalizer 3.3.2
  • click 8.1.7
  • colorama 0.4.6
  • contourpy 1.2.1
  • coolprop 6.6.0
  • cycler 0.12.1
  • decorator 5.1.1
  • docutils 0.20.1
  • et-xmlfile 1.1.0
  • executing 2.0.1
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  • fonttools 4.52.4
  • idna 3.7
  • imagesize 1.4.1
  • iniconfig 2.0.0
  • ipython 8.24.0
  • jedi 0.19.1
  • jinja2 3.1.4
  • kiwisolver 1.4.5
  • latexcodec 3.0.0
  • markupsafe 2.1.5
  • matplotlib 3.9.0
  • matplotlib-inline 0.1.7
  • mypy-extensions 1.0.0
  • numpy 1.26.4
  • numpydoc 1.7.0
  • openpyxl 3.1.3
  • packaging 24.0
  • pandas 2.2.2
  • parso 0.8.4
  • pathspec 0.12.1
  • pexpect 4.9.0
  • pillow 10.3.0
  • platformdirs 4.2.2
  • pluggy 1.5.0
  • prompt-toolkit 3.0.45
  • ptyprocess 0.7.0
  • pure-eval 0.2.2
  • pybtex 0.24.0
  • pybtex-docutils 1.0.3
  • pydantic 2.7.2
  • pydantic-core 2.18.3
  • pydata-sphinx-theme 0.15.3
  • pygments 2.18.0
  • pyparsing 3.1.2
  • pytest 8.2.1
  • python-dateutil 2.9.0.post0
  • pytz 2024.1
  • pyyaml 6.0.1
  • pyzotero 1.5.19
  • requests 2.32.3
  • scipy 1.13.1
  • setuptools 70.0.0
  • sgmllib3k 1.0.0
  • six 1.16.0
  • snowballstemmer 2.2.0
  • soupsieve 2.5
  • sphinx 7.3.7
  • sphinx-book-theme 1.1.2
  • sphinx-design 0.6.0
  • sphinx-rtd-theme 2.0.0
  • sphinx-tabs 3.4.5
  • sphinx-togglebutton 0.3.2
  • sphinxawesome-theme 5.1.5
  • sphinxcontrib-applehelp 1.0.8
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  • sphinxcontrib-devhelp 1.0.6
  • sphinxcontrib-htmlhelp 2.0.5
  • sphinxcontrib-jquery 4.1
  • sphinxcontrib-jsmath 1.0.1
  • sphinxcontrib-qthelp 1.0.7
  • sphinxcontrib-serializinghtml 1.1.10
  • stack-data 0.6.3
  • tabulate 0.9.0
  • toml 0.10.2
  • traitlets 5.14.3
  • typing-extensions 4.12.0
  • tzdata 2024.1
  • urllib3 2.2.1
  • wcwidth 0.2.13
  • wheel 0.43.0
pyproject.toml pypi
  • black ^24.4.2 develop
  • bump2version ^1.0.1 develop
  • ipython ^8.24.0 develop
  • numpydoc ^1.7.0 develop
  • pytest ^8.2.1 develop
  • pyzotero ^1.5.19 develop
  • sphinx ^7.3.7 develop
  • sphinx-book-theme ^1.1.2 develop
  • sphinx-design ^0.6.0 develop
  • sphinx-rtd-theme ^2.0.0 develop
  • sphinx-tabs ^3.4.5 develop
  • sphinx-togglebutton ^0.3.2 develop
  • sphinxawesome-theme ^5.1.5 develop
  • sphinxcontrib-bibtex ^2.6.2 develop
  • CoolProp ^6.6.0
  • PyYAML ^6.0.1
  • cycler ^0.12.1
  • matplotlib ^3.9.0
  • numpy ^1.26.4
  • openpyxl ^3.1.2
  • pandas ^2.2.2
  • pydantic ^2.7.2
  • python ^3.11
  • scipy ^1.13.1
  • toml ^0.10.2
environment.yaml pypi