Macroscopic Model Research Articles

Numerical analysis of L-shaped wrinkling behavior of 3D woven preforms based on a novel hybrid element yarn model

Numerical simulation is a key means to evaluate the forming performance of 3D woven preforms (3DWPs). However, the macroscopic continuous model of 3DWPs cannot predict the orientation and deformation of yarns, while the microscopic discrete model is unsuitable for large-size samples forming simulation due to computational constraints. A novel mesoscopic hybrid element yarn model is thus proposed to establish a large-size 3D woven virtual yarn preform (VYP) model and the L-shaped virtual forming simulation model. The L-shaped forming experiment of the 3DWP is designed and executed, and the accuracy of the numerical simulation models is verified from three aspects: the mechanical curve, the sample's macroscopic morphology, and the local meso-structure features. Subsequently, the deformation behavior of the yarn structure inside the 3DWP during the L-shaped forming process is analyzed by combining experimental and simulation results. Furthermore, the effects of the L-shaped angle and chamfer radius on the deformation behavior of the 3DWP are studied, which helps guide the structural design of 3DWPs.

Simulation of 1D atmospheric pressure dielectric barrier discharge in argon

This work aims at modelling an atmospheric-pressure homogeneous barrier discharge in argon, using a time-dependent 1D fluid model coupled to the electric field and plasmo-chemical kinetic equations. The model is chosen to mimic a discharge when a sinusoidal 1 kV voltage at 10 MHz is applied to the terminals. Energy and mass transfer are considered for a macroscopic fluid representation, while energy transfer in molecular collisions and chemical reactions is treated at the microscopic level. The macroscopic model is represented by a set of coupled partial differential equations. Microscopic effects are studied within a discrete model for electronic and molecular collisions in the frame of ZDPlasKin, a plasma modelling numerical tool. The BOLSIG+ solver is employed in solving the electronic Boltzmann equation. An operator splitting technique is used to separate microscopic and macroscopic models. The spatial and temporal evolution of such species and electron transport parameters are presented and discussed.

A hierarchy of the optimal velocity model with optimal path for pedestrian evacuation: From microscopic to macroscopic models

We study the evacuation process of pedestrians by adopting an optimal velocity model. The Eikonal equation is coupled to the optimal velocity model for optimal path to guide the movement directions of pedestrians. It depends on pedestrian density. We derive the corresponding mean field equation, hydrodynamic and scalar models from the scaled microscopic optimal velocity model. Several numerical experiments are performed in a corridor with two exits. We show and compare results on the microscopic as well as on the hydrodynamic and scalar models. Results from microscopic model are closed to the hydrodynamic and scalar models when a large number of particles are considered in microscopic simulation. The computation time increases as number of particles in microscopic simulation increases. The computation times of the hydrodynamic and scalar models are less than the computation time of the microscopic model with large number of particles. Hence it is beneficial to apply the hydrodynamic and scalar models over the microscopic model when a large number of particles in microscopic system are considered.

Effect of moving walkway arrangement on unidirectional crowd flow characteristics

As moving walkways (MWs) are being used in larger airport terminals, train stations and so on, there is growing concern about their impact on the crowd movement. Some existing applications have shown that these passenger conveyors are not always capable of providing a better level of service, therefore, it is necessary to examine the effect of MWs on crowd dynamics thoroughly. In this work, we first construct more realistic numerical experimental scenarios and utilize a macroscopic continuum pedestrian model to simulate crowd movement in long corridors with the MWs under various conditions, including demand level, operation speed, position, landing area, and layout. Then, based on the comparison of experiment results such as total travel time, density distribution, queue size, and total crowd pressure, we analyze the effect of MWs on unidirectional crowd dynamics. From the results, we prove that the presence of a landing area between two segments of MWs can help to split the flow in the main direction and enhance the safety of crowd movement. Moreover, according to our research, compared to increasing the operation speed, modifying the position and layout of MWs is more effective and cost-saving in improving traffic efficiency. In summary, this paper investigates the effect of MWs and provides a better understanding to organizers when designing pedestrian conveyor systems.

An improved continuum model for hypersonic thermal nonequilibrium flow in the near-continuum regime

In this work, the rarefied Couette flow of diatomic gases with thermal nonequilibrium effects is investigated by the direct simulation Monte Carlo (DSMC) method, and a macroscopic computational model is developed to consider the local rarefaction effects for diatomic gases in the near-continuum regime. The nonlinear transport properties of the diatomic gases are studied, indicating that effective viscosity and effective translational thermal conductivity in the shear nonequilibrium state are affected by translational nonequilibrium effects, which obey the same laws for both monatomic and diatomic gases. The transport coefficients of internal energy modes are affected by both translational nonequilibrium and internal energy relaxation, therefore, the effective rotational and vibrational thermal conductivities are related to the effective viscosity through a modified Eucken relation that accounts for internal energy relaxation. Conclusively, effective constitutive relations are newly established as a function of the shear nonequilibrium parameter and the modified Eucken factors for thermal nonequilibrium flows, and these are integrated into the macroscopic two-temperature model. Subsequently, it is assessed in the simulation of hypersonic flows over flat plates and cylinders at various Knudsen numbers. The results show that the surface shear stress and heat flux obtained by the proposed model agree well with the DSMC results, indicating significantly improved performance compared to the conventional Navier–Stokes two-temperature model for hypersonic flows in the near-continuum regime.

Moments of inertia in light deformed nuclei: Pairing and mean-field impacts

The dependence of the moment of inertia [Formula: see text] on the pairing and axial quadrupole deformation [Formula: see text] in [Formula: see text]Mg and [Formula: see text]Ne was investigated. The study is based on quadrupole-constrained calculations with three cranking approaches for [Formula: see text] (Inglis–Belyaev, Thouless–Valatin, adiabatic time-dependent Hartree–Fock) and a representative set of Skyrme forces (SVbas, SkM*, SLy6). At variance with macroscopic collective models, the calculations predict the specific regime [Formula: see text] at [Formula: see text] ([Formula: see text]Mg) and [Formula: see text] ([Formula: see text]Ne), where the pairing breaks down. This regime is explained by two effects: full break up of the pairing and specific evolution of a single dominant particle–hole (1ph) configuration with [Formula: see text]. The analysis of experimental data for the ground-state rotational bands in [Formula: see text]Mg and [Formula: see text]Ne shows that such regime is possible at low spins.

Saddle-node bifurcation control of macroscopic traffic flow model considering vehicle braking effect

Saddle-node bifurcation control of macroscopic traffic flow model considering vehicle braking effect

Modeling Sorption of Water and Vanadium Cations by Ion-Exchange Membranes

We develop a macroscopic model for sorption of ions and solvent by ion-exchange membranes and use it to investigate how different membranes behave when exposed to electrolytes for vanadium flow batteries. The model combines a classical expression for the Gibbs free energy of a moderately concentrated electrolyte containing several ions with a term that describes elastic swelling of a polymer membrane. Parameters describing pairwise interactions between mobile ions and fixed membrane ions were fit to published experimental data and discussed in the context of the competition for sites in ion-exchange membranes. The model provides insights into why sorption of vanadium cations by Nafion is lower than predicted by Donnan theory and why switching to an anion-exchange membrane does not dramatically reduce vanadium sorption.

Multi-scale investigation of heat and momentum transfer in packed-bed TES systems up to 800 K

With the rising cost of energy and the advancement of corporate social responsibility, there is a growing interest in addressing the challenge of recovering and storing high-temperature waste heat. Sensible heat storage in packed beds stands out as a cost-effective and seemingly straightforward solution for high-temperature Thermal Energy Storage (TES). Engineering models developed to design low-temperature TES systems were tentatively used to design this new generation of high-temperature systems. Delving into the physics of coupled heat and mass transfer reveals a lack of validation of this approach. This study seeks to establish a comprehensive bottom-up methodology - from the particle scale up to the system level - to provide informed and validated engineering models for the design of high-temperature TES systems. To achieve this goal, we developed a multi-scale numerical model to explore the physics of heat and momentum transfer in packed-bed TES systems. At the microscopic scale (pore/particle), we consider the flow of a compressible high-temperature gas between the particles, coupled to transient heat conduction within the particles, with particular attention given to incorporating accurate temperature-dependent viscosity for the gas phase and thermal conductivity and density for both solid and gas phases. At the macroscopic scale (engineering), we propose a high-temperature extension of state-of-the-art two-equation TES models. The governing equations considered are the volume-average conservation laws for gas-mass, gas-momentum and energy of both phases. The multi-scale strategy is applied to a randomly packed bed of spherical particles generated with the discrete element method (DEM) software LIGGGHTS. Numerical models for both scales were implemented in the Porous material Analysis Toolbox based on OpenFoam (PATO), which is made available in Open Source by NASA. Microscopic scale simulations were used to infer the effective parameters needed to inform the macroscopic model, namely, permeability, Forchheimer coefficient, effective thermal conductivities, and the heat transfer coefficient. The informed macroscopic model reproduces with excellent accuracy the average temperature fields of the physics-based microscopic model. Pore-scale analysis shows highly three-dimensional flow characterized by reverse flow and strong cross-flow in the packed bed system. Moreover, it indicates the coupling between temperature and velocity fields, where a nonuniform velocity field results in uneven temperature distributions across the fluid and the solid spheres within the packed bed, subsequently affecting the macroscopic heat transfer coefficient. The overall strategy is validated by comparison to available experimental data. This bottom-up methodology contributes to the understanding and opens new perspectives for a more precise design and monitoring of high-temperature TES systems.

Micro-macro modeling of tensile behavior of a friction stir welded hybrid joint of AlSi10Mg parts produced by powder bed fusion and casting

Additive manufacturing (AM) has gained considerable interest due to its ability to produce lightweight parts with hierarchical microstructures. However, the current constraints on the build chamber size in powder-bed fusion type AM processes limit its industrial application. A hybrid welded joint, consisting of an AM-processed and a conventionally manufactured part, can be employed to produce larger components. Due to the varying processing conditions, these hybrid welded joints contain a wide range of microstructural heterogeneities, which influences the mechanical properties of the joint. Using a numerical model to predict the mechanical behavior of welded joints by considering the microstructural variations is essential for the safe and reliable implementation of hybrid welded joints. This study aims to predict the local tensile behavior of each region of a hybrid friction-stir welded joint of AlSi10Mg produced by laser-based powder bed fusion and casting using a microstructure-sensitive model as well as the global tensile behavior by considering the properties of each region using a joint macroscopic model. The results from this modeling approach agree well with the experimental results. Therefore, this method can predict the mechanical behavior of hybrid welded joints and can establish the structure–property relationship in each weld region.

A macroscopic model of mixed traffic flow Considering the characteristics of the vicinity of traffic signals from CPS perspective

A macroscopic model of mixed traffic flow Considering the characteristics of the vicinity of traffic signals from CPS perspective

Optimal Placement of Sensors in Traffic Networks Using Global Search Optimization Techniques Oriented towards Traffic Flow Estimation and Pollutant Emission Evaluation

The relationship between estimating traffic flow and evaluating pollutant emissions lies in understanding how vehicular traffic patterns affect air quality. Traffic flow estimation is a complex field that involves a variety of analytical techniques to understand, predict, and manage the flow of vehicles on road networks. Different types of analyses commonly employed in this area are statistical analysis (e.g., descriptive statistics, inferential statistics, time series analysis), mathematical modeling (macroscopic models, microscopic models, mesoscopic models), computational methods (e.g., simulation modeling, machine learning, and AI techniques), geospatial analysis (e.g., geographic information systems (GISs), spatial data analysis), network analysis (e.g., graph theory and network flow models). In sensor network setups, the strategic placement of sensors is crucial, primarily due to the challenges posed by limited energy supplies, restricted storage capabilities, and the demands on processing and communication, all of which significantly impact maintenance costs and hardware limitations. To mitigate the burden on processing and communication, it is essential to deploy a limited number of sensors strategically. In practical applications, achieving an optimal layout of physical sensors (i.e., placing sensors within the network in such a way as to meet a specific optimality criterion, such as identifying the minimum number of sensors required to ensure the ability to design reliable state observers capable of reconstructing the network’s state based on the available data) is essential for the accurate monitoring of large-scale systems, including traffic flow or the distribution networks of water and gas. In the context of traffic systems, addressing the challenge of full link flow observability, that is, the ability to accurately monitor and assess the flow of entities (i.e., vehicles) across all the links or pathways within a network, entails selecting the smallest number of traffic sensors from a larger set to install. The goal is to choose a subset of p sensors, which may include redundancies, from a pool of n>>p potential sensors. This is conducted to maintain the structural observability of the entire traffic network. This concept pertains to deducing the complete internal state (traffic volume on each road link in the network) from external outputs and inputs (measurements from sensors). The traditional concept of system observability serves as a criterion for sensor placement. This article presents the development of a simulated annealing heuristic to address the selection problem. The selected sensors are then applied to construct a Luenberger observer, a mathematical construct used in control theory to accurately estimate the internal state of a dynamic system based on its inputs and outputs. Numerical simulations are carried out to demonstrate the effectiveness of this method, and a performance analysis using a digital twin of a transport network, designed using the Aimsun Next software, are also carried out to assess traffic flow and associated pollutant emissions. In particular, we examine a traffic network comprising 21 roads. We address the sensor selection problem by identifying an optimal set of six sensors, which facilitates the design of a Luenberger observer. This observer enables the reconstruction of traffic flow across the network with minimal estimation error. Furthermore, by integrating this observer with data from the Aimsun Next software, we assess the pollutant emissions related to traffic flow. The results indicate a high accuracy in estimating pollutant levels.

High‐Temperature Thermal Conductivity of Fiber Hybrid Ceramic Matrix Composites with Different Modes

Thermal conductivity (TC) is one of the important thermal property evaluation parameters for high‐temperature‐resistant fiber‐reinforced composites. Herein, based on the flexible‐oriented three‐dimensional (3D) woven technology, fiber hybrid ceramic matrix composites (FH‐CMCs) with different hybrid modes are designed and fabricated by using carbon fiber and silicon carbide fiber. The representative volume element (RVE) models of fiber bundle and matrix with mesoscopic characteristics and the macroscopic RVE models with hybrid structure characteristics are established. The heat flux distribution and effective TCs of FH‐CMCs in the thickness and in‐plane directions at different temperatures are obtained by finite element analysis (FEA) and experimental tests. The results show that the FEA and experimental results of the TCs of different FH‐CMCs are in good agreement. In the range of 25–1000 °C, the effective TCs of FH‐CMC gradually decrease with the increase in temperature. The TCs in the thickness direction of different FH‐CMCs are directly proportional to the hybrid ratio, and the TCs in the in‐plane direction are inversely proportional to the hybrid ratio. The heat flux and temperature field distributions of the FH‐CMCs are mainly affected by the TCs of the components, fiber hybrid ratio, and orientation, and the distribution along different directions are obviously different. The research results are helpful to provide theoretical and experimental reference for the structural design and performance improvement of hybrid composites.

Mechanical properties and design of hybrid composites of carbon and jute fibers with polypropylene

Natural fibers have both environmentally friendly and ecological advantages in the fiber material industry. Improving the mechanical properties and durability of natural fiber composites is an important research approach. As one of the representatives of synthetic fibers, carbon fiber has excellent mechanical properties and chemical stability, but its high cost and low toughness limit further application. Combining environmentally friendly, low-cost natural fibers with carbon fibers can broaden the development of natural fiber-reinforced composites in industrial applications. In this paper, the composites made of jute fibers and polypropylene (PP) as well as carbon fibers coupled with KH550 modification were prepared and their properties were investigated. By introducing a macroscopic model similar to the core-shell structure, an environmentally friendly material with better mechanical properties was achieved, with carbon fiber woven fabric serving as the shell and jute fiber mat as the core. The results show that a low-cost hybrid composite was successfully prepared by using a small amount of carbon fiber woven fabric instead of jute fiber mat, and the tensile, flexural and impact properties of the composites were improved by 488.27%, 70.75% and 463.39%, respectively, compared with those of the pure jute fibers composites. This study provides a rapid and reliable approach to improve the mechanical properties of the natural fiber composites.

Incorporating Driving Behavior Metrics Derived from Naturalistic Driving Data into Macroscopic Safety Modeling

This research leveraged datasets from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) to explore the potential benefits of incorporating macroscopic measures derived from NDS into traditional safety modeling. Large datasets with traversals from more than 1,700 unique drivers were used to extract driving behavior on freeway segments. New sets of time series totaling over 1,600 h of driving were developed, including vehicle dynamics exclusively during car-following, while also tracking the spacing between the instrumented vehicle and the vehicle being followed. This paper focuses on the statistical modeling of crash frequency incorporating macroscopic metrics derived from the new time-series datasets as regards the mean, median, variance, and 85th percentile of vehicle spacing, vehicle speed, and traffic density. Results of this exploration indicate that an increase in the traffic density variance, an increase in the speed variance, and a decrease in the mean vehicle spacing had significant effects associated with increases in multi-vehicle crash frequencies. These results can be used to estimate the safety effect of countermeasures that may change speed, density, and or spacing, along with changes in annual average daily traffic (AADT).

Wear evaluation of hard disk drive head based on a converter-like neural network

Hard disk drives (HDDs) are indispensable industrial products in modern information society, with their heads playing a critical role in read/write performance, which is directly affected by head wear. Head wear at nanoscale challenges the applicability of traditional macroscopic physical wear models, necessitating innovative solutions for accurate wear estimation. Data-driven methodologies have emerged as an effective paradigm, yet facing limitations when addressing the multiple phases of head wear degradation. Herein, building on insights into nanoscale wear and interface dynamics, we devised a "converter" module. Simultaneously, a physical constraint is incorporated into the network to guarantee the physical compliance consistency of wear outcomes. The effectiveness of this method is validated on real worn heads. An interpretive analysis is performed, involving the visualization of GRU module's hidden units and the quantification of feature’s activity contribution. Our investigations revealed that the converter results in a higher proportion of neurons becoming actively engaged and discriminative in wear evaluation, thereby enhancing the network's sensitivity to wear-related variations. Meanwhile, the trace ratios of scatter indicate that the converter improves the discriminability of the input data, thereby bolstering the network's capacity to differentiate between wear states.

Data‐driven models for traffic flow at junctions

The simulation of traffic flow on networks requires knowledge on the behavior across traffic intersections. For macroscopic models based on hyperbolic conservation laws, there exist nowadays many ad‐hoc models describing this behavior. Based on real‐world car trajectory data we propose a new class of data‐driven models with the requirements of being consistent to networked hyperbolic traffic flow models. To this end, the new models combine artificial neural networks with a parametrization of the solution space to the half‐Riemann problem at the junction. A method for deriving density and flux corresponding to the traffic close to the junction for data‐driven models is presented. The model parameters are fitted to obtain suitable boundary conditions for macroscopic first‐ and second‐order traffic flow models. The prediction of various models are compared considering also existing coupling rules at the junction. Numerical results imposing the data‐fitted coupling models on a traffic network are presented exhibiting accurate predictions of the new models.

A comparison of the accumulation-based, trip-based and time delay macroscopic fundamental diagram models

Macroscopic fundamental diagram (MFD) is widely applied in network modelling and management, such as route guidance and vehicle relocation, which are formulated as generalised dynamic traffic assignment (DTA) problems. MFD can effectively reduce the spatial dimension thus making the generalised DTA problems computationally efficient. In the literature, three MFD models, the accumulation-based model, the trip-based model, and the time delay model, were proposed to capture the traffic flow propagation under different traffic conditions and demand scenarios. However, no consensus has been reached on their computational efficiency and which model should be chosen under certain traffic conditions and demand scenarios. In this paper, we revisit these models regarding two important theoretical properties regarding flow propagation in the DTA, i.e. the first-in-first-out (FIFO) principle and causality. Corresponding dynamic network loading algorithms are designed to compare their numerical accuracy and computational efficiency. Numerical comparisons with Lighthill-Whitham-Richards (LWR) model and a micro simulator confirm that the accumulation-based model is valid in saturation, the trip-based model is valid in free-flow, while the time delay model provides a good approximation in both free-flow and saturation scenarios. On the other hand, violation of strict causality is observed in the accumulation-based and trip-based models, rendering it hard to pursue analytical DTA. This issue is not observed in the time delay model. Overall, the time delay model is a promising alternative for dynamic network loading in large-scale network applications.

Investigating built environment and traffic flow impact on crash frequency in urban road networks

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

A Constitutive Model for Stud Connection in Composite Structures

The complexity of finite element analysis for composite structures can be significantly reduced by representing the connector and adjacent concrete as a macroscopic element. Nevertheless, the prevailing macroscopic models for shear connections predominantly employ nonlinear elastic theory. This approach introduces inaccuracies in estimating structural stiffness and load-bearing capabilities, primarily due to its inability to precisely capture the cumulative effects of plastic damage. In response, this study introduces a novel macroscopic elastoplastic model grounded in plasticity theory, aimed at accurately characterizing the nonlinear behavior of stud connections subjected to concurrent shear and tensile forces. This paper meticulously delineates the implementation of the elastoplastic constitutive model using the backward Euler method for numerical integration. It further articulates the derivation of the consistent tangent stiffness, which aligns with the convergence efficiency of the Newton–Raphson iterative approach. The computation of the element stiffness matrix for a two-node element is executed via the governing equation inherent to the finite element method. An exemplar macroelement test conducted in ABAQUS affirms the implicit backward Euler scheme’s stability and consistency across varying tolerances. Validation of the elastoplastic model against empirical test outcomes corroborates its efficacy, demonstrating the model’s precision in predicting the load–displacement behavior of stud connections under the influence of shear and tensile forces.