GALÆXI Validation: Taylor-Green Vortex

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Kurz, Marius; Kempf, Daniel; Blind, Marcel P.; Kopper, Patrick; Offenhäuser, Philipp; Schwarz, Anna; Starr, Spencer; Keim, Jens; Beck, Andrea, 2024, "GALÆXI Validation: Taylor-Green Vortex", https://doi.org/10.18419/DARUS-4139, DaRUS, V1, UNF:6:Q71FGjHTEHnKVAq/VkFpXA== [fileUNF]

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Description	This Dataset contains the test case definition and reference data for the Taylor-Green Vortex (TGV) test case which builds the validation test case in the GALÆXI Paper (Section 5). Incompressible TGV (Ma=0.1) according to (Link): J. DeBonis, Solutions of the Taylor–Green vortex problem using high- resolution explicit finite difference methods, in: 51st AIAA Aerospace Sciences Meeting, 2013, p. 382 Compressible TGV (Ma=1.25) according to (Link): J.-B. Chapelier, D. J. Lusher, W. Van Noordt, C. Wenzel, T. Gibis, P. Mossier, A. D. Beck, G. Lodato, C. Brehm, M. Ruggeri, C. Scalo, N. Sandham, Comparison of high-order numerical methodologies for the simulation of the supersonic taylor-green vortex flow, Physics of Fluids 2024; 36 (5): 055146. Executable of FLEXI/GALÆXI can be built using the `build.py` script: `python3 build.py ./build-folder ./userblock.txt` Note: Please ensure that all necessary dependencies of GALÆXI/FLEXI are available (including CUDA) and a Python3 environment is installed on the system.
Subject	Computer and Information Science; Engineering; Physics
Keyword	Discontinuous Galerkin, High-Performance Computing, GPUs, Accelerators, Turbulence, Compressible Flow
Related Publication	Kurz, M., Kempf, D., Blind, M., Kopper, P., Offenhäuser, P., Schwarz, A., Starr, S., Keim, J., & Beck, A. (2024). GALÆXI: Solving complex compressible flows with high-order discontinuous Galerkin methods on accelerator-based systems. Computer Physics Communications, 109388.doi: 10.1016/j.cpc.2024.109388
Notes	This work was funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) and Sweden, Germany, Spain, Greece, and Denmark under grant agreement No 101093393. Moreover, this research presented was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2075 – 390740016, by the DFG Rebound – 420603919, and in the framework of the research unit FOR 2895. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech). The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC) as well as the support and the computing time on “Hawk” and its “Hawk-AI” extension provided by the Supercomputing Centre Stuttgart (HLRS) through the project “hpcdg”. This work was completed in part at the Helmholtz GPU Hackathon, part of the Open Hackathons program. The authors would like to acknowledge OpenACC-Standard.org, JSC, HZDR, and HIDA for their support.
License/Data Use Agreement	CC BY 4.0

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	1 to 10 of 69 Files	Original Format Archival Format (.tab)
	build.py build/Python Source Code - 5.4 KB Published May 3, 2024 18 Downloads MD5: c220e07653c938bb2c136b9e1c6450e1 Python Script for automatically cloning, patching and building the FLEXI/GALÆXI executable. Command: python3 ./build.py ./build-folder userblock.tmp Build	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	FLEXI_CPU_TGV_compressible_userblock.txt build/Plain Text - 12.5 KB Published May 3, 2024 30 Downloads MD5: 6fd0dcffd863d3130e0216e4148ebc34 Userblock file for automatically building FLEXI using the build.py script. Build	Preview "build/FLEXI_CPU_TGV_compressible_userblock.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	FLEXI_CPU_TGV_incompressible_userblock.txt build/Plain Text - 2.3 KB Published May 3, 2024 35 Downloads MD5: b926410a30ce724e5386e5eb84aa7781 Userblock file for automatically building FLEXI using the build.py script. Build	Preview "build/FLEXI_CPU_TGV_incompressible_userblock.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	GALÆXI_GPU_TGV_compressibe_userblock.txt build/Plain Text - 1.9 KB Published May 3, 2024 31 Downloads MD5: 208ac4569001a03bc23cffb1634b8993 Userblock file for automatically building GALÆXI using the build.py script. Build	Preview "build/GALÆXI_GPU_TGV_compressibe_userblock.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	GALÆXI_GPU_TGV_incompressible_userblock.txt build/Plain Text - 1.8 KB Published May 3, 2024 38 Downloads MD5: d79b0f923de0f0100a44818f334d6f76 Userblock file for automatically building GALÆXI using the build.py script. Build	Preview "build/GALÆXI_GPU_TGV_incompressible_userblock.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	CART_HEX_PERIODIC_016_mesh.h5 TGV_compressible_Ma1p25/CPU/TGV_CPU_Ma1p25_16x16x16_N7/Hierarchical Data Format - 1.6 MB Published May 3, 2024 12 Downloads MD5: 27053f66f6fcbe05b9a7928fc0eca039 Mesh file for the simulation. Setup	Access File File Access Public Download Options Hierarchical Data Format Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX Download Auxiliary Files XML from NetCDF/HDF5 (NcML) NcML (0.1)
	parameter_flexi.ini TGV_compressible_Ma1p25/CPU/TGV_CPU_Ma1p25_16x16x16_N7/Unknown - 3.5 KB Published May 3, 2024 17 Downloads MD5: 1307a12ef25fe0791f04bc9243963549 Parameter file to execute the simulation with GALÆXI/FLEXI. Setup	Access File File Access Public Download Options Original File Format Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	parameter_hopr.ini TGV_compressible_Ma1p25/CPU/TGV_CPU_Ma1p25_16x16x16_N7/Unknown - 2.8 KB Published May 3, 2024 13 Downloads MD5: 2feedd4ee7758f3ea8803615979137c0 Parameter file to generate the mesh using HOPR (https://github.com/hopr-framework/hopr). Setup	Access File File Access Public Download Options Original File Format Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	TGV_Re1600_TGVAnalysis.tab TGV_compressible_Ma1p25/CPU/TGV_CPU_Ma1p25_16x16x16_N7/Tabular Data - 246.6 KB Published May 3, 2024 31 Downloads 16 Variables, 633 Observations UNF:6:bJX0KqQ1NRLlYT6NuMz8Vg== Results	Preview "TGV_compressible_Ma1p25/CPU/TGV_CPU_Ma1p25_16x16x16_N7/TGV_Re1600_TGVAnalysis.tab" Access File File Access Public Download Options Comma Separated Values (Original File Format) Tab-Delimited RData Download Metadata DDI Codebook v2 Data File Citation Download EndNote XML Download RIS Download BibTeX
	CART_HEX_PERIODIC_032_mesh.h5 TGV_compressible_Ma1p25/CPU/TGV_CPU_Ma1p25_32x32x32_N7/Hierarchical Data Format - 12.9 MB Published May 3, 2024 16 Downloads MD5: ba8378bde8dfc921f6008f0e25d816a1 Mesh file for the simulation. Setup	Access File File Access Public Download Options Hierarchical Data Format Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX Download Auxiliary Files XML from NetCDF/HDF5 (NcML) NcML (0.1)

Citation Metadata

Persistent Identifier	doi:10.18419/DARUS-4139
Publication Date	2024-05-03
Title	GALÆXI Validation: Taylor-Green Vortex
Author	https://ror.org/04vnq7t770009-0005-0844-0769 https://ror.org/04vnq7t770000-0002-1445-536X https://ror.org/04vnq7t770000-0002-4737-9359 https://ror.org/04vnq7t770000-0002-7613-0739 Hewlett Packard Enterprise0009-0001-1674-7980 https://ror.org/04vnq7t770000-0002-3181-8230 https://ror.org/04vnq7t770009-0005-3737-3374 https://ror.org/04vnq7t770000-0002-2338-1497 https://ror.org/04vnq7t770000-0003-3634-7447
Point of Contact	Use email button above to contact. Numerics Research Group (University of Stuttgart)
Description	This Dataset contains the test case definition and reference data for the Taylor-Green Vortex (TGV) test case which builds the validation test case in the GALÆXI Paper (Section 5). Incompressible TGV (Ma=0.1) according to (Link): J. DeBonis, Solutions of the Taylor–Green vortex problem using high- resolution explicit finite difference methods, in: 51st AIAA Aerospace Sciences Meeting, 2013, p. 382 Compressible TGV (Ma=1.25) according to (Link): J.-B. Chapelier, D. J. Lusher, W. Van Noordt, C. Wenzel, T. Gibis, P. Mossier, A. D. Beck, G. Lodato, C. Brehm, M. Ruggeri, C. Scalo, N. Sandham, Comparison of high-order numerical methodologies for the simulation of the supersonic taylor-green vortex flow, Physics of Fluids 2024; 36 (5): 055146. Executable of FLEXI/GALÆXI can be built using the `build.py` script: `python3 build.py ./build-folder ./userblock.txt` Note: Please ensure that all necessary dependencies of GALÆXI/FLEXI are available (including CUDA) and a Python3 environment is installed on the system.
Subject	Computer and Information Science; Engineering; Physics
Keyword	Discontinuous Galerkin http://www.wikidata.org/entity/Q428273 (Wikidata) High-Performance Computing http://www.wikidata.org/entity/Q1190465 (Wikidata) GPUs http://www.wikidata.org/entity/Q183484 (Wikidata) Accelerators Turbulence https://doi.org/10.29172/062f0300-c33d-4782-b24c-43996b0469f3 (PhySH) Compressible Flow https://doi.org/10.29172/7a4e6b3d-d685-4a65-a562-4720bd830099 (PhySH)
Topic Classification	Fluid Mechanics (DFGFO) https://w3id.org/dfgfo/2024/422-03
Related Publication	Kurz, M., Kempf, D., Blind, M., Kopper, P., Offenhäuser, P., Schwarz, A., Starr, S., Keim, J., & Beck, A. (2024). GALÆXI: Solving complex compressible flows with high-order discontinuous Galerkin methods on accelerator-based systems. Computer Physics Communications, 109388. doi 10.1016/j.cpc.2024.109388 https://doi.org/10.1016/j.cpc.2024.109388
Notes	This work was funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) and Sweden, Germany, Spain, Greece, and Denmark under grant agreement No 101093393. Moreover, this research presented was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2075 – 390740016, by the DFG Rebound – 420603919, and in the framework of the research unit FOR 2895. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech). The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC) as well as the support and the computing time on “Hawk” and its “Hawk-AI” extension provided by the Supercomputing Centre Stuttgart (HLRS) through the project “hpcdg”. This work was completed in part at the Helmholtz GPU Hackathon, part of the Open Hackathons program. The authors would like to acknowledge OpenACC-Standard.org, JSC, HZDR, and HIDA for their support.
Producer	Numerics Research Group (NRG) https://numericsresearchgroup.org/
Funding Information	European Commission: info:eu-repo/grantAgreement/EC/HE/101093393 DFG: EXC 2075 - 390740016 DFG: 420603919 DFG: FOR 2895 - 406435057

Process Metadata

Software	GALÆXI Version: 0772bc7310559e35ed5c40904f4c9dc9ffdf1c0b (https://github.com/flexi-framework/galaexi) GALÆXI Version: b10b4740957bd280ac52c1c268f74bd555821550 (https://github.com/flexi-framework/galaexi) FLEXI Version: d5533b58753afd8fe4b6f7eddc25b48030dc4524 (https://github.com/flexi-framework/flexi)

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