Hydraulic Fracture Containment in Sand

The mechanism of hydraulic fracturing in soft, high permeability material is considered fundamentally different from that in hard, low permeability rock, where a tensile fracture is created and conventional linear elastic fracture mechanics (LEFM) applies. The fracturing and associated modeling work is then a relatively new area. Particularly, the fracture containment in layered formations remains unknown. This research is aiming to capture the basic physics of the process of hydraulic fracture initiation and propagation in such materials, and further the fracture containment in layered samples. It consists of experimental and simulation studies with application in the petroleum industry. Laboratory tests are performed on lightly cohesive/cohesionless sands. They are pure sand, sand with silt and sand with cement. The mechanical behavior is independently determined in triaxial tests at different confining stress (up to 60 MPa) and porosities. The material is described within the framework of elasto-plasticity. Material parameters are then derived from the simulations, which are performed to match stress-strain behavior of uniform deformation in triaxial tests. In addition, particle breakage and boundary induced deformation localization in large strain tests are also investigated. Hydraulic fracturing injection tests are first performed using different fluids to select an appropriate fracturing fluid. They are viscous Newtonian fluid, bentonite slurry, linear gel, crosslinked gel, and BXLG. Fluid rheology and leak-off have a strong influence on the tendency to fracturing. The test observation shows that BXLG is a fairly efficient fracturing fluid at high stress, so that the following injection tests are performed with BXLG. Injection tests are carried out on sand at different confining stress (up to 20 MPa). Utilizing an X-ray CT scanner provides real-time visualization of the fracture geometry during injection. This technique helps reveal the mechanism of the fracturing. Based of injection tests and associated simulations, the considered fracturing behavior involves leak-off, initiation pressure, propagation behavior, effect of material parameters, and fracture closure. Leak-off in high permeability material is characterized by two-dimensional whole gel leak-off. Both external and internal filtercakes are observed. This results in the fracture tip lagging behind the fluid leak-off front; also, a significant part of the pressure drop occurs across the internal filtercake. Pressurization of the borehole is intrinsically related to the fracture initiation. The onset of shear bands of a pressurized borehole can be considered as the upper bound of fracture initiation. The observed high pressure is then determined by the borehole instability due to shearing. The propagation behavior is related to the leak-off and the associated change in pore pressure. Shear failure occurs at the fracture tip within the internal filtercake. BXLG that builds a relatively efficient filtercake results in smooth closure of the fracture tip. The fracture initiates and propagates at an oblique angle. This is consistent with the mechanism that it propagates in shear. Simulation of fracture propagation shows that besides the confining stress, all the considered constitutive parameters have influence on the predicted pressure and geometry. A higher pressure is required for material of larger Young’s modulus, smaller Poisson ratio, smaller friction angle and larger dilation angle. Furthermore, dilatancy plays a more significant role for fracturing in soft, high permeability material than in hard rock. Fracture recession is an important phenomenon during closure, which can be explained by more intensive leak-off at the tip. Also, CT scans show that the fracture closes mechanically far below the confining stress. The most important deviation from elastic prediction is a larger injection pressure, larger width, and closure pressure much lower than the confining stress. In addition, injection tests on cemented sands of different strength show that the threshold value of soft material and hard rock is about 0.5 MPa in tensile strength, below which the dominant fracturing mechanism appears to be failure in shear. The fracture initiation and propagation across layers are tested on layered samples, which have a difference in permeability or strength. A uniform confining stress is applied over the entire sample so that any containment would be determined by material properties. In tests with a permeability contrast, the sand layer has permeability some 3-4 times larger than the sand+silt layer; and, they both have no tensile strength. The tests show that fractures may be strongly contained by the high permeability layer. The simulation of a two-dimensional layered model qualitatively explains the mechanism. When the tip penetrates the high permeability layer, the pressure must increase to open the fracture. Also, the fluid needs to leak-off to build enough effective stress. This requires a relatively long time. In a three-dimensional situation it may explain the larger propagation rate in the other direction, within the low permeability layer. That means that the fracture will propagate much further into low permeability layers. In another kind of layered test, the two layers are sand with cement and sand with silt. They differ in tensile strength and shear resistance but they have the same permeability. The fracture develops better in the cemented sand. The simulation shows that this can be explained by the combined influence of the constitutive parameters. Comparing qualitatively strength and permeability contrast, most fracture containment is observed in the low permeability layer. Under the conditions of the present study, permeability is more important than strength for containment.GeotechnologyCivil Engineering and Geoscience

Elderly house Den Haag

Architecture and The Built EnvironmentArchitectur

Optimization of Coordinated Flow Control and Skip-stopping Schemes for Urban Rail Stations Considering Platform Carrying Capacity

The platform carrying capacity of urban rail transit stations is limited and overcrowding of the platform will lead to serious safety risks for passengers and trains. It is significant to collaborate on the optimization of passenger flow strategy and skip-stopping scheme to alleviate traffic pressure and ensure platform safety. This study proposes and solves the joint optimization problem of coordinated flow control and skip-stopping scheme considering platform carrying capacity. Firstly, platform demand constraints and platform stranded constraints are designed according to the maximum carrying capacity of the platform to control the number of allowable arrivals ensuring platform safety. Secondly, train arrival variable and train stop variable are introduced to generate train skip-stopping index. Finally, considering the characteristics of passengers' continuous arrival and platform carrying capacity, a mixed integer programming model is established to minimize the number of passengers outside the station and the number of passengers stranded on the platform. Based on empirical data, this study takes Beijing Batong line as a case study and uses the established model to generate flow control strategy and skip-stopping schemes for each station during morning rush hours. Experimental results show that compared to the baseline without implementing the two proposed strategies, the proposed collaborative optimization method can effectively reduce the demand for staying at the platform and increase the number of boarding passengers at downstream stations. Thus, the balance between train capacity and passenger flow demand is maintained while ensuring platform safety. Moreover, the proposed method can also avoid overcrowding at downstream stations.Transport and Plannin

Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss

Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pretrain the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the art results, while the training time can be substantially shortened.Transport and Plannin

Impact of soil moisture data resolution on soil moisture and surface heat flux estimates through data assimilation: A case study in the Southern Great Plains

The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2-3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2-3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007m 3 m -3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar.Water Resource

Formation design for single-pass GEO InSAR considering earth rotation based on coordinate rotational transformation

The single-pass geosynchronous synthetic aperture radar interferometry (GEO InSAR) adopts the formation of a slave satellite accompanying the master satellite, which can reduce the temporal decorrelation caused by atmospheric disturbance and observation time gap between repeated tracks. Current formation design methods for spaceborne SAR are based on the Relative Motion Equation (RME) in the Earth-Centered-Inertial (ECI) coordinate system (referred to as ECI-RME). Since the Earth rotation is not taken into account, the methods will lead to a significant error for the baseline calculation while applied to formation design for GEO InSAR. In this paper, a formation design method for single-pass GEO InSAR based on Coordinate Rotational Transformation (CRT) is proposed. Through CRT, the RME in Earth-Centered-Earth-Fixed (ECEF) coordinate system (referred to as ECEF-RME) is derived. The ECEF-RME can be used to describe the accurate baseline of close-flying satellites for different orbital altitudes, but not limited to geosynchronous orbit. Aiming at the problem that ECEF-RME does not have a regular geometry as ECI-RME does, a numerical formation design method based on the minimum baseline error criterion is proposed. Then, an analytical formation design method is proposed for GEO InSAR, based on the Minimum Along-track Baseline Criterion (MABC) subject to a fixed root mean square of the perpendicular baseline. Simulation results verify the validity of the ECEF-RME and the analytical formation design method. The simulation results also show that the proposed method can help alleviate the atmospheric phase impacts and improve the retrieval accuracy of the digital elevation model (DEM) compared with the ECI-RME-based approach.Mathematical Geodesy and Positionin

Sequential Neural Network Model with Spatial-Temporal Attention Mechanism for Robust Lane Detection Using Multi Continuous Image Frames

Lane detection serves as a fundamental task for automated vehicles and Advanced Driver Assistance Systems. However, current lane detection methods can not deliver the versatility of accurate, robust, and realtime compatible lane detection in real-world scenarios especially under challenging driving scenes. Available vision-based methods in the literature do not consider critical regions of the image and their spatial-temporal salience regarding the detection results, thus they deliver poor performance in peculiar difficult circumstances (e.g., serious occlusion, dazzle lighting). This study aims to introduce a novel sequential neural network model with a spatial-temporal attention mechanism that can focus on key features of lane lines and exploit salient spatial-temporal correlations among continuous image frames for the purpose of enhancing the accuracy and robustness of lane detection. Under the regular encoder-decoder structure and with the implementation using common neural network backbones, the proposed model is trained and evaluated on three large-scale opensource datasets. Extensive experiments demonstrate the strength and the robustness of the proposed model outperforming available state-of-the-art methods in various testing.Transport and PlanningCognitive Robotic

Geosynchronous spaceborne-airborne bistatic moving target indication system: Performance analysis and configuration design

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR), consisting of GEO transmitter and airborne receiver, has stable coverage for a long time and benefits moving target detection. However, the performance of GEO SA-BSAR moving target indication (MTI) system varies widely between bistatic configurations. The traditional configuration design for GEO SA-BSAR system only considers the imaging performance, which may cause the poor MTI performance. In this paper, we propose a bistatic configuration design method to jointly optimize the MTI and SAR imaging performance for GEO SA-BSAR MTI system. The relationship between the MTI performance and bistatic configuration parameters is derived analytically and analyzed based on the maximum output signal to clutter and noise ratio (SCNR) criterion. Then, the MTI performance and SAR imaging performance are jointly considered to model the configuration design problem as a multi-objective optimization problem under the constrained condition. Finally, the optimal configuration for GEO SA-BSAR MTI system is given.Mathematical Geodesy and Positionin

Research on the natural hazard emergency cooperation behavior between governments and social organizations based on the hybrid mechanism of incentive and linkage in China

Social organizations have become an important component of the emergency management system by virtue of their heterogeneous resource advantages. It is of great significance to explore the interaction between the local government and social organizations and to clarify the key factors affecting the participation of social organizations in natural hazard emergency responses. With the aim of exploring the relationship between the local government and social organizations, based on evolutionary game theory, the emergency incentive game model and the emergency linkage game model of natural hazard emergency responses were constructed. The evolutionary trajectories of the emergency incentive game system and the emergency linkage game system were described by numerical simulation. Meanwhile, the influence mechanism of government decision parameters on the strategy selection of both game subjects was analyzed. The results show that both governmental incentive strategy and linkage strategy can significantly improve the enthusiasm of social organizations for participating in natural hazard emergency responses. Moreover, they could encourage social organizations to choose a positive participation strategy. Nevertheless, over-reliance on incentives reduces the probability of the local government choosing a positive emergency strategy. In addition, we found that, when both game subjects tend to choose a positive strategy, the strategy selection of the local government drives that of social organizations.Transport and Logistic

Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network

As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security mechanisms. To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection. Both traditional machine learning models (including single classifier and ensemble models) and neural network based deep learning models are evaluated. Furthermore, this study proposed a deep autoencoder based semi-supervised learning method applied for CAN message anomaly detection and verified its superiority over other semi-supervised methods. Extensive experiments show that the fully-supervised methods generally outperform semi-supervised ones as they are using more information as inputs. Typically the developed XGBoost based model obtained state-of-the-art performance with the best accuracy (98.65%), precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the literature.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin