{"title":"机器学习优化 3D 打印流动反应器的几何形状","authors":"Jeffrey A. Bennett, Milad Abolhasani","doi":"10.1038/s44286-024-00095-5","DOIUrl":null,"url":null,"abstract":"The geometric design space of continuous flow reactors for optimal process intensification is prohibitively large for a comprehensive search, but incorporation of multi-fidelity optimization techniques using computer simulations and additive manufacturing can rapidly improve reactor performance.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"501-503"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine-learning optimization of 3D-printed flow-reactor geometry\",\"authors\":\"Jeffrey A. Bennett, Milad Abolhasani\",\"doi\":\"10.1038/s44286-024-00095-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The geometric design space of continuous flow reactors for optimal process intensification is prohibitively large for a comprehensive search, but incorporation of multi-fidelity optimization techniques using computer simulations and additive manufacturing can rapidly improve reactor performance.\",\"PeriodicalId\":501699,\"journal\":{\"name\":\"Nature Chemical Engineering\",\"volume\":\"1 8\",\"pages\":\"501-503\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Chemical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s44286-024-00095-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Chemical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44286-024-00095-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine-learning optimization of 3D-printed flow-reactor geometry
The geometric design space of continuous flow reactors for optimal process intensification is prohibitively large for a comprehensive search, but incorporation of multi-fidelity optimization techniques using computer simulations and additive manufacturing can rapidly improve reactor performance.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
