System Dynamics Simulation Research Articles

Modeling Trends and Projections of Riding with Alcohol-Impaired Drivers in Fatal Crashes among Young Adults: A System Dynamics Approach.

The purpose of the study was to investigate factors contributing to the decline in the number of passengers riding with alcohol-impaired drivers involved in fatal crashes since 1982, and to examine the impact of simulated interventions on this group through 2050. Historical data were obtained from the Fatality Analysis Reporting System. We applied linear regression to analyze changes in the average numbers of passengers per alcohol impaired young driver involved in fatal crashes between 1982 and 2020 by age and sex. We also extended our existing system dynamics simulation model developed to examine driving while impaired (DWI) behaviors of U.S. male and female drivers aged 15 to 24 and explored riding with an impaired driver (RWI) behaviors and corresponding interventions. We conducted sensitivity analyses to examine the likely trajectories of alcohol impaired drivers' passengers in fatal crashes across multiple scenarios through 2050. Our findings show that the decline in passengers of alcohol impaired drivers in fatal crashes primarily stems from a decrease in the number of impaired drivers, rather than a change in average number of passengers per impaired drivers. The simulation model replicated historical trends from 1982 to 2020, and the sensitivity analyses show that the policies reducing DWI trips also decrease RWI trips. Wide adoption of a comprehensive strategy, combining increased enforcement, an alcohol truth campaign, the provision of alternative transportation, and the enactment of a new DWI restrictive law, could significantly reduce the number of passengers in fatal crashes involving alcohol-impaired drivers, while minimizing possible unintended consequences.

Calibration of parameters and predictive control strategy of a wind turbine for improvement of energy harvesting

Applied mathematical modeling of the various energy resources has contributed widely in their design and operation improvements. Among renewable energy sources, wind energy systems have gained a significant attention for their rapid and continuous growth, which resulted from their cost effectiveness and clean power generation. However, research effort on the industry and university levels is still needed to enhance the characteristics of existing wind turbines and allow larger amounts of their share. This thesis presents an accurate model of a wind turbine (TWT-1.65) system for time-based dynamic simulations. The parameters of the power coefficient have been identified, calibrated and verified using different meta-heuristic optimization techniques, which are the Wild Horse Optimizer (WHO), Whale Optimizer (WO), and Genetic Algorithm (GA) to enhance the model accuracy over the previously related studies. The new version of the parameters has resulted in a higher accuracy while being practically meaningful, with the best achieved MSE of 0.006. The system model has been then integrated with two predictive controllers which are the linear Model Predictive Control (MPC) and Neural Network Predictive Control (NNPC) to regulate the pitch angle trends for the target of maximum energy harvesting. With proper selection of the controllers’ parameters, off-line simulation studies have shown improved production trends of the power output with constrained and safe trajectory of the pitch angle, which can be translated to be an improve in the total average harvested power of 34% and 44.5% over the complete time window of 5760 min using NNPC and Linear MPC respectively.

Using routine health care data to develop and validate a system dynamics simulation model of frailty trajectories in an ageing population

ABSTRACT Frailty is common in older adults and has a substantial impact on patient outcomes and service use. Information to support service planning, including prevalence in middle-aged adults and patterns of frailty progression at population level, is scarce. This paper presents a system dynamics model describing the dynamics of frailty and ageing within a population of patients aged ≥50, based on linked data for 2.2 million patients from primary care practices in England. The purpose of the model is to estimate the incidence and prevalence of frailty in an ageing population over time. The model was developed in consultation with stakeholders (patients, carers, clinicians, and commissioners) and validated against another large dataset (1.38 million patients) from Wales. It was then scaled up to the population of England, using Office for National Statistics projections (to 2027). The baseline results, subject to the assumption that the frailty transition parameters remain constant over this period, suggest that the number of people living with frailty will increase as the population ages, and that those with mild-moderate frailty are likely to have the greatest impact on demand. This paper focuses on model development and validation, highlighting the benefits and challenges of using large routine health datasets.

Enhancing chromium supply chain security through resilience strategies: decision support based on system dynamics simulations

Enhancing chromium supply chain security through resilience strategies: decision support based on system dynamics simulations

AllohubPy: Detecting allosteric signals through an information-theoretic approach.

Allosteric regulation is crucial for biological processes like signal transduction, transcriptional regulation, and metabolism, yet the mechanisms and macromolecular properties that govern it are still not well understood. Several methods have been developed over the years to study allosterism through different angles. Among the possible ways to study allosterism, information-theoretic approaches, like AlloHubMat or GSAtools, can be particularly effective due to their use of robust statistics and the possibility to be combined with graph analysis. These methods capture local conformational changes associated with global motions from molecular dynamics simulations through the use of a Structural Alphabet, which simplifies the complexity of the Cartesian space by reducing the dimensionality down to a string of encoded fragments, representing sets of internal coordinates that still capture the overall conformation changes. In this work, we present "AllohubPy," an improved and standardized methodology of AlloHubMat and GSAtools coded in Python. We analyse the performance, limitations and sampling requirements of AllohubPy by using extensive molecular dynamics simulations of model allosteric systems and apply convergence analysis techniques to estimate result reliability. Additionally, we expand the methodology to use different dimensionality reduction Structural Alphabets, such as the 3DI alphabet, and integrate Protein Language Models (PLMs) to refine allosteric hub communication detection by monitoring the detected evolutionary constraints. Overall, AllohubPy expands its preceding methods and simplifies the use and reliability of the method to effectively capture dynamic allosteric motions and residue pathways. AllohubPy is freely available on GitHub (https://github.com/Fraternalilab/AlloHubPy) as a package and as a Jupyter Notebook.

Urban pandemic governance personal protective equipment allocation strategies: a system dynamics simulation

In the early days of the urban pandemic, many cities had personal protective equipment (PPE) shortages, which adversely affected urban pandemic governance. Using the COVID-19 strategies employed in Wuhan as the pivotal case study, this study sought to determine effective strategies to optimize city PPE distribution. System dynamics modeling was employed to explore the influence of PPE allocation strategies on pandemic control measures. It was found that the most effective method for controlling a pandemic was to supply PPE in a specific order: medical staff, patients, and out-of-home citizens. Further, prioritizing universal PPE access over adhering to recommended replacement frequencies was found to be more effective in protecting public health. These findings offer vital insights for policy formulation and pandemic preparedness planning to reduce infection rates and fatalities.

Direct statistical simulation of the Lorenz96 system in model reduction approaches

Direct statistical simulation (DSS) of nonlinear dynamical systems bypasses the traditional route of accumulating statistics by lengthy direct numerical simulations by solving the equations that govern the statistics themselves. DSS suffers, however, from the curse of dimensionality as the statistics (such as correlations) generally have higher dimensions than the underlying dynamical variables. Here we investigate two approaches to reduce the dimensionality of DSS, illustrating each method with numerical experiments with the Lorenz96 dynamical system. The forms of DSS chosen here involve approximate closures at second and third order in the equal-time cumulants. We demonstrate significant reduction in computational effort that can be achieved without sacrificing the accuracy of DSS. The methods developed here can be applied to turbulent fluid and magnetohydrodynamical systems. Published by the American Physical Society 2025

Energy Performance Study of a Data Center Combined Cooling System Integrated with Heat Storage and Waste Heat Recovery System

The energy efficiency of data centers has become an urgent problem as it is enjoying rapid development. This study proposes an integrated energy system involving a data center with different renewable energy sources and waste heat recovery, which can consider the partial and unsteady working load of data center. A dynamic and sophisticated system simulation model is established, which can provide both reliable and fast evaluations but also allow flexible extension of additional components. It is found that the free natural resource cooling system can cover about 28% of cooling demand. Compared to the reference condition, the proposed energy system achieves significant energy-saving benefits, with an energy-saving rate of 16.4%. The system COP increases from 3.88 to 4.64, and the PUE decreases from 1.36 to 1.30, resulting in a 23.45% reduction in electricity expenses. By integrating a waste heat recovery system, the heat pump can absorb approximately 3.57 million kWh of heat from the data center, providing approximately 4.587 million kWh of heating energy for users. The rooftop PV system generates approximately 370,000 kWh of electricity annually, covering approximately 8% of the total electricity consumption of the data center. This study can offer a new channel for the energy efficiency enhancement of data centers.

Analytical treatment of proton double-quantum NMR intensity buildup: multi-spin couplings and the flip-flop term

Abstract. A modified Anderson–Weiss approximation for describing double-quantum (DQ) NMR experiments in systems with many I = 1/2 spins is proposed, taking inter-spin flip-flop processes into special consideration. In this way, an analytical result is derived for multi-spin systems for the first time. It is shown that in the initial stages of DQ intensity buildup, the probability of flip-flop processes in DQ experiments is half as large as in analogous Hahn echo or free induction decay experiments. Their influence on the experimentally observed DQ NMR signal becomes dominant at times t>9/21/2T2eff≈2.12T2eff, where T2eff is the effective spin–spin relaxation time measured by the Hahn echo. Calculations and a comparison with spin dynamics simulations of small spin systems of up to eight spins reveal a satisfactory agreement.

Design of the Focus on Restaurant Engagement to Strengthen Health (FRESH) study: leveraging systems science to work with independently-owned restaurants to increase access to and promotion of healthful foods.

High dietary quality can protect against diet-related chronic diseases. In the United States, racial and ethnic minorities and those with lower incomes consistently exhibit lower dietary quality. Independently-owned restaurants are a common prepared food source in minority low-income communities, but there are significant knowledge gaps on how to work with these restaurants to offer healthy food, due to underlying and dynamic complexities associated with providing healthy food options. The Focus on Restaurant Engagement to Strengthen Health (FRESH) study addresses this complex problem by leveraging systems science approaches to work with independently-owned restaurants. FRESH has two interrelated objectives: (1) to test impact on regular customer dietary quality via a multisite cluster randomized controlled trial in two low-income urban areas (Baltimore and the Washington DC metropolitan area), and (2) to use systems science approaches to develop, parameterize, and calibrate a simulation model. The intervention is theory-and practice-based, comprising three phases: restaurant engagement, low-sugar beverages and healthy meals. The FRESH intervention will be implemented for 12 months in a total of 24 intervention and 24 comparison restaurants. The study is powered to detect a 5-point change in the Healthy Eating Index (HEI) score of regular customers, which would signify a meaningful shift toward healthier eating patterns. The FRESH study will test a novel, multilevel, multisite intervention that aims to improve access to healthier prepared food options among small, independently-owned restaurants located in under-resourced settings. The design of the FRESH intervention and its evaluation are described, as well as plans for the development of a system dynamics simulation model for policymakers and other stakeholders to virtually test future restaurant-based interventions. https://clinicaltrials.gov, identifier, NCT05869149.

To Compete or Cooperate: The Application of System Dynamics Simulation on the Choice of Chinese Banks

To Compete or Cooperate: The Application of System Dynamics Simulation on the Choice of Chinese Banks

Inverse Uncertainty Quantification Assisted Forward Uncertainty Quantification in Power System Dynamic Simulations

Inverse Uncertainty Quantification Assisted Forward Uncertainty Quantification in Power System Dynamic Simulations

System dynamics simulation for reducing the number of lifestyle-related disease patients and medical costs in Japan by promoting behavioural changes in multiple generations

System dynamics simulation for reducing the number of lifestyle-related disease patients and medical costs in Japan by promoting behavioural changes in multiple generations

Collaborative Problem-Solving for the ‘Polycrisis’

The paper focuses on the “Polycrisis,” which is first defined and then positioned as the context for a subsequent discussion about both the formal and social conditions for effective collaborative problem-solving. It then highlights the potential for diagrammatic reasoning to contribute to solving what some commentators choose to call ‘wicked-problems’ including those associated natural, economic, and social forms of fragility. This discussion is informed by pertinent advances in applied category theory, which has a wide range of application-domains including cyber-physical systems, scientific systems, dynamical systems, software engineering, and machine learning. The formal constructs and techniques to be surveyed include optics and parametric lenses, as well as David Spivak’s organisational categories. Optics and lenses have not only been applied to software engineering but also to the modelling of dynamical systems and machine learning. They possess a diagrammatic representation (as string diagrams), which serves as an aid in their deployment (e.g. AlgebraicJulia, Symbolica AI, Haskell applications). The paper argues that these developments should assist users in their collaborative modelling of the Polycrisis by: (i) integrating machine learning, differential programming, and calibration and simulation of dynamical systems; (ii) accounting for subsystem interactions within a larger system that features different stock-flow rates; (iii) accommodating non-linear mappings and weight-sharing; (iv) formally supporting a variety of stochastic influences over the systems that are being modelled; and, (iv) imposing on the whole, the internal logic associated with a topos.

Determining the stability of a fire truck tanker against overturning while driving through crossed forest terrain

The object of this study is to consider dynamic processes in the tank of a fire truck with different levels of water filling. The task to be solved is to determine the data on the fluctuation of the center of mass of a dynamic system, which consists of the water tank of a fire truck tanker truck, when it moves through rough forest terrain at different speeds. The data make it possible to predict the danger of a fire truck tipping over. In the process of solving the task, an estimation model of a fire truck tanker truck was built together with a water tank, using the LS-Dyna dynamic systems simulation software package. In order to reproduce the dynamic impact on the fire truck tanker with the water capacity exerted by the relief of the rugged forest area, the time dependences of the angles of current position of the fire truck tanker – roll angle, yaw, and pitch – were established. The resulting dependences were used as boundary conditions to reproduce the dynamic influence of the terrain. The turning angles of the water tank of a fire truck were determined depending on the unevenness of the terrain, the geometry of which was calculated by a pseudo-random number generator. Using the explicit method for integrating the dynamics equations, implemented in the code of the LS-Dyna software package, the patterns of fluctuations of the center of mass of the water tank of the fire truck tanker depending on the level of filling and speed of movement were defined. Using results from the mathematical modeling of dynamic processes in the fire truck tanker, a numerical algorithm was developed for this fire truck tanker to maintain its stability against overturning. Therefore, the results of this study have a direct practical application in the field of fire truck tanker design safety and could be used to improve and devise new technologies in this field

Nonlinear system dynamics simulation of thermal processes in the VVER-1200 reactor core

Abstract This study presents developing and validating a nonlinear dynamic model for simulating thermal processes in the VVER-1200 reactor core, a third-generation pressurized water reactor with enhanced safety features and improved thermal efficiency. The model employs a point kinetic mathematical approach, incorporating key neutronic parameters extracted from previous analyses of the VVER-1200 under normal operating conditions. These parameters, including neutron flux distribution, power peaking factors, and reactivity coefficients, were integrated into the reactor kinetic model to capture complex feedback mechanisms. In this study, the variation of reactor power with control rod insertion is examined under normal operating conditions. The analysis focuses on how different levels of control rod insertion influence reactor performance, providing insights into optimizing operational efficiency and safety. Monte Carlo code MCNP6 was used to estimate neutronic and kinetic parameters for the proposed model. The model’s validation involved comparing simulation results against detailed Monte Carlo N-Particle calculations, specifically focusing on power changes due to reactivity variations induced by control rod movements. Results showed strong agreement between the nonlinear dynamic model and MCNP calculations. The validated model provides valuable insights into the VVER-1200 core’s thermal behavior under steady-state and transient conditions. It can investigate critical hot spots, identify thermal limits, and assess system perturbation responses. The model’s computational efficiency makes it a practical tool for further optimization and safety analyses of the VVER-1200 design.

Equipartitioning of Molecular Degrees of Freedom in MD Simulations of Gaseous Systems via an Advanced Thermostatization Strategy.

This work introduces a dedicated thermostatization strategy for molecular dynamics simulations of gaseous systems. The proposed thermostat is based on the stochastic canonical velocity rescaling approach by Bussi and co-workers and is capable of ensuring an equal distribution of the kinetic energy among the translational, rotational, and vibrational degrees of freedom. The outlined framework ensures the correct treatment of the kinetic energy in gaseous systems, which is typically not the case in standard approaches due to the limited number of collisions between particles associated with a large free mean path. Additionally, an efficient strategy to effectively correct for intramolecular contributions to the virial in quantum mechanical simulations is presented. The equipartitioning thermostat was successfully tested by the determination of pV diagrams for carbon dioxide and methane at the density functional tight binding level of theory. The results unequivocally demonstrate that the equipartitioning thermostat can effectively achieve an equal distribution of the kinetic energy among the different degrees of freedom, thereby ensuring correct pressure in gaseous systems. Furthermore, RDF calculations show the capability of the proposed method to accurately depict the structure of gaseous systems, as well as enable an adequate treatment of gas molecules under confinement, as exemplified by an MD simulation of (CO2)50@MOF-5.

BIEM_CH: An Efficient Algorithm for Dynamic Rupture Simulation of Complex Fault Systems with Unstructured Meshes and Half-Space Green’s Function

Abstract In this study, a fast 3D dynamic rupture simulation algorithm, named BIEM_CH (Boundary Integral Equation Method for Complex fault systems in Half-space), is presented. This algorithm, based on exact half-space Green’s functions, supports both structured and unstructured discretization schemes, allowing for the effective handling of a wide range of fault geometries, from simple to complex. Because of the semianalytical nature of the boundary integral equation method, the integral kernel (surface integral of the spatial derivatives of Green’s functions) and rupture processes can be computed separately, making BIEM_CH particularly suitable for applications requiring numerous forward simulations, such as dynamic source inversion, for which only the rupture process needs to be recalculated once the integral kernel is obtained. The performance of the algorithm has been significantly enhanced, achieving up to a hundredfold speed increase through the use of exact closed-form solutions for the time-domain half-space Green’s function and matrix operations leveraging graphical processing unit acceleration, resulting in dynamic rupture simulations that can be completed in a matter of seconds. Moreover, BIEM_CH maintains excellent stability when the mesh dimension does not exceed 375 m, irrespective of whether structured or unstructured discretization schemes are used. In addition, this algorithm demonstrates good agreement with other methods in benchmark exercises conducted by the Southern California Earthquake Center and the U.S. Geological Survey’s dynamic rupture code verification project.

Research on the wheel wear prediction of heavy-haul freight wagon based on the roller rig test and line test

Purpose To reduce the wheel maintenance costs caused by wheel wear and to transition from traditional periodic maintenance to condition-based maintenance for railway freight wagons, it is necessary to investigate the prediction of wheel wear and understand the evolution rule of wheel profile wear. Design/methodology/approach This paper established a wheel wear prediction model for railway freight wagons based on Archard’s wear theory and proposed a prediction method that combines vehicle system dynamic, interpolation iteration and intelligent simulation. The wear coefficients in the model were obtained through wheel wear tests by using the roller rig. The model’s effectiveness was further verified through line testing and simulation models, and the corrected wear coefficient can be used for wear prediction of heavy-haul freight wagons in China. Findings The wheel wear prediction showed that the results of the wheel wear prediction model by adopting the wear coefficients obtained from the roller rig tests are close to the actual wheel wear, with the difference of the maximum in wear depth at the nominal rolling circle being within 7%. Originality/value This paper proposed a method that can establish a database of wheel wear coefficients for predicting wheel wear of railway freight wagons under similar operating conditions. The revised wear coefficient can be used for wear prediction of heavy-haul freight wagons in China. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0329/

A System Dynamics Stability Model for Discrete Production Ramp-Up

Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail to meet targets due to unpredictable equipment failures, operator errors, and system complexity. We propose a system dynamics model that captures the unique dynamics of ramp-up phases by integrating stability and disturbance factors that influence the key performance indicators overall equipment effectiveness, process capability, and production output. A systematic literature review informed the identification of stability factors, which were validated through expert interviews in the automotive industry. Our system dynamic simulation results indicate that control factors realistically influence production system behaviour during different ramp-up phases. Despite some limitations regarding the effects of maintenance personnel and engineering changes on key performance indicators, our model effectively simulates realistic ramp-up behaviour. The findings highlight the need for tailored models that consider specific ramp-up contexts and emphasize the importance of data acquisition for enhanced performance prognosis in future research.