Bond Graph Research Articles

MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning.

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. 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. The code and data are publicly available at https://github.com/taowang11/MS.

Bond-Graph model based stepper motor output characteristics simulation implemented in LabVIEW environment

In the industry, simulations are of great importance. They enable measurements to be made in different conditions about a virtual device, which are highly comparable to measurements made in a real life scenarios. Because of their wide range of usage in lower power drive systems, where precision and simplicity is a must, the subject of study is a permanent magnet stepper motor. For precise positioning purposes, it is essential to know the positioning behaviour of these devices. The model construction process involved an intermediate step, which consisted of creating the Bond-Graph of the motor based on pre-defined models available in the literature in this field. In the next step, the Bond-Graph model was converted to a block diagram of the motor. This permitted the direct implementation of the motor model in LabVIEW visual programming environment. The preliminary steps allows us to check and confirm the functionality and correctness of the model. This article covers in detail the model conversion and implementation steps of the simulation. At the end, the functionality of the simulation was tested.

Wheel Torque Distribution Control Strategy for Electric Vehicles Dynamic Performance With an Electric Torque Vectoring Drive Axle

The ability of active chassis control for dynamic performance improvement of electric vehicles with centralized drive system is limited duo to the driving torque between two-side wheels always equally distributed. To solve this problem, this paper proposes a novel electric torque vectoring (TV) drive-axle and its torque distribution control strategy, which can realize the function of arbitrary distribution of the left/right wheel driving torque in the form of centralized drive, thereby improving the dynamic performance of the vehicle. First, the structure and torque distribution principle of TV drive-axle are analyzed theoretically. Subsequently, an accurate mathematical model of TV drive-axle is established based on bond graph theory. Next, a wheel torque distribution control strategy of TV drive-axle is proposed based on hierarchical control approach to improve the vehicle’s handling stability. Wherein, the target yaw rate motion tracking controller as the upper-level controller is designed based on the second-order super-twist sliding mode control (SS-SMC) algorithm, and the wheel drive anti-slip controller in lower-level is used to restrict possible wheels slipping. Finally, the off-line simulation and hardware-in-the-loop (HIL) experiment results suggest that the proposed novel TV drive-axle and control strategy can significantly improve the handling stability and steering flexibility of the vehicle.

Model development of bond graph based wind turbine

<span>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.</span>

Bond Graph-CNN based hybrid fault diagnosis with minimum labeled data

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.

Analysis of electromechanical coupling vibration characteristics of motor-gear system based on bond graph theory

In recent years, there has been a growing demand in the field of engineering systems for research on the vibration response of motor-gear systems under non-steady state conditions. However, conventional modeling and simulation techniques are primarily restricted to systems within a single energy domain. When dealing with intricate electromechanical coupling systems, the modeling process becomes increasingly intricate. Consequently, this study adopts bond graph theory as the foundation for analyzing the vibration characteristics of motor-gear coupling systems. In addressing the vibration issue of the motor-gear transmission system, a nonlinear bond graph model is established that takes into account the time-varying mesh stiffness, static errors, and tooth surface friction, among other nonlinear factors. Then, according to the coupling relationship between motor mechanical systems and electrical systems, the electromechanical coupling dynamic model of the motor-gear system is established using the method of bond graph. In addition, the state equation of the system is derived based on the bond graph model. Finally, the system is simulated using 20-sim software, and the electromechanical coupled vibration characteristics of the simulation results are analyzed. This article lays the foundation for the design and analysis of motor-gear coupling systems and multi-energy domain complex systems.

A Proof of Finite Crystallization via Stratification

We devise a new technique to prove two-dimensional crystallization results in the square lattice for finite particle systems. We apply this strategy to energy minimizers of configurational energies featuring two-body short-ranged particle interactions and three-body angular potentials favoring bond-angles of the square lattice. To each configuration, we associate its bond graph which is then suitably modified by identifying chains of successive atoms. This method, called stratification, reduces the crystallization problem to a simple minimization that corresponds to a proof via slicing of the isoperimetric inequality in ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell ^1$$\\end{document}. As a byproduct, we also prove a fluctuation estimate for minimizers of the configurational energy, known as the n3/4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n^{3/4}$$\\end{document}-law.

An arteriovenous mock circulatory loop and accompanying bond graph model for invitro study of peripheral intravascular bioartificial organs.

Silicon nanopore membrane-based implantable bioartificial organs are dependent on arteriovenous implantation of a mechanically robust and biocompatible hemofilter. The hemofilter acts as a low-resistance, high-flow network, with blood flow physiology similar to arteriovenous shunts commonly created for hemodialysis access. A mock circulatory loop (MCL) that mimics shunt physiology is an essential tool for refinement and durability testing of arteriovenous implantable bioartificial organs and silicon blood-interfacing membranes. We sought to develop a compact and cost-effective MCL to replicate flow conditions through an arteriovenous shunt and used data from the MCL and swine to inform a bond graph mathematical model of the physical setup. Flow physiology through bioartificial organ prototypes was obtained in the MCL and during extracorporeal attachment to swine for biologic comparison. The MCL was tested for stability overtime by measuring pressurewave variability over a 48-h period. Data obtained invitro and extracorporeally informed creation of a bond graph model of the MCL. The arteriovenous MCL was a cost-effective, portable system that reproduced flow rates and pressures consistent with a pulsatile arteriovenous shunt as measured in swine. MCL performance was stable over prolonged use, providing a cost-effective simulator for enhanced testing of peripherally implanted bioartificial organ prototypes. The corresponding bond graph model recapitulates MCL and animal physiology, offering a tool for further refinement of the MCL system.

Modeling and Simulation of Physical Systems Formed by Bond Graphs and Multibond Graphs

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.

Bond graph model study of the jet dispensing valve

The jet dispensing structure includes glue liquid delivery, mechanical drive, pneumatic drive, electromagnetic control, etc. It is a complex electromechanical hydraulic and pneumatic system. The comprehensive application of relevant professional knowledge in various fields is difficult to model and the accuracy of simulation is difficult to guarantee. This article presents the structure of valve-controlled needle jet dispensing valve which is for medium to high viscosity electronic packaging adhesives, studies the bond graph model of the jetting system with this valve as the main control object, and conducts system simulation based on a 20-sim system, laying the foundation for the research of electromechanical, hydraulic, and pneumatic multi domain coupling mathematical models.

Research into a Marine Helicopter Traction System and Its Dynamic Energy Consumption Characteristics

As countries attach great importance to the ocean-going navigation capability of ships, the energy consumption of shipborne equipment has attracted much attention. Although energy consumption analysis is a guiding method to improve energy efficiency, it often ignores the dynamic characteristics of the system. However, the traditional dynamic analysis method hardly considers the energy consumption characteristics of the system. In this paper, a new type of electric-driven helicopter traction system is designed based on the ASIST system. Combined with power bond graph theory, a system dynamic modeling method that considers both dynamic and energy consumption characteristics is proposed, and simulation analysis is carried out. The results indicate that the designed traction system in this study displays high responsiveness, robust, steady-state characteristics, and superior energy efficiency. When it engages with helicopter-borne aircraft, it swiftly transitions to a stable state within 0.2 s while preserving an efficient speed tracking effect under substantial load force, and no significant fluctuations are detected in the motor rotation rate or the helicopter movement velocity. Moreover, it presents a high energy utilization rate, achieving an impressive energy utilization rate of 84% per single working cycle. Simultaneously, the proposed modeling methodology is validated as sound and effective, particularly apt for the dynamic and power consumption analysis of marine complex machinery systems, guiding the high-efficiency design of the transmission system.

The Performance of a Lip-Sync Imagery Model, New Combinations of Signals, a Supplemental Bond Graph Classifier, and Deep Formula Detection as an Extraction and Root Classifier for Electroencephalograms and Brain–Computer Interfaces

Many current brain–computer interface (BCI) applications depend on the quick processing of brain signals. Most researchers strive to create new methods for future implementation and enhance existing models to discover an optimal feature set that can operate independently. This study focuses on four key concepts that will be used to complete future works. The first concept is related to potential future communication models, whereas the others aim to enhance previous models or methodologies. The four concepts are as follows. First, we suggest a new communication imagery model as a substitute for a speech imager that relies on a mental task approach. As speech imagery is intricate, one cannot imagine the sounds of every character in every language. Our study proposes a new mental task model for lip-sync imagery that can be employed in all languages. Any character in any language can be used with this mental task model. In this study, we utilized two lip-sync movements to indicate two sounds, characters, or letters. Second, we considered innovative hybrid signals. Choosing an unsuitable frequency range can lead to ineffective feature extractions. Therefore, the selection of an appropriate frequency range is crucial for processing. The ultimate goal of this method is to accurately discover distinct frequencies of brain imagery activities. The restricted frequency range combination presents an initial proposal for generating fragmented, continuous frequencies. The first model assesses two 4 Hz intervals as filter banks. The primary objective is to discover new combinations of signals at 8 Hz by selecting filter banks with a 4 Hz scale from the frequency range of 4 Hz to 40 Hz. This approach facilitates the acquisition of efficient and clearly defined features by reducing similar patterns and enhancing distinctive patterns of brain activity. Third, we introduce a new linear bond graph classifier as a supplement to a linear support vector machine (SVM) when handling noisy data. The performance of the linear support vector machine (SVM) significantly declines under high-noise conditions. To complement the linear support vector machine (SVM) in noisy-data situations, we introduce a new linear bond graph classifier. Fourth, this paper presents a deep-learning model for formula recognition that converts the first-layer data into a formula extraction model. The primary goal is to decrease the noise in the formula coefficients of the subsequent layers. The output of the final layer comprises coefficients chosen by different functions at various levels. The classifier then extracts the root interval for each formula, and a diagnosis is established based on these intervals. The final goal of the last idea is to explain the main brain imagery activity formula using a combination formula for similar and distinctive brain imagery activities. The results of implementing all of the proposed methods are reported. The results range between 55% and 98%. The lowest result is 55% for the deep detection formula, and the highest result is 98% for new combinations of signals.

Bond-Graph-Based Approach to Teach PID and Sliding Mode Control in Mechatronics

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.

Unravelling hydrogen bond network in methanol-propanol mixtures via molecular dynamics simulation and experimental techniques

ABSTRACT Hydrogen-bonded networks in 1-propanol-methanol binary mixture are studied using molecular dynamics simulation along with experimental techniques (FTIR, dielectric spectroscopy and refractive index measurements) for the entire concentration range. The structure of hydrogen-bonded networks is studied through radial distribution function, hydrogen bond statistics and graph theoretical analysis from molecular dynamics. It is observed that the probability of hydrogen bonding is maximum in methanol molecules followed by hydrogen bonding between methanol and 1-propanol molecules and minimum among 1-propanol molecules, as the concentration changes. The graph theory results show the formation of linear chain hydrogen bond networks, with no caged structures, which is validated from the experimental results. Further, the deconvolution of the FTIR OH peak suggests the presence of linear multimers in all concentrations of the binary system. Average degree of the hydrogen-bonded networks from graph theory indicates a complex networks among pure alcohols and dimers between methanol and 1-propanol in their binary mixtures at all concentrations. The negative values of excess permittivity indicate that the components of the mixture interact in a manner such that the net dipole polarisation decreases.

Simulation and experimental investigation of high-pressure pneumatic pilot-driven on/off valve with high transient performances for compressed air ejection

A high-pressure pneumatic pilot-driven on/off valve (HPPV) that quickly switches an air inlet line on and off was designed to meet the requirements of a compressed air ejection device with high pressure, high flow rate, and continuous ejection. An HPPV multi-physics field simulation model was established using AMESim simulation software based on the bond graph modeling approach that considered mechanical, gas flow, pipe heat exchange, and pilot valve operation, and experiments were conducted to check the simulation model accuracy. The effects of real gas thermodynamics, pipeline heat exchange, and pilot valve operation on the simulation model accuracy and those of different initial parameter conditions on the dynamic characteristics of the HPPV were investigated in the validated simulation model. The results indicated that the tolerance between simulation and experiment was less than 7.3%, so the simulation model was realistic and reliable. In addition, among the different influencing factors, the pipeline heat transfer significantly affected the simulation model accuracy, and the tolerance between the simulation results without considering the pipeline heat exchange and the experimental results exceeded 10%. The real gas thermal effect slightly affected the simulation model accuracy, and the tolerance between the simulation results under the action of each gas state equation was less than 3%. The pilot valve operation affected only the opening and closing moments of the HPPV and had no effect on the simulation model accuracy. The dynamic characteristics of the HPPV were stable and reliable under different initial parameter conditions.

Power Consumption Modeling of Wheeled Mobile Robots With Multiple Driving Modes

Wheeled mobile robots (WMRs) are being used in many real-world applications. The main challenge in deployment of WMRs is their limited battery power. In order to complete a given task autonomously, it becomes necessary to estimate a priori the total energy consumption for the task assigned to a WMR. An accurate power consumption model allows to estimate the energy consumption of a WMR. This paper presents the power consumption modeling of WMRs using a power-based graphical modeling tool called bond graph. First, the dynamic bond graph model is developed for the generalized configuration of a WMR according to number and types of wheels attached in chassis. Then, the developed bond graph model is utilized for the power consumption modeling of a WMR. The proposed power consumption model includes the various driving modes of a WMR, by considering its degree of steerability. Finally, the proposed methodology is applied to a reconfigurable outdoor WMR with a degree of steerability varying from 0 to 2. The estimated power from the model is compared with the measured power from the experiments. Further, the proposed power model is used for the energy planning in a trajectory tracking task based on changing driving modes of a reconfigurable WMR.

Bond-graph analysis and modelling of a metal detector as an example of electro-magnetic system

The modelling, analysis and simulation of different parts of electronic, electrical or any complex equipment, including sensors, help researchers and the companies to design and implement the equipment without loses due to the tries to physically realize the equipment. This article proposes novel bond-graph models and a bond-graph analysis of a metal detector as an illustration of an electro-magnetic system that contains electrical, magnetic and mechanic parts that cannot be usually modelled, designed and analysed as an integral part. The 20SIM simulation software was used to develop the proposed bond-graph models and the bond-graph analysis of a metal detector. The objective of this article is to show that the proposed bond-graph models and analysis correspond (in terms of waveform and values) to a real practical implementation of a metal detector and this is demonstrated by comparing the obtained results with the results obtained by practical measurements on a physical implementation of the metal detector. The obtained results can be extrapolated to other complex systems using the proposed models.

Study on nonlinear dynamics characteristics of dual speed dual clutch transmission system based on bond graph

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.

BondGraphs.jl: Composable energy-based modelling in systems biology.

BondGraphs.jl is a Julia implementation of bond graphs. Bond graphs provide a modelling framework that describes energy flow through a physical system and by construction enforce thermodynamic constraints. The framework is widely used in engineering and has recently been shown to be a powerful approach for modelling biology. Models are mutable, hierarchical, multi-scale, multi-physics, and BondGraphs.jl is compatible with the Julia modelling ecosystem. BondGraphs.jl is freely available under the MIT license. Source code and documentation can be found at https://github.com/jedforrest/BondGraphs.jl. Supplementary data are available at Bioinformatics online.

20-Sim Bondgraph for Solar Home Applications

Designing of solar generation for home applications using MATLAB/sim electronics and Bond Graph model 20- sim used in this paper. The devices required for home applications are solar panel, irradiance and temperature insists as input and generates DC as output to the solar panel.  As the solar panels generates DC output but our home requires 240 v AC supply, to convert this DC to AC power electronic device inverter requires. This inverter converts DC to pulsating AC supply. To convert pulsating AC to pure AC needs LC filter. The output DC of solar depends upon temperature and irradiance, no more the temperature is not constant depends on atmosphere varying from morning to evening at afternoon only maximum temperature, high power attains and at morning and evening low temperature, low power attains but any time the home needs constant power supply to the load for 24 hours for this purpose boost converter requires for continuous supply. The device Boost converter is connected between solar panels and inverter. Instead of boost converter the device Transformer can also be used between LC filter and load. The cost of transformer is high and requires more space but in boost converter only inductor, diode, capacitor, one switch uses and obtains continuous supply, reduces the ripple factor. First all the devices solar panel, boost converter, single phase inverter are designed individually and combined  by using MATLAB/ sim electronics and Bond Graph 20-sim for both software applications better output waveforms are obtained.