System Dynamics Research Articles

System dynamics applied in enterprise engineering – a systematic literature review

Purpose This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises. Design/methodology/approach A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook. Findings The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified. Originality/value The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.

Optimizing power systems and microgrids: A novel multi-objective model for energy hubs with innovative algorithmic optimization

This article delves into the dynamics of power system and microgrid planning, emphasizing the emerging significance of energy hubs. The study introduces a pioneering multi-objective model that integrates economic considerations and microgrid pollution mitigation. The focus is on renewable sources such as wind and solar, coupled with advanced energy storage systems like E-fuel. Sophisticated probabilistic modeling techniques address uncertainties in renewable resources, complemented by a demand-side management program for cost reduction during peak hours. Transforming into an optimization problem, an innovative algorithm inspired by African vulture foraging behaviors provides a distinctive approach to energy landscape optimization. In a comparative analysis with established models, this approach demonstrates proficiency in simulating market behavior and providing optimal strategies for profit maximization. In a simulation analysis of four scenarios, the impact of risk assessment, a novel electronic fuel storage market, and an innovative energy storage solution on operational costs and pollution emissions is scrutinized. Notably, the proposed method excels in Case 4, showcasing a significant reduction in both operational costs and emissions. Case 4 demonstrates a substantial reduction in operational costs to $1745.732 and pollutant releases to 10753.621 kg, outperforming existing models with lower operational costs and pollutant releases.

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state stability (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.

Dynamical Modeling and Dynamic Characteristics Analysis of a Coaxial Dual-Rotor System

The dual-rotor structure is the primary excitation source of aero-engine vibration. It is essential to study the dynamical model and analyse the dynamic characteristics such as critical speed and the unbalanced response for rotor system dynamics. The coaxial dual-rotor support scheme of an aero-engine was introduced in the previous work, and the physical model with a high-speed flexible inner rotor of a sizeable length-diameter ratio has been designed. Then a finite element dynamic model based on the Timoshenko beam elements and rigid body kinematics of the dual-rotor system is modelled, with the Newmark-β method and Newton-Raphson method used for the numerical calculation to study the dynamic characteristics of the system. Three different simulation models, including beam-based FE (1D finite element) model, solid-based FE (3D finite element) model, and TM (transfer matrix) model, were designed to study the characteristics of mode and the critical speed characteristic of the dual-rotor system. The unbalanced response of the dual-rotor system was analysed to study the influence of mass unbalance on the rotor system. The effect of different disc unbalance phases and different speed ratios on the dynamic characteristics of the dual-rotor system were investigated in detail. The experimental result shows that the beam-based FE model is effective and suitable for studying the dual-rotor system. Conflict of Interest Statement The authors declare no conflicts of interest.

Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.

The dynamics of smallholder cattle business systems in Zambia: Geographical opportunities, challenges and sustainability business implications

The dynamics of smallholder cattle business systems in Zambia: Geographical opportunities, challenges and sustainability business implications

Enhancement of vibrationally assisted energy transfer by proximity to exceptional points, probed by fluorescence-detected vibrational spectroscopy

Emulation of energy transfer processes in natural systems on quantum platforms can further our understanding of complex dynamics in nature. One notable example is the demonstration of vibrationally assisted energy transfer (VAET) on a trapped-ion quantum emulator, which offers insights for the energetics of light harvesting. In this work, we expand the study of VAET simulation with trapped ions to a non-Hermitian quantum system comprising a PT-symmetric chromophore dimer weakly coupled to a vibrational mode. We first characterize exceptional points (EPs) and non-Hermitian features of the excitation energy transfer processes in the absence of the vibration, finding a degenerate pair of second-order EPs. Exploring the non-Hermitian dynamics of the whole system including vibrations, we find that energy transfer accompanied by absorption of phonons from a vibrational mode can be significantly enhanced near such a degenerate EP. Our calculations reveal a unique spectral feature accompanying the coalescing of eigenstates and eigenenergies that provides a unique approach to probe the degenerate EP by fluorescence-detected vibrational spectroscopy. Enhancement of the VAET process near the EP is found to be due to maximal favorability of phonon absorption at the degenerate EP, enabling multiple simultaneous excitations. Our work on improving VAET processes in non-Hermitian quantum systems paves the way for leveraging non-Hermiticity in quantum dynamics related to excitation energy transfer. Published by the American Physical Society 2024

Understanding service ecosystem dynamics: a typology

PurposeThe service environment is becoming increasingly turbulent, leading to calls for a systemic understanding of it as a set of dynamic service ecosystems. This paper advances this understanding by developing a typology of service ecosystem dynamics that explains the varying interplay between change and stability within the service environment through distinct behavioral patterns exhibited by service ecosystems over time.Design/methodology/approachThis study builds upon a systematic literature review of service ecosystems literature and uses system dynamics as a method theory to abductively analyze extant literature and develop a typology of service ecosystem dynamics.FindingsThe paper identifies three types of service ecosystem dynamics—behavioral patterns of service ecosystems—and explains how they unfold through self-adjustment processes and changes within different systemic leverage points. The typology of service ecosystem dynamics consists of (1) reproduction (i.e. stable behavioral pattern), (2) reconfiguration (i.e. unstable behavioral pattern) and (3) transition (i.e. disrupting, shifting behavioral pattern).Practical implicationsThe typology enables practitioners to gain a deeper understanding of their service environment by discerning the behavioral patterns exhibited by the constituent service ecosystems. This, in turn, supports them in devising more effective strategies for navigating through it.Originality/valueThe paper provides a precise definition of service ecosystem dynamics and shows how the identified three types of dynamics can be used as a lens to empirically examine change and stability in the service environment. It also offers a set of research directions for tackling service research challenges.

Non-Hermitian Quantum Rényi Entropy Dynamics in Anyonic-PT Symmetric Systems

We reveal the continuous change of information dynamics patterns in anyonic-PT symmetric systems that originates from the continuity of anyonic-PT symmetry. We find there are three information dynamics patterns for anyonic-PT symmetric systems: damped oscillations with an overall decrease (increase) and asymptotically stable damped oscillations, which are three-fold degenerate and are distorted using the Hermitian quantum Rényi entropy or distinguishability. It is the normalization of the non-unitary evolved density matrix that causes the degeneracy and distortion. We give a justification for non-Hermitian quantum Rényi entropy being negative. By exploring the mathematics and physical meaning of the negative entropy in open quantum systems, we connect negative non-Hermitian quantum Rényi entropy and negative quantum conditional entropy, paving the way to rigorously investigate negative entropy in open quantum systems.

Quantum control of classical motion: piston dynamics in a Rabi-coupled Bose-Einstein condensate

Abstract We develop a model and explore the dynamics of a hybrid classical-quantum system consisting of a classical piston and a self-interacting pseudospin 1/2 Bose-Einstein condensate with a time-dependent Rabi coupling. We investigate the mechanical work produced by the piston moving as a result of the quantum pressure of the condensate. The time-dependent Rabi field redistributes the condensate density between the spin components and, as a result, causes a time-dependent pressure acting on the piston. Correspondingly, the motion of the piston produces quantum evolution of the condensate mass- and spin density profiles. We show how by optimised design of the time-dependent direction of the Rabi field based on a quasi-stationary quantum pressure approximation, one can control both the position and velocity of the piston.

Navigating Power Structures: Symbolism, Realism, and Naturalism in The Grapes of Wrath through a Postcolonial Lens

This paper explores the representation and negotiation of power systems in John Steinbeck’s famous book “The Grapes of Wrath” by using a postcolonial framework. This study seeks to identify the manner in which questions of power, oppression, and resistance are depicted in the book by analyzing its themes of symbolism, realism, and naturalism through the lens of postcolonial theory. This study examines the symbolic meaning of people, situations, and events connected to hegemonic systems and colonial legacies through rigorous textual analysis. Additionally, it explores how the novel’s naturalism and realism illuminate the historical, socio-economic and environmental surroundings, emphasizing the connection between systemic inequality and power dynamics. This study clarifies how colonial ideas sustain exploitation and disenfranchisement by examining the experiences of marginalized groups, including tenant farmers, migrant labourers, and indigenous communities. Finally, this study advances our knowledge of how literature can be a platform for opposing and criticizing established power systems. It provides insights into the resilience and resistance strategies used by marginalized people.

A Study on the Dynamic Evolution Paths of Social Risks in PPP Projects of Water Environmental Governance—From the Vulnerability Perspective

The Chinese economy is transitioning from high-speed development to high-quality development, and water environmental governance is a key factor promoting economic transformation. Due to low returns and high investment in China’s water environmental governance, the PPP (public–private-partnership) model is often adopted. However, the PPP model has historically faced challenges adapting to local conditions in China, leading to social risks in the PPP projects of water environmental governance. To reduce these risks, this paper takes a vulnerability perspective, employing the system dynamics simulation method to explore the dynamic evolution process of social risks in the PPP projects of water environmental governance. The main results show the following: (1) the external manifestations of social risks during the construction and operation periods vary significantly, exhibiting notable fluctuations; (2) during the construction period, there is a surge in social risks followed by a returns to lower than normal levels, then a gradual upward trend; during the operation period, social risks initially decrease to a lower level before gradually increasing; and (3) city class, relevant legal systems, and resource reserves emerge as critical factors influencing vulnerability and social risks. The higher the city class, the lower the project vulnerability; the soundness of the legal system for PPP projects can effectively reduce vulnerability and social risks; sufficient resource reserves can reduce social risks. Based on the above findings, this paper proposes several suggestions aiming to reduce the vulnerability and social risks in PPP projects, optimize the process of water environmental governance, and further promote the sustainable development of water environmental governance and the high-quality economy of China.

Data-driven modeling using system dynamics simulation to provide relief in earthquake based on different scenarios.

Effective relief reduces damages and protects people during natural disasters, such as earthquakes. This research proposes a data-driven model based on sustainability, taking into account the pre and post-crisis simultaneously. Real data was used to validate the model in various earthquake scenarios. The study addresses questions regarding the amount and allocation of relief goods during earthquakes. This research is carried out in two phases: simulation and modeling. The purpose of the simulation phase is to estimate the number of relief goods in different scenarios. Additionally, in the modeling phase, a data-based multi-objective model is presented, considering sustainability, to minimize the lack of relief goods, the number of untreated wounded, and supply chain costs. Using the dynamic simulation system, and after designing the structure of the earthquake effects on urban infrastructure, the actions and effects of the earthquake on vital arteries are investigated in different scenarios, and scenarios with a higher degree of risk are identified. The results showed that the highest and lowest demands for relief goods were related to the "Mosha-day fault" and "North Tehran-night fault" scenarios, respectively.

Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems

Exploring the intersection of deterministic and stochastic dynamics, this paper delves into Lagrangian discovery for conservative and non-conservative systems under stochastic excitation. Traditional Lagrangian frameworks, adept at capturing deterministic behavior, are extended to incorporate stochastic excitation. The study critically evaluates recent computational methodologies for learning Lagrangians from observed data, highlighting the limitations in interpretability and the exclusion of stochastic excitation. To address these gaps, an automated data-driven framework is proposed for the simultaneous discovery of Lagrange densities and diffusion coefficients of stochastically excited dynamical systems by leveraging sparse regression. This novel framework offers several advantages over existing approaches. Firstly, it provides an interpretable description of the underlying Lagrange density, allowing for a deeper understanding of system dynamics under stochastic excitations. Secondly, it identifies the interpretable form of the diffusion coefficient of generalized stochastic force, addressing the limitations of existing deterministic approaches. Additionally, the framework demonstrates robustness and versatility through numerical case studies encompassing both stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs), with results showing almost exact approximations to true system behavior and minimal relative error in derived equations of motion.

QM/MM Free Energy Calculations of Long-Range Biological Protonation Dynamics by Adaptive and Focused Sampling.

Water-mediated proton transfer reactions are central for catalytic processes in a wide range of biochemical systems, ranging from biological energy conversion to chemical transformations in the metabolism. Yet, the accurate computational treatment of such complex biochemical reactions is highly challenging and requires the application of multiscale methods, in particular hybrid quantum/classical (QM/MM) approaches combined with free energy simulations. Here, we combine the unique exploration power of new advanced sampling methods with density functional theory (DFT)-based QM/MM free energy methods for multiscale simulations of long-range protonation dynamics in biological systems. In this regard, we show that combining multiple walkers/well-tempered metadynamics with an extended system adaptive biasing force method (MWE) provides a powerful approach for exploration of water-mediated proton transfer reactions in complex biochemical systems. We compare and combine the MWE method also with QM/MM umbrella sampling and explore the sampling of the free energy landscape with both geometric (linear combination of proton transfer distances) and physical (center of excess charge) reaction coordinates and show how these affect the convergence of the potential of mean force (PMF) and the activation free energy. We find that the QM/MM-MWE method can efficiently explore both direct and water-mediated proton transfer pathways together with forward and reverse hole transfer mechanisms in the highly complex proton channel of respiratory Complex I, while the QM/MM-US approach shows a systematic convergence of selected long-range proton transfer pathways. In this regard, we show that the PMF along multiple proton transfer pathways is recovered by combining the strengths of both approaches in a QM/MM-MWE/focused US (FUS) scheme and reveals new mechanistic insight into the proton transfer principles of Complex I. Our findings provide a promising basis for the quantitative multiscale simulations of long-range proton transfer reactions in biological systems.

On Numerical Simulations of Variable-Order Fractional Cable Equation Arising in Neuronal Dynamics

In recent years, various complex systems and real-world phenomena have been shown to include memory and hereditary properties that change with respect to time, space, or other variables. Consequently, fractional partial differential equations containing variable-order fractional operators have been extensively resorted for modeling such phenomena accurately. In this paper, we consider the two-dimensional fractional cable equation with the Caputo variable-order fractional derivative in the time direction, which is preferable for describing neuronal dynamics in biological systems. A point-wise scheme, namely, the Crank–Nicolson finite difference method, along with a group-wise scheme referred to as the explicit decoupled group method are proposed to solve the problem under consideration. The stability and convergence analyses of the numerical schemes are provided with complete details. To demonstrate the validity of the proposed methods, numerical simulations with results represented in tabular and graphical forms are given. A quantitative analysis based on the CPU timing, iteration counting, and maximum absolute error indicates that the explicit decoupled group method is more efficient than the Crank–Nicolson finite difference scheme for solving the variable-order fractional equation.

Reinforcement Learning for Closed-loop Regulation of Cardiovascular System with Vagus Nerve Stimulation: A Computational Study.

(Objective). Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study. (Approach). Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate (HR) and the mean arterial pressure (MAP) for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control (PILCO). (Main results). Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of Deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems. (Significance). We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Video camera and seismic monitoring of water bulge explosion at Strokkur Geyser, Iceland

Eruptions of volcanoes and geysers share many fundamental similarities: for example, they are manifestations of Earth’s geothermal energy, involving the pressure-driven expulsion of fluids from the Earth’s interior. However, while volcanoes can produce spectacular lava bubbles that burst, water bubbles are rarely observed on the surface of geysers. It is still unclear why some of these low-viscosity geyser systems produce none, while others produce them regularly. There is no quantification of the size, speed, and height of these bubbles at geysers, which is the gap we fill here. Strokkur creates a water bulge in its surface pool (bulge stage). When the bulge bursts, water is ejected into the air (jet stage). The steam then continues to rise buoyantly and drift away (drift stage). Here we study the evolution of the three stages using records from video camera campaigns and a local seismic network. We find that larger bulges are associated with larger ascent velocities and cause larger jet heights. As energy is channeled into a high jet, small seismic ground motions are recorded. The bulge formation itself is barely visible seismically. Our work suggests that the 0.74±0.27 s-long bulge stage can be used as a first-order proxy for predicting eruption height. This study might also be relevant for understanding fluid dynamics in volcanic systems.

Greybody factors, quasi-normal modes and thermal fluctuations of quantum-corrected Schwarzschild black hole surrounded by quintessence

In this study, we examine the effects of a quintessence field-enclosed Schwarzschild black hole that has undergone quantum corrections on a number of different physical phenomena. The effective potential and its graphical features across different values of the quantum correction and quintessence field as well as relevant physical parameters are analyzed by deriving the radial equation from the Klein–Gordon equation and converting it into a Schrodinger wave equation using the tortoise coordinate. Both the event and cosmic horizons are found in the solutions to the radial equation. We provide a detailed analysis of the greybody component and its behavior by merging these solutions in the intermediate regime. By analyzing the black hole metric function, effective potential, and absorption probabilities, the study uncovers the complex relationship between black hole characteristics, quantum corrections, and the surrounding quintessence field. Investigating the BH’s quasinormal modes and thermal fluctuations provides insights into its stability as well as thermodynamic properties. The findings offer a comprehensive understanding of how the black hole interacts with its environment and responds to environmental changes, shedding light on the rich dynamics of black hole systems in the presence of quantum corrections and quintessence fields.

Study of the factors influencing the life cycle cost of green buildings using SEM-SD method

ABSTRACT The recent surge in carbon emissions from the building sector has raised significant environmental concerns. While green building practices have proven effective, their widespread adoption has been hindered by high costs. This study aims to explore the factors impacting the life cycle costs of green buildings, with the aim of achieving “double carbon” reduction. Through an analysis of 42 articles, 24 factors were distilled based on expert opinion. After collecting 322 data through questionnaires, a model of the factors influencing the life cycle cost of green buildings was constructed using structural equation modeling, and a cost simulation study was carried out through the system dynamics method in the practical case to examine how the cost changed. The findings highlight the pivotal role of planning and design, implementation, and operation and maintenance phases. Notably, green building design programs, green construction programs, and the property management level stand out as critical factors emerged as critical factors. These insights can inform strategies to reduce the cost of green buildings, thereby fostering energy conservation and emission reduction in the construction industry.