{"dcterms:modified":"2023-12-08","dcterms:creator":"DaRUS","@type":"ore:ResourceMap","@id":"https://nfldevdataverse2.rus.uni-stuttgart.de/api/datasets/export?exporter=OAI_ORE&persistentId=https://doi.org/10.18419/darus-1151","ore:describes":{"citation:keyword":[{"citation:keywordValue":"microstructure"},{"citation:keywordValue":"representative volume element (RVE)"},{"citation:keywordValue":"homogenization"},{"citation:keywordValue":"effective properties"},{"citation:keywordValue":"regression"}],"EngMeta:engMetaControlledVar":[{"EngMeta:engMetaControlledVarName":"volume fraction","engMetaControlledVarUnit":"%","engMetaControlledVarValueFrom":"18","engMetaControlledVarValueTo":"84"},{"EngMeta:engMetaControlledVarName":"overlap","engMetaControlledVarUnit":"%","engMetaControlledVarValueFrom":"0","engMetaControlledVarValueTo":"100"},{"EngMeta:engMetaControlledVarName":"inclusion radius","engMetaControlledVarValueFrom":"0.06","engMetaControlledVarValueTo":"0.45"}],"citation:dsDescription":{"citation:dsDescriptionValue":"The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions.\r\n30.000 images are contained and split into two inclusion subclasses of circular and rectangular inclusions.\r\nFeatures computed via the 2-point correlation function can be found. The features and effective properties have been used for a regression problem in the related paper. Example code to access the data and recreate the features is attached.","citation:dsDescriptionDate":"2021-02-08"},"citation:datasetContact":[{"citation:datasetContactName":"Lißner, Julian","citation:datasetContactAffiliation":"Universität Stuttgart"},{"citation:datasetContactName":"Felix Fritzen","citation:datasetContactAffiliation":"Universität Stuttgart"}],"publication":{"publicationCitation":"Lißner, Julian, and Felix Fritzen. \"Data-Driven Microstructure Property Relations.\" Mathematical and Computational Applications 24.2 (2019): 57.","publicationIDType":"doi","publicationIDNumber":"10.3390/mca24020057","publicationURL":"https://doi.org/10.3390/mca24020057"},"process:processMethodsPar":[{"process:processMethodsParName":"inclusion heat conductivity","process:processMethodsParValue":"0.2","process:processMethodsParUnit":"W/(mK)"},{"process:processMethodsParName":"matrix heat conductivity","process:processMethodsParValue":"1.0","process:processMethodsParUnit":"W/(mK)"}],"process:processMethods":[{"processMethodsName":"Generation","process:processMethodsDescription":"The microstructures are sampled with a random adsortion algorithm with the inclusion overlap being the admissibility criterion. A wide range of volume fraction and overlap is allowed, yielding highly heterogeneous microstructures."},{"processMethodsName":"Homogenization","process:processMethodsPars":"inclusion heat conductivity, matrix heat conductivity","process:processMethodsDescription":"The effective property was computed using the Fourier accelerated nodal solver (FANS) with constant inclusion and matrix phase properties for all images."},{"processMethodsName":"Feature extraction","process:processMethodsDescription":"The spatial two point correlation function is used to extract features of the microstructure. A reduced basis method was deployed to computed eigenmodes/convolutional filters to extract low dimensional features retaining the most variance in the data. The method is described in detail in the linked paper."}],"author":{"citation:authorName":"Lißner, Julian","citation:authorAffiliation":"Universität Stuttgart","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0002-2286-5211"},"processSoftware":[{"processSoftwareName":"FANS","processSoftwareCitation":"Leuschner, Matthias, and Felix Fritzen. \"Fourier-accelerated nodal solvers (FANS) for homogenization problems.\" Computational Mechanics 62.3 (2018): 359-392. doi: 10.1007/s00466-017-1501-5"},{"processSoftwareName":"Python","processSoftwareVersion":"3.7.9","processSoftwareURL":"https://www.python.org/"},{"processSoftwareName":"h5py","processSoftwareVersion":"2.10.0","processSoftwareURL":"https://www.h5py.org/","processSoftwareLicence":"BSD-3-Clause License"},{"processSoftwareName":"numpy","processSoftwareCitation":"Harris, C.R., Millman, K.J., van der Walt, S.J., Gommers R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N.J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M.H., Brett, M., Haldane, A., del Río, J.F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C. Oliphant, T.E. \"Array programming with NumPy. \" Nature 585, 357–362 (2020). doi: 10.1038/s41586-020-2649-2","processSoftwareVersion":"1.19.1","processSoftwareURL":"https://numpy.org/","processSoftwareLicence":"BSD-3-Clause License"},{"processSoftwareName":"matplotlib","processSoftwareCitation":"J. D. Hunter, \"Matplotlib: A 2D Graphics Environment\", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007. doi: 10.1109/MCSE.2007.55","processSoftwareVersion":"3.3.1","processSoftwareURL":"https://matplotlib.org/"}],"citation:depositor":"Lißner, Julian","title":"2d microstructure data","dateOfDeposit":"2021-02-08","EngMeta:engMetaMode":"Simulation","subject":["Computer and Information Science","Engineering"],"citation:relatedMaterial":"Surrogate model - application GUI [100 MiB]","citation:project":{"citation:projectName":"SimTech EXC 2075, PN3-1: Processing uncertain microstructural data"},"@id":"https://doi.org/10.18419/darus-1151","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"2.0","schema:name":"2d microstructure data","schema:dateModified":"Mon Feb 15 08:10:57 CET 2021","schema:datePublished":"2020-11-19","schema:license":"http://creativecommons.org/licenses/by/4.0","dvcore:fileTermsOfAccess":{"dvcore:fileRequestAccess":false},"schema:includedInDataCatalog":"DaRUS","schema:isPartOf":{"schema:name":"Data Analytics in Engineering","@id":"https://nfldevdataverse2.rus.uni-stuttgart.de/dataverse/mib-dae","schema:isPartOf":{"schema:name":"Institute of Applied Mechanics (MIB)","@id":"https://nfldevdataverse2.rus.uni-stuttgart.de/dataverse/mib","schema:isPartOf":{"schema:name":"DaRUS","@id":"https://nfldevdataverse2.rus.uni-stuttgart.de/dataverse/darus","schema:description":"This is the data Repository of the University of Stuttgart."}}},"ore:aggregates":[{"schema:description":"hdf5 file containing all the data","schema:name":"2d_microstructures.h5","dvcore:restricted":false,"schema:version":1,"dvcore:datasetVersionId":918,"@id":"doi:10.18419/darus-1151/5","schema:sameAs":"https://nfldevdataverse2.rus.uni-stuttgart.de/api/access/datafile/:persistentId?persistentId=doi:10.18419/darus-1151/5","@type":"ore:AggregatedResource","schema:fileFormat":"application/x-h5","dvcore:filesize":1014871287,"dvcore:storageIdentifier":"s3://fokus-dv-prod-1:177819a2178-ea6be9faaf59","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"8d93bd9af6585b0f63257c571920752c"}},{"schema:description":"code to recompute/reproduce the data used for the regression problem","schema:name":"compute_regression_data.py","dvcore:restricted":false,"schema:version":1,"dvcore:datasetVersionId":918,"@id":"doi:10.18419/darus-1151/3","schema:sameAs":"https://nfldevdataverse2.rus.uni-stuttgart.de/api/access/datafile/:persistentId?persistentId=doi:10.18419/darus-1151/3","@type":"ore:AggregatedResource","schema:fileFormat":"text/x-python","dvcore:filesize":2401,"dvcore:storageIdentifier":"s3://fokus-dv-prod-1:177819a3d52-4485bf5d8a83","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"8088ddd98656e04611bdf589220baf99"}},{"schema:description":"an example on how to access data stored in the hdf5 file","schema:name":"example_data_access.py","dvcore:restricted":false,"schema:version":1,"dvcore:datasetVersionId":918,"@id":"doi:10.18419/darus-1151/6","schema:sameAs":"https://nfldevdataverse2.rus.uni-stuttgart.de/api/access/datafile/:persistentId?persistentId=doi:10.18419/darus-1151/6","@type":"ore:AggregatedResource","schema:fileFormat":"text/x-python","dvcore:filesize":1993,"dvcore:storageIdentifier":"s3://fokus-dv-prod-1:177819a4521-cd82582964bd","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"be74203ddeb47af439f3cbc33594ac92"}},{"schema:description":"dependency file for 'compute_regression_data.py'","schema:name":"processing_functions.py","dvcore:restricted":false,"schema:version":1,"dvcore:datasetVersionId":918,"@id":"doi:10.18419/darus-1151/4","schema:sameAs":"https://nfldevdataverse2.rus.uni-stuttgart.de/api/access/datafile/:persistentId?persistentId=doi:10.18419/darus-1151/4","@type":"ore:AggregatedResource","schema:fileFormat":"text/x-python","dvcore:filesize":3857,"dvcore:storageIdentifier":"s3://fokus-dv-prod-1:177819a3d59-3350107dab5e","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"24355f5d0f71131cf836137a375fce75"}}],"schema:hasPart":["doi:10.18419/darus-1151/5","doi:10.18419/darus-1151/3","doi:10.18419/darus-1151/6","doi:10.18419/darus-1151/4"]},"@context":{"EngMeta":"https://nfldevdataverse2.rus.uni-stuttgart.de/schema/EngMeta#","author":"http://purl.org/dc/terms/creator","authorIdentifier":"http://purl.org/spar/datacite/AgentIdentifier","authorIdentifierScheme":"http://purl.org/spar/datacite/AgentIdentifierScheme","citation":"https://dataverse.org/schema/citation/","dateOfDeposit":"http://purl.org/dc/terms/dateSubmitted","dcterms":"http://purl.org/dc/terms/","dvcore":"https://dataverse.org/schema/core#","engMetaControlledVarUnit":"https://schema.org/unitCode","engMetaControlledVarValueFrom":"https://schema.org/minValue","engMetaControlledVarValueTo":"https://schema.org/maxValue","ore":"http://www.openarchives.org/ore/terms/","process":"https://nfldevdataverse2.rus.uni-stuttgart.de/schema/process#","processMethodsName":"https://schema.org/measurementTechnique","processSoftware":"https://schema.org/SoftwareApplication","processSoftwareCitation":"https://schema.org/citation","processSoftwareLicence":"https://schema.org/license","processSoftwareName":"https://schema.org/name","processSoftwareURL":"https://schema.org/downloadUrl","processSoftwareVersion":"https://schema.org/version","publication":"http://purl.org/dc/terms/isReferencedBy","publicationCitation":"http://purl.org/dc/terms/bibliographicCitation","publicationIDNumber":"http://purl.org/spar/datacite/ResourceIdentifier","publicationIDType":"http://purl.org/spar/datacite/ResourceIdentifierScheme","publicationURL":"https://schema.org/distribution","schema":"http://schema.org/","subject":"http://purl.org/dc/terms/subject","title":"http://purl.org/dc/terms/title"}}