Microscopic Simulation Research Articles

MicroSimACC: an open database for field experiments on the potential capacity impact of commercial Adaptive Cruise Control (ACC)

Commercial availability of vehicle automation has become mainstream. Most of today’s new vehicles can perform longitudinal car following autonomously via Adaptive Cruise Control (ACC). Field experiments demonstrate that today’s commercially available ACC vehicles provide similar headways and capacities as human-driven vehicles on freeways under steady-state and free-flow conditions. However, field tests also demonstrated that the design of today’s commercially available ACC vehicles can lead to further capacity reduction when operating in non-steady-state conditions where queues are present and speeds frequently fluctuate. These experiments generated MicroSimACC, a comprehensive set of field data that encompasses full speed range car following with interruptions from lane change manoeuvres. This will benefit the research community by providing benchmark data for developing models to be integrated into microscopic simulations for more prospective analyses and planning.

Determining Desired Speeds from Vehicle Trajectory Data

Desired speed or free speed distributions are important input parameters for microscopic traffic flow simulations. Whereas driven speeds can be measured, desired speeds are not detectable for all vehicles because of vehicles constraining each other. An established approach to estimating desired speed distributions is the modified Kaplan–Meier approach, which estimates desired speed distributions based on single vehicle data from stationary detectors. We propose a novel approach to determine desired speeds based on vehicle trajectory data. The proposed approach, as well as the modified Kaplan–Meier approach, is applied to a trajectory dataset recorded on a German freeway. With the modified Kaplan–Meier approach, we observed that the resulting desired speed distributions vary by approximately 5 km/h depending on the position of the stationary detectors. The desired speed distributions obtained from the trajectory-based approach are approximately 5 to 10 km/h higher than those estimated by the modified Kaplan–Meier approach. This difference in results is probably because the trajectory-based approach observes each vehicle over a longer distance rather than just at stationary points. Nevertheless, it can be concluded that the estimation of desired speed distributions is subject to a certain degree of inaccuracy. The analysis of the vehicle trajectory data revealed a notable intra-vehicle instability in desired speeds, with a difference of 5 to 7 km/h observed for 40% of the vehicles between different periods of free driving. These findings should be considered in the context of calibrating microscopic traffic flow simulations.

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.

Ultrafast dephasing in solid-state high harmonic generation: macroscopic origin revealed by real-space dynamics [Invited

Using a fully real-space perspective on high harmonic generation (HHG) in solids, we examine the relationship between microscopic response, macroscopic propagation of this response to the far-field, and the extremely short dephasing times routinely used in the theoretical simulations of experimentally measured solid-state HHG spectra. We find that far-field propagation naturally reduces the contribution to the observed HHG emission from electrons that do not return to the lattice site where they have been injected into the conduction band. We then show that extremely short dephasing times routinely used in microscopic simulations suppress many electron trajectories that contribute to the far-field spectra, leading to significant distortions of the true high harmonic response. We show that a real-space based dephasing mechanism, which preferentially suppresses trajectories that veer too far away from their original lattice site, yields HHG spectra that faithfully retain those trajectories that contribute to the far-field spectra while filtering out those that do not, already at the microscopic level. Our findings emphasize the similarities between atomic and solid-state HHG by highlighting the importance of the intensity-dependent phase of HHG emission and address the longstanding issue regarding the origin of extremely short dephasing times in solid-state HHG.

Safety-oriented urban pavement design and evaluation: integrating microscopic simulation and tyre-pavement friction

ABSTRACT Tyre-pavement friction significantly impacts traffic safety performance, yet integrating these topics is often overlooked in pavement engineering. This paper addresses this gap by utilising microscopic traffic simulation and surrogate measures of safety to assess vehicular conflicts severity in diverse urban scenarios and establish friction thresholds for practical implementation. A pre-construction materials selection approach is proposed, employing a predictive model for tyre-pavement friction. Findings reveal that combinations of fine-graded mixtures and low surface texture mineral aggregates can lead to increased conflict severity in urban environments. Furthermore, a post-construction texture assessment using 3D computer vision was applied for verifying compliance with friction thresholds for asphalt pavement surfaces. This study emphasises the potential for cost-effective integration of traffic simulation and safety analysis to an enhanced urban pavement design and evaluation. By incorporating safety-based tyre-pavement friction considerations, decision-making related to materials selection and friction conditions can be improved, leading to safer urban transportation networks.

Analysis of reservoir damage and microscopic seepage simulation in low permeability oil and gas reservoirs based on pore topology structure

This paper investigates the potential factors contributing to reservoir damage in low permeability reservoirs, based on the micro-characteristics of pore throats. Through the utilization of numerical simulations, we investigate the micro-permeability behavior of fluids within these low permeability reservoirs. The simulations produce a micro-numerical field distribution of fluid within the reservoir (comprising velocity field, pressure field, and phase field), facilitating an analysis of evolving patterns and influencing mechanisms between oil and water phases during displacement processes. The simulation results indicate that with an increase in the number and diameter of plugged pores, there is a significant decrease in reservoir permeability. The permeability of pores smaller than 3.0 μm decreases to 92 % of the original value, while that of pores smaller than 3.5 μm drops to 87 %, and that of pores smaller than 5.0 μm decreases to 82 %. The rock stress sensitivity effect leads to a reduction in permeability, with a 94 % decrease when pore deformation reaches 95 %, and an 86.5 % decrease at a deformation level of 85 %. Leveraging these simulation results and considering potential formation damage that may occur during oil and gas field development processes, we propose targeted formation damage controlling measures to provide theoretical underpinning for efficient development strategies in low permeability reservoirs.

Numerical simulation of fingering in the underground hydrogen storage

Underground hydrogen storage has garnered interest in recent years owing to the considerable demand for clean energy. Hydrogen is more diffusive than air and has relatively low density and viscosity. These unique properties induce distinct hydrodynamic phenomena during hydrogen storage. Cushion gas has been proven to be a potential remedy for attenuating the adverse impacts of underground hydrogen storage. To investigate the influence of the cushion gas, a microscopic numerical simulation was performed with Fluent software using the Eulerian multi-fluid model. Carbon dioxide, nitrogen, and methane are usually used as the preferred candidates for cushion gases in underground hydrogen storage systems. In this study, nitrogen was used as the cushion gas and was injected along with hydrogen into heterogeneous porous media with volume fractions ranging from 0% to 70%. A parameterization study was then performed to elucidate the influences of the injection rate and viscosity of the fluid on the fingering pattern. Two representative types of fingering, viscous fingering and capillary fingering, were observed under different gas mixtures and boundary conditions. After the simulation, an image analysis was performed to capture the evolution of the fingering pattern. The specific fingering area, number of branches, and fractal dimensions are proposed as geometric indices to describe the shape of the fingering pattern. The results showed that there was a remarkable enhancement in saturation due to the injection of the cushion gas, depending on the concentration of the gas mixture. This study offers insight on the design of gas mixture injection in underground hydrogen storage and can be further extended to the hydrochemo–mechanical coupled numerical simulation of multiphase gas injection in porous media.

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.

Comparison of probabilistic cruising-for-parking time estimation models

Models of parking search time predominantly rely on the binomial approximation, the calculation of which is dependent solely on the occupancy of the parking network. This paper sheds light on the importance of accounting for competition between parking searchers and the effects of dynamically changing occupancy. To this end, we investigate the accuracy of two probabilistic models, namely the binomial approximation and a model proposed by Cao and Menéndez (CaM). We utilize an agent-based microscopic parking simulation model as a benchmark for comparison purposes. Our findings highlight the significance of considering the competitive dynamics among drivers in the quest for parking spaces and the fluctuations in parking occupancy rates over short time intervals. We highlight the ranges of occupancies for which the binomial approximation and the CaM model provide more reliable estimations of parking search times. The CaM model provides, in general, more accurate estimations of search times since it accounts for various parking network characteristics such as competition and varying occupancy. The binomial approximation underestimates parking search times more substantially at high occupancies with a large number of searching vehicles, whereas the CaM model performs worse for lower spacing between parking spots and lower parking occupancies. Insights from this research are then used to discuss policy implications of accounting for competition and short-term occupancy variations when designing either parking models or parking-related control strategies.

A methodology of cooperative driving based on microscopic traffic prediction

We present a methodology of cooperative driving in vehicular traffic, in which for short-time traffic prediction rather than one of the statistical approaches of artificial intelligence (AI), we follow a qualitative different microscopic traffic prediction approach developed recently (Kerner and Klenov, 2022). In the microscopic traffic prediction approach used for the planning of the subject vehicle trajectory, no learning algorithms of AI are applied; instead, microscopic traffic modeling based on the physics of vehicle motion is used. The presented methodology of cooperative driving is devoted to application cases in which microscopic traffic prediction without cooperative driving cannot lead to a successful vehicle control and trajectory planning. For the understanding of the physical features of the methodology of cooperative driving, a traffic city scenario has been numerically studied, in which a subject vehicle, which requires cooperative driving, is an automated vehicle. Based on microscopic traffic prediction, in the methodology first a cooperating vehicle(s) is found; then, motion requirements for the cooperating vehicle(s) and characteristics of automated vehicle control are predicted and used for vehicle motion; to update predicted characteristics of vehicle motion, calculations of the predictions of motion requirements for the cooperating vehicle and automated vehicle control are repeated for each next time instant at which new measured data for current microscopic traffic situation are available. With the use of microscopic traffic simulations, the evaluation of the applicability of this methodology is illustrated for a simple case of unsignalized city intersection, when the automated vehicle wants to turn right from a secondary road onto the priority road.

Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations.

Soil conditioning of clay based on interface adhesion mechanism: Microscopic simulation and laboratory experiment

Clogging frequently occurs in the cutter head, excavation chamber or screw conveyor when an earth pressure balance (EPB) shield machine is tunneling in soft or silty clay ground with high clay mineral content. In this paper, montmorillonite, kaolinite, and illite were selected as research objects, and molecular dynamics simulation and laboratory experiment were adopted. At the microscopic scale, dynamic contact behavior and interfacial mechanical behavior of the interface between clay minerals and water/surfactant solution was simulated and the interfacial adhesion and conditioning mechanism between clay minerals and water/surfactant solution was revealed. Thus, sodium dodecyl benzene sulfonate surfactant was selected as the main composition of the soil conditioner. Then, the adhesion stress before and after soil conditioning and the contact angles between clay minerals and water/surfactant solution were tested and analyzed at the macroscopic scale. The result shows that the contact angle between droplet and clay mineral surface is an important parameter to characterize soil adhesion. The simulation rules of the microscopic contact angle are consistent with the experiment results. Furthermore, the adsorption energy between microscopic substances is dominated by electrostatic force, which can reflect the adhesion stress between macroscopic substances. Soil adhesion stress can be effectively decreased by adding the surfactant to the soil conditioner.

The Feasibility and Operational Performance of Implementation Median U-turn Intersections: A CRITIC Method

Currently, a growing number of cities are adopting the Median-U-Turn (MUT) intersection design to enhance road capacity and traffic efficiency. The critical question in selecting the right intersection design is how significantly the implementation of MUT can enhance intersection performance, focusing on three key aspects: intersection efficiency, capacity, and the environmental impact of the design. To address this question, an evaluation of operational performance under various prevailing conditions (roadway and control) was conducted using VISSIM, a microscopic simulation platform. This evaluation involved five scenarios: conventional intersections (with increased cycle length, grade separation with a signalized at-grade intersection, grade separation with a roundabout), MUT, and signalized crossover MUT at a dense urban arterial intersection in Amman, Jordan's capital. The performance was compared using several metrics: average control delay, number of stops, average travel speed and time, average stopped delay, Carbon Monoxide (CO) emissions, fuel consumption, and vehicle safety. The Criteria Importance Through Intercriteria Correlation (CRITIC) technique was subsequently used to select the optimal design. The findings indicate that the existing intersection configuration is the least effective, while the MUT with signals at the crossing U-turn points is the most efficient solution.

Enhancing parameter calibration for micro-simulation models: Investigating improvement methods

Calibrating microscopic traffic simulation models is a prerequisite for simulation applications. This study proposes three novel methods to improve the accuracy and interpretability of the calibration model. The proposed approach involves selecting the calibration parameter, refining the model parameter system, and optimizing the calibration results. The first method expands the single-point mean into a multi-point distribution. The cumulative distribution curve of delay was selected as the calibration parameter. The second method divides the parameter system into global and local parameters. Global parameters were calibrated using NGSIM measured data, and local parameters were calibrated through intelligent algorithms. The third method proposes a methodology of parameter clustering recursion based on the genetic algorithm results, with information entropy selected as the analysis index. To evaluate the effectiveness of the proposed optimization methods, this study used NGSIM trajectory data as a case study. Eight simulation schemes based on the three optimization methods were designed, and simulation experiments were conducted using the VISSIM platform. The results show that the accuracy of the multi-point distribution calibration and parameter value optimization method is significantly higher than the default method. Additionally, the optimization method with calibration of both global and local parameters was more consistent with actual driving characteristics. This study provides a theoretical foundation for improving the practical application of traffic simulation technology, which has significant implications for transportation planning and management.

An Approach to Microscopic Cortical Bone Fracture Simulation: Enhancing Clinical Replication

The acquisition of bone models to perform simulations is a complex and expensive process. The hierarchical structure of bones is very complex, so that studies are mainly focused on the larger scales of bones. The objective of this work is to perform a fracture simulation at the microscale level. For this purpose, the first part of the process focuses on segmenting a bone model and selecting an area of it to generate a representation of the microstructures that make up the bone tissue from a microscopic point of view. The second part is dedicated to carry out a fracture simulation in the microscopic bone model. The developed algorithm follows a statistical approach and solves the main problems of the traditional approach (FEM) to perform a bone fracture simulation. The method returns the path that a fracture follows and demonstrates how bone structures affect fracture growth. The parameters used are configurable and can be adapted for specific cases. In addition, users can reproduce as many clinical cases as desired within seconds without have to manually segment images obtained from a microscope. The data obtained may be exported to obtain synthetic images that could be used to generate datasets for machine learning tasks or other purposes.

Safety of cyclists interacting with autonomous vehicles: A combined microscopic simulation and SSAM analysis

The aim of this paper is to investigate the safety of cyclists while interacting with autonomous vehicles (AVs) in an uncontrolled T-intersection using PTV Vissim microsimulation combined with the Surrogate Safety Assessment Model (SSAM). For the microsimulation, three cycling behaviours were constructed (cautious, normal, and aggressive), and two AV behaviours (cautious and normal) were adopted. 87 h of microsimulation resulted in 230 conflicts. The type and nature of these conflicts, as well as two temporal indicators and four speed-related indicators were analysed. Nearly 96% of the conflicts were rear-end collisions, and almost 70% were conflicts where the cyclist was the first to arrive at the conflict point. Interestingly, the least involved cycling behaviour in conflicts was aggressive and the most involved was cautious. A significant relationship was found between severity clusters of temporal indicators and cycling behaviour. It is also concluded that an important future research direction would be the construction of a two-dimensional conflict diagram with severity thresholds specifically for bicycle-vehicle interactions.

Eco-Driving on Hilly Roads in a Mixed Traffic Environment: A Model Predictive Control Approach

Human driving behavior significantly affects vehicle fuel economy and emissions on hilly roads. This paper presents an ecological (eco) driving scheme (EDS) on hilly roads using nonlinear model predictive control (NMPC) in a mixed traffic environment. A nonlinear optimization problem with a relevant prediction horizon and a cost function is formulated using variables impacting the fuel economy of vehicles. The EDS minimizes vehicle fuel usage and emissions by generating the optimum velocity trajectory considering the longitudinal motion dynamics, the preceding vehicle’s state, and slope information from the digital road map. Furthermore, the immediate vehicle velocity and angle of the road slope are used to tune the cost function’s weight utilizing fuzzy inference methods for smooth maneuvering on slopes. Microscopic traffic simulations are used to show the effectiveness of the proposed EDS for different penetration rates on a real hilly road in Fukuoka City, Japan, in a mixed traffic environment with the conventional (human-based) driving scheme (CDS). The results show that the fuel consumption and emissions of vehicles are significantly reduced by the proposed NMPC-based EDS compared to the CDS for varying penetration rates. Additionally, the proposed EDS significantly increases the average speed of vehicles on the hilly road. The proposed scheme can be deployed as an advanced driver assistance system (ADAS).

Enhanced Hydrogen Bonding by Urea Functionalization Tunes the Stability and Biological Properties of Peptide Amphiphiles.

Self-assembled nanostructures such as those formed by peptide amphiphiles (PAs) are of great interest in biological and pharmacological applications. Herein, a simple and widely applicable chemical modification, a urea motif, was included in the PA's molecular structure to stabilize the nanostructures by virtue of intermolecular hydrogen bonds. Since the amino acid residue nearest to the lipid tail is the most relevant for stability, we decided to include the urea modification at that position. We prepared four groups of molecules (13 PAs in all), with varying levels of intermolecular cohesion, using amino acids with distinct β-sheet promoting potential and/or containing hydrophobic tails of distinct lengths. Each subset contained one urea-modified PA and nonmodified PAs, all with the same peptide sequence. The varied responses of these PAs to variations in pH, temperature, counterions, and biologically related proteins were examined using microscopic, X-ray, spectrometric techniques, and molecular simulations. We found that the urea group contributes to the stabilization of the morphology and internal arrangement of the assemblies against environmental stimuli for all peptide sequences. In addition, microbiological and biological studies were performed with the cationic PAs. These assays reveal that the addition of urea linkages affects the PA-cell membrane interaction, showing the potential to increase the selectivity toward bacteria. Our data indicate that the urea motif can be used to tune the stability of a wide range of PA nanostructures, allowing flexibility on the biomaterial's design and opening a myriad of options for clinical therapies.

Molecular dynamics simulation of methane hydrates: Prediction of the phase equilibria using extracted microscopic parameters from SAFT-VR Mie EOS

This paper explores the three-phase equilibrium of structure-I methane hydrate by utilizing the extracted Mie potential parameters for non-bonded molecular interactions in the LAMMPS molecular dynamics simulator. An in-house code was developed to numerically solve the SAFT-VR Mie equation of state (EOS). To reduce numerical errors, unlike common practices, we incorporated all partial derivatives analytically in the code. The microscopic simulation parameters for methane and water molecules were selected so that saturation pressure and liquid density data from EOS were fitted on experimental data. Using this approach, the three-phase (V−Lw−H) equilibrium temperatures for pressure values of 30, 70, 100, and 300 bar were calculated. The solubility of methane in the aqueous phase at three-phase equilibrium was also determined. The results were validated against available laboratory data. To evaluate the performance of Mie potential parameters, the MD simulations were repeated using three well-known force fields for methane molecules, namely, Optimized Potentials for Liquid Simulations-United Atom (OPLS-UA), Universal force field (UFF), and General Amber force field (GAFF). The TIP4P/Ice force field was used for water molecules in all cases. The comparison with experimental data reveals that while the accuracy of the UFF force field is the highest, our simulation approach outperforms OPLS-UA and GAFF force fields in predicting methane solubility in the aqueous phase. Besides molecular dynamics simulation, the van der Waals-Platteeuw model was combined with the SAFT VR Mie EOS to account for the hydrate phase. In our case study, the SAFT-VR Mie + vdW-P model proved to be more accurate in predicting the three-phase equilibrium data compared to the MD approach.

Microscopic simulation study on the penetration property of pure hydrogen sulfide gas in polyethylene and polyvinylidene fluoride

The H2S penetration in the nonmetallic biogas pipelines and reinforced thermoplastic composite pipelines can lead to severe environmental pollution and disrupt the regular operations of oil and gas fields. Thus, delving into and comprehending the H2S penetration in polyethylene (PE) or polyvinylidene fluoride (PVF2) holds immense significance for both environmental preservation and industrial progress. In this study, Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) are employed to investigate and contrast the H2S penetration property within PE and PVF2 at 300–360 K and 0.1–1.5 MPa. The results indicate the diffusion and penetration coefficients decrease with decreasing temperature and pressure, while the solubility coefficient increases with decreasing temperature. Compared to PE, PVF2 demonstrates stronger resistance to H2S penetration, mainly due to H2S is difficult to diffuse in PVF2. The relationship between penetration coefficient and temperature is consistent with Arrhenius law. In 0.1 MPa, the activation energy of H2S in PE and PVF2 are 31,911 and 42,247 J/mol, respectively. Moreover, the relationship between activation energy and pressure is power function. The study also reveals the H2S diffusion in both PE and PVF2 follows the leap mechanism. These research findings can offer valuable guidance for reducing environmental pollution and ensuring industrial development.