FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384038118> ?p ?o ?g. }

• W4384038118 endingPage "8086" @default.
• W4384038118 startingPage "8086" @default.
• W4384038118 abstract "Most granular flow in nature and industrial processing has the property of polydispersity, whereas we are always restricted to using the monodisperse drag force model in simulations since the drag force model with polydispersity is difficult to establish. Ignoring polydispersity often results in obvious deviations between simulation and experimental outcomes. Generally, it is very hard for us to describe the characteristics of polydispersity in drag force by using a function with analytic expression. Recently, the artificial neural network (ANN) model provides us the advantages of estimating these kinds of outcomes with better accuracy. In this work, the ANN is adopted to model the drag force in polydisperse granular flows. In order to construct a reasonable ANN algorithm for modeling the polydisperse drag force, the structures of ANN are elaborately designed. As training for the ANN drag model, a direct numerical simulation method is proposed, based on the lattice Boltzmann method (LBM), to generate the training data, and an adaptive data filtering algorithm, termed as the optimal contribution rate algorithm (OCRA), is introduced to effectively improve the training efficiency and avoid the over-fitting problems. The results support that the polydispersity of the system can be well scaled by the ANN drag model in a relatively wide range of particle concentrations, and the predicted results coincide well with the experimental ones. Moreover, the ANN drag model is not only effective for polydisperse systems, but compatible with monodisperse systems, which is impossible using traditional drag models." @default.
• W4384038118 created "2023-07-13" @default.
• W4384038118 creator A5022526821 @default.
• W4384038118 creator A5023896153 @default.
• W4384038118 creator A5040205022 @default.
• W4384038118 creator A5054594981 @default.
• W4384038118 date "2023-07-11" @default.
• W4384038118 modified "2023-10-18" @default.
• W4384038118 title "Modeling of the Drag Force in Polydisperse Gas–Solid Flow via an Efficient Supervised Machine Learning Approach" @default.
• W4384038118 cites W1780926925 @default.
• W4384038118 cites W1966386069 @default.
• W4384038118 cites W1977341407 @default.
• W4384038118 cites W1981484092 @default.
• W4384038118 cites W1985906280 @default.
• W4384038118 cites W1994505190 @default.
• W4384038118 cites W2015867820 @default.
• W4384038118 cites W2032464697 @default.
• W4384038118 cites W2035149449 @default.
• W4384038118 cites W2055787424 @default.
• W4384038118 cites W2059354167 @default.
• W4384038118 cites W2068470667 @default.
• W4384038118 cites W2071530432 @default.
• W4384038118 cites W2080953862 @default.
• W4384038118 cites W2098772492 @default.
• W4384038118 cites W2108850740 @default.
• W4384038118 cites W2117242079 @default.
• W4384038118 cites W2135000584 @default.
• W4384038118 cites W2158286628 @default.
• W4384038118 cites W2166776469 @default.
• W4384038118 cites W2321193544 @default.
• W4384038118 cites W2339680970 @default.
• W4384038118 cites W2416964000 @default.
• W4384038118 cites W2480862060 @default.
• W4384038118 cites W2557151320 @default.
• W4384038118 cites W2582164253 @default.
• W4384038118 cites W2730397861 @default.
• W4384038118 cites W2759791353 @default.
• W4384038118 cites W2890054601 @default.
• W4384038118 cites W2895791676 @default.
• W4384038118 cites W2903431909 @default.
• W4384038118 cites W2907522752 @default.
• W4384038118 cites W2951899946 @default.
• W4384038118 cites W2964223364 @default.
• W4384038118 cites W2976561278 @default.
• W4384038118 cites W2994316685 @default.
• W4384038118 cites W3011940857 @default.
• W4384038118 cites W3012518767 @default.
• W4384038118 cites W3028212721 @default.
• W4384038118 cites W3040821245 @default.
• W4384038118 cites W3043333766 @default.
• W4384038118 cites W3046609048 @default.
• W4384038118 cites W3097194793 @default.
• W4384038118 cites W3128524438 @default.
• W4384038118 cites W3159820150 @default.
• W4384038118 cites W3183406235 @default.
• W4384038118 cites W4213349714 @default.
• W4384038118 cites W4220974482 @default.
• W4384038118 cites W4295094175 @default.
• W4384038118 cites W4308978469 @default.
• W4384038118 cites W4309315525 @default.
• W4384038118 cites W4312164129 @default.
• W4384038118 cites W4361001834 @default.
• W4384038118 cites W4362520905 @default.
• W4384038118 cites W4367843690 @default.
• W4384038118 doi "https://doi.org/10.3390/app13148086" @default.
• W4384038118 hasPublicationYear "2023" @default.
• W4384038118 type Work @default.
• W4384038118 citedByCount "0" @default.
• W4384038118 crossrefType "journal-article" @default.
• W4384038118 hasAuthorship W4384038118A5022526821 @default.
• W4384038118 hasAuthorship W4384038118A5023896153 @default.
• W4384038118 hasAuthorship W4384038118A5040205022 @default.
• W4384038118 hasAuthorship W4384038118A5054594981 @default.
• W4384038118 hasBestOaLocation W43840381181 @default.
• W4384038118 hasConcept C11432220 @default.
• W4384038118 hasConcept C121332964 @default.
• W4384038118 hasConcept C126255220 @default.
• W4384038118 hasConcept C188027245 @default.
• W4384038118 hasConcept C192562407 @default.
• W4384038118 hasConcept C33923547 @default.
• W4384038118 hasConcept C38349280 @default.
• W4384038118 hasConcept C41008148 @default.
• W4384038118 hasConcept C57879066 @default.
• W4384038118 hasConcept C72921944 @default.
• W4384038118 hasConceptScore W4384038118C11432220 @default.
• W4384038118 hasConceptScore W4384038118C121332964 @default.
• W4384038118 hasConceptScore W4384038118C126255220 @default.
• W4384038118 hasConceptScore W4384038118C188027245 @default.
• W4384038118 hasConceptScore W4384038118C192562407 @default.
• W4384038118 hasConceptScore W4384038118C33923547 @default.
• W4384038118 hasConceptScore W4384038118C38349280 @default.
• W4384038118 hasConceptScore W4384038118C41008148 @default.
• W4384038118 hasConceptScore W4384038118C57879066 @default.
• W4384038118 hasConceptScore W4384038118C72921944 @default.
• W4384038118 hasFunder F4320321001 @default.
• W4384038118 hasIssue "14" @default.
• W4384038118 hasLocation W43840381181 @default.
• W4384038118 hasOpenAccess W4384038118 @default.

• next