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Passive vibration isolation techniques with low-frequency characteristics have been a hot topic in the aerospace field. A hydraulic inertial vibration isolator is a highly effective type of isolator for controlling low-frequency vibrations. It typically consists of a main spring, a minor spring, an inertial mass, and a fluid domain. Due to its multi-domain nature, analyzing the isolation mechanism of this type of isolator is challenging. The bond graph method is employed to establish the dynamic model of the isolator. Subsequently, the state equations of the isolator are derived, and the energy equations of both the mechanical and the fluid parts of the isolator are obtained. Based on this, the energy transfer characteristics between the mechanical and fluid domains inside the isolator under external excitation are discussed. The time-domain response of the forces transmitted to the foundation is analyzed. It is shown that the anti-resonance frequency occurs when the forces transmitted to the foundation generated by the main spring and the fluid pressure are equal to that of the minor spring. To verify the proposed method's correctness, a prototype of the isolator is designed and a carefully designed experiment is conducted. The acceleration transmissibility of the isolator is used to conduct a comparative study. The results show that the theoretical results are in good agreement with the experimental results. To depict the dynamic characteristics of the isolator under large amplitude vibration, the nonlinear dynamic model of the isolator is developed, and the corresponding force transmissibility of the isolator is formulated. The energy flow between the mechanical and the fluid domains under this condition is also analyzed. The results indicate that the energy flow responses exhibit a similar change tendency to the force transmissibility. However, the peak of the energy ratio between the mechanical subsystem and the fluid is the same as the linear condition, suggesting that this value is determined by the amplification ratio of the isolator. This research provides enhanced physical insight to understand the dynamic characteristics of this type of isolator and will help to shorten the design cycle of the isolator. [ABSTRACT FROM AUTHOR]

Due to the intermittent contact with the wellbore, determining torque and drag for deviated wells is difficult. Most models have ignored drill string stiffness and assumed continual contact to simplify derivation. However, the accuracy of these ‘soft-string’ models is restricted, especially at high dogleg severities. On the other hand, most ‘stiff-string’ models rely on computationally intensive approaches or continuous contact assumptions. To mitigate these issues, a computationally efficient penalty-based wellbore contact algorithm has been developed based on vector calculation, which at most requires two dot products and three arithmetic operations to determine contact locations. This algorithm is incorporated into a 3D multibody dynamics (MBD) model, which utilizes rigid drill-string segments based on the Newton-Euler formulation, connected via axial, shear, torsional, and bending springs to capture drill string flexibility. This model performs simulations faster than real-time and has been validated using surface measurements from a completed well.

A regression model based on graph neural network, tailored for electric circuit dynamics prediction is introduced, providing converter performance predictions on converter circuit level and internal parameter variations. Regardless of the number of components or connections present in a converter circuit, the proposed model can be readily scaled to incorporate different converter circuit topologies. Moreover, the model can be used to analyse converter circuits with any number of circuit components and any control parameters variation. To enable the use of machine learning methods and applications, all physical and switching circuit properties such as converter circuits operating in continuous conduction mode or discontinuous conduction mode are accurately mapped to graph representation. Three of the most common converters (Buck, Boost, and Buck-boost) are used as example circuits applied to model and the target is to predict the gain and current ripples in inductor. The model achieves 99.51% on the R 2 measure and a mean square error of 0.0263. [ABSTRACT FROM AUTHOR]

Recently, environmental regulations have been strengthened due to climate change. This change comes in a way that limits emissions from ships in the shipbuilding industry. According to these changes, the trend of ship construction is changing installing pollutant emission reduction facilities such as scrubbers or applying alternative fuels such as low sulfur oil and LNG to satisfy rule requirements. However, these changes are focused on large ships. Small ships are limited in size. So, it is hard to install large facilities such as scrubbers and LNG propulsion systems, such as fishing boats that require operating space. In addition, in order to apply the pure electric propulsion method, there is a risk of marine distress during battery discharge. Therefore, the application of the electric–diesel hybrid propulsion method for small ships is being studied as a compromised solution. Since hybrid propulsion uses various energy sources, a method that can estimate effective efficiency is required for efficient operation. Therefore, in this study, a Bond graph is used to model the various energy sources of hybrid propulsion ships in an integrated manner. Furthermore, based on energy system modeling using the Bond graph, the study aims to propose a method for finding the optimal operational scenarios and reduction ratios for the entire voyage, considering the navigation feature of each different maritime region. In particular, the reduction gear is an important component at the junction of the power transmission of the hybrid propulsion ship. It is expected to be useful in the initial design stage as it can change the efficient operation performance with minimum design change. [ABSTRACT FROM AUTHOR]

Motivation Accurately predicting molecular metabolic stability is of great significance to drug research and development, ensuring drug safety and effectiveness. Existing deep learning methods, especially graph neural networks, can reveal the molecular structure of drugs and thus efficiently predict the metabolic stability of molecules. However, most of these methods focus on the message passing between adjacent atoms in the molecular graph, ignoring the relationship between bonds. This makes it difficult for these methods to estimate accurate molecular representations, thereby being limited in molecular metabolic stability prediction tasks. Results We propose the MS-BACL model based on bond graph augmentation technology and contrastive learning strategy, which can efficiently and reliably predict the metabolic stability of molecules. To our knowledge, this is the first time that bond-to-bond relationships in molecular graph structures have been considered in the task of metabolic stability prediction. We build a bond graph based on 'atom-bond-atom', and the model can simultaneously capture the information of atoms and bonds during the message propagation process. This enhances the model's ability to reveal the internal structure of the molecule, thereby improving the structural representation of the molecule. Furthermore, we perform contrastive learning training based on the molecular graph and its bond graph to learn the final molecular representation. Multiple sets of experimental results on public datasets show that the proposed MS-BACL model outperforms the state-of-the-art model. Availability and Implementation The code and data are publicly available at https://github.com/taowang11/MS. [ABSTRACT FROM AUTHOR]

The bond-graph (BG) enables to model multi-physics systems in the energy domains. This paper shows that BG models are an alternative, more intuitive and simpler to interpret, graphical tool as compared to existing dynamic models for performance prediction of the line-start permanent-magnet synchronous motors (LSPMSMs). It includes electrical, magnetic, and mechanical energy domains where the stator windings, rotor bars, and permanent magnets are the main components of the model. The dynamical equations of the proposed BG are extracted and solved using the MATLAB ordinary differential equation solver for a sample LSPMSM. The dynamics of the studied LSPMSM is investigated under two operating conditions (1) a healthy motor and (2) a motor with broken rotor bars. The simulation results are compared with the finite-element analysis (FEA) as the most accurate method to simulate the electric motor behaviour. It is shown that the proposed BG model accurately predicts the performance parameters of the motor such as current, torque, and speed under various operating conditions. In addition to the modelling validation, the transient behavior of the LSPMSM are discussed to describe the possible reasons for its behaviour during transient and steady-state operating conditions. [ABSTRACT FROM AUTHOR]

Just recently, wind energy continues to be one of most important renewable energy resources, by cause of its production is ecologically friendly; for that reason, the technology built for the renewable energy production by way of wind turbines takes excessive challenges in the study. Due to several physical domains prevailing in wind turbine, namely aerodynamical, mechanical and electrical, the modeling of wind turbine is problematic; thus, modeling based on physical techniques has a superior reliability in these circumstances. One of these approaches is bond graph modeling that model system evolved from conservation law of both of mass and energy comprising in the structures. This study presents modeling the parts of bond graph-based wind turbine. Then, sub models are connected together to attain the entire model of wind turbine for simulation based on 20-Sim software. The proposed wind turbine is 2.5 kW of variable velocity wind turbine with three blades, gearbox, tower, and doubly-fed induction generator type. The effectiveness of bond graph modeling system on wind turbine has been proven in simulation results. [ABSTRACT FROM AUTHOR]

This article proposes an integration between a graph framework for circuit representation and a Graph neural network (GNN) model suitable for different machine learning (ML) applications. Furthermore, the paper highlights design steps for tailoring and using the GNN-based ML model for converter performance predictions based on converter circuit level and internal parameter variations. Regardless of the number of components or connections present in a converter circuit, the proposed model can be readily scaled to incorporate different converter circuit topologies and may be used to analyze such circuits regardless of the number of components used or control parameters varied. To enable the use of ML methods and applications, all physical and switching circuit properties including operating mode, components and circuit behavior must be accurately mapped to graph representation. The model scalability to other circuit types and different connections and circuits elements is also tested, while being studied in the most common DC-AC inverter in grid connected systems including filter and filterless configurations. The filtered and filterless DC-AC inverter circuits are used to evaluate the model, scoring $R^{2}$ greater than 99% in most cases and a mean square error (MSE) tending to zero.

In addressing challenges associated with the constrained enhancement of range performance in pure electric tractors, the difficulty in achieving genuinely eco-friendly agricultural machinery with hybrid powertrains, and the outdated approaches to energy optimization in hydrogen fuel cell tractors, a comprehensive methodology covering the entire spectrum of system design, optimization, and validation is presented. This method constructs an integrated electromechanical and hydraulic power bond graph model for an agricultural tractor, encompassing energy systems, drive systems, and lifting systems. It achieves rapid interaction between 20-sim software and Matlab/Simulink software through a script toolbox. In order to tackle the real-time applicability challenges of dynamic programming, a hierarchical dynamic programming is proposed. The dynamic programming strategy results serve as the upper-level output. At the lower layer, preprocessing of impact value variables is carried out to filter the input variables for the general regression neural network. This enables the real-time application of the dynamic programming algorithm and significantly reduces training time. Finally, validation is conducted through both model-in-the-loop and hardware-in-the-loop. The results demonstrate the correctness and superiority of the designed tractor simulation model and energy management strategy model compared to the power-following strategy. Specifically, in plowing conditions, the dynamic programming and hierarchical dynamic programming strategies exhibited reductions of 10.278% and 6.728%, respectively, in equivalent hydrogen consumption compared to power-following strategy. In transportation conditions, the reductions are 16.02% for dynamic programming strategy and 4.87% for hierarchical dynamic programming strategy, both relative to power-following strategy. This study lays the theoretical groundwork for modeling dynamic systems of tractors and optimizing energy management.

Passive vibration isolation techniques with low-frequency characteristics have been a hot topic in the aerospace field. A hydraulic inertial vibration isolator is a highly effective type of isolator for controlling low-frequency vibrations. It typically consists of a main spring, a minor spring, an inertial mass, and a fluid domain. Due to its multi-domain nature, analyzing the isolation mechanism of this type of isolator is challenging. The bond graph method is employed to establish the dynamic model of the isolator. Subsequently, the state equations of the isolator are derived, and the energy equations of both the mechanical and the fluid parts of the isolator are obtained. Based on this, the energy transfer characteristics between the mechanical and fluid domains inside the isolator under external excitation are discussed. The time-domain response of the forces transmitted to the foundation is analyzed. It is shown that the anti-resonance frequency occurs when the forces transmitted to the foundation generated by the main spring and the fluid pressure are equal to that of the minor spring. To verify the proposed method’s correctness, a prototype of the isolator is designed and a carefully designed experiment is conducted. The acceleration transmissibility of the isolator is used to conduct a comparative study. The results show that the theoretical results are in good agreement with the experimental results. To depict the dynamic characteristics of the isolator under large amplitude vibration, the nonlinear dynamic model of the isolator is developed, and the corresponding force transmissibility of the isolator is formulated. The energy flow between the mechanical and the fluid domains under this condition is also analyzed. The results indicate that the energy flow responses exhibit a similar change tendency to the force transmissibility. However, the peak of the energy ratio between the mechanical subsystem and the fluid is the same as the linear condition, suggesting that this value is determined by the amplification ratio of the isolator. This research provides enhanced physical insight to understand the dynamic characteristics of this type of isolator and will help to shorten the design cycle of the isolator.

Current physical systems are built in more that one coordinate: for example, electrical power systems, aeronautical systems and robotic systems can be modeled in multibond graphs (M B G) . However, in these systems, some elements use only one axis or dimension—for example, actuators and controllers—which can be modeled in bond graphs (B G) . Therefore, in this paper, modeling of systems in multibond graphs and bond graphs ( M B G - B G ) is presented. Likewise, the junction structure of systems represented by ( M B G - B G ) is introduced. From this structure, mathematical modeling in the state space is presented. Likewise, modeling of systems on a platform ( M B G - B G ) can be seen as symmetric to the mathematical model that represents these systems. Finally, a synchronous generator modeled by ( M B G - B G ) as a case study is developed, and simulation results using 20-Sim software are shown. Furthermore, an electrical power system connected to the power supply of a DC motor as another case study is explained. [ABSTRACT FROM AUTHOR]

Recently, environmental regulations have been strengthened due to climate change. This change comes in a way that limits emissions from ships in the shipbuilding industry. According to these changes, the trend of ship construction is changing installing pollutant emission reduction facilities such as scrubbers or applying alternative fuels such as low sulfur oil and LNG to satisfy rule requirements. However, these changes are focused on large ships. Small ships are limited in size. So, it is hard to install large facilities such as scrubbers and LNG propulsion systems, such as fishing boats that require operating space. In addition, in order to apply the pure electric propulsion method, there is a risk of marine distress during battery discharge. Therefore, the application of the electric–diesel hybrid propulsion method for small ships is being studied as a compromised solution. Since hybrid propulsion uses various energy sources, a method that can estimate effective efficiency is required for efficient operation. Therefore, in this study, a Bond graph is used to model the various energy sources of hybrid propulsion ships in an integrated manner. Furthermore, based on energy system modeling using the Bond graph, the study aims to propose a method for finding the optimal operational scenarios and reduction ratios for the entire voyage, considering the navigation feature of each different maritime region. In particular, the reduction gear is an important component at the junction of the power transmission of the hybrid propulsion ship. It is expected to be useful in the initial design stage as it can change the efficient operation performance with minimum design change.

The main contribution of this article is creating synergy between subjects; this means that students use the same graphical tool in several subjects. So far, the bond graph has not been used in control theory, but it is the "native language" of mechatronics engineers, so we would like to introduce it into the teaching of control theory. The bond graph method is proposed as a novel teaching method to teach mechatronics subjects in the paper. The bond graph is a graphical alternative to ordinary differential equations from a mathematical standpoint. Traditionally, control theory employs ordinary differential equations, as they are familiar to control theorists. However, mathematically, both approaches are equivalent but require a slightly different approach in their application. This article highlights the mathematical similarities between the two approaches while emphasizing the distinctions in graphical representation. Another contribution is that the PID and sliding mode controller are represented using the bond graph method. In the meantime, through the use of practical examples, we effectively illustrate how the same problem can be solved using either approach. In the training materials, the PID controller and an adaptive robust sliding mode controller (ARSMC) with the bond graph are utilized as examples to demonstrate synergy in mechatronics. Finally, we present proof that mechatronic engineers achieve superior outcomes when utilizing the bond graph approach, based on test results from undergraduate students. [ABSTRACT FROM AUTHOR]

The effectiveness of model-based diagnosis strongly depends on the model's authenticity and is impacted by various uncertainties. Measurement uncertainty is governed by the probability method, and parameter uncertainty can be handled by the linear fractional transformation, whereas structure uncertainty and errors are rarely considered. In this paper, an improved bond graph (BG) model is proposed, which adopts subsystems to substitute modelling errors, namely parameter uncertainty and structure uncertainty. A multi-dimensional Fibonacci optimization algorithm is developed to identify the parameters of subsystems to obtain the subsystem-based diagnostic hybrid BG (SDHBG) model. Fault diagnosis is realized by comparing the residuals and thresholds derived from the SDHBG. Experiments are conducted to validate the key concepts of the proposed methods. Subsequently, the results suggest its effectiveness. [ABSTRACT FROM AUTHOR]

A new approach to model the motion of floating bushings in external gear pumps is presented in this article, where lubrication conditions have been introduced using dimensional analysis. This model is based on Bond Graph diagrams and has been experimentally validated in lab tests measuring the movement of the floating bushing inside the gear pump by means of laser micrometers. The novelty of this research is the creation of a simple and experimentally validated tool for the behaviour study of these types of pumps, which allows the simulation of a dynamic rigid solid in a fluid boundary with clearances of the order of microns, without using powerful CFD tools, with very short execution times, and using conventional computational tools. The qualitative behaviour of the model with respect to the experimental results is very similar, adjusting the numerical values with very acceptable accuracies by taking into account the precision of the experimental measurements, and allows us to use the model to interpret the volumetric and mechanical efficiency variations according the operating conditions. [ABSTRACT FROM AUTHOR]

The authors' previously published results on a bond-graph-compatible and nonholonomic momentum form of Kane's equations are extended, from scleronomic to rheonomic systems. The momentum form is found to be partially Hamiltonian, and bond-graph-compatible velocity forms are also found, of which one is partially Lagrangian. The results are derived for general particle systems and then specialized to rigid-body systems. Direct dependence of kinetic energy upon time is avoided, making the results power-conserving and suitable for use with bond graphs. A new nonholonomic IC (NIC) bond-graph element is defined, and bond graphs for the partially Hamiltonian and partially Lagrangian forms are exhibited using this element. [ABSTRACT FROM AUTHOR]

The variable speed transmission system employs compound planetary gear trains and clutches. Compared to traditional aviation transmission systems, the added control parts in the variable transmission system result in a stronger coupling between components and more complex dynamic response characteristics. Modeling using the central mass method is complex and abstract. This study proposes a bond graph-based method for modeling and analyzing the dynamic behavior of the variable transmission system. The transmission system is divided into multiple component-level real-time dynamic models, and obtain a nonlinear multi-energy domain shift dynamic models based on energy flow paths coupling. The mathematical model is numerically solved using the Runge-Kutta method. Simulation results show that there is a delay in the speed response during the shifting process. Further analysis reveals that this “give and take” phenomenon is related to the mechanical structure and passive working principle of the one-way clutch. Subsequently, the time-domain response of the output speed under the hydraulic loading characteristic curves of linear, exponential, S-shaped, and composite functions, as well as the clutch torque, were studied. The impact of different hydraulic loading durations based on the exponential curve on component speed and torque was also analyzed. The simulation results provide a theoretical basis for designing low-shift impact, high-reliability, and high-performance variable transmission systems.

Early estimation of the fatigue life of an oil well drill string reduces the risk associated with drill string fatigue failures. In this study, a low-order computationally efficient bond graph model of a vertical well drill string and a component-level higher-order finite element model of a drill pipe threaded connection are employed to predict the fatigue damage of a given drill pipe. The bond graph is a 3D lumped segment model developed using the Newton–Euler formulation and body fixed coordinates. It is parameterized using finite element modelling simulations. The stress history from the top-level model is applied to the component-level model that contains details such as threaded geometry. Then, a multi-axial, non-proportional, and variable amplitude (MNV) fatigue estimation is performed using an open-source finite element analysis code. The fatigue prognosis approach is then demonstrated in a drill string design case study that optimizes the placement of vibration stabilizers in the wellbore to avoid severe vibrations while minimizing fatigue damage. Optimal placement of stabilizers predicts a 200% increase in fatigue life of the most vulnerable component with reference to the worst-case scenario.

Recent results on the passivity analysis and control of physical systems, based on the balance of dissipated and internally generated energy, are generalised to nonlinear systems represented by bond graphs. For linear systems, the internally generated energy associated with modulated sources can be coupled with the dissipative field, so that if external energy sources are excluded, then the system is passive (or dissipative) if the resulting composite multiport field is passive. Such a result for linear systems was previously conveniently expressed in terms of a characteristic matrix being positive semi-definite. Parasitic elements of previous works are no longer required, which allows working on the original bond graph of lower dimension than the augmented bond graph and for no-linear systems avoid inverting the dissipative nonlinear constitutive relations. For nonlinear systems, passivity is now considered through the explicit difference between the dissipated and the internally generated energy. If this energy difference is positive, the system is passive. For control systems, the current work proposes that the controller is designed to have a structure similar to the plant (linear or nonlinear), and its parameters are chosen to assure that in closed-loop the difference between the dissipated and the internally generated energy is positive. In particular, the control parameters can be chosen to assign a desired dissipated energy or to cancel by feedback the internally generated energy and to add damping, therefore achieving sufficient conditions for the passivity of the closed-loop system. [ABSTRACT FROM AUTHOR]

Dynamic interaction between train wheel and high-speed slab is an important issue when evaluating the contact force at the wheel–rail interface under the influence of an irregularity either on the train wheel or on the rail. In this paper, an influence of amplitude of irregularity along with the vehicle speed on the dynamics of wheel–rail interaction for high-speed railway tracks is being analyzed. For this purpose, single-wheel high-speed railway (HSR) track interaction models are developed using the bond graph modeling technique. The HSR track model consists of two layers of beam, i.e., rail and concrete slab. Both the rail and slab track is modeled using the Euler–Bernoulli beam theory. The Hertzian contact theory at the wheel–rail interface has been considered for this analysis. The vertical dynamic interaction between a train wheel and a high-speed slab track is compiled in the time domain using a bond graph approach coupled with a technique known as modal superposition. Irregularity present on the wheel is characterized as smooth, moderate, and severe depending upon the variation of irregularity amplitude. An expeditious increase of maximum contact force has been observed between the speed range of 200 and 250 km/h. Beyond the speed of 250 km/h, there is a gradual increment of contact force up to its peak value. When the train speed is beyond 288 km/h, there is a gradual decrease in maximum contact force. This kind of several other useful dynamic responses in terms of wheel acceleration and wheel–rail overlap are also evaluated. [ABSTRACT FROM AUTHOR]

The modeling of three-phase electrical systems in the coordinates d , q , 0 in a simple and direct way in a multibond graph approach is presented. From the graphical model obtained, a mathematical model in state space can be determined. Thus, studies of the electromagnetic transients of electrical systems can be analyzed. Likewise, this modeling in the physical domain can be applied to balanced or unbalanced systems and the storage elements (inductances and capacitances) can be connected to be linearly independent or dependent. Structural properties such as stability, controllability, observability or controller design can be analyzed in multibond graphs. The proposed methodology is applied to two examples and the simulation results are shown using 20-Sim software. [ABSTRACT FROM AUTHOR]

A detailed methodology to develop a digital twin has many useful applications in the era of technology 4.0. This study provides a framework to develop a digital twin for vibration prediction and fatigue life prognosis of a vertical oil well drillstring. The nature and the severity of the down-hole vibrations are identified and estimated based on the vibrational and operational parameter measurements made at the surface level. Because of the difficulty in accessing full-scale industrial drilling data, a reduced-scale drillstring was constructed that could exhibit bit bounce, stick-slip and whirl. A bond graph simulation model was tuned to match the apparatus, and then used to generate synthetic training data. The trained machine learning algorithm can classify the incoming surface monitoring data from the physical twin into different types and severities of vibration states which are not otherwise observable. Moreover, the classified vibration condition is used to re-configure the bond graph model with appropriate complexity to generate a loading history for fatigue life prognosis. The fatigue life estimation uses a novel combination of a low-complexity model of the entire drillstring and a high fidelity finite element model of components where stress concentrations are most severe. The digital twin detected the vibration type and its severity and estimated the remaining fatigue life of the physical system using only measurements of the motor current, rig floor axial vibration, and rotary speed.

This paper proposes a method of transferring physical continuous and switching/converter circuits working in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) to graph representation, independent of the connection or the number of circuit components, so that machine learning (ML) algorithms and applications can be easily applied. Such methodology is generalized and is applicable to circuits with any number of switches, components, sources and loads, and can be useful in applications such as artificial intelligence (AI) based circuit design automation, layout optimization, circuit synthesis and performance monitoring and control. The proposed circuit representation and feature extraction methodology is applied to seven types of continuous circuits, ranging from second to fourth order and it is also applied to three of the most common converters (Buck, Boost, and Buck-boost) operating in CCM or DCM. A classifier ML task can easily differentiate between circuit types as well as their mode of operation, showing classification accuracy of 97.37% in continuous circuits and 100% in switching circuits.

The oilwell drilling fluid flows cause viscous and hydrodynamic forces on drill strings. This effect is ignored or treated as a constant in most drill string models. The present study introduces mathematical models for lateral vibration damping and axial drag forces that are employable in lumped segment drill string models. First, the variables to which drilling fluid-generated forces are most sensitive were identified and the Response Surface Method was applied to design the experiment matrix. The lateral vibration-damping experiments, which were validated using a scaled-down physical model, and the axial drag experiments were done using Fluid-Structure Interaction simulations. The results were statistically analysed to acquire the models and were implemented in a 3D lumped segment bond graph developed using the Newton-Euler formulation and body-fixed coordinates. The results indicate a considerable effect of the extended treatment of damping and axial drag on bending moment fluctuation, wellbore interactions, and weight on bit.

Vertical Axis Wind Turbine (VAWT) technology has been identified to be a potential disruptor to the wind energy industry. However, this technology is yet to deliver the promised performance, mainly due to its system design. To help designers reveal the VAWTs’ technological potential, the current study focuses on developing a physics-based, interrelated multi-domain dynamic model of the whole system to be used for systematic design optimisation and integrated system performance analysis. To achieve this the subsystems and design parameters of a typical VAWT are first identified. Then by dynamically coupling domain-independent models of the involved components with DMST, an integrated parametric modelling platform is generated and executed in MATLAB/Simulink. The developed parametric tool is then validated at a system level using performance data from a 6kW VAWT-X Energy case study turbine, showing a high level of accuracy and robustness. Finally, the system dynamics are studied through sensitivity analyses to evaluate the suitability of the platform for further dynamic analysis and design optimisation. The simulated results indicate the existence of component interaction induced dynamics with significant impact on system performance if neglected.

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method. [ABSTRACT FROM AUTHOR]

An industrial linear stage is a device that is commonly used in robotics. To be precise, an industrial linear stage is an electro-mechanical system that includes a motor, electronics, flexible coupling, gear, ball screw, and precision linear bearing. A tight fit can provide better precision but also generates a difficult-to-model friction that is highly nonlinear and asymmetrical. Herein, this paper proposes an advanced trajectory controller based on a digital twin framework incorporated with artificial intelligence (AI), which can effectively control a precision linear stage. This framework offers several advantages: detection of abnormalities, estimation of performance, and selective control over any situation. The digital twin is developed via Matlab's Simscape and runs concurrently having a real-time controller. [ABSTRACT FROM AUTHOR]

Current physical systems are built in more that one coordinate: for example, electrical power systems, aeronautical systems and robotic systems can be modeled in multibond graphs (MBG). However, in these systems, some elements use only one axis or dimension—for example, actuators and controllers—which can be modeled in bond graphs (BG). Therefore, in this paper, modeling of systems in multibond graphs and bond graphs (MBG-BG) is presented. Likewise, the junction structure of systems represented by (MBG-BG) is introduced. From this structure, mathematical modeling in the state space is presented. Likewise, modeling of systems on a platform (MBG-BG) can be seen as symmetric to the mathematical model that represents these systems. Finally, a synchronous generator modeled by (MBG-BG) as a case study is developed, and simulation results using 20-Sim software are shown. Furthermore, an electrical power system connected to the power supply of a DC motor as another case study is explained.

The oilwell drilling fluid flows cause viscous and hydrodynamic forces on drill strings. This effect is ignored or treated as a constant in most drill string models. The present study introduces mathematical models for lateral vibration damping and axial drag forces that are employable in lumped segment drill string models. First, the variables to which drilling fluid-generated forces are most sensitive were identified and the Response Surface Method was applied to design the experiment matrix. The lateral vibration-damping experiments, which were validated using a scaled-down physical model, and the axial drag experiments were done using Fluid-Structure Interaction simulations. The results were statistically analysed to acquire the models and were implemented in a 3D lumped segment bond graph developed using the Newton-Euler formulation and body-fixed coordinates. The results indicate a considerable effect of the extended treatment of damping and axial drag on bending moment fluctuation, wellbore interactions, and weight on bit. [ABSTRACT FROM AUTHOR]

• The reliability model is established using fault energy dissipation as a key parameter. • Energy port for mechatronic component is defined to emphasize the fault energy dissipation. • Fault energy dissipation is calculated under three categories based on bond graph. • The reliability calculation method for the system composed of series components is derived. The failures of mechatronic systems, such as wear, fatigue, and aging, result in energy dissipation. This paper develops a reliability model from the perspective of fault energy dissipation for mechatronic systems. An energy port for the mechatronic component is presented, and the fault energy dissipation is calculated based on bond graph. A reliability model for mechatronic components is developed using fault dissipated energy as a pivotal parameter. By accounting for energy transfers among upstream and downstream components, a fault propagation model based on interaction equations between energy ports is studied, which allows to obtain the fault dissipated energy within the mechatronic system. Moreover, a generic method for calculating the reliability of mechatronic systems is proposed based on the total fault dissipated energy of the entire system. Considering the Electro-Hydrostatic actuator (EHA) as an example, the proposed method is compared with existing reliability models. The obtained results show that the proposed model outperforms the existing ones, and therefore it can be considered as a promising reliability evaluation method for mechatronic systems. [ABSTRACT FROM AUTHOR]

In this paper, a novel Fault Detection and Isolation (FDI) method for Proton Exchange Membrane (PEM) electrolyzers is presented. The challenge of limited availability of labeled fault data is addressed through the utilization of Bond Graphs (BG) and Self-Supervised Learning (SSL). The Linear Fractional Transformation-Bond Graph (LFT-BG) model is employed to generate uncertain residuals and pseudo labels, enabling the training of a deep neural network through self-supervised learning. Additionally, the BG-XAI method is introduced, leveraging eXplainable AI (XAI) and structural equations from Bond Graphs to provide explanations for the decisions made by the deep learning model. The superior performance of the proposed approach, particularly in dealing with limited labeled data, is demonstrated through comparative assessments against various state-of-the-art SSL methods. The demo code of the proposed method is available in this repository: https://github.com/mohan696matlab/SSL_based_Hybrid_FDI. • Uses LFT-bond graph model to generate residuals and pseudo labels for pre-training. • Introduces BG-XAI, a novel explainable AI technique using occlusion. • The proposed hybrid FDI is applied to a PEM electrolyzer stack, showing superior performance. • It deals with limited labeled fault data in renewable energy, offering a reliable solution for diagnosis. [ABSTRACT FROM AUTHOR]

The main contribution of this article is creating synergy between subjects; this means that students use the same graphical tool in several subjects. So far, the bond graph has not been used in control theory, but it is the “native language” of mechatronics engineers, so we would like to introduce it into the teaching of control theory. The bond graph method is proposed as a novel teaching method to teach mechatronics subjects in the paper. The bond graph is a graphical alternative to ordinary differential equations from a mathematical standpoint. Traditionally, control theory employs ordinary differential equations, as they are familiar to control theorists. However, mathematically, both approaches are equivalent but require a slightly different approach in their application. This article highlights the mathematical similarities between the two approaches while emphasizing the distinctions in graphical representation. Another contribution is that the PID and sliding mode controller are represented using the bond graph method. In the meantime, through the use of practical examples, we effectively illustrate how the same problem can be solved using either approach. In the training materials, the PID controller and an adaptive robust sliding mode controller (ARSMC) with the bond graph are utilized as examples to demonstrate synergy in mechatronics. Finally, we present proof that mechatronic engineers achieve superior outcomes when utilizing the bond graph approach, based on test results from undergraduate students.

A new approach to model the motion of floating bushings in external gear pumps is presented in this article, where lubrication conditions have been introduced using dimensional analysis. This model is based on Bond Graph diagrams and has been experimentally validated in lab tests measuring the movement of the floating bushing inside the gear pump by means of laser micrometers. The novelty of this research is the creation of a simple and experimentally validated tool for the behaviour study of these types of pumps, which allows the simulation of a dynamic rigid solid in a fluid boundary with clearances of the order of microns, without using powerful CFD tools, with very short execution times, and using conventional computational tools. The qualitative behaviour of the model with respect to the experimental results is very similar, adjusting the numerical values with very acceptable accuracies by taking into account the precision of the experimental measurements, and allows us to use the model to interpret the volumetric and mechanical efficiency variations according the operating conditions.

Cyber-Physical Systems (CPSs) engineering profoundly relies on modeling methods to represent the system and study the operation and cybersecurity of CPSs. The operation of a CPS is the result of the collaboration between Information Technology (IT) and Operational Technology (OT) components. While OT focuses on the system’s process physics, the emphasis of IT is on information flow. Consequently, different system models are utilized to study various aspects of CPSs, which may infer different views of the same system. The increasing complexity of CPSs and the high number of cyberattacks against Industrial Control Systems (ICSs) and CPSs in recent years have highlighted the necessity of considering these interrelations based on a unified model to analyze cybersecurity of CPSs. However, the diversity of engineering fields and implicit relations and dependencies between them have made it difficult to integrate the modeling methods towards a unified IT&OT model of CPSs.In this paper, we propose a comprehensive method, based on bond graphs, to model CPS and analyze their cybersecurity. Unlike existing methods, modeling the cyber layer along with the physical layer based on the system flow provides a holistic graphical representation of a CPS, which facilitates collaboration between IT and OT experts.

During the study, the simulation of a robotic drive was realised using the 20-sim simulation software. The assumed robotic drive consists of an ideal permanent magnetic DC motor, a gear pair to realise the speed reduction, a torque transfer torsion shaft and a differential gear pair to change the direction and inertia. The drive is controlled by a speed control loop using a PID controller. In line with the purpose and result of the study, the simulation proved the effectiveness of Bond Graph-based modelling. [ABSTRACT FROM AUTHOR]

A model based diagnosis for Proton Exchange Membrane (PEM) water electrolyser has been presented in this paper using Linear Fractional Transformation (LFT) bond graph technique. LFT helps in calculating the adaptive thresholds in order to make the diagnosis robust against parameters and measurement uncertainties, hence avoiding false alarms. A modular approach has been followed that allows the model to be adapted to different configurations of PEM electrolyser. The developed diagnosis has been first tested offline by injecting the faults into behavioural model of the PEM electrolyser and then implemented online for a single cell PEM electrolyser. • Software based robust diagnosis has been proposed for PEM electrolyser. • For the first time, diagnosis of cell/stack as well as auxiliaries has been proposed. • The proposed diagnosis is based on Linear Fractional Transformation Bond Graph. • Modular approach makes it adaptable to different configurations of PEM electrolyser. • Tested offline (using behavioural model) and online (on real PEM electrolyser). [ABSTRACT FROM AUTHOR]

The bond graph methodology for modelling an integrated energy distillation column is applied in this paper. The distillation column is built by five trays for a binary mixture. However, due to its modular construction in a bond graph, the number of trays can be increased. In order to link the analysis tools of systems modeled in the bond graph to the mathematical model given to a distillation column, a junction structure of the proposed bond graph is presented. Hence, this junction structure is a way to obtain the state space representation of the modeled column in bond graphs. Likewise, it is well known that distillation columns determine a class of nonlinear systems, so throughout this paper, these systems in a bond graph approach can be analyzed. In order to learn the behavior of the distillation column in the physical domain, simulation results using 20-Sim software are shown. In addition, with the simulation of two case studies consisting of two mixtures with different relative volatilities, the versatility of the column model in a bond graph is presented. In both cases, the increase in the feed flow, the mole fraction of the light component in the feed or the distillate reflux that enriches the concentration of light in the column determine an increase in the mole fraction of light in the distillate and in the bottom reflow. Further, the control design for a distillation column in the physical domain can be extended. [ABSTRACT FROM AUTHOR]

A fault diagnosis method using bond graphs in an expert system is proposed for a reactor coolant system. Firstly, the time causality graph and the variable relationship graph are derived from the bond graph. Secondly, the fault signature matrix is obtained by combining the change relationship of fault parameters. Finally, the fault signature matrix is used as the rule of the inference engine design in the expert system for fault diagnosis. In this paper, the key equipment of the reactor coolant system is used to verify the fault diagnosis method of the bond graph expert system, and the path reasoning relationship between alarms is obtained, which can accurately obtain the deep knowledge required by the operators. A new idea for fault diagnosis in a nuclear power plant's expert system is provided by this method. [ABSTRACT FROM AUTHOR]

The modeling of three-phase electrical systems in the coordinates d,q,0 in a simple and direct way in a multibond graph approach is presented. From the graphical model obtained, a mathematical model in state space can be determined. Thus, studies of the electromagnetic transients of electrical systems can be analyzed. Likewise, this modeling in the physical domain can be applied to balanced or unbalanced systems and the storage elements (inductances and capacitances) can be connected to be linearly independent or dependent. Structural properties such as stability, controllability, observability or controller design can be analyzed in multibond graphs. The proposed methodology is applied to two examples and the simulation results are shown using 20-Sim software.

This work presents a scheme of a dual input–singleoutput closed-circuit hydraulic drive consisting of a pump and a hydraulic motor both variable displacement axial-piston type. It analyzes the dynamics of the hydraulic drive system through modeling and simulation. The model considers state-dependent non-linear losses and the temporal behaviors of the rotary actuators identified from the test data and these models are also considered in the simulation studies. The work also discusses the effects of nominal changes in the values of the decisive parameters on the drive's predicted performance to justify its practical range of application. Without dismantling the system components, comparing the test data with the simulation results verifies the system model for the drive's various working conditions. This aspect is essential for a priori analysis of a system and lends itself to be helpful for application engineers for the initial selection and development of a similar drive used in the mobile equipment. • Dynamics of a system with variable displacement pump and motor is analyzed. • The system is characterized using semi-empirical relations from the test data. • The influences of fluid bulk modulus and load inertia on transients are studied. [ABSTRACT FROM AUTHOR]

The objective of this work is to develop a supervisory HMI performed with WinCC software using a bond graph approach which is a generic and general tool that allows to give realistic and reasonable performances of a nonlinear dynamic system. We have also introduced an optimal database to improve the control of our system. Simulations are presented to highlight the usefulness of the proposed design and its modelling. Our contribution consists of the modelling, control and visualisation of a real FESTO AFB system by integrating two 'PID' technology objects for level control on the one hand and temperature control on the other hand. [ABSTRACT FROM AUTHOR]

In musculoskeletal actuation systems, it is essential to understand and analyze the extension and force patterns generated in the muscle-tendon units (MTUs) responsible for the motion of a phalange. This work proposes a systematically developed bond graph model for the muscle-tendon actuation system for the desired motion of the phalange of the hand. The phalange is represented by a cylindrical rigid body, actuated by four MTUs attached to it symmetrically. The MTU is based on Hill's muscle model. The role of the central nervous system (CNS) that commands desired motions to the phalange is emulated through a virtual domain in the model. The virtual domain decides the activation pattern of MTUs. Accordingly, the MTUs apply forces on the phalange to achieve the desired motion. Simulation results for important motions such as flexion-extension, adduction-abduction, and circumduction show that the model effectively captures the dynamics of the musculoskeletal actuation system. [ABSTRACT FROM AUTHOR]

Fault Isolation is a critical step in any fault diagnosis method, and the difficulty increases with the complexity of the system. When there are not enough sensors in the system, the traditional model-based methods have trouble isolating the fault. The current data-driven FDI techniques generally emphasize accuracy and rarely draw attention to the lack of readily accessible labeled data in the industry. This study aims to develop a hybrid fault diagnosis method by combining the well-established graphical technique of Bond-Graph (BG) with the powerful pattern recognition ability of Convolutional Neural Network (CNN) to improve the overall fault isolation performance. A new formalism named BG-CNN method is proposed, which can utilize the residuals generated from the BG model in a CNN for improved fault isolation with a minimal number of labeled data. The single incipient faults as well as multiple simultaneous faults can be isolated by this method. The BG-CNN method demonstrates a high level of performance for the FDI of a Direct Current (DC)-motor with a relatively small number of labeled samples. In comparison, the traditional CNN method using raw sensor data requires a significantly larger number of labeled samples to achieve a similar level of performance. • Traditional model-based and data-driven fault diagnosis methods have certain drawbacks. • A hybrid approach, combining both methods can enhance fault isolation with limited labeled data. • The novel BG-CNN method uses residuals from a bond graph model in a CNN, boosting fault isolation. • BG-CNN excels in isolating both single and multiple simultaneous faults. • It surpasses traditional methods in fault isolation, requiring fewer labeled samples for comparable results. [ABSTRACT FROM AUTHOR]

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.

An industrial linear stage is a device that is commonly used in robotics. To be precise, an industrial linear stage is an electro-mechanical system that includes a motor, electronics, flexible coupling, gear, ball screw, and precision linear bearing. A tight fit can provide better precision but also generates a difficult-to-model friction that is highly nonlinear and asymmetrical. Herein, this paper proposes an advanced trajectory controller based on a digital twin framework incorporated with artificial intelligence (AI), which can effectively control a precision linear stage. This framework offers several advantages: detection of abnormalities, estimation of performance, and selective control over any situation. The digital twin is developed via Matlab’s Simscape and runs concurrently having a real-time controller.

In this paper, the bond graph modeling for the control of tracking systems has been applied. The closed loop system is built by the bond graph model of the system to be controlled, an additional bond graph according to the tracking input signal, and feedback gains in the physical domain. Hence, a procedure to obtain the closed loop tracking system is proposed. The proposal of modeling and tracking control systems in this paper determines symmetries in the bond graph approach with respect to the traditional algebraic approach. The great advantage of this graphical approach is that the mathematical determination of the system model is not necessary. Moreover, the coefficients of the characteristic polynomial using unidirectional causal loops of the closed loop system modeled in bond graphs are obtained. A case of study of a DC motor connected to an electrical supply network and a mechanical load is considered. Tracking control for the step, ramp, and acceleration type input signals in a bond graph approach are applied. In order to show the effectiveness of the proposed procedure, the simulation results are shown. [ABSTRACT FROM AUTHOR]