{"id":"https://openalex.org/W7171816114","doi":"https://doi.org/10.48550/arxiv.2607.26078","title":"Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate","display_name":"Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate","publication_year":2026,"publication_date":"2026-07-20","ids":{"openalex":"https://openalex.org/W7171816114","doi":"https://doi.org/10.48550/arxiv.2607.26078"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.26078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.26078","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.2607.26078","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064728176","display_name":"Fangnian Wang","orcid":"https://orcid.org/0000-0003-3029-6562"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fangnian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125114239","display_name":"Nicholas Tan Jerome","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jerome, Nicholas Tan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119912306","display_name":"Thomas Jordan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jordan, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5144001579","display_name":"Frank Simon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simon, Frank","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/T11371","display_name":"Wind and Air Flow Studies","score":0.4032999873161316,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11371","display_name":"Wind and Air Flow Studies","score":0.4032999873161316,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11619","display_name":"Combustion and Detonation Processes","score":0.1647000014781952,"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/T11317","display_name":"Fire dynamics and safety research","score":0.10329999774694443,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/genetic-algorithm","display_name":"Genetic algorithm","score":0.6413000226020813},{"id":"https://openalex.org/keywords/computational-fluid-dynamics","display_name":"Computational fluid dynamics","score":0.48500001430511475},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4828000068664551},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48249998688697815},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.4104999899864197},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.40880000591278076},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.36910000443458557},{"id":"https://openalex.org/keywords/leak","display_name":"Leak","score":0.3619999885559082}],"concepts":[{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.6413000226020813},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4964999854564667},{"id":"https://openalex.org/C1633027","wikidata":"https://www.wikidata.org/wiki/Q815820","display_name":"Computational fluid dynamics","level":2,"score":0.48500001430511475},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4828000068664551},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48249998688697815},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4115000069141388},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3684999942779541},{"id":"https://openalex.org/C2780378346","wikidata":"https://www.wikidata.org/wiki/Q1349983","display_name":"Leak","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.34880000352859497},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C66251956","wikidata":"https://www.wikidata.org/wiki/Q7451086","display_name":"Sensor array","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C200457457","wikidata":"https://www.wikidata.org/wiki/Q584049","display_name":"Ventilation (architecture)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C2776902269","wikidata":"https://www.wikidata.org/wiki/Q5165493","display_name":"Continuous monitoring","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.2741999924182892},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.27090001106262207},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C159063594","wikidata":"https://www.wikidata.org/wiki/Q248382","display_name":"Ignition system","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.26078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.26078","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.2607.26078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.26078","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":[{"display_name":"Industry, innovation and infrastructure","score":0.6306506395339966,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Hydrogen":[0],"infrastructure":[1],"in":[2,141],"enclosed":[3],"environments,":[4],"such":[5],"as":[6],"parking":[7],"facilities":[8],"for":[9,47,75,225,234],"fuel":[10],"cell":[11],"vehicles,":[12],"presents":[13],"significant":[14],"safety":[15],"challenges":[16],"due":[17],"to":[18,133,136,181,212],"hydrogen's":[19],"low":[20],"ignition":[21],"energy":[22],"and":[23,62,94,110,115,129,166,176,207,221,237],"wide":[24],"flammability":[25],"range.":[26],"Current":[27],"monitoring":[28,236],"systems":[29,233],"are":[30],"largely":[31],"reactive,":[32],"detecting":[33],"leaks":[34],"only":[35],"after":[36],"hazardous":[37],"concentrations":[38],"have":[39],"formed.":[40],"This":[41],"study":[42],"develops":[43],"a":[44,63,76,107,121,145,155,223],"computational":[45,54,167],"framework":[46,217],"proactive":[48],"sensor":[49,209,228],"placement":[50,103],"optimization":[51,187,203],"by":[52,164,169],"integrating":[53,226],"fluid":[55],"dynamics":[56],"(CFD),":[57],"genetic":[58],"algorithm":[59],"(GA)":[60],"optimization,":[61],"DeepSets":[64,149],"neural":[65],"surrogate.":[66],"A":[67],"CFD":[68,162],"database":[69],"of":[70,124,172],"180":[71],"scenarios":[72],"was":[73,104],"generated":[74],"representative":[77],"50":[78],"m":[79,82,85],"x":[80,83],"30":[81],"3":[84],"garage,":[86],"covering":[87],"multiple":[88],"leak":[89],"positions,":[90],"rates":[91],"(1-150":[92],"g/s),":[93],"ventilation":[95],"conditions":[96],"(ACH":[97],"=":[98],"3-10":[99],"per":[100],"hour).":[101],"Sensor":[102],"optimized":[105,227],"using":[106],"multi-objective":[108],"GA":[109,119],"compared":[111,211],"with":[112,154,230],"uniform,":[113],"random,":[114],"surrogate-assisted":[116,186],"approaches.":[117],"The":[118,148,215],"achieved":[120],"detection":[122,205],"rate":[123],"96.1%":[125],"within":[126],"60":[127],"s":[128],"reduced":[130],"blind":[131],"areas":[132],"0.12%,":[134],"corresponding":[135],"an":[137],"approximately":[138],"5%":[139],"improvement":[140],"composite":[142],"fitness":[143,156],"over":[144],"uniform":[146],"baseline.":[147],"surrogate":[150],"reproduced":[151],"near-optimal":[152],"configurations":[153],"gap":[157],"below":[158],"0.01":[159],"while":[160,192],"reducing":[161],"evaluations":[163],"89%":[165],"time":[168],"two":[170],"orders":[171],"magnitude.":[173],"Detection":[174],"performance":[175],"spatial":[177],"coverage":[178],"remained":[179],"comparable":[180],"the":[182,198],"GA,":[183],"demonstrating":[184],"that":[185,201],"can":[188],"retain":[189],"solution":[190],"quality":[191],"enabling":[193],"rapid":[194],"design":[195],"iteration.":[196],"Overall,":[197],"results":[199],"show":[200],"CFD-informed":[202],"improves":[204],"effectiveness":[206],"reduces":[208],"requirements":[210],"conventional":[213],"layouts.":[214],"proposed":[216],"supports":[218],"scalable":[219],"deployment":[220],"provides":[222],"foundation":[224],"networks":[229],"digital":[231],"twin":[232],"real-time":[235],"risk":[238],"assessment.":[239]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2026-07-31T00:00:00"}