Complex Multi-domain Systems Research Articles

Higher-order transformations for the generation of synchronization infrastructures in blended modeling

IntroductionBlended modeling aims at boosting the development of complex multi-domain systems by enabling seamless multi-notation modeling. The synchronization mechanisms between notations are embodied in model transformations. Manually defining model transformations requires specific knowledge of transformation languages, and it is a time-consuming and error-prone task. Moreover, whenever any of the synchronized languages or notations evolves, those transformations become obsolete.MethodsIn this paper, we propose an automated solution for generating synchronization transformations in an industrial setting.ResultsThe approach entails i) the specification of mapping rules between two arbitrary domain-specific modeling languages leveraging a mapping modeling language, appositely defined for this purpose, and ii) the automatic generation of synchronization model transformations driven by the mapping rules.DiscussionWe validated the proposed approach in two use cases. Although our main goal was to provide a solution for synchronization between graphical and textual notations of UML-RT state machines, the proposed approach is language- and notation-agnostic.

Simulation and experimental research of electric tractor drive system based on Modelica

The electric tractor has the advantages of zero-emission, high efficiency, and low noise, which is the direction of future development and transformation of agricultural power machinery. Aim at the problem that the simulation methods commonly used in the development of electric tractor drive system are poorly generalized and cannot meet the simulation needs of complex multi-domain physical systems. This paper proposes a modeling method for an electric tractor drive system, takes the YTO-500 tractor as the research object, designs and calculates the overall scheme and parameters of its drive system, divides the drive system into modules, establishes the energy system, motor system and mechanical parts model based on Modelica, and integrates the simulation model of electric tractor drive system on this basis. The traction performance and transportation working conditions were simulated and tested. With compared and analyzed, in the traction characteristics, the simulation and test results of maximum speed, maximum traction force, and maximum traction power of each gear are consistent; within 400s transportation simulation conditions, the speed range of electric tractor is 13~28km·h-1, which is consistent with the speed range of electric tractor transportation gear. The results show that the simulation and the test results are consistent, which verifies the credibility of the simulation and the correctness of the model built, providing a basis for future research and development of agricultural machinery.

Spark Ignition Engine Modeling Using Optimized Artificial Neural Network

The spark ignition engine is a complex multi-domain system that contains many variables to be controlled and managed with the aim of attending to performance requirements. The traditional method and workflow of the engine calibration comprise measure and calibration through the design of an experimental process that demands high time and costs on bench testing. For the growing use of virtualization through artificial neural networks for physical systems at the component and system level, we came up with a likely efficiency adoption of the same approach for the case of engine calibration that could bring much better cost reduction and efficiency. Therefore, we developed a workflow integrated into the development cycle that allows us to model an engine black-box model based on an auto-generated feedfoward Artificial Neural Network without needing the human expertise required by a hand-crafted process. The model’s structure and parameters are determined and optimized by a genetic algorithm. The proposed method was used to create an ANN model for injection parameters calibration purposes. The experimental results indicated that the method could reduce the time and costs of bench testing.

Development of virtual lab module for wind and solar energy systems using 3DEXPERIENCE platform of Dassault Systemes

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

통합마스트-송수신 모듈의 열화인자 분석을 통한 결함진단 및 잔여수명 예측 연구

Frequently installed on latest destroyers, integrated mast (I-mast) conducts tasks including coast and air-space surveillances. I-mast is a complex multi-domain system with various mechanical and electrical subsystems and components. In the perspective of diagnosis and maintenance, it is important to technologically manage this system for long operating time periods and eventually increase its survivability. We explore the prognostics and health management (PHM) process of I-mast and transmitter receiver module (TRM) using the framework of Modelica-based system simulation. With Modelica, one can reuse components employed in a model, easily model a system expressed in mathematical language, and conduct a multi-dimensional, multi-physics analysis. First, failure mode effects and criticality analysis (FMECA) was conducted to extract the major failure and degradation factors of an I-mast structure and TRM under the vessel’s vibrating and operating conditions. To quantitatively assess the risk priority number (RPN), we propose the use of design of experiments (DoE) and analysis of variance (ANOVA). To preprocess the vibration acceleration signals, principal component analysis (PCA) was used to identify major features from statistical multi-components. Subsequently, based on the diagnosis of fault states, the prediction of remaining useful life (RUL) of I-mast and TRM has been discussed in the context of PHM.

Аналіз операційного середовища та ймовірні сценарії застосування Повітряних Сил Збройних Сил України

У статті розглянуто основні чинники, що визначають умови операційного середовища, в якому діють Повітряні Сили Збройних Сил України. Проаналізовано сучасні сили та засоби повітряного нападу, засоби протиповітряної оборони, тенденції їх розвитку, застосування та впливу на операційне середовище. Розглянуто можливі сценарії застосування Повітряних Сил Збройних Сил України в умовах сучасного та майбутнього операційного середовища.Проведений аналіз основних чинників і умов, які визначатимуть майбутнє операційне середовище та ймовірні сценарії застосування Повітряні Сили Збройних Сил України, свідчить, що потрібно бути готовими до виконання нових “нетипових” завдань в таких нових формах дій, як багатодоменні операції (битви).Саме умови операційного середовища визначатимуть напрями розвитку форм дій і способів застосування Повітряних Сил Збройних Сил України. Зміна поглядів щодо застосування повітряних сил в сучасних операціях у відповідності до доктрин та стандартів країн НАТО вимагатиме об’єднання різнорідних сил та засобів усіх видів збройних сил в складну багатодоменну систему.Внутрішньовидова інтеграція окремих систем розвідки, управління та ураження Повітряних Сил в складну систему дозволить оперативно реагувати на зміни бойової обстановки, адаптувати (змінювати або уточнювати) завдання Повітряних Сил з випередженням противника у прийнятті рішень, а в подальшому – інтегрувати її в єдину складну систему видів Збройних Сил України.Тому вже сьогодні потрібно визначити шляхи реформування Повітряних Сил Збройних Сил України та напрями розробки та модернізації їх озброєння та військової техніки, орієнтовані на мережеве бойове застосування.Реалізація концепцій “бойові дії в єдиному інформаційному просторі” та “багатодоменна операція (битва)”, дозволить Повітряним Силам Збройних Сил України на якісно новому рівні вести спільні дії у складі об’єднаних угруповань різновидових, різнорідних сил.

Evaluation of gas fuel and biofuel usage in turbine

<p>Modern gas turbines are a significant source for power generation and prime mover for marine propulsion. The depleting fossil fuel sources have provided a cue for broader implementation and usage of renewable energy. Biofuel has been touted as a substitute for natural gas to power gas turbines. To confirm the dependability and reliability of this attempt in a complex multi-domain system, for example, the gas turbine, the fuel system of the micro-gas turbine is designed and modelled using MATLAB Simulink. The model; simulates the; transient and steady state of a gas turbine’s nominal functional situations. Evaluations between the field data and; simulation outcomes validate a high degree of correspondence. The fuel system in the micro;-gas turbine simulation model is also optimized with the experimental data. </p>

Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving

The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of parameters, constraints, and objective functions of the optimization model. The simulation during the optimization iterative process needs only to resolve the variables on the MSG, and therefore this method could decrease the simulation time greatly during every iterative step of the optimization process. As an example, the parameter optimization on economy of fuel for a heavy truck was realized to demonstrate the efficiency of this solving strategy. This method has been implemented in MWorks—a Modelica-based simulation platform.

Innovative Digital Manufacturing Curriculum for Industry 4.0

Manufacturing companies across all major industries are facing serious challenges trying to competitively design and manage modern products, which are becoming increasingly complex multi-domain systems or “systems of systems”. Model-based systems driven product development (or SDPD, for Systems Driven Product Development) has been proposed as a solution based on driving the product lifecycle from the systems requirements and tracing back performance to stakeholders’ needs through a RFLP (Requirement, Functional, Logical, Physical) traceability process. The SDPD framework integrates system behavioral modeling with downstream product design and manufacturing process practices to support the verification/validation of the systems behavior as products progress through all phases of the lifecycle, as well as the optimization of trade-offs decisions by maintaining the cross-product digital twin and thread for global decision optimization in an efficient and effective way. We have developed an innovative digital manufacturing curriculum (designed around the SDPD paradigm) that is based on the digitalization of the SE (Systems Engineering) process through the integration of modelling and simulation continuum, in the form of Model-based Systems Engineering (MBSE), with Product lifecycle management (PLM). At the core of this curriculum is a shift of focus from theory to implementation and practice, through an applied synthesis of engineering fundamentals and systems engineering, that is driven by a state-of-the-art digital innovation platform for product (or system) development consisting of integrated software (digital) tools spanning the complete lifecycle. The curriculum consists of three key components, namely, modelling and simulation continuum, traceability, and digital thread. The curriculum provides a foundation for implementing the digital twin and supports the training of the next generation of engineers for Industry 4.0. The digital manufacturing (or SDPD) framework is applied in the design and optimization of an electric skateboard. The implementation demonstrates: 1) The benefits of digitalization/model-based engineering when developing complex multi-domain products or systems; 2) The ability of students to effectively complete a real-life modern product development within the time line of one semester; 3) The provision of MBSE curriculum for Engineering Education 4.0, characterized by key, integrated skills for the digital enterprise and Industry 4.0.

Virtual prototyping for maritime crane design and operations

This paper presents the implementation of the virtual prototyping system for maritime crane design and operations. The study is designed to bridge the following gaps in maritime crane system simulations. First, the virtual prototyping system introduces an open and flexible platform oriented to overall product and system design, modeling, simulation and visualization. Second, the virtual simulator for operations based on the proposed framework is reinforced by high fidelity models of physics and dynamics. The paper discusses the challenges in virtual prototyping of complex multi-domain systems from the perspectives of modern complex engineering system design, modeling and simulation of multi-domain dynamic systems, large set of data exchange for communication and visualization. The software architecture of the system is based on the application of the functional mock-up interface standard. This utilizes the current available modeling and simulation tools, and allows for the exchange and reuse of models. Simulations in a virtual environment permit the evaluation of multiple trade-offs and alternative solutions from early design stages. As a case study and verification, the knuckle boom crane systems were implemented. The virtual crane simulator proved the effectiveness of the proposed virtual prototyping system in solving the long-existing challenges in simulations of complex multi-domain systems.

Adaptive Fuzzy Sugeno Large of Maxima Optimization for Gas Turbine Biofuel Speed Controllers

Modern day gas turbines are important sources of electrical power generation, and are regarded as the prime movers in aerospace, marine propulsion. The depletion of fossil fuel resources has paved the way for the usage of biofuel and renewable energy. The challenge to this is the fact that biofuel has been considered as an alternative to fossil fuel for power generation to ensure the reliability and dependability of renewable fuels in a complex multi-domain systems such as gas turbines. The main objective of this study is to optimize the fuel control system parameters of the Rowen gas turbine model and the turbine speed controllers. Furthermore, to render the Rowen model simulation relevant for biofuel applications, real-time experimental data on speed and loads were obtained. Thus, this paper proposes micro turbines that run on biofuel-based tuned Fuzzy Sugeno Large of Maxima (FSLOM). Four controllers were used to track the speed and load data. The experimental results show that, fuzzy controllers with online gain tuning, result in the best speed controllers performance (2.0- 1.05 Sec) for the biofuel operation compared to other controllers.

Livestock system sustainability and resilience in intensive production zones: which form of ecological modernization?

Changes in agriculture during the twentieth century led to high levels of food production based on increasing inputs and specialization of farms and agricultural regions. To address negative externalities of these changes, two forms of ecological modernization of agriculture are promoted: “weak” ecological modernization, mainly based on increasing input efficiency through crop and animal monitoring and nutrient recycling, and “strong” ecological modernization, based on increasing agrobiodiversity at different space and time scales and within or among farms to develop ecosystem services and in turn reduce industrial inputs even more. Because characterizing the sustainability of these two forms of ecological modernization remains an issue, we review the literature on livestock systems to compare their advantages and drawbacks. After defining the livestock system as a local social–ecological system embedded in a complex multi-level and multi-domain system, we characterize the two forms of ecological modernization (weak vs. strong). When sustainability is defined as a state that should be maintained at a certain level and assessed through a set of indicators (environmental, economic, and social), we highlight that one ecological modernization form might have an advantage for certain sustainability criteria, but a disadvantage for others. When sustainability is viewed as a process (resilience), we find that these two forms of ecological modernization are based on different properties: governance of the entire agri-food chain for weak ecological modernization versus local governance of agriculture and its biophysical and social diversity and connectivity, and management of slow variables for strong ecological modernization. The relevance of this sustainability-analysis approach is illustrated by considering different types of dairy livestock systems, organic agriculture and integrated crop–livestock systems.

Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method

The calibration of the model is one of the most important steps in the development of models of engineering systems. A new approach is presented in this study to calibrate a complex multi-domain system. This approach respects the real characteristics of the circuit, the accuracy of the results, and minimizes the cost of the experimental phase. This paper proposes a complete method, the Global Optimization Method for Parameter Calibration (GOMPC). This method uses an optimization technique coupled with the simulated model on simulation software. In this paper, two optimization techniques, the Genetic Algorithm (GA) and the two-level Genetic Algorithm, are applied and then compared on two case studies: a theoretical and a real hydro-electromechanical circuit. In order to optimize the number of measured outputs, a sensitivity analysis is used to identify the objective function (OBJ) of the two studied optimization techniques. Finally, results concluded that applying GOMPC by combining the two-level GA with the simulated model was an efficient solution as it proves its accuracy and efficiency with less computation time. It is believed that this approach is able to converge to the expected results and to find the system's unknown parameters faster and with more accuracy than GA.

Static and dynamic modeling of a diesel fed fuel cell power supply

Fuel cell supplied auxiliary power units could ease the development of fuel cell systems in transportation application if they are fed by conventional hydrocarbons like diesel. Then a fuel processor has to be used to convert the hydrocarbon in a hydrogen rich gas mixture with a low rate of contaminant. The temperatures of the fuel processor modules and the mass flows have to be controlled. The energetic macroscopic representation (EMR) is a causal, graphic modeling tool for complex multi-domain systems that can be used for the design of the control structure through the inversion of model. In this work EMR is used to model a diesel supplied low temperature fuel cell unit including the fuel processor, the fuel cell stack (HTPEM) as well as the supply system of the mass flows. The presented fuel processor and HTPEM models are validated against experimental results. The structure of the temperature and mass flow controls in the fuel processor and supply system are derived. Both the model and the control are implemented in Matlab/Simulink™ and validated.

Inversion-Based Control of a Proton Exchange Membrane Fuel Cell System Using Energetic Macroscopic Representation

This paper introduces a fuel cell system model based on energetic macroscopic representation (EMR). EMR is a causal graphic modeling approach to describe complex multidomain systems, which facilitates inversion-based control structure development called maximum control structure (MCS). The EMR model is derived for a commercially available fuel cell system and corresponds well with experimental results. An inversion-based control is proposed for the air supply subsystem. The control corresponds well with the unknown internal control. The application of EMR and MCS marks a promising approach: Control structure development based on experience is replaced by a systematic approach. This is especially meaningful for complex multidomain systems such as fuel cell systems.

Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming

This paper suggests a unified and automated design methodology for synthesizing designs for multi-domain systems, such as mechatronic systems. A multi-domain dynamic system includes a mixture of electrical, mechanical, hydraulic, pneumatic, and/or thermal components, making it difficult use a single design tool to design a system to meet specified performance goals. The multi-domain design approach is not only efficient for mixed-domain problems, but is also useful for addressing separate single-domain design problems with a single tool. Bond graphs (BGs) are domain independent, allow free composition, and are efficient for classification and analysis of models, allowing rapid determination of various types of acceptability or feasibility of candidate designs. This can sharply reduce the time needed for analysis of designs that are infeasible or otherwise unattractive. Genetic programming is well recognized as a powerful tool for open-ended search. The combination of these two powerful methods is therefore an appropriate target for a better system for synthesis of complex multi-domain systems. The approach described here will evolve new designs (represented as BGs) with ever-improving performance, in an iterative loop of synthesis, analysis, and feedback to the synthesis process. The suggested design methodology has been applied here to three design examples. The first is a domain-independent eigenvalue placement design problem that is tested for some sample target sets of eigenvalues. The second is in the electrical domain––design of analog filters to achieve specified performance over a given frequency range. The third is in the electromechanical domain––redesign of a printer drive system to obtain desirable steady-state position of a rotational load.