Matches in SemOpenAlex for { <https://semopenalex.org/work/W3109736701> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W3109736701 endingPage "108023" @default.
- W3109736701 startingPage "108023" @default.
- W3109736701 abstract "Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society (ANS). Afterwards, the appropriate architecture of FFNN (i.e. the appropriate number of hidden neurons and hidden layers) and the appropriate input patterns features are investigated. The resulted FFNN is trained using the modeled BFs and the selected category of features. In the test process, the BFs of the master alloys (i.e. Fe-Al%50, Cu-Fe50%, Al-Cu50%) are estimated. To evaluate the performance of the proposed FFNN for training/estimation of the new elements/alloys, Si is added to the training process and the BFs of the Al-Si35% is estimated. Average mean relative error (AMRE) and cumulative distribution function (CDF) of the errors show the acceptable accuracy of the estimating the BFs of the alloys. The noticeable advantages of the proposed technique are: 1- The BFs of the different alloys are estimated only by using the BFs of the constituent elements of the alloys. 2- The time needed to estimate the new BFs by the proposed technique can be neglected versus the time needed to model the new BFs by Monte Carlo. 3- The proposed technique can generalize its ability for estimating the BFs of the new alloys. 4- Monte Carlo codes need the trained person to model the BFs of the alloys while the FFNN generates the new BFs easily." @default.
- W3109736701 created "2020-12-07" @default.
- W3109736701 creator A5006489067 @default.
- W3109736701 creator A5045466386 @default.
- W3109736701 creator A5085307867 @default.
- W3109736701 date "2021-03-01" @default.
- W3109736701 modified "2023-09-25" @default.
- W3109736701 title "Estimating buildup factor of alloys based on combination of Monte Carlo method and multilayer feed-forward neural network" @default.
- W3109736701 cites W151946278 @default.
- W3109736701 cites W1583515380 @default.
- W3109736701 cites W1659720364 @default.
- W3109736701 cites W1965139466 @default.
- W3109736701 cites W1966991711 @default.
- W3109736701 cites W1967692887 @default.
- W3109736701 cites W1977692226 @default.
- W3109736701 cites W1990257170 @default.
- W3109736701 cites W2023110604 @default.
- W3109736701 cites W2057824432 @default.
- W3109736701 cites W2061953987 @default.
- W3109736701 cites W2111051539 @default.
- W3109736701 cites W2137356002 @default.
- W3109736701 cites W2272168277 @default.
- W3109736701 cites W2334770677 @default.
- W3109736701 cites W2432639806 @default.
- W3109736701 cites W2512281945 @default.
- W3109736701 cites W2534753755 @default.
- W3109736701 cites W2759354848 @default.
- W3109736701 cites W2941058675 @default.
- W3109736701 cites W2976411560 @default.
- W3109736701 cites W2979075388 @default.
- W3109736701 cites W2979836850 @default.
- W3109736701 cites W2992808985 @default.
- W3109736701 cites W54778000 @default.
- W3109736701 doi "https://doi.org/10.1016/j.anucene.2020.108023" @default.
- W3109736701 hasPublicationYear "2021" @default.
- W3109736701 type Work @default.
- W3109736701 sameAs 3109736701 @default.
- W3109736701 citedByCount "3" @default.
- W3109736701 countsByYear W31097367012022 @default.
- W3109736701 countsByYear W31097367012023 @default.
- W3109736701 crossrefType "journal-article" @default.
- W3109736701 hasAuthorship W3109736701A5006489067 @default.
- W3109736701 hasAuthorship W3109736701A5045466386 @default.
- W3109736701 hasAuthorship W3109736701A5085307867 @default.
- W3109736701 hasConcept C105795698 @default.
- W3109736701 hasConcept C11413529 @default.
- W3109736701 hasConcept C154945302 @default.
- W3109736701 hasConcept C192562407 @default.
- W3109736701 hasConcept C19499675 @default.
- W3109736701 hasConcept C33923547 @default.
- W3109736701 hasConcept C41008148 @default.
- W3109736701 hasConcept C47702885 @default.
- W3109736701 hasConcept C50644808 @default.
- W3109736701 hasConceptScore W3109736701C105795698 @default.
- W3109736701 hasConceptScore W3109736701C11413529 @default.
- W3109736701 hasConceptScore W3109736701C154945302 @default.
- W3109736701 hasConceptScore W3109736701C192562407 @default.
- W3109736701 hasConceptScore W3109736701C19499675 @default.
- W3109736701 hasConceptScore W3109736701C33923547 @default.
- W3109736701 hasConceptScore W3109736701C41008148 @default.
- W3109736701 hasConceptScore W3109736701C47702885 @default.
- W3109736701 hasConceptScore W3109736701C50644808 @default.
- W3109736701 hasLocation W31097367011 @default.
- W3109736701 hasOpenAccess W3109736701 @default.
- W3109736701 hasPrimaryLocation W31097367011 @default.
- W3109736701 hasRelatedWork W1604847762 @default.
- W3109736701 hasRelatedWork W1905705329 @default.
- W3109736701 hasRelatedWork W2014323024 @default.
- W3109736701 hasRelatedWork W2258992572 @default.
- W3109736701 hasRelatedWork W2357447513 @default.
- W3109736701 hasRelatedWork W2386387936 @default.
- W3109736701 hasRelatedWork W2386767533 @default.
- W3109736701 hasRelatedWork W2391384657 @default.
- W3109736701 hasRelatedWork W2392110728 @default.
- W3109736701 hasRelatedWork W2899084033 @default.
- W3109736701 hasVolume "152" @default.
- W3109736701 isParatext "false" @default.
- W3109736701 isRetracted "false" @default.
- W3109736701 magId "3109736701" @default.
- W3109736701 workType "article" @default.