System Dynamics Research Articles

Long-term sustainability of the water-agriculture-energy nexus in Brazil's MATOPIBA region: A case study using system dynamics.

The global demand for agricultural commodities has driven extensive land conversion to agriculture in Brazil, especially in the MATOPIBA region. This area encompasses the Rio Grande Basin, a major tributary of the São Francisco Basin that is known for expanding intensive irrigated agriculture and hydropower generation. However, recent data reveal declining precipitation and aquifer recharge, potentially exacerbating ongoing water and land conflicts. This study investigates the long-term sustainability of agricultural expansion amid the worsening water scarcity using a system dynamics model. Findings suggest that rising costs and decreasing profits due to irrigation water shortages may hinder the expansion of irrigated land. By 2040, the irrigation demand may remain partly unmet, while downstream flow and baseflow could decrease. Additionally, agricultural expansion will significantly raise energy demand, posing a developmental challenge. We suggest that ensuring the sustainability of the Rio Grande Basin depends on improved water management and exploring alternative energy sources to address existing constraints.

The Dynamics of African traditional justice systems: Perspectives and prospective

ABSTRACT This study investigates the dynamics of African traditional justice systems in four African countries: Sierra Leone, Malawi, Uganda, and Zimbabwe. These countries have a rich history of traditional justice systems deeply rooted in their respective societies. These systems are regarded as essential elements of African communities, embodying unique philosophies, rituals, and practices that have historically played a crucial role in resolving conflicts and fostering peace. The evolution of law and order in these African countries has been heavily influenced by these traditional justice systems, contributing to the establishment of a robust governance structure. By conducting an analysis of customary justice frameworks in the sub-Saharan African context, this study examines the perceptions of local populations regarding the traditional justice system as a legitimate institution. It explores how the traditional justice system can function as an alternative or complementary form of justice within traditional societies. Drawing on previous research conducted in Sierra Leone, Malawi, Uganda, and Zimbabwe, this study reveals the challenges associated with integrating traditional justice systems into the broader legal framework. Nevertheless, it concludes that the integration of these systems can contribute to a comprehensive approach to sustainable peace-building, conflict resolution, and the establishment of a just rule of law in these African countries.

Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach

Incidents like the COVID-19 pandemic or military conflicts disrupted global supply chains, causing long-lasting shortages in multiple sectors. This so-called ripple effect denotes the propagation of disruptions to further elements of the supply chain. Due to the severity of the impact that the ripple effect has on revenues, service levels, and reputation among supply chain entities, it is essential to understand the related implications. Given the unpredictable nature of disrupting events, this study emphasises the value of a reactive development of effective recovery policies on an operational level. In this article, a system dynamics model for a supply chain is used as framework to investigate the ripple effect. Based on this model, recovery policies are generated using reinforcement learning (RL), which represents a novel approach in this context. As main findings, the experimental results demonstrate the applicability of the proposed approach in mitigating the ripple effect based on secondary data from a major aerospace and defence supply chain and furthermore, the results indicate a broad applicability of the approach without the need for complete information about the disruption characteristics and supply chain entities. With further refinement and real-world implementation, the presented approach provides the potential to enhance supply chain resilience in practice.

Hypothetical mechanisms driving physical activity levels in ethnic minority groups living in Europe: a systematically identified evidence-based conceptual systems model

BackgroundIn Europe, physical activity levels tend to be lower in ethnic minority groups than the general population. Interventions and policies based on research examining isolated determinants of physical activity have had limited success in increasing physical activity levels. This study used systems dynamics theory and the capability approach theoretical framework to develop a conceptual model of how individual characteristics, institutional and physical environments and the migration context may interact to promote or hinder physical activity in ethnic minority groups living in Europe.MethodsA systematic update of Langøien et al.’s 2017 review of the determinants of physical activity in ethnic minority groups living in Europe was conducted. Our target population included individuals of all ages who reported a familial migration background from any low- and middle-income countries or belonging to minority indigenous population in Europe. Outcomes pertaining to non-work related physical activity of light, moderate or vigorous intensity performed in any setting were included. Included studies provided an evidence base from which to derive the causal loop diagrams comprising our conceptual model. Sub-system causal loop diagrams were interpreted in co-author review sessions to explicate non-linear system mechanisms, such as reinforcing and balancing feedback loops.ResultsForty-one studies were identified, of which the majority was qualitative. The conceptual model consisted of 4 causal loop diagrams relating to psychosocial constructs; sociocultural constructs; health and health communication and social and material resources, in interaction with environmental/migration context. Four hypothetical mechanisms were identified, e.g. hypothesizing that participation in organised activities leads to increased self-efficacy, thereby enabling further participation.ConclusionsThis study contributes an evidence-based conceptual systems model which elucidates how low levels of physical activity in ethnic minority groups in Europe could be supported by reinforcing and balancing mechanisms involving factors relating to physical and institutional environments, migration context and individuals. A pluralistic approach to literature review, integrating complexity methods such as CLDs into more conventional systematic literature review, supports novel insights into how factors could interact to support persistently low levels of activity, moving beyond the identification of potential relationships between isolated factors to indicating the ways in which these relationships are sustained and could be modified by intervention or policy.

Influence of extended working hours and physical recovery on absenteeism in the footwear industry from a system dynamics model

Objectives. Work-related musculoskeletal disorders (WMSDs) are recurrent in the footwear industry, resulting in absenteeism. This study aimed to quantitatively analyze the influence of overtime work and physical recovery time on the occurrence of WMSD-related absenteeism using a system dynamics model. As ergonomic methods have limitations in quantitatively simulating the behavior of these relationships, the integration of computational modeling techniques has emerged as a methodological alternative to bridge this gap. Methods. An ergonomic work analysis (EWA) was developed in a production cell of a large company. A model of causal relationships (causal loop diagram) and a simulation model (flow and stock diagram) were then developed, where three scenarios for overtime and physical recovery time were analyzed. Results. Working an additional hour resulted in a 42% increase in physical overload, leading to 7.62 leave requests per year and 78.7 days of employee absenteeism. Increasing the physical recovery time by 15 min reduced the overload to 36.5%, resulting in 6.8 leave requests per year and 71.1 days of employee absenteeism. Conclusions. Properly managing excess workload and providing adequate physical recovery for professionals is necessary to mitigate the productivity impacts of absenteeism in the footwear industry.

Quantum nature of reactivity modification in vibrational polariton chemistry.

In this work, we present a mixed quantum-classical open quantum system dynamics method for studying rate modifications of ground-state chemical reactions in an optical cavity under vibrational strong-coupling conditions. In this approach, the cavity radiation mode is treated classically with a mean-field nuclear force averaging over the remaining degrees of freedom, both within the system and the environment, which are handled quantum mechanically within the hierarchical equations of motion framework. Using this approach, we conduct a comparative analysis by juxtaposing the mixed quantum-classical results with fully quantum-mechanical simulations. After eliminating spurious peaks that can occur when not using the rigorous definition of the rate constant, we confirm the crucial role of the quantum nature of the cavity radiation mode in reproducing the resonant peak observed in the cavity frequency-dependent rate profile. In other words, it appears necessary to explicitly consider the quantized photonic states in studying reactivity modification in vibrational polariton chemistry (at least for the model systems studied in this work), as these phenomena stem from cavity-induced reaction pathways involving resonant energy exchanges between photons and molecular vibrational transitions.

Exciton energy transfer inside cavity-A benchmark study of polaritonic dynamics using the surface hopping method.

Strong coupling between the molecular system and photon inside the cavity generates polaritons, which can alter reaction rates by orders of magnitude. In this work, we benchmark the surface hopping method to simulate non-adiabatic dynamics in a cavity. The comparison is made against a numerically exact method (the hierarchical equations of motion) for a model system investigating excitonic energy transfer for a broad range of parameters. Surface hopping captures the effects of the radiation mode well, both at resonance and off-resonance. We have further investigated parameters that can increase or decrease the rate of population transfer, and we find that surface hopping in general can capture both effects well. Finally, we show that the dipole self-energy term within our parameter regime does not significantly affect the system's dynamics.

Multiple Misaligned Outflows and Warped Accretion Flows in the Proto-multiple System Per-emb-8 and 55

To investigate the formation process of multiple systems, we have analyzed the Atacama Large Millimeter/submillimeter Array archival data of the 1.3 mm continuum, 12CO (2-1) and C18O (2-1) emission in a proto-multiple system consisting of a Class 0 protostar Per-emb-8 and a Class I protobinary Per-emb-55 A and B. The 1.3 mm continuum emission is likely to primarily trace their protostellar disks, and the Keplerian disk rotation is observed in Per-emb-8 and Per-emb-55 A in the emission lines. In Per-emb-8, we identify two arm-like structures with a length of ∼1000 au connecting the eastern and western of its disk in the continuum and C18O emission. Our analysis suggests that these arm-like structures are most likely infalling flows. In the 12CO emission, we discover a second bipolar outflow associated with Per-emb-8. The two bipolar outflows in Per-emb-8 are possibly launched along the normal axes of the misaligned inner and outer parts of its warped protostellar disk. In Per-emb-55, we find that the red- and blueshifted lobes of its bipolar outflow are misaligned by 90°. The presence of the warped disk, multiple misaligned outflows, and asymmetric infalling flows suggest complex dynamics in proto-multiple systems, and these could be related to the tidal interactions between the companions and/or the turbulent environments forming this proto-multiple system.

Molecular Dynamical and Quantum Mechanical Exploration of the Site-Specific Dynamics of Cy3 Dimers Internally Linked to dsDNA.

Performing spectroscopic measurements on biomolecules labeled with fluorescent probes is a powerful approach to locating the molecular behavior and dynamics of large systems at specific sites within their local environments. The indocarbocyanine dye Cy3 has emerged as one of the most commonly used chromophores. The incorporation of Cy3 dimers into DNA enhances experimental resolution owing to the spectral characteristics influenced by the geometric orientation of excitonically coupled monomeric units. Various theoretical models and simulations have been utilized to aid in the interpretation of the experimental spectra. In this study, we employ all-atom molecular dynamics simulations to study the structural dynamics of Cy3 dimers internally linked to the dsDNA backbone. We used quantum mechanical calculations to derive insights from both the linear absorption spectra and the circular dichroism data. Furthermore, we explore potential limitations within a commonly used force field for cyanine dyes. The molecular dynamics simulations suggest the presence of four possible Cy3 dimeric populations. The spectral simulations on the four populations show one of them to agree better with the experimental signatures, suggesting it to be the dominant population. The relative orientation of Cy3 in this population compares very well with previous predictions from the Holstein-Frenkel Hamiltonian model.

Investigating 3D microbial community dynamics of the rhizosphere using quantitative phase and fluorescence microscopy

Microbial interactions in the rhizosphere contribute to soil health, making understanding these interactions crucial for sustainable agriculture and ecosystem management. Yet it is difficult to understand what we cannot see; among the limitations in rhizosphere imaging are challenges associated with rapidly and noninvasively imaging microbial cells over field depths relevant to plant roots. Here, we present a bimodal imaging technique called complex-field and fluorescence microscopy using the aperture scanning technique (CFAST) that addresses these limitations. CFAST integrates quantitative phase imaging using synthetic aperture imaging based on Kramers–Kronig relations, along with three-dimensional (3D) fluorescence imaging using an engineered point spread function. We showcase CFAST’s practicality and versatility in two ways. First, by harnessing its depth of field of more than 100 μm, we significantly reduce the number of captures required for 3D imaging of plant roots and bacteria in the rhizoplane. This minimizes potential photobleaching and phototoxicity issues. Second, by leveraging CFAST’s phase sensitivity and fluorescence specificity, we track microbial growth, competition, and gene expression at early stages of colony biofilm development. Specifically, we resolve bacterial growth dynamics of mixed populations without the need for genetically labeling environmental isolates. Moreover, we find that gene expression related to phosphorus sensing and antibiotic production varies spatiotemporally within microbial populations that are surface attached and appears distinct from their expression in planktonic cultures. Together, CFAST’s attributes overcome commercial imaging platform limitations and enable insights to be gained into microbial behavioral dynamics in experimental systems of relevance to the rhizosphere.

Witness of non-Markovian dynamics based on Bhattacharyya quantum distance

Non-Markovian effects due to quantum memory in the dynamics of open systems typically correspond to information backflows from the surrounding environment to the system. We propose a witness to quantify the non-Markovianity of quantum evolutions using the Bhattacharyya distance (BD), a specific quantum statistical distance. This witness has the advantage of not requiring the calculation of the evolved density matrix and only computes through the initial and final states of the system, therefore leading to the improvement of quantum metrology. It means that we calculate the quantum angle between two states to detect non-Markovian effects. This proposal is investigated by considering several instances of open quantum systems, such as two and three-level atoms interacting in single and two-mode fields, respectively, and two effective two-level atoms interacting locally with two independent environments. We demonstrate that the suggested BD-based non-Markovianity witness identifies memory effects, consistent with well-established witnesses based on Bures distance, quantum Fisher information, and Hilbert-Schmidt speed, showing sensitivity to information backflows.

An Optimization Method for Stiffness and Damping Mistuning Identification From Blade Tip Timing Data

Abstract System identification of dynamic properties is of large interest in the turbomachinery industry to create more accurate computational models and more effective designs. Previous identification techniques were able to accurately capture the blade variability, known as mistuning, in the stiffness as well as the average damping of all the blades. Mistuning is vital to accurately identify and study because the symmetry of the system is broken and can lead to vibration localization and high amplitudes. In this work, a new method is proposed to not only capture the stiffness mistuning values, but also the blade-to-blade variability in damping. These damping mistuning values have been shown to have a significant effect on the dynamics of bladed disk systems. Incorporation of the damping mistuning into the computational model can be done utilizing an augmented component mode mistuning method with either structural or proportional damping. The mistuning values for this new identification method were compared to a well-established direct method and a previously studied optimization method. Blade responses were then found using a harmonic analysis and the newly identified mistuning values. These blade responses were then compared to experimental tip timing data from full scale rotating experiments. These comparisons show that the new model is able to better reproduce experimental data using computational models that incorporate both stiffness and damping mistuning values.

DISCOVERING SYMBOLIC LAWS DIRECTLY FROM TRAJECTORIES WITH HAMILTONIAN GRAPH NEURAL NETWORKS

Abstract The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derived as functionals based on observables such as positions and velocities. Discovering these governing symbolic laws is the key to comprehending the interactions in nature. Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory. We demonstrate the performance of HGNN on n-springs, n-pendulums, gravitational systems, and binary Lennard Jones systems; HGNN learns the dynamics in excellent agreement with the ground truth from small amounts of data. We also evaluate the ability of HGNN to generalize to larger system sizes, and to a hybrid spring-pendulum system that is a combination of two original systems (spring and pendulum) on which the models are trained independently. Finally, employing symbolic regression on the learned HGNN, we infer the underlying equations relating to the energy functionals, even for complex systems such as the binary Lennard-Jones liquid. Our framework facilitates the interpretable discovery of interaction laws directly from physical system trajectories. Furthermore, this approach can be extended to other systems with topology-dependent dynamics, such as cells, polydisperse gels, or deformable bodies.

Digital nomads: a systematic literature review and future research agenda

Purpose This study aims to address the need for robust conceptual foundations in the digital nomadism discourse through a systematic literature review (SLR). It delves into digital nomadism within sustainable tourism, providing valuable insights to foster community development. Design/methodology/approach This study uses a rigorous eight-step process that combines an SLR and a system dynamics approach. In the SLR phase, the authors use the theory, context, characteristics and methodology framework to identify key variables for a dynamic model of sustainable tourism through digital nomadism, following detailed search and selection criteria based on PRISMA guidelines. The second phase uses causal loop diagrams (CLDs) from the literature review to visualize relationships and inform future research directions. The CLD model is validated through literature-based and stakeholder interaction processes, focusing on the social, economic and environmental dimensions, resulting in the development of a robust conceptual model. Findings This study identified 28 theories, including agency–structure theory, travel career ladder theory, stakeholder theory and grounded theory, among others. Semi-structured interviews were the primary research method. Major themes explored encompass community interactions, work lifestyles, cultural aspects, financial considerations, infrastructure and policy. The proposed conceptual model aims to scrutinize relationships across economic, environmental and social dimensions, providing a foundation for investigating and promoting sustainable tourism. Research limitations/implications Policy implications involve refining taxation policies to maintain tax bases, promoting collaborative economic models and enforcing environmental regulations. Additionally, integrating community planning infrastructure and implementing demographic strategies to manage potential population shifts are crucial. Policies supporting education, skill transfer, health and well-being initiatives contribute significantly to fostering sustainable tourism practices and enhancing community vitality. Originality/value To the best of authors’ knowledge, this study is the first in the digital nomadism space to propose a conceptual model for sustainable tourism adoption.

Frequency-Dependent Bouc–Wen Modeling of Magnetorheological Damper Using Harmonic Balance Approach

Magnetorheological dampers (MRDs) are of great interest in engineering due to their continuously adjustable damping characteristics. Accurate models are essential for optimizing MRDs and analyzing system dynamics. However, conventional methods widely overlook the impact of excitation frequency and amplitude. To address this issue, this work proposes a modified Bouc–Wen model that can be adapted to various excitation conditions. The model’s parameters depend on the current, excitation frequency, and amplitude. The mechanical characteristics of the MRD were analyzed by the tests. The parameters in the Bouc–Wen model were identified by combining the harmonic balance method and the genetic algorithm. The modified Bouc–Wen model was established by analyzing the variation of each parameter with current, excitation frequency, and amplitude. Finally, the agreement between the modified prediction model and the test results was verified under sinusoidal excitation of 80 mm and 1 Hz. The average relative errors were 3.87%, 2.82%, 2.45%, 2.19%, and 3.27% for current excitations of 0 A, 0.5 A, 1 A, 1.5 A, and 2.0 A, respectively. Since the MRD in this paper operates from 0.5 Hz to 2 Hz, the modified model was validated in the same range. Experiments demonstrate that the modified Bouc–Wen model efficiently and accurately describes the mechanical properties of the MRD under various excitation conditions.

Characteristic value decomposition: A unifying paradigm for data-driven modal analysis

Data-driven modal analysis is an indispensable means to understand the dynamic behavior of engineered structures and natural systems. It effectively captures active dynamic behavior and embeds them in a set of identified linear modal subspaces, unveiling complex dynamic behavior via the superposition of simpler hierarchical spatiotemporal dynamics. Despite the existence of many methods, there is no unified perspective connecting these modal analysis techniques. This paper proposes a new modal decomposition method, the characteristic value decomposition (CVD), that provides a unifying paradigm for data-driven modal analysis. CVD identifies identical spatial modes and temporal coordinates from two measurement data matrices. The modal parameters are identified through an eigendecomposition to a quotient of auto-covariance and cross-covariance matrices of the two data matrices. This paper shows that several data-driven modal analysis methods, namely, the state-variable decomposition, the dynamic mode decomposition, the Ibrahim time domain method, and the eigensystem realization algorithm, are special cases of CVD when the two data matrices are appropriately selected. Moreover, the CVD identifies the modal parameters with higher accuracy and is robust to added noise compared to established methods, as demonstrated using numerical experiments. It is also shown that the CVD flexibly utilizes data from multiple experiments and undersampled data sets to unveil system dynamics.

On the operational similarities of bladed rotor vibrations with casing contacts

AbstractRotor-to-stator rubbing in rotating machinery, resulting from tight clearances, introduces complex dynamics that can potentially lead to high vibrations and machine failure. Historically, the rubbing models were addressed using cylinder-to-cylinder contacts; however, recent attention has shifted towards examining blade-tip contact in turbines, which affects the systems dynamics and efficiency. This study investigates the impact of the variations in blade number on bladed rotor systems, emphasizing on the types of motion that occur as function of the operational speed in the sub-critical range. A simplified bladed rotor model has been developed, using a Jeffcott rotor with blades represented as damped elastic pendulums. The equations of motion are derived and numerical simulations are performed to explore the system’s behaviour with varying blade numbers (3, 5, 7, and 10) in order to analyse displacements, contact forces and bifurcation diagrams as function of the rotating speed. Results reveal distinct regions: periodic motion (I and III) and chaotic motion (II and IV) appear alternatively in the bifurcation diagram, with the chaotic regions occurring at specific fractions of the natural frequency and the number of blades. The study concludes that chaotic motions are associated with larger displacements and higher contact forces, and the vibrational behaviour becomes less hazardous as the number of blades increases. In addition, the appearance of periodic and chaotic motions occur in the same regions by scaling the rotating speed with the number of blades and natural frequency of the system. From an operational perspective, this dynamic investigation offers valuable insights into the severity of blade rubbing in industrial systems. It can guide the implementation of mitigation solutions to prevent worst-case failure scenarios and help to perform adjustments to either operational or design parameters.

A Real-Time Power Sharing Strategy for a Multi-Energy System While Considering Individual System Dynamics

For a sustainable and resilient energy supply, multi-energy systems (MES) are becoming more prominent. Real-time efficient power-sharing in an MES consisting of different generation sources, uncontrollable loads, multiple storage options, and Power-to-X technologies is a challenging task. The challenge arises due to constantly fluctuating generation and load, as well as the different system dynamics of each element in the setup. Optimizing the power-sharing among the various controllable elements in an MES could be broken down into a two-level process. The top-level process, commonly known as super-ordinate control, defines the power-sharing over a longer timeframe based on load and generation forecasts and multiple other factors. The lower-level process, commonly known as sub-ordinate control, based on the inputs from the super-ordinate control and live sensor data refines the control signals of the individual elements. This work focuses on developing a control framework for the sub-ordinate control process while considering the individual element dynamics such as dead time and rise time to a control signal. This is important as not considering differences in the system dynamics results in sub-optimal control, causing dynamic mismatches. This work proposes a simple rule-based power-splitting method backed up with individual PID regulators and Smith Predictor for each element in the MES, which are further coupled to each other for more precise and efficient control. The proposed method induces a cooperative behavior among the MES elements and improves the reaction time to a control signal while also improving the target tracking by 3-8%.

Research on the control of thrust vectoring turbojet aircraft with uncertainties and input saturation based on fixed‐time control

AbstractOne‐dimensional thrust vectoring turbojet vertical takeoff and landing (VTOL) aircraft have attracted research attention due to their high thrust‐to‐weight ratios and their solution for the slow speed response of turbojet engines. However, there are some uncertainties and physical limitations in their systems, such as external disturbances, unknown parameters and input saturation. To improve the accuracy and convergence speed of trajectory tracking, a fixed‐time control method that is robust to saturation is proposed. The system dynamics are established, and an auxiliary system is built within a fixed time control framework to address the influence of input saturation and increase the error convergence rate. Nonsingular fast terminal sliding mode technology is combined with a few adaptive laws to ensure the robustness of the closed‐loop system against dynamic uncertainties and improve the precision of steady‐state control. The stability of the control system is proven based on Lyapunov, and the controller parameters are optimized based on particle swarm optimization (PSO). The proposed method based on fixed‐time stability guarantees that the states of the closed‐loop system can reach the residual set around zero within the designed time, and an expression for the upper bound on the convergence time is given. Finally, numerical simulations demonstrate the superiority of the proposed method based on the error integral criterion.

Early warning signals of complex critical transitions in deterministic dynamics

AbstractEarly Warning Signals (EWS) have generated much excitement for their potential to anticipate transitions in various systems, ranging from climate change in ecology to disease staging in medicine. EWS hold particular promise for bifurcations, a transition mechanism in which a smooth, gradual change in a control parameter of the system results in a rapid change in system dynamics. The predominant reason to expect EWS is because many bifurcations are preceded by Critical Slowing Down (CSD): if assuming the system is subject to continuous, small, Gaussian noise, the system is slower to recover from perturbations closer to the transition. However, this focus on warning signs generated by stochasticity has overshadowed warning signs which may already be found in deterministic dynamics. This is especially true for higher-dimensional systems, where more complex attractors with intrinsic dynamics such as oscillations not only become possible—they are increasingly more likely. The present study focuses on univariate and multivariate EWS in deterministic dynamics to anticipate complex critical transitions, including the period-doubling cascade to chaos, chaos-chaos transitions, and the extinction of a chaotic attractor. In a four-dimensional continuous-time Lotka–Volterra model, EWS perform well for most bifurcations, even with lower data quality. The present study highlights three reasons why EWS may still work in the absence of CSD: changing attractor morphology (size, shape, and location in phase space), shifting power spectra (amplitude and frequency), and chaotic transitional characteristics (density across attractor). More complex attractors call for different warning detection methods to utilise warning signs already contained within purely deterministic dynamics.