FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295364673> ?p ?o ?g. }

• W4295364673 endingPage "107350" @default.
• W4295364673 startingPage "107350" @default.
• W4295364673 abstract "As US dairy operations consolidate to meet an increasing demand for dairy products, dairy producers have begun to seek more economically efficient dairy-barn designs and ventilation systems to combat the pernicious effects of heat stress. The metabolic changes that heat stress induces not only put the animals at risk of serious illness but also diminish the herd’s productivity. Lactation, being a metabolic process, generates a great deal of body heat thereby increasing the risk of heat stress during warm weather, and that risk increases when the animals commingle in an enclosed space. For this reason, predicting heat stress and estimating its severity have taken on special importance. Recently, producers and barn builders have contended with the conditions that promote heat stress by implementing closed mechanical ventilation systems. However, if a proposed ventilation system’s performance was not accurately predicted, the system would not work effectively or efficiently. The traditional ways of assessing ventilation performance, which involved either hand-held sensors or stationery sensing monitors, tended to be time-consuming and the larger the barn the more labor-intensive. Additionally, the complex microenvironmental system that exists inside a dairy barn is further complicated by the myriad moving elements, both animals and operational machines. Given these complicating parameters, researchers have lately turned to computation fluid dynamics (CFD) when analyzing such environments because, once properly validated, CFD can accurately predict the heat and mass transfer occurring at any location inside a computer-generated model of a dairy barn. However, operating CFD correctly requires not only an extensive background knowledge and a significant amount of hands-on experience, but also high-end hardware, which is often unavailable to dairy operators and non-CFD--experts. This study proposed CFD-ML, a machine-learning (ML) based “simulator,” as a means to achieving computational predictions that are accurate enough to use as a basis for dairy-barn design. A convolutional neural network (CNN), a representative ML model, allows complex mapping between input barn geometry and the velocity and temperature fields occurring in the barn, resulting in faster prediction time and simpler usage processes. As a result, the CFD-ML model can achieve outcomes comparable to those obtained using CFD, with R2 values greater than 0.85, in far less computing time required by CFD. This finding should provide a foundation that could facilitate future CFD-ML advances in dairy science research as well as the development of a user-friendly Graphical User Interface (GUI) that bypasses the complex CFD procedures." @default.
• W4295364673 created "2022-09-13" @default.
• W4295364673 creator A5028145203 @default.
• W4295364673 creator A5049407947 @default.
• W4295364673 creator A5067936903 @default.
• W4295364673 creator A5070538645 @default.
• W4295364673 creator A5074975599 @default.
• W4295364673 date "2022-11-01" @default.
• W4295364673 modified "2023-09-30" @default.
• W4295364673 title "Application of machine-learned metadata-driven model for dairy barn ventilation simulation" @default.
• W4295364673 cites W2088726485 @default.
• W4295364673 cites W2147434934 @default.
• W4295364673 cites W2229193821 @default.
• W4295364673 cites W2515505748 @default.
• W4295364673 cites W2749001100 @default.
• W4295364673 cites W2750319426 @default.
• W4295364673 cites W2753021168 @default.
• W4295364673 cites W2753135167 @default.
• W4295364673 cites W2896620600 @default.
• W4295364673 cites W2913866198 @default.
• W4295364673 cites W2943949089 @default.
• W4295364673 cites W2968333881 @default.
• W4295364673 cites W2981646906 @default.
• W4295364673 cites W3024511949 @default.
• W4295364673 cites W3114014284 @default.
• W4295364673 cites W3125373488 @default.
• W4295364673 cites W3138062520 @default.
• W4295364673 cites W3139412186 @default.
• W4295364673 cites W3159903628 @default.
• W4295364673 cites W3165769551 @default.
• W4295364673 cites W3208854259 @default.
• W4295364673 cites W4200073116 @default.
• W4295364673 cites W4284993088 @default.
• W4295364673 doi "https://doi.org/10.1016/j.compag.2022.107350" @default.
• W4295364673 hasPublicationYear "2022" @default.
• W4295364673 type Work @default.
• W4295364673 citedByCount "1" @default.
• W4295364673 countsByYear W42953646732023 @default.
• W4295364673 crossrefType "journal-article" @default.
• W4295364673 hasAuthorship W4295364673A5028145203 @default.
• W4295364673 hasAuthorship W4295364673A5049407947 @default.
• W4295364673 hasAuthorship W4295364673A5067936903 @default.
• W4295364673 hasAuthorship W4295364673A5070538645 @default.
• W4295364673 hasAuthorship W4295364673A5074975599 @default.
• W4295364673 hasConcept C118552586 @default.
• W4295364673 hasConcept C127413603 @default.
• W4295364673 hasConcept C140793950 @default.
• W4295364673 hasConcept C146978453 @default.
• W4295364673 hasConcept C147176958 @default.
• W4295364673 hasConcept C15744967 @default.
• W4295364673 hasConcept C1633027 @default.
• W4295364673 hasConcept C18762648 @default.
• W4295364673 hasConcept C200457457 @default.
• W4295364673 hasConcept C21880701 @default.
• W4295364673 hasConcept C2777080012 @default.
• W4295364673 hasConcept C2780883654 @default.
• W4295364673 hasConcept C2987418964 @default.
• W4295364673 hasConcept C39432304 @default.
• W4295364673 hasConcept C41008148 @default.
• W4295364673 hasConcept C44154836 @default.
• W4295364673 hasConcept C78519656 @default.
• W4295364673 hasConcept C86803240 @default.
• W4295364673 hasConceptScore W4295364673C118552586 @default.
• W4295364673 hasConceptScore W4295364673C127413603 @default.
• W4295364673 hasConceptScore W4295364673C140793950 @default.
• W4295364673 hasConceptScore W4295364673C146978453 @default.
• W4295364673 hasConceptScore W4295364673C147176958 @default.
• W4295364673 hasConceptScore W4295364673C15744967 @default.
• W4295364673 hasConceptScore W4295364673C1633027 @default.
• W4295364673 hasConceptScore W4295364673C18762648 @default.
• W4295364673 hasConceptScore W4295364673C200457457 @default.
• W4295364673 hasConceptScore W4295364673C21880701 @default.
• W4295364673 hasConceptScore W4295364673C2777080012 @default.
• W4295364673 hasConceptScore W4295364673C2780883654 @default.
• W4295364673 hasConceptScore W4295364673C2987418964 @default.
• W4295364673 hasConceptScore W4295364673C39432304 @default.
• W4295364673 hasConceptScore W4295364673C41008148 @default.
• W4295364673 hasConceptScore W4295364673C44154836 @default.
• W4295364673 hasConceptScore W4295364673C78519656 @default.
• W4295364673 hasConceptScore W4295364673C86803240 @default.
• W4295364673 hasFunder F4320332299 @default.
• W4295364673 hasFunder F4320332605 @default.
• W4295364673 hasLocation W42953646731 @default.
• W4295364673 hasOpenAccess W4295364673 @default.
• W4295364673 hasPrimaryLocation W42953646731 @default.
• W4295364673 hasRelatedWork W2012791660 @default.
• W4295364673 hasRelatedWork W2070197186 @default.
• W4295364673 hasRelatedWork W2073225146 @default.
• W4295364673 hasRelatedWork W2472553574 @default.
• W4295364673 hasRelatedWork W2899084033 @default.
• W4295364673 hasRelatedWork W3030324398 @default.
• W4295364673 hasRelatedWork W3048832799 @default.
• W4295364673 hasRelatedWork W3129383238 @default.
• W4295364673 hasRelatedWork W4252719677 @default.
• W4295364673 hasRelatedWork W4312198238 @default.
• W4295364673 hasVolume "202" @default.
• W4295364673 isParatext "false" @default.
• W4295364673 isRetracted "false" @default.

• next