The tilt coordination technique is used in driving simulation for reproducing a sustained linear horizontal acceleration by tilting the simulator cabin. If combined with the translation motion of the simulator, this technique increases the acceleration rendering capabilities of the whole system. To perform this technique correctly, the rotational motion must be slow to remain under the perception threshold and thus be unnoticed by the driver. However, the acceleration to render changes quickly. Between the slow rotational motion limited by the tilt threshold and the fast change of acceleration to render, the design of the coupling between motions of rotation and translation plays a critical role in the realism of a driving simulator. This study focuses on the acceptance by drivers of different configurations for tilt restitution in terms of maximum tilt angle, tilt rate, and tilt acceleration. Two experiments were conducted, focusing respectively on roll tilt for a 0.2 Hz slaloming task and on pitch tilt for an acceleration/deceleration task. The results show what thresholds have to be followed in terms of amplitude, rate, and acceleration. These results are far superior to the standard human perception thresholds found in the literature.
Zarrin M and Azadeh A. Simulation optimization of lean production strategy by considering resilience engineering in a production system with maintenance policies. Simulation. Epub ahead of print 8 September 2016. DOI:
In this article, the second author was left off in error in the original OnlineFirst version. The latest online version has been corrected to reflect this.
The corresponding author in the original OnlineFirst version was also incorrect. The latest online version has been corrected to display Professor Ali Azadeh as the corresponding author.
Design of parallel algorithms for edge detection is extremely important for image analysis and understanding. Cellular automata are the most common and simple models of parallel computation and over the last decade, numerous cellular automata techniques have already been proposed. This paper presents a novel method for edge detection of optical character images based on a variant of cellular automata, called non-linear cellular automata. The method consists of three stages and each stage is simple to understand and implement. A standard binarization technique is applied to a grayscale image and boundary conditions are added to the resultant image. Finally, non-linear cellular automata rules are designed and applied simultaneously to all pixels of the image. The suggested scheme has been validated on optical characters (handwritten as well as printed) of different languages. Furthermore, results are compared with standard edge detection techniques in terms of different performance parameters like entropy, kappa values, true positives, false negatives, etc. It is observed that the suggested scheme is superior to other standard schemes. Hence, the scheme has the potential application for character recognition.
To this day, debugging support for the DEVS formalism has been provided, at best, in an ad-hoc way. The intricacies of dealing with the interplay of different notions of (simulated) time, formalism semantics, and user input have not been thoroughly investigated. This paper presents a visual modeling, simulation, and debugging environment for Parallel DEVS, which builds on a theoretical foundation for debugging DEVS models. We take inspiration from both code debugging and the simulation world to model our environment; we transpose a set of useful code debugging concepts onto Parallel DEVS, and combine those with simulation-specific operations, such as as-fast-as-possible simulation and (scaled) real-time execution. Apart from these common debugging operations, we introduce new features to the debugging of Parallel DEVS models, such as "god events," which can alter the model state during simulation, and reversible debugging, which allows one to go back in time. To achieve this, the PythonPDEVS simulator is deconstructed and reconstructed: the modal part of the simulator–debugger, as well as the debugging operations, are modeled using the Statecharts formalism. These models are combined, resulting in a model of the timed, reactive behavior of a debuggable simulator for Parallel DEVS. The code for the simulator is automatically synthesized from this model. To improve usability, we combine the simulator with a visual modeling environment, allowing for visual and interactive live debugging.
The traditional inertial navigation system-based indoor pedestrian navigation method is not suitable to all the conditions, since its data fusion model is designed only for a single condition. In order to achieve better performance, a two-mode navigation method for indoor pedestrian navigation is proposed in this work, which includes the stance mode and the swing mode. When the person’s foot is in a stance phase, the stance mode is used to correct the velocity error and yaw error. When the person’s foot is in a swing phase, the swing mode works and only the yaw error is able to be corrected. For verification, a real indoor test has been done to assess the performance of the proposed two-mode method. The position root-mean-square error (RMSE) value is 1.9265 m during a 176-m traverse, and the RMSE of yaw is 0.20734 rad. These results demonstrate that our method is effective to reduce the error compared with the conventional schemes.
In the motivation of tapping the strong potential of computational intelligence in discovering knowledge of protective relay operations using data mining, modeling and simulation of an actual industrial numerical distance relay and its recording facility are a vital requisite. This is justified by the practicality and necessity of divulging the decision algorithm hidden in the recorded relay event report using computational intelligence-based data mining. Thus, this paper studies the detailed modeling and simulation of an industrial AREVA MiCOM P441 distance relay, accommodated together with its own simulated event report recording facility. The idea is to provide the flexibility of allowing the simulated event report to have sufficiently significant depth and breadth for data mining purposes. The modeled relay has operated correctly and discriminatively in deciding circuit breaker pole(s) to be tripped in stipulated assertion times as per the Malaysian licensee’s requirements for various faults and protected zones along a transmission line. It has subsequently been validated successfully by the performance of the real field AREVA MiCOM P441 distance relay belonging to the Malaysian licensee. With the successful modeling and simulation of the AREVA MiCOM P441 distance relay and its recording facility, such subsequent works as data extraction and preparation, computational intelligence-based data mining for relay decision algorithm discovery and finally a relay analysis expert system development can certainly be executed. These subsequent applications are briefly explored as a demonstration of the benefit offered after the successful modeling of the relay.
Hardware-in-the-loop (HIL) is a type of real-time simulation test that is different from a pure real-time simulation test due to a real component added to the loop. Since HIL includes numerical and physical components, a transfer system is required to link these parts. The transfer system typically consists of a set of actuators and sensors. In order to get accurate test results, the transfer system dynamic effects need to be mitigated. The fuel control unit (FCU) is an electro-hydraulic component of the fuel control system in gas turbine engines. Investigation of FCU performance through HIL technique requires the numerical model of other related parts, such as the jet engine and the designed electronic control unit. In addition, a transfer system is employed to link the FCU hardware and the numerical model. The objective of this study was to implement the HIL simulation of the FCU. To get accurate simulation results, the inverse and polynomial compensation techniques were proposed to compensate time delays resulting from inherent dynamics of the transfer system. Finally, the results obtained by applying both of the methods were compared.
Land transportation systems in a port city could contribute to the competitiveness of the port and the economic development of the city. However, in port cities the land transportation system, which includes many interrelated factors, is complex. A system dynamics approach was applied to analyze the land transportation system, which was divided into economy, transport network and transportation investment subsystems. Then, system dynamics models were built to simulate the interactive relationship between the land transportation system in a port city and the economy in the port. The proposed system dynamics models analyze the impact of different transportation levels and the proportion of road and railway investment on the land transportation systems in a port city. Finally, the research takes Tianjin city as an example for numerical analysis, and analyzes the impact of different combinations of transport investment on the land transportation system in a port city.
Cloud computing systems can benefit from the use of personal and non-dedicated computers, which are currently employed in volunteer computing systems. Being non-dedicated, these resources show random behavior regarding the times they are online (available) and offline. Accordingly, their availability levels are lower than those of traditionally employed dedicated resources. Thus, in order to use non-dedicated resources in cloud computing environments, it is necessary first to solve the problem of how to attain high availability levels for the Internet services deployed over them. Most approaches on how to guarantee high service availability levels with non-dedicated resources are based on the introduction of high degrees of redundancy into the system. However, this praxis leads to an inefficient usage of computational resources and, therefore, to higher operational costs. Accordingly, the focus of this paper is the problem of minimizing the cost of a service deployment over non-dedicated resources while providing a high level of service availability. In order to solve this stochastic optimization problem, the paper proposes a hybrid algorithm that combines a metaheuristic component with a discrete-event simulation component. The metaheuristic component is used to search for an efficient configuration of resources. The simulation component is integrated inside the metaheuristic and used to estimate the service availability of each promising configuration. A numerical experiment section, comparing the performance of several algorithms, contributes to validating the proposed approach as well as to illustrate its potential applications.
Increasing computing capability and high-resolution digital tracing of human behavior make large-scale computational models for individual-based realistic simulation available. Reconstructing a virtual computational environment is crucial for designing and implementing individual interactions in an artificial society as human beings behave in the real world. In this paper, we propose a methodology to recreate a virtual city by utilizing statistical data and geographic information. The synthetic population and physical environment are baseline components of the virtual city. Individual-based modeling is used to specify individuals’ demographic characteristics, and each individual is endowed with heterogeneous social attributes. Various physical environments are generated with geographic locations and mapped with individuals to support daily mobility, migration, and interaction. A series of algorithms are proposed to bridge the gap between macroscopic data and microscopic models, and guarantee equivalence between them. Based on the methodology, we reconstructed a virtual city of Beijing, and presented the statistical analysis of population structure, spatial distribution of physical environments, human travel characteristics, and spatial topologies of social networks. Our synthetic population can represent individual actors in the form of households and household members, and the synthetic population is statistically equivalent to a real population. The proposed methodology is efficient to recreate a synthetic virtual city and can serve as a base for computational experiments.
This paper presents some ongoing research carried out in the context of the PRISE Project (Research Platform for Embedded Systems Engineering). This platform has been designed to evaluate and validate new embedded system concepts and techniques through a special hardware and software environment. Since much actual embedded equipment is not available, corresponding behavior is simulated within a high-level architecture (HLA) federation implemented with a run-time infrastructure (RTI) called CERTI and developed at ONERA. HLA is currently largely used in many simulation applications, but the limited performances of the RTIs raise doubts over the feasibility of HLA federations with real-time requirements. This paper addresses the problem of achieving real-time performances with the HLA standard. Several experiments are discussed using well-known aircraft simulators such as Microsoft Flight Simulator, FlightGear, and X-plane connected with the CERTI RTI. The added value of these activities is to demonstrate that according to a set of innovative solutions, HLA architecture is well suited to achieve hard real-time constraints. Finally, a formal model guaranteeing the schedulability of concurrent processes is also proposed.
As a result of rapid urbanization in numerous cities around the world, the demand for transportation has increased rapidly, resulting in emission of high levels of exhaust pollutants into the atmosphere. This is a major cause of deterioration in the local air quality, with consequent escalating risk of adverse health conditions amongst urban inhabitants. Understanding dispersion of pollutants in street canyons, local urban configurations, meteorological processes, and other physical factors are essential for predicting and assessing air quality. This article presents a comprehensive review of the state-of-the-art research works relevant to the investigation of flow structures and pollutant dispersion phenomena in urban street canyons. Various factors, including building geometries, local atmospheric conditions, static and dynamic obstructions, as well as chemical reactions of exhaust pollutants, are critically discussed by taking into account field measurements, wind tunnel experiments, operational modeling techniques, and computational fluid dynamics (CFD). The most critical pollutant levels in street canyons under several physical circumstances are identified. Elements leading to discrepancies and resulting in inconsistencies of different research methods are briefly addressed and suggestions for future research are offered.
This study utilized energy simulation in support of a forensic pathology time-of-death analysis for a corpse discovered in a single-family residence two years prior to the study. In order to produce an accurate estimate of the interior temperature profile at the time of death, a thermal model was constructed using EnergyPlus and calibrated using environmental monitoring data from the site. The calibration methods used in the study draw from several precedents and are presented in detail. The thermal model was able to predict the temperature in the room of interest within 1.4°C (2.5°F) with 90% confidence. This model was then altered to account for known differences between the monitoring period and the period of interest, and used to predict what the temperature profile had been at the time of death. This study adds to a small body of work that compares simulated to measured performance data for unconditioned spaces, which should have a growing relevance as building energy performance simulation tools are used to model passive strategies.
Only recently, research communities and professional organizations have started to incorporate the factor of climate change in software-based environmental simulation with a view to inform climate adaptation planning and design. Based on the results from simulating a neighborhood design proposed for New Cairo, Egypt, we develop a conceptual framework and an environmental simulation workflow aimed at achieving Climate Change–conscious Urban Neighborhood Design (C3UND). Central to the C3UND approach is the coupling of neighborhood outdoor simulation and building indoor simulation and taking into account climate change scenarios as projected by today’s meteorological modeling. Utilizing two existing software systems, ENVI-met for urban neighborhood outdoor simulation and Ecotect for building indoor simulation, we demonstrate how a workflow can be implemented to play out climate change scenarios on urban neighborhoods and the buildings located within. The C3UND simulation framework and workflow was further applied to a neighborhood site at the Sheffield University campus in England with weather data input of the present day (2012) and of the 2050s generated by the CCWorldWeatherGen tool. Our current study suggests that environmental simulation of climate change scenarios at an urban neighborhood scale is currently achievable but not without considerable gaps. Use of additional three-dimensional virtual neighborhood models, for instance, is required to bring outdoor and indoor simulation outcomes together through graphic overlay to enable more intuitive and holistic understanding of potential climate change impacts. The implications of the C3UND framework for sustainable urban and architecture design are discussed, leading to a list of research questions to be further investigated.
Discrete event simulations can be used to analyze natural and artificial phenomena. To this end, one provides models whose behaviors are characterized by discrete events in a discrete timeline. By running such a simulation, one can then observe its properties. This suggests the possibility of applying on-the-fly verification procedures during simulations. In this work we propose a method by which this can be accomplished. It consists of modeling the simulation as a transition system (implicitly), and the property to be verified as another transition system (explicitly). The latter we call a simulation purpose and it is used both to verify the success of the property and to guide the simulation. Algorithmically, this corresponds to building a synchronous product of these two transitions systems on-the-fly and using it to operate a simulator. By the end of such an algorithm, it may deliver either a conclusive or inconclusive verdict. If conclusive, it becomes known whether the simulation model satisfies the simulation purpose. If inconclusive, it is possible to adjust certain parameters and try again. The precise nature of simulation purposes, as well as the corresponding satisfiability relations and verification algorithms, are largely determined by methodological considerations important for the analysis of simulations, whose computational characteristics we compare with empirical scientific procedures. We provide a number of ways in which such a satisfiability relation can be defined formally, the related algorithms, and mathematical proofs of soundness, completeness and complexities. Two application examples are given to illustrate the approach.
Auscultation, the act of listening to the heart and lung sounds, can reveal substantial information about patients’ health and other cardiac-related problems; therefore, competent training can be a key for accurate and reliable diagnosis. Standardized patients (SPs), who are healthy individuals trained to portray real patients, have been extensively used for such training and other medical teaching techniques; however, the range of symptoms and conditions they can simulate remains limited since they are only patient actors. In this work, we describe a novel tracking method for placing virtual symptoms in correct auscultation areas based on recorded ECG signals with various stethoscope diaphragm orientations; this augmented reality simulation would extend the capabilities of SPs and allow medical trainees to hear abnormal heart and lung sounds in a normal SP. ECG signals recorded from two different SPs over a wide range of stethoscope diaphragm orientations were processed and analyzed to accurately distinguish four different heart auscultation areas, aortic, mitral, pulmonic and tricuspid, for any stethoscope’s orientation. After processing the signals and extracting relevant features, different classifiers were applied for assessment of the proposed method; 95.1% and 87.1% accuracy were obtained for SP1 and SP2, respectively. The proposed system provides an efficient, non-invasive, and cost efficient method for training medical practitioners on heart auscultation.
Surgical planners are used to achieve the optimal outcome for surgery. They are especially desired in procedures where a positive aesthetic outcome is the primary goal, such as the Nuss procedure which is a minimally invasive surgery for correcting pectus excavatum (PE) – a congenital chest wall deformity which is characterized by a deep depression of the sternum. The Nuss procedure consists of placement of a metal bar(s) underneath the sternum, thereby forcibly changing the geometry of the ribcage. Because of the prevalence of PE and the popularity of the Nuss procedure, the demand to perform this surgery is greater than ever. Therefore, a Nuss procedure surgical planner is an invaluable planning tool ensuring an optimal physiological and aesthetic outcome. We propose the development and validation of the Nuss procedure planner. First, a generic model of the ribcage is developed. Then, the computed tomography (CT) data collected from actual patients with PE is used to create a set of patient-specific finite element models (FEM). Based on finite element analyses (FEA) a force–displacement data set is created. This data is used to train an artificial neural network (ANN) to generalize the data set. In order to evaluate the planning process, a methodology which uses an average shape of the chest for comparison with results of the Nuss procedure planner is developed. Haptic feedback and inertial tracking is also implemented. The results show that it is possible to utilize this approximation of the force–displacement model for a Nuss procedure planner and trainer.
The modeling and simulation of social networks is an important approach to better understanding complex social phenomena, especially when the inner structure has remarkable impact on behavior. With the availability of unprecedented data sets, simulating large-scale social networks of millions, or even billions, of entities has become a new challenge. Current simulation environments for social studies are mostly sequential and may not be efficient when social networks grow to a certain size. In order to facilitate large-scale social network modeling and simulation, this paper proposes a framework named SUPE-Net, which is based on a parallel discrete event simulation environment YH-SUPE for massively parallel architectures. The framework is designed as a layered architecture with utilities for network generation, algorithms and agent-based modeling. Distributed adjacency lists are used for graph modeling and a reaction–diffusion paradigm is adapted to model dynamical processes. Experiments are performed using PageRank and the susceptible–infected–recovered (SIR) model on social networks with millions of entities. The results demonstrate that SUPE-Net has achieved a speedup of 12, and increased the event-processing rate by 11%, with good scalability and effectiveness.
This paper presents a new optimization method for designing the parameters of a power system stabilizer (PSS) using a smart bacteria foraging algorithm (SBFA). The proposed technique, which is a modification of the bacteria foraging method (BFA), can direct bacteria by performing a tumble with a smart unit of length, decreasing the cost function better than the conventional BFA method. This algorithm not only considers social intelligence, but also emphasizes the individual intelligence of bacteria for finding a better nutritional path. A new cost function in the proposed SBFA has been used for specifying the direction of movement after a tumble. This approach led to a higher convergence speed and also better performance than the BFA. The effectiveness of the proposed method has been tested on a multi-machine power system while considering a frequency error-based objective function to enhance damping of the electromechanical oscillation modes. Simulation results for the proposed method are compared with conventional PSS, BFA- and fuzzy-based PSS methods. The results show the superior performance of the proposed SBFA-based PSS in comparison with other techniques for damping power system oscillations.
This paper addresses the problem of formally modeling processes related to the operation of surgical pavilions and an anesthesia unit in a Chilean hospital. To perform this modeling, we used Specification and Description Language (SDL), which resulted in a graphic layout consisting of one system, 11 blocks, 52 processes, 135 channels of communication, and 137 information signals. The model includes as system environment to the emergency units, clinical services, and support units. The model was created to document and understand the tacit knowledge of the personnel working in the anesthesia and pavilion surgical units. The aim of the proposed methodology was to design a model that can represent the system in a modular and standard way and that will also assist with hospital management and facilitate simulation.
This paper describes a single simulation framework to perform interactive cataract surgery simulations. Contributions includes advanced bio-mechanical models and intensive use of modern graphics hardware to provide fast computation times. Surgical devices are replicated and located in a real-time thanks to infrared tracking. Combination of a high-fidelity simulation and actual surgical tools are able to improve surgeon immersion while training. Preliminary tests have been performed by experienced ophthalmologists to qualitatively assess the face-validity of the simulator and the faithfulness of the behavior of the anatomical structures as well as the interactions with the surgical tools.
Peer-to-peer (P2P) traffic has increased rapidly over the past few years, with file sharing providing the main drive behind such traffic. Therefore, modeling P2P systems and studying their impact on the performance of the underlying network is a vital research topic. In this work we describe a flexible, efficient and easily expandable P2P simulation framework developed for the OPNET simulation package. The framework can be used to study a wide variety of issues regarding the packet-level and/or the flow-level performance of P2P systems, including the impact of cooperation incentives on file dissemination; mobility and quality of service (QoS) support in P2P distribution; and the effects of free-riding, resource pollution, and malicious attacks on P2P networks.
Endoscopic third ventriculostomy is a procedure used to treat hydrocephalus by making a perforation in the floor of the third ventricle of the brain under endoscopic guidance. We report on our initial experience in developing an endoscopic third ventriculostomy simulator for neurosurgery residents, including the definition of the simulation content and integration on NeuroTouch, a simulator for microneurosurgery training. The simulator includes exercises in which the trainee is asked to choose the location of the burr hole and the orientation of the trajectory to the foramen of Monro, or is required to find the third ventricle with a neuro-endoscope and perforate its floor. The simulator provides feedback on trainee performance either graphically or using quantitative metrics. The simulator allows easily switching from endoscopic third ventriculostomy mode to microscopic craniotomy mode using a retractable stereoscope based on a single screen and mirrors, detachable plastic heads, and quick-connect tool handles to give more realistic haptic feedback.
A reconfigurable cellular manufacturing system (RCMS) consists of multiple reconfigurable machining cells, each of which has one or more reconfigurable machine tools (RMTs), a setup station, and an automatic material handling and storage system. As part of the RCMS design process, similar parts must be grouped into part families and the RMTs must be arranged to form parallel cell configurations. A RCMS is designed at the outset for rapid changes in its components, allowing the production of multiple part families in each parallel cell. This paper proposes a new approach to simultaneously solve the cell formation and the scheduling of part families for an effective working of a RCMS. A new mixed integer linear programming model is used to represent both problems at the same time with the objective of minimizing production costs. Two types of production costs are considered: reconfiguration (i.e. setup) costs for changing from one family to the next one, and under-utilization costs for not using the RMT resources. A small size example is used to illustrate this integrated methodology. Computational experiments have been carried out adapting some larger instances from the literature on cellular manufacturing systems. Solving large instances optimally becomes prohibitive in terms of computational effort. That is why an approximate method, based on a Tabu search (TS) algorithm, has also been developed. Results show the ability of this algorithm to find good-quality production schedules of part families in a RCMS without requiring long computing times. It can be concluded that a RCMS can attain manufacturing flexibility without losing cost-effectiveness and that the approach proposed in this paper can efficiently solve real-world problems.
With ever-rising energy demand and diminishing sources of inexpensive energy resources, energy conservation has become an increasingly important topic. Building heating, ventilation, and air conditioning (HVAC) systems are considered to be a prime target for energy conservation due to their significant contribution to commercial buildings’ energy consumption in the US. Knowing a building’s occupancy plays a crucial role in implementing demand-response HVAC controls, with a corresponding potential for reduction of HVAC energy consumption, especially in office buildings. This paper evaluates occupancy modeling (both binary detection and multi-class estimation) using twelve ambient sensor variables. Performance of six machine-learning techniques is evaluated in both single-occupancy and multi-occupancy offices. Of the six, the decision-tree technique yielded the best overall accuracy (i.e. 96.0% to 98.2%) and root mean square error (RMSE) (i.e. 0.109 to 0.156). The contribution of each individual ambient sensor variable is evaluated via information gain. It is found that CO2, door status, and light variables have important contributions to the final modeling results. It is observed that the overall accuracy generally increases as the number of sensors increases. This paper also examines the possibility of building a global occupancy model, and explores the reasons for low performance of global occupancy estimation. Lastly, the occupancy model is used to estimate and visualize the accumulative room and thermal zone usage in an office test-bed building for three months. The results reveal that the effective vacancy accounts for a substantial portion of the operational hours, varying from 19.8% to 29.8% with an average of 23.3%, which bears significant potential for energy savings. Furthermore, the authors simulated HVAC energy consumption of the test-bed building for three months in DesignBuilder and EnergyPlus, and compared energy consumption of occupancy-based demand-response HVAC controls using the authors’ occupancy-modeling results to the conventional HVAC controls currently implemented in the test-bed building. The results demonstrate that 20% of gas and 18% of electricity could be effectively saved if occupancy-based demand-response HVAC control is implemented.
Agent-based simulation models are an increasingly popular tool for research and management in many fields. In executing such simulations "speed" is one of the most general and important issues because of the size and complexity of simulations. But another important issue is the effectiveness of the solution, which consists of how easily usable and portable the solutions are for the users, i.e. the programmers of the distributed simulation. Our study, then, is aimed at efficient and effective distribute simulations by adopting a framewor-level approach, with our design and implementation of a framework, D-Mason, which is a parallel version of the Mason library for writing and running simulations of agent-based simulation models. In particular, besides the efficiency due to workload distribution with small overhead, D-Mason at a framework level proves itself effective since it enables the scientists that use the framework (domain expert but with limited knowledge of distributed programming) only minimally aware of the fact that the simulation is running on a distributed environment. Then, we present tests that compare D-Mason against Mason in order to assess the improved scalability and D-Mason capability to exploit heterogeneous distributed hardware. Our tests also show that several massive simulations that are impossible to execute on Mason (e.g. because of CPU and/or memory requirements) can be easily performed using D-Mason.
Energy saving and environmental protection are the two main themes of today’s auto industry development. The hybrid electric vehicle (HEV) has become one of the most practical significant ways to solve energy and emission problems with good fuel economy and lower emissions. Aimed at the present HEV control methods, which have problems such as power loss, low efficiency of the system, the deterioration of the lubrication conditions, and so on, from the points of view of the overall efficiency of drive system, an optimal control method for a HEV is proposed to solve these problems. Firstly, all the possible operating modes are formulated. Then the efficiency evaluation equations of different modes are constructed. Next, according to the battery state of charge, this method determines the possible operating modes, and then the efficiency of different modes is calculated by means of the demand torque. Comparing the efficiency of different modes, the mode with the highest efficiency is obtained so that the engine torque and motor torque are distributed to enable the engine and motor output to correspond with this torque. Finally, the proposed method is simulated; the results show that it reduces system power loss and vehicle fuel consumption and emissions, and that it also protects the life of the transmission parts and lubrication conditions to some extent, achieving significant improvements.
This paper describes a lane-change model for connected vehicles, and evaluates the environmental impact per road level of service (LOS) when a host vehicle makes a lane change. During manual driving, the host vehicle accelerates or decelerates to create a safe distance before the vehicle makes a lane change. During automated driving, however, the host vehicle makes lane changes in response to the acceleration or deceleration of the vehicle in front of it through vehicle-to-vehicle communications. The gap, safe distance, and variation in vehicle speed depending on the LOS are analyzed using simulation for both automated and manual driving. For lane changing from a faster to a slower lane, the reduction in CO2 emissions of the connected vehicle was in the range 4770–54,291 g/km in comparison to the manual vehicle. For lane changing from a slower to a faster lane, the CO2 reductions were in the range 40,788–91,884 g/km. In more complex traffic situations, the CO2 reductions of the connected vehicles were larger than in simple traffic situations. This study indicates that the use of connected vehicles can result in a reduction of CO2 emissions.
The aim of this paper is to provide a mathematical method for minimizing the vibrations produced by hydraulic dampers, while maintaining the same damping force characteristics. The vibration level depends on the force–pressure characteristics of valve systems, which determine the damping force and high-frequency acceleration characteristic of a damper, and which need to be optimally tuned to lower the noise level. The paper considers a model-based approach to obtain the optimal pressure–flow characteristic via simulations conducted with the use of coupled models, including the damper and the servo-hydraulic tester model. The objectives of this work were as follows: (i) develop or adapt a double-tube damper model including pressure–flow valve characteristics; (ii) define key parameters of the valve characteristics influencing the high-frequency piston-rod acceleration, which is considered as a measure of vibration level; (iii) identify the parameter values (trends) minimizing the piston-rod acceleration using two alternative methods, namely a quick-and-dirty method based on a design of experiment (DOE) plan and a nonlinear programming method; (iv) obtain the optimal pressure–flow characteristic minimizing the vibration level by means of simulation; and (v) perform an experimental study comparing the high-frequency content of acceleration produced by the damper assembled with the original and optimized valve system using a laboratory setup.
Many modern models contain changes that affect the structure of their underlying equation system, e.g. the breaking of mechanical devices or the switching of ideal diodes. The modeling and simulation of such systems in current equation-based languages frequently poses serious difficulties. In order to improve the handling of variable-structure systems, a new modeling language has been designed for research purposes. It is called Sol and it caters to the special demands of variable-structure systems while still representing a general modeling language. This language is processed by a new translation scheme that handles the differential-algebraic equations in a highly dynamic fashion. In this way, almost arbitrary structural changes can be processed. In order to minimize the computational effort, each change is processed as locally as possible, preserving the existing computational structure as much as possible. Given this methodology, truly object-oriented modeling and simulation of variable-structure systems is made possible. The corresponding process of modeling and simulation is illustrated by two examples from different domains.
Multi-objective combinatorial optimization (MOCO) is an essential concern for the implementation of large-scale distributed modeling and simulation (MS) system. It is more complex than general computing systems, with higher dynamics and stricter demands on real-time performance. The quality and speed of the optimal decision directly decides the efficiency of the simulation. However, few works have been carried out for multi-objective combinatorial optimization MOCO especially in large-scale and service-oriented distributed simulation systems (SoDSSs). The existing algorithms for MOCO in SoDSSs are far from enough owing to their low accuracy or long decision time. To overcome this bottleneck, in this paper, a quantum multi-agent evolutionary algorithm (QMAEA), for addressing MOCO in large-scale SoDSSs is proposed. In QMAEA, the concept and characteristics of agent and quantum encoding are introduced for high intelligent searching. Each agent represented by a quantum bit, called a quantum agent (QAgent), is defined as a candidate solution for a MOCO problem, and each QAgent is assigned an energy, which denotes the fitness or objective function value of the candidate solution represented by it. Each QAgent is connected by four other QAgents nearby, and all QAgents are organized by an annular grid, called a multi-agent grid (MAG). In a MAG system, the population of QAgents can reproduce, perish, compete for survival, observe and communicate with the environment, and make all their decisions autonomously. Several operators, i.e. energy-evaluation-operator, competition-operator, crossover-operator, mutation-operator and trimming-operator, are designed to specify the evolvement of the MAG. The theory of predatory search strategy of animals is introduced in the evolution of QMAEA. Multiple evolutionary strategies, such as local-evolution-strategy, local-mutation-strategy and global-mutation-strategy are designed and used to balance the exploration (global search ability) and the exploitation (local search ability) of QMAEA. The framework and procedures of QMAEA are presented in detail. The simulation and comparison results demonstrate the proposed method is very effective and efficient for addressing MOCO in SoDSSs.
The simulation of urban mobility is a modeling challenge due to the complexity and scale. The complexity in modeling a social agent is due to three reasons: (i) the agent is behaviorally complex itself due to several interrelated/overlapping modeling aspects; (ii) the setting in which a social agent operates usually demands a multi-resolution approach; and (iii) the consideration of real spatial and population data is the underpinning that has to be realized. In this paper, we propose an agent-based parallel geo-simulation framework of urban mobility based on necessary modeling aspects. The aspect-oriented modeling paradigm relates the models vertically as well as horizontally and highlights the situations requiring multi-resolution interfacing. The framework takes into consideration the importance of technological foot-prints embedded with social behavior along with essential space and mobility features keeping focus on the importance of the city-scale scenario. We have used a real, high-quality raster map of a medium-sized city in central Europe converting it into a cellular automata (CA). The fine-grained CA readily supports pedestrian mobility and can easily be extended to support other mobility modes. The urban mobility simulation is performed on a real parallel and distributed hardware platform using a CA compatible software platform. Considering city-wide mobility in an emergency scenario, an analysis of the simulation efficiency and agent behavioral response is presented.
Work related to the tuning of the first-principle model of a feedwater heater operating in a coal-fired power unit is presented, along with discussion concerning the most efficient and accurate tuning algorithms based on direct-search, first- and second-order optimization techniques. The objective of this work is to find the most efficient and accurate algorithm to tune the model parameters, that is, heat transfer coefficients based on the algorithms’ benchmarking study. The model variables (e.g. variability of the power rate of energy exchange) and estimated parameter values were used to formulate key performance indicators intended for a model-driven diagnostics approach. The computational process was organized in an iterative process of updating model parameters and indicators. The validation was successfully performed using operational data from a 225 MW coal-fired power unit.
This paper describes a model capable of simulating large-scale traffic in an urban environment. The goal is of this work is the realistic and detailed simulation of the traffic, reproducing the behavior of each vehicle involved in the environment individually. This model has been developed in order to be integrated in an immersive driving simulator, where the driving position is the center of the simulation and the traffic model reproduces what happens around them. The general behavior of the traffic model is based on the following theory. Depending on the size of the urban environment to be simulated, the number of vehicles involved, and the traffic density, the environment can be studied as a whole or segmented in adjacent areas. Each vehicle model has two components. First, the behavior of the vehicle is simulated individually, modeling acceleration and braking, depending on the type and characteristics of each vehicle (mass, power, size, etc.). Second, the behavior of the drivers is also modeled, by type (passive, moderate, aggressive), playing various maneuvers also common in urban traffic circulation, such as lane changes, behavior at crossings and intersections, etc. A traffic light regulation model and the complete signposting of the urban environment are also included. As a result, the developed traffic model is applicable to large-scale traffic simulation integrated in an immersive driving simulator and is very useful when investigating complex behaviors of these environments. The model has been validated comparing it with results obtained from various references and very satisfactory results have been obtained.
Computer simulation is an important tool for studying epidemic dynamics. Owing to the scales and runtime speed requirements, the simulations of large-scale pandemic diseases such as severe acute respiratory syndrome (SARS) and H1N1 influenza usually require high-performance parallel simulation or computation. Previous works on the parallel simulations of large-scale epidemic were implemented on traditional general purpose CPU-based platforms or clusters. As more and more high-performance computation clusters are being built with so-called general-purpose graphics cards, this paper presents the implementation and optimization techniques for social contact network-based large-scale epidemic simulation on GPU clusters. Compared with previous works, this paper focuses on (1) how to efficiently implement the contact network-based parallel epidemic simulation on GPUs, and (2) how to hide communication latencies between processing nodes to improve scalability. Our proposed techniques are implemented and tested on a commodity cluster whose processing nodes are equipped with GPGPUs. The experimental results show that, for the simulation of 20 million individuals and 1.2 billion host contacts on 80 nodes, the execution on GPUs can achieve 7.4x – 11.7x speedup over the execution on CPUs.
While the overall performance of buildings has been established to be heavily impacted by design decisions made during the early stages of the design process, design professionals are typically unable to explore design alternatives, or their impact on energy profiles, in a sufficient manner during this phase. The research presents a new design simulation methodology based on incorporating a prototype tool (H.D.S. Beagle) that combines parametric modeling with multi-objective optimization through an integrated platform for enabling rapid iteration and trade-off analysis across the domains of design, energy use intensity, and finance. The research evaluates how the proposed method impacts design simulation processes, by either enabling and/or disrupting the early stages of design decision making. This simulation technology is presented through two major experiment sets: (1) a series of hypothetical cases emulating the architecture, engineering, and construction (AEC) design modeling and simulation process using our integrated simulation framework and technology; and (2) a pedagogically based experiment used for establishing benchmarks. Through these experiment data sets, both quantitative and qualitative data are collected, including human designer and computational analysis speeds, quantity of generated design alternatives, and quality of resulting solution space as defined by the evaluation metric of this research. The affordances for incorporation of real world design complexity into our computational design prototype and simulation methodology are discussed through both the enabling and the disruptive impact on the early stages of the design process.
This paper proposes an Investment Planning Model (IPM) that can be employed in determining the optimal period to inject new investments on some critical sections of any transmission network in the Wholesale Electricity Market in order to sustain its reliability and availability. The mathematical models for realization of the IPM are obtained by capturing the salient effect of increasing Existing Transmission Commitment on Available Transfer Capability. These mathematical models are implemented and executed with the help of embedded Matlab function blocks in the Matlab environment. The input units of the IPM were obtained via the PSAT 2.1.2 software package. In the course of simulations, the proposed IPM was used to predict the investment periods of all the transmission paths of a 14-bus IEEE test system as quickly as possible.
In the era of web2.0, marketers are eager to benefit from viral advertising. In this paper we propose a computational network model of viral advertising to examine the maximization of influence within social networks. For our network model we combine both the independent cascade model and the threshold model. We use a spreading threshold to trigger the cascading process, to examine the ways in which advertisements spread across the social network. We also investigate the procedures for choosing an initial set of people to maximize the performance of advertisement spreading. Furthermore, we analyse the impact of network structures on the dynamics of diffusion, and a strategy for combining viral advertising with mass marketing in e-commerce. We also run simulations using a real dataset to check the diffusion of advertisements in an online social network. Ultimately we discovered that a combination of viral advertising and mass marketing is better to diffuse advertisements than either method wholly by itself. Using an optimal algorithm improves diffusion performance, but using ‘degree’ is also an alternative way of choosing initial nodes when the whole structure of network is unknown. Integrating simulations to build a real-time decision support platform will make the diffusion of advertisements more efficient.
This paper proposes the integration of life cycle analysis within the production system models as a tool for decision making (whether at the strategic, tactical or operational levels) attending not only economic and technical criteria but also the environmental impact. This methodological proposal advances over the traditional approach of calculating the value of the environmental impact of a particular product, by proposing the use of models to determine the environmental impact of the product, according to the decisions made in the production system. That is, it does not provide an impact value of the product, but rather a model to determine the impact in terms of the decisions made in the production process; therefore, it can be used, especially by means of simulation, for the optimization of the production system, based on multiple criteria (including environmental impact).
The methodological approach is exemplified by a case study, which is used to validate the proposal and to expose it more precisely and clearly, although the methodology is equally applicable to any production process, especially processes highly automated or with different alternative production techniques.
This case study, based on the wine production sector of the Rioja Qualified Designation of Origin, in Spain, was made with actual data after several years of research in representative wineries; therefore, besides an application example, it is a support tool for sustainability in wineries, by reducing the environmental impact of wine production (especially in La Rioja and Spain, but generally throughout the world).
Real-time embedded and cyber-physical systems challenge simulation disciplines due to the heterogeneous tools used to model components in the system exploration and design phases. Termed "heterogeneity," the mixed-model problem challenges multi-simulator coordination, where event causality must be preserved among simulators with different models of computation, signals, criteria for time advancement, and levels of abstraction. SimConnect and SimTalk enable heterogeneous, distributed, hardware/software co-simulation with a simplified backplane approach, emphasizing the simulation of software interacting with simulated world-model electrical, mechanical, and physical effects. The structure of SimConnect and SimTalk is described, adhering to the properties of a Kahn Process Network. Application of the tools to the coordination of three different simulators (TExaS, Ngspice, and Simulink) is presented to simulate closed-loop, hardware/software-based, Proportional-Integral-Derivative/Pulse-Width-Modulated control of a direct current motor. Results demonstrate agreement among simulator coordinations with configurable tradeoffs in speed versus accuracy.
In a distributed virtual environment (DVE), participants located in different places may observe inconsistent views of the simulated virtual world due to message delay and loss in the network. This paper investigates how to compensate for the impact of message delay and loss on consistency in the DVE. We focus on dead reckoning (DR)-based update mechanisms and measure inconsistency by the time–space inconsistency (TSI) metric. We theoretically analyze the TSI of an entity and derive the condition under which the impact of message delay and loss on consistency can be fully compensated for by reducing the DR threshold. Based on the analysis, a compensatory update scheduling algorithm is proposed. Experiments using real traces of a racing car game are conducted to evaluate the compensatory algorithm. The results confirm that if the condition derived in our theoretical analysis is fulfilled, the compensatory algorithm can decrease the TSI of a racing car to the level of the case without message delay and loss when there is sufficient network bandwidth available. Under severe bandwidth constraints, the compensatory algorithm still leads to comparable TSIs of the racing car among the participants regardless of their network conditions so as to enable fair competition.
A nonlinear robust control strategy is proposed in this paper to deal with wing rock motion. The method is based on the disturbance observer enforced contraction theory results. The disturbance observer calculates and robustly cancels system uncertainties and input disturbances. The information regarding the known part of the nonlinear plant dynamics is used by the controller, while the uncertainties and disturbances are treated as an additional input to the system and dealt with by the disturbance observer. The algorithm demonstrates good performance in damping the oscillations while rejecting the disturbances. Simulations are provided to demonstrate the effectiveness of the proposed method via an application to an experimentally derived delta wing rock model operating in both nominal and uncertain environments.
Aeronautics and air transport is a vital sector of our society and economy. Air transport logistics is one of the key players to support efficient globalization; however, sustainable mobility is at stake, due to facts such as the interdependencies with the financial system, climate change and an increasing scarcity of resources. This paper highlights the consequences of a lack of a proper understanding of air-side and land-side, leading to an unsustainable air transport system. A system approach for knowledge sharing between air traffic controllers, handling operators, airlines and airport managers is justified by means of causal models to design a mitigation mechanism to tackle perturbations instead of increasing the latent capacity. The simulation benefits to design indicators of sustainability are also mentioned.
In the last decade, agent-based modeling and simulation (ABMS) has been applied to a variety of domains, demonstrating the potential of this technique to advance science, engineering, and policy analysis. However, realizing the full potential of ABMS to find breakthrough research results requires far greater computing capability than is available through current ABMS tools. The Repast for High Performance Computing (Repast HPC) project addresses this need by developing a useful and useable next-generation ABMS system explicitly focusing on larger-scale distributed computing platforms. Repast HPC is intended to smooth the path from small-scale simulations to large-scale distributed simulations through the use of a Logo-like system. This article’s contribution is its detailed presentation of the implementation of Repast HPC as a useful and usable framework, a complete ABMS platform developed explicitly for larger-scale distributed computing systems that leverages modern C++ techniques and the ReLogo language.
In keeping with intent of this special issue—innovative ways to use modeling and simulation as an enabling technology—this paper addresses a global call for change in medical practice due to shifting populations, rising health risks, and increased expectation of governments to ensure patient safety. In advanced countries the basic economics of supply and demand are making a therapeutic commodity—blood—a costly treatment. In advancing states the rapid deformation of stored red blood cells and the prevalence of patient infection make standard transfusion medicine hazardous. As a result the World Health Organization has issued a call for alternatives to transfusion practice within the medical community. This paper introduces the implementation of patient blood management as that alternative standard of care, and it outlines an effective means to educate medical professionals via a web-based immersive simulation training tool. This tool was developed from evidence-based medicine, engineering and mathematical modeling, and simulations drawn from patient case studies. The medical instruction comprising this tool and its portability can readily serve a global audience of practitioners who are unfamiliar with these techniques and who are without an expedient means to obtain training. The tool is a multidisciplinary effort drawing on engineering, computer science, social science, and medical expertise. And just as this special issue stresses that simulation represents probably the only methodology to provide the human race with a tool for enabling control of mankind’s evolution. Representing the cornerstone for anticipating future critical situations, this tool responds to an imminent dilemma in the global medical community.
Unmanned Aerial Vehicles (UAVs) are used for many missions, including weather reconnaissance, search and rescue assisting operations over seas and mountains, aerial photographing and mapping, fire detection, and traffic control. Autonomous operation of UAVs requires the development of control systems that can work without human support for long time periods. The path planners, which generate collision-free and optimized paths, are needed to provide autonomous operation capabilities to the UAVs. The optimization of the flight trajectory is a multi-objective problem dealing with variable terrain features as well as dynamic environment conditions. This paper presents a simulation environment for offline path planning of unmanned aerial vehicles on three-dimensional terrains. Our path planner aims to identify the shortest path and/or flight envelope in a given line of sight by avoiding terrain collisions, traveling on a path that stays within the restricted minimum and maximum distances above the terrain, traveling far from the specified threat zones, and maneuvering with an angle greater than the minimum curvature radius. We present two meta-heuristics (genetic algorithms and hyper-heuristics) in order to construct the paths for UAV navigation and compare our results with a reference work given in the literature. A comparative study over a set of terrains with various characteristics validates the effectiveness of the proposed meta-heuristics, where the quality of a solution is measured with the total cost of a constructed path, including the penalties of all constraints.