Matches in SemOpenAlex for { <https://semopenalex.org/work/W2998962824> ?p ?o ?g. }
- W2998962824 endingPage "108483" @default.
- W2998962824 startingPage "108483" @default.
- W2998962824 abstract "Uranium dioxide (UO2) is widely used in nuclear reactors. This fuel has a low thermal conductivity (TC). Increasing its TC can effectively enhance the safety of reactors and fuel efficiencies. A prevalent approach to increasing the TC of UO2 is to inject a second phase material with a high TC into a UO2 matrix. Due to operational difficulties in the fabrication, deployment, and testing of such composite fuels, measurement data regarding effective thermal conductivity (ETC) of these composite fuels are rarely available, which hinders the development of these composites. To overcome such a barrier, finite element method is utilized to generate massive simulated measurements over the concerned composites. Subsequently, a novel algorithmic method is developed that automatically learns from gathered simulation results to accurately and reliably: 1) predict the ETC of a composite fuel according to its given structural characteristics, and 2) reversely infer the structural characteristics of a composite fuel from its expected ETC. The relative error of forward prediction and inverse design is <5% by the new algorithm. The new computational solution provides a novel and effective approach to developing new composite fuels with significant design acceleration and cost reduction." @default.
- W2998962824 created "2020-01-23" @default.
- W2998962824 creator A5013553103 @default.
- W2998962824 creator A5030409288 @default.
- W2998962824 creator A5034223028 @default.
- W2998962824 creator A5037240748 @default.
- W2998962824 creator A5045177386 @default.
- W2998962824 creator A5046202286 @default.
- W2998962824 creator A5061178696 @default.
- W2998962824 creator A5064667217 @default.
- W2998962824 creator A5071006086 @default.
- W2998962824 creator A5091402932 @default.
- W2998962824 date "2020-04-01" @default.
- W2998962824 modified "2023-09-27" @default.
- W2998962824 title "Bi-directional prediction of structural characteristics and effective thermal conductivities of composite fuels through learning from finite element simulation results" @default.
- W2998962824 cites W1746194456 @default.
- W2998962824 cites W1971182549 @default.
- W2998962824 cites W1976431068 @default.
- W2998962824 cites W1980169269 @default.
- W2998962824 cites W1981213253 @default.
- W2998962824 cites W1986534258 @default.
- W2998962824 cites W1997014239 @default.
- W2998962824 cites W1999478804 @default.
- W2998962824 cites W2003633660 @default.
- W2998962824 cites W2005734026 @default.
- W2998962824 cites W2008740465 @default.
- W2998962824 cites W2011486642 @default.
- W2998962824 cites W2013464658 @default.
- W2998962824 cites W2032174474 @default.
- W2998962824 cites W2033612910 @default.
- W2998962824 cites W2041768064 @default.
- W2998962824 cites W2048414560 @default.
- W2998962824 cites W2051913665 @default.
- W2998962824 cites W2072024713 @default.
- W2998962824 cites W2072414015 @default.
- W2998962824 cites W2299267226 @default.
- W2998962824 cites W2337110853 @default.
- W2998962824 cites W2749159454 @default.
- W2998962824 cites W2753062524 @default.
- W2998962824 cites W2760710953 @default.
- W2998962824 cites W2764289613 @default.
- W2998962824 cites W2782634521 @default.
- W2998962824 cites W2790581753 @default.
- W2998962824 cites W2791408101 @default.
- W2998962824 cites W2793730276 @default.
- W2998962824 cites W2801494011 @default.
- W2998962824 cites W2804109744 @default.
- W2998962824 cites W2884184140 @default.
- W2998962824 cites W2884430236 @default.
- W2998962824 cites W2888395196 @default.
- W2998962824 cites W2899341660 @default.
- W2998962824 cites W2902390267 @default.
- W2998962824 cites W2913198522 @default.
- W2998962824 cites W2919115771 @default.
- W2998962824 cites W2929390436 @default.
- W2998962824 cites W2944052326 @default.
- W2998962824 cites W2969697705 @default.
- W2998962824 cites W3099236691 @default.
- W2998962824 doi "https://doi.org/10.1016/j.matdes.2020.108483" @default.
- W2998962824 hasPublicationYear "2020" @default.
- W2998962824 type Work @default.
- W2998962824 sameAs 2998962824 @default.
- W2998962824 citedByCount "10" @default.
- W2998962824 countsByYear W29989628242020 @default.
- W2998962824 countsByYear W29989628242021 @default.
- W2998962824 countsByYear W29989628242022 @default.
- W2998962824 countsByYear W29989628242023 @default.
- W2998962824 crossrefType "journal-article" @default.
- W2998962824 hasAuthorship W2998962824A5013553103 @default.
- W2998962824 hasAuthorship W2998962824A5030409288 @default.
- W2998962824 hasAuthorship W2998962824A5034223028 @default.
- W2998962824 hasAuthorship W2998962824A5037240748 @default.
- W2998962824 hasAuthorship W2998962824A5045177386 @default.
- W2998962824 hasAuthorship W2998962824A5046202286 @default.
- W2998962824 hasAuthorship W2998962824A5061178696 @default.
- W2998962824 hasAuthorship W2998962824A5064667217 @default.
- W2998962824 hasAuthorship W2998962824A5071006086 @default.
- W2998962824 hasAuthorship W2998962824A5091402932 @default.
- W2998962824 hasBestOaLocation W29989628241 @default.
- W2998962824 hasConcept C104779481 @default.
- W2998962824 hasConcept C116915560 @default.
- W2998962824 hasConcept C121332964 @default.
- W2998962824 hasConcept C127413603 @default.
- W2998962824 hasConcept C135628077 @default.
- W2998962824 hasConcept C136525101 @default.
- W2998962824 hasConcept C142724271 @default.
- W2998962824 hasConcept C159985019 @default.
- W2998962824 hasConcept C192562407 @default.
- W2998962824 hasConcept C204530211 @default.
- W2998962824 hasConcept C204787440 @default.
- W2998962824 hasConcept C207467116 @default.
- W2998962824 hasConcept C21880701 @default.
- W2998962824 hasConcept C2524010 @default.
- W2998962824 hasConcept C2779819667 @default.
- W2998962824 hasConcept C33923547 @default.
- W2998962824 hasConcept C41008148 @default.
- W2998962824 hasConcept C66938386 @default.
- W2998962824 hasConcept C7083945 @default.