Application Of Simulation Research Articles

Model for investigating cathode dual-population microfluidic microbial fuel cells

Microfluidic microbial fuel cells (MMFC) are one of the most promising power sources. However, due to the lack of clarity in the internal operating mechanism, the output performance is suboptimal. Thus, a comprehensive two-dimensional cathode dual-population model is developed to gain deeper insights into internal workings. Based on verifying the accuracy of the model, the influence of temperature, ionic strength, and spacing of electrode on the performance and microbial growth of MMFC are explored. The finding reveal a nonlinear trend in the performance of MMFC at temperatures of 293.15 K and 313.15 K. Furthermore, the impact of electrode spacing and ionic strength on the performance of MMFC is examined, thereby emphasizing the applicability in experimental research and numerical simulation. This study provided insights into the operating mechanism of dual-population microbial microfluidic fuel cells.

Introducing a simple convex hull method to calibrate diffusion coefficients in Lagrangian particle models

Calibrating Lagrangian particle models (LPMs) is crucial for simulating oil spill trajectories, enabling emergency responses, and mitigating harmful effects on the environment. However, there is a lack of rigorous observation data on oil spill events, especially in water bodies with no serious spills but perceived increasing risk, hampering LPM application. This study utilized trajectory data from three drifters in Lake Erie, collected in 2016, 2018, and 2019. We then proposed a simple and efficient convex hull method to identify the smallest envelope of the simulated particle cloud, covering all drifter trajectories while simultaneously controlling over-diffusion. This approach was employed to calibrate the General NOAA Operational Modeling Environment oil spill model (an LPM) and determine optimal diffusion coefficients. Based on 362 one-day-long drifter trajectory simulation cases, the recommended diffusion coefficient for particle trajectory simulation in Lake Erie was determined to be 14 m2 s−1. The calibrated model demonstrated the ability to effectively capture the movement direction of drifters and control the discrepancy between simulated and observed trajectories. Meanwhile, it underestimated drifter travel distances due to errors in input currents, the primary force driving drifter movement. These results promote LPM application for particle trajectory simulations.

Application of situational simulation based on the Mini-CEX and DOPS rating scales in urology clinical training

ObjectiveThe objective of the study was to investigate the application effect of scenario simulation teaching based on the Mini-Clinical Evaluation Exercise (Mini-CEX) and direct observation of procedural skills (DOPS) rating scales in urology clinical apprenticeship training.Materials and methodsA total of 32 students from the class of 2015–2017 who completed their traineeship in the Department of Urology of our hospital were selected and divided into a research group and a routine group. Routine teaching was implemented for the trainees in the control group, while situational simulation teaching based on the Mini-CEX and DOPS scoring scales was implemented for the trainees in the research group. The Mini-CEX and DOPS scores and student satisfaction were compared at the time of admission and discharge between the two groups.ResultsAt the time of admission, there was no statistically significant difference between the Mini-CEX and DOPS scores of the two groups of trainees (p > 0.05). However, at the time of discharge, the Mini-CEX and DOPS scores of the trainees in the study group were 53.21 ± 4.52 and 81.23 ± 3.57, respectively, which were significantly higher than those in the conventional group (p < 0.001), and the trainees in the study group’s satisfaction with teaching was (20.11 ± 2.31), which was significantly higher than that of the conventional group (p < 0.001).ConclusionImplementing scenario simulation teaching based on the Mini-CEX and DOPS rating scales in urology clinical traineeship can significantly improve trainees’ surgical skills while also leading to high levels of satisfaction with the teaching method.

Evaluation of production line expansion efficiency using computer simulation

Abstract The article discusses the application of computer simulation in the optimization of production processes, particularly in the context of analyzing scenarios related to the addition of new production lines. The conducted research and simulations have shown that computer simulation is a key tool for precise modeling and analysis of various options, allowing for better understanding and optimization of production activities. The article presents the theoretical foundations of simulation along with practical examples of its application, focusing on assessing the impact of different production line configurations on the overall system’s efficiency. The analysis of benefits includes shortening the production cycle time, increasing flexibility, and improving operational efficiency. The challenges associated with implementing computer simulation, such as the need for specialized knowledge and the necessity for continuous updates of simulation models, are also discussed. Based on the research and analyses conducted, the article demonstrates that computer simulation is an effective tool supporting strategic and operational decision-making in production management, particularly in the context of expanding production infrastructure.

Fractal model for heterogeneous effective diffusion and its application in compositional simulations of CO2 flooding in tight reservoirs

Fractal model for heterogeneous effective diffusion and its application in compositional simulations of CO₂ flooding in tight reservoirs

Band Alignment Semimetal Heterojunction-Based Ultrabroadband Photodetector for Noncontact Gesture Interaction with Low Latency.

Non-contact gesture recognition and interaction (NGRI) revolutionizes the natural user interface, fundamentally transforming human interactions with daily-use technology. Conventional NGRI systems frequently encounter obstacles such as pronounced latency and environmental disturbances, including humidity or lighting conditions, resulting in compromised system fluidity and robustness. This study highlights the utilization of silicon-based semimetal heterojunction photodetectors for precise gesture recognition and seamless human-machine interaction. Through the application of band alignment theory and sophisticated TCAD simulation, heterojunction barriers are successfully optimized by fine-tuning parameters including Si doping concentration and semimetal thickness. By strategically aligning vertical material growth and implementing vertical heterojunction configuration, a room temperature detector with exceptional sensitivity (specific detectivity (D*): ≈1011 Jones), ultra-broad spectral range (405-10600 nm), and rapid response time (≈ µs) is achieved. Harnessing its distinguished speed and sensitivity in detecting human infrared radiation, in conjunction with an advanced spatial-temporal comparison algorithm and a multi-channel high-frequency sampling processing design, a NGRI system with low latency, high precision, minimal energy consumption, and versatility across diverse scenarios has been developed. The results pave the way for non-contact sensor design and may further enhance the practicality and user experience of non-contact human-machine interaction systems.

Mobile network and computer software test path selection application in environmental data collection simulation

Mobile network and computer software test path selection application in environmental data collection simulation

A novel modelling approach for realistically-wrapped state recycled aggregates and its application in meso-scale tensile behaviour simulation of recycled concrete

Most of the existing Finite Element (FE) simulations on the mechanical behaviour of Recycled Aggregate Concrete (RAC) generally simplified the Recycled Aggregates (RAs) as simple shapes (e.g., circular / elliptical and polygonal), with residual mortar created by proportional scaling or segmentation of these shapes, which is not consistent with the real situation. Hence, this study proposed an irregular shapes overlay method and a placement algorithm to randomly generate the real-shaped RAs and build the RAC’s mesoscale geometric model. Then, RAC’s mesoscale FE model was established based on the cohesive zone model and got calibrated. The effects of various key parameters (i.e., the RAs substitution percentage, the residual mortar content of substituted RAs and the relative strength of residual to new mortar) on the RAC’s tensile behaviour were studied. The proposed framework can effectively generate RAC’s mesoscale geometric models with various RAs substitution percentages and residual mortar contents. Embedding zero-thickness interface elements between all solid elements can perfectly simulate the RAC’s crack development under tensile loading, offering more realistic cracking paths compared to RAC with simple shape RAs. For the case of new mortar strength larger than the residual one, the tensile strengths of RAC decrease as the RAs substitution percentage and residual mortar content, with a reduction of up to 15%. Otherwise, the tensile strengths show no significant decrease.

Exploring the application of molecular simulation technology in food sector: Focusing on food component interactions and food quality

The intricate interactions between component molecules that occur during food processing and storage are challenging to elucidate with traditional experimental techniques at the molecular level. Molecular simulation technology, conversely, offer an expedient methodology for investigating these molecular behaviors and anticipating macroscopic experimental phenomena. This review outlines the fundamental principles of molecular simulation techniques commonly employed in the field of food science and their associated computational procedures. It also examines their applications in the context of food component interactions and the quality control of proteins, carbohydrates, and lipids. This technique can be employed to elucidate the interactions between food nutrients, facilitate the rapid detection of pesticide and veterinary drug residues, identify foodborne pathogens and biotoxins, and conduct molecular mechanism analysis of toxicological effects at the atomic and molecular levels. By dynamically characterizing changes in molecular conformation, electron transfer and bonding processes, molecular simulation has become an important tool for optimizing food processing and storage conditions. Further studies should investigate the relationship between molecular conformational changes and food quality in greater depth and seek to optimize the application of molecular simulation in food science in order to provide critical insights into practical operations.

Bayesian mesh optimization for graph neural networks to enhance engineering performance prediction

Abstract In engineering design, surrogate models are widely employed to reduce the computational costs of simulations by approximating design variables and geometric parameters from computer-aided design (CAD) models. However, traditional surrogate models often lose critical information when simplified to lower dimensions, and face challenges in handling the complexity of 3D shapes, especially in industrial datasets. To address these limitations, we propose a Bayesian graph neural network (GNN) framework that directly learns geometric features from CAD mesh representations for accurate engineering performance prediction. Our framework leverages Bayesian optimization (BO) to dynamically determine the optimal mesh element size, significantly improving model accuracy while balancing computational efficiency. This approach addresses the challenge of mesh quality by optimizing mesh resolution, ensuring that critical geometric features are preserved in 3D deep-learning-based surrogate models. Unlike existing models that rely on static or predefined mesh resolutions, our method adapts mesh size based on the task, making it highly flexible for a wide range of engineering applications. Experimental results demonstrate that mesh quality directly impacts prediction accuracy, and the proposed BO-EI GNN model outperforms state-of-the-art models such as 3D CNN, SubdivNet, GCN, and GNN in predicting mass, rim stiffness, and disk stiffness. Our method also significantly reduces the computational costs compared to traditional optimization techniques. The proposed framework shows promising potential for application in finite element analysis (FEA) and other mesh-based simulations, enhancing the utility of surrogate models across various engineering fields.

M630L MUTATION DISRUPTS THE STRUCTURE CONFORMATION OF BRUTON TYROSINE KINASE (BTK) DOMAIN IN PATIENT WITH X-LINKED AGAMMAGLOBULINEMIA: INSIGHTS FROM IN SILICO

X-Linked Agammaglobulinemia (XLA) is a rare inherited disease, attributed to mutations found in the Bruton Tyrosine Kinase (BTK) gene. This research outlines the application of Molecular Modelling and Simulation to predict the effects of a novel non-synonymous Single Nucleotide Polymorphism (nsSNP) reported in the BTK kinase domain protein of a male XLA patient. The mutation at position c.1888A>T causes a substitution of p.M630L within the kinase domain of the BTK protein. Functional assessment using in silico prediction tools (SIFT, Polyphen-2, PROVEAN, MutationAssessor, PANTHER, FATHMM, MutPred and MutationTaster) predicted the mutation to be deleterious, potentially disrupting both the structure and function of the protein. Nevertheless, the mutation is not located at the active site of the kinase domain. Consequently, the molecular dynamics (MD) simulations were performed to investigate the impact of amino acid substitution to the three-dimensional (3D) conformational structure of the BTK protein, contributing to the disease phenotype observed in the reported patient. MD analysis, clustering analysis and Principle Component Analysis (PCA) revealed that the 3D conformational structure of the kinase domain of mutant protein to be more compact and rigid compared to the wildtype. Given that the alterations in protein structure can influence their functional characteristics, the p.M630L mutation within the BTK protein kinase domain might disrupt the protein function, potentially impeding the maturation of B cells and contributing to the onset of XLA disease.

Development of system analysis code and its application in transient simulation of supercritical CO2 Brayton cycle direct cooled reactors

The supercritical carbon dioxide (s-CO2) Brayton cycle direct cooled reactor system has the characteristics of high cycle efficiency, compact equipment, and simple layout, which has attracted the attention of researchers recently. The use of s-CO2 Brayton cycle and the elimination of intermediate heat exchangers make the transient characteristics of this reactor different from light water reactors which have rich operating experience. Transient simulation and safety analysis with system code is an essential stage in reactor design and safety verification. Therefore, a system code, named NUSAS (NUclear Safety Analysis and Simulation), was developed in this study for the transient simulation of s-CO2 Brayton cycle direct cooled reactor system. This code is developed based on the advanced two-fluid two-pressure seven-equation model and has the capability of modelling the turbomachinery in s-CO2 Brayton cycle direct cooled reactor system. The transient characteristics during core power step and precooler cooling water mass flow rate step were analyzed. The load tracking and operation control strategy of the reactor was designed, and transient simulations of the wide range (100% to 50%) load tracking process were conducted. According to the simulation results of NUSAS, the transient characteristics of s-CO2 Brayton cycle direct cooled reactor were demonstrated when operating conditions change.

Offset curves: An application in road simulation.

Routable Digital Maps represent a recent advancement driven by the growth of Intelligent Transportation System applications. Efforts have been made to develop and utilize maps within computer systems that can autonomously identify optimal routes. Typically, online map visualizations lack essential information such as traffic patterns, construction zones, or lane counts, which are crucial for designing accurate and realistic routes. This research introduces a route planning method that aggregates and reuses various data points to establish a robust framework for developing new routing plans. The primary goal is to analyze the collected data and create a framework for estimating a geometrically smooth centerline of a road map route. This method involves generating a series of parallel curves, known as offset curves, to facilitate the creation of new routes and alternative lanes. The following examples illustrate some empirical results obtained from this approach.

Application of Computational Fluid Dynamic Simulation of Parent Blood Flow in the Embolization of Unruptured A1 Aneurysms

To investigate the effect of microcatheter shaping based on the parent artery mainstream line of blood flow simulated using the computational fluid dynamics (CFD) technique on embolization of unruptured aneurysms on the posterior wall of the anterior cerebral artery (ACA) A1 segment. Patients with unruptured cerebral aneurysms on the posterior wall of the ACA A1 segment were retrospectively enrolled and treated with endovascular embolization after microcatheter shaping. The clinical, embolization and follow-up data were analyzed. Eight patients were enrolled and treated with endovascular embolization. Eight microcatheters were steam shaped in vitro and were all successfully navigated to the right location in the in vitro experiment. During the embolization procedure, seven microcatheters were successfully navigated to the right location for embolization. In the remaining one patient who had tortuous cerebral arteries, reshaping of the microcatheter based on the parent artery mainstream of blood flow made successful navigation of the microcatheter to the right place. Complete occlusion was obtained in seven (87.5%) aneurysms and residual aneurysm neck in the remaining one (12.5%). At angiographic follow-up in 6 (75%) patients, the Raymond grade was I in 5 (83.3%) and II in the rest one (16.7%). Use of CFD simulation of parent artery blood flow for microcatheter shaping in the embolization of unruptured aneurysms on the posterior wall of the ACA A1 segment is safe and effective in navigating the microcatheter to the right location for embolization, resulting in good stability and support for the embolization.

Optimization of marine hybrid power energy consumption

Abstract The energy and power system of the ship is the core of the ship, and the composite energy storage plays a great role in reducing the load power fluctuation of the ship, which effectively optimizes the energy efficiency index and reliability of the ship. [Objective] To better optimize the ship energy management system, a control model is established according to the characteristics of the energy system of hybrid electric ships. [Method] Energy consumption optimization is carried out by using a rule-based energy control strategy and energy control strategy based on equivalent fuel consumption minimum (ECMS). The dynamic control and strategy of the hybrid ship were optimized, and the data simulation and real ship application were carried out on the hybrid experiment platform. [Results] According to the data and results of the real ship application, the following results were shown: The daily fuel saving rate of the ECMS strategy is 14.6% in normal operating conditions and 13.3% in cruise operating conditions. [Conclusion] It has a great effect on the improvement of ship energy efficiency.

Finite-time region-partitioning intermittent stabilization of memristor-based neural networks and its application

Currently, most memristor-based neural networks (MBNNs) models are switching systems with fixed jumping values, which may limit their applications. To tackle this issue, this paper first constructs a new MBNNs system based on HP memristors. Then the finite time (FT) intermittent stabilization of this MBNNs is investigated via a new finite time stability lemma and the designed no-chattering region-partitioning intermittent control (RPIC). Compared to previous intermittent control, the work or rest intervals in our RPIC strategy are not predesigned, but defined based on the relationship between the constructed V(t) and two auxiliary functions V˘(t), Vˆ(t). Finally, the validity of the proposed MBNNs and the theoretical method is verified through numerical simulation and image encryption application.

A comparative study of the performance of new ridge estimators for multicollinearity: Insights from simulation and real data application

This paper addresses the challenge of multicollinearity in regression models, a condition that inflates the standard errors of coefficients, leading to unreliable estimates and wider confidence intervals. Multicollinearity, characterized by high intercorrelations among independent variables, undermines model accuracy by increasing the variance of the estimated coefficients, making them more sensitive to changes in the data and difficult to interpret. In this paper, we introduce three novel ridge estimators specifically designed to analyze data affected by multicollinearity. These are the Balanced Log Ridge Estimator (BLRE), the Inverse Influence Ridge Estimator (IIRE), and the Adaptive Shrinkage Ridge Estimator (ASRE), all designed to enhance estimation accuracy and stability in the presence of high multicollinearity and noise. Through Monte Carlo simulations and empirical analysis on a highly correlated real dataset, ASRE consistently demonstrates superior performance, achieving the lowest mean squared error compared to existing ridge estimators. The IIRE and BLRE also perform well; however, ASRE proves to be the most robust, especially in extreme scenarios. In contrast, the ordinary least squares estimator performs poorly under these conditions, underscoring the effectiveness of the new estimators. ASRE is recommended for most situations, with IIRE as a reliable alternative, offering significant improvements in handling multicollinearity.

Instrument-Assisted Soft Tissue Mobilization 2-Handed Grip Force Production Consistency During Simulated Treatment: A Technical Report.

Instrument-assisted soft tissue mobilization (IASTM) continues to increase in popularity and utilization among manual therapists. Despite its popularity, little is known about the consistency in peak or average forces that clinicians apply when performing IASTM treatments with a 2-handed grip. The purpose of this study was to examine intraclinician consistency in peak and average forces when applying a 2-handed IASTM grip. Randomized crossover study conducted in a university biomechanics laboratory. Five (5) licensed athletic trainers with prior IASTM training used 5 different IASTM instruments to apply simulated treatment. Average peak forces (Fpeak) and average mean forces (Fmean) were collected via force plate for all 5 IASTM instruments with a skin simulant attached. Descriptive statistics, coefficients of variation (CVs), box and density plots, and Bland-Altman plots were assessed. The clinicians' average Fpeak ranged from 3.0N to 11.6N and average Fmean from 1.9N to 8.1N. Fpeak CVs for all instruments ranged from 14% to 31%, and Fmean CVs ranged from 15% to 35%. Bland-Altman plots indicated that for both Fpeak and Fmean, 97% of the data points fell within the limits of agreement across instruments and clinicians. Mean differences across instruments ranged from 0.9N (91.8g) to 4.1N (418.1g) for Fpeak and from 1.0N (102.0g) to 2.8N (285.5g) for Fmean. Thus, CVs, box and density plots, and Bland-Altman plots supported general force application consistency. Trained IASTM clinicians produced consistent treatment application forces (ie,Fpeak and Fmean) within treatment sessions during 2-handed simulated application.

Application of Simulation in the Management of the Operational Warehouse, A Systematic Literature Review

A significant issue in the application of simulation tools in production processes is the difficulty in effectively integrating these tools into existing logistics systems, which often leads to underutilization and suboptimal performance. According to a study by McKinsey & Company (2017), only 17% of companies have successfully integrated advanced simulation tools into their logistics processes, resulting in considerable inefficiencies. Additionally, a report by Gartner (2020) highlighted that 55% of organizations face significant challenges in aligning simulation technologies with their operational workflows, often due to a lack of expertise and inadequate technological infrastructure. This document provides a qualitative analysis to present a current overview of the application areas and benefits of simulation tools in intralogistics processes through a systematic literature review of 108 articles published between 2011 and 2023. The primary focus to explore is about operational warehouses processes, examining applications by country, knowledge area, simulation tool type, practical or theoretical application and implementation challenges of simulation.

Comparative Analysis and Application of Mass and Heat Transfer Simulation in Fractured Reservoirs Based on Two Fracture Models

The projection-based embedded discrete fracture model (pEDFM) and its counterpart, the embedded discrete fracture model (EDFM), have become standard tools for the depiction of the fractures in reservoir simulations. Despite their widespread use, there are still some unclear areas in modeling the complex processes of mass and heat transfer within fractured reservoirs, particularly in both single-phase and multiphase flow scenarios. Our research introduces a numerical methodology for simulating the mass and heat transfer in fractured reservoirs which is developed by extending the framework of the pEDFM and EDFM. To gauge the effectiveness of these models, we devised two cases which were designed to evaluate the adaptability of the pEDFM and EDFM in scenarios involving an ultra-low permeability fracture or a high permeability fracture under single-phase and multiphase conditions. By using local grid refinement (LGR) as a reference, the results of the pEDFM were in reasonably good agreement with the LGR in terms of pressure, temperature, and saturation distributions. This comparison suggests that the pEDFM has a significant advantage in depicting the mass and heat transfer at the matrix–fracture interface compared to the EDFM. Furthermore, a comprehensive analysis of the flow trajectories in both the pEDFM and EDFM provided a reasonable explanation for their differences. Furthermore, the numerical applications involving the heat extraction of Enhanced Geothermal Systems (EGSs) and the water flooding in fractured reservoirs illustrate the adaptability of the pEDFM in the numerical simulation for complex geological conditions. The insights and conclusions obtained in this paper can enhance our understanding of the distinctions between the pEDFM and EDFM, aiding in the selection of the most suitable method for characterizing the fractures in numerical simulations.