Functional Mock-up Units Research Articles

Development of a Reduced Order Model-Based Workflow for Integrating Computer-Aided Design Editors with Aerodynamics in a Virtual Reality Dashboard: Open Parametric Aircraft Model-1 Testcase

In this paper, a workflow for creating advanced aerodynamics design dashboards is proposed. A CAD modeler is directly linked to the CFD simulation results so that the designer can explore in real time, assisted by virtual reality (VR), how shape parameters affect the aerodynamics and choose the optimal combination to optimize performance. In this way, the time required for the conception of a new component can be drastically reduced because, even at the preliminary stage, the designer has all the necessary information to make more thoughtful choices. Thus, this work sets a highly ambitious and innovative goal: to create a smart design dashboard where every shape parameter is directly and in real-time linked to the results of the high-fidelity analyses. The OPAM (Open Parametric Aircraft Model), a simplified model of the Boeing 787, was considered as a case study. CAD parameterization and mesh morphing were combined to generate the design points (DPs), while Reduced Order Models (ROMs) were developed to link the results of the CFD analyses to the chosen parameterization. The ROMs were exported as FMUs (Functional Mockup Units) to be easily managed in any environment. Finally, a VR design dashboard was created in the Unity environment, enabling the interaction with the geometric model in order to observe in a fully immersive and intuitive environment how each shape parameter affects the physics involved. The MetaQuest 3 headset has been selected for these tests. Thus, the use of VR for a design platform represents another innovative aspect of this work.

LiDICS: a light dynamics interface for multi-domain simulation based on the FMI-standard

The assessment of adaptive façades faces several challenges, including the representation of adjustable material and geometric properties within building performance simulation. This paper introduces a methodical and model-based interface dedicated to this issue. Adjustable material and geometric façade properties can be captured and continuously adapted within the building performance simulation using this model, without the need to reinitialise the simulation as required with matrix-based methods. The method comprises two steps: model training and export. Model training transforms ray-tracing-based sampling outcomes into an algebraic function. Model export generates a Functional Mock-up Unit. The LiDICS method is applied to switchable membrane cushion constructions with complementary printing patterns. The model training achieves a high accuracy ( r 2 = 99.4 % ). Subsequently, an inter-model validation is conducted using annual simulation from the RADIANCE 3-phase method, likewise resulting in high accuracy ( r 2 = 98.5 % ). Lastly, a brief analysis is conducted to compare four operation strategies regarding coupled comfort and energy assessment.

Unified automatic construction method of multi-source language functional mock-up unit

Abstract Model simulation plays an important role in modern engineering design and system development. It simulates and validates complex systems through a virtual environment, reduces the cost and risk of physical tests, and improves the design efficiency and accuracy. Generating a functional mock-up unit (FMU) is the core of model simulation. Most FMU is generated by Modelica, but this generation method is not only very complicated but also requires experimentalists to be familiar with Modelica. Nowadays, physical models are becoming more and more complex. With the development of artificial intelligence, more and more intelligent models are involved in the simulation process. Therefore, it is essential to realize the simulation process of automatic and rapid generation of FMU by multi-source languages. In this paper, by combining Python and Modelica, two methods of automatic FMU generation are designed. Through simulation tests, it is verified that the FMU generated by these two methods can be applied to the simulation process, which brings significant efficiency improvement to the model design.

Multi-physics system modelling based on bond graph theory for offshore hydrogen production

Hydrogen is expected to take a major role in the energy sector. To address the increasing demand, specially designed systems for dedicated hydrogen production, e.g., an offshore hydrogen wind turbine, are developed. Especially in these multi-physics systems, each aspect of the entire system requires expert knowledge for modelling and sometimes specific simulation tools. A key challenge lies in the integration of component models into the full system model. We propose a method based on bond graph theory to identify single components and their interfaces for dynamic simulation. This top-down approach leads to a structured processes for the development of multi-physics systems with a diverse team. We present a design of a new 15 MW green hydrogen offshore wind turbine to study the performance in turbulent wind conditions. The single components are decoupled in their development and therefore, can be tested independently or combined with other components. All component models are utilized as Functional Mock-Up Units and combined in the full model with Co-Simulation. The development method is applicable to other complex energy systems.

FEM-Circuit co-simulation of superconducting synchronous wind generators connected to a DC network using the homogenized J–A formulation of the Maxwell equations

High-temperature superconductors (HTS) are greatly appealing for the development of high efficient, and high energy density power devices. They are particularly relevant for applications requiring light and compact machines such as wind power generation. In this context, to ensure the proper design of the superconducting machines and their reliable operation in power systems, it is then important to develop models that can accurately include their physics but also can describe properly their interaction with the system. To achieve such a goal, one approach is the co-simulation. This numerical technique can bring fine geometrical and physical details of the machines through a finite element model (FEM) meanwhile dealing with the operation of the whole system that incorporates the machine and a subset of the power grid represented by an external electrical circuit. The goal of the present work is to put to use this numerical technique when superconducting components are involved. Here, a case study is proposed involving a 15 MW hybrid superconducting synchronous generator (HTS rotor and conventional stator) coupled to a direct current network via a rectifier and its associated filter. The case study related to wind power application allows grasping the technical issues when employing co-simulation dealing with HTS machines. The FEM of the generator is done in the commercial software COMSOL Multiphysics, which interacts with the circuit simulator Simulink through the built-in Functional Mock-up Unit. For the present study, a new version of the latest J–A formulation combined with homogenization technique is introduced allowing an even faster computation time compared to the T–A formulation. Distributed variables and global variables such as current density, magnetic flux density, and local losses for the former and voltage, current, electromagnetic torque, and power quality for the latter are estimated and compared for both formulations. The idea is to find the best-suited combination FEM-circuit under criteria of computational speed, accuracy, and numerical stability. Thus, it is shown that all formulations generate an error of less than 5% on the machine parameters and that the J–A formulation with first order elements stands out with a significant 4-fold reduction in computational costs.

Optimizing Fuel Injection Timing for Multiple Injection Using Reinforcement Learning and Functional Mock-up Unit for a Small-bore Diesel Engine

<div>Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [<span>1</span>]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NO<sub>x</sub>–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy. Change of fuel injection quantity was not attempted in this study as the main objective was to demonstrate the use of RL algorithms while maintaining a constant indicated mean effective pressure. A change in fuel quantity has a larger influence on the indicated mean effective pressure than a change in fuel injection timing. The focus of this work was to present a novel methodology of using the 3D combustion data from analysis software in the form of a functional mock-up unit (FMU) and showcasing the implementation of a RL algorithm in Python language to interact with the FMU to reduce the NO and soot emissions by suggesting changes to the main injection timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms identified the operating injection strategy, i.e., main injection timing for a pilot–main and pilot–main–post-injection strategy, reducing NO emissions from 38% to 56% and soot emissions from 10% to 90% for a range of fuel injection strategies.</div>

Virtual design on the heat pump refrigeration cycle: challenges and approaches

Virtual product design (VPD) of the refrigeration cycle has long been a major subject of many engineers and manufacturers. Issues should be the question of how simulation gets close to the real world in virtual ways. It is especially true when we consider variable refrigerant flow (VRF) heat pumps, having various types of indoor unit combinations, long and complicated piping configurations, and simultaneous cooling and heating functions under extreme weather conditions. In this regard, model-based systems engineering (MBSE) or model-based design (MBD) guides a methodology to make a virtual model easily. Now, there are plenty of engineering platforms and tools to make a strong simulation model, which provides interfacing technology among multiple physics, different dimensions, and codes in different languages. In the study, the authors share their experience in the virtual design for a VRF heat pump system including the architecture, multiple physics, and the connection with the control SW. Modelica, an acausal and object-oriented modeling language, constructs the backbone of the model to describe the dynamic behavior of refrigerants and to combine external components in the form of functional mock-up units (FMUs). The developed virtual model is validated against measured data, showing it can reproduce all the essential physics of interest both in qualitative and quantitative ways.

A co-simulation approach to onboard support of marine operation: a Palfinger crane path planning case

Marine cranes are one of the most important industrial equipment in the maritime field. The base of a marine crane is dynamically moving as the motion of the ship’s six degrees of freedom that is affected by offshore environmental loads. There is a coupling between the crane and the ship, which means the crane operation and the ship motion affect each other. In this paper, co-simulation technology is employed to construct the virtual marine operation system which is composed of diverse Functional Mock-Up Units (FMUs) exported using the Functional Mock-Up Interface (FMI) standard and System Structure and Parameterization (SSP) standard to define the structure and parameters based on the co-simulation platform Vico. A path planning case for the Palfinger crane is implemented using the A* algorithm. The physical three-dimensional working space of the crane is discretized into a finite number of nodes in joint space. The cost is defined by the variable of the ship motion to optimize the marine operation. The obtained discrete nodes are smoothed to get the velocity of the actuators as control signals. Simulation of the crane operation is carried out in the virtual operating system following the planned path.

Investigating the Functional Mock-up Interface as a Coupling Framework for the multi-fidelity analysis of nuclear reactors

This paper investigates the use of the Functional Mock-up Interface (FMI) for code coupling in nuclear engineering. The FMI is a standard that defines a container and an interface to exchange dynamic simulation models. It has been developed and refined by several actors over the past two decades. This coupling standard allows seamless integrations of independent objects called Functional Mock-up Units (FMU). Since communication among FMUs is standardized, encapsulating a simulation tool and model within an FMU allows this tool and model to be coupled with any other FMU. This approach is opposed to creating dedicated code-to-code coupling interfaces and enables a more sustainable approach to code coupling in nuclear engineering. The paper showcases the utilization of the FMI standard for the simulation of an operational load-follow scenario in a Lead-cooled Fast Reactor. The primary circuit and balance of the plant are modeled using higher and lower-fidelity codes, respectively, with a third tool employed to model a simplified control system. The paper investigates the pros and cons of the proposed approach by exercising it throughout the following workflow: incorporation of the FMI standard into an existing code; setting up of models using different codes; coupling of these codes based on different architectures; simulation and post-processing of results. As an outcome, implementing an FMI interface presents itself as a judicious long-term investment for simulation software. However, users and developers should be aware of the limited FMI capabilities for the coupling of partial differential equations. In addition, a coupling standard by itself cannot address some difficulties, such as simulation restart, that are associated with the handling of a heterogeneous set of tools.

Integration of geometry modelling and behavior simulation based on graph-based design languages and functional mockup units

This paper explores the automated creation of a digital twin, employing a framework based on graph-based design languages (GBDLs) and functional mock-up units (FMUs). Through this framework, all entities that represent a digital twin can be represented geometrically and enriched with behavioral properties. The goal is to create not only geometric entities, but also simulation architectures automatically. Kinematics, pneumatics, hydraulics and many other disciplines can be mapped and linked through this kind of simulation architecture. For this purpose, either a monolithic simulation, which transfers all domains into one simulation, is possible, or the setup of a co-simulation. In this case, each simulation is modeled separately and stored as a FMU. Each interface for input and output parameters of an FMU is defined by the functional mock-up interface (FMI) standard and therefore unique. This uniqueness allows different FMUs to be linked together and built up into a co-simulation. The links are defined via a simulation architecture and present the interface between two domains. This simulation architecture, like the whole digital twin, relates to GBDLs. This paper discusses the approach through the exemplary creation of a simulation architecture for a Festo™ assembly system.

Impact of Flexibility Implementation on the Control of a Solar District Heating System

Renewable energy sources, distributed generation, multi-energy carriers, distributed storage, and low-temperature district heating systems, among others, are demanding a change in the way thermal networks are conceived, understood, and operated. Governments around the world are moving to increase the renewable share in energy distribution networks through legislation like the European Directive 2012/27 in Europe, and solar energy integration into district heating systems is arising as an interesting option to reduce operation costs and carbon footprint. This conveys an important investment that adds complexity to the management of thermal networks and often delays the return on investment due to the unpredictability of renewable energy sources, like solar radiation. To this end, this paper presents an optimisation methodology to aid in the operative control of an existing solar district heating system located in the northwest of France. The modelling of the system, which includes a large-scale solar field, a biomass boiler, a gas boiler, and thermal energy storage, was previously built in Dymola. The optimisation of this network was performed using MATLAB’s genetic algorithm (GA) and running the Dymola model as functional mock-up units, FMUs, using Simulink’s FMI Kit. The results show that the methodology presented here can reduce the current operation costs and improve the use of the daily storage of the DH system by a combination of mass flow control and the implementation of a flexibility function for the end-users. The cost-per-kWh was reduced by as much as 16% in a single day, and the share of heat supplied by the solar field on this day was increased by 5.22%.

Development of a surrogate model of a trans-critical CO2 heat pump for use in operations optimization using an artificial neural network

Conventional physics-based models can demand substantial computational resources when employed for operational optimization. To allow faster system simulations that can be employed for operational optimization, a surrogate model of the CO2 heat pump has been developed using an artificial neural network (ANN). The ANN model takes in six (6) inputs: evaporator water-side mass flow, its temperature, gas cooler water-side mass flow, its temperature, set-point output temperature, and high-side heat pump pressure. The model’s outputs comprise the electrical energy needed to run the heat pump, the heat from the gas coolers, the temperature of the heat pump-heated fluid, and the outlet temperature of the heat pump’s evaporator. Data used for training, validating, and testing the ANN model were generated by running a calibrated Modelica model of the CO2 heat pump for various combinations of input parameters obtained from Latin hypercube sampling. The ANN model developed includes an input layer with 6 inputs, 2 hidden dense layers, each with 30 neurons, and an output layer for 4 outputs (6-30-30-3). The ReLU activation function was implemented on each hidden layer and no regularizations were imposed. The Adam optimizer was used with a learning rate of 0.001 specified. Early stopping (patience = 2000) was implemented to ensure that the training data was not overfitted. A maximum of 30000 epochs was specified. The resulting Mean Square Error (MSE) obtained for the training, validation, and testing data sets were 1.38x10−5, 2.05x10−5, and 3.65x10−5, respectively. When tested against one-week operational runs generated by Modelica, the Root Mean Square Errors (RMSEs) for coefficient of performance (COP)s for spring, summer, autumn, and winter operations obtained were 0.232, 0.346, 0.089 and 0.076, respectively. The resulting surrogate ANN model can be integrated into the system model as a functional mock-up unit within Modelica to facilitate faster simulations for operational optimization.

Modeling and Simulating Wind Energy Generation Systems by Means of Co-Simulation Techniques

This paper presents the development of a wind energy conversion system co-simulation based on the Functional Mock-up Interface standard aiming at contributing to the development of co-simulation of large electrical power systems by means of open-source and standardized computational tools. Co-simulation enables the computational burden of a monolithic simulation to be shared among several processing units, significantly reducing processing time. Through the Functional Mock-up Interface standard, developed models are encapsulated into Functional Mock-up Unit, providing an extra means for the protection of intellectual property, a very appealing feature for end users, both in industry and academia. To achieve the decoupling of the subsystems, the Bergeron ideal transmission line model will be used, with travel time equal to the simulation time-step. The computational performance and effectiveness of the proposed co-simulation technique was evaluated with a wind power plant with 50 wind turbines. The system digital models were developed into Modelica language, while co-simulation was implemented in Python.

Demand response via optimal pre-cooling combined with temperature reset strategy for air conditioning system: A case study of office building

Demand response (DR) through air conditioning (AC) systems in buildings is a promising way to balance the power grid. However, the majority of existing DR strategies are rule-based. The other ones are model-based but generally have adopted oversimplified AC system models. Consequently, the effect of model-based optimal control cannot be evaluated properly. To address these limitations, a co-simulation framework is developed to minimize power consumption and electricity costs by adopting both pre-cooling and temperature reset strategies. The co-simulation framework combines an EnergyPlus model that is calibrated by measured data and the genetic algorithm through Functional Mock-up Unit socket. Both time-of-use electricity prices and the constraints of thermal comfort are considered in the optimization problem. A real office building with a variable refrigerant flow (VRF) system is used to test the co-simulation framework. Compared with rule-based strategies, the power consumption of the VRF system following the optimal control strategy during the DR period is reduced by 80.12%, 74.70% and 55.60%, and the daily electricity cost is reduced by 14.98%, 12.11% and 8.35%, respectively. The quantitative analysis also reveals that 37.96% of the cold energy stored by the envelope in the pre-cooling period is released into the air during the DR period.

A comparison between Sobol’s indices and Shapley’s effect for global sensitivity analysis of systems with independent input variables

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.

Free-running CFD simulations to assess a ship-manoeuvring control method with motion forecast in waves

In this study, Computational Fluid Dynamics (CFD) based free running simulations of a tanker model were performed to assess a ship-manoeuvring control method with motion forecasting in waves. A modified Time-Varying Auto-Regressive (TVAR) model was developed as a forecast model for predicting the changes in the heading angle. Furthermore, Functional Mock-up Unit (FMU) was developed and embedded in the CFD model to perform the steering operation based on the predicted heading angle. The results confirmed that the motion-forecasting control method improves the course keeping and course changing performance, showing faster heading change, decreased heading overshoot and reduced rudder manipulation.

Virtual Hardware-in-the-Loop FMU Co-Simulation Based Digital Twins for Heating, Ventilation, and Air-Conditioning (HVAC) Systems

In this paper, a novel self-adaptive control method based on a digital twin is developed and investigated for a multi-input multi-output (MIMO) nonlinear system, which is a heating, ventilation, and air-conditioning system. For this purpose, hardware-in-loop (HIL) and software-in-loop (SIL) are integrated to develop the digital twin control concept in a straightforward manner. A nonlinear integral backstepping (NIB) model-free control technique is integrated with the HIL (implemented as a physical controller) and SIL (implemented as a virtual controller) controllers to control the HVAC system without the need for dynamic feature identification. The main goal is to design the virtual controller to minimize the distinction between system outputs in the SIL and HIL setups. For this purpose, Deep Reinforcement Learning (DRL) is applied to update the NIB controller coefficients of the virtual controller based on the measured data of the physical controller. Since the temperature and humidity of HVAC systems should be regulated, the NIB controllers in the HIL and SIL are designed by the DRL algorithm in a multi-objective scheme (MO). In particular, the simulations of the HIL and SIL environments are coupled by a new advanced tool: function mockup interface (FMI) standard. The Functional Mock-up Unit (FMU) is adopted into the FMI interface for data exchange. The extensive research of HIL and SIL controllers shows that the system outputs of the virtual controller are controlled exactly according to the physical controller.

Cosimulation of Integrated Organic Photovoltaic Glazing Systems Based on Functional Mock-Up Unit

This study presents an approach to simulating building-integrated photovoltaic glazing systems composed of semitransparent organic photovoltaic (ST-OPV) elements. The approach consists of a mathematical cosimulation model based on the energy balance of complex glazing systems, considering heat transfer as conduction, mixed convection, and radiation effects. The cosimulation method is based on a functional mock-up unit (FMU) developed in Python and the building simulation program Domus. This work aims at presenting a cosimulation technique that can be easily applied to building energy simulation tools for the assessment of photovoltaic energy generation in glazing systems. The cosimulation glazing model was verified according to ANSI/ASHRAE Standard 140-2011, and the zone temperature was kept within with a root medium square error (RMSE) of 1.45 °C. The simulated building with an ST-OPV system showed promising results and could be applied to near-zero energy buildings since each 6-m2 glazing has a power generation of around 77 W, equivalent to 9% of available solar resource.

NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications

The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU.

Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces.

This work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error of simulated and measured time domain signal amplitude is . In addition, the of the number of points and mean intensity values received from the virtual and real targets are and , respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points and mean intensity values up until now. Moreover, the distance error is below the range accuracy of the actual LiDAR sensor, which is 2 cm for this use case. In addition, the proving ground measurement results are compared with the state-of-the-art LiDAR model provided by commercial software and the proposed LiDAR model to measure the presented model fidelity. The results show that the complete signal processing steps and imperfections of real LiDAR sensors need to be considered in the virtual LiDAR to obtain simulation results close to the actual sensor. Such considerable imperfections are optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by the sunlight.