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The paper presents a fault region identification method for the active distribution network (ADN) with limited PMU. First, PMU configuration and region division strategies are proposed based on the network topology. Next, the difference in three-phase current variations between the normal and fault regions of the ADN is analyzed. A multi-dimensional state monitoring matrix is built using the measured current data. The spectral norm ratio coefficient is constructed based on the 2-norm to lower the complexity of the multi-dimensional state monitoring matrix and quantify the difference in state changes before and after the fault occurs in each region. Then, the fault region is identified by combining each region's spectral norm ratio coefficient and the change of the current phase. Finally, an IEEE 33-node simulation model is created to simulate faults under different working conditions. According to the simulation results, the suggested approach is less impacted by fault type, neutral point grounding mode, and transition resistance. Furthermore, even if the communication does not match the rigorous synchronization requirements, the proposed method can still complete the fault identification of the distribution network correctly and has high robustness. [ABSTRACT FROM AUTHOR]

A chemo-mechanical model is developed to investigate the effects on the stress development of the coating of polycrystalline Ni-rich LiNixMnyCozO2 (x ⩾ 0.8) (NMC) particles with poly(3,4-ethylenedioxythiophene) (PEDOT). The simulation results show that the coating of primary NMC particles significantly reduces the stress generation by efficiently accommodating the volume change associated with the lithium diffusion, and the coating layer plays roles both as a cushion against the volume change and a channel for the lithium transport, promoting the lithium distribution across the secondary particles more homogeneously. Besides, the lower stiffness, higher ionic conductivity, and larger thickness of the coating layer improve the stress mitigation. This paper provides a mathematical framework for calculating the chemo-mechanical responses of anisotropic electrode materials and fundamental insights into how the coating of NMC active particles mitigates stress levels. [ABSTRACT FROM AUTHOR]

Simulation models are used to emulate real-world phenomena, and errors are inevitable in the numerical computation process. Owing to that, simulation models need to be verified and validated to ensure the models and their implementations are correct. In this paper, V&V has been done for the micro3d discrete dislocation dynamics (DDD) model by comparing simulation results with corresponding theory, including any analytical solutions, other numerical solutions and experimental data. DDD simulations are a powerful simulation methodology that can help researchers better understand the plastic behavior of crystalline materials. In this study, parametric analyses for DDD simulations parameters have been performed. In addition, simulation results are verified and validated. [ABSTRACT FROM AUTHOR]

Short-circuit (SC) of power components in inverters is one of the most serious faults that are vulnerable to occur. It is critical to quickly and accurately detect and locate SC faults in power devices, especially to determine their severity. Therefore, the paper proposes a fault diagnosis algorithm that combined the rough set genetic algorithm (RS-GA) and the Bayesian network (BN). The algorithm uses the RS for attribute reduction and the GA for global optimization to reduce numerous fault attributes and obtains several kinds of reduction results. According to the reduction results, BN diagnostic models are established for optimization, and the optimal RS-GA-BN diagnostic model is determined by an optimization evaluation algorithm based on time and accuracy. More importantly, by the fault attribute values of the optimal reduction attribute of the RS-GA-BN model, the fault severity can be diagnosed, and the fault and its trend can be predicted in advance, which provides an important idea for the prevention of SC. The experimental platform and simulation model are built to obtain fault diagnosis data. The experimental results show that the RS-GA-BN model presents higher fault diagnosis accuracy than the BP neural network, and the experiment verifies the feasibility and correctness of the method. [ABSTRACT FROM AUTHOR]

This paper presents a simulation technique for optimizing a hydrogen integration network. By applying this technique, the minimum fresh hydrogen consumption can be determined. Quantitative relationship between sources and sinks streams were studied to get the flow rates of coupled source and sink, hydrogen consumption and hydrogen concentration in each stream. The introduced technique was applied on twelve sources and twelve sinks with any purity of hydrogen concentration. The hydrogen integration network was designed through two steps, the first step considers applying the data given in the LINGO program, while the second step considers using the LINGO results in the introduced excel program to obtain the retrofitted hydrogen integration network. The proposed technique was applied on several case studies to achieve the minimum consumption of fresh hydrogen for the obtained hydrogen integrated networks. The introduced model for simulation and retrofitting of mass exchange networks is easy to understand and the results showed that this model is more efficient for fertilizer, petrochemical and refinery plants. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

To solve the problem of multiple noise interference when tunable diode laser absorption spectroscopy (TDLAS) technology is used for gas concentration detection, the principles of Kalman, moving average, wavelet transform, and singular value decomposition four filtering algorithm principles and system noise sources are analyzed. MATLAB simulation software is used to simulate the relationship curve of thermal noise, shot noise, relative intensity noise of lasers, and the characteristic value of harmonic signals. Four filtering algorithms are used to process the noise, and the best filtering algorithm is selected. Moreover, a TDLAS experimental system with ethylene gas as the detection object is designed in this paper. The experimental results show that the singular value decomposition method among the four filtering algorithms has the best effect in removing thermal noise, shot noise, and laser noise. After filtering is performed, the accuracy of the continuous detection of the system is improved, and the purpose of reducing or even eliminating system noise can be achieved. After processing occurs, the signal-to-noise ratio of the detected signal of the system is improved by 57 dB, and the noise removal rate of the system reaches 66.7%. [ABSTRACT FROM AUTHOR]

This paper introduces the hhsmmR package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov models. These models are flexible models with both Markovian and semi-Markovian states, which are applied to situations where the model involves absorbing or macro-states. The left-to-right models and the models with series/parallel networks of states are two models with Markovian and semi-Markovian states. The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission distribution using penalized B-splines, prediction of future states and the residual useful lifetime estimation in the predict function. The commercial modular aero-propulsion system simulation (C-MAPSS) data-set is also included in the package, which is used for illustration of the application of the package features. The application of the hhsmm package to the analysis and prediction of the Spain's energy demand is also presented. [ABSTRACT FROM AUTHOR]

This paper presents a simulation technique for optimizing a hydrogen integration network. By applying this technique, the minimum fresh hydrogen consumption can be determined. Quantitative relationship between sources and sinks streams were studied to get the flow rates of coupled source and sink, hydrogen consumption and hydrogen concentration in each stream. The introduced technique was applied on twelve sources and twelve sinks with any purity of hydrogen concentration. The hydrogen integration network was designed through two steps, the first step considers applying the data given in the LINGO program, while the second step considers using the LINGO results in the introduced excel program to obtain the retrofitted hydrogen integration network. The proposed technique was applied on several case studies to achieve the minimum consumption of fresh hydrogen for the obtained hydrogen integrated networks. The introduced model for simulation and retrofitting of mass exchange networks is easy to understand and the results showed that this model is more efficient for fertilizer, petrochemical and refinery plants. [ABSTRACT FROM AUTHOR]

Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has grown to become a central focus of applied ecology and conservation science. Several computational algorithms have been developed to understand and map connectivity, and many studies have validated their predictions using empirical data. Yet at present, there is no published comparative analysis which uses a comprehensive simulation framework to measure the accuracy and performance of the dominant methods in connectivity modelling. Given the widespread usage of such models in spatial ecology and conservation science, a thorough evaluation of their predictive abilities using simulation techniques is essential for guiding their appropriate and effective application across different contexts. In this paper, we address this by using the individual-based movement model Pathwalker to simulate different connectivity scenarios generated from a wide range of possible movement behaviours and spatial complexities. With this simulated data, we test the predictive abilities of three major connectivity models: factorial least-cost paths, resistant kernels, and Circuitscape. Our study shows the latter two of these three models to consistently perform most accurately in nearly all cases, with their abilities varying substantially in different contexts. For the majority of conservation applications, we infer resistant kernels to be the most appropriate model, except for when the movement is strongly directed towards a known location. We conclude this paper with a review and interdisciplinary discussion of the current limitations and possible future developments of connectivity modelling. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulation, an atomic simulation technique, plays a significant role in studying mechanism of ultra-precision cutting. MD simulation of nanometric cutting with extremely high cutting speeds involves considerable computational time and significant memory usage, which can provide microscopic analysis to better understand the ultra-precision cutting process. Considerable researches relating to MD simulation might help improve manufacturing technologies, which are of particular interest to early studies. However, a number of studies embracing ultra-precision cutting with MD simulation have not been reviewed systematically before, especially for the surface generation mechanism. In this paper, the principles and fundamentals, the development and application, and advantages and current limitations of MD simulation of surface generation mechanism in ultra-precision cutting are reviewed. Relevant MD simulation studies of surface generation mechanism are summarized with a focus on (a) the minimum cutting thickness; (b) the effect of tool geometry parameters: rake angle, edge radius; (c) the influence of cutting parameters: feed rate, cutting depth and cutting speed; (d) the influence of cutting force and cutting temperature; (e) the effect of crystal orientation. This paper provides a brief review of surface generation mechanism in ultra-precision cutting with the MD simulation to preliminary understand MD simulation and how it applied in studying nanosurface generation mechanism. [ABSTRACT FROM AUTHOR]

With the introduction of new technologies, the scope of miniaturization has broadened. The conditions under which complicated products are designed, manufactured, and assembled ultimately influence how well they perform. The intricacy and crucial functionality of products are frequently only fulfilled through the use of high-precision components such as micro gears. In power transmission systems, gears are used in a variety of industries. Micro gears or gears with micro features, with tolerances of less than 5 μm, are pushing manufacturing processes to their technological limits. Monte-Carlo simulation methods enable an accurate forecast of inaccuracies in compliance. The complexity of the micro gear's design, on the other hand, increases the simulation computation and runtime. An alternative method for simulation is to create a surrogate model to predict the behavior. This paper proposes a statistical surrogate model to predict the conformity of a pair of micro gears. Afterward, the advantage of the surrogate model enables the optimal tolerance assignment while taking gear functionality and production cost into account. [ABSTRACT FROM AUTHOR]

Bonded contact models have been increasingly used in the discrete element method (DEM) to study cemented and sintered particulate materials in recent years. Several popular DEM bond models have been proposed in the literature; thus it is beneficial to assess the similarities and differences between the different bond models before they are used in simulations. This paper identifies and discusses two fundamental types of bond models: the Spring Bond Model where two bonded particles are joined by a set of uniform elastic springs on the bond's cross-section, and the Beam Bond Model in which a beam is used to connect the centres of two particles. A series of cantilever beam bending simulation cases were carried out to verify the findings and assess the strength and weakness of the bond models. Despite the numerous bond models described in the literature, they can all be considered as a variation of these two fundamental model types. The comparative evaluation in this paper also shows that all the bond models investigated can be unified to a general form given at a predefined contact point location. [ABSTRACT FROM AUTHOR]

The pyrolysis oil fraction is highly attractive for pyrolysis products. A simulated flowsheet model of a waste tire pyrolysis process is presented in this paper. A kinetic rate-based reaction model and equilibrium separation model are created in the Aspen Plus simulation package. The simulation model is effectively proven against experimental data of literature at temperatures of 400, 450, 500, 600 and 700 °C. Also, the developed model was employed to investigate the impact of temperature on the pyrolysis procedure and demonstrated that there is an optimum temperature for chain fractions. The optimum temperature to have the highest amount of limonene (as a precious chemical product of waste tire pyrolysis process) was found 500 °C. The findings indicated that the pyrolysis process is ecologically benign, although there is still space for development. In addition, a sensitivity analysis was carried out to see how altering the heating fuel in the process would affect the non-condensable gases produced in the process. Reactors and distillation columns in the Aspen Plus® simulation model was developed to assess the technical functioning of the process (e.g., upgrading the waste tires into limonene). Furthermore, this work focuses on the optimization of the operating and structure parameters of the distillation columns in the product separation unit. The PR-BM, as well as NRTL property models, were applied in the simulation model. The calculation of non-conventional components in the model was determined using HCOALGEN and DCOALIGT property models. [ABSTRACT FROM AUTHOR]

The numerical control machine tool is an important part of manufacturing. The speed and accuracy of the machine tool simulation and the monitoring of machine physical model movement, model collision, and operation health are the most common bottlenecks. The previous studies on these problems are discrete and poor in real time. In this paper, combined with the digital twin (DT) technology, a perception-monitor-feedback system architecture is constructed. Based on the improved Gilbert–Johnson–Keerthi (GJK) distance algorithm, the collision information of tool and machine tool can be better detected during the simulation and monitoring of machine tool movement, and the more real workpiece shape can be displayed. Based on the model synchronous motion driven by the production perception data and its potential information extraction, the tool wear online monitoring is carried out. The tool wear online monitoring is performed based on the production perception data–driven model synchronous movement and extraction of its potential information. Finally, the usability and efficiency of the system are verified by an example of a typical shaft part processed by a numerical control machine tool. [ABSTRACT FROM AUTHOR]

When the simulation of a system, or the verification of its model, needs to be resumed in an online context, we face the problem that a particular starting state needs to be reached or constructed, from which the process is then continued. For timed automata, especially the construction of a desired clock state, represented as a difference bound matrix (DBM), can be problematic, as only a limited set of DBM operations is available, which often does not include the ability to set DBM entries individually to the desired value. In online applications, we furthermore face strict timing requirements imposed on the generation process. In this paper, we present an approach to construct a target clock state in a model via sequences of DBM operations (as supported by the model checker Uppaal), for which we can guarantee bounded lengths, solving the present problem of ever-growing sequences over time. The approach forges new intermediate states and transitions based on an overapproximation of the target state, followed by a constraining phase, until the target state is reached. We prove that the construction sequence lengths are independent of the original trace lengths and are determined by the number of system clocks only, allowing for state construction in bounded time. Furthermore, we implement the (re-)construction routines and an extended Uppaal model simulator which provides the original operation sequences. Applying the approach to a test model suite as well as randomly generated DBM operation sequences, we empirically validate the theoretical result and the implementation. [ABSTRACT FROM AUTHOR]

Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time of the ED processes is necessary to guarantee the reliability of the results. However, simulation models concerning EDs are frequently affected by missing data, thus requiring a proper estimation of some unknown parameters. In this paper, a simulation-based optimization approach is used to estimate the incomplete data in the patient flow within an ED by adopting a model calibration procedure. The objective function of the resulting minimization problem represents the deviation between simulation output and real data, while the constraints ensure that the response of the simulation is sufficiently accurate according to the precision required. Data from a real case study related to a big ED in Italy is used to test the effectiveness of the proposed approach. The experimental results show that the model calibration allows recovering the missing parameters, thus leading to an accurate DES model. [ABSTRACT FROM AUTHOR]

Conceptual design of contemporary high-frequency structures is typically followed by a careful tuning of their parameters, predominantly the geometry ones. The process aims at improving the relevant performance figures, and may be quite expensive. The reason is that conventional design methods, e.g., based on analytical or equivalent network models, often only yield rough initial designs. This is especially the case for miniaturized components featuring considerable electromagnetic (EM) cross couplings, or antenna systems with non-negligible radiator coupling (e.g., MIMO, closely-spaced arrays). For reliability reasons, parametric optimization is carried out using EM simulation tools, which is a time-consuming task. In many cases, designer needs to resort to a global search, especially when handling several objectives and constraints is necessary, or the high-frequency structure under design is overly complex. Combination of both aforementioned factors makes it no longer possible to rely on engineering insight, even to detect a promising region of the design space. Unfortunately, nature-inspired algorithms, commonly employed for solving these tasks typically exhibit significant computational expenditures. This paper proposes a simple yet efficient method for globalized search using a response feature approach and inverse regression surrogates. Owing to less nonlinear dependence of the feature point coordinates on the system variables (as compared to the original responses, e.g., S-parameter frequency characteristics), our methodology permits a rapid identification of the most appropriate regions of the parametric space, and further design tuning by means of local routines. At the same time, the overall optimization cost is comparable to the cost of local procedures. The proposed approach is validated using several high-frequency structures (a dual-band antenna, a microstrip coupler, an impedance matching transformer) optimized under different design scenarios. Global search capability and computational efficiency are demonstrated through comprehensive comparisons with multiple-start local search, as well as particle swarm optimizer, a representative nature-inspired algorithm. [ABSTRACT FROM AUTHOR]

High-velocity oxygen-fuel (HVOF) thermal spraying is a coating process involving multidisciplinary aspects, e.g., fuel–oxidant combustion, flame–particle jet, particle deposition, mass and heat transfer, and even robotic kinematics. Like most coating processes, in HVOF processes, coating thickness is a significant property determining the coating performance; hence, this property should be accurately controlled during the process. In view of green, smart, and digital manufacturing, the coating thickness prediction model is demanded to produce high-quality coatings efficiently. This paper presents an approach to parametrically simulate the coating thickness in HVOF processes via an integrated numerical model. Firstly, an axisymmetric computational fluid dynamics (CFD) model is constructed to compute the behaviors of the fuel–oxidant combustion, flame–particle jet, and particle deposition distribution. Secondly, based on the particle distribution in a 2D axisymmetric model, a 3D single coating thickness profile model is developed by constructing a circular pattern using the axis of the nozzle. Further, this profile is smoothened by a Gaussian model, and its mathematical expression is obtained. Finally, a numerical model couples spray paths with the mathematical expression to model the coating thickness distribution on a substrate surface under industrial scenarios. At the end of this paper, to verify the proposed model's effectiveness, four sets of operating parameters with a single straight path were experimentally implemented. The width and height of the bead-like shape coating were in good agreement with the simulated results. The normalized root-mean-square errors of the cross-sectional profile heights were around 10%. [ABSTRACT FROM AUTHOR]

Complex social processes introduce difficulties to validating causal parameters and identifying the correct system structure in modelling. Policy impact assessment for sustainability transitions should therefore not expend too many resources modelling any single set of assumptions about the world. Furthermore, keeping models relatively simple allows more effective communication and stakeholder collaboration. This paper presents an exploratory system dynamics model of urban mode choice. We demonstrate that, despite structural and parametric uncertainty, it is possible to rank alternative policy approaches and identify high-leverage uncertainties as targets of policy action or further analysis. We also show how different narrative theories of change can have drastically different or unintuitive outcomes for the same intervention. Simulation can benefit both impact assessment and the further scrutiny and refinement of change narratives. We argue that the following methodological choices and their synergies made our modelling approach effective: exploratory modelling, focus on endogeneity, coarse resolution and avoidance of abstract variables. [ABSTRACT FROM AUTHOR]

One of the effective methods of vibration isolation of the equipment of biogas treatment plants is the use of vibration supports. Technologies for semi-active vibration damping, the main advantage of which is their stability and adaptability to the operating conditions of process equipment, are actively developing. The paper presents the design of a semi-active vibration support with magnetic fluid, the distinguishing feature of which is an extended range of operating characteristics and reduced wear rate of the components. An analysis of the damping characteristics of a semi-active vibration support with permanent magnets is presented. A simulation model of the considered vibration support has been developed. The simulation results indicate a decrease in the influence of vibration on control points. [ABSTRACT FROM AUTHOR]

In this paper, a bilateral haptic virtual surgery simulation system under a hybrid controller was studied. An analogue controller realized by a field programmable analogue array (FPAA) was paralleled in the operator robot side, which reduced the impact of controller discretisation on the system. A system stability conditions under hybrid control with multiple-operators were deduced. The stability analysis indicates that the addition of analogue derivative term widens the range of haptic controls gains that satisfy the multiple-users' stability conditions. Finally, the human's performance of a stiffness discrimination task was studied in an independently developed minimally invasive surgical (MIS) platform. The experiment results show that, human operators under the hybrid controller achieve the highest task success rates. [ABSTRACT FROM AUTHOR]

Manufacturing large and/or complex structural components is today non-trivial and far-too expensive, due to limitations in the state-of-the-art production processes. Swarm robotics could then bring a different perspective and it may promise flexible, autonomous, and highly robust solutions for a large variety of applications; hence, its adoption in industry and construction may change manufacturing rules. The present contribution introduces a continuous model to capture the behavior of a swarm of manufacturing agents (e.g., drones, 3D printers, etc..) as well as a very simple, but effective, numerical implementation to simulate the evolution of such a swarm. The paper also presents one- and two-dimensional examples, showing the potentiality of the proposed approach in predicting swarm behaviors. [ABSTRACT FROM AUTHOR]

Friction plays an important role in the injection mechanism of squeeze casting machines. To control the injection process of aluminum alloy more accurately, a novel numerical simulation method based on a friction-integrated finite element (FE) model is proposed in this paper. A friction mechanism model of the injection mechanism based on thermohydrodynamic lubrication (THDL) was developed, and with the application of a friction-generating experiment, the influences of temperature, interaction speed, and fit clearance on the friction-related characteristics of an injection mechanism were ultimately evaluated. The THDL model was integrated into a thermomechanical coupling FE model to simulate the multicycle injection process, and the simulation results were compared with those of the general model in the absence of friction. The effects of friction on the temperature, deformation, and fit clearance of the injection mechanism were analyzed. The accuracy of the friction-integrated model was also evaluated in an injection experiment. A multicycle injection experiment platform that measures the temperature and deformation of the injection mechanism online in real-time was developed. When the numerical simulation results of the two models were compared with the experimental data, the friction-integrated model achieved more reliable results than the general model. [ABSTRACT FROM AUTHOR]

A field effect transistor having the active area made of monolayer graphene is simulated by a drift-diffusion model coupled with the Poisson equation. The adopted geometry, already proposed in (Nastasi and Romano in IEEE Trans. Electron. Devices 68:4729–4734, 2021, https://doi.org/10.1109/TED.2021.3096492), gives a good current-ON/current-OFF ratio as it is evident in the simulations. In this paper, we compare the numerical simulations of the standard (non-degenerate) drift-diffusion model, that includes the Einstein diffusion coefficient, with the degenerate case. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25–100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables. [ABSTRACT FROM AUTHOR]

High performance LINPACK (HPL) benchmark is used to evaluate the maximum floating-point performance of a computer cluster. Since the performance of the graphics processing unit (GPU) has been improved rapidly, many researchers start to optimize HPL benchmark through GPU to maximize system utilization. Nevertheless, it is difficult to determine the optimal combination of parameters in this process due to the complexity of the input. Therefore, running HPL on a heterogeneous system is time-consuming and is not flexible under different hardware components. So we propose a simulation model of HPL in this paper. The model is no longer limited by hardware components and able to simulate the execute process of HPL across different computing node in heterogeneous GPU-enhanced clusters at any scale. It can also assist researchers in evaluating the floating-point performance quickly and provide a reference for the hardware investment. [ABSTRACT FROM AUTHOR]

Market diffusion of new products is driven by the actions and reactions of consumers, distributors, competitors, and other stakeholders, all of whom can be heterogeneous in their individual characteristics, attitudes, needs, and objectives. These actors may also interact with others in various ways (e.g., through word of mouth or social influence). Thus, a typical consumer market constitutes a complex system whose behavior is difficult to foresee because stochastic impulses may give rise to complex emergent patterns of system reactions over time. Agent-based modeling, a relatively novel approach to understanding complex systems, is well equipped to deal with this complexity and, therefore, may serve as a valuable tool for both researchers studying particular market effects and practitioners seeking decision support for determining features of products under development or the appropriate combination of measures to accelerate product diffusion in a market. This paper provides an overview of the strengths and criticisms of such tools. It aims to encourage researchers in the field of innovation management, as well as practitioners, to consider agent-based modeling and simulation as a method for gaining deeper insights into market behavior and making better-informed decisions. [ABSTRACT FROM AUTHOR]

The service performance and life of the aero-engine are directly affected by the blade machining accuracy. In the end milling of the flexible blade, the profile tolerance would usually be violated by the deflection errors induced by excessive static deformations under the action of milling force. In order to predict the blade deflection errors, this paper presents an iterative algorithm considering the coupling relationship between milling force and elastic deformation based on the milling finite element simulation model. Then a novel multipoint rod-shaped auxiliary support technology is proposed to suppress the blade deflection errors. Taking the influence of layout parameters on blade deflection errors into account, the layout of auxiliary support is optimized by a genetic algorithm. To verify the effectiveness of the proposed method, an auxiliary support mechanism is designed and manufactured, and the deflection errors suppression experiment is implemented. The measured results show that the prediction error of blade maximum deflection is 17.6%. Furthermore, the blade average deflection error on two measured curves reduces from 45.88 μm to 24.39 μm with the support of the designed mechanism. Thus, this auxiliary support mechanism and layout optimization method can achieve higher accuracy in blade milling. [ABSTRACT FROM AUTHOR]

This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify the structure and capabilities of the production system. An evaluation of a prototype implementation has been conducted on an industrial assembly system. The results indicate that the use of the framework in a typical adaptation process provides a significant reduction in time and resources required. [ABSTRACT FROM AUTHOR]

This special section discusses recent advances in the modeling and simulation of complex systems through the following four papers, one of which is a survey paper and the other three are regular papers. For the evaluation of the solutions within the metaheuristic search techniques, a discrete event simulation model of an urban mass rapid transit system is integrated into the optimization procedure. In the paper by David Schmaranzer, Roland Braune, and Karl F. Doerner, a simulation-based optimization technique for the headway optimization of complex urban mass rapid transit systems is developed. [Extracted from the article]