Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386906382> ?p ?o ?g. }
- W4386906382 endingPage "107035" @default.
- W4386906382 startingPage "107035" @default.
- W4386906382 abstract "This study proposes a conformable artificial neural network model to improve the void fraction prediction in helical heat exchangers. The obtained model had only one neuron in the hidden layer, achieving an algebraic structure simpler than the classic training. Furthermore, this model satisfies the interval condition of [0–1] and is a function of vapor fraction, density ratio, and viscosity ratio. The new conformable ANN void fraction satisfactorily described the two-phase flow in the systems mentioned above because the outlet temperatures were predicted with 2.96% of RMSE, lower than those obtained with other void fraction model analyses. This paper describes the conformable Logistic Sigmoid Transfer Function (CLOGSIG) and its application advantages in the ANN training process. Using CLOGSIG as a transfer function can get different sinusoidal behaviors that can modify the data distribution around the function's sinusoidal area, allowing better data adaptability and improving network performance." @default.
- W4386906382 created "2023-09-21" @default.
- W4386906382 creator A5001043730 @default.
- W4386906382 creator A5001974765 @default.
- W4386906382 creator A5029501554 @default.
- W4386906382 creator A5050650026 @default.
- W4386906382 creator A5065312690 @default.
- W4386906382 creator A5073391985 @default.
- W4386906382 creator A5090078796 @default.
- W4386906382 date "2023-11-01" @default.
- W4386906382 modified "2023-10-02" @default.
- W4386906382 title "A conformable artificial neural network model to improve the void fraction prediction in helical heat exchangers" @default.
- W4386906382 cites W1987055235 @default.
- W4386906382 cites W1991670290 @default.
- W4386906382 cites W2007336590 @default.
- W4386906382 cites W2015663673 @default.
- W4386906382 cites W2015928143 @default.
- W4386906382 cites W2031179271 @default.
- W4386906382 cites W2034834781 @default.
- W4386906382 cites W2063067239 @default.
- W4386906382 cites W2063545850 @default.
- W4386906382 cites W2071593170 @default.
- W4386906382 cites W2080931732 @default.
- W4386906382 cites W2082142907 @default.
- W4386906382 cites W2087661061 @default.
- W4386906382 cites W2088557358 @default.
- W4386906382 cites W2092024822 @default.
- W4386906382 cites W2134837756 @default.
- W4386906382 cites W2495427444 @default.
- W4386906382 cites W2513045195 @default.
- W4386906382 cites W2570096509 @default.
- W4386906382 cites W2740460687 @default.
- W4386906382 cites W2770636204 @default.
- W4386906382 cites W2810987980 @default.
- W4386906382 cites W2908281608 @default.
- W4386906382 cites W2953637813 @default.
- W4386906382 cites W2963055118 @default.
- W4386906382 cites W2963641381 @default.
- W4386906382 cites W2979040987 @default.
- W4386906382 cites W2982905738 @default.
- W4386906382 cites W3109655413 @default.
- W4386906382 cites W3131986220 @default.
- W4386906382 cites W3185306152 @default.
- W4386906382 cites W4211181491 @default.
- W4386906382 cites W4224219574 @default.
- W4386906382 cites W4282935700 @default.
- W4386906382 cites W4283749422 @default.
- W4386906382 cites W4289798260 @default.
- W4386906382 cites W4292737110 @default.
- W4386906382 cites W4296993760 @default.
- W4386906382 cites W4310078126 @default.
- W4386906382 cites W4317746092 @default.
- W4386906382 cites W79134773 @default.
- W4386906382 doi "https://doi.org/10.1016/j.icheatmasstransfer.2023.107035" @default.
- W4386906382 hasPublicationYear "2023" @default.
- W4386906382 type Work @default.
- W4386906382 citedByCount "0" @default.
- W4386906382 crossrefType "journal-article" @default.
- W4386906382 hasAuthorship W4386906382A5001043730 @default.
- W4386906382 hasAuthorship W4386906382A5001974765 @default.
- W4386906382 hasAuthorship W4386906382A5029501554 @default.
- W4386906382 hasAuthorship W4386906382A5050650026 @default.
- W4386906382 hasAuthorship W4386906382A5065312690 @default.
- W4386906382 hasAuthorship W4386906382A5073391985 @default.
- W4386906382 hasAuthorship W4386906382A5090078796 @default.
- W4386906382 hasConcept C107706546 @default.
- W4386906382 hasConcept C121332964 @default.
- W4386906382 hasConcept C149629883 @default.
- W4386906382 hasConcept C154945302 @default.
- W4386906382 hasConcept C159985019 @default.
- W4386906382 hasConcept C177606310 @default.
- W4386906382 hasConcept C178790620 @default.
- W4386906382 hasConcept C185592680 @default.
- W4386906382 hasConcept C18903297 @default.
- W4386906382 hasConcept C192562407 @default.
- W4386906382 hasConcept C41008148 @default.
- W4386906382 hasConcept C50517652 @default.
- W4386906382 hasConcept C50644808 @default.
- W4386906382 hasConcept C57879066 @default.
- W4386906382 hasConcept C6648577 @default.
- W4386906382 hasConcept C81388566 @default.
- W4386906382 hasConcept C86072612 @default.
- W4386906382 hasConcept C86803240 @default.
- W4386906382 hasConcept C97355855 @default.
- W4386906382 hasConceptScore W4386906382C107706546 @default.
- W4386906382 hasConceptScore W4386906382C121332964 @default.
- W4386906382 hasConceptScore W4386906382C149629883 @default.
- W4386906382 hasConceptScore W4386906382C154945302 @default.
- W4386906382 hasConceptScore W4386906382C159985019 @default.
- W4386906382 hasConceptScore W4386906382C177606310 @default.
- W4386906382 hasConceptScore W4386906382C178790620 @default.
- W4386906382 hasConceptScore W4386906382C185592680 @default.
- W4386906382 hasConceptScore W4386906382C18903297 @default.
- W4386906382 hasConceptScore W4386906382C192562407 @default.
- W4386906382 hasConceptScore W4386906382C41008148 @default.
- W4386906382 hasConceptScore W4386906382C50517652 @default.
- W4386906382 hasConceptScore W4386906382C50644808 @default.
- W4386906382 hasConceptScore W4386906382C57879066 @default.