System Simulation Research Articles

Rancang Bangun Sistem Antrian Online Menggunakan Metode Single Channel-Multi Phase Studi Pada Dinas Sosial Kabupaten Labuhanbatu

Labuhanbatu District Social Office is an institution implementing social affairs of government under and responsible to the head of the region (Regent). In this case the Labuhanbatu District Social Service Office applies many arrangements in between what happens. The way that is done is very manual, namely by setting the queue NumberOne by one. With a very small limit on the number of passengers, this causes queues to accumulate, coupled with situations that affect his daily life. In the process of service that is done, there is a way of service that must be done because a lot of attention should be paid to the way the service is done now is not very effective. So it is necessary to create a simulation of community service system that can identify community services in the Office of Social Services Labuhanbatu District. During the service takes place the time needed between one another in getting the service to be efficient first came to be served in the service. The reason for the research using this topic is that research on the topic is still not done in the Labuhanbatu District Social Service with Inter-system using Single Channel-Multi Phase method. So that the Social Service of Labuhanbatu Regency can serve the community to the maximum and the community feels comfortable and fair.

Daily Engine Performance Trending Using Common Flight Regime Identification

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and functional principal component analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm (GA)-assisted orthogonal matching pursuit (OMP) for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.

Reserve Technique in Integrating Large Sustainable Energy Sources: A Case Study of the Tunisian Grid

The increasing integration of sustainable energy sources, such as wind and solar power, into the national electricity grid presents significant challenges in terms of frequency control and grid stability. Additionally, the imbalance between electricity supply and demand introduces dynamic frequency variations. However, according to the literature, the impact of high penetration of renewable energy sources on the Tunisian grid has not been extensively analyzed using power system simulator for engineering (PSS/E). This research paper explores how the primary reserve technique participates to maintain frequency within acceptable ranges in the Tunisian electrical grid. Individual generators contribute to the total power output, thereby influencing frequency deviation. The primary frequency control action by each generator is proportional to its frequency deviation and inversely proportional to its governing drop, which measures the generator’s sensitivity to frequency changes. This paper analyzes frequency stability in the Tunisian grid under scenarios with and without different rates of sustainable energy source penetration, which barely reached 3.5% in 2023. In Tunisia, the use of sustainable energy is essential not only for ensuring grid stability but for combating climate change and reducing carbon emissions, aligning with the country’s environmental goals. The transition to sustainable energy significantly reduces the carbon footprint of the power sector, offering a sustainable solution for mitigating the adverse effects of climate change. Dynamic simulations were conducted for the isolated Tunisian system, separate from the interconnected grid, focusing on the critical scenario of the loss of a large electricity production unit. This study also examined the absence of sustainable energy integration and the effects of integration of different rates of renewable energy to evaluate the impact of reserves on the continuity of the Tunisian electrical service. Simulation results, which considered a 2023 grid model, show that with an integration trial of 20% renewable energy and, in the worst-case scenario, which represents the loss of the largest production group in the grid, the primary reserve of a given group—defined by the quantity of active energy—can be rapidly deployed to restore the balance between electricity supply and demand. Thus, reserves are a crucial solution for maintaining frequency within reasonable limits and ensuring the continuity of electrical services in Tunisia with varying rates from 10% to 20% integration of different sustainable energy sources.

Dynamical system simulation with attention and recurrent neural networks

Dynamical system simulation with attention and recurrent neural networks

Assessment of the viability of photovoltaic system implementation on the New Media Tower of Universitas Multimedia Nusantara using PVSyst software: A feasibility study.

The urgency of addressing climate change, exacerbated by greenhouse gas emissions, necessitates sustainable solutions, including green building practices and renewable energy adoption. This study focuses on the feasibility of implementing solar photovoltaic systems at Universitas Multimedia Nusantara (UMN), particularly in Building C, known as the New Media Tower, which is designed with green building principles. Solar energy, an increasingly prominent renewable source, presents a viable solution to reduce carbon footprints. Before installation, thorough simulations using software like PVSyst are essential to predict energy output and evaluate system efficiency. Several studies have explored PV system feasibility using simulations, highlighting the importance of software selection for accurate assessments. Building C offers potential locations for PV installation, with the rooftop being a primary consideration due to its expansive area and minimal shading. The rooftop PV system simulation shows an annual energy production of 202 MWh, close to the target of 209.64 MWh, while the parking area system only generates 64.5 MWh/year. Technical evaluations reveal that only the rooftop PV system meets electricity generation targets, highlighting its superiority over the parking area system. Financial analysis demonstrates the rooftop system's viability, with a payback period of 8.2 years and a return on investment (ROI) of 115.8%. Although the upfront investment is substantial, the long-term benefits justify implementation. Overall, this study underscores the technical and financial feasibility of rooftop photovoltaic systems on Building C at UMN, offering valuable insights for sustainable energy initiatives in academic institutions.

MATHEMATICAL MODELING AND OPTIMAL CONTROL OF WHEAT STRIPE RUST (Yellow Rust) DISEASE

Wheat Stripe Rust is a devastating fungal disease that is the largest limitation to wheat production and threatens the global food supply. In this work, we proposed a mathematical system of ordinary differential equations to model the wheat Stripe Rust transmission dynamics. First, we showed the feasible region, the positivity of the solution, the model’s equilibrium points and the basic reproduction number. Second, we used the Pontryagin maximum principle to extend the basic system into an optimal control system by embedding three control measures (resistant cultivars, fungicides and cultural practices). Finally, we used a numerical simulation of the optimality system to demonstrate the thorough impacts of resistant wheat cultivars, fungicide treatments and cultural practices in reducing the epidemic.

Finite‐Time Functional Interval Observer Design for Nonlinear Discrete‐Time Switched Descriptor Systems

ABSTRACTIn this article, the finite‐time functional interval estimation method for nonlinear discrete‐time switched descriptor systems with perturbations is investigated. To deal with the nonlinear terms in the switched system, the nonlinear error system is converted into a linear parameter varying form using the reformulated Lipschitz property. Firstly, a functional observer with finite‐time gain is constructed for the nonlinear switched descriptor system. Then, based on the proposed finite‐time functional observer, the reachability analysis is applied to design a finite‐time functional interval observer. Finally, the superiority and effectiveness of the interval estimation method presented in this paper are verified through a simulation of the circuit system.

Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as its accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits is challenging due to limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) for determining the ideal parameters for the circuit model. The presented EBOA incorporates several modifications aimed at improving its searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support the BOA settings to be adjusted in an adaptive manner. Additionally, it integrates key mechanisms from the Hippopotamus Optimization (HO) to strengthen the algorithm’s exploitation potential by leveraging surrounding knowledge for more effective position updates while also improving the balance between global and local search processes. The EBOA was subjected to extensive mathematical validation through the application of benchmark functions to rigorously assess its performance. Also, PV parameter estimation was achieved by combining the EBOA with a Newton–Raphson approach. Numerous module and cell varieties, including RTC France, STP6-120/36, and Photowatt-PWP201, were assessed using double-diode and single-diode PV models. The higher performance of the EBOA was shown by a statistical comparison with many well-known metaheuristic techniques. To illustrate this, the root mean-squared error values achieved by our scheme using (SDM, DDM) for RTC France, STP6-120/36, and PWP201 are as follows: (8.183847 × 10−4, 7.478488 × 10−4), (1.430320 × 10−2, 1.427010 × 10−2), and (2.220075 × 10−3, 2.061273 × 10−3), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence.

Sustainable and Self-Sufficient Fresh Water Through MED Desalination Powered by a CPV-T Solar Hybrid Collector: A Numerical and Experimental Study

The effects of global warming are severely recognizable and, according to the OECD, 47% of the world’s population will soon live in regions with insufficient drinking water. Already, many countries depend on desalination for fresh water supply, but such facilities are often powered by fossil fuels. This paper presents an energy self-sufficient desalination system that runs entirely on solar power. Sunlight is harvested using parabolic trough collectors with an effective aperture area of 1.5 m × 0.98 m and a theoretical concentration ratio of 150 suns, in which a concentrator photovoltaic thermal (CPV-T) hybrid-absorber converts the radiation to electricity and heat. This co-generated energy runs a multi-effect distillation (MED) plant, whereby the waste heat of multi-junction concentrator solar cells is used in the desalination process. This concept also takes advantage of synergy effects of optical elements (i.e., mirrors), resulting in a cost reduction of solar co-generation compared to the state of the art, while at the same time increasing the overall efficiency to ~75% (consisting of an electrical efficiency of 26.8% with a concurrent thermal efficiency of 48.8%). Key components such as the parabolic trough hybrid absorber were built and characterized by real-world tests. Finally, results of system simulations, including fresh water output depending on different weather conditions, degree of autonomy, required energy storage for off-grid operation etc. are presented. Simulation results revealed that it is possible to desalinate around 2,000,000 L of seawater per year with a 260 m2 plant and 75 m3 of thermal storage.

Periodic external driving of transmission-delay-coupled phase oscillator system: Switching between different twisted states.

We consider the effects of an external periodic forcing on a spatially extended system that consists of identical phase oscillators coupled with transmission delays on a ring. Analyzing the continuum limit N→∞ of the model system along the Ott-Antonsen invariant manifold, we obtain the stability diagram for two regimes, called the forced and drifting entrainments. The former exhibits a spatially homogeneous solution trying to lock onto the drive, of which the stability boundary is rigorously determined. The latter represents a spatially organized group of oscillators that entrain one another at a frequency different from that of the drive. We show that in the drifting entrainment the external driving triggers the occurrence of unusual twisted states, characterized by nonuniform phase gradient as well as by the traveling wave of the order parameter amplitude. Moreover, it is found that by increasing or decreasing the forcing strength one can effectively switch between twisted states with different winding numbers. Our theoretical and numerical results for the reduced system are supported by the direct numerical simulations of the model system.

Molecular dynamics simulation of combustion reaction process and products of oxygen-containing functional groups in coal based on Machine Learning Potential

Oxygen-containing functional groups are the main heat source of coal spontaneous combustion, but their complex reaction pathways and microscopic mechanisms are still unclear. In this study, the molecular dynamics simulation of the composite combustion reaction model system with four oxygen-containing functional groups was conducted using the Machine Learning (ML) potential force field. The results showed that the stability order of these four oxygen-containing functional groups in the combustion reaction system is as follows: ‒OH < ‒COOH < ‒C‒O < ‒C=O. These simulation findings align with those obtained from in-situ FTIR experiments, thereby validating the accuracy of the ML potential force field. Notably, ‒C‒O exhibits the highest tendency for CO2 conversion (58.57%); ‒COOH displays the highest tendency for H2O conversion (45.00%); and ‒OH demonstrates the highest tendency for CO conversion (59.17%). The essence of the oxidative combustion reaction pathway involving oxygen-containing functional groups lies in the heat accumulation resulting from the oxidative dehydrogenation effect.

A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation.

We consider the problem of efficiently simulating stochastic models of chemical kinetics. The Gillespie stochastic simulation algorithm (SSA) is often used to simulate these models; however, in many scenarios of interest, the computational cost quickly becomes prohibitive. This is further exacerbated in the Bayesian inference context when estimating parameters of chemical models, as the intractability of the likelihood requires multiple simulations of the underlying system. To deal with issues of computational complexity in this paper, we propose a novel hybrid τ-leap algorithm for simulating well-mixed chemical systems. In particular, the algorithm uses τ-leap when appropriate (high population densities), and SSA when necessary (low population densities, when discrete effects become non-negligible). In the intermediate regime, a combination of the two methods, which uses the properties of the underlying Poisson formulation, is employed. As illustrated through a number of numerical experiments, the hybrid τ offers significant computational savings when compared with SSA without, however, sacrificing the overall accuracy. This feature is particularly welcomed in the Bayesian inference context, as it allows for parameter estimation of stochastic chemical kinetics at reduced computational cost.

Dynamic simulation of wind-powered alkaline water electrolysis system for hydrogen production

Alkaline water electrolysis (AWE) is a mature and cost-effective technology, especially suited for large-scale deployment, yet faces challenges in adapting to the dynamic nature of renewable energy sources. Evaluating AWE's adaptability to fluctuating renewable energy inputs is crucial. This study introduces a dynamic model to assess the influence of wind energy on a 250 kW industrial-scale AWE system for hydrogen production. This paper pioneers the utilization of Aspen Plus Dynamics for dynamic modeling, offering a comprehensive approach that incorporates all vital components and controllers of the balance of plant, such as gas-liquid separator, heat exchangers, deionized water supply, pumps, and the cooling loop. This methodology allows comprehensive simulation at the system level, revealing the real dynamic characteristics of the system under fluctuating wind energy conditions. Due to the absence of a dedicated module for AWE in Aspen Plus Dynamics, an integrated dynamic operation unit for the electrolyzer is constructed using Aspen Custom Modeler. The study analyzes the dynamic characteristics of the AWE system, specifically focusing on temperature, voltage, and liquid level. The study compares two temperature control strategies, before-stack and after-stack, with the refined after-stack method effectively addressing over-temperature issues and improving system energy efficiency by 1.44%.

Thermodynamic simulation of a water-injected twin-screw steam compressor

Abstract Decarbonisation of industrial heating and process steam generation is crucial for the mitigation of advancing climate change. High-temperature heat pumps can meet these demands while effectively integrating with renewable energy sources, outperforming traditional heating and boiling systems, especially when operating with natural refrigerants. Since compressor efficiency is critical for the effectiveness of heat pump systems, particular care is required in compressor design and specification. Detailed thermodynamic simulations of the compressor system are indispensable in this process. The compression of fluids with a negative saturation vapour curve leads to challenging temperature increases during compression, with substantial superheating of the working fluid. In this context, a promising approach is the injection of liquid working fluid into the compressing chambers for internal cooling. This introduces new complexity to compressor simulations for heat-pump applications. In this work, a novel two-phase approach for multi-chamber model simulations of positive displacement compressors is presented. Initially, a quasi-isentropic reference process for vapour compression with internal liquid injection is introduced and efficiency ratings are derived. Furthermore, the simulation process is described in detail, and comprehensive thermodynamic simulations of a water-injected twin-screw steam compressor are carried out.

Numerical simulation of the dual-loop different velocity air curtain system in the operating room

The problem of cross-infection between patients and surgeons in the operating room had been widely concerned by the international community. The purpose of this study had been to explore the potential of a Dual-loop Different Velocity Air Curtain System (DDVACS) being applied in the operating room to reduce cross-infection. In this study, the performance of the system was studied using the computational fluid dynamics (CFD) numerical simulation method, with the operating conditions of different air supply velocities inside and outside being set. The RNG k-ε model, Discrete Phase Model (DPM), and Species Transport model were used to simulate the air distribution, particle diffusion, and virus diffusion. The main research results showed that, when the DDVACS was operated at an inner air supply velocity of 0.24 m/s and an outer air supply velocity of 0.45 m/s, the virus concentration in the surgical area had decreased by 89%, the particulate matter concentration in the surgical area had decreased by 78.08%, but the deposition of particulate matter had increased by 21.62%. This study concluded that DDVACS had been found to effectively reduce the concentration of cross-infection. Finally, the velocity difference between the inner and outer sides was controlled within 2 times. This setting had been arranged to make the DDVACS work better. This study's contribution was to further verify the application of the DDVACS in operating rooms, providing a new perspective and empirical support for related fields and possessing certain practical application value.

Simulation and Optimization of A Dual-loop Organic Rankine Cycle-Onboard Carbon Capture System for LNG-powered Ships

Simulation and Optimization of A Dual-loop Organic Rankine Cycle-Onboard Carbon Capture System for LNG-powered Ships

Bi-level programming model of location and charging electric vehicle stations in distribution networks

Electric vehicles (EVs) are currently a focal point in distribution network research due to their potential benefits. Integrating EVs into distribution networks can enhance voltage profile stability. However, unplanned EV connection and charging can lead to increased peak load power consumption, voltage profile irregularities, and heightened network losses. Addressing the placement of charging stations in distribution networks is crucial to prevent voltage drop crises and mitigate other issues. This study proposes a Bi-level particle programming optimization programming approach, utilizing the Particle Swarm Optimization (PSO) algorithm to minimize losses and determine optimal station locations. The algorithm iteratively minimizes losses while considering location and operating costs until a stopping condition is met. Additionally, an EV charging planning algorithm is introduced to manage EV connections without exacerbating peak load or causing severe voltage drops. Simulations conducted on an IEEE 33-bus system using MATLAB analyze the proposed design's impact on power consumption, system losses, and network voltage. Simulations in an IEEE 33-bus system show that the proposed model shifts peak power consumption by about 4.6% to about 11.6% under different EV penetration scenarios. It achieves a reduction in the network losses by up to 0.39%, while the voltage deviations in critical buses are minimized to enhance stability during periods of high demand for EVs. Results demonstrate that the programmed charging algorithm effectively reduces power consumption at peak load, minimizes network losses, and smooths the daily voltage profile.

A ranking improved teaching-learning-based optimization algorithm for parameters identification of photovoltaic models

Solar energy is an important clean energy source, primarily applied for photovoltaic (PV) power generation. The precise identification of PV system parameters is critical for system control and simulation, posing a challenge due to the models' non-linearity, implicitness, and multiple optimal properties. So, a ranking improved teaching-learning-based optimization (RITLBO) is developed in this work to solve the problem of identifying the parameters of the PV model. RITLBO is a meta-heuristic algorithm based on teaching-learning-based optimization (TLBO) that simulates classroom teacher-student interaction. In RITLBO, learners are classified into inferior and superior groups based on their fitness ranking. During the teacher phase, superior learners emulate the top three agents with the highest fitness for local search, while inferior learners engage in guided mutual learning for global search, effectively utilizing computing resources. In the learner phase, superior learners receive guided information, while inferior learners engage in broader information exchange, balancing exploration and exploitation. RITLBO and fourteen algorithms are used to identify the parameters for five different PV models to confirm that the RITLBO is effective. Statistical results and analysis demonstrate that RITLBO is accurate and reliable in identifying PV model parameters. RITLBO offers promising prospects in optimizing PV system parameters through its unique strategies.

Coupling a process-driven model with geographic information system enables the spatial predication of cadmium in paddy soils

Policies on the management of paddy fields are usually made at a broad scale and from a long-term perspective, while predicting the spatial extent of cadmium (Cd) contamination in paddy soils remains challenging. In this study, we developed a process-driven spatial model to quantify the transport of Cd in paddy soils and validated it against observed data from a 10-year regional investigation in southern China. Using a geographic information system and Monte Carlo simulation, the model was then applied to evaluate the effectiveness of different remediation strategies for contaminated paddy fields at field-to-regional scales in a 100-year period. In the last decade, atmospheric emissions have accounted for 43.5% of the total Cd input in local paddy soils. However, the local clean air act failed to mitigate Cd contamination in 99.8% of study area over the period of 2020–2120 because straw return became the dominant contributor to Cd inputs. Improving aerosol emission reductions by 3% per year, stopping straw return to soil, and cleaning irrigation channels would take approximately 30 years (2020–2050) to protect 95% of local rice production from causing an excessive human Cd kidney burden, especially in the paddy fields located in mining-affected areas.

A multi-agent system simulation framework with optimized spatial neighborhood search

A multi-agent system simulation framework with optimized spatial neighborhood search