A cellular automaton (CA) model with a finer discretization of space is proposed to simulate a non-emergency evacuation process in a room with an obstacle. During the evacuation process, a triangle "evading region" phenomenon has been observed through simulation and experiment on the upstream side of the spatial obstacle. In this paper, we use a simple method to generate an obstacle floor field corresponding to the triangle. We investigate the relationship between the pedestrian trajectories and the obstacle's position. We also study the effect of the obstacle on evacuation time and average evacuation speed. Our study provides insights into the simulation of obstacle avoidance behavior of pedestrians in simple scenarios.
This paper describes a high-fidelity numerical model that simulates vessel stationkeeping operations in ice-rich waters. The discrete event simulation engine incorporates several novel features, including new ice floe failure models for bow and midships locations; an ice floe creation strategy that facilitates rafting of ice floes; and a vessel thruster model that takes into account physical limitations such as thruster angle slew rates and propeller ramp rates. It accommodates a wide range of ice field specifications and runs in real-time on a standard desktop personal computer (Intel® Core™ i7 Processor or equivalent). The model has been validated using physical measurements of a generic drillship model in several broken ice conditions; it predicted thruster forces and motions that were comparable to those observed during dynamic positioning operations.
Medication administration errors account for a relatively high proportion of medical errors, with more than 50% occurring at the nursing administration stage. Nursing is characterized by a large amount of work, rigid working hours, high information cognitive intensity, and frequent information updates. The high workload of nurses is a significant cause of medication administration errors. In this study, a literature analysis was used to determine the elements of the system dynamics model, and the causal loop diagram was used to draw the relationship framework among the elements. Vensim personal learning edition and interview surveys were then used for model validation and simulation. First, 302 case analyses of medication administration errors collected from the three metropolitan area hospitals were used to construct the causal loop diagram, the stock and flow map of the medication administration error system, and the dynamics model; second, the model was tested from theoretical and historical data simulation perspectives; finally, the system dynamics model proposed in this study was used to simulate a medical institution from overtime and policy perspectives. Through system dynamics modeling, the inducing mechanism of workload on medication administration errors in nursing operations was elucidated, and corresponding suggestions for prevention were provided. In addition, ideas and basis for optimizing the medication administration process, improving workload, and preventing medication administration errors considering workload were provided.
Synchronous generator system is a complicated dynamic system for energy transmission, which plays an important role in modern industrial production. In this article, we propose some predictor-corrector methods and structure-preserving methods for a generator system based on the first benchmark model of subsynchronous resonance, among which the structure-preserving methods preserve a Dirac structure associated with the so-called port-Hamiltonian descriptor systems. To illustrate this, the simplified generator system in the form of index-1 differential-algebraic equations has been derived. Our analyses provide the global error estimates for a special class of structure-preserving methods called Gauss methods, which guarantee their superior performance over the PSCAD/EMTDC and the predictor-corrector methods in terms of computational stability. Numerical simulations are implemented to verify the effectiveness and advantages of our methods.
Frozen heaving failure of fractured rock mass is commonly encountered in engineering in cold regions, which is chiefly caused by the frost heaving pressure arising from the water–ice phase change in the crack. To explore the evolution of frost heaving pressure in penetrating elliptical crack considering water content and water migration, a new theoretical model embodying the frost heaving pressure evolutionary character was established by introducing freezing ratio function. The equivalent thermal expansion coefficient was used to analyze the evolution process of frost heaving pressure under the effect of water–ice phase change, which was then verified. It was found that the evolution process of frost heaving pressure can be divided into three stages: free expansion stage of water–ice phase change, rapid growth stage of frost heaving pressure, and stable stage of frost heaving pressure. Subsequently, the influences of rock thermal expansion effect, properties of rock and ice, and water content of crack on the frost heaving pressure were investigated. The results indicate that the impact of rock thermal expansion on frost heaving pressure is extremely slight, which is negligible. Comparing with the properties of rock, the properties of ice show significant effects on the frost heaving pressure, particularly the Poisson ratio of ice. In the case of identical water migration ratio, the peak frost heaving pressure increases linearly with the water content of crack.
Simulation games are designed to cultivate expertise and rehearse particular skill sets. To yield longitudinal effects, sequences of events must be crafted to yield intended learning outcomes, sometimes by focusing on particularly difficult situations and replaying variants. The present paper develops a logic-based approach for encoding the interrelation between action, events, and objects in a manner that allows the resulting scenario description to immediately be executed in a game development environment. This has the dual effect of decoupling the description of a scenario from the simulation platform itself, as well as supporting iterative and flexible development of learning content. To this end, we provide three interrelated components: First, we develop a scenario description language based on Answer Set Programming. The language is designed to allow an automated reasoner to deduce a schedule of the future events that are caused by an action taken in a given simulation environment. Second, we define a protocol for exchanging actions and computed futures between, respectively, the simulation environment and the external automated reasoner. Finally, as a proof of concept, we develop an Application Programming Interface (API) for the Unity Real-Time Development Platform that implements the protocol and offers a software framework for connecting the computed future events to concrete game objects. This allows the game to evolve coherently from the specification. We argue that the resulting system inherits capabilities for artificial commonsense reasoning from its declarative basis which are useful for reasoning about an evolving emergency incident or training scenario.
Vehicular ad hoc networks (VANETs) can support safety-critical applications such as the safe and fast movement of emergency vehicles while preventing crashes, delays, and congestion involving emergency vehicles. A simulation approach to evaluate ambulance transit using VANET capabilities is presented in this paper for a real-life road network taken in and around the University of Missouri–Kansas City, USA. Three different VANET functional scenarios are compared with a base scenario (SC-1) which depicts the present-day traffic pattern and vehicle behaviors. In the three scenarios, the second one (SC-2) adds vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications along with the present-day traffic pattern; ambulances use VANETs simply to announce their arrival. The third scenario (SC-3) provides functionalities for ambulances to rerouting when they learn about a current or an anticipated road congestion. And the fourth scenario (SC-4) adds vehicle rerouting capabilities, so ambulance routes can be sent to vehicles, and they can avoid the ambulance path. Performance results are presented and evaluated for metrics such as delay, packet drop ratio, the average speed of the ambulance, average vehicle density, and ambulance message interval time. From the simulation results, it was observed that the ambulance transit time reduces by 12.52%, 14.65%, and 18.75% for SC-2, SC-3, and SC-4, respectively, as compared with SC-1, while the average ambulance speed increases by 14.49%, 15.57%, and 20.90% for SC-2, SC-3, and SC-4, respectively, when compared with SC-1. This paper provides substantial evidence that VANET capabilities for emergency vehicles can enhance crash protection and reduce the commute time for emergency response.
Model-based systems engineering (MBSE) focuses on using models to support the design, optimization, simulation, and ultimately deployment of complex cyber-physical systems (CPSs). These models enable reasoning about and predicting the behavior of the (realized) real-world system in silico. The value of using such (predictive) model depends on its validity against its real-world counterpart. As such, the validity context of a model is critical to ensure correct model use. Reasoning on validity is only possible if the validity of the model was captured explicitly at design time. In previous work, the validity frame (VF) was presented as a way to explicitly capture a model's validity; however, no guidance on the integration process within MBSE processes was given. Within this article, we present the creation and evolution of the model and its VFs to ensure model validity consistency and completeness. This evolution results in a set of interrelated models and VFs. By capturing these relations, we create a lightweight frame-enabled library of model variants. We show our contribution using an F1/10 vehicle simulation test bench.
Traditionally, parallel discrete-event simulations of agent-based models in continuous time are organized around logical processes exchanging time-stamped events, which clashes with the properties of models in which tightly coupled agents frequently and instantaneously access each other’s states. To illustrate the challenges of such models and to derive a solution, we consider the domain-specific modeling language ML3, which allows modelers to succinctly express transitions and interactions of linked agents based on a continuous-time Markov chain (CTMC) semantics. We propose synchronous optimistic synchronization algorithms tailored toward simulations of fine-grained interactions among tightly coupled agents in highly dynamic topologies and present implementations targeting multicore central processing units (CPUs) as well as many-core graphics processing units (GPUs). By dynamically restricting the temporal progress per round to ensure that at most one transition or state access per agent, the synchronization algorithms enable efficient direct agent interaction and limit the required agent state history to only a single current and projected state. To maintain concurrency given actions that depend on dynamically updated macro-level properties, we introduce a simple relaxation scheme with guaranteed error bounds. Using an extended variant of the classical susceptible-infected-recovered network model, we benchmark and profile the performance of the different algorithms running on CPUs and on a data center GPU.
Harmful algal and cyanobacterial blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world's water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces Discrete Event System Specification-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with model-based system engineering and cyber-physical systems infrastructure for dynamic environments.
The relationship between military expenditures and economic productivity has taken the attention of many researchers and there exist an important number of studies approaching the topic through several techniques. However, there is no consensus among the scholars whether military expenditures trigger economic growth, productivity, and other macroeconomic indicators. Such arguments are mainly due to unclear results obtained from the existing studies, in which the complex relationships between military expenditures and macroeconomics are not fully incorporated. Considering the bidirectional and nonlinear relationships among macroeconomic indicators and complex feedback mechanisms, a system dynamics (SD) model for examining the impacts of military expenditures on economic productivity in Turkey is proposed. The proposed SD model aims to reflect the complex environment surrounding the military spending–economic productivity nexus and to analyze the feedback structures that lead to miscellaneous consequences with delays. A stock–flow model is developed to represent the complex nonlinear relationships and causalities between the variables. Data from SIPRI, the World Bank, and several local statistical sources covering the years 2009–2018 are utilized to simulate the existing case, warfare in neighbors, economic shrinkage scenarios, and the combination of the latter two. The results obtained from the scenarios suggest that short fixes such as importing military products instead of national investments give rise to chronic issues like continual dependence on foreign supply, hence, leading to decrease in overall economic growth. To the best of our knowledge, this is the first attempt to integrate SD methodology with military expenditure and economic productivity analysis.
Simple and computationally efficient drill string models running real-time describing motion in all axes in directional wells are important for the implementation of closed-loop control and assisted monitoring during drilling operations. This paper proposes a new simplified three-dimensional model based on a parametric curve and lumped-parameter modeling, where Kane's method is used to establish the equations of motion. Validation of the steady-state motion and convergence for the lumped model in vertical and horizontal alignment was compared with a finite-element model. The configuration and restoring forces show good results compared with finite-element analysis. Hence, the model demonstrate the axial contraction as a function of the body restoring forces being oriented to the inertial frame, inherently producing nonlinear coupled axial tension forces. The qualitative response of the model is confirmed in simulation case studies, being showcased by a deviated J-well configuration. Traveling block velocity and top drive torque are included as actuated inputs to analyze off-bottom friction and contact along the wellbore. The model is proposed to act as a virtual sensor for drilling directional wells.
Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such agents that can individually deliberate about their own knowledge, goals, and preferences, and can adapt their behavior based on other agents' behaviors and on their attitude toward complying with norms. We showcase the applicability and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex behaving agents and investigate behavioral interventions over a period of time of months.
Simulation-based metamodels or surrogate models are simplified models that capture the relationship between inputs and outputs of the simulation model. The analytical expression of metamodels is defined using a sample of input/output points obtained from the simulation model. This analytical expression is then embedded in an optimization process that usually provides solutions much faster than other simulation-based optimization techniques such as metaheuristics or mathematical modeling. The goal of this paper is to describe the simulation-based metamodeling approach and to provide a thorough review of the literature on its applications to emergency healthcare systems. For this purpose, we examine the recent literature (journals and conference proceedings) published in the last 15 years (2008–2022). Finally, we identify findings and avenues of research in simulation-based metamodeling that deserve special attention from the scientific community and allow the potential of this approach to be used for better decision-making in emergency healthcare.
This article presents an hybrid and hierarchical model in which two modeling and simulation approaches, discrete event system specification simulation (DEVS) and semantic technologies, were used together in order to help in the analysis of a major healthcare problem, the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). Indeed, the complexity of the SARS-CoV-2 replication process, and the range of hierarchical scales over which it interacts with cellular components (extending from genomic and transcriptomic to proteomic and metabolomic scales), and the intricate way in which they are interwoven, make its understanding very challenging. It is therefore crucial to model the different scales of the replication process, by taking into account all interactions with the infected cell. By combining the advantages of both DEVS simulation and ontological modeling, we propose a hierarchical ontology-based DEVS simulation model of the SARS-CoV-2 viral replication at both the micro-molecular (proteomic and metabolomic) and macro-molecular (genomic and transcriptomic) scales. First, we demonstrate the usefulness of combining DEVS simulation and semantic technologies in a common modeling framework to face the complexity of the SARS-CoV-2 viral replication at different scales. Second, the modeling and simulation of the SARS-CoV-2 replication process on different levels provide valuable information on the different stages of the virus's life cycle and lays the foundation for a system to anticipate future mutations selected by the virus.
As knowledge and data increase in scale and complexity, it is more difficult to apply these two key assets to achieve optimal effectiveness in engagement simulation. The aim of this study was to investigate the techniques of knowledge and data integration with respect to the development of smart agents to predict accurate behaviors in tactical engagements. To reduce the complexity of combat behavior representation, with respect to the functions, we represented subject matter expert operational knowledge by proposing multiple levels of cascaded hierarchical structure, namely, the function decision tree, to increase the readability and maintainability of the behavioral model. For decision points in a behavioral model, smart agents can be trained based on data samples collected from rounds of constructive simulations which provide validated physical models and tactical principles. As a proof of concept, we constructed a simulation testbed of multi-warhead ballistic missile penetration, which generated 129,600 constructive simulations over a total of 84 h. Thereafter, we selected 5817 data samples (i.e. ~4.5% of the simulations) using an operational metric of total rewards exceeding 100. The data samples are used to train an artificial neural network and then this network is used to develop a deep reinforcement learning agent. The results revealed that the training process iterated nearly 17,000 epochs until the policy loss decreased to an acceptable low value. The smart agent increased the ratio of ballistic missile target hits by 18.96%, a significant increase when compared with the traditional rule-based behavioral model.
Nowadays, the introduction of digital technology improves the condition of the workplace and employees' productivity, but the unstable behavior of employees is still typical in Internet enterprises in China. Sometimes, employees frequently show their behavior reversals between cooperation and conflict. An integrated analysis method with three steps is performed to explore its reason. First, an evolutionary game model is employed to examine the strategies of individual employee's behavior selection between cooperation and conflict. Second, the cellular automata are developed to simulate the evolution of employee group behavior selection over time. The frequent behavior turnovers between cooperation and conflict are illustrated. Third, catastrophe theory and method are used to identify the hidden cusp catastrophe patterns under the evolution of group behavior selection. Research results reveal that individual employee selects cooperation if the penalty exceeds half the cost. Simulation results show intense and sudden changes in employee group behavior selection, in which cusp catastrophe patterns exist. The cusp catastrophe model can intuitively interpret the mechanism by which factors, such as average perceived payoff and proportion of cooperation employees, influence the behavior state of the employee group. The mechanism of catastrophe in frequent behavior turnover is explored. This methodology, which is based on the theoretical framework of social exchange theory, integrates evolutionary game theory, simulation, and catastrophe theory to identify the catastrophe mechanism in behavior turnover and make theoretical and practical contributions to behavior selection research.
Simulating three-dimensional acoustic problems with traditional finite element models (FEMs) may result in a large dispersion error that is difficult to manage. To control dispersion error caused by "overly stiff" features of FEM, a face-based smoothed FEM (FS-FEM) model was used to analyze the sound field of a high-speed train. Based on the acoustic Galerkin method's weak form, smooth regions were reconstructed within the original finite element area, and numerical simulations were performed in MATLAB. Modal analysis of a test example revealed that results obtained using FS-FEM are more accurate than those obtained using the traditional FEM. Finally, it was used to calculate the sound field of China Railway High-speed 380B (CRH380B) high-speed trains in time and frequency domains, achieving higher accuracy than the traditional approach. Results were more akin to the realistic solution, which demonstrated the performance of the FS-FEM.
Negative pressure wards are significant in preventing the spread of infectious pathogens which play a crucial role in fighting against COVID-19. Owing to the negative pressure, contaminated air with pathogens is not able to flow from the wards to non-contaminated zones while fresh filtered air will be transported to the ward via the ventilation system. As airflow controlled by ventilation systems affects the motion of pathogens, for example, infectious aerosol particles, the ability of a negative pressure ward to reduce the risk of infection highly relies on an effective ventilation system. In this investigation, impacts of airflow patterns under various human postures and ventilation processes aerosols diffusion are analyzed via the computational fluid dynamics (CFD) simulation. According to the results, among three airflow patterns, the highest contaminant removal efficiency is 57% at 200 s with the top supply and bottom return mode; besides, in three postures, in the case that the patient is in a standing position, the contaminant removal efficiency is the highest. Furthermore, it is found that the best airflow scheme is a slit tuyere in the ward, with a top supply and side return mode and a sitting position for the patient. This study may provide a reference for the design of airflow in negative pressure isolation wards, control of contaminants, and prevention of viral infections, so as to ensure a good working and recovery environment for medical staff and patients.
