393 research outputs found

    Python-Modelica framework for automated simulation and optimization

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    Modeling and simulation are essential for the development of complex engineering systems, such as wind turbines. Thus, Fraunhofer IWES (Fraunhofer Institute for Wind Energy Systems) has developed the MoWiT (Modelica for Wind Turbines) library for fully-coupled aero-hydro-servo-elastic simulations of wind turbine systems. To meet the needs for detailed assessment and design development of such sophisticated engineering systems, which imply iterative steps for design optimization, a Python-Modelica framework is set up and presented in this paper. By means of this, the simulation of MoWiT models can easily be managed, including redefinition of model parameters, specification of output sensors and simulation settings, integration of optimization algorithms, post-processing of simulation results, as well as parallel execution of several simulations. The application of this Python-Modelica framework is shown based on the example of a design optimization task of a floating wind turbine support structure

    Optimisation Of Controller Parameters For Adaptive Building Envelopes Through A Co-Simulation Interface: A Case Study

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    Adaptive building envelopes can dynamically adapt to environmental changes, often supported by a control system. While building performance simulation (BPS) tools can be employed to test different design alternatives, representing control strategies within current BPS tools can be challenging, especially for systems with a fast, dynamic response. Another challenge in current BPS tools is the ability to tune and select parameters for the particular use case. In this study, a modelling approach is presented for the integrated analysis of control strategies of adaptive building envelopes linking thermal performance and control with an optimisation algorithm. The proposed modelling approach was evaluated using a case study with an automated motorised blind with two distinct control strategies. Simulation results suggest that the window heat gains were 72.7 % lower when the controller model was coupled with an optimiser to identify optimised controller parameters compared to a baseline control strategy. The results of this study are suggestive of the benefits that can be obtained from adjusting the dynamic aspects of the building envelope. The results support the thesis of using optimisation as standard building envelope design practice in the future

    Workshop - Systems Design Meets Equation-based Languages

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    An optimization-based approach to automated design

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    We propose a model-based, automated, bottom-up approach for design, which is applicable to various physical domains, but in this work we focus on the electrical domain. This bottom-up approach is based on a meta-topology in which each link is described by a universal component that can be instantiated as basic components (e.g., resistors, capacitors) or combinations of basic components via discrete switches. To address the combinatorial explosion often present in mixed-integer optimization problems, we present two algorithms. In the first algorithm, we convert the discrete switches into continuous switches that are physically realizable and formulate a parameter optimization problem that learns the component and switch parameters while inducing design sparsity through an L1L_1 regularization term. The second algorithm uses a genetic-like approach with selection and mutation steps guided by ranking of requirements costs, combined with continuous optimization for generating optimal parameters. We improve the time complexity of the optimization problem in both algorithms by reconstructing the model when components become redundant and by simplifying topologies through collapsing components and removing disconnected ones. To demonstrate the efficacy of these algorithms, we apply them to the design of various electrical circuits

    Special Session on Industry 4.0

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