[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127438-en":3,"doc-seo-127438-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127438,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Machine learning-assisted design automation workflow of non-uniform flow distributors for multipass crossflow device","Multipass crossflow devices play a key role in process engineering across diverse applications, and additive manufacturing enables improved device efficiency through non-uniform flow distribution that can enhance heat transfer uniformity and reduce temperature gradients. This study tackles non-uniform distribution control by proposing a design that locally varies channel diameters at predefined positions to reach user-defined flow ratio targets. A machine learning-assisted design automation workflow integrates algorithmic modeling and CFD simulations to handle design complexity, with validation using experiments on an incompressible gas flow distributor supporting the reported findings.","Results in Engineering 27 (2025) 106931  \nContents lists available at ScienceDirect  \nResults in Engineering  \njournal [homepage:](homepage: www.sciencedirect.com/journal/results-in-engineering)[ www.sciencedirect.com/journal/results-in-engineering](homepage: www.sciencedirect.com/journal/results-in-engineering)  \n| Research paper\u003Cbr>Machine learning-assisted design automation workflow of non-uniform flow distributors for multipass crossflow device\u003Cbr>Mertcan Kaya a, ,∗ , Julian Ferchow b, , Mirko Meboldt c, Christoph Klahn a,d,\u003Cbr> |  |  |  |\n| --- | --- | --- | --- |\n| a Karlsruhe Institute of Technology (KIT), Institute for Micro Process Engineering (IMVT), 76344 Eggenstein-Leopoldshafen, Germany b Inspire AG (ipd|z), 8092, Zürich, Switzerland\u003Cbr>c ETH Zurich (pd|z), 8092, Zürich, Switzerland\u003Cbr>d Karlsruhe Institute of Technology (KIT), Institute of Mechanical Process Engineering and Mechanics (MVM), 76131 Karlsruhe, Germany |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>ML driven design Design automation Chemical engineering Additive manufacturing Flow distribution Bifurcation design |  | Multipass crossflow devices are essential in the process engineering industry due to their broad range of applications. The growing use of additive manufacturing techniques offers opportunities to improve the efficiency of these devices by enabling non-uniform flow distribution, which can enhance heat transfer uniformity to avoid temperature gradients across individual channels in chemical engineering applications such as heat exchangersand packed bed reactors. This paper addresses the challenge of controlling non-uniform flow distribution by developing a novel design that locally varies channel diameters at predefined positions within the distributor, to achieve user-defined target flow distribution ratios. The proposed design combines machine learning-assisted design automation workflow with algorithmic modeling and computational fluid dynamics (CFD) simulations to manage the complexity of non-uniform flow distribution effectively. The results are further validated through an experimental study of an incompressible gas flow distributor, which supports the findings presented in this work. |  |\n\n1. Introduction  \nMultipass crossflow devices, such as heat exchangers, are widely employed in process engineering for heating and cooling applications. The selection of a suitable flow arrangement depends on multiple factors, including thermal efficiency, fluid paths, space constraints, temperature levels, and production costs [1–3]. Owing to their compact design, multipass crossflow heat exchangers offer significant advantages in industrial settings. However, their optimal performance depends on maintaining a uniform heat transfer coefficient across all channels, which is strongly influenced by the inlet flow distribution [4–7]. Even slight deviations in channel flow can create local variations in heat transfer coefficients, particularly in microchannel exchangers, thereby degrading thermal performance compromising the overall thermal performance of the system [5]. Notably, Guo et al. demonstrated that, under specific conditions, controlled non-uniform flow can enhance heat transfer [4]. This strategy enhances overall heat transfer while promoting uniform heat transfer across all channels through a controlled, non-uniform flow distribution [8]. Such enhancements are beneficial not only for multipass crossflow heat exchangers but also for crossflow reactors used in exothermic processes. Isothermal conditions in these reactors en-  \n* Corresponding author. [E-mail address:](E-mail address: mertcan.kaya@kit.edu)[ mertcan.kaya@kit.edu](E-mail address: mertcan.kaya@kit.edu) (M. Kaya).  \nable higher conversion rates, emphasizing the importance of isothermal crossflow devices in these applications [9]. These findings highlight the broad applicability of controlled non-uniform flow distribution in devices ranging from heat exchangers to chemical reactors and separa","cbCaijSXhgeRjpF4","https://ap.wps.com/l/cbCaijSXhgeRjpF4","pdf",1806270,1,9,"English","en",105,"# Introduction\n## Multipass crossflow devices and the role of flow distribution\n## Consecutive manifolds (U-type and Z-type) and their limitations\n## Bifurcation geometries and additive manufacturing potential","[{\"question\":\"Why is controlling non-uniform flow distribution important for multipass crossflow devices?\",\"answer\":\"Flow distribution strongly affects heat transfer coefficients across channels. Small maldistribution can create local thermal variations, degrading overall thermal performance.\"},{\"question\":\"How does the proposed design achieve user-defined target flow ratios?\",\"answer\":\"It locally varies channel diameters at predefined positions inside the distributor to match specified flow distribution ratios.\"},{\"question\":\"What methods are used in the machine learning-assisted design automation workflow?\",\"answer\":\"The workflow combines algorithmic modeling with computational fluid dynamics (CFD) simulations, and it is validated with experiments using an incompressible gas flow distributor.\"}]","Machine learning-assisted design automation workflow of non-uniform flow distributors for multipass crossflow device | PDF",1785938863,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-assisted-design-automation-workflow-of-non-uniform-flow-distributors-for-multipass-crossflow-device","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-assisted-design-automation-workflow-of-non-uniform-flow-distributors-for-multipass-crossflow-device/127438/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is controlling non-uniform flow distribution important for multipass crossflow devices?","Question",{"text":76,"@type":77},"Flow distribution strongly affects heat transfer coefficients across channels. Small maldistribution can create local thermal variations, degrading overall thermal performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed design achieve user-defined target flow ratios?",{"text":81,"@type":77},"It locally varies channel diameters at predefined positions inside the distributor to match specified flow distribution ratios.",{"name":83,"@type":74,"acceptedAnswer":84},"What methods are used in the machine learning-assisted design automation workflow?",{"text":85,"@type":77},"The workflow combines algorithmic modeling with computational fluid dynamics (CFD) simulations, and it is validated with experiments using an incompressible gas flow distributor.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]