{"id":"https://openalex.org/W4406354440","doi":"https://doi.org/10.1109/access.2025.3529472","title":"A Deep Learning-Based Approach for Two-Phase Flow Pattern Classification Using Void Fraction Time Series Analysis","display_name":"A Deep Learning-Based Approach for Two-Phase Flow Pattern Classification Using Void Fraction Time Series Analysis","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4406354440","doi":"https://doi.org/10.1109/access.2025.3529472"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3529472","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3529472","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3529472","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068589308","display_name":"Jefferson dos Santos Ambrosio","orcid":"https://orcid.org/0000-0002-6271-2236"},"institutions":[{"id":"https://openalex.org/I1283613182","display_name":"Universidade Tecnol\u00f3gica Federal do Paran\u00e1","ror":"https://ror.org/002v2kq79","country_code":"BR","type":"education","lineage":["https://openalex.org/I1283613182"]},{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jefferson Dos Santos Ambrosio","raw_affiliation_strings":["Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; (UTFPR), Curitiba, Brazil","Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; &#x2014; UTFPR, Av. Sete de Setembro, Curitiba, PR, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-6271-2236","affiliations":[{"raw_affiliation_string":"Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; (UTFPR), Curitiba, Brazil","institution_ids":["https://openalex.org/I1283613182","https://openalex.org/I52418104"]},{"raw_affiliation_string":"Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; &#x2014; UTFPR, Av. Sete de Setembro, Curitiba, PR, Brazil","institution_ids":["https://openalex.org/I1283613182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017487342","display_name":"Marco J. da Silva","orcid":"https://orcid.org/0000-0003-1955-8293"},"institutions":[{"id":"https://openalex.org/I121883995","display_name":"Johannes Kepler University of Linz","ror":"https://ror.org/052r2xn60","country_code":"AT","type":"education","lineage":["https://openalex.org/I121883995"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Marco Jose da Silva","raw_affiliation_strings":["Institute of Measurement Technology, Johannes Kepler University Linz, Linz, Austria","Institute of Measurement Technology, Johannes Kepler University Linz, Altenberger Str. 69, Linz, Austria"],"raw_orcid":"https://orcid.org/0000-0003-1955-8293","affiliations":[{"raw_affiliation_string":"Institute of Measurement Technology, Johannes Kepler University Linz, Linz, Austria","institution_ids":["https://openalex.org/I121883995"]},{"raw_affiliation_string":"Institute of Measurement Technology, Johannes Kepler University Linz, Altenberger Str. 69, Linz, Austria","institution_ids":["https://openalex.org/I121883995"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088729127","display_name":"Andr\u00e9 Eug\u00eanio Lazzaretti","orcid":"https://orcid.org/0000-0003-1861-3369"},"institutions":[{"id":"https://openalex.org/I1283613182","display_name":"Universidade Tecnol\u00f3gica Federal do Paran\u00e1","ror":"https://ror.org/002v2kq79","country_code":"BR","type":"education","lineage":["https://openalex.org/I1283613182"]},{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Andr\u00e9 Eug\u00eanio Lazzaretti","raw_affiliation_strings":["Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; (UTFPR), Curitiba, Brazil","Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; &#x2014; UTFPR, Av. Sete de Setembro, Curitiba, PR, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-1861-3369","affiliations":[{"raw_affiliation_string":"Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; (UTFPR), Curitiba, Brazil","institution_ids":["https://openalex.org/I1283613182","https://openalex.org/I52418104"]},{"raw_affiliation_string":"Universidade Tecnol&#x00F3;gica Federal do Paran&#x00E1; &#x2014; UTFPR, Av. Sete de Setembro, Curitiba, PR, Brazil","institution_ids":["https://openalex.org/I1283613182"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.9622,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83788887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"11778","last_page":"11791"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10864","display_name":"Fluid Dynamics and Mixing","score":0.9434000253677368,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10864","display_name":"Fluid Dynamics and Mixing","score":0.9434000253677368,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.901199996471405,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5846468210220337},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5636847615242004},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.523699164390564},{"id":"https://openalex.org/keywords/two-phase-flow","display_name":"Two-phase flow","score":0.4919690787792206},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47260552644729614},{"id":"https://openalex.org/keywords/fraction","display_name":"Fraction (chemistry)","score":0.4565941095352173},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4356600046157837},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3459036350250244},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2859281599521637},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2173166275024414},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10083511471748352},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.07441821694374084},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06874281167984009}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5846468210220337},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5636847615242004},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.523699164390564},{"id":"https://openalex.org/C144308804","wikidata":"https://www.wikidata.org/wiki/Q232997","display_name":"Two-phase flow","level":3,"score":0.4919690787792206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47260552644729614},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.4565941095352173},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4356600046157837},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3459036350250244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2859281599521637},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2173166275024414},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10083511471748352},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07441821694374084},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06874281167984009},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3529472","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3529472","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:714688df840c4fbb96a65807870ab734","is_oa":true,"landing_page_url":"https://doaj.org/article/714688df840c4fbb96a65807870ab734","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 11778-11791 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3529472","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3529472","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6787822224","display_name":null,"funder_award_id":"306569/2022-1","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"},{"id":"https://openalex.org/F4320322349","display_name":"Ministry of Education, Science and Technology","ror":"https://ror.org/01p262204"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2004025592","https://openalex.org/W2027589229","https://openalex.org/W2064675550","https://openalex.org/W2076422580","https://openalex.org/W2100537585","https://openalex.org/W2152278508","https://openalex.org/W2194775991","https://openalex.org/W2262590373","https://openalex.org/W2551393996","https://openalex.org/W2555077524","https://openalex.org/W2588524044","https://openalex.org/W2754051771","https://openalex.org/W2788478361","https://openalex.org/W3025293388","https://openalex.org/W3029134161","https://openalex.org/W3033215007","https://openalex.org/W3086569303","https://openalex.org/W3089215229","https://openalex.org/W3112018172","https://openalex.org/W3120260026","https://openalex.org/W3188872815","https://openalex.org/W3216249187","https://openalex.org/W3216625121","https://openalex.org/W4281651918","https://openalex.org/W4290058871","https://openalex.org/W4293219921","https://openalex.org/W4294980275","https://openalex.org/W4311771706","https://openalex.org/W4313201727","https://openalex.org/W4313829995","https://openalex.org/W4385245566","https://openalex.org/W4394957446","https://openalex.org/W4396573561","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6923860363"],"related_works":["https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660","https://openalex.org/W1971346788","https://openalex.org/W2320316818","https://openalex.org/W2002601706","https://openalex.org/W2064547363","https://openalex.org/W2183500742","https://openalex.org/W2352511163"],"abstract_inverted_index":{"Flow":[0],"regime":[1,33,215],"classification":[2,34,98,166,216],"is":[3],"essential":[4],"for":[5,104,140,213,226,238],"analyzing":[6],"and":[7,17,50,67,74,102,110,130,150,186,218],"modeling":[8],"two-phase":[9,105],"flows,":[10],"as":[11],"it":[12,132],"demarcates":[13],"the":[14,19,37,48,60,78,81,115,119,125,145,160,182,195,200,206,209],"flow":[15,32,106,214],"behavior":[16],"influences":[18],"selection":[20],"of":[21,53,80,118,208,224],"appropriate":[22],"predictive":[23],"models.":[24],"Machine":[25],"learning-based":[26],"approaches":[27],"have":[28,75],"gained":[29],"relevance":[30,207],"in":[31,36,137,168,173,191,228],"research":[35,148,227],"last":[38],"few":[39],"years.":[40],"However,":[41,70],"they":[42,71],"are":[43,72,236,242],"still":[44],"solidly":[45],"based":[46],"on":[47,59],"construction":[49],"careful":[51],"definition":[52],"hand-crafted":[54],"features.":[55],"Deep":[56],"learning":[57],"approaches,":[58],"other":[61,84],"hand,":[62],"can":[63,198],"provide":[64],"more":[65],"robust":[66,233],"end-to-end":[68,94],"solutions.":[69],"underexplored":[73],"not":[76],"evaluated":[77,183],"generalization":[79,116],"models":[82,120],"to":[83],"data":[85],"or":[86],"acquisition":[87],"systems.":[88],"Hence,":[89],"this":[90,229],"work":[91],"proposes":[92],"using":[93],"state-of-the-art":[95],"(SOTA)":[96],"time-series":[97],"methods":[99],"(ResNet,":[100],"LSTM-FCN,":[101],"TSTPlus)":[103],"patterns":[107],"(churn,":[108],"bubbly,":[109],"slug).":[111],"We":[112],"also":[113],"present":[114],"analysis":[117,210],"with":[121,127,133],"cross-dataset":[122,174],"experiments,":[123],"training":[124],"model":[126],"one":[128],"dataset":[129,135],"testing":[131],"another":[134,138],"collected":[136],"system":[139],"two":[141],"datasets:":[142],"HZDR":[143],"(from":[144,152],"Helmholtz-Zentrum":[146],"Dresden-Rossendorf":[147],"laboratory)":[149],"TUD":[151],"Technische":[153],"Universit\u00e4t":[154],"Dresden).":[155],"The":[156],"results":[157],"demonstrate":[158],"that":[159,235],"approach":[161],"chosen":[162],"here":[163,212],"presents":[164],"superior":[165],"metrics":[167,184],"all":[169,181,192],"cases":[170],"evaluated,":[171],"particularly":[172],"experiments.":[175],"With":[176],"our":[177],"proposed":[178,211],"SOTA":[179],"methods,":[180],"(accuracy":[185],"F1-Score)":[187],"consistently":[188],"surpass":[189],"85%":[190],"cases,":[193],"while":[194],"baseline":[196],"method":[197],"decrease":[199],"performance":[201],"under":[202],"75%.":[203],"This":[204],"demonstrates":[205],"literature":[217],"opens":[219],"up":[220],"a":[221],"new":[222],"set":[223],"possibilities":[225],"area,":[230],"aiming":[231],"at":[232,244],"solutions":[234],"viable":[237],"practical":[239],"use.":[240],"Codes":[241],"available":[243],"<uri":[245],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[246],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/ambrosioj/two-phase-time-series-deep-learning</uri>.":[247]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}