System Simulation Approach Research Articles

Use of a Modified Threshold Function in Fuzzy Cognitive Maps for Improved Failure Mode Identification

Fuzzy cognitive maps (FCMs) provide a rapid and efficient approach for system modeling and simulation. The literature demonstrates numerous successful applications of FCMs in identifying failure modes. The standard process of failure mode identification using FCMs involves monitoring crucial concept/node values for excesses. Threshold functions are used to limit the value of nodes within a pre-specified range, which is usually [0, 1] or [-1, + 1]. However, traditional FCMs using the tanh threshold function possess two crucial drawbacks for this particular.Purpose(i) a tendency to reduce the values of state vector components, and (ii) the potential inability to reach a limit state with clearly identifiable failure states. The reason for this is the inherent mathematical nature of the tanh function in being asymptotic to the horizontal line demarcating the edge of the specified range. To overcome these limitations, this paper introduces a novel modified tanh threshold function that effectively addresses both issues.

A novel approach for hybrid system simulation and design based on an enhanced load flow calculation methodology

This article presents a modified method of performing power flow calculations as an alternative to pure energy-based simulations of off-grid hybrid systems. The enhancement consists in transforming the scenario-based power flow method into a discrete time dependent algorithm with the inclusion of bus and controller dynamics. The output of the simulation can be then used to run economic calculations as well as for the evaluation of the technical feasibility of a given network configuration.

How to promote GBTs innovation through multi-agent collaboration: An evolutionary game system simulation approach

Facing the serious pollution and carbon emission problem, China actively develops and applies GBTs (green building technologies) to realize the dual-carbon target, while the innovation of GBTs is deeply influenced by multiple subjects and factors. This study constructs a tripartite evolutionary game model of GACEs-ARIs, analyzes the inner mechanism of the collaborative innovation process of GBTs through simulation. It is found that: (1) GA is the most crucial subject in the process of collaborative innovation, which plays the role of coordinating the decision-making of the rest of subjects; (2) The trust degree and the benefit distribution coefficient are the influencing factors that play an important role in determining the start and continuation of collaborative innovation; (3) To actively promote the collaborative innovation of GBTs, GA should play a good role as an intermediary to ensure that the trust between the two sides is at a high level, and negotiate a reasonable distribution ratio, actively mobilizing the cooperation of CEs and ARIs by incentives and penalties.

Improving Warehouse Layout Effectiveness and Process Picking Efficiency with the Discrete Event System Simulation Approach

Field conditions show process picking inefficiencies caused by the layout and arrangement of products in the warehouse that are not optimal. The objective of this research is to estimate the actual system performance and propose a new design so that the system becomes more effective and efficient. The research methodology combines Discrete-Event System (DES) Simulation to estimate the performance of the system and Double ABC Analysis to determine the re-arrangement of the item's position based on the pickup frequency and outer volume of the item. The statistical results identified the moving time has a significant difference between the actual and proposed layout. This study concludes that the re-arrangement of items can reduce the picking time of operators in the operational warehouse process.

Intended and unintended consequences of strategies to meet performance benchmarks for rheumatologist referrals in a centralized intake system.

Timely assessment of a chronic condition is critical to prevent long-term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge. We applied a system simulation approach to model a high-volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change. The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178-269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178-43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit. An MWTG policy can result in intended and unintended consequences-ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance.

Simulating the dynamic behavior of a Triple Screw Pump without CFD

Pressure pulsations in screw pumps lead to noise, vibration and system strain. In order to increase the amount of dissolved and undissolved gas in the fluid while keeping noise, vibration and stress on the system at an acceptable level, the pump behavior must be understood in detail. Physical tests and CFD are common methods in modern pump design. However, both methods are not able to assess numerous design changes and machine integration challenges quickly. The system simulation approach tackles both challenges efficiently and allows to evaluate machine dynamics. Siemens Simcenter Amesim has been used to model a Leistritz Flexcore 45/90 triple screw pump. The model, containing components of a multi-physics library, calculates the movement of the chambers along the spindle package length by having virtual chambers alternately change their volume depending on the spindle rotation. Fluid properties, pressure dependent aeration and inertia effects are considered. The generation and displacement of the chamber volumes cause the suction and discharge of the fluid. The overlay of the single chambers results in the total volume flow of the pump. Internal leakage between the chambers is also considered, turning the model into a realistic digital twin of the screw pump. For the overall model and each individual chamber, the model gives volume flow rates, discharge pressure, pressure pulsation and internal pressure evolution. The air content has a significant influence on the pressure pulsations. To verify the model, measurements of operating points on a real pump have been carried out. Simulated volume flows and pressure pulsation show very good agreement with measurements. This new method for modeling screw pumps dramatically increases the speed of simulating pump behavior and opens the possibility of integrating the models into higher-level systems. The reproduction of dynamic effects and system responses for control tasks is also possible. The existing model can be extended towards further physics like friction modeling or additional system components like an electrical power unit.

Thermal energy storage for increased waste heat recovery at a silicon production plant in Norway

• Case study of a heat recovery system in a Norwegian silicon production plant. • Novel field of application for Ruths steam accumulator. • Combined approach of techno-economic optimization and dynamic system simulation. • Thermal storage increases annual electricity production by up to 500 MW h. • Public funds required to achieve desirable payback periods. The production of silicon is an energy-intensive process, which requires high temperatures. Sudden release of high-temperature gas to the exhaust system is an inevitable part of silicon furnace operation and causes strong fluctuations in the waste heat recovery system. This makes waste heat recovery challenging and leads to frequent throttling of the produced steam reducing the efficiency of the system. To avoid this throttling and thus increase system efficiency, retrofitting a thermal energy storage to the existing waste heat recovery system is analyzed for a silicon production plant in Norway. A steam accumulator installed in parallel to existing bypasses, which does not interfere with the existing waste heat recovery system, is found to be ideally suited for the case. The analysis is carried out using real plant data, which is used to calibrate a dynamic simulation model modelled in the Modelica language. Design parameters of the steam accumulator are identified based on economic optimizations and their performance is verified with the dynamic simulation model. The simulation results shows that the simplifications in the economic optimizations are acceptable and that storage sizes of less than 10 m 3 can lead to annual profits of around 23 k€. Investment costs excluding transport and on-site installation are around 120 k€, yielding payback times from three to six years, depending on public funds. Including transport and on-site installation increases the payback times to about seven to ten years. The integration of a storage of this size was deemed realistic by the plant operators. The results will therefore be used for the creation of a business case with a more detailed cost analysis.

Design and off-design system simulation of concentrated solar super-critical CO2 cycle integrating a radial turbine meanline model

The proper design of a Concentrated solar power (CSP) plant and the associated thermal energy storage requires a careful analysis of the yearly expected irradiance and environmental conditions. System simulations based on accurate components models allow the prediction of the performance of the plant and the analysis of the components interactions. The simulations of various scenarios provide information that will help tuning the design of the components to improve the performance of the whole system at design and off-design conditions. In this paper, the focus is set on the radial turbine expander which is one of the most important component. Classical system simulation approaches use map based performance model for the turbine. This require to generate the performance map either with meanline models, CFD simulations or experiments. This is not very convenient when iterating on the system design and if the actual turbine does not exist yet. In this work, a meanline model for radial inflow turbine operating with real gas has been implemented in MODELICA language for the first time to the authors’ best knowledge. This turbine model has been coupled with the available CSP sCO2 Brayton cycle model in MODELON ThermalPower library. A parametric study on a virtual test bench show the turbine performances for various rotational speed and inlet guide vane angle. The study focus then on the performance prediction of on and off design of the system with a 8 MW turbine obtained with a simple preliminary design calculation. The investigations showed that for the varying load conditions in the solar plant system, an improved operating point has been identified by adjusting the rotational speed and the inlet guide vane angle. Future work will focus on the use such system model with multi objective meta heuristic optimization.

Simulasi Sistem Dinamis Bahan Pangan Tambahan Guna Pemenuhan Kebutuhan Konsumen dan Peningkatan Keuntungan Petani

Shallots (Allium cepa var ascalonicum L.) is one of the most popular food additives in Indonesia. These ingredients are used to add flavor to Indonesian cuisine. The increase in community needs caused by the increase in population has an impact on increasing the demand for shallots every year. This increase also occurred in Bantul Regency. The demand for shallots in Bantul is not accompanied by a balance in the amount of supply obtained from shallot-producing areas. This is because most farmers in Bantul Regency only plant shallots in the dry season. Therefore, this study seeks to propose how to balance the supply of shallots by farmers with market demand to increase farmers' profits. This study uses a dynamic system simulation approach with the help of Powersim studio 9 software. The first step is to build a Causal Loop Diagram (CLD) to see the causal or reciprocal relationship between variances and stock-flow diagrams to compile a mathematical formulation of the system model. dynamic. The simulation results are then validated to determine the credibility of the model. After the model is declared valid, 3 (three) scenarios of implementing the strategy in the shallot supply chain model will be generated. The best model scenario simulation results are scenario 1 (one), namely by replacing the use of tuber seeds in all activities.

Investigation of a nonlinear piezoelectric energy harvester with advanced electric circuits with the finite element method

In this contribution, a system simulation method based on the finite element method (FEM) is applied to simulate a strongly coupled bimorph piezoelectric vibration-based energy harvester (PVEH) with various nonlinear, non-ideal and active circuits: The standard circuit, the synchronized switch harvesting on inductor circuit and the synchronized electric charge extraction circuit are considered. Furthermore, nonlinear elastic behavior of the piezoelectric material is taken into account and harmonic base excitations of different magnitudes at a fixed frequency are applied. The holistic FEM-based system simulation approach solves the complete set of piezoelectric equations together with the equation of the electric circuit such that all electromechanical coupling phenomena are taken into account. This fully coupled numerical analysis enables the detailed evaluation of the influences of the electric circuits on the vibrational behavior and the harvested energy of the PVEH with respect to the magnitude of base excitation. Results from literature on the efficiency of electric circuits are confirmed and interactions between mechanical and electrical nonlinearities of PVEHs are revealed.Article highlightsSystem simulations of a mechanically and electrically nonlinear piezoelectric energy harvester are performed using only one software tool.The infuence of electric circuits on the vibration behavior and the effciency of an energy harvester are investigated in detail.Interactions between mechanical and electrical nonlinearities of an energy harvester are revealed.

Multi-method Simulation Approach for Software Project Management Training in-process Decision Support System: a Conceptual-operative Framework

Software project management (SPM) is a discipline that comprises of different topics, practice and theories. There are two dimensions in the knowledge of SPM, theoretical or concepts of SPM and; practical or experience of SPM. These two dimensions although grow separately but come across at one point that is experiential knowledge of SPM. To present these dimensions through a proper training, a practitioner needs to have a proper view on SPM process. In this paper we present a new framework for practicing SPM through a distinct simulation technique to bring on an in-process decision support system, supporting the proper training approach. The framework developed based on three different methods of simulation technique, discrete event simulation (DES), system dynamics (SD) and partially observable Markov decision process (POMDP).1

Behavioral modelling of PROFET™ devices for system-level simulation of mission profiles in automotive environment applications

In this work we present a system-simulation approach to model the behavior of PROFET™ devices for automotive applications. Attention has been paid to the simulation speed, given the necessity to simulate long mission profiles in a short amount of time. PLECS has been chosen as a simulation tool, thanks to its system-level approach to the solution of electrical circuits and systems; the work shows how to implement the basic behavioral features described in the PROFET™ datasheet, and how some typical electric features, which would normally be analyzed with a SPICE-like simulator, have been included.

Simulation Modeling of a Photovoltaic-Green Roof System for Energy Cost Reduction of a Building: Texas Case Study

This study aims at introducing a modeling and simulation approach for a green roof system which can reduce energy cost of a building exposed to high temperatures throughout the summer season. First, to understand thermal impact of a green roof system on a building surface, a field-based study has been conducted in Commerce, Texas, U.S., where the average maximum temperature in summer is 104 °F (40 °C). Two types of analyses were conducted: (1) comparison of temperature between different plant type via Analysis of variance (ANOVA) and (2) polynomial regression analysis to develop thermal impact estimation model based on air temperature and presence of a green roof. In addition, an agent-based simulation (ABS) model was developed via AnyLogic® University 8.6.0 simulation software, Chicago, IL, U.S., in order to accurately estimate energy cost and benefits of a building with a photovoltaic-green roof system. The proposed approach was applied to estimate energy reduction cost of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas (33.2410° N, 95.9104° W). As a result, the proposed approach was able to save $740,325.44 in energy cost of a heating, ventilation, and air conditioning (HAVC) system in the subject building. The proposed approach will contribute to the implementation of a sustainable building and urban agriculture.

Computational challenges to test and revitalize Claude Lévi-Strauss transformational methodology

The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in his structural analysis of myth. After an analysis of Lévi-Strauss’s transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment. The validation of the method involves a discrete-event system specification simulation based on the extension of discrete-event system specification models dedicated to provide a dynamic Google Earth visualization of the selected myth. Future work around the discrete-event system specification formalism in anthropology is described as well as future applications regarding the impact of computational models (discrete-event system specification formalism, Bayesian inferences, and object-oriented features) to new contemporary anthropological domains.

Measuring the efficiency of VAT reforms: a demand system simulation approach

AbstractUsing an estimated demand model, we simulate household-level demand responses to the recent Slovak value added tax (VAT) reform and its hypothetical alternatives. We use simulation results, instead of the standard approximate measures, for constructing an improved efficiency indicator of tax reforms as a ratio of welfare and fiscal revenue effects and call it marginal welfare gain (MWG). We also contribute to the literature on optimal structure of indirect taxes and on marginal tax reforms with new evidence of actual and hypothetical reforms that could increase the welfare of households at zero fiscal costs. Regarding the general policy implications of our results, we conclude that using a demand model to rank commodities according to their MWGs can be helpful for designing both efficient and equitable VAT reforms.

Plant Maintenance Modelling Through Availability Analysis In Raw Mill of Cement Production

Maintenance activities are a particular concerned at PT XYZ, which is the largest cement producer in Eastern Indonesia. PT XYZ has five main factories located in Pangkep, South Sulawesi, units 1,2,3,4, and 5. In 2019, PT XYZ’s five clinker production only reached 2,445,515 tons or 93, 64% of the target. The details, raw meal production was not achieved, namely 4,041,377 tons or 96.54% of the target set by the company due to the unplanned shutdowns. This research tries to control the breakdowns of raw mill machine by evaluating the availability and day of operation (calendar day). The availability analysis method used is the discrete system simulation approach because the discrete system simulation can accommodate random behavior and dependence between variables. In the existing condition, system availability is 72.83% and calendar day is 239 days. These two parameters are still less than the target. To overcome this problem, experimentation was carried out with 3 scenarios. The best simulation results shows that the preventive maintenance activities changes the value of TTF (Time to Failure), TTR (Time to Repair), and reduces the duration of overhaul activities. This can increase the availability to 83.04% and calendar day to 255 days.

Study of emerging air taxi network operation using discrete-event systems simulation approach

This research focuses on understanding the air taxi operations to determine the number of air taxis required to fulfill the demand for urban air mobility in New York City (NYC). We leverage the Define, Measure, Analyze, Design, and Verify (DMADV) framework and integrate it with the systems simulation approach. Upon investigation, we find that all the parameters linearly impact the vehicle utilization, while other measures are robust, specifically with respect to the seating capacity. It is also recommended to operate initially with 70 air taxis in NYC to achieve a trade-off between customer wait time and vehicle utilization. The proposed approach can act as a recommender system for air taxi companies.

Earth air heat exchanger, Trombe wall and green wall for passive heating and cooling in premium passive refugee house in Sweden

With the increasing number of migrants and refugees, there is a need for energy-efficient and low impact temporary housing that can accommodate millions of displaced peoples worldwide. This study describes a design proposal for a premium passive refugee house that uses three main passive heating and cooling solutions (Earth Air Heat Exchanger, Trombe wall, and green wall) and is suited to the Swedish climate. The purpose of the combination of the three passive systems is to reduce cooling and heating loads to conserve a significant amount of primary energy and thus mitigate the impact of the house’s energy use on the environment through a reduction in emissions. The house is designed to fulfill its energy needs from renewable sources and produce an annual surplus of 180 kWh/m2/annum. The methodology applied is a dynamic system modeling and simulation approach using TRNSYS and ANSYS software. The simulation results showed a heating load of 7.9 kWh/m2/annum and a cooling load of 2.8 kWh/m2/annum, with total energy consumption reaching 18.4 kWh/m2/annum. Preliminary feasibility costing showed a payback time of 7.4 years out of the 25-years suggested lifetime of building using the three passive solutions. The amount of CO2 emissions is 231.1 kg CO2e/annum with a primary energy demand of 0.032 GJ/m2/annum. As a follow-up to the initial study, a proof of concept has been implemented in Lund, Sweden, in an urban living lab to verify the simulation results through a 12-month post-occupancy monitoring and evaluation study.

Internet of things and simulation approach for decision support system in lean manufacturing

Internet of things and simulation approach for decision support system in lean manufacturing

A System Dynamics Modeling Support System Based on Computational Intelligence

System dynamics (SD) is a complex systems modeling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing fields such as data sciences. This may prove promising for the development of novel support methods that augment human cognition and improve efficiencies in the model building process. A few different directions have been explored recently to support individual modeling stages, such as the generation of model structure, model calibration and policy optimization. However, an integrated approach that supports across the board modeling process is still missing. In this paper, a prototype integrated modeling support system is presented for the purpose of supporting the modelers at each stage of the process. The proposed support system facilitates data-driven inferring of causal loop diagrams (CLDs), stock-flow diagrams (SFDs), model equations and the estimation of model parameters using computational intelligence (CI) techniques. The ultimate goal of the proposed system is to support the construction of complex models, where the human power is not enough. With this goal in mind, we demonstrate the working and utility of the proposed support system. We have used two well-known synthetic reality case studies with small models from the system dynamics literature, in order to verify the support system performance. The experimental results showed the effectiveness of the proposed support system to infer close model structures to target models directly from system time-series observations. Future work will focus on improving the support system so that it can generate complex models on a large scale.