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Energy","sub_category":"Wind Energy","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Kynema-SGF (formerly AMR-Wind)\n\n[Documentation](https://kynema.github.io/kynema-sgf) | [Nightly test dashboard](http://my.cdash.org/index.php?project=Kynema) \n\n[![Powered by AMReX](https://amrex-codes.github.io/badges/powered%20by-AMReX-red.svg)](https://amrex-codes.github.io/amrex/) [![Build Status](https://github.com/Kynema/kynema-sgf/workflows/Kynema-SGF-CI/badge.svg)](https://github.com/Kynema/kynema-sgf/actions) [![OpenSSF Best Practices](https://www.bestpractices.dev/projects/9284/badge)](https://www.bestpractices.dev/projects/9284)\n\n\nKynema-SGF (formerly AMR-Wind), wherein SGF stands for structured-grid fluid dynamics, is a \nmassively parallel, block-structured adaptive-mesh, incompressible\nflow solver. The codebase was initiated in 2019 from [incflo](https://github.com/AMReX-Codes/incflo). The solver\nis built on top of the [AMReX library](https://amrex-codes.github.io/amrex).\nAMReX library provides the mesh data structures, mesh adaptivity, as well as the\nlinear solvers used for solving the governing equations. Kynema-SGF is actively\ndeveloped and maintained by a dedicated multi-institutional team from [Lawrence\nBerkeley National Laboratory](https://www.lbl.gov/), [National Laboratory of the Rockies](https://nlr.gov), and [Sandia National\nLaboratories](https://sandia.gov).\n\nThe primary applications for Kynema-SGF are: performing large-eddy simulations\n(LES) of atmospheric boundary layer (ABL) flows, simulating wind farm\nturbine-wake interactions using actuator disk or actuator line models for\nturbines, and as a background solver when coupled with a near-body solver (e.g.,\n[Kynema-UGF](https://github.com/kynema/kynema-ugf)) with overset methodology to\nperform blade-resolved simulations of multiple wind turbines within a wind farm.\nFor offshore applications, the ability to model the air-sea interaction effects\nand its impact on the ABL characteristics is another focus for the code\ndevelopment effort. As with other codes in the\n[Kynema](https://github.com/kynema) ecosystem, Kynema-SGF shares the following\nobjectives:\n\n- an open, well-documented implementation of the state-of-the-art computational\n  models for modeling wind farm flow physics at various fidelities that are\n  backed by a comprehensive verification and validation (V\u0026V) process;\n\n- be capable of performing the highest-fidelity simulations of flow fields within\n  wind farms; and \n\n- be able to leverage the high-performance leadership class computing\n  facilities available at DOE national laboratories.\n\n## References\nTo cite Kynema-SGF and to learn more about the methodology, use the following [journal article](https://doi.org/10.1002/we.70010) as well as the [ExaWind reference](https://doi.org/10.1002/we.2886):\n\n```\n@article{amrwind2025,\n    author = {Kuhn, Michael B. and {Henry de Frahan}, Marc T. and Mohan, Prakash and Deskos, Georgios and Churchfield, Matthew and Cheung, Lawrence and Sharma, Ashesh and Almgren, Ann and Ananthan, Shreyas and Brazell, Michael J. and {Martinez-Tossas} Luis A. and Thedin, Regis and Rood, Jon and Sakievich, Philip and Vijayakumar, Ganesh and Zhang, Weiqun and Sprague, Michael A.},\n    title = {AMR-Wind: A performance-portable, high-fidelity flow solver for wind farm simulations},\n    journal = {Wind Energy},\n    volume = {28},\n    number = {5},\n    pages = {-},\n    doi = {10.1002/we.70010},\n    url = {https://onlinelibrary.wiley.com/doi/full/10.1002/we.70010},\n    eprint = {},\n    year = {2025}\n}\n\n@article{exawind2024,\n    author = {Sharma, Ashesh and Brazell, Michael J. and Vijayakumar, Ganesh and Ananthan, Shreyas and Cheung, Lawrence and deVelder, Nathaniel and {Henry de Frahan}, Marc T. and Matula, Neil and Mullowney, Paul and Rood, Jon and Sakievich, Philip and Almgren, Ann and Crozier, Paul S. and Sprague, Michael},\n    title = {ExaWind: Open-source CFD for hybrid-RANS/LES geometry-resolved wind turbine simulations in atmospheric flows},\n    journal = {Wind Energy},\n    volume = {27},\n    number = {3},\n    pages = {225-257},\n    doi = {10.1002/we.2886},\n    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/we.2886},\n    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/we.2886},\n    year = {2024}\n}\n```\n\n## Documentation\n\nDocumentation is available at https://kynema.github.io/kynema-sgf, which \nincludes a walkthrough tutorial, a user manual, notes on theory,\nand tips for developers. We also provide a developer-focused API\ndocumentation at the same link. You can either\nbrowse the docs online by following the links, or you can generate them locally\nafter downloading the code. Please follow the instructions in user manual to\nbuild documentation locally.\n\n## Compilation and usage\n\nKynema-SGF is built upon the [AMReX library](https://amrex-codes.github.io/amrex).\nA snapshot of the AMReX library is distributed along with the Kynema-SGF source\ncode as a `git-submodule`. In addition to the AMReX library, you will require a\nmodern C++ compiler that supports the C++17 standard. Users wishing to execute\nthe code on high-performance computing (HPC) systems will also need MPI\nlibraries installed on their system. The code can also be compiled using MPI+X, \nwhere X can be OpenMP for CPU shared memory parallelism,\nCUDA to target NVIDIA GPUs, ROCM for AMD GPUs, or SyCL for Intel GPUs.\n\n### Contributing, reporting bugs, and requesting help\n\nTo report issues or bugs please [create a new\nissue](https://github.com/kynema/kynema-sgf/issues/new) on GitHub.\n\nWe welcome contributions from the community in form of bug fixes, feature\nenhancements, documentation updates, etc. All contributions are processed\nthrough pull-requests on GitHub. Please refer to the \n[coding guidelines](https://kynema.github.io/kynema-sgf/developer/coding_guidelines.html) as\na reference for the best practices currently used to develop Kynema-SGF.\n\nTo pass the code format checking, use this command with a new version of `clang-format`:\n```\nfind src unit_tests \\( -name \"*.cpp\" -o -name \"*.H\" -o -name \"*.h\" \\) -exec clang-format -i {} +\n```\n\nPlease acknowledge as a publication co-author any developer that has\nsignificantly contributed to implementing or improving specific\ncapability that was used for that publication.\n\n### User discussion, feedback, and community support\n\nThe development team manages a mailing list for Kynema-SGF users. Invites for quarterly user meetings,\nalong with occasional announcements, are sent to this list.\nQuarterly meetings provide development updates and a forum for discussion and feedback.\nIf you would like to join this mailing list, please send a request to michael.kuhn@nlr.gov,\nand we will be happy to add your email address. The GitHub page (issues, discussions, pull requests) is preferred\nfor the majority of questions.\n\n## Versioning and tags\n\nKynema-SGF uses a type of semantic versioning to help users navigate different versions of the code, \nwhich are labeled with GitHub tags. These tagged versions are not exhaustive, and they adhere to\nthe following convention. Given a version number MAJOR.MINOR.PATCH:\n1. MAJOR version for changes to input file compatibility for key aspects of the solver, when a key model is changed to significantly affect results of simulations, when a major new capability is added\n2. MINOR version for when a significant feature is added (in a backward compatible manner), accumulation of smaller features, or changes to input file compatibility for less central aspects of the solver (e.g., post-processing, forcing terms)\n3. PATCH version for backward compatible bug fixes\n\n## License\n\nKynema-SGF is licensed under BSD 3-clause license. Please see the\n[LICENSE](https://github.com/kynema/kynema-sgf/blob/development/LICENSE) included in\nthe source code repository for more details.\n\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1002/we.70010","https://doi.org/10.1002/we.2886"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["wind-turbines","wind","adaptive-mesh-refinement","hpsf","cfd","low-mach","wind-energy","exawind","les","atmospheric-modelling"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/352497","html_url":"https://ost.ecosyste.ms/projects/352497"}