Modelica - A Language for Physical System Modeling, Visualization and Interaction

Modelica is an object-oriented language for modeling of large, complex and heterogeneous physical systems. It is suited for multi-domain modeling, for example for modeling of mechatronics including cars, aircrafts and industrial robots which typically consist of mechanical, electrical and hydraulic subsystems as well as control systems. General equations are used for modeling of the physical phenomena, No particular variable needs to be solved for manually. A Modelica tool will have enough information to do that automatically. The language has been designed to allow tools to generate efficient code automatically. The modeling effort is thus reduced considerably since model components can be reused and tedious and error-prone manual manipulations are not needed. The principles of object-oriented modeling and the details of the Modelica language as well as several examples are presented

Comparison of moving boundary and finite-volume heat exchangers models in the Modelica language

When modelling low capacity energy systems such as a small (5–150 kWel) organic Rankine cycle unit, the governing dynamics are mainly concentrated in the heat exchangers. As a consequence, accuracy and simulation speed of the higher level system model mainly depend on the heat exchanger model formulation. In particular, the modelling of thermodynamic systems characterized by evaporation or condensation, requires heat exchanger models capable of handling phase transitions. To this aim, the finite volume (FV) and the moving boundary (MB) approaches are the most widely used. The two models are developed and included in the open-source ThermoCycle Modelica library. In this contribution a comparison between the two approaches is performed. Their performance is tested in terms of model integrity and accuracy during transient conditions. Furthermore the models are used to simulate the evaporator of an ORC system and their responses are validated against experimental data collected on an 11 kWel ORC power unit

Hybrid Simulation Safety: Limbos and Zero Crossings

Physical systems can be naturally modeled by combining continuous and discrete models. Such hybrid models may simplify the modeling task of complex system, as well as increase simulation performance. Moreover, modern simulation engines can often efficiently generate simulation traces, but how do we know that the simulation results are correct? If we detect an error, is the error in the model or in the simulation itself? This paper discusses the problem of simulation safety, with the focus on hybrid modeling and simulation. In particular, two key aspects are studied: safe zero-crossing detection and deterministic hybrid event handling. The problems and solutions are discussed and partially implemented in Modelica and Ptolemy II

Energy efficient renovation of heritage residential buildings using modelica simulations

Historic homesteads can be found on a large scale in Europe and particularly in Flanders. In Flanders there are hundreds of homesteads in desperate need of renovation. Within the framework of the Europe 2020 objectives both CO2 emission and energy use need to be reduced with 20% by 2020. Unlike for the average residential building renovation, focus lies on synergy between respect to heritage and achieving an optimal energetic effectiveness. The object of this research is a case study homestead in Bruges, named the Schipjes. The first step in energy efficient renovation is to lower energy use by optimizing the building physics, therefore dynamic simulations in Modelica are performed to evaluate primary energy demand, especially for heating, and thermal comfort. The second step is the choice of the most energy efficient technical installations for a district heating system as will be used for Schipjes. Five different scenarios or combinations of heat production and distribution systems are developed as input options for future research simulations and energetic equations in Modelica

Building fault detection data to aid diagnostic algorithm creation and performance testing.

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions