System Dynamics Research Articles

Managing sustainability using system dynamics: Selected papers from the Tenth European System Dynamics Workshop, at the University of Stuttgart, Germany

Managing sustainability using system dynamics: Selected papers from the Tenth European System Dynamics Workshop, at the University of Stuttgart, Germany

Building credibility for human systems integration in model‐based systems engineering

AbstractRobust and trusted digital human representations are necessary to successfully account for human considerations in model‐based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility. A literature review was completed on mathematical, physics‐based, software development, discrete event simulation, agent‐based, system dynamics, and MBSE models with the goal of proposing a process for performing VV&A on digital engineering (DE) and MBSE models for sociotechnical systems. However, this research also revealed the need for a broader framework to characterize the risk associated with using these models for making high‐consequence decisions. While accomplishing the literature review, another approach to building credibility was identified that is used heavily in the financial industry, namely model risk management (MRM). This process is extended by leveraging MRM approaches from within the financial community to propose a framework for sociotechnical model users to characterize the risk of using MBSE models to make programmatic decisions. The primary contribution of this work is to document a meta‐analysis of model VV&A while proposing an alternative approach to characterizing and communicating credibility that was discovered during this analysis. This approach could be a viable option for ensuring the credibility of human systems integration in MBSE models.

Research on Knowledge Payment Product Supply Chain Governance Based on Blockchain: An Evolutionary Game Model

ABSTRACTBlockchain provides a new opportunity to govern the dilemmas in the knowledge payment product supply chain. This paper introduces blockchain into the evolutionary game to explore the behavioral strategies of knowledge providers, platforms, and consumers and to analyze in depth the influence of initial willingness and blockchain‐related factors by the system dynamics. The analysis shows that (1) improving the tripartite initial willingness is conducive to accelerating the process of supply chain governance and making the system reach an ideal state: knowledge providers choose compliance creation, platforms adopt blockchain, and consumers buy authorized product; (2) there are specific thresholds for the blockchain adoption cost and the premium of product brought by blockchain, only when the cost and premium are lower than the thresholds, the system evolves towards the ideal state; (3) when blockchain is adopted, it can effectively inhibit knowledge providers' illegal creation behavior and consumers' piracy behavior by increasing the punishments of platforms on knowledge providers and consumers. The results provide guidance to implement effective governance and the orderly development of knowledge payment industry.

A system dynamics model based on ISM for risk perception in emergency by employees in chemical industrial parks

Risk perception is one of the factors that guide human behavior in the workplace. In accident prevention and emergency response, the risk perception of employees affects the safety behaviour and efficiency of emergency disposal. Besides, risk perception is a complicated process restricted by many influencing factors. The study aims to identify the most important factors affecting risk perception. This study had three phases. In the first, factors affecting the risk perception were extracted based on the questionnaire survey and the expert scoring. Then, the Interpretive Structure Model (ISM) was used to stratify the 14 influencing factors of risk perception and obtain a hierarchical structure chart. Finally, by analyzing the influence paths in the ISM, the system dynamics feedback loop diagram was constructed. The model took the state variable “risk perception→ risk response→ risk identification→ risk communication→ risk perception” as the main circular loop, and was supplemented and perfected by multiple feeder loop circuits. Research indicates risk experience is the most fundamental factor affecting risk perception. In the aspect of sensitivity analysis, the study shows that the risk perception of employees is related to the distance of the risk source. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing risk perception in chemical industry park.

Resilience Components in Mexican Whale-Watching Regulation

Whale watching (WW) is a growing tourist activity that is at risk of becoming unsustainable. Legislation regarding WW must mitigate adverse effects on species and address the dynamics of complex systems. This study proposes analyzing this interaction using a socio-ecological resilience framework, considering both social and ecological components. Resilient governance is characterized by four features: flexibility to respond to change, adaptability, multi-level governance, and participation. The aim of this study was to assess how Mexican WW regulations contribute to the resilience of the socio-ecosystem through a literature review on compliance with the regulations and the presence of resilience elements in the regulations. Non-compliance with 12 guidelines was identified, with vessel crowding, distance, and unauthorized vessels being the most frequently reported issues. The analysis of the regulations revealed the presence of all four elements of resilient governance; however, participation is limited to certain key stakeholders, which undermines whale conservation due to non-compliance. In conclusion, the Mexican regulations contribute to resilient governance; however, to ensure whale conservation and socio-ecological resilience, it is essential that all involved parties understand their roles and actively participate in decision-making processes.

Unsteady Free Convection of Nanofluid Past an Infinite Vertical Sheet Under the Effects of Inclined Magnetic Field and Cogley Radiation

Background: An infinite vertical sheet is the medium of inquiry for studying the copper- water nanofluid behavior with an inclined magnetic field and Cogley radiation. The research contains significant aspects. The use of this nanofluid is of great practical importance, especially in the areas of energy efficiency, thermal management, radiative cooling for space technology, environmental impact reduction, and improving heat transfer efficiency in electronic device cooling operations. Moreover, the research enhances the understanding and manipulation of sophisticated materials. The uttermost significance of this resides in its capacity to uncover issues inherent in the stages of planning, developing, analyzing, and improving manufacturing processes, possibly securing new solutions via patent protection. Objective: This research aims to provide a comprehensive analysis of the unsteady natural convection of a nanofluid along an infinite vertical sheet. The study focuses on understanding how an inclined magnetic field, Cogley radiation, heat source, and varying nanoparticle volume concentrations impact the velocity, thermal, and concentration profiles of the nanofluid. Additionally, the investigation seeks to evaluate the Nusselt number, Sherwood number, and skin friction coefficient across different concentration profiles to derive meaningful insights. Methods: The Laplace transform (L.T.) method, in combination with the MATLAB symbolic computing program, is used to develop numerical solutions for the main boundary value issues. The Laplace transform technique is used to clarify dimensionless partial differential equations (PDEs) and their limiting situations. An in-depth study is conducted on the profiles of momentum, energy, and concentration for various kinds of nanofluids with changing volume concentrations of nanoparticles. This research provides a comprehensive examination and also identifies prospective prospects in this field that might be protected by patents. Result: The work provides an in-depth understanding of how inclined magnetic fields, Cogley radiation, heat generation, and nanoparticle volume concentration affect the velocity, energy, and mass profiles of the nanofluid. Graphical representations depict these effects, offering a visual comprehension of the system's behavior. The Nusselt number and skin friction coefficient are determined by analytical and numerical methods. These findings demonstrate that the Nusselt number and skin friction coefficient have an inverse relationship with changes in concentration profiles. Conclusion: The work improves our comprehension of the fluctuating natural convection of nanofluids across an unbounded vertical surface, taking into account variables such as an inclined magnetic field, heat source, Cogley radiation, and nanoparticle volume concentration. The practical implications of the discoveries, together with the analytical and numerical results, enhance our comprehension of the dynamics of nanofluid systems. Ensuring the protection and widespread distribution of patented technology is essential for promoting sustainable advancements.

Modeling of excitation dynamics in large-size molecular systems: Hierarchical equations with compartmentalization.

We describe the new method that can be useful for calculation of the excitation dynamics in large molecular arrays that can be split into compartments with weak exciton coupling between them. In this method, the dynamics within each compartment is evaluated nonperturbatively using hierarchical equations of motion (HEOM), whereas transfers between the exciton states belonging to different compartments are treated by the generalized Förster (gF) theory. In a combined HEOM-gF approach, the number of equations increases linearly when adding new compartments as opposed to pure HEOM, where a depth of hierarchy exhibits strong non-linear grows when scaling the total number of molecules. Comparing the combined HEOM-gF method with an exact HEOM solution enabled us to estimate the parameters corresponding to a validity range of the proposed theory. The possibility of using the method for modeling of energy transfers in photosynthetic antenna supercomplexes is discussed.

Rationalised experiment design for parameter estimation with sensitivity clustering

Quantitative experiments are essential for investigating, uncovering, and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been hindered by oversimplified assumptions and computational overhead. To address this, we present PARameter SEnsitivity Clustering (PARSEC), an MBDoE framework that identifies informative measurable combinations through parameter sensitivity (PS) clustering. We combined PARSEC with a new variant of Approximate Bayesian Computation-based parameter estimation for rapid, automated assessment and ranking of experiment designs. Using two kinetic model systems with distinct dynamical features, we show that PARSEC-based experiments improve the parameter estimation of a complex system. By its inherent formulation, PARSEC can account for experimental restrictions and parameter variability. Moreover, we demonstrate that there is a strong correlation between sample size and the optimal number of PS clusters in PARSEC, offering a novel method to determine the ideal sampling for experiments. This validates our argument for employing parameter sensitivity in experiment design and illustrates the potential to leverage both model architecture and system dynamics to effectively explore the experimental design space.

Exploring the perceived dynamic system driving inequities in the community obesogenic environment

Abstract Introduction Persons in socioeconomically disadvantaged situations (PSEDS) are more susceptible and disproportionally exposed to obesogenic environments, resulting in poorer dietary outcomes and lower levels of physical activity. This study aimed to map the complex dynamic system within the community obesogenic environment driving unhealthy eating and insufficient physical activity among PSEDS in Flanders, and to identify potential local actions. Methods Participatory group model building (GMB), a qualitative system dynamics method, was used to engage PSEDS (n = 7), local decision makers (n = 7) and local stakeholders (n = 5) in two Flemish municipalities. A two-day GMB session was held in each municipality during which a causal loop diagram (CLD) was created and potential local actions were identified by considering causal pathways and reinforcing loops in the system. Results The CLD was designed around two main variables (i) healthy eating and (ii) physical activity among PSEDS. Seven sub-systems could be identified: 1) social environment, 2) facilities and activities, 3) mobility options, 4) information, 5) affordability, 6) safety (specifically for physical activity), and 7) industry and advertisement (specifically for eating behaviour). Several actions to improve the local environment to promote eating behaviour and physical activity among PSEDS were identified. Conclusions The CLD illustrates the complex interactions between individual and environmental determinants contributing to inequalities in eating behaviour and physical activity by including multiple perspectives of community members in socioeconomically disadvantaged situations, local decision makers and local stakeholders. Both the participatory GMB approach and the final CLD visualization can provide the basis for planning local actions for promoting healthy eating behaviour and physical activity among PSEDS. Key messages • Group model building is an effective approach to explore and understand a dynamic complex system through a shared understanding among diverse stakeholders. • A systems approach is valuable to provide a more comprehensive and context-specific understanding of the perceived factors contributing to inequities in the obesogenic environment.

A hybrid approach to investigating major management factors for effective highway preventive maintenance

To resolve the problem of the low management capacity of highway preventive maintenance (HPM), this paper identified and evaluated the major HPM management factors to improve management effectiveness and achieve sustainable highway development. The study conducted a literature review and exploratory factor analysis (EFA) to identify the major HPM management factors. Social network analysis (SNA) was used to distinguish the degree of importance of these factors. A system dynamics (SD) model was developed to explore their patterns of influence. The research identified six dimensions of HPM management, including the management system, management resources, management cognition, management decisions, management technology, and external conditions, along with 26 major management factors. Moreover, information acquisition, system perfection, etc., are key factors; system execution, manager capability, etc., are hub factors; and route selection, machinery allocation, etc., are non-key factors. These factors have a positive impact on HPM management, leading to an upward trend in management effectiveness. The main innovation provided a hybrid and comprehensive approach to identify and evaluate the major management factors for effective HPM. This study can guide managers in developing effective HPM plans, allocating resources more efficiently, improving the overall quality of highway maintenance and forming a sustainable transportation system.

Research on nonlinear dynamics of planetary gear system of wind turbine gearbox considering wear

Research on nonlinear dynamics of planetary gear system of wind turbine gearbox considering wear

Second Order Extended Ensemble Filter for Non-linear Filtering

Whenever the state of a system must be estimated from noisy information, a state estimator is employed to fuse the data with the model to produce an accurate estimate of the state. When the system dynamics and observation models are linear, the Kalman Filter which is optimal, is used. However, in most applications of interest the system dynamics and observations equations are not- linear and suitable extensions of the Kalman Filter have been developed; for example, the Extended Kalman Filter(EKF). The Extended Kalman Filter is based on linearization by the Taylor series expansion about the mean of the state. This filtering process is however computationally expensive especially in high dimensional data. The cause for this is the high cost of integrating the equation of evolution of covariances. Due to this complexity in integration, new methods were sought known as the particle filters. It replaces linearization of non-linearities with Monte Carlo methods. The particle filter formed a basis for Ensemble Kalman Filter (EnKF) an extension of Kalman filter to non-linear filtering. The EnKF reduced the computational cost but its innovation process does not capture information sufficiently hence there is need to improve its performance. This study has developed a new filter, Second order Extended Ensemble Filter (SoEEF).We derived it from stochastic state models by expansion of expected values to the second order by use of Taylor series together with Monte Carlo method and Matlab. We used Lorenz 63 system of ordinary equations and differential Matlab to test the performance of the new filter. Then we compared its performance with four other filters like Bootstrap Particle Filter (BPF), First order Kalman Bucy Filter (FoEKBF),Second order Kalman Bucy Filter (SoKBF) and First order Extended Ensemble Filter (FoEEF). SoEEKF performs much better than the other four filters.

Heart rate variability and insomnia in depressed patients with breast cancer

ObjectivesDepression is associated with unhealthy autonomic regulation. However, whether patients with breast cancer (BC) with different degrees of depression can be identified from linear and non-linear dynamics in the autonomic...

Dynamics of high-dimensional quantum systems coupled to a harmonic bath. General theory and implementation via multiconfigurational wave packets and truncated hierarchical equations for the mean-fields.

Modeling the dynamics of a quantum system coupled to a dissipative environment becomes particularly challenging when the system's dimensionality is too high to permit the computation of its eigenstates. This problem is addressed by introducing an eigenstate-free formalism, where the open quantum system is represented as a mixture of high-dimensional, time-dependent wave packets governed by coupled Schrödinger equations, while the environment is described by a multi-component quantum master equation. An efficient computational implementation of this formalism is presented, employing a variational mixed Gaussian/multiconfigurational time-dependent Hartree (G-MCTDH) ansatz for the wave packets and propagating the environment dynamics via hierarchical equations, truncated at the first or second level of the hierarchy. The effectiveness of the proposed methodology is demonstrated on a 61-dimensional model of phonon-driven vibrational relaxation of an adsorbate. G-MCTDH calculations on 4- and 10-dimensional reduced models, combined with truncated hierarchical equations for the mean fields, nearly quantitatively replicate the full-dimensional quantum dynamical results on vibrational relaxation while significantly reducing the computational time. This approach thus offers a promising quantum dynamical method for modeling complex system-bath interactions, where a large number of degrees of freedom must be explicitly considered.

Hot carrier transfer from plasmon decay in Ag20at H-Si(111) surface: Real-time TDDFT simulation in Wannier gauge.

Plasmon decay is believed to play an essential role in inducing hot carrier transfer at the interfaces between plasmonic nanoparticles and semiconductor surfaces. In this work, we employ real-time time- dependent density functional theory (RT-TDDFT) simulation in the Wannier gauge to gain quantum- mechanical insights into the nonlinear dynamics of the plasmon decay in the Ag20 nanoparticle at a semiconductor surface. The first-principles simulations show that the plasmon decay is more than two times faster when the Ag20 nanoparticle is adsorbed on a hydrogen-terminated Si(111) surface, taking place within 100 femtoseconds of the plasmon excitation. Hot carrier transfer across the interface is observed as the plasmon decay takes place, and nearly 30% of holes are generated deep in the valence band of the semiconductor surface. The use of Wannier gauge in RT-TDDFT simulation is particularly convenient for gaining quantum-mechanical insights into non-equilibrium electron dynamics in complex heterogeneous systems.

Regional energy consumption policy practice to promote low-carbon economy

IntroductionEnergy is the fundamental driving force for world economic and social development. However, the growth of carbon dioxide emissions and increasing environmental challenges are becoming increasingly problematic, limiting the pace of economic development.MethodsTo optimize the energy consumption structure and promote the development of low-carbon economy, this study took Shaanxi Province as an example to analyze the impact of low-carbon economy policy variables on the energy consumption structure dominated by coal. The purpose of this study is to examine the influence of policy variables on the evolution of energy consumption structure by constructing a system dynamics model. In addition, this study will explore policy optimization issues.ResultsThe error of simulation results was within ±10%. The consumption of non-fossil energy increased from 2,196,400 tons of standard coal in 2009 to 22.578 million tons in 2030, which is in line with the development trend. Under these four policy combinations, the share of coal will fall to 57.8 percent by 2030. In addition, the share of non-fossil energy sources will increase to 21 percent and the share of natural gas will reach 16 percent.DiscussionThe policy suggestions put forward in this paper are of great significance to the optimization of energy structure led by coal. The model constructed can be effectively applied to practical policy simulation and adjustment, and can provide reference for the development of low-carbon economic policies in other regions.

Modelling interrelations between C-ITS impact categories: a system-dynamics approach using causal loop diagrams

The growing number of connected vehicles has led to an increased focus on Vehicle-to-Everything (V2X) communication in the field of transport research. This communication paradigm facilitates cooperation between vehicles and infrastructure to address traffic challenges such as efficiency, sustainability and safety. The development and standardisation of such Cooperative Intelligent Transport Systems (C-ITS) has been pursued in several projects. Beyond technical considerations, assessing the effect of these applications in terms of various impact categories is of paramount importance. However, existing research tends to examine impact categories such as efficiency, sustainability, safety, psychological or socioeconomic impacts separately, often overlooking potential interactions and interdependencies. This approach is inadequate as impacts on one category can have both cascading effects on others and rebound effects. To address this gap, this paper proposes a system dynamics approach using Causal Loop Diagrams (CLD) to illustrate the interconnectedness of impact categories and the potential impacts of C-ITS services. By depicting general relationships, interdependencies and feedback loops between impact category elements, the model accommodates the introduction of single or multiple C-ITS services as separate modules, allowing an analysis of their combined effects on the overall system. To this end, two use cases demonstrate the applicability of the developed CLD and illustrate some of the multiple interrelations between the effects of C-ITS services. The results of this paper support road operators and researchers when setting up the impact assessment of C-ITS services by revealing the dynamic and intertwined nature of different impact categories.

Parametric analysis of a fully coupled USV-type wave energy converter: An approach based on wave-to-grid modelling

The integration of the wave energy converter and the unmanned surface vehicle (USV), which has the power generation capability and the mobile capability, is attracting increasing attention in concept design, control, and parametric study, as the integrated system can play a crucial role in the maritime sectors of commerce, science, and military. Enhancing the power generation of a USV-integrated wave energy converter contributes to prolonged USV operations or grid supply, but the power generation performance of this new-type device is significantly influenced by its various coupled subsystems, which requires a systematic methodology for distinguishing key design parameters. To address that problem, a comprehensive wave-to-grid model that reflects all subsystem coupling, energy losses, and constraints is developed to comprehensively describe system dynamics, and then a Taguchi method is employed to investigate the influence of selected design parameters on electric power generation. Results reveal that the piston area of the hydraulic cylinder and the accumulator pressure are the two most influential parameters affecting the time-averaged electric power of the proposed USV-type wave energy converter. The proposed methodology provides guidelines for technicians on how to design wave energy converters optimally.

The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system.

Indirect interspecific effects (IIEs) occur when one species affects another through a third intermediary species. Understanding the role of IIEs in population dynamics is key for predicting community-level impacts of environmental change. Yet, empirically teasing apart IIEs from other interactions and population drivers has proven challenging and data-demanding, particularly in species-rich communities. We used stochastic population models parameterized with long-term time series of individual data to simulate population trajectories and examine IIEs in a simple high-arctic vertebrate food chain consisting of the wild Svalbard reindeer, its scavenger (the Arctic fox) and the barnacle goose, a migratory prey of the fox. We used the simulated population trajectories to explore co-fluctuations between the species within the food chain. Additionally, we adjusted the model in two ways: first, to isolate the impact of fluctuations in the abundance of a species by keeping its abundance constant; and second, to isolate the impact of a trophic interaction on the dynamics of other species by setting the abundance of the influencing species to zero. We found that fluctuations in reindeer carcasses shaped fox abundance fluctuations, which subsequently affected goose population dynamics. Reindeer and goose population growth rates were nevertheless only weakly correlated, probably in part due to demographic and environmental stochasticity, density dependence and lagged dynamics in the geese. However, removing the fluctuations in reindeer abundance or setting reindeer abundance to zero indeed demonstrated strong underlying IIEs on goose population dynamics and extinction probability. This study thus highlights the importance of species interactions, including IIEs, on species coexistence and communities in the long-term, that is beyond immediate effects and covariation in short-term fluctuations.

Cell-nanoparticle stickiness and dose delivery in a multi-model in silico platform: DosiGUI

BackgroundIt is well-known that nanoparticles sediment, diffuse and aggregate when dispersed in a fluid. Once they approach a cell monolayer, depending on the affinity or “stickiness” between cells and nanoparticles, they may adsorb instantaneously, settle slowly – in a time- and concentration-dependent manner - or even encounter steric hindrance and rebound. Therefore, the dose perceived by cells in culture may not necessarily be that initially administered. Methods for quantifying delivered dose are difficult to implement, as they require precise characterization of nanoparticles and exposure scenarios, as well as complex mathematical operations to handle the equations governing the system dynamics. Here we present a pipeline and a graphical user interface, DosiGUI, for application to the accurate nano-dosimetry of engineered nanoparticles on cell monolayers, which also includes methods for determining the parameters characterising nanoparticle-cell stickiness.ResultsWe evaluated the stickiness for 3 industrial nanoparticles (TiO2 – NM-105, CeO2 – NM-212 and BaSO4 – NM-220) administered to 3 cell lines (HepG2, A549 and Caco-2) and subsequently estimated corresponding delivered doses. Our results confirm that stickiness is a function of both nanoparticle and cell type, with the stickiest combination being BaSO4 and Caco-2 cells. The results also underline that accurate estimations of the delivered dose cannot prescind from a rigorous evaluation of the affinity between the cell type and nanoparticle under investigation.ConclusionAccurate nanoparticle dose estimation in vitro is crucial for in vivo extrapolation, allowing for their safe use in medical and other applications. This study provides a computational platform – DosiGUI – for more reliable dose-response characterization. It also highlights the importance of cell-nanoparticle stickiness for better risk assessment of engineered nanomaterials.