System Dynamics Simulation Research Articles

Dynamic simulation and analysis of dichloromethane-acetone, dichloromethane-trichloromethane and dichloromethane-toluene VOC mixture abatement systems under transient feed conditions

Minimisation of environmental footprint in line with sustainability goals rests at the top of the agenda of the pharmaceutical industry. Volatile Organic Compounds (VOCs), while essential to primary pharmaceutical manufacturing, are solvents whose emissions pose a risk to human health and ecosystems. Adsorption on activated carbon beds is an established technology for end of pipe emissions control, which, however, faces efficiency limitations by quick and irregular bed saturation due to complex, transient feeds. This study employs a validated nonisothermal, multicomponent adsorption model to quantitatively assess the exact effect and potential value of waste stream feed sequencing towards activated carbon bed utilisation optimisation. Our results indicate that gradual increase of dichloromethane feed concentration in combination with a low, constant inlet concentration of the strongly adsorbing component leads to the latest breakthrough onset for the dichloromethane-acetone, dichloromethane-chloroform and dichloromethane-toluene mixtures. Transient vs. constant feeds usage and their interplay clearly affects bed behaviour, paving the way to industrial VOC emission abatement scheduling and process optimisation.

Simulation-based business model innovation process for business-to-business contexts

Innovating service-oriented business models in industrial business-to-business (B2B) contexts presents a complex and risky endeavor. Recently, System Dynamics (SD) modelling and simulation has been suggested as a tool for prototyping and experimentation in business model innovation (BMI). However, knowledge of how to best utilise SD in BMI is scarce. Therefore, our research objective was to develop a new simulation-based approach for BMI, particularly for B2B contexts. We conducted a two-and-a-half-year action design research study with two industrial firms, Alpha (start-up) and Beta (incumbent firm). We developed and simulated new service-oriented business models as part of the two BMI teams. Our study resulted in the simulation-based BMI process containing phases, tools/techniques, and goals. Our findings demonstrate that SD, as a dynamic and visual modelling language, facilitates collaborative and cognitive activities during BMI, such as communication, design, evaluation, and decision-making.

Wishful Thinking about Consciousness

We contrast three very distinct mathematical approaches to the hard problem of consciousness: quantum consciousness, integrated information theory, and the very large-scale dynamical systems simulation of a network of networks. We highlight their features and their associated hypotheses, and we discuss how they are aligned or in conflict. We suggest some challenges to these theories, in considering how they might apply to the human brain as it develops both cognitive and conscious sophistication, from infancy to adulthood. We indicate how an evolutionary perspective challenges the distinct approaches to aver performance advantages and physiological surrogates for consciousness.

Deciphering Stakeholder Strategies in Electric Vehicle Battery Recycling: Insights from a Tripartite Evolutionary Game and System Dynamics

Given that the global fleet of electric vehicles is expanding rapidly, leading to a surge in demand for power batteries, the significance of battery recycling has come sharply into focus. In this paper, we develop a tripartite evolutionary game model coupled with a system dynamics simulation approach to analyze the behavioral mechanisms of stakeholders in electric vehicle battery recycling and to identify the key factors influencing their strategic transformations. The model involves three key stakeholders: local governments, battery cathode manufacturers, and battery pack manufacturers. Through profit analysis and the formulation of a replicator dynamics system, coupled with the application of Vensim for system dynamics modeling, we analyze the effects of ten crucial exogenous variables on stakeholder strategies. Our research highlights the crucial role of government actions, such as subsidies and penalties, in directing corporate strategies. Notably, excessively high subsidies (over 2.1 million Chinese Yuan) may lead to dependency among cathode manufacturers. Market factors, including increased battery sales and the preference for low-carbon procurement by electric vehicle manufacturers, are shown to significantly motivate cathode manufacturers to adopt recycling strategies and encourage pack manufacturers to develop recyclable designs. Additionally, the introduction of EU battery regulations and the integration of cathode manufacturers into carbon trading schemes have been identified as positive catalysts for strategic shifts, especially for cathode manufacturers, while exerting minimal influence on pack manufacturers. This paper elucidates the intricate interaction of the behavioral patterns and key motivators affecting stakeholder strategies in electric vehicle battery recycling.

Towards complex dynamic physics system simulation with graph neural ordinary equations

The great learning ability of deep learning facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour both in academia and industry. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting particles and varying temporal dependencies between particle system states in different time stamps, which dominate particles’ interacting behavior and the physical systems’ evolution patterns. Existing learning based methods fail to fully account for the complexities, making them unable to yield satisfactory simulations. To better comprehend the complex physical laws, we propose a novel model – Graph Networks with Spatial–Temporal neural Ordinary Differential Equations (GNSTODE) – that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework. Through training with real-world particle–particle interaction observations, GNSTODE can simulate any possible particle systems with high precisions. We empirically evaluate GNSTODE’s simulation performance on two real-world particle systems, Gravity and Coulomb, with varying levels of spatial and temporal dependencies. The results show that GNSTODE yields better simulations than state-of-the-art methods, showing that GNSTODE can serve as an effective tool for particle simulation in real-world applications. Our code is made available at https://github.com/Guangsi-Shi/AI-for-physics-GNSTODE.

Unified Dynamic Simulation System for Multi-Energy Flows of Electricity, Heat, and Gas in Integrated Energy Systems

Integrated energy systems (IES) coordinate heterogeneous energy flows of electricity, heat, and gas to meet diverse load demands and enhance energy efficiency, is a new generation of energy systems that promote sustainable energy development. Simulation systems are essential tools for the analysis, control, and optimization of IES. However, due to the strong coupling of heterogeneous energy flows in IES, existing simulation software often lacks integration, typically focusing on electricity or relying on separate simulations of different energy flows. To address these challenges, this paper proposes a unified simulation technology for electricity, heat, and gas. First, the thermodynamic dynamic simulation capabilities of MATLAB/Simulink are extended using the Thermolib toolbox to create a unified modeling and simulation environment for coupled multi-energy flows. On this foundation, the energy conversion relationships and operating mechanisms of key multi-energy coupling equipment are studied, and dynamic Simulink models of the critical energy devices within the system are developed. Finally, a comprehensive dynamic simulation model for the multi-energy flows of the IES is constructed by connecting energy device models through an energy flow bus. The simulation results demonstrate that this system can effectively simulate the characteristics of electricity, heat, and gas flows across multiple time scales and the complex operating conditions of various energy devices within the MATLAB environment. This approach simplifies the multi-energy flow simulation structure and improves data transmission efficiency for multi-energy flows.

An Investigation of Real-Time Robotic Polishing Motion Planning Using a Dynamical System

When addressing the technical challenges of achieving precise force tracking during the local polishing process of polishing robots, controlling the contact state between the robot and the workpiece surface is essential. To this end, a contact motion-planning strategy based on dynamic systems is designed to generate trajectory routes during local polishing. The trajectory simulation of the local modulation dynamic system is achieved through the employment of the support vector regression (SVR) algorithm with a Gaussian kernel, facilitating the learning process. The feasibility and stability of planning local paths are validated using the local modulation dynamic system. To maintain a constant contact force between the end-effector polishing robot and the workpiece, an integral adaptive impedance control strategy is utilized, enabling the robot’s compliant control. Subsequently, an experimental system for the polishing robot is constructed in order to verify the effectiveness of the motion-planning system. The experimental results demonstrate that the proposed motion-planning approach is applicable in practical polishing processes, ensuring smooth contact and maintaining the desired contact force when scanning nonlinear surfaces, and thus showcasing stability and practicality.

System Dynamics Simulation and Influencing Factors of the Interaction between Urbanization and Eco-Environment in Hebei Province, China

System Dynamics Simulation and Influencing Factors of the Interaction between Urbanization and Eco-Environment in Hebei Province, China

Shifting analysis of grain carriers using System Dynamics Simulation

PT PFS is a subsidiary of PT BJ specially serving of port services. PT PFS need to shift FSO Ardjuna Sakti from jetty 1.6-1.7 to the new jetty (jetty 4.5-4.6) because PT PFS has some problems and activity plans. The problems are teh increased of commercial demand, the development jetty 3 of port CDG, the Berth Occupation Ratio (BOR) which already exceeds 65%, the expansion plan of PT BJ and the need for flexibility on the jetty 1.5-1.8. Therefore, it is necessary to do the analysis of shifting the FSO Ardjuna Sakti. The method use in this study is comparing the financial analysis using manual calculation and system dynamics simulation. The financial analysis uses in this study are Internal Rate of Return (IRR), Payback Period, Net Present Value (NPV) and comparing revenue and profit between FSO Ardjuna Sakti in jetty 1.6-17 and jetty 4.5-4.6. The result of the study showed that the IRR is 14,73%, it is exceed than the MARR of BI rate 6,5%, Payback Period is 5 years so investment will comeback at 5 next years, and the value of the NPV with manual and system dynamics simulation has both positive value or exceed than 0 (zero). The result of manual calculation of NPV is Rp.15.369.374.455 and the result of system dynamics simulation is Rp.1.184.863.983.489. Comparing manual calculation and system dynamics simulation of revenue and profit of FSO Ardjuna Sakti in jetty 1.6-1.7 respectively is Rp.2.384.292.880 and Rp.2.125719.660 whilst in jetty 4.5-4.6 respectively is Rp.9.015.993.344 and Rp.6.761.995.008. From those financial methods compared manually and by system dynamics simulation, we can conclude the investment decisions are feasible. And activity of shifting FSO Ardjuna Sakti from jetty 1.6-1.7 to the jetty 4.5-4.6 is appropriate to do.

Simulink Model for LMV and Conceptualization of 2 Speed E-Axle

Heavy-duty trucks are the primary contributor to the global emissions of greenhouse gases. Because of this, electric haulage has seen significant growth in popularity. Electric heavy-duty trucks aren't widely adopted because of their limited range, payload capacity, and high prices. Electric axle systems, sometimes known as "E-Axles," are capable of electrifying heavy-duty vehicles effectively. The motor, power electronics, and gearbox are all combined into one unit in an E-axle. E-axles that are devoid of motors and gearboxes have the potential to boost performance while also reducing weight and fuel consumption. Heavy-duty automobiles are similar to them. E-axles have the potential to increase the fuel efficiency, flexibility, and emissions of heavy-load trucks. The e-axles are being inspected. E-axles for heavy-duty trucks bring both issues and opportunities concerning finances, infrastructure, and costs. Examine the performance standards as well as the technology behind batteries. This paper provides an introduction to MATLAB Simulink, which focuses on engineering and science. Simulink facilitates the design, simulation, and evaluation of systems. This article demonstrates the fundamental principles, features, and benefits of Simulink through the application of system modeling, control design, and dynamic simulation. It presents Simulink methodologies, libraries, and blocks, as well as interactive simulations.
 Keywords: MATLAB, Simulink, EV Control systems, EV simulation, EV powertrain modeling, EV test and validation Simulink, WLTP drive cycle

Navigating climate change complexity and deep uncertainty: approach for building socio-ecological resilience using qualitative dynamic simulation

The consequences of climate change on different sectors of society are interrelated. The threats posed by rising global temperatures, intensifying extreme weather events, and shifting climatic patterns are becoming increasingly evident all around the world. Policymakers face the daunting task of assessing climate change risks, encompassing impacts and response strategies, to guide sustainability transformations. In this study, we introduce a four-step qualitative Decision Making Under Deep Uncertainty (DMDU) approach in the context of Climate Change Impact Assessments (CCIA). Our goal is to enhance the integration of CCIA into spatial planning, particularly in the Global South, using qualitative system dynamics simulation. Emphasizing the value of qualitative DMDU, we explore vulnerability and resilience through a lens of multi-sectoral and multi-scalar socio-ecological processes. We exemplify our approach by applying CCIA to the coastal zone of Yucatán, Mexico, accounting for social and environmental heterogeneity across the four Regions in which it is administered. Results identify the optimal allocation of climate change mitigation and adaptation policies to address specified resilience in each Region, all of which are required to achieve the overall resilience of the coastal zone. We argue that our qualitative DMDU approach provides an analytical platform to address the trade-offs inherent in the ranking of multiple vulnerabilities related to achieving general resilience.

Vibration suppression of smart composite beam using model predictive controller

Abstract This work presents an adaptive model predictive control (MPC) strategy to suppress the vibration in a laminated composite beam. The control method incorporates a system identification algorithm to estimate the system parameters online, which provides a precise simulation of system dynamics. A fixed-free cantilever composite beam equipped with piezoelectric actuators was used to evaluate the efficacy of the control method. The sensors and actuators are securely bonded to the upper and lower surfaces at arbitrary locations along the beam’s length. A unified mechanical displacement field is applied to all layers, while displacements are considered independently for each layer. The beam is composed of eight layers of material, each with a thickness of 0.2 mm and orientations specified as (90°/0°/90°/0°). To achieve the best performance, the parameters of the MPC were adjusted numerically. The numerical analysis revealed that placing the actuator near the clamped end at the fixed end resulted in superior control outcomes, with a settling time of approximately 1.8 s. Conversely, the longest settling time occurred when the actuator was positioned at the free end, taking around 4 s. This model could potentially be expanded to address vibration in more intricate beams exhibiting nonlinear characteristics. The deflection readings measured at the end of the beam have been utilized as feedback control signals for predicting future behavior over a predetermined control horizon. The subsequent cost function is minimized through a quadratic equation to determine the sequence of optimal yet constrained control inputs. The suggested active vibration control system is then implemented and assessed numerically to examine the effectiveness of the control method.

Trajectories of socio-ecological systems: A case study in the tropical Andes.

Scenario and policy assessments in socioeconomic and environmental studies face significant challenges in socio-ecological systems (SES). There are a limited number of studies that have looked at the impact of different scenarios within integrated approaches, and many have used a static approach with a single driver of change. The present work analyzes the SES dynamics for a strategic basin in the Colombian Andes when implementing and analyzing scenarios and policies related to land cover and land use change using a system dynamics simulation model. The model includes natural, ecosystem services, sociocultural, and economic components. Scenarios and policy options are analyzed both individually and jointly to identify synergies or trade-off effects between the different SES components. The results showed the different trajectories of the socio-ecological system according to the cases studied, and its impact on different variables in the analyzed components. Some counterintuitive effects were also identified, such as the importance of intrinsic motivations in decision-making processes, and determinants in land management and policy design.

Machine-Learned Kohn-Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics.

In this work, we report a simple, efficient, and scalable machine-learning (ML) approach for mapping non-self-consistent Kohn-Sham Hamiltonians constructed with one kind of density functional to the nearly self-consistent Hamiltonians constructed with another kind of density functional. This approach is designed as a fast surrogate Hamiltonian calculator for use in long nonadiabatic dynamics simulations of large atomistic systems. In this approach, the input and output features are Hamiltonian matrices computed from different levels of theory. We demonstrate that the developed ML-based Hamiltonian mapping method (1) speeds up the calculations by several orders of magnitude, (2) is conceptually simpler than alternative ML approaches, (3) is applicable to different systems and sizes and can be used for mapping Hamiltonians constructed with arbitrary density functionals, (4) requires a modest training data, learns fast, and generates molecular orbitals and their energies with the accuracy nearly matching that of conventional calculations, and (5) when applied to nonadiabatic dynamics simulation of excitation energy relaxation in large systems yields the corresponding time scales within the margin of error of the conventional calculations. Using this approach, we explore the excitation energy relaxation in C60 fullerene and Si75H64 quantum dot structures and derive qualitative and quantitative insights into dynamics in these systems.

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

Research on vibration characteristics of cone valve based on steady state hydrodynamics

The steady-state fluid dynamic force of the cone valve core reduces the stability of the cone valve, thereby affecting its vibration characteristics. This study first derived the formula for calculating the steady-state fluid dynamic force of the valve core using the momentum theorem. Then, the study used the Computational Fluid Dynamics (CFD) method to explore the variation of steady-state fluid dynamic force with the opening degree of the cone valve and the pressure difference at the inlet and outlet. The study also analyzed the steady-state fluid dynamic force of the improved structure cone valve. Combining the CFD calculation results with the established axial vibration and dynamic models of the valve core spring system, a system dynamics simulation model was developed. Innovatively, the study combined experimental and simulation methods to investigate the vibration characteristics of the cone valve and the impact of steady-state fluid dynamic force on these characteristics. The experimental results aligned well with the simulation results, showing that the influence of steady-state fluid dynamic force on vibration characteristics is similar to that of spring force. At a small opening degree, the compensation structure of steady-state fluid dynamic force can reduce axial vibration amplitude. Increasing the inlet pressure from 2.0 MPa to 2.5 MPa results in a decrease in axial amplitude and an increase in the numerical value of the vibration balance position. Increasing the outlet pressure from 0.5 MPa to 0.9 MPa initially decreases axial amplitude and axial waveform factor, followed by an increase. Increasing the spring preload from 9.4 mm to 13.4 mm leads to a decrease in the numerical value of the balance position, a reduction in amplitude, and a decrease in the waveform factor of the axial vibration of the valve core. The study reveals systematic effects of inlet pressure, outlet pressure, and spring preload on the vibration characteristics of the cone valve. The research results in this paper can provide theoretical support for the structural design of hydraulic valves, reduce the impact of fluid dynamics on vibration, and thereby improve the stability of hydraulic system operation.

Scenarios for offshore wind co-existence opportunities and trade-offs

This paper introduces the MARCO (MARine CO-existence scenario building) concept for using scenario exploration in stakeholder engagement processes in offshore wind. MARCO builds on spatial analyses using geographic information systems (GIS), and projections over time using system dynamics simulation models. We position the concept within the existing literature on tools for decision support and stakeholder participation, and provide a preliminary status on the spatial baselines, as well as example scenarios for area usage in offshore wind and implications, including risks and co-existence opportunities, on other sectors and nature.

PRINCIPLES OF MATHEMATICAL SIMULATION OF MINING VEHICLES USING APPLIED MATHEMATICAL SOFTWARE

The transportation of rock mass and coal in mining operations relies heavily on the efficiency and reliability of mining transport systems. This study delves into the intricate dynamics of chassis functions during transportation, emphasizing the critical role of transmitting dynamic stresses and mass to the road or rail structures. With a focus on increasing productivity, modern mining locomotives and heavy dump trucks now carry substantial adhesion adhesive weights, allowing for the hauling of heavier loads even on steep slopes. The integration of scientific simulations and research-based design methods aids in identifying and managing dynamic loading effects within mining vehicle chassis. This approach minimizes the transfer of dynamic loads onto the bolster structure, enhancing system reliability and operational efficiency. Computer-aided software plays a vital role in stream-lining the development process, reducing the need for costly physical prototypes and extensive testing in challenging mining environments. The study outlines simplified calculation schemes for mining transport utilities, balancing the need for accuracy with computational efficiency. By utilizing mathematical models and simulation techniques, the dynamics of mine locomotives and dump trucks can be accurately evaluated, guiding design decisions and operational strategies. The application of Lagrange equations and software tools like “Wolfram Mathematica” facilitates the generation of differential equations for dynamic analysis, providing insights into vehicle dynamics and road interactions. Overall, this research underscores the importance of advanced modeling and simulation techniques in optimizing mining transport systems, enhancing safety, productivity, and reliability in demanding mining environments. Purpose. To highlight the application of mathematical software as an instrument for mechanical system dynamics study, that allows scientifically substantiate the usage of new technical solutions during their development. Method. Development a system of differential equations using Lagrange equations of the 2nd order, which are solved in Wolfram Mathematica. Preparation of initial conditions and mass-inertia data can be done by any known software. The generalized mathematical model can be used for any vehicle suspending unnecessary coordinates. Results. Implementation of new technical solutions without scientific ground could be resulted in difficulties while exploitation. For its removal necessary to receive dynamical characteristics. Existing engineering calculation methods are not suitable, especially because of tight time and limited financing. The customized mathematical models can be developed and solved on demand using available software. A scientific novelty. Enhancement of theoretical and experimental research become possible owing to the usage of mod- ern approaches of dynamic systems simulation using applied mathematical software Wolfram Mathematica. Obtained model can be modified in few operation from one mass task into multibody system with maximum 69 free displacements (coordinates). Practical value. The possibility to receive customized simulation model fast, verified with increased quality for scientific purposes.

Towards low-carbon steel: System dynamics simulation of policies impact on green hydrogen steelmaking in China and the European Union

Green hydrogen (GH) has been included in the steel emission reduction plans of many countries. This study evaluates the status quo of GH and steel industry and the related subsidy and regulatory policies in two major steelmaking countries/regions: China and the European Union. A system dynamics model coupling GH and steel industry is established to conduct simulation analysis of the low-carbon transition path of GH replacing fossil fuels in these two countries/regions' steel industry under several carbon emission reduction scenarios. Results verify the feasibility of using GH in the steelmaking and GH's contribution to the steel industry's carbon emission reduction. And this study assesses the policy impacts on the development of green hydrogen steel and gives specific policy implications on the timing and measures. The methodology and policy tools in this study also provide the reference for other countries who are still in wait-and-see attitude towards the development of GH steel.

A hybrid modeling approach to simulate complex systems and classify behaviors

Many important systems, both natural and artificial, are complex in nature, and models and simulations are one of the main instruments to study them. In this paper, we present an approach where a complex social system is represented at a high level of abstraction as a network, thereby addressing several challenges such as quantification, intervention, adaptation and validation. The network represents the factors that influence the mental health and wellbeing in children and young people. In this article, we present an approach that links a system dynamics simulation to simulate the network and ranking algorithms to measure the vertices’ behaviors. The network is enhanced by adding edge strengths in the form of correlations between vertices (established through literature). Such an approach allows us to exploit the network structure to qualify and quantify the vertices of the network, to overlay different processes over the network topology, to add and remove new vertices, and therefore interact dynamically. This in turn allows for the qualification of vertices’ importance and network resilience. System dynamics simulation allows for policy analysis, where different scenarios are analyzed by stimulating a set of vertices and the effect over the network is observed. This approach allows for an abstract, flexible, yet comprehensive way of analyzing a complex social network at any scale.