Simulation Modeling Approach Research Articles

Discrete-Event Simulation Integrates an Improved NEH Algorithm for Practical Flowshop Scheduling Problems in the Satellite Industry

The production of multiple types of satellites based on a common manufacturing platform represents a permutation flowshop scheduling problem (PFSP) with complex constraints. This is a highly complex scheduling problem, yet there is still a gap between theoretical research and practical application, particularly in the satellite industry. Therefore, we propose a more practical method that integrates discrete-event simulation modelling and an improved NEH algorithm to solve a more realistic PFSP. The discrete-event simulation-based method includes the following three main components: a flexible PFSP simulation modelling approach, an improved NEH algorithm, and an interaction mechanism between the simulation model and the optimisation algorithm. The proposed method allows automatic and flexible simulation modelling according to the characteristics of the actual satellite manufacturing workshop, which determines the practical nature of the approach proposed in this paper and then achieves excellent scheduling results based on the special interaction mechanism. The computational results demonstrate that this is a 9.18% improvement over the initial NEH algorithm and a 1.40% improvement over the best current improved NEH algorithm.

Designing silvopastoral systems: a simulation model approach

Designing silvopastoral systems: a simulation model approach

A Combined Experimental and Computational Study on the Effect of the Reactor Configuration and Operational Procedures on the Formation, Growth and Dissociation of Carbon Dioxide Hydrate

Clathrate hydrate-based technologies are considered promising and sustainable alternatives for the effective management of the climate change risks related to emissions of carbon dioxide produced by human activities. This work presents a combined experimental and computational investigation of the effects of the operational procedures and characteristics of the experimental configuration, on the phase diagrams of CO2-H2O systems and CO2 hydrates’ formation, growth and dissociation conditions. The operational modes involved (i) the incremental (step-wise) temperature cycling and (ii) the continuous temperature cycling processes, in the framework of an isochoric pressure search method. Also, two different high-pressure PVT configurations were used, of which one encompassed a stirred tank reactor and the other incorporated an autoclave of constant volume with magnetic agitation. The experimental results implied a dependence of the subcooling degree, (P, T) conditions for hydrate formation and dissociation, and thermal stability of the hydrate phase on the applied temperature cycling mode and the technical features of the utilized PVT configuration. The experimental findings were complemented by a thermodynamic simulation model and other calculation approaches, with the aim to resolve the phase diagrams including the CO2 dissolution over the entire range of the applied (P, T) conditions.

Computational Development of New Inhibitors for Apoptosis Stimulating Protein of p53 (iASPP) Using Pharmacophore Modeling, Docking, and MD Simulation Approaches

Inhibitor of apoptosis-stimulating protein of p53 (iASPP) overexpression is associated with diverse human tumors, including lung cancer, colorectal cancer, prostate cancer, acute leukemia, hepatocellular carcinoma, cervical cancer, and ovarian cancer. This study aims to find new and potent inhibitors for the iASPP drug target. Using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation an integrated strategy was developed to find highly effective iASPP inhibitors. Subsequently, the pharmacophore model was employed as a screening query to find promising inhibitors from the ZINC database. Total 36/12000 hits were identified against the iASPP drug target. The binding mode of the promising identified inhibitors was predicted by a molecular docking study. To evaluate the stability of the newly identified inhibitors top 03 best docking scores compounds were subjected to MD simulation. MD simulation and binding energy calculation confirmed that the compounds including ZINC001361049159, ZINC001361124195, and ZINC001545869876 were stable. The MD simulation analysis indicated that among all the compounds ZINC001361049159 was the most potent. These newly designed iASPP inhibitors could be used as starting material to identify new and potent anti-cancer drugs.

On the move: Influence of animal movements on count error during drone surveys.

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost- and time-efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone-based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent-based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems.

Detecting bias in abundance estimates of spawning fish from closed‐capture models using remote and physical capture data

AbstractObjectiveMixed‐data models incorporating remote antenna detections from PIT‐tagged fish together with physical capture data (mixed data) can improve precision of mark–recapture abundance estimates, particularly for spawning fish. However, if the entire population is not available for physical capture during the mark–recapture sampling period, abundance estimates will be biased low. Our objectives were to examine bias and precision of two modeling approaches and develop a simple diagnostic to determine whether the entire population was sampled.MethodsWe use a simulation modeling approach to compare specified abundances with abundance estimates from closed‐capture models using mixed data (the mixed‐data approach) and using only marked (e.g., PIT‐tagged) individuals divided by the proportion of individuals that are marked (proportional approach). We use the difference in bias between the two models as our diagnostic of whether a population was randomly sampled. We then applied our diagnostic to two case studies: a spawning population of adfluvial June Sucker Chasmistes liorus in Utah Lake, Utah, 2008–2020, and a spawning population of Bull Trout Salvelinus confluentus in the Imnaha River, a tributary of the Snake River, Oregon, 2013–2020.ResultSimulation experiments revealed that the mixed‐data approach became increasingly negatively biased as the proportion of the population that was unavailable for physical capture increased, yet the proportional approach remained unbiased. Abundance estimates from the proportional approach averaged approximately 6× greater for June Sucker and 2× greater for Bull Trout compared to the mixed‐data approach, suggesting a proportion of the population was not available for physical capture in both case studies.ConclusionUnderstanding the magnitude of bias in abundance estimates is particularly important for the management of imperiled species that are subject to recovery plans. Comparing estimates using the unbiased proportional approach that we described here with those from a mixed‐data approach when PIT tag detection data are used to estimate abundance is a straightforward method to evaluate bias in these estimates.

Analysing Double Shearing Mechanism in Fiberglass Rock Bolting Systems: a Comprehensive Analytical Model and Numerical Simulation Approach

Analysing Double Shearing Mechanism in Fiberglass Rock Bolting Systems: a Comprehensive Analytical Model and Numerical Simulation Approach

Efficiency of Inventory in Thai Hospitals: Comparing Traditional and Vendor-Managed Inventory Systems

Background: Traditional procurement and price negotiation methods in public hospitals in Thailand involve group purchasing agreements and bulk orders, posing challenges to improving inventory management efficiency. The vendor-managed inventory (VMI) model is a promising alternative for enhancing hospital performance, especially during crises. This study aimed to assess the potential cost savings in implementing VMI in a large general hospital in Thailand. Methods: A simulation modeling approach was used to compare the current inventory system with three VMI models: VMI1, focused on improving inventory turnover rate (ITR); VMI2, emphasized frequent replenishment with a 1-month supply; and VMI3, eliminated safety stock. Results: The results demonstrated significant cost savings, with potential reductions in total inventory management expenses. Specifically, VMI1 improved ITR from 6.31 to 7.76, reducing average inventory by 36% and cutting management costs by 40%. VMI2, with an ITR of 12.80, reduced inventory by 44% and saved 47% in management costs, while VMI3 achieved a 70% reduction in inventory and a 69% saving in management costs. Conclusions: This study highlights the VMI’s transformative potential in hospital inventory management, demonstrating significant cost savings. However, in the public sector, the feasibility of procurement regulations requires further exploration.

Evaluation of two constitutive modeling approaches for finite element simulation of ballistic impact of varying thickness monolithic aluminum plates by steel spheres

Evaluation of two constitutive modeling approaches for finite element simulation of ballistic impact of varying thickness monolithic aluminum plates by steel spheres

From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

Long-term responses of internal environment dynamics in a freshwater lake to variations in external nutrient inputs: A model simulation approach

Lake restoration usually focuses on reducing external nutrient sources. However, when sediments contain nutrients accumulated over multiple years, internal nutrient release can delay restoration progress. In lake restoration and management, it is important to understand the dynamic relationship between nutrient concentrations in a lake and internal and external nutrient sources. In this study, we quantified external nutrient inputs through measurements and compared them with internal sediment release from simulation using the PCLake+ model. Additionally, we evaluated alterations in the internal nutrient release, lake nutrient concentrations, and algae biomass (chlorophyll-a) within the lake following varying degrees of reduction in external nutrient loads. The results demonstrate that the PCLake+ effectively simulated the lake's nutrient concentration and algae biomass. Based on the PCLake+ estimates, internal nutrient loads accounted for 51 % of the total nitrogen (N) and 80 % of the total phosphorus (P) loadings in Lake Erhai in 2019. In 2020, the total contributions were 43 % for TN and 72 % for TP. We simulated four scenarios where external nutrient inputs were reduced to 25 %, 50 %, 75 %, and 99.99 % of their original levels. The 40-year simulation showed that the lake's ecological system initially exhibited a fast internal response but reached equilibrium after eight years. P concentrations took longer to reach equilibrium compared to N concentrations, probably due to the stronger binding characteristics of P. To meet the water quality target in the future, it is necessary to reduce external N and P inputs into Lake Erhai by at least 23 % and 15 %, respectively, under current conditions. Although reducing external nutrient loads can indirectly lower internal nutrient loads, water management should address both external and internal loads simultaneously, as internal release cannot be effectively reduced by external reductions alone. Additionally, the lake's internal release may continue for several years, even with reductions in external inputs.

Multi-Scale Modeling and Simulation of All-Solid-State Sodium-Ion Batteries with Polymer-Ceramic Hybrid Electrolytes

Lithium-ion batteries (LIBs) have been widely considered as the most promising power source for mobile devices. However, LIBs cannot meet the increasing global demand for electrochemical energy storage in the foreseeable future. Sodium ion-batteries (SIBs) are based on more ecofriendly and earth-abandoned materials. Commonly researched SIBs are based on liquid electrolytes. They pose leakage and thermal runaway risks. Solid polymer electrolytes solve these problems but need to operate at temperatures between 60-80°C to compensate insufficient ionic conductivity of polymer electrolytes at ambient temperatures.We present a polymer-ceramic hybrid electrolyte which overcomes the necessity to operate at high temperatures. It combines the advantages of high ion-conducting solid ceramic particles and flexible polymers. Sodium metal serves as the anode and PEO with inorganic fillers is used as cathode electrolyte. Due to their sustainability and high efficiency —comparable to LIBs— sodium-ion batteries are a promising candidate for small scale stationary energy storage applications or mobile energy storage applications with less constrains regarding energy density (e.g. trains, ships, submarines etc.). This contribution highlights a modeling approach for simulations of a single battery cell to develop an optimal cell design of the aforementioned battery system. A newly developed multiscale modeling method for hybrid polymer-ceramic sodium ion batteries reflects the different length scales of the active particle level and the electrode level. We precisely describe the spatially resolved mass and charge transport as well as electrochemical reactions in the active particle and the surrounding electrolyte employing a finite volume method [1]. Both domains are resolved in 3D and the model framework was validated against experimental data. The model output is incorporated in the connected pseudo-two-dimensional battery model for fast simulations of the overall battery cell considering mass and charge transport. The results support the importance of particle (active particle and ceramic filler particle) properties and configurations as well as electrolyte composition. Based on this, we exploit an evolutionary algorithm to suggest optimized battery designs for different use cases. In Addition, we present the evaluation of electrolyte properties with solid-state NMR spectroscopy and their model implementation [2]. μCT imaging experimentally investigates the cathode half-cell morphology. The derived virtual reconstructions enable realistic microstructure simulations of the battery cells. In this presentation, we will suggest two cell designs: The high-energy cell optimizes material efficiency and energy density. The high-power cell opts for high power densities (fast charging).In summary, we present a novel all-solid-state polymer-ceramic hybrid sodium-ion battery type for small stationary or large mobile energy storage units. Our contribution outlines a simulation approach that sheds light on macroscopic and microscopic processes inside the battery-cell using a multiscale modeling method. As a result, a high-energy and a high-power design is derived. Gerbig, M. Holzapfel and H. Nirschl, J. Electrochem. Soc., 170(4), 40517 (2023).Moradipour, A. Markert, T. Rudszuck, N. Röttgen, G. Dück, M. Finsterbusch, F. Gerbig, H. Nirschl and G. Guthausen, J Energ Power Technol, 05(04), 1–21 (2023).

Design considerations of CdSe solar cells for indoor applications under white LED illumination

This work sheds light on the potential of Cadmium Selenide (CdSe) solar cells for indoor applications. CdSe boasts a wide direct bandgap, high carrier mobility, and a high absorption coefficient, making it an attractive candidate for harnessing ambient indoor light. Our study centers around an experimental solar cell architecture composed of FTO/CdSe/PEDOT:PSS/CuI/ITO, which exhibits a power conversion efficiency (PCE) of 6.00 %. Through a meticulous analysis of the core technological aspects of this cell, we successfully replicate the measured current-voltage characteristics and other experimental data, affirming the validity of our simulation modeling approach. Moving forward, we delve into the design and optimization of CdSe-based solar cells under white LED illumination. We emphasize the pivotal role of a double-hole transport layer (HTL) configuration over a single HTL, with a focus on optimizing the alignment between the HTL/back contact and HTL/absorber interfaces. The strategic incorporation of a heavily doped p-type HTL material, boasting both a deep valence band maximum (VBM) and a shallow conduction band minimum (CBM), is identified as paramount, especially for a deep VBM absorber like CdSe. Adding double HTL materials also facilitates efficient hole collection within the CdSe thin film while mitigating undesirable electron-hole recombination at the critical interface between the hole collection layer and the electrode. The implementation of a double HTL configuration based on CuI/ZnTe:Cu or CuI/BCS significantly enhances performance, resulting in a PCE in the order of 20 % under 200 lux and 2900 K LED illumination. Moreover, we introduce the single HTL design to provide other alternatives for efficiency boosting. Upon increasing the work function of the front contact, it is found that the valence band offset between the HTL and the absorber can be engineered, resulting in a PCE above 21.5 %.

Mixed modeling approach for efficient simulation of single-phase PWM inverters

Mixed modeling approach for efficient simulation of single-phase PWM inverters

Framework for accurate estimation of freezing time and convective heat transfer coefficient for freezing of a food product in domestic refrigerator: a numerical and simulation modeling approach

Framework for accurate estimation of freezing time and convective heat transfer coefficient for freezing of a food product in domestic refrigerator: a numerical and simulation modeling approach

Evaluating different CFD surrogate modelling approaches for fast and accurate indoor environment simulation

Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.

Exploring sustainable agriculture: Investigating the impact of controlled release fertilizer damage through bonded particle modeling

Controlled release technology of chemical fertilizers is an important cornerstone for the efficient development of sustainable agriculture. Damage to the coating layer and fragmentation of particles during the application of controlled release fertilizers can lead to the loss of their controlled release properties, making the development of effective damage reduction operations a key focus in the study of such agricultural materials. Therefore, this paper employs the bonded particle model (BPM) method within the discrete element method (DEM) framework to investigate the mechanism of coating damage in controlled-release fertilizers. It establishes a classification of damage types based on the level of damaged particles as an effective analytical approach. Furthermore, a comparative analysis is conducted between the discrete element particle model (DEPM) method and the bonded particle model method in terms of their performance in discharge mechanics and flow characteristics. The results indicate that the breakage rate of fertilizer discharge is influenced by both the formation of a steady state and the depletion of the fertilizer load within the container, with an average breakage rate of 1.511% over the total period. Differences in the average maximum compression force and mass flow rate between the discrete element particle model group and the bonded particle model group were 60.355 N and 1.998 g/s, respectively, reflecting limitations imposed by the fertilizer models on expected damage deformation behaviors. This study elucidates the process and potential impacts of fertilizer particle damage and deformation, revealing the strengths and limitations of two simulation model approaches under different application conditions, providing insights for sustainable agricultural production and technological innovation in fertilization equipment.

A CFD-based compartmental modelling approach for long-term dynamic simulation of water resource recovery facilities

ABSTRACT This article presents a methodology for compartmental model (CM) creation for long-term simulation of water resource recovery facilities (WRRFs). CMs are often focused on reproducing with a lower computational cost, previously simulated scenarios. In contrast, the methodology presented here can represent variable hydraulic conditions, based on the interpolation of data gathered from a set of computational fluid dynamics (CFD) simulations that reproduce representative hydraulic scenarios. This is achieved by modelling with bidirectional flows the exchange flows between fixed compartments, which are defined based on the geometry of the reactors. The resultant hydraulic surrogate model can be implemented in commercial water treatment software to solve biochemical kinetics. The methodology was applied to simulate in WEST®-DHI a WRRF in Vila-Real, Spain. In this contribution, the CM was validated with real plant data. The developed CM provided a quick response simulation with a high level of hydraulic and biochemical detail. This allowed observation of a spatial distribution of component concentration, which could help with sensor location or plant optimisation. The methodology presented here could also be a useful enabler of digital twins to be implemented in WRRF.

Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach

Urban energy poverty remains a critical challenge affecting millions worldwide, with significant implications for socio-economic development and sustainability. This study employs Monte Carlo simulations to analyze the variability in energy poverty, as measured by the Multidimensional Energy Poverty Index (MEPI). Through this approach, we explored how different socio-economic predictors affect MEPI scores across five scenarios: economic growth and diversification, energy transition and technological innovation, climate change impacts and vulnerability, policy intervention and social safety nets, and stagnation and rising inequality. The sensitivity analysis revealed that household income, size, and food security are paramount in influencing urban energy poverty levels. Scenarios involving economic growth and technological innovation demonstrated positive impacts on reducing energy poverty, while scenarios of climate change and economic stagnation highlighted the vulnerabilities and widening disparities within populations. The results suggest that to effectively alleviate urban energy poverty, policy interventions must be comprehensive, targeting the identified influential factors. The study emphasizes the need for targeted policy interventions on social safety nets to alleviate energy poverty.

A Simulation Modeling Approach for the Techno-Economic Analysis of the Integration of Electric Vehicle Charging Stations and Hybrid Renewable Energy Systems in Tourism Districts

Electric vehicles (EVs) play a crucial role in tertiary sectors due to their eco-friendliness and sustainability when powered by clean energy. Integrating EV charging stations with renewable energy systems is essential to alleviate energy issues and grid pressure. Exploring this integration’s feasibility is imperative for sustainable transportation. This study aims to provide a clear approach and methodology for examining the potential of integrating renewable energy technologies with EV charging stations at the district level. Additionally, the study investigates the energy, economic, and environmental benefits of an integrated system comprising photovoltaic/wind turbines (PV/WTs) connected to the electricity grid to meet the energy demand of a tertiary district consisting of five hotels in Egypt. Through the development of a simulation model, the paper verifies whether the proposed energy system can meet the district’s energy demand. In addition, the simulation model has been employed to conduct a sensitivity analysis for investigating the impact of different charging rates on economic feasibility. The results indicate that a hybrid renewable energy system (HRES) integrated with an EV charging station can effectively relieve pressure on the electricity grid and provide electricity at competitive prices compared to the national grid. Moreover, the proposed energy system significantly reduces environmental emissions by up to 510 tons of CO2 per year and has the potential to decrease fossil fuel usage by 248 tons per year. Sensitivity analysis highlights the significant impact of charging prices on project profitability.