{"title":"考虑非局域性的基因表达式规划:边界层向湍流过渡的建模","authors":"Alexander Bleh, Christian Morsbach","doi":"10.1007/s10494-025-00654-7","DOIUrl":null,"url":null,"abstract":"<div><p>The consideration of the inherently non-local characteristics of turbulence is an open challenge and subject to many investigations. Recent approaches rely on the utilization of spatially configured Neural Networks such as e.g. Convolutional Neural Networks to account for non-local effects (Comput. Methods Appl. Mech. Eng. 384:113927, 2021). Nevertheless, approaches featuring Neural Networks are not easily available for Gene Expression Programming. An alternative option, to consider non-local effects, is the use of partial differential equations (PDE) like an additional convection-diffusion equation as is done for example in several transition models such as the <span>\\(\\gamma\\)</span>- model by Menter et al. (Flow Turbul. Combust. 583–619, 2015). Consequently, instead of only modeling a local correction factor directly using GEP, we equip the input quantities with an additional optional convection-diffusion equation of which we model the production term, diffusion constants and boundary type. The methodology is applied on a set of low pressure turbine testcases in order to find transition models. Resulting expressions are further analysed in terms of underlying mechnims and logical foundations.</p></div>","PeriodicalId":559,"journal":{"name":"Flow, Turbulence and Combustion","volume":"115 :","pages":"1133 - 1155"},"PeriodicalIF":2.4000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10494-025-00654-7.pdf","citationCount":"0","resultStr":"{\"title\":\"Consideration of Non-Locality for Gene Expression Programming: Modeling the Transition to Turbulence in the Boundary Layer\",\"authors\":\"Alexander Bleh, Christian Morsbach\",\"doi\":\"10.1007/s10494-025-00654-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The consideration of the inherently non-local characteristics of turbulence is an open challenge and subject to many investigations. Recent approaches rely on the utilization of spatially configured Neural Networks such as e.g. Convolutional Neural Networks to account for non-local effects (Comput. Methods Appl. Mech. Eng. 384:113927, 2021). Nevertheless, approaches featuring Neural Networks are not easily available for Gene Expression Programming. An alternative option, to consider non-local effects, is the use of partial differential equations (PDE) like an additional convection-diffusion equation as is done for example in several transition models such as the <span>\\\\(\\\\gamma\\\\)</span>- model by Menter et al. (Flow Turbul. Combust. 583–619, 2015). Consequently, instead of only modeling a local correction factor directly using GEP, we equip the input quantities with an additional optional convection-diffusion equation of which we model the production term, diffusion constants and boundary type. The methodology is applied on a set of low pressure turbine testcases in order to find transition models. Resulting expressions are further analysed in terms of underlying mechnims and logical foundations.</p></div>\",\"PeriodicalId\":559,\"journal\":{\"name\":\"Flow, Turbulence and Combustion\",\"volume\":\"115 :\",\"pages\":\"1133 - 1155\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10494-025-00654-7.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Flow, Turbulence and Combustion\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10494-025-00654-7\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Flow, Turbulence and Combustion","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10494-025-00654-7","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MECHANICS","Score":null,"Total":0}
Consideration of Non-Locality for Gene Expression Programming: Modeling the Transition to Turbulence in the Boundary Layer
The consideration of the inherently non-local characteristics of turbulence is an open challenge and subject to many investigations. Recent approaches rely on the utilization of spatially configured Neural Networks such as e.g. Convolutional Neural Networks to account for non-local effects (Comput. Methods Appl. Mech. Eng. 384:113927, 2021). Nevertheless, approaches featuring Neural Networks are not easily available for Gene Expression Programming. An alternative option, to consider non-local effects, is the use of partial differential equations (PDE) like an additional convection-diffusion equation as is done for example in several transition models such as the \(\gamma\)- model by Menter et al. (Flow Turbul. Combust. 583–619, 2015). Consequently, instead of only modeling a local correction factor directly using GEP, we equip the input quantities with an additional optional convection-diffusion equation of which we model the production term, diffusion constants and boundary type. The methodology is applied on a set of low pressure turbine testcases in order to find transition models. Resulting expressions are further analysed in terms of underlying mechnims and logical foundations.
期刊介绍:
Flow, Turbulence and Combustion provides a global forum for the publication of original and innovative research results that contribute to the solution of fundamental and applied problems encountered in single-phase, multi-phase and reacting flows, in both idealized and real systems. The scope of coverage encompasses topics in fluid dynamics, scalar transport, multi-physics interactions and flow control. From time to time the journal publishes Special or Theme Issues featuring invited articles.
Contributions may report research that falls within the broad spectrum of analytical, computational and experimental methods. This includes research conducted in academia, industry and a variety of environmental and geophysical sectors. Turbulence, transition and associated phenomena are expected to play a significant role in the majority of studies reported, although non-turbulent flows, typical of those in micro-devices, would be regarded as falling within the scope covered. The emphasis is on originality, timeliness, quality and thematic fit, as exemplified by the title of the journal and the qualifications described above. Relevance to real-world problems and industrial applications are regarded as strengths.