Multilevel Research Articles

Estimation of uncertainties in the density driven flow in fractured porous media using MLMC

AbstractWe use the Multi Level Monte Carlo method to estimate uncertainties in a Henry-like salt water intrusion problem with a fracture. The flow is induced by the variation of the density of the fluid phase, which depends on the mass fraction of salt. While the fracture’s location is fixed, its aperture is uncertain. In our setting, porosity and permeability vary spatially and recharge is time-dependent. So we introduce three random variables, one controlling both the porosity and permeability fields, one for the fracture width and one for the intensity of recharge. For each realization of these uncertain parameters, the evolution of mass fraction and pressure fields is modeled using a system of non-linear, time-dependent PDEs with a solution discontinuity at the fracture. These uncertainties propagate, affecting the distribution of salt concentration, a key factor in water resource quality. We show that the MLMC method can be successfully applied to this problem. It significantly reduces the computational cost compared to classical Monte Carlo methods by effectively balancing discretisation and statistical errors, and by evaluating multiple scenarios over different spatial and temporal mesh levels. The deterministic PDE solver, using the ug4 library, runs in parallel to compute all stochastic scenarios.

A Queuing Theory Approach for Monitoring Indoor Parking Management System

A Queuing Theory Approach for Monitoring Indoor Parking Management System

Hydrogen Leakage Risks and Mitigation Measures in Large Underground Garages

Abstract Hydrogen fuel cell vehicles, characterized by zero emissions, pollution-free operation, and high efficiency, have emerged as a key focus in the development of the global automotive industry. The operating pressure for onboard hydrogen storage tanks commonly ranges from 30 to 70 MPa. Due to hydrogen's wide combustion and explosion concentration range and its exceptionally rapid combustion rate, there is a high risk of explosions and other accidents once equipment failure happens during storage and transportation. The research presented in this paper focuses on the analysis of hydrogen leakage from storage tanks in an underground garage using FLUENT simulations. The findings reveal that released hydrogen forms a jet from the storage tank under high pressure, dispersing along the ceiling upon reaching it and accumulating at the edges and corners. Moreover, larger leakage ports on the storage tank result in a greater mass flow of hydrogen, leading to an expanded diffusion range of the hydrogen cloud and decreased local concentration. To mitigate the risk of hydrogen combustion and explosion within the garage, this study introduces 16 extraction vents on the garage ceiling and 6 natural vents on its sides. The validation of the proposed hydrogen risk mitigation measures demonstrates their effectiveness in reducing the concentration and range of flammable clouds within the garage, especially when dealing with larger leakage ports.

A hybrid expert neural network for predicting hydrogen concentration under the ceiling in underground garage

In the event of a hydrogen leak, the build-up of hydrogen near the ceiling of an underground garage poses a significant safety risk. Fast and accurate estimation of hydrogen concentration distribution is crucial for risk assessment. This study proposes a novel neural network named multi-expert variational hybrid network (MEVHN) to predict the distribution of hydrogen concentration under the ceiling when the peak concentration reaches its maximum value during a leakage event. The model utilizes data from discrete sensors to make predictions. It incorporates a mixture of experts (MoE) framework to transform the sensor data into latent variables, which are then used by a variational autoencoder (VAE) decoder to predict the hydrogen concentration distribution. Constraints are added to the loss function to improve the prediction accuracy further. The results show that the MEVHN has an inference time of 1.3 seconds, a coefficient of determination (R²) of 0.977, a mean absolute error (MAE) of 1.86E-3, and a mean squared error (MSE) of 3.15E-5. These results indicate that the model performs well in predicting the 2D hydrogen concentration distribution.

Assessment of the Physicochemical Properties of Ultrafine Particles (UFP) from Vehicular Emissions in a Commercial Parking Garage: Potential Health Implications.

Vehicular emissions are a major culprit in the rise of urban air pollution. The particulate matter (PM) emitted from vehicular sources includes primarily ultrafine particles (UFPs) with aerodynamic diameters less than 0.1 µm (PM0.1) and is linked to adverse respiratory and cardiovascular health effects. Despite this knowledge, few exposure assessment studies exist that detail the physicochemical properties of PM in parking garages. In this study, airborne PM emitted by vehicles in a parking garage of a hospital in New Jersey was sampled, during winter and summer seasons, and physicochemically characterized. The results indicate that the mass concentrations of the UFPs in the garage were 2.51 µg/m3 and 3.59 µg/m3, respectively. These UFPs contained a large percentage of elemental carbon and toxic elements. They also contained polycyclic aromatic hydrocarbons (PAHs), having deleterious health effects. An inhalation particle modeling revealed that 23.61% of these UFPs are deposited in the pulmonary region of the lung, translating to a dose of 10.67 µg for winter and 15.25 µg for summer, over a typical 40 h work week. These high deposited levels of UFPs and their complex chemistry levels further warrant the need for toxicological assessment of UFPs related to vehicular emissions.

Analytical study on fire resistance performance of precast concrete hollow-core slabs for electric vehicle fire in parking structures

This study evaluated the effects of fires that occur in internal combustion engine vehicles (ICEVs) and battery electric vehicles (BEVs) on the structural safety of hollow-core slabs (HCS) installed in parking structures. A fire simulation of a prototype parking structure was performed using the time–heat release rate curves of ICEVs and BEVs derived from fire experiments, and fire behavior and temperature history were derived according to the fire curve. Additionally, nonlinear finite element analysis was conducted using a general-purpose finite element analysis program, and the fire resistance performance of HCS was evaluated according to the fire curve and slab load ratio. In the case of a fire in ICEVs, the performance criteria of ISO 834–1 were met even at a slab load ratio of 0.7. However, in the case of a fire in BEVs, the standard for insulation was not met as the temperature change of the upper surface of HCS exceeded 180 K, and the load-bearing capacity was also not satisfied as the limiting deflection reached a slab load ratio of 0.7.

Experimental study on the distributioncharacteristics of hydrogen leakage in three-dimensional parking garages

Experimental study on the distributioncharacteristics of hydrogen leakage in three-dimensional parking garages

Learning super-resolution and pyramidal convolution residual network for vehicle re-identification

Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods.

Vocs pollution and respiratory exposure in commercial and residential underground parking garages

The indoor air quality in Underground Parking Garages (UPGs) has deteriorated significantly, primarily due to the high concentrations of particulate matter and volatile organic compounds (VOCs) emitted by idling or low-speed motor vehicles. However, the compositional characteristics and respiratory exposure to VOCs in UPGs have not been quantitatively analyzed. To establish a method for investigating the respiratory exposure to VOCs among different populations in UPGs, a three-dimensional dynamic diffusion model of indoor pollutants was developed based on monitoring data from 116 components in various UPGs in a large city in northern China. The results indicated that air pollution in underground spaces poses significant health risks to workers and children. Furthermore, a comprehensive approach is essential for improving ventilation capacity and air quality in underground transportation spaces.

Vehicle Tracking System in Underground Parking Lots Using Smartphone

Vehicle Tracking System in Underground Parking Lots Using Smartphone

Three-Dimensional (3D) Flood Simulation Aids Informed Decision Making: A Case of a Two-Story Underground Parking Lot in Beijing

Flooding in underground spaces, such as subway stations, underground malls, and garages, has increased due to intensified rainfall, urbanization, and population growth. Traditional 2D simulations often overlook crucial vertical flow variations, especially in steep transitions like stairs and ramps. The current study aims to investigate the flood dynamics in large underground geometries by taking a parking lot in Beijing, China, as a study case. The model overcomes the limitations of previous simulations by adapting a full 3D mesh-based simulation with reasonable computational cost. Unlike earlier studies, this model employs a high temporal resolution transient inflow at the inlet to the underground space. Simulation scenarios consider different return periods (5, 20, and 100 years) and inlet water depths, providing an analysis of their impact on flood status in the underground structure. The model generates high spatial–temporal results, enabling precise detection of flood-prone locations, evacuation times, and suggested mitigation techniques. The results recommend evacuating from hazard areas before the 10th minute during extreme flood events. Additionally, the study estimates a 40% increase in flood hazards for scenarios with direct connections between levels. Overall, the study highlights the importance of 3D simulations for accurate risk assessment.

Intensive construction technology for urban underground parking shaft

Intensive construction technology for urban underground parking shaft

Smoke and Hot Gas Removal in Underground Parking Through Computational Fluid Dynamics: A State of the Art and Future Challenges

Smoke and Hot Gas Removal in Underground Parking Through Computational Fluid Dynamics: A State of the Art and Future Challenges

The problem of noise generated during the use of garage parking platforms in residential buildings

The problem of noise generated during the use of garage parking platforms in residential buildings

მიწისქვეშა ავტოსადგომების კვამლისაგან დაცვა ხანძრის დროს

The work examines current issues of fire, sanitary, hygienic and environmental safety of ventilation of underground parking lots. It has been established that, according to European standards, smoke removal systems with jet fans act much more efficiently than air ducts, quickly displacing smoke and exhaust gases towards the air intake grilles, from where and they come out. The design and parameters of the ventilation system are selected based on its ability to quickly and safely ensure the evacuation of people through smoke-free exits, as well as provide air parameters in the parking lot that meet the requirements of the design and installation of a ventilation unit in accordance with industry regulations of Georgia.

Computer Aided Based Performance Analysis of Glioblastoma Tumor Detection Methods using UNET-CNN

Brain tumors are the life killing and threatening disease which affects all age groups around the world. The timely detection and followed by the perspective treatments saves the human life. The tumor regions in brain are detected and segmented using UNET-CNN architecture in this paper. During training process of the proposed work, both Glioblastoma and Healthy brain Magnetic Resonance Imaging (MRI) is preprocessed and then multi level transform is applied on the preprocessed image. The features are further computed from the transformed coefficients and these features are trained by UNET-CNN architecture to obtain trained vectors. During testing process of the proposed work, the test brain MRI image is preprocessed and then decomposed coefficients are obtained by multi level transform. Features are computed from these decomposed coefficients and they are classified using UNET-CNN architecture with the trained vectors. The simulation results of the developed methodology are compared with similar studies on both BRATS 2017 and BRATS 2018 datasets

Exploring gaps in residential and parking location choice models for autonomous vehicles: a proposed evaluation framework

ABSTRACT The advent of autonomous vehicles (AVs) is poised to transform urban landscapes, notably by altering perceptions of travel time value, reshaping parking structures, and introducing additional travel demand through empty-cruising and self-parking capabilities. These changes are anticipated to influence both residential and parking location choices. This study delves into the complexities of these decisions by integrating insights from 28 modelling studies on residential and/or parking location choices in the context of AVs and by proposing an evaluation framework for future modelling efforts. A critical gap identified is the oversight of empty-cruising and parking costs in the context of residential relocation choice for owners of privately-owned AVs. Furthermore, no study has adopted a joint-choice modelling approach for residential and parking location choices. By offering guidelines for the incorporation of these elements, this study contributes to the development of a holistic residential and parking location choice model with AVs, thus providing a valuable roadmap for future research in urban planning and transportation economics.

Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.

An Indoor-Outdoor Layered HD Map Construction for Unmanned Ground Vehicles

Abstract. High-Definition Maps (HD Maps) are lane-level 3-dimensional road network models that ensure the safe operation of autonomous vehicles and drive the continuous advancement of autonomous driving technology to higher levels. However, HD Maps currently face challenges regarding limited coverage and description of indoor environments (e.g. underground parking), as well as the lack of uniformity between indoor and outdoor map formats. Therefore, this paper proposes a Layered HD Map model for both indoor and outdoor environments. The map model consists of 6 layers, allowing effective representation of indoor and outdoor environments, as well as transitional areas. The paper also establishes an Unmanned Ground Vehicle (UGV) to collect and update map data using LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) and autonomous exploration technology, aiming to enhance the efficiency and automation of HD Map acquisition. In order to verify the validity of the proposed map model, this paper conducted a navigation experiment in a double decker parking lot containing entrances and exits. The results of the experiments demonstrate that the Layered HD Map generated through autonomous LiDAR SLAM can effectively describe the indoor and outdoor environments, and enable successful navigation of UGVs.

Evaluation of Çankırı and Iğdır salt caves within the scope of health and recreation tourism

With the increasing popularity of healing methods involving salt, the importance of salt mines has grown significantly over time. Today, as salt therapy (Speleotherapy) becomes more widespread, salt caves in many countries around the world have begun to transform into salt therapy centers. Salt caves in Spain (Cardona), Belarus (Soliharsk), Romania (Slanic, Salina Turda, and Praid), Poland (Wieliczka), Pakistan (Khewra), Germany (Berchtesgaden), Azerbaijan (Nakhchivan), and Turkey (Çankırı and Iğdır Tuzluca) are used for therapeutic purposes. Particularly, Çankırı Salt Cave and Iğdır Tuzluca Salt Cave are important resources in Turkey that can be evaluated within the scope of health tourism. In this study, a literature review, official website analysis, and on-site observations were conducted. In the literature review, previous research on the subject was examined. Through website analysis, the spatial units and recreational activities of the salt caves were identified. The spatial units and recreational activities of each salt cave were determined using the collected data. Subsequently, on-site observations were made by visiting the salt caves in Çankırı and Iğdır. During these observations, the spatial units within the salt caves were visited and photographed individually. Then, the data obtained from the literature, official website analyses, and on-site observations were combined. Finally, the characteristics of all the examined caves were tabulated, and based on the created table, the caves were evaluated. As a result of the evaluation, the Cardona Salt Mine in Spain stands out for its historical and cultural activities; the Nakhchivan in Azerbaijan, Soligorsk in Belarus, Slanic and Praid Salt Mines in Romania are treatment centers offering salt therapy services; the Salina Turda Salt Mine in Romania is an underground theme park; the Wieliczka Salt Mine in Poland, Khewra Salt Mine in Pakistan, and Berchtesgaden Salt Mine in Germany are primarily used for recreational purposes; while the developing Çankırı and Iğdır Tuzluca Salt Mines in Turkey are also noted for their recreational use.