{"id":"https://openalex.org/W7162791086","doi":"https://doi.org/10.48550/arxiv.2605.30277","title":"Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor","display_name":"Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162791086","doi":"https://doi.org/10.48550/arxiv.2605.30277"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30277","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30277","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.30277","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137333847","display_name":"Minseo Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Minseo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137336187","display_name":"Seongmin Oh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oh, Seongmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111143291","display_name":"Chaehyeon Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Chaehyeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051409728","display_name":"BumJin Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cho, Bumjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113389011","display_name":"Shilaj Baral","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baral, Shilaj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061890720","display_name":"Sudeep Khanal","orcid":"https://orcid.org/0000-0002-0855-8143"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khanal, Sangam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137320140","display_name":"Minseop Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Minseop","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137346248","display_name":"Joongoo Jeon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeon, Joongoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.5475000143051147,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.5475000143051147,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12560","display_name":"Nuclear Engineering Thermal-Hydraulics","score":0.13490000367164612,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11372","display_name":"Hydraulic and Pneumatic Systems","score":0.04039999842643738,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.6599000096321106},{"id":"https://openalex.org/keywords/computational-fluid-dynamics","display_name":"Computational fluid dynamics","score":0.6347000002861023},{"id":"https://openalex.org/keywords/pressure-drop","display_name":"Pressure drop","score":0.6047999858856201},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.579200029373169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5159000158309937},{"id":"https://openalex.org/keywords/electromagnetic-coil","display_name":"Electromagnetic coil","score":0.44780001044273376},{"id":"https://openalex.org/keywords/transient","display_name":"Transient (computer programming)","score":0.4081000089645386},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.37400001287460327}],"concepts":[{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.6599000096321106},{"id":"https://openalex.org/C1633027","wikidata":"https://www.wikidata.org/wiki/Q815820","display_name":"Computational fluid dynamics","level":2,"score":0.6347000002861023},{"id":"https://openalex.org/C114088122","wikidata":"https://www.wikidata.org/wiki/Q1261069","display_name":"Pressure drop","level":2,"score":0.6047999858856201},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.579200029373169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5555999875068665},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C30403606","wikidata":"https://www.wikidata.org/wiki/Q2981904","display_name":"Electromagnetic coil","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.37400001287460327},{"id":"https://openalex.org/C90278072","wikidata":"https://www.wikidata.org/wiki/Q216320","display_name":"Fluid dynamics","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C2780013297","wikidata":"https://www.wikidata.org/wiki/Q19596153","display_name":"Boiler (water heating)","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3521000146865845},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33820000290870667},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.336899995803833},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32510000467300415},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.32100000977516174},{"id":"https://openalex.org/C140820882","wikidata":"https://www.wikidata.org/wiki/Q732722","display_name":"Vortex","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.295199990272522},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2946000099182129},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2937999963760376},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C2781121602","wikidata":"https://www.wikidata.org/wiki/Q3504403","display_name":"Modular neural network","level":4,"score":0.27379998564720154},{"id":"https://openalex.org/C2780113671","wikidata":"https://www.wikidata.org/wiki/Q3285020","display_name":"Natural circulation","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30277","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30277","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30277","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30277","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-time":[0],"thermal-hydraulic":[1],"simulation":[2],"is":[3],"essential":[4],"for":[5,55,111,120,148],"digital":[6],"twin":[7],"(DT)":[8],"technology":[9],"that":[10,72,215],"supports":[11],"the":[12,83,90,129,136,164,172,178,190,197,229],"safe":[13],"and":[14,115,124,162,186,192,201,228],"efficient":[15],"operation":[16],"of":[17,59,89,166,232],"small":[18],"modular":[19],"reactors":[20],"(SMRs).":[21],"Computational":[22],"fluid":[23],"dynamics":[24,182],"(CFD)":[25],"provides":[26],"high-fidelity":[27],"flow":[28,200,233],"analysis,":[29],"but":[30],"its":[31,193],"computational":[32],"cost":[33],"prevents":[34],"direct":[35],"use":[36],"in":[37,183],"DT":[38,221],"applications.":[39],"AI-based":[40],"surrogate":[41],"modeling":[42],"has":[43],"been":[44,64],"actively":[45],"investigated":[46],"to":[47,82,100,134,158,219],"address":[48],"this":[49],"limitation,":[50],"yet":[51],"neural":[52,79,142],"operator--based":[53],"surrogates":[54],"CFD-level":[56],"transient":[57],"analysis":[58],"SMR-specific":[60],"geometries":[61],"have":[62],"not":[63],"reported.":[65],"This":[66],"study":[67],"presents":[68],"an":[69,107],"integrated":[70],"framework":[71],"combines":[73],"a":[74,116,211],"reduced-order":[75],"model":[76],"(ROM)":[77],"with":[78,128],"operators,":[80],"applied":[81],"helical":[84],"coil":[85],"steam":[86],"generator":[87],"(HCSG)":[88],"System-integrated":[91],"Modular":[92],"Advanced":[93],"Reactor":[94],"(SMART).":[95],"Two":[96],"ROM":[97],"strategies":[98],"tailored":[99],"each":[101,125,217],"CFD":[102,225],"data":[103,114,226],"type":[104,227],"were":[105],"compared,":[106],"MLP-based":[108],"autoencoder":[109,118],"(AE)":[110],"unstructured":[112],"mesh":[113,122],"convolutional":[117],"(CAE)":[119],"structured":[121],"data,":[123],"was":[126,145,153],"coupled":[127],"deep":[130],"operator":[131,143],"network":[132],"(DeepONet)":[133],"construct":[135],"latent":[137],"DeepONet":[138],"(L-DeepONet).":[139],"The":[140,174],"Fourier":[141],"(FNO)":[144],"additionally":[146],"adopted":[147],"comparison.":[149],"A":[150],"multi-scale":[151,175,194],"technique":[152],"incorporated":[154],"into":[155],"both":[156,184],"frameworks":[157],"mitigate":[159],"spectral":[160],"bias":[161],"improve":[163],"prediction":[165],"K\u00e1rm\u00e1n":[167],"vortex":[168,181],"streets":[169],"developing":[170],"inside":[171],"HCSG.":[173],"L-DeepONet":[176],"captured":[177],"instantaneous":[179],"periodic":[180],"velocity":[185],"pressure":[187,204],"fields,":[188],"while":[189],"FNO":[191],"variant":[195],"predicted":[196],"time-averaged":[198],"mean":[199],"provided":[202],"reliable":[203],"drop":[205],"estimates.":[206],"These":[207],"complementary":[208],"characteristics":[209],"provide":[210],"practical":[212],"model-selection":[213],"guideline":[214],"links":[216],"architecture":[218],"specific":[220],"objectives":[222],"based":[223],"on":[224],"required":[230],"level":[231],"resolution.":[234]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-30T00:00:00"}
