F. Verdiere, A. Rezgui, S. Gaaloul, B. Delinchant, L. Gerbaud, F. Wurtz, X. Brunotte
{"title":"Modelica Models Translation into Java Components for Optimization and DAE Solving Using Automatic Differentiation","authors":"F. Verdiere, A. Rezgui, S. Gaaloul, B. Delinchant, L. Gerbaud, F. Wurtz, X. Brunotte","doi":"10.1109/UKSim.2012.56","DOIUrl":null,"url":null,"abstract":"Modelica modelling language is increasingly used in engineering. It defines differential algebraic equations (DAE) which can be solved using numerical algorithms. In order to solve a DAE, Jacobian of the model is required. In this paper, a full Java implementation of Modelica model generator is made using Automatic Differentiation (AD). The generated model is packaged in a software component standard in order to be plugged in different solvers (DAE, ODE, and Optimization). Depending on the static or dynamic nature of Modelica models, the treatment is adapted in order to take advantage of automatic differentiation. Especially, in the case of static model, a Jacobian is given as sensitivity information to the optimization algorithm.","PeriodicalId":405479,"journal":{"name":"2012 UKSim 14th International Conference on Computer Modelling and Simulation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 UKSim 14th International Conference on Computer Modelling and Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UKSim.2012.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Modelica modelling language is increasingly used in engineering. It defines differential algebraic equations (DAE) which can be solved using numerical algorithms. In order to solve a DAE, Jacobian of the model is required. In this paper, a full Java implementation of Modelica model generator is made using Automatic Differentiation (AD). The generated model is packaged in a software component standard in order to be plugged in different solvers (DAE, ODE, and Optimization). Depending on the static or dynamic nature of Modelica models, the treatment is adapted in order to take advantage of automatic differentiation. Especially, in the case of static model, a Jacobian is given as sensitivity information to the optimization algorithm.