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The variation in the cyclic performance of air conditioning systems (conforming with the latest rating standards) is investigated. Two code testers of very different sizes but meeting the standards’ requirements are used to evaluate the cyclic degradation coefficient (Cd) for the same air conditioner. The measured Cd from the two code testers shows a deviation of about 20%. A validated Modelica model is used to analyze the code testers’ thermal inertia as it is suspected to be a significant contributor. A new parameter called “thermal inertia factor” is defined to account for these thermal inertia effects on the measured Cd. However, all the deviations in Cd are not resolved at this point. The measurement uncertainty is simulated with the Monte Carlo method and discovered to be a critical contributor to the Cd deviation. The current study is the first of its kind and is expected to facilitate improved test procedures by accounting for the variations arising from differences in code tester designs at different laboratories.

The model-based system engineering approach consists of assembling subsystems together to model a complete system. In this context, some functional blocks can have a considerable influence on the overall behaviour of the system. A preliminary identification of the influence of the subsystems on the output responses can help reducing the complexity of the overall system, with a negligible impact on the overall accuracy. Therefore, pertinent indicators must be introduced to achieve this goal. To this purpose, in this work, some well-established methods and algorithms for global sensitivity analysis (GSA) of linear and non-linear systems with independent input variables, i.e., approaches based on Sobol’s indices (different algorithms are considered), and Shapley’s effect, are compared on both benchmark functions and real-world engineering problems.Specifically, in this paper, real-world engineering problems dealing with linear and non-linear systems are modelled through commercial finite element software and/or dedicated programming languages for solving complex non-linear dynamics models, like Modelica. Regarding Modelica models, an efficient strategy based on functional mock-up units is presented to speed up the simulation of highly non-linear dynamic systems. All numerical models are interfaced with the algorithms used for GSA through ad-hoc routines coded in Python environment. For each problem, a systematic comparison between the results provided by the different algorithms making use of Sobol’s indices and Shapley’s indices is performed, in terms of reliability, accuracy and computational costs.

This paper describes new additions to the Rapid Parameter Identification Toolbox (RaPId), which is an open-source MATLAB toolbox for parameter estimation using models developed with the Modelica language and exported with the functional mock-up interface (FMI) Standard. These additions include an updated graphical user interface (GUI), an optimization method utilizing multiple starting points for a gradient descent optimization, and examples for different cyber-physical system applications such as the Duffing-Holmes equation modeling in a form of electrical circuit and a hydroelectric power plant modeling.

Solar and wind energy are the renewable energy sources that have the great potential to fulfill the electricity needs of the world. They are available in abundance and also while generating electricity, they produce little or no global warming emissions. A lot of work has been carried out in the literature in the field of solar and wind energy systems. This has been taught as courses in various universities. 3DEXPERIENCE platform of Dassault Systemes has an integrated mathematical Modelling application known as Dymola. This integrated Dymola software allows solving complex multi-domain system modeling and analysis by using modelica and simulation technology. In this work, a virtual lab module for wind and solar PV systems have been designed and developed in 3DEXPERIENCE platform. The characteristic Plots of solar PV system for various configurations are plotted and effect of partial shading is also analyzed. The variation of mechanical power of wind system for various pitch angles are plotted.

The development of the industrial steam heating system has made power and thermal system more closely linked. Accordingly, the use of the steam network’s energy storage capability to improve the rapid load change capacity of thermal plants has become a new topic. The industrial steam heating system (ISHS) contains a large number of pipes and heat exchange equipment. The key is to understand the energy storage capability of the system by analogy and quantitative study. This study carries out the heat storage capability analysis of the industrial steam heating system through dynamic modeling. This study builds an actual pipe model in a chemical park by Modelica language and validates the model against field data collected on the site. The model deviation is less than 3%. Based on the validated model, this study carries out heat storage and discharge characteristics quantification by investigating three basic regulation modes: temperature regulation mode (quality regulation), the mass flow rate regulation mode (quantity regulation), and the hybrid mode. Through comparison, this study finds that the steam heating system has the highest analogized storage volume and longest heating discharge time under hybrid mode. From the perspective of utilizing system heating storage characteristics and saving the investment, hybrid regulation mode outperforms quality and quantity regulation mode. ISHS with hybrid mode has the potential to participate in short-term coordination control of electricity and heat in industrial parks.

The cascade refrigeration system and cascade air source heat pump have both been a well-known advanced technology for providing cooling and heating under certain circumstances. The intermediate temperature has been proven as a key factor in affecting the operating performance for both the refrigeration and heat pump system. However, the optimal intermediate temperature varies along with the change of the ambient temperature, cooling/heating demand. So achieving the real-time optimal intermediate temperature in a realistic operating circumstance is critical for improving the performance of cascade refrigeration or heat pump system. In this paper, the extreme seeking control strategy is proposed to optimize the coefficient of performance via seeking the real-time optimal intermediate temperature. A cascade refrigeration system model and a cascade air source heat pump water heater model are built based on Modelica, and the ASHP water heater model is validated by experimental data. The simulations are conducted under fixed and variable operating conditions to validate the effectiveness of the proposed strategy.

Future launch vehicle concepts and technologies for expendable and reusable launch vehicles are currently investigated by the DLR research projects Akira and X-tras . In particular, the winged Liquid Fly-back Booster concept Lfbb based on an LOX/LH2 propellant combination for vertical takeoff and vertical landing (VTVL), as well as the delta-winged horizontal takeoff and horizontal landing (HTHL) concept Aurora based on an LOX/Kerosene propellant combination are considered in these projects. Because of the complexity and risks involved in on-line trajectory optimization, off-line reference trajectories are still considered important for tracking purposes. In that sense, the goal of this paper is to investigate an off-line and general-purpose guidance and control (G&C) architecture for preliminary studies of reusable launch vehicles. This is done by using trajectory optimization combined with Modelica models for the generation of optimal guidance commands, and then trajectory tracking is performed by means of inner-loop feedback controls in terms of nonlinear dynamic inversion with prescribed desired dynamics. We showcase the advantages of this baseline G&C architecture in terms of early stability and controllability aspects during the preliminary design studies of an example configuration of a reusable launch vehicle investigated in the context of the research projects above mentioned.

Wiped film evaporators (WFE) or agitated thin film evaporators are efficient equipment for separating highly thermosensitive and viscous mixtures. There are different approaches to tailor residence time distribution (RTD) for specific applications and to maximise heat and mass transfer by equipment design or process parameter adjustment, like liquid load or wiper speed. However, the fundamental principles of fluid dynamics are only known to a limited extent. Although flow patterns and mass and heat transfer are highly interdependent, the effects of different wipers on liquid flow and, finally, on residence time behaviour are poorly understood. In this work, a new model describing fluid dynamics and its impact on RTD is presented. The model is implemented in the equation-based modelling language Modelica. Several system variables – e.g. film thickness, hold-up or velocity profiles within the liquid film – can be estimated. The effect of varying process parameters, like liquid load and fluid temperature, can also be evaluated. The model has been calibrated with results from RTD experiments in a WFE using a roller wiper. It is capable of describing the residence time behaviour of the roller wiper system with good accuracy. Mean residence times can be reproduced within approx. ±20%. Further results, such as film thickness or hold-up, provide additional insight into the fluid dynamics of characteristic regions – like bow wave, wiping and thin film zone – and the effects of several process parameters.

Reduced models enable real-time optimization of large-scale processes. We propose a reduced model of distillation columns based on multicomponent nonlinear wave propagation (Kienle 2000). We use a nonlinear wave equation in dynamic mass and energy balances. We thus combine the ideas of compartment modeling and wave propagation. In contrast to existing reduced column models based on nonlinear wave propagation, our model deploys a hydraulic correlation. This enables the column holdup to change as load varies. The model parameters can be estimated solely based on steady-state data. The new transient wave propagation model can be used as a controller model for flexible process operation including load changes. To demonstrate this, we implement full-order and reduced dynamic models of an air separation process and multi-component distillation column in Modelica. We use the open-source framework DyOS for the dynamic optimizations and an Extended Kalman Filter for state estimation. We apply the reduced model in-silico in open-loop forward simulations as well as in several open- and closed-loop optimization and control case studies, and analyze the resulting computational speed-up compared to using full-order stage-by-stage column models. The first case study deals with tracking control of a single air separation distillation column, whereas the second one addresses economic model predictive control of an entire air separation process. The reduced model is able to adequately capture the transient column behavior. Compared to the full-order model, the reduced model achieves highly accurate profiles for the manipulated variables, while the optimizations with the reduced model are significantly faster, achieving more than 95% CPU time reduction in the closed-loop simulation and more than 96% in the open-loop optimizations. This enables the real-time capability of the reduced model in process optimization and control.

Future concepts and key technologies for reusable launch vehicles are currently investigated by the DLR project Akira , focusing on vertical takeoff and horizontal landing (VTHL), as well as horizontal takeoff and horizontal landing (HTHL) concepts. Dedicated developments of multidisciplinary frameworks for launch vehicle modeling and preliminary design optimization have been presented in the relevant literature. These activities are often performed by several independent and discipline-specific tools; such an approach can only account for limited interactions of the involved disciplines with the overall system dynamics. Therefore, the objective of this paper is to focus on a multidisciplinary launch vehicle dynamics modeling, guidance, and control framework to support reusable launch vehicle design activities at DLR while taking into account the highly interconnected disciplines involved and changing environmental conditions. The modeling framework is based on the object-oriented, multidisciplinary, and equation-based modeling language Modelica . Dedicated 3-DOF and 6-DOF model implementations, covering the kinematics and dynamics formulation, environmental effects, aerodynamics, and propulsion models are presented. Within this framework, a method to obtain a direct connection between 3-DOF and 6-DOF models is shown. This is done by considering results from the trajectory optimization package ‘trajOpt’ in combination with nonlinear 6-DOF inverse models obtained automatically by Modelica . Angular rates and resulting moments can be retrieved by this intermediate 6-DOF modeling approach for subsequent controllability studies. We discuss some of these benefits in terms of nonlinear flight control simulations for an HTHL reusable launch vehicle concept.

To achieve further substantial reductions in the cost of concentrating solar-thermal power systems, recent efforts have been directed towards changing the working fluid in the receiver, adopting new approaches to thermal energy storage, and incorporating higher-efficiency power cycles. In this study, we present a novel CSP system configuration under development in ASTRI research program in Australia, incorporating a tubular sodium receiver, a high-temperature phase-change material storage unit, and a supercritical CO2 power block. The model is implemented using the SolarTherm framework in the Modelica language, and simulated using Dymola software. The novel system was compared to a ‘reference’ system with a molten salt receiver, two-tank storage, and subcritical steam Rankine cycle. For verification, the reference system was also modelled using the System Advisor Model software, and differences of 1% in annual receiver output and 2% in net electrical output were achieved when comparing Modelica and SAM simulations, despite difference operating strategies and time resolution. The results of the analysis of the novel system showed an improvement of annual solar-to-electricity efficiency to 19.6%, up from 15.6% for the reference system. This was achieved as a result of higher power cycle efficiency, despite the reduction in optical and receiver efficiencies resulting from the higher working fluid temperature. The ASTRI system was economically optimised for financial performance using 2012 as the basis year, and reached a minimum levelised cost of electricity of 11.42 ¢AUD/kWh for a solar multiple of 2.2 and storage capacity of 8–9 h at the location of Alice Springs, Australia.

In recent years, increasing the energy efficiency of the building has become one of the objectives of facility managers. In this sense, monitoring-based commissioning and automated fault detection have demonstrated to be effective ways to reduce the overall building energy consumption. Machine learning strategies provide accurate results in the field, but in many applications, the historical data available, especially related to faults, is just not enough. Therefore, the use of a physics-based model of the facility can be useful to generate synthetic data for a fault detection scheme. or be used for the monitoring of the system. However, the calibration of these models is usually done manually based on domain knowledge of the modeller and detailed information about the facility. In this study, a methodology for the calibration of an integrated model with the coupled interaction between the building and HVAC dynamics using Heteroscedastic Evolutionary Bayesian Optimization (HEBO) is presented. The proposed framework is demonstrated in a real use case by simultaneously calibrating 45 different parameters for a Modelica model of an air-handling unit coupled to a building using 15 days of data gathered from the building management system. The results obtained show accurate predictions when compared to the real measured data, with a temperature CV(RMSE) error of 0.38% and a MAE below 1 °C, which is under the common value found in other studies. Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles

International audience; Modern modeling languages for general physical systems, such as Modelica, Amesim, or Simscape, rely on Differential Algebraic Equations (DAEs), i.e., constraints of the form f(x′,x,u) = 0. This drastically facilitates modeling from first principles of the physics, as well as the reuse of models. In this paper, we develop the mathematical theory needed to establish the development of compilers and tools for DAE-based physical modeling languages on solid mathematical bases. Unlike Ordinary Differential Equations (ODEs, of the form x′ = f(x,u)), DAEs exhibit subtle issues because of the notion of differentiation index and related latent equations-ODEs are DAEs of index zero, for which no latent equation needs to be considered. Prior to generating execution code and calling solvers, the compilation of such languages requires a nontrivial structural analysis step that reduces the differentiation index to a level acceptable by DAE solvers. The models supported by tools of the Modelica class involve multiple modes, with mode-dependent DAE based dynamics and state-dependent mode switching. However, multimode DAEs are much more difficult to handle than DAEs, especially because of the events of mode change. Unfortunately, the large literature devoted to the mathematical analysis of DAEs does not cover the multimode case, typically saying nothing about mode changes. This lack of foundations causes numerous difficulties to the existing modeling tools. Some models are well handled, others are not, with no clear boundary between the two classes. In this paper, we develop a comprehensive mathematical approach supporting compilation and code generation for this class of languages. Its core is the structural analysis of multimode DAE systems. As a byproduct of this structural analysis, we propose sound criteria for accepting or rejecting multimode models. Our mathematical development relies on nonstandard analysis, which allows us to cast hybrid system dynamics to discrete-time dynamics with infinitesimal step size, thus providing a uniform framework for handling both continuous dynamics and mode change events.; Les langages modernes de modélisation de systèmes physiques, tels que Modelica, Amesim, ou Simscape, s'appuient sur des Equations Algébro-Différentielles (DAE), qui sont des contraintes de la forme f(x;x;u) = 0. Cette approche rend naturelle la modélisation directe à partir des principes de la physique, ainsi que la réutilisation de modèles. Dans cet article, nous développons les bases mathématiques qui fondent ces langages. Par rapport aux Equations Différentielles Ordinaires (ODE, de la forme x'=g(x,u)), les DAE sont plus compliquées, en raison des notions d'index et d'équations latentes -- les ODE sont des DAE d'index zéro, sans équations latentes. Avant toute génération de code, une analyse structurelle des modèles est requise, afin de ramener l'index à des valeurs acceptables pour les solveurs de DAE (deux ou trois maximum). Les modèles acceptés par les langages de la classe Modelica sont des DAE multi-modes, avec une DAE différente dans chaque mode, tandis que les changements de mode sont provoqués par des conditions portant sur l'état et/ou le temps. Les DAE multi-mode sont beaucoup plus difficiles à traiter que les DAE (mono-mode). La difficulté principale est le traitement des changements de mode et des conditions de redémarrage associées. Ce point n'est pas abordé dans la littérature mathématique sur les DAE multi-mode: rien n'est dit sur comment les changements de mode doivent être traités. Cette situation est source de problèmes importants pour tous les outils existants.Certains modèles sont correctement traités, d'autres non, sans qu'on sache bien pourquoi -- et les cas problématiques ne sont pas pathologiques, mais se rencontrent naturellement. Dans ce travail, nous développons une approche cohérente et complète pour la compilation des DAE multi-mode. Au coeur de celle-ci se trouve une analyse structurelle multi-mode, qui couvre à la fois les dynamiques dans les divers modes, et les changements de mode. Notre approche traite des modèles présentant des valeurs impulsives pour certaines variables, lors des changements de mode. Notre approche permet d'accepter ou de rejeter des modèles sur des critères bien fondés (modèle sur- ou sous-déterminé). Ces travaux eussent été impossibles sans le recours à l'analyse non-standard. Grâce à elle, nous pouvons réinterpréter les dynamiques en temps continu comme du temps discret à pas infinitésimal, ce qui fournit une vision uniforme de toute la trajectoire -- enmode continu ou en changement de mode.

To facilitate the integration of buildings to the power grid, battery-equivalent models have been proposed to characterize and quantify aggregate flexibility from building loads. An analytical method has been proposed to construct such a flexibility model for thermostatically controlled loads. Since this analytical method is derived from a simplified representation of building thermal dynamics, it is necessary to evaluate the resulting flexibility model in a more realistic context. This paper focuses on the validation of this analytical method for estimating aggregate flexibility from residential air conditioning (ACs) systems. A high-fidelity model is first developed to mimic thermal behaviors of a residential AC system through a co-simulation between Modelica and EnergyPlus. A population of virtual residential AC systems is then generated by randomizing parameters of the high-fidelity model. Finally, an aggregate flexibility model is constructed for the residential AC population using the analytical method and validated using results from simulation of the high-fidelity model. It is found that the flexibility estimated using the analytical method is with reasonable accuracy, but can be improved by explicitly incorporating heat gains into the method.

In this paper, we introduce a newly developed open source data center package in the Modelica Buildings library to support modeling and simulation of cooling and control systems of data centers. The data center package contains major thermal and control component models, such as Computer Room Air Handler, Computer Room Air Conditioner, models of different subsystem configurations such as chillers with differently configured waterside economizers, as well as templates for different systems. Two case studies based on this package are performed to investigate the performances of the cooling and electrical system under normal conditions and emergency situations such as a blackout: one is for a data center powered by conventional energy, and the other is for a data center powered by renewable energy. Simulation results show that the dynamic modeling and multi-domain simulation in the Modelica-based tool make it convenient for users to investigate both normal and emergent operations for conventional and renewable data centers.

Model predictive control (MPC), although considered a high-potential control approach, usually requires considerable effort for model-creation and parametrization. Moreover, many models can be too computationally intensive for control applications. Distributed model predictive control (DMPC) is a promising approach that avoids the construction of a complex model of the total system and thus facilitates modeling and supports the use of exact simulation models. DMPC divides the optimization problem into sub-problems, with the advantage that pre-fabricated simulation models from standard libraries, models provided by component manufacturers or purely data-driven models can be used. Moreover, each optimization problem, considered for its own, becomes smaller and, in whole, can be solved faster compared to an integrated system model. The distributed optimizations must be coordinated to achieve near-global-optimum performance. This coordination requires a suitable scheme, which, in many publications, is based on iterative data exchange between the subsystems. In previous works, we developed a non-iterative algorithm based on the exchange of lookup tables. In this paper, we compare and benchmark the previously developed approach against a second iterative approach to show advantages and limitations of both algorithms. We apply both approaches to the Modelica simulation model air-handling unit while using artificial neural network models and a non-linear solver for the DMPC algorithms. We make simplifying assumptions to provide mathematical justification regarding the optimality of the approaches. Judging from the indicators for control quality and the monetary operation costs in the case study, we conclude that the algorithms hold high potential for the application in generic building energy systems.

Model-based optimization can help improve the indoor thermal comfort and energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems. The models used in previous optimization studies either omit the dynamic interaction between indoor airflow and HVAC or are too slow for model-based optimization. To address this limitation, we propose an optimization methodology using coupled simulation of the airflow and HVAC that captures the dynamics of both systems. We implement an optimization platform using the coupled models of a coarse grid Fast Fluid Dynamics model for indoor airflow and Modelica models for HVAC which is linked to the GenOpt optimization engine. Then, we demonstrate the new optimization platform by studying the optimal thermostat placement in a typical office room with a VAV terminal box in the design phase. After validating the model, we perform an optimization study, in which the VAV terminal box is dynamically controlled, and find that our optimization platform can determine the optimal location of thermostat to achieve either best thermal comfort or least energy consumption, or the combined. Finally, the time cost for performing such optimization study is about 6.2 h, which is acceptable in the design phase.

A detailed model of a post-combustion CO2 capture plant (pcc-plant) is developed in the Modelica® language which allows the simulation of start-up and shutdown processes. The model of the pcc-plant is coupled to a simplified model of a coal fired power plant to investigate the coupled process. To guarantee a stable and automatic operation, a sequential control for start-up and shutdown procedures is implemented in both plants. Two case studies, focussing on the pcc-plant, are subsequently performed in this contribution. In the first case study, the influence of different parameters and input variables on the start-up time is investigated in order to analyze the start-up process of the pcc-plant. The varied variables are the steam flow rate to the reboiler, the solvent flow rate and the total amount of solvent in the pcc-plant. In the second case study two different start-up control structures for the carbon capture plant are proposed and their performances are presented and compared.

This paper aims to reveal the coupling correlation of the ORC related system, and verify the boundaries and feasibilities of the system-level modeling. A kW-scale ORC test rig with a self-made expansion valve is set up for the dynamic test under three common freedoms, namely the mass flow rate of working fluid, heat source and cooling source. By imposing the same real-time experimental conditions, the system-level dynamic simulation is performed on the Modelica/Dymola software. The test rig and dynamic model are of high-accuracy and reliable after examining the heat balances and error propagation of the measurement uncertainties, as well as calibrating with theoretical analysis. Results show that the internal disturbance of mass flow rate of working fluid significantly affect the states parameters. The external disturbance of mass flow rate of cooling source has a less effect on the states parameter, while the states parameters are insensitive to the external disturbance of mass flow rate of heat source. The simulation results are in agreement with the experimental results. The average overshoot of the outlet temperature of cooling source is 0.7 °C, and the average overshoot of the evaporation pressure is 48 kPa, which coincide with the results from the experiment under the disturbance of working fluid mass flow rate. It shows the potential of such simulation model to be a guided model, which also predicts that the maximum desired power output of the ORC test rig is about 1874.7 W, with the thermal efficiency of 6.94%.

A large part of energy usage in buildings occurs during the operational phase, emphasising the need for efficient and improved facility management, operation and control. Model Predictive Control (MPC) or Fault Detection and Diagnosis (FDD) are among the strategies that allow minimising energy use and costs during operation. However, the need for fast and accurate dynamic models (e.g. grey box model), which are time-consuming and challenging to implement, precludes their systematic integration in the built environment. A typical grey-box modelling approach consists of manually implementing several grey-box model structures with an increasing level of complexity before performing a forward selection procedure to identify the optimal configuration. The link between the different grey-box models and the monitored data is also established manually. Such an approach can be both time-consuming and error-prone and involves a significant cost that hampers the broad adoption of strategies such as MPC and FDD. This study proposes a tool-chain that uses BIM to automatically generate several grey-box structures with added complexities stemming from the specific geometry and design of the building. More specifically, an existing rule-based IFC to Modelica interface is extended to automatically create several Modelica-based grey box models that gradually take into account the building’s specific information and characteristics. Additional rules are also proposed to automate the connection between the models and the building monitoring system. As a forward selection approach, a multi-objective optimisation using the NSGA-2 algorithm is adopted. The application of the tool-chain on two case studies shows that the integration of BIM to automate the implementation of grey box models, not only reduces the human involvement in the modelling process but can also produce more accurate models. Besides, this study shows that the use of multi-objective optimisation with datasets from two different seasons results in models that are valid for all seasons. ispartof: Energy And Buildings vol:188 pages:209-225 status: published

Nowadays, a large part of the scientific community efforts, dedicated to Concentrated Solar Power (CSP) technologies, are focused on improving the efficiency and reducing the costs. This work presents a flexible, highly customizable, dynamic model that simulates the transient thermal behavior of a solar field in order to use it for optimization, performance evaluation and control tasks purposes. The model is based on the detailed physical principles described in a previously paper which has been extended with an exergetic approach, recommendation made by the author of the previous paper in order to improve the model. The model has been developed using the ThermoCycle library and implemented in the equation-based object-oriented modeling language Modelica. An experimental campaign has been carried out in a real facility in order to calibrate and validate the model. Energetic and exergetic analysis are also presented in this work.

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

An innovative personal conditioning system called Roving Comforter (RoCo) comprises of a miniature vapor compression cycle (VCC), storing its condenser heat in a phase change material (PCM) thermal storage with helical refrigerant tubes. In the present study, we analyze its system performance with different PCM by using a validated dynamic model in Modelica®. Mathematical equations and programming decisions for implementation of PCM model in Modelica are discussed. Higher melting temperature of PCM results in higher power consumption during cooling operation, but reduced power consumption during recharge operation (solidification of PCM). Thus, it is necessary to identify the appropriate temperature of PCM that maximizes its coefficient of performance (COP), since RoCo operates with alternating cooling and recharge operations. The PCM melting and solidification from the helical coil geometry is captured with empirical correlations obtained from experiment. Various regimes observed in these asymmetric processes are discussed. An increase of 11% in the COP is observed. The article highlights modeling details and interesting insights of system performance of a vapor compression cycle integrated with PCM, which are increasingly being developed in recent times.

Water-to-air finned-tube heat exchanger (FTHE) is a common component in air-conditioning systems. Mathematical models of water-to-air FTHE under the wet-cooling condition are necessary in evaluating the performance of air-conditioning system with water-to-air FTHEs during the system design phase. However, existing water-to-air FTHE models are computationally expensive and require detailed geometric data, which hinder the model applications during the system design phase. To address the above limitations, the current paper proposes a new water-to-air FTHE model which is computationally efficient, relatively accurate and only requires nominal data as inputs. The new water-to-air FTHE model is derived using wet-dry transformation method and the heat transfer process is calculated using the nominal data. Then the model is implemented in Modelica, which is an equation-based, object-oriented modeling language. In addition, experimental measurements of a water-to-air FTHE are conducted. The new model is then evaluated by experimental data and an existing model. The results show that the relative deviations of outlet temperatures and heat transfer rate between the modeled and experimental data are within 7% and 11%, respectively, which is much better than the existing model (19% and 13%). In addition, the new model is 1047 times faster than the existing model.

In general, a hardware-in-the-loop (HIL) building simulation has lower cost and fewer practical limitations (e.g., scheduling issues) than field tests in occupied buildings, while also overcoming limitations of simulations alone by capturing the full behavior of some physical systems, equipment, and components. However, the implementation of an HIL can be difficult due to the scarcity of appropriate tools. This paper presents an agent-based framework for HIL simulation. It can be used for investigation of controller performance via controller-in-the-loop simulations and also HIL for system synthesis. In the latter case, both controllers and major equipment participate in tests to ensure that dynamics of equipment operation are correctly captured in addition to controller performance. The HIL simulation framework presented allows such actual physical parts to be included in the framework while representing others for which behaviors are better known and modeled in simulation models. The mechanism implemented in the framework to synchronize simulations in software with real-time operation of physical equipment is described. As an example, use of the HIL simulation framework is illustrated through a brief study of speed control of the supply fan in the air handling unit of a variable-air-volume building heating, ventilating and air-conditioning system.

© 2018 Elsevier B.V. The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers’ poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts’ involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components’ control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems. ispartof: Automation in Construction vol:96 pages:508-526 status: published

A complex system is composed of several subsystems and numerous lower level components. It is inefficient to design the complex system at the system level by using high-fidelity models. A system level approach using a language-based model is required to design such a highly complex system before implementing a detailed design. However, as commercial process integration and design optimization software packages focus on detailed design, which uses the high-fidelity models, it is difficult to perform the design optimization of a highly complex system, such as a combat vehicle. Moreover, as commercial optimization software packages use numerical computation, the gradient calculation cost can increase, and the matrix computation process can be inefficient in terms of optimization time. Therefore, in this study, an integrated interface for efficient design optimization was developed to focus on concept design using a MODELICA language-based model. Additionally, the design variable screening by using analysis of variance, surrogate modeling through sequential design of experiments, and symbolic computation were used to solve the aforementioned problems. These were applied to the design optimization of the combat vehicle system to demonstrate the effectiveness of the integrated interface and symbolic computation. In conclusion, a concept design utilizing a MODELICA language-based model was achieved, and the optimization time achieved by symbolic computation was largely reduced in comparison to the optimization time achieved by numerical computation.

Modern building complexes have simultaneous heating and cooling demands. Therefore, integrated energy systems with heat pumps and long-term thermal storage are a promising solution. An integrated heating and cooling system for a building complex in Oslo, Norway was analyzed in this study. The main components of the system were heat pumps, solar thermal collectors, storage tanks, ice thermal energy storage, and borehole thermal energy storage. Dynamic simulation models were developed in Modelica with focus on the long-term thermal energy storage. One year measurement data was used to calibrate the system model and two COPs were defined to evaluate system performance. The simulation results showed that more heat had to be extracted from the long-term thermal storage during winter than could be injected during summer. This imbalance led to a decrease in ground temperature (3 °C after 5 years) and decreasing long-term performance of the system: both COPs decreased by 10% within five years. This performance decrease could be avoided by increasing the number of solar collectors from 140 to 830 or by importing more heat from the local district heating system. Both measures led to sustainable operation with a balanced long-term thermal storage.

Wind power generation research and application technology have received much attention in the development of renewable energy. However, the traditional blade-rotating type wind power system has a number of drawbacks such as natural landscaping damage, flow-induced noise, and shadow flickering problems. In this paper, we propose a lift-generating disk-type blade power generation mechanism that can effectively generate wind power even with a simple structure considering the problems of the existing systems. Data on the lift force in relation to the shape of the designed blade were derived through a computational fluid dynamics simulation, and the Modelica language was used to model the integrated multi-physics wind power system. Then, a wind tunnel test was conducted using a small-scale model of the disk-type blade created to verify the simulation. The experimental results were in good agreement with the simulated results. Thus, we validated the modeling of the wind power system and applied the law of similarity to obtain the generator power output prediction results for the actual scale model.

This article presents a simulation-based study that estimates the primary energy use of a novel HVAC system for different configurations of a thermal plant. The main characteristic of the system is its ability to provide simultaneous heating and cooling to buildings by using a single hydronic circuit with water temperatures of about 22 °C all year round. Four different configurations of the thermal plant were analyzed. A district heating-cooling network was considered as baseline. Other configurations included the use of a reversible air-to-water heat pump coupled with free cooling devices. In particular, a model of a PCM-based heat exchanger was developed in this work by using the programming language Modelica. This device was designed to store cold energy during night-time and release it during daytime through the water circuit. Results for a typical office building model showed that the integration of free cooling devices can significantly reduce the primary energy use of the novel HVAC system. In particular, the thermal plant configuration including the PCM-based heat exchanger made it possible to almost completely avoid the use of mechanical cooling, leading to annual primary energy savings of about 67% when compared with the baseline thermal plant configuration.

This study presents a model-based dynamic optimization strategy for charging of hot and cold thermal storages with a dual-mode CO 2 ejector expansion heat pump, which could be used as a high-efficiency smart grid enabling option in heating and cooling services for buildings or industry. The dynamic model of this coupled system was developed by Modelica. The outlet water temperatures of hot and cold tanks are used as indicators in dynamic optimal control strategy. To optimize the overall performances during energy process, the transient performances are optimized by genetic algorithm based on dynamic model of system. Single-objective and multi-objective dynamic optimal control strategies were developed and applied for this system. Study results show that these two developed model-based dynamic optimal control strategies can optimize the overall performances of such coupled systems during energy charging. Compared with the control strategy with constant control parameters, these two dynamic optimal control strategies can be useful in the coordinative optimization of multiple control parameters, and multi-objective dynamic optimal control strategy is superior to the single-objective one.

In heating, ventilation and air-conditioning (HVAC) systems, air-to-air plate-fin heat exchangers (PFHEs) can be used as heat recovery devices to reduce the building energy consumption. However, existing heat exchanger models have limitations in simulating the performance of air-to-air PFHEs. For example, some models adopt heat transfer correlations which are not suitable for PFHEs, while others require detailed geometric data which are usually difficult to access, etc. To address these limitations, we developed a new model for air-to-air PFHE without dehumidification. Based on empirical correlations dedicated to air-to-air PFHEs, the mathematical models of the heat transfer and the flow resistance were built. The new model considers the impacts of the changing air flow rates and temperatures. Additionally, it only requires readily available nominal parameters as inputs and does not need any geometric data. Furthermore, no numerical discretization is needed to solve the equations, which makes the model computationally more efficient than models using the finite-element method. To evaluate the performance of the new model, it is implemented using an object-oriented, equation-based modeling language Modelica. Case studies show that the new model can predict the results with a relative deviation less than 10% compared to the experimental data.

The design of future district heating network will focus on two major aspects: The first of which is boosting the share of renewable heat in overall energy mix, and the second of which is to achieve lower network operating temperatures, thus increasing the potential for integrating new alternative heat sources. Renewable heat sources by their nature are highly fluctuating and require thermal storage and sophisticated control strategies to be utilized efficiently. Static modelling and simulation tools are incapable of capturing the dynamic effects and necessary control strategies that come with such complex systems. The work in this paper will outline how Dymola, a Modelica based simulation tool is capable of handling such complexity. The first section will outline an automated workflow to translate CAD drawings into functioning Dymola models. The next sections will upgrade a small heating network model to a future system with the addition of a large thermal storage tank, solar collectors and prosumers. The last section will demonstrate a concept to achieve lower network operating temperatures by simulating the network with a branch supplied by the return pipeline. The addition of the storage tank and solar showed an increase of the combined biomass and solar from 82% to 93% over the reference time frame. The second use case demonstrated a 12% reduction in network heat losses for a given week by implementing the lower temperature branch.

Generic model-assisted control algorithms for building energy systems achieve an energy-efficient building operation while the implementation effort is lower than in the case of a taylor-made automation system. In this paper, a generic distributed simulation-model-assisted and exergy-based control algorithm is presented, which is applicable to typical building energy systems (BES). It exploits dynamic nonlinear simulation models from an open-source Modelica library and makes use of sequential neighbor communication. In the supply chains of a BES, the algorithm performs multiple optimizations of small subsystems by considering pre-defined values of pre-defined coupling variables. The objective function is the sum of the exergy destruction and loss, which is based only on measurements without additional information such as monetary costs. In a simulation case study, an open-loop and a closed-loop version of the algorithm are compared to a control system based on rules and feedback controllers that was optimized manually for this particular scenario to provide a benchmark. The tracking error of the closed-loop algorithm is close to the benchmark. The open-loop algorithm achieves an energy consumption close to the benchmark. These results show that the algorithm finds suitable solutions automatically whereas the rule-based control system cannot be expected to perform equally well if boundary conditions were changed.

The introduction of a heat source water network system using heat from hot spring and drainage water for hot water supply in accommodation facilities in hot spring areas is expected to contribute to increased energy efficiency. In this study, the relationship between the temperature of the heat source water and the energy efficiency of heat pump water heaters introduced in hot spring facilities connected to a single-loop heat source water network was analyzed. The primary energy consumption of the heat source water network system was compared with that of a centralized heat supply system by conducting a dynamic simulation using Modelica in the Dymola environment. The yearly average coefficients of performance of the proposed heat source water network and centralized heat supply systems were estimated to be is 4.56 and 3.24, respectively. The seasonal performance of the proposed system was also investigated based on measurement data. In December, it was estimated that the heat source water network system would achieve a 17.6% reduction in primary energy consumption in comparison with the centralized heat supply system. However, in August, it was estimated that the primary energy consumption of the heat source water network system would be 8.9% higher than that of the centralized system.

This study presents a method used to simulate large-scale thermal models of cities that achieves two improvements compared to the state-of-the-art techniques: 1) Current state-of-the-art methods cannot simulate the dynamic interaction between subcomponents of a smart energy system at urban scale. This method proposes detailed dynamic simulation approaches for large-scale thermal models. 2) Currently applied co-simulation frameworks are not applicable to large-scale models. In the present study, the dynamic building simulation tool IDA Indoor Climate and Energy, which uses parallelization methods for large-scale models, is coupled with a co-simulation platform. The methods are applied to a semi-virtual case study, which consists of 1561 buildings and a new development area. The building stock is analyzed using an automated method based on publicly available data. In contrast, the virtual urban development area is investigated using a co-simulation framework with three dynamic simulation tools: IDA Indoor Climate and Energy for buildings (256 thermal zones and 29 heating systems), TRNSYS for the energy supply unit and Dymola/Modelica for the district heating network. The influence of co-simulation on the accuracy and on the computation time are investigated. The major finding of this study is that the computation time can be significantly reduced by decoupling methods.

In order to reduce the energy cost while improving the process operation, a study which aims to analyze the influence of the operational parameters variations and determine the optimal operating conditions of a pilot multi-effect desalination system (MED) at CIEMAT-Plataforma Solar de Almeriia (PSA) has been performed. An equation-based object-oriented mathematical model of the experimental MED plant, implemented using the modeling language Modelica and previously developed, calibrated and validated, has been adapted to carry out this study. Firstly, an energetic and exergetic analysis of the process under nominal conditions has been carried out, revealing the key sources of energy and exergy consumption. On the one hand, the thermal energy contained in the mass outflows are the main responsible source of the high energy consumption, on the other hand the entropy generation and the heat exchanged with the environment are the responsible of the exergy degradation. Secondly, a study on the influence of the operational parameters shows that the production of the real plant under nominal conditions is far from the maximum simulated values and some operational parameters have not a great influence in the process with respect to the rest. Finally, using a genetic optimization algorithm implemented in the modeling tool, a optimization process taking into account different energy and exergy performance criteria, sets optimal operational set points.

Concentrating solar thermal power plants rely in thermal energy storage systems in order to provide a stable power supply. However, they might not been able to meet power plant demands, mainly because of their storage sizes which are restricted due to economic reasons. One way of mitigating this effect is to control in an optimal way the charging and discharging processes. For the design and validation of advanced control strategies, an accurate dynamic model is essential. For this reason, a dynamic thermal energy tank model intended to be used in concentrating solar thermal power plant models is presented in this paper. The developed tank model is validated in charging and discharging processes and also at rest state in order to validate thermal losses dynamics. Simulation results are compared against experimental data from the CIEMAT-PSA molten salt testing facility.

Modelica is a free, general-purpose object-oriented equation-based modeling language. It is mainly designed to describe systems using the physical modeling approach. Our proposal to describe Agent-Based Models (ABMs) in Modelica is discussed in this manuscript. The contribution of the presented work is twofold: firstly, to analyze the conceptual requirements to describe ABMs in Modelica; and secondly, to develop a prototype implementation following the previous analysis. Agents are described using a message passing communication mechanism previously proposed by the authors. Additional extensions to this mechanism are proposed in order to describe agent interactions. The environment, where the agents live, is described as a two-dimensional cellular automaton. A new Modelica library, named ABMLib, developed to support this functionality, is presented. A prototype implementation of the message passing mechanism and ABMLib models has been performed to demonstrate the functionality of the library as a proof-of-concept for this proposal. Fil: Sanz, Victorino. Universidad Nacional de Educación a Distancia; España Fil: Bergero, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina Fil: Urquia, Alfonso. Universidad Nacional de Educación a Distancia; España

Ground source heat pumps are attractive alternatives for both conventional heating and cooling systems because of their higher energy efficiencies. A ground source heat pump system consists of a conventional heat pump coupled with a ground heat exchanger where water or a water-antifreeze mixture exchanges heat with the ground. Several major design options are available for ground heat exchangers, which are classified by layout to open and closed loops. Closed loop tubing can be installed horizontally as a loop field in trenches or vertically as a series of long U-shapes in wells. Slinky-coil horizontal ground heat exchangers are a compromise between vertical ground heat exchangers and conventional horizontal ground heat exchangers. They combine the relatively small installation cost of conventional horizontal ground heat exchangers and the high heat exchange capacity of vertical ground heat exchangers. These traits make a slinky-coil horizontal ground heat exchanger a suitable alternative for space heating in areas with limitations on land space usage. However, caused by a lack of appropriate dimensioning tools, these systems are often undersized or overdesigned. In this research, the feasibility of modelling and simulation of slinky-coil geothermal heat exchangers in the modelling language Modelica has been demonstrated. A model of a slinky-coil horizontal ground heat exchanger has been developed to simulate the performance of such systems. The model includes a ground heat exchanger and a soil cell. The ground heat exchanger has been modelled in two levels of detail, one with coil-like pipes and one with thin plates instead of pipes. In order to be able to assess the validity of the model, it has been developed based on a field test in Japan, and simulated under the same conditions. Reasonably good agreement has been obtained between the numerical predictions and published experimental measurements, which demonstrates the reliability and accuracy of the model.

We evaluate an ambient, bidirectional thermal network, which uses a single circuit for both district heating and cooling. When in net more cooling is needed than heating, the system circulates from a central plant in one direction. When more heating is needed, the system circulates in the opposite direction. A large benefit of this design is that buildings can recover waste heat from each other directly. We analyze the thermodynamic performance of the bidirectional system. Because the bidirectional system represents the state-of-the-art in design for district systems, its peak energy efficiency represents an upper bound on the thermal performance of any district heating and cooling system. However, because any network has mechanical and thermal distribution losses, we develop a diversity criterion to understand when the bidirectional system may be a more energy-efficient alternative to modern individual-building systems. We show that a simple model of a low-density, high-distribution loss network is more efficient than aggregated individual buildings if there is at least 1 unit of cooling energy per 5.7 units of simultaneous heating energy (or vice versa). We apply this criterion to reference building profiles in three cities to look for promising clusters.

This work presents retQSS, a novel methodology for efficient modeling and simulation of particle systems in reticulated meshed geometries. On the simulation side, retQSS profits from the discrete-event nature of Quantized State System (QSS) methods, which enable efficient particle tracking algorithms that are agnostic of the application domain. On the modeling side, retQSS relies on the standardized Modelica modeling language, yielding compact and elegant specifications of hybrid (continuous/discrete) dynamic systems. Combined together, these features offer a sound, general-purpose framework for modeling and simulation of particle systems. We show how the state-events that arise when particles interact with a reticulated mesh are seamlessly translated into easily tractable time-events. The latter can substantially improve simulation performance in scenarios where discontinuities dominate the computational demand. We showcase the flexibility of our approach by addressing four case studies arising from different application domains. Performance studies revealed that retQSS can perform similarly to, and even outperform, well-known domain-specific particle simulation toolkits while offering a clear and sound accuracy control interface., Accepted for publication in Computer Physics Communications

This work is conducted within the framework of developing a control-oriented modelling and simulation tool focused on the study of the generation III + VVER-1200 plant transient dynamics, in order to conduct necessary stability analyses aimed at providing essential feedbacks required to perform the model-based control system design. An object-oriented modelling technique was applied, based on the reliable, validated and well-documented Modelica language. The pressurizer was modeled based on the Astrom and Bell non-equilibrium approach. The developed model was then adapted to predict the behavior of the VVER-1200 pressurizer free dynamics at different conditions, with different perturbations/excursions. The developed model has been successfully integrated inside a Modelica based VVER-1200 plant model and pressurizer transient response to plant different free dynamics was tested. Test and verification of the developed model were performed against multiple benchmarks, as well as a real reference plant. Good agreement was obtained. Test results has shown that the simulator has provided precise information regarding its steady state as well as transient behavior even for severe excursions. Uncertainty analysis was also performed and showed that the developed model is more sensitive to the input variables than the plant model parameters. The model is valid along the complete operating range, including run-up/down, fast runbacks, as well as violent transients. The produced model is non-linear, time variant, of low order and high-precision. The adopted technique is well suited to the task of performing assessed studies for system dynamics. It fills the gap and overcomes shortages and/or limitations associated with current control-oriented modeling techniques.

ASHRAE Guideline 36: High-performance sequences of operation (SOO) for Heating, Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30% energy under ideal simulation environments. However, HVAC systems are susceptible to various types of faults in a real building operation. There are no existing studies that pertain to a comprehensive fault impact analysis of the high-performance control sequences suggested by ASHRAE Guideline 36 for HVAC systems. How these sequences handle and adapt to the various types of faults is still largely unknown. In this context, a comprehensive fault impact analysis and robustness assessment of the high-performance control sequences is conducted. A Modelica-based medium office virtual testbed is developed following the air-side and the plant-side SOO. A total of 359 fault scenarios in three different seasonal operating conditions (cooling, shoulder, and heating seasons) are injected into the baseline model. The evaluated key performance indexes (KPIs) include the operational cost, source energy, site energy, control loop quality, thermal comfort, ventilation, and power system metrics. The faults of the most negative impact are identified for different seasonal operating conditions over all the KPIs. The results also show that high-performance control sequences are well adapted for the vast majority (∼90%) of all the fault scenarios over all the KPIs in this study.

Net-Zero Energy Communities (NZECs) are critical to assuring the sustainability and resilience of modernized power systems. System modeling helps overcome technical challenges in designing and operating NZECs. In this paper, we present an open-source NZEC virtual testbed in Modelica based on a real NZEC in Florida, USA. This testbed consists of two sets of models: (1) higher-fidelity physics-based models that consider the interaction between subsystems of the studied NZEC and capture fast dynamics, and (2) lower-fidelity data-driven models that require fewer resources to establish and/or run. All models are validated against measurements from this real NZEC. In addition, this testbed includes a simulation framework that streamlines the processes for simulation and thus allows the use of developed models to form a virtual testbed. To demonstrate the usage of the virtual testbed, a case study is conducted where a building-to-grid integration control is evaluated via simulation. The evaluation results suggest that the tested control significantly smooths the power draw of the studied community and does not sacrifice thermal comfort to a great extent.

Model calibration for building systems is a key step to achieving accurate and reliable predictions that reflect the dynamics of real systems under study. Calibration becomes particularly challenging when integrating building and HVAC dynamics, due to large-scale, nonlinear, and stiff underlying differential algebraic equations. In this paper, we describe a framework for calibrating multiple parameters of cou-pled building/HVAC models using scalable Bayesian optimization (BO), whose advantages include global optimization without requiring gradient information, and data-efficiency. The proposed methodology is improved online via two additional steps: domain tightening and domain slicing, both of which leverage the learned calibration cost function to reduce the search space volume and dimension, respectively. We demonstrate effectiveness of the proposed algorithm by simultaneously calibrating 17 parameters (in-cluding emissivities, heat transfer coefficients, and thickness of walls/floors) of a Modelica model of joint building and HVAC dynamics, with 2 weeks worth of building data. This high-dimensional calibration task is solved via our proposed scalable BO calibration method, and yields parameters that are > 90% accurate with < 1000 model simulations; additionally, the outputs of the final calibrated model on unseen testing data complies with standard ASHRAE calibration guidelines. (c) 2021 Elsevier B.V. All rights reserved.

Dynamic modelling of the faulty operation of chiller plants and boiler plants can help identify their impacts and support the development of fault detection methods. However, adequate models are seldom reported in the literature. In this study, we aim to develop high-fidelity models for approximating the dynamic behaviors of chiller plants and boiler plants under control-related faults. Specifically, we first designed a typical configuration of the chiller plants and the boiler plants; we then modeled both the physical systems and controllers of these typical plants with Modelica. When developing the Modelica models, we created a hierarchical model structure while modules in each layer can be redeclared and parameterized at upper layers. This model structure facilitates the implementation of fault scenarios through intuitive model modifiers. At last, we applied the proposed models in a comprehensive fault impact evaluation of the thirteen control-related faults of chiller and boiler plants. In this evaluation, the proposed models are coupled with the EnergyPlus TM thermal load model to study the impact of various faulty scenarios. Based on the fault impact evaluation results, we identified the faults that have the most significant impacts on the operation of the chiller and boiler plants, respectively. We also found that the relationship between the impacts of the studied faults and the severity level of the faults can be highly non-linear. This study contributes to the literature by providing the first dynamic models of chiller plants and boiler plants which can be used to study control-related faults on a large-scale.

Petri nets are a common method for modeling and simulation of systems biology application cases. Usually different Petri net concepts (e.g. discrete, hybrid, functional) are demanded depending on the purpose of the application cases. Modeling complex application cases requires a unification of those concepts, e.g. hybrid functional Petri nets (HFPN) and xHPN. Existing tools have certain limitations which motivated the extension of VANESA, an existing open-source editor for biological networks. The extension can be used to model, simulate, and visualize Petri nets based on the xHPN formalism. Moreover, it comprises additional functionality to support and help the user. Complex (kinetic) functions are syntactically analyzed and mathematically rendered. Based on syntax and given physical unit information, modeling errors are revealed. The numerical simulation is seamlessly integrated and executed in the background by the open-source simulation environment OpenModelica utilizing the Modelica library PNlib. Visualization of simulation results for places, transitions, and arcs are useful to investigate and understand the model and its dynamic behavior. The impact of single parameters can be revealed by comparing multiple simulation results. Simulation results, charts, and entire specification of the Petri net model as Latex file can be exported. All these features are shown in the demonstration case. The utilized Petri net formalism xHPN is fully specified and implemented in PNlib. This assures transparency, reliability, and comprehensible simulation results. Thus, the combination of VANESA and OpenModelica shape a unique open-source Petri net environment focusing on systems biology application cases. VANESA is available at: http://agbi.techfak.uni-bielefeld.de/vanesa. Copyright © 2021. Published by Elsevier B.V.