{"title":"Exergetic port-Hamiltonian systems: modelling basics","authors":"Markus Lohmayer, P. Kotyczka, S. Leyendecker","doi":"10.1080/13873954.2021.1979592","DOIUrl":null,"url":null,"abstract":"ABSTRACT Port-Hamiltonian systems theory provides a structured approach to modelling, optimization and control of multiphysical systems. Yet, its relationship to thermodynamics seems to be unclear. The Hamiltonian is traditionally thought of as energy, although its meaning is exergy. This insight yields benefits: 1. Links to the GENERIC structure are identified, making it relatively easy to borrow ideas from a popular nonequilibrium thermodynamics framework. 2. The port-Hamiltonian structure combined with a bond-graph syntax is expected to become a main ingredient in thermodynamic optimization methods akin to exergy analysis and beyond. The intuitive nature of exergy and diagrammatic language facilitates interdisciplinary communication that is necessary for implementing sustainable energy systems and processes. Port-Hamiltonian systems are cyclo-passive, meaning that a power-balance equation immediately follows from their definition. For exergetic port-Hamiltonian systems, cyclo-passivity is synonymous with degradation of energy and follows from the first and the second law of thermodynamics being encoded as structural properties.","PeriodicalId":49871,"journal":{"name":"Mathematical and Computer Modelling of Dynamical Systems","volume":"27 1","pages":"489 - 521"},"PeriodicalIF":1.8000,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical and Computer Modelling of Dynamical Systems","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/13873954.2021.1979592","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 6
Abstract
ABSTRACT Port-Hamiltonian systems theory provides a structured approach to modelling, optimization and control of multiphysical systems. Yet, its relationship to thermodynamics seems to be unclear. The Hamiltonian is traditionally thought of as energy, although its meaning is exergy. This insight yields benefits: 1. Links to the GENERIC structure are identified, making it relatively easy to borrow ideas from a popular nonequilibrium thermodynamics framework. 2. The port-Hamiltonian structure combined with a bond-graph syntax is expected to become a main ingredient in thermodynamic optimization methods akin to exergy analysis and beyond. The intuitive nature of exergy and diagrammatic language facilitates interdisciplinary communication that is necessary for implementing sustainable energy systems and processes. Port-Hamiltonian systems are cyclo-passive, meaning that a power-balance equation immediately follows from their definition. For exergetic port-Hamiltonian systems, cyclo-passivity is synonymous with degradation of energy and follows from the first and the second law of thermodynamics being encoded as structural properties.
期刊介绍:
Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems.
The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application.
MCMDS welcomes original articles on a range of topics including:
-methods of modelling and simulation-
automation of modelling-
qualitative and modular modelling-
data-based and learning-based modelling-
uncertainties and the effects of modelling errors on system performance-
application of modelling to complex real-world systems.