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Effective fault data is the basis for intelligent fault diagnosis. In actual engineering applications, it is often impossible to obtain sufficiently effective fault signals. Moreover, relying on fault data makes it impossible to design supporting fault diagnosis models for equipment that is being designed and manufactured. In response to the above problems, a method of using digital twin training data to drive high-fidelity fault diagnosis models was proposed. Taking rolling bearings as the research object, the above method was verified. First, a dynamic analysis was performed on the rolling bearing and a bearing model in three states was constructed. Subsequently, a twin model of the rolling bearing was built based on the Modelica language. The vibration signal was obtained by running the model and compared with the theoretical fault characteristic frequency to verify the accuracy of the high-fidelity rolling bearing twin model. Finally, the convolutional neural network (CNN) was trained using the data of the digital twin model and tested using experiments under various working conditions. The results show that the method proposed in this article can achieve higher accuracy and is effective. [ABSTRACT FROM AUTHOR]

This paper proposes a new modeling approach to obtain accurate and fast simulations of pulse width–modulated (PWM) inverters with minimal loss of information. The idea of this methodology combines the use of precise switched models and fast averaged models. Switched models are used during transient evolution, and averaged models on periodic steady-state (PSS) regimen. In this way, the switching behavior of an inverter can be obtained even in the context of a long-term simulation where switched models cannot be used because of the central processing unit (CPU) requirement. The novel modeling strategy implements an algorithm that, based on the evolution of some variables, detects the transient or periodic steady state condition and automatically performs the commutation between both models. Furthermore, the mentioned algorithm also estimates the averaged model parameters when the precise switched model is used. Consequently, it is not necessary to calculate the average parameters to create the model and accurate results are obtained regardless of the operating point. This paper also reports simulation experiments of a single-phase PWM boost inverter model to clarify the idea of the modeling strategy and shows the simulation time and accuracy advantages of the proposed modeling approach. The Modelica language was used to program the novel modeling strategy and build the boost inverter examples. [ABSTRACT FROM AUTHOR]

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Nuclear microreactors are a potential technology to provide heat and electricity for remote microgrids. There is potential for the microgrid on the island of El Hierro to use a microreactor, within an integrated energy system (IES), to generate electricity and provide desalinated water. This work proposes a workflow for optimizing and analyzing IESs for microgrids. In this study, an IES incorporating a microreactor, thermal energy storage (TES) system, combined heat and power plant, and a thermal desalination plant was designed, optimized, and analyzed using Idaho National Laboratory's Framework for Optimization of Resources and Economics (FORCE) toolset. The optimization tool, Holistic Energy Resource Optimization Network (HERON), was used to determine the optimal capacity sizes and dispatch for the reactor and thermal energy storage systems to meet demand. The optimized reactor and TES sizes were found to be 11.61 MWth and 58.47 MWhth, respectively, when optimizing the IES to replace 95% of the island's existing diesel generation needs. A dynamic model of the system was created in the Modelica language, using models from the HYBRID repository, to analyze and verify the dispatch from the optimizer. The dynamic model was able to meet the ramp rates while maintaining reactor power with minimal control adjustments. [ABSTRACT FROM AUTHOR]

The PSDF (photovoltaic, storage, direct current, and flexibility) energy system represents an innovative approach aimed at achieving carbon neutrality. This study focused on rural buildings and utilized Modelica to develop a dynamic simulation model of the PSDF system. The research introduced a framework for direct current distribution microgrid systems with flexible regulatory mechanisms, employing a virtual inertia control strategy to provide stable adjustments for flexible operations and support integration with local grids. Case simulation results indicated that the system equipped with a water tank saved 3.15 kWh compared to the system without a water tank, resulting in an energy savings rate of 22.14%. Compared to traditional photovoltaic systems, the PSDF system significantly enhanced energy management flexibility and system reliability through the integration of thermal storage and battery management. This research made significant contributions to the fields of renewable energy and building energy systems by offering a scalable and practical solution suitable for rural contexts. [ABSTRACT FROM AUTHOR]

For improving thermal efficiency of K-car engine under usual operating conditions, the authors explored the optimized engine parameters applying quality engineering tool for 0-D engine cycle simulation model. In this simulation study, the potential for improving thermal efficiency was clarified by exploring various engine geometrical parameters such as bore and stroke, compression ratio, connecting rod length and intake valve timing, etc. regardless of the engine displacement standard for K-cars. The results indicated that super high expansion ratio engine which has the piston swept volume beyond 660 cm3 significantly improved the thermal efficiency for K-car engines. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and photovoltaics (PVs) are expected to be broadly adopted in future power systems. However, the temporal variability of EV load and PV production presents challenges for integrating them into the power grid. This study evaluates and assesses the synergies between EVs and PV systems to maximize solar energy utilization for EV load coverage. The configurations studied include EV charging via the national grid as a reference case (Case 1) and two solar energy harvesting options: EVs powered directly by vehicle-mounted PVs (Case 2) and EV chargers connected to residential PV installations (Case 3). These cases are evaluated across different urban environments with large EV fleets and dissimilar weather conditions: Berlin and Los Angeles. A customized operation profile based on the worldwide harmonized light-duty test cycle (WLTC) and a charge-right-away (CRA) strategy is used. Energy performance analysis is conducted through dynamic simulations using the Modelica language, with environmental and economic indices derived. Key findings highlight the superior performance of residential PV systems in both cities compared to current solar EV technologies, with both solutions offering significant benefits over the reference case. Cases 2 and 3 result in a 44% and 59% reduction in annual energy consumption, greenhouse gas emissions, and charging costs in Berlin, while in Los Angeles, the reductions are 67% and 98%. The average daily solar driving range reaches 20.3% in Berlin and 30.4% in Los Angeles. [ABSTRACT FROM AUTHOR]

Interest in automated building analytics, including fault detection and diagnostics has been increasing; however, developers of these solutions have lacked access to ground-truth-validated data across a wide range of weather conditions for algorithm development and performance assessment. This study presents the development, and validation of faulted and fault-free models for air handling units (AHUs) – a common HVAC system design. Detailed models for the single-duct AHU (Modelica) and dual-duct AHU (HVACSIM+) were used to conduct annual simulations, for common sensor, mechanical, and control sequence faults. We report lessons learned during the efforts, including challenges and insights regarding how these simulation models, typically used for design applications, can be purposed to accurately reflect real-world system operational behaviours. Finally, we highlight considerations for researchers and FDD developers who may wish to leverage this dataset to assess the performance of their algorithms, and evolving performance of FDD solutions over time. [ABSTRACT FROM AUTHOR]

This article presents a matching algorithm for bipartite graphs containing repetitive structures and represented by intension as Set-Based Graphs. Under certain conditions on the structure of the graphs, the computational cost of this novel algorithm is not affected by the cardinality of the sets of vertices and edges. The main application of the algorithm is that of matching large Equation-Based Models where provided that most equations are defined using for loop statements that iterate over vectors of unknown variables, the computational cost becomes independent of the growth of the vectors involved. Besides introducing the algorithm, the article describes its implementation in a Modelica compiler and studies its performance over different test models. [ABSTRACT FROM AUTHOR]

In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average. [ABSTRACT FROM AUTHOR]

Both the SysML and Modelica standards are used in the field of Systems Engineering (SE) to model systems from different perspectives, on different abstraction levels. SysML is strong when modeling systems on a functional level because it provides different views. With the capabilities of other simulation specifications, the engineers can simulate the system architecture. On the other hand, an open standard, such as Modelica is a key enabler for representing multi‐physical systems described by differential, algebraic, and discrete equations. With the symbolic manipulation, the dynamics of the systems are represented in state space form, and solved by the numerical integration methods fixed or variable step. However, it is clear that the connection between systems engineering and system simulation, with their respective domain knowledge of the actual equipment in their system, is missing. By seeing these complementary values, the authors demonstrate language interaction to integrate SysML and Modelica to achieve complimentary values through bi‐directional transformation and simulation. [ABSTRACT FROM AUTHOR]

Space heating applications account for a high share of global greenhouse gas emissions. To increase the renewable share of heat generation, seasonal thermal energy storage (STES) can be used to make thermal energy from fluctuating renewable sources available in times of high demand. A popular STES technology is pit thermal energy storage (PTES), where heat is stored underground, using water as a storage medium. To evaluate the use of PTES in an energy system, easily adaptable, publicly accessible and tool independent models are needed. In this paper, we improve an existing PTES model developed in the Modelica modeling language. The model is cross-compared with a more detailed and previously validated COMSOL model, considering different amounts of insulation, showing a deviation of 2–13% in the observed annual charged and discharged amount of heat. The results indicate that the presented model is well suited for early design stage and an exemplary case study is performed to demonstrate its applicability in a system context. Dimensions of system components are optimized for the levelized cost of heat (LCOH), both with and without subsidies, highlighting the importance of subsidies for the transition towards climate friendly heating solutions, as the gas boiler use is reduced from 47.6% to 2.7%. [ABSTRACT FROM AUTHOR]

The ongoing research on model predictive control (MPC) for building air conditioning systems predominantly centers on improving the predictive capabilities of system models. In this paper, the impacts of three additional pivotal factors on MPC performance are assessed by examining a generic MPC design for a typical variable air volume (VAV) system that serves large commercial buildings. The three factors encompass the nuanced reformulation of optimization, the judicious relaxation of constraints, and the meticulous tuning of parameters. Detailed case studies with an integrated Modelica and EnergyPlus model of the US Department of Energy's Commercial Reference Building are conducted. The results confirm that the optimization formulation, along with relaxation methods, significantly affects MPC performance in terms of energy savings, zonal thermal comfort level, and computational demand. They also reveal that the impact of the MPC control parameters on the energy savings and thermal comfort may vary by season and can be non-monotonic. [ABSTRACT FROM AUTHOR]

The increasing installment of solar and wind renewable energy systems create a volatile energy demand to be met by electricity providers. A nuclear hybrid energy system is a nuclear reactor with energy storage that integrates into the grid with renewable energy sources. The Natrium design by TerraPower and GE Hitachi is a sodium fast reactor with molten salt energy storage. The Natrium design operates at steady state of 345 MWe and can boost up to 500 MWe for 5.5 hours. This study uses Dymola and the Modelica language to model the Natrium‐based nuclear‐renewable hybrid energy system. The dynamic system model is tested using hourly historical data from the state of Texas 2021 to show how renewables affect the electricity demand and how energy storage affects the Natrium system response to the demand. According to the results, while the available storage will allow the Natrium design to boost electricity production when the demand and electricity price is high making it more economically viable, the current molten salt storage is slightly undersized for the ERCOT market. [ABSTRACT FROM AUTHOR]

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica. [ABSTRACT FROM AUTHOR]

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption. [ABSTRACT FROM AUTHOR]

In current building design practices, the operation and performance of heating, ventilation and air conditioning (HVAC) systems are rarely documented in detail. This is mainly caused by the manual burden of generating detailed HVAC simulation models. To lower the barrier for detailed HVAC simulations, this paper presents an automated toolchain that generates and simulates models in Modelica language utilizing building information structured in a web-based common data environment. This approach differs from previous approaches by its focus on HVAC systems and integration with a common data environment over file-based BIM, which increases interoperability and allows users to run simulation studies in the cloud. The tool successfully generated and simulated a model of the HVAC systems in a small building under ideal and faulty operation of the heating system and thus demonstrated a fully interoperable data exchange between a common data environment and a simulation environment while showcasing the potential of analyzing HVAC systems with Modelica. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is promising for optimizing building's operation but high hardware, software and know-how requirements impede its commercialization. Therefore, rule-based controllers (RBC) are state-of-the-art. Approximate MPC (AMPC) can help bridge this gap by replacing the optimization with an explicit functional relation called approximator. Literature lacks reproducible use cases and benchmarks and a comparison of sophisticated and traditional approximators. This study aims to close this gap by applying AMPC to BOPTEST's two-zone heat pump testcase. The BOPTEST testcase includes predefined RBCs and KPIs promoting repeatability. Then, a comparison was made between artificial neural networks (ANNs), random forest (RF), linear, and logistic regression. The results show that feature selection significantly affects the performance. After adapting the features, the ANNs and RF outperform the RBC with cost savings of up to 33% and discomfort reductions of 70%, while requiring 15% of the MPC's computation time. The traditional approximators fail to outperform the RBC. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for characterizing, implementing, and validating both three-phase and single-phase induction motor models, accompanied by a variable speed drive model. The primary goal is to investigate interactions between the electrical power grid and other dynamic domains (e.g., thermofluidic) that impact motor/load drive behavior. Our approach involves establishing a mechanical interface based on a physically meaningful equation linking motor torque/speed to the electrical model in the phasor domain. This allows seamless integration of diverse domain subsystems into a unified multi-domain model using Modelica v4.0.0 and the OpenIPSL library v3.0.1, overcoming co-simulation limitations. The proposed model, which requires only one Modelica-compliant tool for simulation, introduces additional dynamics through the mechanical interface, enabling explicit simulation of load disturbances based on constitutive physics. This deepens our understanding of dynamic interactions between the electrical power domain and other subsystems connected through the motor. We detail the modeled components using mathematical equations and textual descriptions, emphasizing the Modelica modeling approach. Simulation examples validate the implementation, demonstrating the multi-domain modeling capabilities of the newly developed components. [ABSTRACT FROM AUTHOR]

Dynamic thermo-hydraulic simulations of district heating networks (DHN) are essential to investigate novel concepts for their sustainable design and operation. To develop solutions for a particular case study, numerous long-term simulations are required. Therefore, computational effort for simulation is critical. Heat consumers (HC) are numerous and determine the dynamics of mass flows and return temperatures in the DHN. Thus, the way in which HCs are modeled has significant impact on the computational effort and the results of the simulation. This article presents a novel Modelica-based model for HCs that builds on an existing simplified modeling approach (open-loop design). The calculation of mass flow and return temperature is improved in terms of robustness, plausible behavior and low computational effort. In particular, the model reacts to limited differential pressure and supply temperatures to ensure plausible behavior across all operating conditions, including undersupply situations. The model is successfully tested using an exemplary DHN. The analysis proves that the HC model itself requires little time to simulate. Nevertheless, it significantly influences the simulation time for the entire DHN, which varies by a factor of five for the investigated system depending on the HC model. Fast dynamics, including a bypass in the model and correction of deviations between set point and actual heat load increase the simulation time, so users should sensibly choose how to use these options. HC models triggering many state events result in high computational effort. Compared to other simple HC models, the proposed model produces more plausible results while maintaining at least equal simulation performance (for models without bypass) or even improving it (for models with bypass, CPU time is reduced by at least 35%). [ABSTRACT FROM AUTHOR]

We consider both primal and dual formulations of singular autonomous systems of three different types of fractional‐order differential equations. We present a comprehensive study which proves that by using the spectrum of a linear pencil, a polynomial matrix of first order, and not the fractional‐order pencil of the prime system, we will receive information for all properties for both the prime and its dual system. In addition, by using this spectrum, the solutions for all systems can be obtained by using formulas without additional computational cost. Finally, we provide examples including a computational analysis in Modelica. [ABSTRACT FROM AUTHOR]

Spawn is DOE's next-generation tool chain for whole building energy control simulation. Spawn couples traditional imperative load-based envelope modelling with new equation-based modelling of HVAC and controls. Spawn uses EnergyPlus for the former and the Modelica Buildings Library for the latter. Because it leverages the Modelica Buildings Library, Spawn can evaluate advanced energy systems at the building and district scale, including new architectures and controls for heat pump systems with storage, and the coupling of such systems to electrical distribution networks. Spawn's Modelica integration likewise enables it to simulate realistic control sequences and therefore to bridge energy simulation and control implementation workflows. From EnergyPlus, Spawn inherits efficient envelope simulation and the ability to use existing envelope model authoring tools. This paper describes the architecture and implementation of Spawn, which automatically couples Modelica and EnergyPlus for run-time data exchange. This paper closes with examples that illustrate Spawn's modelling and simulation processes. [ABSTRACT FROM AUTHOR]

Open-source modelling libraries facilitate the standardization and harmonization of model development. In the context of building energy systems, Modelica is a suitable modelling language as it is equation-based and object-oriented. As an outcome of the IBPSA project 1 cooperation, four open-source modelling libraries have been successfully deployed which all share the core library Modelica IBPSA. One of them is the AixLib modelling library. AixLib supports different modelling depths ranging from component to district level and covers all relevant domains in the context of building energy systems. To ensure high-quality simulations, continuous integration has been successfully added to automatically compare simulation results with existing validation data. This paper presents AixLib's key features, scope, and associated tools. We present three use cases that highlight the broad application range of AixLib models. Furthermore, an overview of relevant research and industry projects is provided. Finally, we give an outlook on future development goals. [ABSTRACT FROM AUTHOR]

Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. Good control design for such operation requires a dynamic model of the petroleum flow from the well-bore to the separator. Here, such a dynamic model is considered for liquid production (oil/water) using an electric submersible pump (ESP) to aid in counteracting gravity and friction forces. Based on an existing model used for industrial control design, one goal is to report a complete dynamic model in a single paper. Emphasis is put on dimensionless equipment models for the simple change of units, and the model is developed from physical laws for easy replacement of sub-models, if needed. All the necessary information (equations, parameters) for model implementation is provided, and two candidate equation-based modeling languages are selected and compared: Modelica and ModelingToolkit [MTK] for Julia. The simulation results are virtually identical for the two languages and make sense from physics; however, there is a minor discrepancy in one plot—likely caused by slight differences in accuracy in handling initialization in the implicit algebraic equations. The implementation structures of the model in Modelica and MTK are similar. Modelica is a mature and excellent modeling tool, handles large-scale models, and has tools for producing C code and integration with other tools. MTK is still in rapid development, supports more model types than Modelica, and is integrated in an eco-system with excellent support for control design, optimization, model fitting, and more. To illustrate the suitability of using the developed model for control design, a simple PI controller is designed within the eco-system of MTK/Julia. [ABSTRACT FROM AUTHOR]

A thermo-fluid modeling framework is being developed for ExaDigiT--an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. The work is being conducted in two parts, and discussion is divided into two companion papers. The work documented in this paper focuses on the development of a cooling system library in Dymola for the Frontier supercomputer at Oak Ridge National Laboratory. The second part, outlined in a companion paper, focuses on a templating structure called Auto-CSM for easily creating modelagnostic, physics-based thermo-fluid cooling system models for liquid-cooled supercomputers using a text-based schema. The cooling model is being developed using primarily the open-source Transient Simulation Framework of Reconfigurable Models (TRANSFORM) library. The library follows the templating architecture developed within the TRANSFORM library for modeling subsystems. A full-system validation was performed to validate a very simple model that is integrated with the system controls, and the results are presented herein. [ABSTRACT FROM AUTHOR]

Water-to-air heat pumps are widely used Heating, Ventilation, and Air Conditioning (HVAC) devices due to their versatility and energy efficiency. However, there is a scarcity of readily available Modelica models that support reversible operation (heating and cooling modes), use compressor speed as the control signal, and accurately predict the system performance. To address this gap, this paper presents a speed-input water-to-air heat pump model developed using Modelica. Performance curves are employed to represent the functionality and predict the system's capacity and power usage. To validate the proposed model's effectiveness, manufacturer-provided data are used to generate the performance curves. The model, based on these curves, is then used to simulate testing conditions, which are implemented in a real heat pump testbed. The comparison between simulated and measured values shows that the errors during normal operation stages are within an acceptable range, demonstrating the effectiveness of the developed water-to-air heat pump model. [ABSTRACT FROM AUTHOR]

This paper describes two ways on how to interface Functional Mockup Units (FMUs) and Modelica models through the Pyomo's foreign function interface with Pyomo. Pyomo is a Python-based, open-source optimization modeling language with a diverse set of optimization capabilities. Modelica has arguably much better modeling capabilities than Pyomo, but Pyomo integrates excellent optimization solvers, such as Ipopt (Wächter et al. 2006), and provides a good optimization infrastructure. The Interface has been developed in the context of a NAWI, (National Alliance Water Innovation) Hub project in collaboration with the University of Connecticut and Sandia National Labs. The optimization has been set up and tested within Modelon's Modelica platform Modelon Impact. An unpublished, detailed multi-effect desalination plant developed by Prof. Matt Stuber in the context of (Stuber et al., 2015) has been used to demonstrate the capabilities, as well as simple test models, and design models from Modelon's commercial Libraries. [ABSTRACT FROM AUTHOR]

CasADi is an open-source framework that can be used to efficiently solve optimization problems involving userdefined ODE/DAE models. Supported solution methods include so-called shooting methods, where solvers for initial-value problems in ODEs or DAEs are referenced inside in nonlinear programming (NLP) formulations. In order to solve such NLP formulations with gradient-based algorithms, CasADi implements a fully automatic sensitivity analysis. This analysis includes forward sensitivity analysis, adjoint sensitivity analysis as well as the calculation of higher-order sensitivities for the ODE/DAE models. Because of the variational (differentiate-thenintegrate) approach used, the numerical solution can be performed with variable-step size, variable-order integrators such as those from the SUNDIALS suite. In this work, we present a generalization of the sensitivity analysis support in CasADi to systems with events, as are common in real-world cyber-physical models. In particular, the event extension enables us to formulate and solve optimization problems with such event systems, without a priori knowledge of the number and ordering of events. Ultimately, we expect the proposed approach to be compatible with general cyber-physical models formulated in Modelica or available as model-exchange FMUs. We demonstrate the proposed approach for two proofof- concept examples; the classical bouncing ball written in CasADi directly and a simple hybrid DAE describing a breaking spring formulated in Modelica and imported symbolically into CasADi. In the examples, we show that the forward sensitivities calculated to high precision using the proposed approach are consistent with a cruder finitedifference approximation and provide an example of how they can be embedded into optimization formulations. We discuss how the approach can be extended to handle standard FMUs, adhering to FMI 2 or FMI 3, as well as nontrivial Modelica models imported via a symbolic interface based on the emerging Base Modelica standard. [ABSTRACT FROM AUTHOR]

Context-aware systems are widespread in our daily lives, but modeling languages that address the notion of context are rare. Variable structure systems (VSS) allow for structural and behavioral changes in physical models at runtime (while the simulation is running) based on different situations. It is desirable to explicitly describe under which contextual situation a specific variant of the simulation model should be used and how to implement the switching between these variants at runtime. In this case, contexts could be used to control the variability of context-aware systems. Equation-based modeling languages are suitable for modeling complex multi-domain, multi-physical systems, and among them, Modelica is the state-of-the-art. Unfortunately, the capabilities for VSS in Modelica are strongly limited. As a result, several frameworks have been proposed to address this problem by supporting different VSS types. However, it remains unclear which framework contributes to which VSS type. Furthermore, approaches have been developed to support VSS, but none can explicitly describe contexts and their transitions. In this work, we first introduce VSS and its different types. Then, we provide an overview of which framework targets which VSS type. Finally, we propose a new language extension based on Modelica, ContextModelica, that provides semantics for the direct context definition, enabling the use of context to control and manage variability. [ABSTRACT FROM AUTHOR]

Mathematical optimization is a widespread method in order to improve, for instance, the efficiency of energy systems. A simulation approach based on partial differential equations can typically not be formulated as an optimization problem, thus requiring interfacing to an external optimization environment. This is, among others, also true for the programming language Modelica. Because of high computation time, such coupled approaches are often limited to small-scale optimization problems. Since simulation models tend to get more complex, simulation time and, in turn, associated optimization time rise significantly. To enable proper sampling of the search space, individual optimization runs need to be solved in acceptable times. This paper addresses the search for a proper optimization approach and tool to couple with Modelica/Dymola. The optimization is carried out on an exemplary power plant model from the ClaRa-Library using an evolutionary algorithm (SPEA2-based) with Ansys optiSlang. To verify and evaluate the results, a comparison with the standard Dymola optimization library is performed. Both parallelization and indirect optimization with surrogate models achieved a significant runtime reduction by a factor of up to 5.4. The use of meta models is particularly advantageous for repetitive optimization runs of the same optimization problem but may lead to deviations due to the calculated approximations. [ABSTRACT FROM AUTHOR]

Printed circuit heat exchangers (PCHEs) are widely used as recuperators in the supercritical carbon dioxide (S-CO2) Brayton cycle design. The variation of heat sources will have a great impact on the heat transfer effect of the recuperator. It is of interest to study the fast calculation of flow and heat transfer performance of PCHEs under different operating conditions to obtain the optimal comprehensive performance and provide guidance for the operation control strategy analysis. Herein, a fast calculation method is established through a one-dimensional model of a PCHE based on Modelica. The effects of working medium mass flow rate and inlet temperature on the flow and heat transfer process are analyzed from the three aspects of heat transfer rate, flow pressure drop, and comprehensive performance, and the mass flow rate matching optimization is realized. The results show that increased mass flow rate increases heat transfer rate and flow pressure drop. The efficiency evaluation coefficient (EEC) has a maximum value at which the mass flow rate values of the cold and hot channels are best matched, and the comprehensive performance is optimal. When the mass flow rate of the heat channel is 4.8 g/s, the maximum EEC is 1.42, corresponding to the mass flow rate of the cold channel, 4.2 g/s. Compared with the design condition, the heat transfer rate increases by 62.1%, and the total pump power increases by 14.2%. When the cold channel inlet temperature increases, EEC decreases rapidly, whereas EEC increases when the hot channel inlet temperature increases. The conclusions can provide theoretical support for the design and operation of PCHEs. [ABSTRACT FROM AUTHOR]

DC electrical distribution systems offer many potential advantages over their AC counterparts. They can facilitate easier integration with distributed energy resources, improve system energy efficiency by eliminating AC/DC converters at end-use devices (e.g., laptop chargers), and reduce installation material, time, and cost. However, DC electrical distribution systems present additional design considerations, largely resulting from potentially greater magnitude and variation in cable losses. Modeling and simulation are rarely used to design such systems. However, the greater dependency of DC system energy efficiency on design choices such as distribution voltages, architecture, and integration of PV and BESS suggests that modeling and simulation may be required. Such system performance analysis is currently not a standard practice, in part due to limited availability and validation of capable software tools. This paper characterizes the accuracy of a Modelica-based Building Electrical Efficiency Analysis Model (BEEAM) toolkit, as a precursor for validating its use to perform system performance analysis and inform design decisions. The study builds upon previous verification research by characterizing complete systems comprised of commercially available equipment, and providing a more detailed analysis of simulation results. Five lighting systems with varying electrical distribution architectures were designed using market-available equipment, installed in a laboratory environment, modeled using BEEAM, and simulated using three Modelica integrated development environments (IDEs). Simulated and measured results were compared to characterize toolkit accuracy. Initial results revealed that simulated performance was mostly within ±5% of measured system-level and device-level performance. While simulation results were not found to be dependent on the IDE, some Modelica compiler interoperability issues were identified. Although the BEEAM toolkit showed promise for the targeted use case, further work is needed to determine whether the demonstrated 5% accuracy is sufficient for making real-world design decisions, and for BEEAM to advance from an interesting research tool to one that can impact real-world building projects. [ABSTRACT FROM AUTHOR]

Renewable energy source-fed heat pumps (HPs) may perform up to very high-efficiency standards, offering a promising tool in the wider residential heat decarbonization effort. In this context, this paper investigates different heating configurations utilizing various renewable thermal sources in conjunction with an HP-based system in order to determine the optimal configuration in terms of efficiency, using an existing, fully functioning residential building in Zaragoza, Spain, as our case study, comprising 40 dwellings. Four different HP configurations are investigated:, (i) an air-source system, (ii) a ground-source system, (iii) a dual-source system with solar thermal collectors, and (iv) a triple-source system based on solar, geothermal, and ambient sources. For the purpose of such investigation, detailed dynamic energy simulations are conducted through the use of the INTEMA.building tool (developed in Modelica), applying a multi-objective optimization process that aims at minimizing both the annual electricity consumption and the net present cost. It is demonstrated that the renewable thermally driven HPs are more efficient than the conventional, air-source ones, with the seasonal coefficient of performance increasing by 9.98% (ground source), 4.57% (dual source), and 17.40% (triple source), compared to the air-source heat pump system. Finally, it is revealed (via integrated techno-economic analyses) that the most effective and economical design is the dual source system, while the most expensive is the ground-source configuration. These findings can guide the ongoing design efforts on green residential heat solutions at both research and commercial implementation level. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic panels and heat pumps in domestic environments is a topic that has been studied extensively. Due to their electrical nature and the presence of elements that add thermal inertia to the system (water tanks and the building itself), the functioning of compression heat pumps can be manipulated to try to fulfill a certain objective. In this paper, following a rule-based control concept that has been identified in commercial solutions and whose objective is to improve the self-consumption of the system by actively modulating the heat pump compressor, a parametric analysis is presented. By making use of a lab-tested model, the performance of the implemented control algorithm is analyzed. The main objective of this analysis is to identify and quantify the effects of the main parameters in the performance of the system, namely the climate (conditioning both heating and cooling demands), the photovoltaic installation size, the thermal insulation of the building and the control activation criteria. A total of 168 yearly simulations have been carried out. The results show that the average improvement in self-consumption is around 13%, while the cost is reduced by 2.5%. On the other hand, the heat from the heat pump and the power consumed increase by 3.7% and 5.2%, respectively. Finally, a linear equation to estimate the performance of the controller is proposed. [ABSTRACT FROM AUTHOR]

The exploitation of offshore wind energy by means of floating wind turbines is gaining traction as a suitable option to produce sustainable energy. Multi‐rotor floating wind turbines have been proposed as an appealing option to reduce the costs associated with manufacturing, logistics, offshore installations, and operation and maintenance of large wind turbine components. The development of such systems is forestalled by the lack of a dedicated tool for dynamics and load analysis. Standard codes, such as FAST by NREL, offer the desired fidelity level but are not able to accommodate multi‐rotor configurations. A few experimental codes have been also proposed, which may accommodate multi‐rotor systems, but low flexibility makes them impractical to study a vast range of innovative multi‐rotor FWTs concepts. To close the gap, this work presents the development and comprehensive benchmark of a fully coupled aero‐hydro‐servo‐elastic tool able to easily accommodate arbitrary platform and tower geometries and the number of wind turbines employed. Development is carried out in Modelica, which allows for the employment of the same code functionality in a virtually unlimited number of physical configurations. Full blade‐element momentum capabilities are achieved by integrating into Modelica the well‐established NREL aerodynamic module AeroDyn v15 within FAST v8. Structural dynamics of tower and blades are implemented through a lumped‐element approach. Hydrodynamic loads are computed by employing the DNV software SESAM WADAM. Thorough benchmark is performed against FAST, and positive results are obtained. The dynamic performance of a two‐rotor floating wind turbine is finally assessed considering different turbulence spectrums. [ABSTRACT FROM AUTHOR]

Modelica is a general-purpose modeling language mainly designed to facilitate the development, reusability and exchange of models. It represents the state-of-the-art in equation-based modeling of continuous-time systems. Modelica libraries facilitate the description of multi-formalism and multi-domain models. However, the description of agent-based models (ABMs) in Modelica is not currently supported, mainly due to the characteristics of the language and its simulation algorithm. The combination of ABMs with continuous-time equations provides a powerful tool for describing and analyzing complex systems. An approach for describing ABMs using the Modelica language is presented in this manuscript, with the objective of facilitating the combination of ABMs with the rest of Modelica functionality. Agent behavior is described using a process-oriented modeling approach. Agents are described as individual entities that move across a flowchart diagram, that represents the processes that agents undergo. Processes are formally described using the Parallel DEVS formalism, extended to describe the interface with other Modelica models. The environment where agents interact is described as a cellular automaton. This approach has been implemented in a free Modelica library, named ABMLib. Three case studies are discussed to illustrate the modeling functionality of the library and its combination with other models: a basic traffic model, a sheep–wolves predator–prey model and a consumer market model. [ABSTRACT FROM AUTHOR]

The aim of this document is to present an efficient and systematic method of model‐based predictive control synthesis. Model predictive control requires using a model of a dynamical system, that can be linear, time‐varying, non‐linear, or identified from data. Finding a model that is both precise and simulatable at low computational cost can be challenging and time consuming due to requiring extensive knowledge of the system and physics as well as a large volume of data with relevant scenarios and sometimes a complicated identification work. (filtering noises and bias, data formatting, etc.) The proposed methodology begins with fine‐scale multi‐physics modeling, which is possible thanks to open model libraries (see Modelica). The obtained model is then simulated by considering ad hoc scenarios to generate data, which are then used to identify a neural network, that will support the predictive control syntheses. The systematic methodology is detailed and applied to the widely used control benchmark known as the quadruple tanks process. Results show that the methodology is accurately applied to optimize hyperparameters in finding a neural network model and to control the quadruple tanks process with the predictive controller. [ABSTRACT FROM AUTHOR]

This paper presents the development of ADMLib, a new high-productivity and efficient Modelica package to model and simulate anaerobic digestion systems inside the structured modeling framework. Library components were organized into subpackages to encompass growth kinetics, non-biochemical reaction kinetics, acid-base, heat transfer, and inhibition processes, as well as the characteristics of substances and gas phase. A validation of the dynamic behavior response was performed where the implemented functions were used to simulate different bibliographic models. A brief performance analysis was carried out, in order to evaluate the component-based approach of ADMLib against the traditional differential algebraic equation (DAE) systems. The implementation testing demonstrated that the developed package was reliable, usable, and performant. [ABSTRACT FROM AUTHOR]

A new approach is introduced to model and simulate equation-based systems where variables can appear and disappear during simulation without re-generation and re-compilation of code when the numbers of equations and states change during events. The method is presented in a generic, mathematical way and can be in principle applied to all types of declarative, equation-based modelling languages, such as Modelica. A concrete implementation is given for the Julia-based experimental modelling language Modia, which is similar to Modelica. However, Modia features far simpler semantics based on hierarchical collections of name/value pairs and has the ability to support domain-specific algorithms, especially for multibody systems with collision handling. The new method is demonstrated with heat-transfer in a rod, separation of stages of a rocket and gripping operations of a robot. [ABSTRACT FROM AUTHOR]

The marine steam power system includes a large amount of thermal equipment; meanwhile, the marine environment is harsh and the working conditions change frequently. Operation management involves many disciplines, such as heat, machinery, control, electricity, etc. It is a complex multi-discipline physical system with typical nonlinear, multi-parameter, strong coupling characteristics. In order to realize the health management of a marine steam power system, based on digital twin technology combined with the Modelica language, modular modeling, etc., this paper conducts in-depth research on the multi-domain modeling of the marine steam power system, characteristic analysis of variable working conditions, fault simulation, etc. The analysis results show that the dynamic response trend of the model is consistent with the actual operation, the error of the main steam flow at 1800 s is the largest and is −4.9%, and the error of the main steam flow, steam turbine output power, cooling water outlet temperature and other key parameters is within ±5%. Virtual reality mapping between the digital model and the physical equipment is realized, which lays a foundation for mastering the dynamic characteristics of the marine steam power system. [ABSTRACT FROM AUTHOR]

Model-based optimization is an important means by which to analyze the energy-saving potential of chiller plants. To obtain reliable energy-saving results, model calibration is essential, which strongly depends on operating data. However, sufficient data cannot always be satisfied in reality. To improve the prediction accuracy of the model with limited data, a model calibration method based on error reverse correction was investigated. A traditional optimization-based calibration method was first used for preliminary model calibration to obtain simulation data and simulation errors. Then, the sources of the simulation errors were analyzed to determine the distribution characteristics of the corresponding operating conditions of the model. Finally, the performance of the model was reversely corrected by adding a correction term to the original model. The proposed calibration method was tested on a chiller plant in Xiamen, China. The results showed that the proposed calibration method improved prediction accuracy by 2.61% (the coefficient of variation of the root mean square error (CV (RMSE)) was reduced from 3.96% to 1.35%) compared to the traditional method. The maximum mean bias error (MBE) for monthly chiller energy consumption was 2.66% with the proposed calibration method, while it was 10.42% with the traditional method. Overall, in scenarios with limited data, the proposed calibration method can effectively improve the accuracy of simulation results. [ABSTRACT FROM AUTHOR]

In designing the heat exchanger model, the existing epsilon-number of transfer units (NTU) method cannot reflect factors inside the heat exchanger, such as the local flow rate changes. Furthermore, it is difficult to apply a three-dimensional model to a one-dimensional analysis since high computational power is required. In contrast, a pseudo-three-dimensional heat exchanger model which has higher accuracy than the existing epsilon-NTU method and has faster speed for application in one-dimensional analysis. However, pseudo-three-dimensional model has still improvements in the simulation time as the mesh counts increase. In this study, the improved pseudo-three-dimensional heat exchanger model was developed and integrated within a one-dimensional analysis loop capable of simulating vehicle driving and the cooling system. The integrated model calculated the change in the energy consumption of the entire cooling system with the change in the aspect ratio of the heat exchanger. Also, the optimal operation strategy of the cooling system, which incorporated a proportional integral derivative controller of the three-way valve, was determined to reduce the parasitic losses. As a result, parasitic loss of the cooling system decreased by 14.1 % as aspect ratio increased by 46 % under FTP-72 (UDDS) driving schedule and simulation time was reduced 90.1 % while accuracy degrades only 2.7 %. [ABSTRACT FROM AUTHOR]

Direct evaporative coolers (DECs) are a low-energy cooling alternative to conventional air conditioning in hot-dry climates. The key component of DEC is the cooling pad, which evaporatively cools the air passing through it. While detailed numerical models of heat and mass transfer have been proposed for the cooling pad, these require many input parameters that are not readily accessible. Alternatively, simplified models lack accuracy and are confined to common types of cooling pad. To address these limitations, we developed and validated a physics-based model, that only needs the nominal data to compute the heat and mass transfer with considerable accuracy. The proposed model is implemented in Modelica, an equation-based object-oriented modeling language. For comparison, a basic lumped model from EnergyPlus based on the efficiency curve of the cooling pad is also implemented. The physics-based model exhibits <2% error from the experimental data and the lumped model exhibits a 12.3% error. [ABSTRACT FROM AUTHOR]

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Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. In this article, we propose several contributions to this multifaceted issue, namely: an efficient and scalable multimode extension of the structural analysis of Modelica models; a systematic way of rewriting a multimode Modelica model, based on this analysis, so that the rewritten model is guaranteed to be correctly compiled by state-of-the-art Modelica tools; a proposal for the handling of the consistent initialization of multimode models; multimode structural analysis algorithms that handle both multiple modes and mode change events in a unified framework, coupled with a compile-time algorithm for identifying and quantifying impulsive behaviors at mode changes. Our approach is illustrated on relevant example models, and the performance of our implementations is assessed on a variable dimension large-scale model. [ABSTRACT FROM AUTHOR]

Electromagnetic Transient (EMT) simulation tools are typically developed using conventional procedural programming languages. On the other hand, modern high-level and equation-based programming languages, such as Modelica, are currently available. Modelica allows formulating models that are easy to develop, maintain and understand by expressing what needs to be computed without stating how it should be computed. This paper presents a Modelica-based simulator for electromagnetic transients. It is demonstrated that this approach offers significant advantages for developing sophisticated models. Computational performance and accuracy are compared to a conventional EMT-type simulation tool. [ABSTRACT FROM AUTHOR]

The new generation of low-earth-orbit (LEO) satellite constellation systems has the characteristics of low delay, strong signal and global coverage, and it is an important direction for the development of next-generation communication technology. A major disadvantage is that the constellation system is huge, often composed of hundreds or thousands of satellites, which puts forward high requirements for the design and health management of the constellation system, and the existing telemetry data monitoring system cannot meet the actual needs. CPS is a multidimensional complex system that integrates computation, communication and control (3C). Through the deep integration and cooperation of 3C, the real-time monitoring and dynamic control of large-scale engineering systems are realized, which is completely suitable for the operation and maintenance requirements of the satellite constellation system. This paper firstly establishes the entire satellite constellation system model, which is integrated from the satellite multidomain system, the constellation orbit environment system and the communication link system. Then, according to the technical concept of cyber-physical systems (CPS), an implementation framework of a prognostics and health (PHM) system driven by a model–data hybrid for satellite constellation systems is proposed. The framework is based on model simulation data and telemetry data and combines virtual and real data fusion, fault diagnosis, simulation prediction and other technologies to generate enhanced data to drive the effective operation of the PHM system. Finally, a verification case is designed to prove that the satellite constellation health management system implemented under this framework has a positive effect on the reliable operation and maintenance of the satellite constellation system. [ABSTRACT FROM AUTHOR]

The trajectory planning method with dynamics is the key to improving the motion performance of manipulators. The optimal control method (OCM) is a key technology to solve optimal problems with dynamics. There are direct and indirect methods in OCM; indirect methods are difficult to apply to engineering applications, and so direct methods are widely applied instead. The direct collocation method (DCM) is a technology in OCM to transform an optimal control problem (OCP) to a nonlinear problem (NLP), so that plenty of solvers can be used directly. However, the general DCM, for which it has been found that the explicit form of the right-hand-side (RHS) functions of state equations of the complex system in the OCP is hard to derive, is limited to solving the OCP of three-axis manipulators. This paper proposes an improved DCM to solve the OCP of six-axis manipulators, which can find the solution of the time-optimal trajectory for the motion of six-axis manipulators based on the improved DCM. The proposed method derives the RHS equations implicitly by introducing a Functional Mock-up Unit (FMU), which simplifies the representation of the RHS equations as a black-box model, so that the DCM can be applied to the OCP of six-axis manipulators. A simulation case of a three-axis manipulator accomplished in a related study works as a reference compared with our improved method to verify the solution consistence between the DCM using the explicit RHS equations or using the implicit RHS equations, and the loss of computational efficiency is acceptable. In the meantime, a simulation solution and an experiment of six-axis manipulators, which is a novel advancement, are presented to validate the proposed method. [ABSTRACT FROM AUTHOR]

Home energy retrofit has recurred in public policy throughout recent decades. However, the savings in energy usage attributable to home retrofit have remained difficult to accurately predict. Occupants cause prediction inaccuracies by varying different factors, especially heating setpoints temperatures and heating patterns. Acting together, such occupant factors result in distributions — not single values — of heat-energy usage, even among similar homes. Datasets of heat-energy distributions can be found by building performance simulation using modern grey-box models. This study presents a methodology to simulate grey-box models of home heating through ranges of heating setpoints and patterns. An entire process to calibrate, validate and simulate at a large scale is described, and then demonstrated using case studies. Grey-box models, written in Modelica language, can conveniently simulate through large ranges of occupant factors. The case studies exploited this advantage of grey-box models to simulate empirical data on occupant factors. (For instance, empirical data found that home heating setpoints shifted before and after home energy retrofit.) In doing so, the datasets of simulation results enabled the exploration of home heat-energy usage with the normal and Weibull statistical distributions. Additionally, the heat-energy distributions of case-study homes were statistically tested, first for retrofit savings, second for equality to each other and third for equality to an official heat-energy estimate. Results demonstrate that home heat-energy usage, at a large scale, is best expressed as a Weibull distribution not normality. After home energy retrofit, heat-energy usage displays less variation (in general), less skewness, and thus becomes closer to normality. Occupant factors were found to vary home heat-energy usage into distinct distributions, even within similar homes. Therefore, in most case-study homes, heat-energy usage did not equal an official estimate. Finally, shallow retrofit of a modern home in Ireland fails to save heat-energy usage by most occupants. [ABSTRACT FROM AUTHOR]

Nowadays, industrial products are getting increasingly complex, and time-to-market is significantly shorter. Modeling and simulation tools for cyber-physical systems need to keep up with the increased complexity. This paper presents OpenModelica.jl, a modular and extensible Modelica compiler framework in Julia targeting ModelingToolkit.jl and supporting Variable Structured Systems. We extended the Modelica language with three new operators to support continuous-time mode-switching and reconfiguration via recompilation at runtime. Therefore, our compiler supports the Modelica language and variable structure systems via the aforementioned extensions. To our knowledge, there are no other Modelica tools available that support both standard Modelica and variable structure systems. We evaluated our framework using a standardized benchmark suite, in terms of simulation, compilation and recompilation performance. The results concerning compilation and simulation time performance were compared with the results of running the existing OpenModelica compiler with the same set of models. A custom benchmark was devised to estimate the cost in terms of recompilation when simulating variable structure systems. The performance experiments showed that OpenModelica.jl is currently about four times slower in terms of compilation time when compiling a transmission line model with tens of thousands of equations and variables. The difference in simulation performance between the two compilers was negligable. Furthermore, the impact of recompilation during the simulation was usually small compared with the simulation time for long simulations. The results are promising for a prototype, and we outline approaches to further improve both compilation and simulation performance as future research. [ABSTRACT FROM AUTHOR]