Time-driven Method Research Articles

Advantages of the Event Method for the Simulation of Water Quality in Pressurised Water Systems

In this paper, several methods for the calculation of water quality evolution in drinking water distribution networks are analysed. The Lagrangian Time-Driven method has been implemented in the Epanet simulation software since version 2.0. In version 2.2, some improvements were implemented to deal with mass imbalances (Lagrangian Time-Driven improved method). However, it sometimes presents inaccuracies in calculations, especially when there are short-length pipes. To solve this problem, the implementation of the Lagrangian Event-Driven method is proposed, which provides more accurate quality results. In order to detail the differences and similarities of the results of the different methods and to determine under what conditions the results provided by Epanet are sufficiently adjusted, two practical examples have been carried out, one of them on a hydraulic model of a real network.

Condition-driven probabilistic adversarial autoencoder with nonlinear Gaussian feature learning for nonstationary process monitoring

For industrial processes, the observed data usually has strong nonlinearity and non-Gaussian properties. The whole industrial process shows typical nonstationary characteristics due to frequent time-varying behaviors. This makes the nonstationary process monitoring a challenging task along time direction. It is recognized that the nonstationary process characteristic can change with a certain rule along condition direction instead of time direction. In the present work, a novel condition-driven probabilistic adversarial autoencoder (CPAAE) algorithm is designed to address this problem. CPAAE divides the whole nonstationary process into multiple condition slices by cutting the indicator variables of the industrial process into multiple equal intervals. Subsequently, probabilistic adversarial autoencoder (PAAE) models for different condition slices can be established to extract nonlinear Gaussian features. The condition slices will be aggregated into different condition modes by evaluating the similarity of Gaussian features, and multiple monitoring models can be established for different condition modes to replace the conventional time-driven method. In this way, the nonstationary changes along the time direction can be restored to different condition modes, revealing similar process characteristics in the same condition mode. Finally, the nonstationary industrial process can be monitored by checking the changes of both Gaussian features and reconstruction errors for different condition modes. A numerical case and a real thermal power plant process are adopted to validate the feasibility of the proposed method.

Reconstruction-based Monte Carlo method for accurate and efficient breakage simulation

Monte Carlo (MC) methods have been proven effective in population balance modeling; yet, in dealing with particle breakage problems, their performances in terms of statistical noise and computational efficiency still demand further improvement. In this work, we propose a time-driven MC method based on weighted particles that are reconstructed periodically by matching the shape of the number density function, which is defined on an adaptive subdomain mesh. By accounting for the weighted particles of all subdomains in a global sense rather than only a selected few in the traditional ways, this solver yields promising agreement with the analytical solutions of both binary- and multi-breakage cases. Compared with existing methods, the proposed method is able to resolve the particle size distribution as well as moments with much lower statistical noise, and is more suitable for long-time simulations. Moreover, its accuracy is less dependent on the weighted particle number or time increment.

A novel cost-management system for container terminals using a time-driven Activity-Based Costing approach

Cost-management systems play a significant role in the operations management of container terminal companies; however, in complex loading and discharging operations, traditional Activity-Based Costing (ABC) systems are costly and difficult to be built and maintained. A modified Time-Driven ABC method, considering all the procedures required to obtain the final product and estimating resource usage accordingly, has emerged in response to this challenge. This study provides terminal operators with an effective cost analysis tool using a time-driven ABC method to identify bottleneck in handling process and calculate the handling cost of cost objects. Also, this research uses a container vessel and ten shipping lines calling at Shanghai East Container Terminal as case studies, and presents a full cost analysis of the activities and their respective costs. This investigation provides insights into the causes of excessive logistics costs and highlights the benefits of applying Time-Driven ABC method in the container terminal industry, along with shortcomings and potential future research.

A time-driven activity-based costing approach for identifying variability in costs of childbirth between and within types of delivery

BackgroundRecently, time-driven activity-based costing (TDABC) is put forward as an alternative, more accurate costing method to calculate the cost of a medical treatment because it allows the assignment of costs directly to patients. The objective of this paper is the application of a time-driven activity-based method in order to estimate the cost of childbirth at a maternal department. Moreover, this study shows how this costing method can be used to outline how childbirth costs vary according to considered patient and disease characteristics. Through the use of process mapping, TDABC allows to exactly identify which activities and corresponding resources are impacted by these characteristics, leading to a more detailed understanding of childbirth cost.MethodsA prospective cohort study design is performed in a maternity department. Process maps were developed for two types of childbirth, vaginal delivery (VD) and caesarean section (CS). Costs were obtained from the financial department and capacity cost rates were calculated accordingly.ResultsOverall, the cost of childbirth equals €1894,12 and is mainly driven by personnel costs (89,0%). Monitoring after birth is the most expensive activity on the pathway, costing €1149,70. Significant cost variations between type of delivery were found, with VD costing €1808,66 compared to €2463,98 for a CS. Prolonged clinical visit (+ 33,3 min) and monitoring (+ 775,2 min) in CS were the main contributors to this cost difference. Within each delivery type, age, parity, number of gestation weeks and education attainment were found to drive cost variations. In particular, for VD an age > 25 years, nulliparous, gestation weeks > 40 weeks and higher education attainment were associated with higher costs. Similar results were found within CS for age, parity and number of gestation weeks.ConclusionsTDABC is a valuable approach to measure and understand the variability in costs of childbirth and its associated drivers over the full care cycle. Accordingly, these findings can inform health care providers, managers and regulators on process improvements and cost containment initiatives.

Energy Efficient Data Transmission Approaches for Wireless Industrial Automation

Background: The industrial wireless automation system enables the monitoring and control of processes. It may be difficult to recharge the battery of sensors installed in harmful environments. Hence optimization of the power is the major issue to be addressed while implementing the network. The proposed hybrid data transmission approaches optimize data accuracy and energy efficiency of a wireless sensor node deployed in any industry. Methods: In the time-driven method, the sensor nodes periodically sense the environment and transmit the data continuously over time. In the event-driven method, the sensor nodes transmit data only when there is a drastic change in the occurrence of a certain event. Results: Based on the nature of the process, applications are classified as, less critical, critical and most critical. The time-driven based hybrid transmission approach is suggested for the most critical applications because they need to be monitored continuously so as to attain data accuracy. In the case of critical applications, the data is not required to be sent continuously, but instead it can be sampled and transmitted once in two seconds. Though the above suggested methods intended to provide better outcomes in terms of power utilization, in the case of process control applications, most critical and critical applications need to be monitored continuously. Hence such applications could be done as a heterogeneous industrial automation network, which is the combination of wired and wireless connectivity. This can be implemented by replacing all the signal cables with wireless communication system, regular power supply must be provided for the radio module attached with final control elements and also to the transmitters involved in most critical applications. For the least critical applications, the data can be sampled and transmitted once in four seconds. Conclusion: Simulation has been performed for time-driven based and duty-cycling based hybrid transmission approaches. The results can guide process engineers in selecting the transmission approach for optimizing the power of IWAS based on the critical level of the process. In the case of a critical process, the time-driven based hybrid transmission approach may be used, and in the case of a less critical process, the duty-cycling based hybrid transmission approach could be selected. By selecting appropriate transmission approach the life time of IWAS could be improved. Keywords: Controllers, process control, transceivers, transmitters, temperature sensors, wireless sensor networks.

A novel time-event-driven algorithm for simulating spiking neural networks based on circular array

Abstract The computing of synaptic currents occupies a major part of computational cost when simulating a large scale spiking neural network. Based on the observation that the probability of a neuron receiving at least one spike from any synapses during a very tiny simulation time step is very small, we propose a time-driven algorithm corrected by an event-driven process (a hybrid time-event-driven algorithm) which consists of two procedures of computation. In the first procedure of the synaptic current computation, we suppose that the neuron in question receives no spike during the simulation time step, and thereby propose a time-driven method of joint decay process to reduce the computational complexity of the synaptic current. In the second procedure of the computation, we suppose that the neuron in question receives spikes during the simulation time step, and propose an event-driven local correction process to correct the total synaptic current that is calculated in the first procedure of the computation. We design a data structure of circular two-dimensional array for storing both conductance coefficients related with presynaptic neurons and correcting conductance related with postsynaptic neurons. Furthermore, in order to realize the local correction process quickly and effectively, we propose a new event-processing method to realize the local correction process based on the data structure of circular two-dimensional array. By theoretically comparing with that of traditional time-driven algorithm, it is found that the proposed time-event-driven algorithm reduces computational cost of synaptic current substantially. The simulation results further show the efficiency of the proposed algorithm.

A time-driven constant-number Monte Carlo method for the GPU-simulation of particle breakage based on weighted simulation particles

Monte Carlo (MC) simulations based on weighted particles offer novel and more precise techniques for the solution of the population balance equation for particulate systems. A recent constant-number approach named stochastic weighted algorithm (SWA) (Lee et al. (2015), J. Comput. Phys. (303) 1–18) has been developed, which renders the breakage of a simulation particle by an alteration of its properties, without the creation of novel simulation particles. The theoretic justification of the general formulation for all possible SWAs is limited to binary breakage kernels. We present a novel approach for the derivation of the properties of the MC particles representing fragments, which is applicable for all sorts of breakage kernels. This general scheme encompasses the already introduced SWA schemes, especially a number-based (SWA1, named NBS in this paper) and volume-based (SWA2, named VBS in this paper) breakage scheme, and it makes novel formulations possible: the low volume scheme (LVS), which renders preferably lower fragment sizes, and the combination of LVS with the NBS (LVS-NBS) or VBS (LVS-VBS). The implementation of these breakage schemes in the context of a GPU-based time-driven method is presented and the gained results are validated by comparison with results of the analytic solutions of a homogeneous binary breakage kernel. It is found, that the SWA methods (NBS and VBS) are only able to render large particle sizes, and that LVS, NBS-LVS and VBS-LVS are able to render the whole spectrum of particle sizes. Smaller noise levels are found for VBS and specific VBS-LVS schemes, making both more suitable for prolonged simulations than the other presented methods.

Event-driven optimization of complex HVAC systems

Real-time optimization (RTO) has been developed to improve the cost and/or energy efficiency of complex heating, ventilation and air-conditioning (HVAC) systems. In current literature, almost all of the developed real-time optimization methods belong to the type of time-driven optimization, in which the action of optimization is triggered by “time”. As optimization should be done when the system operating conditions experience a change that is large enough to cause current operational setting not optimal any more, optimization strategies should recognize ‘significant’ changes and perform optimization when necessary. Since the time-driven optimization is a periodic mechanism in nature while those ‘significant’ changes may not be periodic, the time-driven optimization cannot capture ‘significant’ changes and do the optimization promptly. Therefore, this paper proposes an event-driven optimization (EDO) for complex HVAC systems, the key idea of which is to use “event” rather than “time” to trigger the action of optimization. A systematic event-driven optimization method is illustrated, where its main tasks, including event definition and event identification, will be discussed. The proposed method will be compared with a conventional time-driven method using case studies, through which the main advantages of the proposed method will be identified.

Time step criterions for nonlinear dense packed granular materials in time-driven method simulations

Discrete-element method (DEM) is a powerful tool for simulations of granular materials. As one branch of DEM,time-driven method (TDM) is suitable for the materials in dense phase. Time step is an important parameter in TDM simulations, which decides the CPU time and the computational accuracy. When a granular system under gravity is deep and nonlinear stress–strain relationship is used to model the particle–particle interaction, some current time step models are not applicable anymore, because the nature frequency of a particle is affected by its boundary conditions. To amend this shortage, a new time step criterion is presented in this paper, which involves the effect from the depth of the system due to gravity. Between this new criterion and that based on Rayleigh time, the smaller one should be the upper limitation of time step in TDM simulations.

DEM SIMULATION OF PARTICULATE FLOW USING MULTI-SCALE TIME STEPS

Event-driven method (EDM) and the time-driven method (TDM) are two main branches of discrete element methods (DEM) for the simulations of granular materials. A new algorithm is introduced in this paper where different models are used for different local volume fractions. TDM time step is used for the cases with high volume fractions and EDM time step is used for the cases with low volume fractions. Whether the local volume fraction is low or high is determined by the number of neighbors in the Verlet table. While the time step updating the Verlet table is the same as the time step for those particles without neighbors in the Verlet table.

A time-driven transmission method for well logging networks

Long delays and poor real-time transmission are disadvantageous to well logging networks consisting of multiple subnets. In this paper, we proposed a time-driven transmission method (TDTM) to improve the efficiency and precision of logging networks. Using TDTM, we obtained well logging curves by fusing the depth acquired on the surface, and the data acquired in downhole instruments based on the synchronization timestamp. For the TDTM, the precision of time synchronization and the data fusion algorithm were two main factors influencing system errors. A piecewise fractal interpolation was proposed to fast fuse data in each interval of the logging curves. Intervals with similar characteristics in curves were extracted based on the change in the histogram of the interval. The TDTM is evaluated with a sonic curve, as an example. Experimental results showed that the fused data had little error, and the TDTM was effective and suitable for the logging networks.

Reducing Statistical Noise and Extending the Size Spectrum by Applying Weighted Simulation Particles in Monte Carlo Simulation of Coagulation

The direct simulation Monte Carlo (DSMC) method is widely utilized to simulate microscopic dynamic processes in dispersed systems that give rise to the population balance equation. In conventional DSMC approaches, simulation particles are equally weighted, even for broad size distributions where number concentrations in different size intervals are significantly different. The resulting statistical noise and limited size spectrum severely restrict the application of these DSMC methods. This study proposes a new Monte Carlo (MC) method, the differentially weighted time-driven method, which captures the coagulation dynamics in dispersed systems with low noise and is simultaneously able to track the size distribution over the full size range. Key elements of this method include constructing a new jump Markov process based on a new coagulation rule for two differentially weighted simulation particles, and restricting the number of simulation particles in each size interval within prescribed bounds. The method...

A distributed time-driven simulation method for enabling real-time manufacturing shop floor control

This paper proposes a distributed simulation approach for scheduling discrete-events in manufacturing shop floors. The proposed approach employs a time-driven method to simulate occurrence of discrete-events using distributed entities that replicate physical entities in the manufacturing shop floor. In specific, the proposed approach iteratively controls the timing of discrete-events occurrence using a control theoretic model. In this approach, changing the speed of the simulation clock, termed time-scaling factor, can accelerate or decelerate the simulation speed resulting in simpler synchronizations of discrete-events and faster simulation than standard distributed discrete-event simulations according to the capability of the communication networks. Computational experiments are conducted to test the performance of the proposed system with different values of the time-scaling factor, and the relationship between the system performance and the time-scaling factor is investigated through analysis of the system model. Results obtained from the computational experiments show significant successes in speeding up discrete-event simulations in such a way that the proposed approach can be used for the control of manufacturing shop floors, providing real-time decision supports.

Multi-monte-carlo method for general dynamic equation considering particle coagulation

Monte-Carlo (MC) method is widely adopted to take into account general dynamic equation (GDE) for particle coagulation, however popular MC method has high computation cost and statistical fatigue. A new Multi-Monte-Carlo (MMC) method, which has characteristics of time-driven MC method, constant number method and constant volume method, was promoted to solve GDE for coagulation. Firstly MMC method was described in details, including the introduction of weighted fictitious particle, the scheme of MMC method, the setting of time step, the judgment of the occurrence of coagulation event, the choice of coagulation partner and the consequential treatment of coagulation event. Secondly MMC method was validated by five special coagulation cases in which analytical solutions exist. The good agreement between the simulation results of MMC method and analytical solutions shows MMC method conserves high computation precision and has low computation cost. Lastly the different influence of different kinds of coagulation kernel on the process of coagulation was analyzed: constant coagulation kernel and Brownian coagulation kernel in continuum regime affect small particles much more than linear and quadratic coagulation kernel, whereas affect big particles much less than linear and quadratic coagulation kernel.

Modified Lagrangian method for modeling water quality in distribution systems

Previous work has shown that Lagrangian methods are more efficient for modeling the transport of chemicals in a water distribution system. Two such methods, the Lagrangian Time-Driven Method (TDM) and Event-Driven Method (EDM) are compared for varying concentration tolerance and computational water quality time step. A new hybrid method (EDMNET) is developed which improves the accuracy of the Lagrangian methods. All the above methods are incorporated in an existing hydraulic simulation model. The integrated model is run for different network problems under varying conditions. The TDM-generated solutions are affected by both concentration tolerance and water quality time step, whereas EDM solutions are dependent on concentration tolerance. The EDMNET solutions are less sensitive to variations in these parameters. The threshold solutions are determined for all the methods and compared. The hybrid method simulates the nodal concentrations accurately with least maximum segmentation of network and reasonable computational effort as compared to the other Lagrangian methods.

Asynchronous game of life

This paper proposes the 8-state asynchronous equivalence of the well-known game of life (GL). Our cellular automaton, called asynchronous game of life, simulates exactly the behavior of the GL, such as universal computation and self-organization, no matter whether the update of cells is simultaneous or independent according to some updating scheme, like a step-driven or time-driven method [see BioSystems 51 (1999) 123]. We employ the updating scheme of Blok and Bergersen [Phys. Rev. E 59 (1999) 3876] such that at every time step each cell has a certain probability to be updated, and investigate the statistical properties of our model through power spectral analyses.

Asynchronous game of life

This paper proposes the 8-state asynchronous equivalence of the well-known game of life (GL). Our cellular automaton, called asynchronous game of life, simulates exactly the behavior of the GL, such as universal computation and self-organization, no matter whether the update of cells is simultaneous or independent according to some updating scheme, like a step-driven or time-driven method [see BioSystems 51 (1999) 123]. We employ the updating scheme of Blok and Bergersen [Phys. Rev. E 59 (1999) 3876] such that at every time step each cell has a certain probability to be updated, and investigate the statistical properties of our model through power spectral analyses.

Numerical simulation of the motion of granular material using object-oriented techniques

The objective of this contribution is to present a numerical simulation method to model the motion of a packed bed on a moving grate or in a rotary kiln using object-oriented techniques. The packed bed can be described as granular material consisting of a large number of particles. The method chosen is the Lagrangian time-driven method and it uses the position, the orientation, the velocity and the angular velocity of particles as independent variables. These are obtained by time integration of the three-dimensional dynamics equations which were derived from the classical Newtonian mechanics approach based on the second law of Newton for the translation and rotation of each particle in the granular material. This includes keeping track of all forces and moments acting on each particle at every time-step. Particles are treated as contacting visco-elastic bodies which can overlap each other. Contact forces depend on the overlap geometry, material properties and dynamics of particles and include normal and tangential components of repulsion force with visco-elastic models for energy dissipation through internal and surface friction. The resulting equations of particle motion are solved by the Gear predictor–corrector scheme of fifth-order accuracy. The simulation method is based on object-oriented methodologies and programmed in the programming language C++. This approach supports objects which can be used for three-dimensional particles of various shapes and sizes and for walls as boundaries. The programming modules are implemented in the TOSCA (tools of object-oriented software for continuum mechanic applications) software package which allows for a high degree of flexibility and for shortening the duration of the software development process. As methods for particle motion may deal with particles of different sizes and materials, the approach allows to describe transport processes in technical applications.

Numerical Methods for Modeling Water Quality in Distribution Systems: A Comparison

A comparison is made between the formulation and computational performance of four numerical methods for modeling the transient behavior of water quality in drinking-water-distribution systems. Two are Eulerian-based (the finite-difference and discrete-volume methods) and two are Lagrangian-based (the time-driven and event-driven methods). The Eulerian approaches move water between fixed grid points or volume segments in pipes as time is advanced in uniform increments. The Lagrangian methods update conditions in variable-sized segments of water at either uniform time increments or only at times when a new segment reaches a downstream pipe junction. Each method is encoded into an existing distribution-system simulation model and run on several pipe networks of varying size under equal accuracy tolerances. Results show that the accuracies of the methods are comparable. The Lagrangian methods are more efficient for simulating chemical transport. For modeling water age, the time-driven Lagrangian method is the most time-efficient while the Eulerian methods are more memory-efficient.