Journal Description
Modelling
Modelling
is an international, peer-reviewed, open access journal on theory and applications of modelling and simulation in engineering science, published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, EBSCO, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.8 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review and reviewer names are published annually in the journal.
Latest Articles
Stepwise Regression for Increasing the Predictive Accuracy of Artificial Neural Networks: Applications in Benchmark and Advanced Problems
Modelling 2024, 5(1), 153-179; https://doi.org/10.3390/modelling5010009 - 12 Jan 2024
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A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression
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A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression between the output and the input data. In this study, the SR procedure adds quadratic terms and products of the input variables on pairs. Afterwards, the ANN is trained based on the enhanced input data obtained by SR. After testing the proposed SR-ANN algorithm in four benchmark function approximation problems found in the literature, six examples of multivariate training data are considered, of two different sizes (big and small) often encountered in engineering applications and of three different distributions in which the diversity and correlation of the data are varied, and the testing performance of the ANN for varying sizes of its hidden layer is investigated. It is shown that the proposed SR-ANN algorithm can reduce the prediction error by a factor of up to 26 and increase the regression coefficient between predicted and actual data in all cases compared to ANNs trained with ordinary algorithms.
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Open AccessArticle
Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control
Modelling 2024, 5(1), 117-152; https://doi.org/10.3390/modelling5010008 - 03 Jan 2024
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Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy
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Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model.
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(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems)
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Modeling the Market-Driven Composition of the Passenger Vehicle Market during the Transition to Electric Vehicles
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Modelling 2024, 5(1), 99-116; https://doi.org/10.3390/modelling5010007 - 27 Dec 2023
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The automotive market is currently shifting away from traditional vehicles reliant on internal combustion engines, favoring battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). The widespread acceptance of these vehicles, especially without government subsidies, hinges on market
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The automotive market is currently shifting away from traditional vehicles reliant on internal combustion engines, favoring battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). The widespread acceptance of these vehicles, especially without government subsidies, hinges on market dynamics, particularly customers opting for vehicles with the lowest overall cost of ownership. This paper aims to model the total cost of ownership for various powertrains, encompassing conventional vehicles, HEVs, PHEVs, and BEVs, focusing on both sedans and sports utility vehicles. The modeling uses vehicle dynamics to approximate the fuel and electricity consumption rates for each powertrain. Following this, the analysis estimates the purchase cost and the lifetime operational cost for each vehicle type, factoring in average daily mileage. As drivers consider vehicle replacements, their choice tends to lean towards the most economical option, especially when performance metrics (e.g., range, acceleration, and payload) are comparable across the choices. The analysis seeks to determine the percentage of drivers likely to choose each vehicle type based on their specific driving habits. Advances in battery technology will reduce the battery weight and cost; further, the cost of electricity will decrease as more renewable energy sources will be integrated into the grid. In turn, the total cost of ownership will decrease for the electrified vehicles. By following battery trends, this study is able to model the makeup of the automotive market over time as it transitions from fossil-fuel based vehicles to fully electric vehicles. The model finds until the cost of batteries and electricity is significantly reduced, the composition of the vehicle market is a mixture of all vehicle types.
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DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts
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Modelling 2024, 5(1), 85-98; https://doi.org/10.3390/modelling5010006 - 27 Dec 2023
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The remarkable growth of process mining applications in care pathway monitoring is undeniable. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms,
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The remarkable growth of process mining applications in care pathway monitoring is undeniable. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of our approach.
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(This article belongs to the Special Issue Promoting Interoperability within Modelling and Simulation Applications)
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Shell-Based Finite Element Modeling of Herøysund Bridge in Norway
Modelling 2024, 5(1), 71-84; https://doi.org/10.3390/modelling5010005 - 23 Dec 2023
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This paper thoroughly examines the application of the Finite Element Method (FEM) to the numerical modal analysis of Herøysund Bridge, focusing on the theoretical backdrop, the construction process, and FEM techniques. This work examines the specific applied FEM approaches and their advantages and
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This paper thoroughly examines the application of the Finite Element Method (FEM) to the numerical modal analysis of Herøysund Bridge, focusing on the theoretical backdrop, the construction process, and FEM techniques. This work examines the specific applied FEM approaches and their advantages and disadvantages. This Herøysund Bridge analysis employs a two-pronged strategy consisting of a 3D–solid model and a shell model. To forecast the physical behavior of a structure, assumptions, modeling methodologies, and the incorporation of specific components such as pillars are applied to both approaches. This research also emphasizes the importance of boundary conditions, examining the structural effects of standard Earth gravity, a post-tensioned load, and a railing and asphalt load. The Results section thoroughly explores the mode shapes and frequencies of the 3D–solid and shell models. The conclusion of this work includes findings obtained from the study, implications for Herøysund Bridge, and a comparison of both modeling strategies. It also incorporates ideas for future research and guides employing FEM 3D–solid and shell methods to design and construct more efficient, resilient, and durable bridge structures.
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(This article belongs to the Section Modelling in Engineering Structures)
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Life-Cycle Assessment of an Office Building: Influence of the Structural Design on the Embodied Carbon Emissions
Modelling 2024, 5(1), 55-70; https://doi.org/10.3390/modelling5010004 - 22 Dec 2023
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In 2020, 37% of global CO2eq. emissions were attributed to the construction sector. The major effort to reduce this share of emissions has been focused on reducing the operational carbon of buildings. Recently, awareness has also been raised on the role
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In 2020, 37% of global CO2eq. emissions were attributed to the construction sector. The major effort to reduce this share of emissions has been focused on reducing the operational carbon of buildings. Recently, awareness has also been raised on the role of embodied carbon: emissions from materials and construction processes must be urgently addressed to ensure sustainable buildings. To assess the embodied carbon of a building, a life-cycle assessment (LCA) can be performed; this is a science-based and standardized methodology for quantifying the environmental impacts of a building during its life. This paper presents the comparative results of a “cradle-to-cradle” building LCA of an office building located in Luxembourg with 50 years of service life. Three equivalent structural systems are compared: a steel–concrete composite frame, a prefabricated reinforced concrete frame, and a timber frame. A life-cycle inventory (LCI) was performed using environmental product declarations (EPDs) according to EN 15804. For the considered office building, the steel–concrete composite solution outperforms the prefabricated concrete frame in terms of global warming potential (GWP). Additionally, it provides a lower GWP than the timber-frame solution when a landfill end-of-life (EOL) scenario for wood is considered. Finally, the steel–concrete composite and timber solutions show equivalent GWPs when the wood EOL is assumed to be 100% incinerated with energy recovery.
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(This article belongs to the Section Modelling in Engineering Structures)
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Finite Element In-Depth Verification: Base Displacements of a Spherical Dome Loaded by Edge Forces and Moments
Modelling 2024, 5(1), 37-54; https://doi.org/10.3390/modelling5010003 - 21 Dec 2023
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Nowadays, engineers possess a wealth of numerical packages in order to design civil engineering structures. The finite element method offers a variety of sophisticated element types, nonlinear materials, and solution algorithms, which enable engineers to confront complicated design problems. However, one of the
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Nowadays, engineers possess a wealth of numerical packages in order to design civil engineering structures. The finite element method offers a variety of sophisticated element types, nonlinear materials, and solution algorithms, which enable engineers to confront complicated design problems. However, one of the difficult tasks is the verification of the produced numerical results. The present paper deals with the in-depth verification of a basic problem, referring to the axisymmetric loading by edge forces/moments of a spherical dome, truncated at various roll-down angles, . Two formulations of analytical solutions are derived by the bibliography; their results are compared with those produced by the implementation of the finite element method. Modelling details, such as the finite element type, orientation of joints, application of loading, boundary conditions, and results’ interpretation, are presented thoroughly. Four different ratios of the radius of curvature, and shell’s thickness, and are examined in order to investigate the compatibility between the implementation of the finite element method to the “first-order” shell theory. The discussion refers to the differences not only between the numerical and analytical results, but also between the two analytical approaches. Furthermore, it emphasizes the necessity of contacting even linear elastic preliminary verification numerical tests as a basis for the construction of more elaborated and sophisticated models.
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Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs
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Modelling 2024, 5(1), 16-36; https://doi.org/10.3390/modelling5010002 - 20 Dec 2023
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The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR,
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The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°.
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Machine Learning-Assisted Characterization of Pore-Induced Variability in Mechanical Response of Additively Manufactured Components
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Modelling 2024, 5(1), 1-15; https://doi.org/10.3390/modelling5010001 - 19 Dec 2023
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Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total
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Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material’s behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes.
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Modelling the Acoustic Propagation in a Test Section of a Cavitation Tunnel: Scattering Issues of the Acoustic Source
Modelling 2023, 4(4), 650-665; https://doi.org/10.3390/modelling4040037 - 08 Dec 2023
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The prediction of the underwater-radiated noise for a vessel is classically performed at a model scale and extrapolated by semi-empirical laws. The accuracy of such a method depends on many parameters. Among them, the acoustic propagation model used to estimate the noise measured
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The prediction of the underwater-radiated noise for a vessel is classically performed at a model scale and extrapolated by semi-empirical laws. The accuracy of such a method depends on many parameters. Among them, the acoustic propagation model used to estimate the noise measured at a model scale is important. The present study focuses on the impact of the presence of a source in the transverse plane. The scattering effect, often neglected in many studies, is here investigated. Applying different methods for computation, we perform several simulations of the acoustic pressure field to show the influence of the scattered field. We finally discuss the results and draw some conclusions about the scattering effect in our experimental configuration.
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Finite Element Modeling and Analysis of Perforated Steel Members under Blast Loading
Modelling 2023, 4(4), 628-649; https://doi.org/10.3390/modelling4040036 - 01 Dec 2023
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Perforated steel members (PSMs) are now frequently used in building construction due to their beneficial features, including their proven blast-resistance abilities. To safeguard against structural failures from explosions and terrorist threats, perforated steel beams (PSBs) and perforated steel columns (PSCs) offer a viable
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Perforated steel members (PSMs) are now frequently used in building construction due to their beneficial features, including their proven blast-resistance abilities. To safeguard against structural failures from explosions and terrorist threats, perforated steel beams (PSBs) and perforated steel columns (PSCs) offer a viable alternative to traditional steel members. This is attributed to their impressive energy absorption potential, a result of their combined high strength and ductile behavior. In this study, numerical examinations of damage assessment under the combined effects of gravity and blast loads are carried out to mimic real-world scenarios of external explosions close to steel structures. The damage assessment for PSBs and PSCs considers not just the initial deformation from the blast, but also takes into account the residual capacities to formulate dependable damage metrics post-explosion. Comprehensive explicit finite element (FE) analyses are performed with the LSDYNA software. The FE model, when compared against test results, aligns well across all resistance phases, from bending and softening to tension membrane regions. This validated numerical model offers a viable alternative to laboratory experiments for predicting the dynamic resistance of PSBs and PSCs. Moreover, it is advisable to use fully integrated solid elements, featuring eight integration points on the element surface, in the FE models for accurate predictions of PSBs’ and PSCs’ behavior under blast loading. A parametric study is presented to investigate the effect of web-opening shapes, retrofitting, and different blast scenarios. The results obtained from the analytical FE approaches are used to obtain the ductile responses of PSMs, and are considered an important key in comparisons among the studied cases.
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Generalized Fiducial Inference for the Generalized Rayleigh Distribution
Modelling 2023, 4(4), 611-627; https://doi.org/10.3390/modelling4040035 - 17 Nov 2023
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This article focuses on the interval estimation of the generalized Rayleigh distribution with scale and shape parameters. The generalized fiducial method is used to construct the fiducial point estimators as well as the fiducial confidence intervals, and then their performance is compared with
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This article focuses on the interval estimation of the generalized Rayleigh distribution with scale and shape parameters. The generalized fiducial method is used to construct the fiducial point estimators as well as the fiducial confidence intervals, and then their performance is compared with other methods such as the maximum likelihood estimation, Bayesian estimation and parametric bootstrap method. Monte Carlo simulation studies are carried out to examine the efficiency of the methods in terms of the mean square error, coverage probability and average length. Finally, two real data sets are presented to demonstrate the applicability of the proposed method.
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Modelling Detection Distances to Small Bodies Using Spacecraft Cameras
Modelling 2023, 4(4), 600-610; https://doi.org/10.3390/modelling4040034 - 17 Nov 2023
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Small bodies in the Solar System are appealing targets for scientific and technological space missions, owing to their diversity in intrinsic and extrinsic properties, besides orbit and other factors. Missions to small bodies pass through the critical onboard object detection phase, where the
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Small bodies in the Solar System are appealing targets for scientific and technological space missions, owing to their diversity in intrinsic and extrinsic properties, besides orbit and other factors. Missions to small bodies pass through the critical onboard object detection phase, where the body’s light becomes visible to the spacecraft camera. The relative line-of-sight to the object is acquired and processed to feed relative guidance and navigation algorithms, therefore steering the spacecraft trajectory towards the target. This work assesses the distance of detection for each small body in the Solar System considering the target radiometric properties, three typical spacecraft camera setups, and the relative observation geometry by virtue of a radiometric model. Several uncertainties and noises are considered in the modelling of the detection process. The detection distances for each known small body are determined for small-, medium-, and large-class spacecraft. This proves useful for early mission design phases, where a waypoint for detection needs to be determined, allowing the shift from an absolute to a relative guidance and navigation phase. The work produces an extensive dataset that is freely accessible and useful for teams working on the design phases of space missions.
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An Extension of the Susceptible–Infected Model and Its Application to the Analysis of Information Dissemination in Social Networks
Modelling 2023, 4(4), 585-599; https://doi.org/10.3390/modelling4040033 - 15 Nov 2023
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Social media significantly influences business, politics, and society. Easy access and interaction among users allow information to spread rapidly across social networks. Understanding how information is disseminated through these new publishing methods is crucial for political and marketing purposes. However, modeling and predicting
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Social media significantly influences business, politics, and society. Easy access and interaction among users allow information to spread rapidly across social networks. Understanding how information is disseminated through these new publishing methods is crucial for political and marketing purposes. However, modeling and predicting information diffusion is challenging due to the complex interactions between network users. This study proposes an analytical approach based on diffusion models to predict the number of social media users engaging in discussions on a topic. We develop a modified version of the susceptible–infected (SI) model that considers the heterogeneity of interactions between users in complex networks. Our model considers the network structure, abandons the assumption of homogeneous mixing, and focuses on information diffusion in scale-free networks. We provide explicit algorithms for modeling information propagation on different types of random graphs and real network structures. We compare our model with alternative approaches, both those considering network structure and those that do not. The accuracy of our model in predicting the number of informed nodes in simulated information diffusion networks demonstrates its effectiveness in describing and predicting information dissemination in social networks. This study highlights the potential of graph-based epidemic models in analyzing online discussion topics and understanding other phenomena spreading on social networks.
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Using Discrete-Event Simulation to Balance Staff Allocation and Patient Flow between Clinic and Surgery
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Modelling 2023, 4(4), 567-584; https://doi.org/10.3390/modelling4040032 - 15 Nov 2023
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We consider the problem of system-level balanced scheduling in a pediatric hospital setting. A hospital clinic has a queue for patients needing care. After being seen in clinic, many require follow-up surgery, for which they also wait in a queue. The rate-limiting factor
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We consider the problem of system-level balanced scheduling in a pediatric hospital setting. A hospital clinic has a queue for patients needing care. After being seen in clinic, many require follow-up surgery, for which they also wait in a queue. The rate-limiting factor is physician availability for both clinic visits and surgical cases. Although much existing work has been done to optimize clinic appointments, as well as to optimize surgical appointments, this novel approach models the entire patient journey at the system level, through both clinic and surgery, to optimize the total patient experience. A discrete-event simulation model of the system was built based on historic patient encounter data and validated. The system model was then optimized to determine the best allocation of physician resources across the system to minimize total patient wait time using machine learning. The results were then compared to baseline.
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Finite Element Modeling Aspects of Buried Large Diameter Steel Pipe–Butterfly Valve Interaction
Modelling 2023, 4(4), 548-566; https://doi.org/10.3390/modelling4040031 - 10 Nov 2023
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Buried flexible pipes are allowed to deflect up to 2–5% of the pipe diameter, which can become problematic for the connected direct-bury, large-diameter butterfly valves. The complex behavior of the pipe–valve–soil system makes it difficult to predict the deflection of the pipe/valve system.
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Buried flexible pipes are allowed to deflect up to 2–5% of the pipe diameter, which can become problematic for the connected direct-bury, large-diameter butterfly valves. The complex behavior of the pipe–valve–soil system makes it difficult to predict the deflection of the pipe/valve system. In the absence of field/experimental studies, the application of finite element analysis (FEA) seems necessary to predict deflection and stresses and to avoid potential downtime associated with disruption of service. This paper described the FEA of a large-diameter pipe–valve system, with different backfills under gravity, overburden, and internal pressure loads. The effects of modeling different components of the system (e.g., flanges, bearing housing, gate disc, etc.) were described and investigated. The goal of this study was to provide insight into the design and installation of direct-bury pipe–valve systems and evaluate current installation methods in the absence of guidelines. In addition, the level of modeling details required for FEA to yield accurate results was discussed.
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The Data Assimilation Approach in a Multilayered Uncertainty Space
Modelling 2023, 4(4), 529-547; https://doi.org/10.3390/modelling4040030 - 08 Nov 2023
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The simultaneous consideration of a numerical model and of different observations can be achieved using data-assimilation methods. In this contribution, the ensemble Kalman filter (EnKF) is applied to obtain the system-state development and also an estimation of unknown model parameters. An extension of
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The simultaneous consideration of a numerical model and of different observations can be achieved using data-assimilation methods. In this contribution, the ensemble Kalman filter (EnKF) is applied to obtain the system-state development and also an estimation of unknown model parameters. An extension of the Kalman filter used is presented for the case of uncertain model parameters, which should not or cannot be estimated due to a lack of necessary measurements. It is shown that incorrectly assumed probability density functions for present uncertainties adversely affect the model parameter to be estimated. Therefore, the problem is embedded in a multilayered uncertainty space consisting of the stochastic space, the interval space, and the fuzzy space. Then, we propose classifying all present uncertainties into aleatory and epistemic ones. Aleatorically uncertain parameters can be used directly within the EnKF without an increase in computational costs and without the necessity of additional methods for the output evaluation. Epistemically uncertain parameters cannot be integrated into the classical EnKF procedure, so a multilayered uncertainty space is defined, leading to inevitable higher computational costs. Various possibilities for uncertainty quantification based on probability and possibility theory are shown, and the influence on the results is analyzed in an academic example. Here, uncertainties in the initial conditions are of less importance compared to uncertainties in system parameters that continuously influence the system state and the model parameter estimation. Finally, the proposed extension using a multilayered uncertainty space is applied on a multi-degree-of-freedom (MDOF) laboratory structure: a beam made of stainless steel with synthetic data or real measured data of vertical accelerations. Young’s modulus as a model parameter can be estimated in a reasonable range, independently of the measurement data generation.
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(This article belongs to the Section Modelling in Engineering Structures)
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Supply-Driven Analysis for a Continuous Water Supply Network Based on a Pressure-Based Outflow at the House Outlets under Peak Withdrawal Scenarios
Modelling 2023, 4(4), 515-528; https://doi.org/10.3390/modelling4040029 - 08 Nov 2023
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This research brings a new analysis method for a continuous water supply distribution network. The number of house service connections in different story buildings, rather than the nodal peak demand, shall be accounted for in the analysis. This work aims to consider the
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This research brings a new analysis method for a continuous water supply distribution network. The number of house service connections in different story buildings, rather than the nodal peak demand, shall be accounted for in the analysis. This work aims to consider the flow when pipes are opened in the house plumbing systems. The approach deviates from a traditional peak demand-based analysis of the water distribution network. The analysis gives the flow rate at each nodal point that could be observed in the different story buildings. The methodology is applied to a hypothetical network and shows how much flow and nodal pressure can occur when different percentages of consumers are in an active state. This study indicates that the peak demand-based sizing of the supply pipes could have a deficient capacity under real scenarios. The proposed analysis method will help to understand the actual behavior of the network.
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(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems)
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Reduced-Dimension Surrogate Modeling to Characterize the Damage Tolerance of Composite/Metal Structures
Modelling 2023, 4(4), 485-514; https://doi.org/10.3390/modelling4040028 - 07 Nov 2023
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Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization. To overcome this curse of dimensionality and to minimize computational resource requirements, this research demonstrates a user-friendly approach to
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Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization. To overcome this curse of dimensionality and to minimize computational resource requirements, this research demonstrates a user-friendly approach to formulating a reduced-dimension surrogate model that represents a high-dimensional, high-fidelity source model. This approach was developed specifically for a non-expert using commercially available tools. In this approach, the complex physical behavior of the high-fidelity source model is separated into individual, interacting physical behaviors. A separate reduced-dimension surrogate model is created for each behavior and then all are summed to formulate the reduced-dimension surrogate model representing the source model. In addition to a substantial reduction in computational resources and comparable accuracy, this method also provides a characterization of each individual behavior providing additional insight into the source model behavior. The approach encompasses experimental testing, finite element analysis, surrogate modeling, and sensitivity analysis and is demonstrated by formulating a reduced-dimension surrogate model for the damage tolerance of an aluminum plate reinforced with a co-cured bonded E-glass/epoxy composite laminate under four-point bending. It is concluded that this problem is difficult to characterize and breaking the problem into interacting mechanisms leads to improved information on influential parameters and efficient reduced-dimension surrogate modeling. The disbond damage at the interface between the resin and metal proved the most difficult mechanism for reduced-dimension surrogate modeling as it is only engaged in a small subspace of the full parameter space. A binary function was successful in engaging this damage mechanism when applicable based on the values of the most influential parameters.
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(This article belongs to the Special Issue Modeling Dynamic Fracture of Materials)
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Autoignition Problem in Homogeneous Combustion Systems: GQL versus QSSA Combined with DRG
Modelling 2023, 4(4), 470-484; https://doi.org/10.3390/modelling4040027 - 25 Oct 2023
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The global quasi-linearization (GQL) is used as a method to study and to reduce the complexity of mathematical models of mechanisms of chemical kinetics. Similar to standard methodologies, such as the quasi-steady-state assumption (QSSA), the GQL method defines the fast and slow invariant
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The global quasi-linearization (GQL) is used as a method to study and to reduce the complexity of mathematical models of mechanisms of chemical kinetics. Similar to standard methodologies, such as the quasi-steady-state assumption (QSSA), the GQL method defines the fast and slow invariant subspaces and uses slow manifolds to gain a reduced representation. It does not require empirical inputs and is based on the eigenvalue and eigenvector decomposition of a linear map approximating the nonlinear vector field of the original system. In the present work, the GQL-based slow/fast decomposition is applied for different combustion systems. The results are compared with the standard QSSA approach. For this, an implicit implementation strategy described by differential algebraic equations (DAEs) systems is suggested and used, which allows for treating both approaches within the same computational framework. Hydrogen–air (with 9 species) and ethanol–air (with 57 species) combustion systems are considered representative examples to illustrate and verify the GQL. The results show that 4D GQL for hydrogen–air and 14D GQL ethanol–air slow manifolds outperform the standard QSSA approach based on a DAE-based reduced computation model.
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