Simulation Models Of Systems Research Articles

Simulation and Experimental Study of a Novel Negative-Pressure Flapper-Nozzle Mechanism.

As the manufacturing industry evolves, the significance of control valve positioners in chemical production escalates. The flapper-nozzle system, the heart of control valve positioners, directly influences the linearity of system control. Presently, studies on the flapper-nozzle system primarily focus on dynamic system modeling and computational fluid dynamics simulations. However, traditional flapper-nozzle mechanisms often fail to achieve linear control objectives. This paper proposes a novel negative-pressure nozzle structure to tackle this issue, combining computational fluid dynamics and experimental methods, and considering gas compressibility during high-speed flow. Both simulation and experimental results suggest that the new structure improves the supply air pressure and broadens the linear pressure output range of the system, showing significant potential for practical applications.

Логико-временная формализация имитационных моделей систем железнодорожной автоматики и телемеханики

The purpose of the article is to present the results of developing a method for formalizing simulation models of railway automation and remote control systems as queuing systems with a rigidly regulated sequence of using service devices while processing streams of requests of various types. For the software implementation of simulation models with such properties, the GPSS World tool is traditionally used. The logical time formalization is proposed, and the concept of the proposed formalization of simulation models is formulated. Its main provisions include: the type of request is determined solely by the composition and sequence of occupation and release of servicing devices; the use of transaction parameters to organize their multiphase processing for each type of request; checking the value of Boolean functions for each procedure of processing requests for each type; the regulated time in the procedure of processing requests is considered as one of the many logical conditions of Boolean functions; logical and parametric description of the modeled system should be performed at the level of initial data without changing the text of the modeling program. Objects in GPSS World sufficient for implementing this concept have been identified. Using the selected objects, an alphabet has been compiled, and logical diagrams of modeling algorithms have been developed in the obtained alphabet. This serves as a demonstration of the application of the logical time concept for the formalization of simulation models of complex queuing systems. A GPSS program has been developed to confirm the possibility and feasibility of using the proposed method for formalizing simulation models of railway automation systems with a rigid structure.

Simulation model of a vehicle in the Simulink environment for determining fuel consumption in driving cycles

BACKGROUND: The paper shows that the problem of determining objective indicators of fuel consumption in operation based on calculation methods remains relevant. One of these methods is simulation modeling. Current state of computer technology significantly simplifies building of system simulation models, for example, using the Simulink software by Mathworks. In the process of model development, it is necessary to keep the balance of generalization and detailing, since excessive detailing leads to an unreasonably deep consideration of individual elements of the system and to a significant increase in simulation time.
 AIMS: Search for a reasonable structure of model of the driver car environment system, which makes it possible to simulate the car in driving cycles or other driving scenarios.
 METHODS: The MATLAB/Simulink simulation environment was used in the study. Based on the analysis of the car design and the capabilities of individual Simulink tools, a block diagram to study fuel consumption using the example of a passenger vehicle was synthesized. The obtained model was debugged for the stable motion simulation of the vehicle in various driving cycles using graphical analysis.
 RESULTS: The obtained results show that the developed system simulation model is well behaved. Control systems ensure the vehicle motion according to the mixed driving cycle modes in compliance with the required tolerances. It is possible to register fuel consumption and other simulation results of interest.
 CONCLUSIONS: The model showed the relative simplicity of setting up and the ability of quick update for other vehicles. A minimal amount of vehicle data is required to bring the model to an operable condition. Particular unknown parameters are supposed to be refined using identification methods.

Parameter Extraction of Photovoltaic Models by Honey Badger algorithm and Wild Horse Optimizer

Analyzing the processes ranging from the determination of the installation configuration of the photovoltaic (PV) systems to the operation at the maximum power, from the technical and economic feasibility study to the positive contribution to the region where the production is planned are just possible with the accurate and efficient simulation models of the PV systems. PV parameter extraction, which is a topic frequently discussed recently, is crucial for the detailed modeling of PV cells and modules and simulating the behavior of these systems. For this reason, the current study examined PV parameter extraction and solved this optimization problem with the honey badger algorithm (HBA) and wild horse optimizer (WHO). PV cells and modules were modeled with the single diode model (SDM) and double diode model (DDM) and tested with actual measurement data. The root-mean-square error (RMSE) was chosen as the objective function, and the results were compared with the evaluation metrics for computational accuracy and time. Based on four PV model results, RMSE values were calculated between 9.9318E-04 to 1.7011E-03 for HBA and between 9.8602E-04 and 1.7298E-03 for WHO. As a result, even though both algorithms produce successful, stable, and fast results in PV parameter extraction, the WHO yielded better results.

Automated model generation framework for material flow simulations of production systems

Owing to increasingly important drivers for change, such as automation, digitisation, and dynamic demand patterns, simulation models of production systems become outdated rapidly. At the same time, building simulation models often requires much time, cost, and expertise, especially when dealing with complex job shop production systems. To address these challenges, an automated simulation model generation (ASMG) framework for material flow simulation of production systems is presented. This framework contains multiple approaches to infer routeing, control and temporal aspects from event-based data. To achieve this, methods from process mining (PM) and machine learning (ML) are applied. Additionally, the suitability of Coloured Petri Nets (CPNs) to serve as conceptual and operational simulation models is examined. The inferred simulation models have high validity when compared to the real system concerning the KPIs machine utilisation, throughput, and work in process. It is shown, that most model elements can be inferred very well, in particular process routes, processing times, and resource selection rules. This proof of concept serves as a foundation for research on detection approaches inferring further model elements such as setup times accurately.

Simulation modeling of systems with a complex stochastic data processing process using colored Petri nets

The article considers the process of building simulation models of systems with stochastic data processing based on colored Petri nets. A formal description of models based on colored Petri nets is presented. For simulation modeling of data processing tasks, the use of temporal Petri nets is substantiated, which allows to define and describe in detail the time intervals of the simulated process. The algorithm for building simulation models based on colored Petri nets is presented. The peculiarities of the use of temporal Petri nets in the construction of simulation models with complex stochastic data processing processes are determined. Special functions are used to assign random values. A list of functions with their detailed description and ranges of permissible values for input parameters is provided. As an example, the construction of a simulation model of the work process of the application processing center of a commercial firm is considered. The model was built in the CPN Tools environment. System parameters, variables, functions and model parameters are defined and investigated. The method of accumulating information in positions was used to accumulate statistics on the results of the models. The analysis of the results of simulation modeling of the work process of the application processing center of a commercial firm is presented.

From natural language to simulations: applying AI to automate simulation modelling of logistics systems

ABSTRACT Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models.

A CFD results-based reduced-order model for latent heat thermal energy storage systems with macro-encapsulated PCM

Macro-encapsulation of phase change material (PCM) is a promising approach to overcome a serious drawback of many latent heat thermal energy storage systems (LHTESSs): their low thermal power. Simulations are often used to support the design of these storage systems, but the simulation of the charging process of such an LHTESS with detailed CFD models is too computationally expensive. To obtain information about the behavior of a complete LHTESS, highly simplified system simulation models are usually applied. A new approach to create a reduced-order model is herein presented that aims to increase the accuracy of these system simulation models. The first step consists of performing a set of detailed CFD simulations of one capsule with different boundary conditions. The results are written into look-up tables that contain the charging power of one capsule as a function of the enthalpy stored and the boundary conditions. These look-up tables are then implemented into the reduced-order model. The temporal mean deviation of the energy content in the storage unit between experiments and the reduced-order model is only 5 % and the simulation time of the fastest reduced-order model was 5 s, while the CFD simulations took up to about two weeks on a workstation. Finally, for the conditions tested, the heat transfer fluid (HTF) does not have to be included in the CFD simulation, but can be replaced by a properly defined convective boundary condition. The capsule wall, however, needs to be included in the CFD model (especially for capsule wall materials with a distinctively higher thermal conductivity than the PCM) to account for the heat flow towards the bottom of the capsule supporting close contact melting.

Development of a water flow and nutrients concentration simulation model for cascade hydroponic systems

Development of a water flow and nutrients concentration simulation model for cascade hydroponic systems

Thermal analysis of PCM magnesium chloride hexahydrate using various machine learning and deep learning models

The heat storage through Phase change material (PCM) is important for sustainable development. Previously a set of experiments were carried out on 14.5 kg of PCM, Magnesium chloride hexa hydrate in a shell and tube heat exchanger. The results are used to build machine learning and deep learning models such as Support vector machine, Random forest, Extreme gradient boost, Catboost and Artificial neural network to replicate the experiment virtually. The input variables are heat transfer fluid’s inlet and outlet temperature and time elapsed while PCM’s temperature is predicted in both charging and discharging cycles. The hyperparameter tuning is done using hyperband. Catboost model outperformed all other models with highest variance in both the cycles (0.9945 and 0.9299) and highest score: 0.993 and 0.9466. It has got the lowest MAPE (0.2% and 0.1 %). Hence this model can be recommended over these other models for doing system simulation of problems involving both charging and discharging cycles of the PCM.

Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

Understanding recent deep‐learning techniques for identifying collective variables of molecular dynamics

AbstractHigh‐dimensional metastable molecular dynamics (MD) can often be characterised by a few features of the system, that is, collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning‐based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning‐based approaches for finding CVs, either by computing leading eigenfunctions of transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples.

Bifurcation analysis of a North Atlantic Ocean box model with two deep-water formation sites

The tipping of the Atlantic Meridional Overturning Circulation (AMOC) to a ‘shutdown’ state due to changes in the freshwater forcing of the ocean is of particular interest and concern due to its widespread ramifications, including a dramatic climatic shift for much of Europe. A clear understanding of how such a shutdown would unfold requires analyses of models from across the complexity spectrum. For example, detailed simulations of sophisticated Earth System Models have identified scenarios in which deep-water formation first ceases in the Labrador Sea before ceasing in the Nordic Seas, en route to a complete circulation shutdown. Here, we study a simple ocean box model with two polar boxes designed to represent deep-water formation at these two distinct sites. A bifurcation analysis reveals how, depending on the differences of freshwater and thermal forcing between the two polar boxes, transitions to ‘partial shutdown’ states are possible. Our results shed light on the nature of the tipping of AMOC and clarify dynamical features observed in more sophisticated models.

Dynamical coarse-grained models of molecular liquids and their ideal and non-ideal mixtures.

Coarse-grained (CG) simulation models of condensed-phase systems can be derived with well-established methods that perform coarse-graining in space and provide an effective Hamiltonian with which some of the structural and thermodynamic properties of the underlying fine-grained (FG) reference system can be represented. Coarse-graining in time potentially provides CG models that furthermore represent dynamic properties. However, systematic efforts in this direction have so far been limited, especially for moderately coarse-grained, chemistry-specific systems with complicated conservative interactions. With the aim of representing structural, thermodynamic, and dynamic properties in CG simulations of multi-component molecular systems, we investigated a recently introduced method in which the force on a CG particle originates from conservative interactions with surrounding particles and non-Markovian dissipative interactions, the latter introduced by means of a colored-noise thermostat. We examined two different methods to derive isotropic memory kernels required for integrating the corresponding generalized Langevin equation (GLE) of motion, based on the orthogonal dynamics of the FG forces and on an iterative optimization scheme. As a proof of concept, we coarse-grain single-component molecular liquids (cyclohexane, tetrachloromethane) and ideal and non-ideal binary mixtures of cyclohexane/tetrachloromethane and ethanol/tetrachloromethane, respectively. We find that for all systems, the FG single particle velocity auto-correlation functions and, consequently, both the short time and long time diffusion coefficients can be quantitatively reproduced with the CG-GLE models. We furthermore demonstrate that the present GLE-approach leads to an improved description of the rate with which the spatial correlations decay, which is artificially accelerated in the absence of dissipation.

Optimization of Heat Dissipation and Control Scheme for Wind Turbine Gearbox Based on FloMaster-Simulink co-Simulation

Due to favorable wind conditions and steadily rising temperatures, the demand for heat dissipation for the gearbox of wind turbines rises every spring and summer, and the energy consumption of the heat dissipation system rises as well. In cases where the gearbox oil temperature is excessively high due to poor heat dissipation, the wind turbine unit may only operate at a limited load or shut down. This article suggests a restoration strategy that uses seawater to cool the gearbox lubrication system, based on the operation of the gearbox cooling system of wind turbines. The original wind turbine gearbox cooling system and the enhanced seawater cooling system simulation models are built at the same time, and dynamic working conditions simulation research is done by utilizing the FloMaster simulation platform. To achieve automatic management of the gearbox temperature, we alter the fan or pump’s speed by the oil temperature at the gearbox’s outlet. We design a control scheme based on the working principle and use the FloMaster-Simulink co-simulation to calculate the operating characteristics of the system under dynamic working conditions. The results show that by simultaneously controlling the speed of the pump and the speed of the fan, the cooling system has the best control effect and the lowest energy consumption, providing a reference for the development of the gearbox cooling system of wind turbines.

Numerical simulation of cascaded absorption-two-stage 4-bed adsorption cooling cycles for efficient low-grade heat utilization

Thermal-driven sorption cooling technology was developed to address some pressing challenges including high energy consumption, rising fuel prices, and environmental issues associated with traditional electrical-driven cycles. Nevertheless, this technology needs to be improved in terms of thermal performance and energy efficiency. This study investigates the performance of new cascade single-effect absorption (ABC) and two-stage four-bed adsorption (4ADC) cooling systems powered by low-grade heat source temperatures. Four different configurations are proposed to make proper use of low-temperature heat sources between 45 and 70 °C and improve heat recovery. The use of this temperature range to drive conventional ABC is still considerably lacking in the literature. The ABC does not typically operate below 60 °C. Additionally, the effects of operating conditions on performance are also investigated. The simulation models for both ABC and 4ADC systems are experimentally validated separately and showed excellent agreement. Results showed that the maximum cooling capacity obtained from the proposed system ranges between (5.4–17.1) kW with COP of 0.46–0.47. The COP is found to be relatively insensitive to regeneration temperatures. Increasing the solution pump flow rate increases the system performance by about 25% over the ranges of the regeneration temperature. The proposed configurations provide high energy performance compared with previous related studies. The obtained results proved that the proposed cascade configurations can recover the available low-grade heat source and operate effectively with temperatures between 45 and 70 °C.

Load frequency control of power system based on improved AFSA-PSO event-triggering scheme

Aiming at the impact of redundant information transmission on network resource utilization in current power systems, an improved event-triggered scheme based on particle swarm optimization and artificial fish swarm algorithm for power system load frequency control (LFC) with renewable energy is proposed. First of all, to keep the stability and security of power systems with renewable energy, the load frequency control scheme is investigated in this paper. Then, to relieve the communication burden and increase network utilization, an improved event-triggered scheme based on the particle swarm algorithm and artificial fish swarm algorithm is explored for the power system load frequency control. Then, by utilizing improved Lyapunov functional and the linear matrix inequality method, sufficient condition for the H∞ stability of the load frequency control system is established. Finally, a two-area load frequency control system and IEEE-39 node simulation models are constructed to verify the effectiveness and applicability of the proposed method.

Control for Power Quality Improvement of Solar Photovoltaic-Distributed Static Synchronous Compensator Interfaced with Weak Grid Using Multi-Variable Filter Dual Second-Order Generalized Integrator Phase-Locked Loop

This study proposes a control method for transferring power generated by a solar photovoltaic system. The control algorithm, called MVF-DSOGI-PLL or Multi-Variable Filter Dual Second-Order Generalized Integrator Phase-Locked Loop), accurately estimates symmetrical components, phase angle, and positive sequence voltages of the Common Point of Interface (CPI) in distorted grid conditions. The approach creates a compensatory current to reduce PQ difficulties by extracting the size of the basic current load undergoing operation employing the SOGI method. These systems can operate in either modes of PV-DSTATCOM or DSTATCOM, depending on solar insolation’s availability. The simulation models of the grid-interfaced SPV systems are developed in Matlab/Simulink to evaluate the performances of the suggested control method. The results of simulation demonstrate that the suggested control systems efficiently transfers the power generated by PV arrays to a grid while performing tasks like load balancing, power factor correction (PFC), and harmonics reduction. Furthermore, the MVF-DSOGI-PLL-based control algorithm successfully mitigates PQ issues in distribution networks with weak grids, ensuring that PQ levels remain between a limited set by the IEEE-519 standard. A laboratory prototype of the proposed system is implemented to assess its steady states and dynamic efficient under fluctuating solar insolation, and load imbalance distortions.

Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data

Abstract Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time consuming and by the time the forecast is delivered, new observations are already available, making it obsolete from the release date. Moreover, producing such predictions is computationally demanding, and their production frequency is restricted. We tested the potential of a computationally cheap weighting average technique that can continuously adjust such probabilistic forecasts—in between production intervals—using newly available data. The method estimates local positive weights computed with a Bayesian framework, favoring members closer to observations. We tested the approach with the Norwegian Climate Prediction Model (NorCPM), which assimilates monthly sea surface temperature (SST) and hydrographic profiles with the ensemble Kalman filter. By the time the NorCPM forecast is delivered operationally, a week of unused SST data are available. We demonstrate the benefit of our weighting method on retrospective hindcasts. The weighting method greatly enhanced the NorCPM hindcast skill compared to the standard equal weight approach up to a 2-month lead time (global correlation of 0.71 vs 0.55 at a 1-month lead time and 0.51 vs 0.45 at a 2-month lead time). The skill at a 1-month lead time is comparable to the accuracy of the EnKF analysis. We also show that weights determined using SST data can be used to improve the skill of other quantities, such as the sea ice extent. Our approach can provide a continuous forecast between the intermittent forecast production cycle and be extended to other independent datasets.

Complex Adaptive Systems Simulation Modeling to Address Cardiovascular Disparities: Complex Science for a Complex Problem.

Complex Adaptive Systems Simulation Modeling to Address Cardiovascular Disparities: Complex Science for a Complex Problem.