Study on the Cyclic Loading Effects to the Railway Ballast

2013 ◽  
Vol 724-725 ◽  
pp. 1736-1739
Author(s):  
Xue Jun Wang ◽  
Bin Hua ◽  
Yi Lin Chi ◽  
Xue Yu Zhao ◽  
Fu Yu Li
Keyword(s):  
Cyclic Loading ◽  
Research Method ◽  
Flow Simulation ◽  
Railway Ballast ◽  
Compaction Rate ◽  
Simulation Process ◽  
Loading Effects ◽  
Simulation Results ◽  
Frequency Changes ◽  
Particle Flow Simulation

When ballast materials are subjected to cyclic loading, as a result, the change of particles micromechanical properties will lead to ballast degradation, permanent deformations on the railways step by step. In this paper, it presented a coupling discrete particle-flow simulation model of the railway ballast for cyclic tamping loading. Tamping frequency changes from 25HZ to 60HZ in numerical simulation process. Simulation results that the ballast compaction rate increases linearly with frequency up to a characteristic frequency 35HZ and then it declines in inverse proportion to tamping frequency. The aim of this paper is to study on the effects on the railway ballast under cyclic loading. The study shows that the discrete element method is a valid method for investigation of the microscopic properties of railway ballast now, while we have no other better research method.

Study Behavior of Railway Ballast under Cyclic Loading

2014 ◽  
Vol 919-921 ◽  
pp. 1155-1159
Author(s):  
Tao Yong Zhou ◽  
Bin Hu ◽  
Bin Hua
Keyword(s):  
Numerical Simulation ◽  
Cyclic Loading ◽  
Discrete Element Method ◽  
Discrete Element ◽  
Railway Track ◽  
Experimental Facility ◽  
Railway Ballast ◽  
Study Behavior ◽  
Rate Of Increase ◽  
Simulation Results

Densification and settlement are two important behavior parameters of railway ballast under cyclic loading. This paper presents numerical simulation using discrete element method which studies the evolution of densification of railway ballast in specified region under cyclic loading. The simulation results show that with the increase of cyclic loading time, the densification of railway ballast under sleeper is also increasing, and the rate of increase becomes smaller and smaller. This paper presents a new full-scale complete railway track loading experimental facility which studies the evolution of settlement of ballasted railway track under cyclic loading. The experimental results show that with the increase of number of load cycles, the settlement of ballasted railway track is also increasing, and the rate of increase becomes smaller and smaller.

scholarly journals Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

2021 ◽  
Author(s):  
Seyed Kourosh Mahjour ◽  
Antonio Alberto Souza Santos ◽  
Manuel Gomes Correia ◽  
Denis José Schiozer
Keyword(s):  
Flow Simulation ◽  
Reduction Process ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Dynamic Data ◽  
Scenario Reduction ◽  
Field Development ◽  
Data Usage ◽  
Simulation Process ◽  
Uncertainty Space

AbstractThe simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

scholarly journals Micro-mechanical investigation of railway ballast behavior under cyclic loading in a box test using DEM: effects of elastic layers and ballast types

2019 ◽  
Vol 21 (4) ◽  
Author(s):  
Nishant Kumar ◽  
Bettina Suhr ◽  
Stefan Marschnig ◽  
Peter Dietmaier ◽  
Christof Marte ◽  
...  
Keyword(s):  
Cyclic Loading ◽  
Early Stage ◽  
Damage Model ◽  
Simulation Models ◽  
Railway Track ◽  
Maintenance Cost ◽  
Particle Movement ◽  
Railway Ballast ◽  
Elastic Layers ◽  
The Impact

Abstract Ballasted tracks are the commonly used railway track systems with constant demands for reducing maintenance cost and improved performance. Elastic layers are increasingly used for improving ballasted tracks. In order to better understand the effects of elastic layers, physical understanding at the ballast particle level is crucial. Here, discrete element method (DEM) is used to investigate the effects of elastic layers – under sleeper pad ($$\text {USP}$$USP) at the sleeper/ballast interface and under ballast mat ($$\text {UBM}$$UBM) at the ballast/bottom interface – on micro-mechanical behavior of railway ballast. In the DEM model, the Conical Damage Model (CDM) is used for contact modelling. This model was calibrated in Suhr et al. (Granul Matter 20(4):70, 2018) for the simulation of two different types of ballast. The CDM model accounts for particle edge breakage, which is an important phenomenon especially at the early stage of a tamping cycle, and thus essential, when investigating the impact of elastic layers in the ballast bed. DEM results confirm that during cyclic loading, $$\text {USP}$$USP reduces the edge breakage at the sleeper/ballast interface. On the other hand, $$\text {UBM}$$UBM shows higher particle movement throughout the ballast bed. Both the edge breakage and particle movement in the ballast bed are found to influence the sleeper settlement. Micro-mechanical investigations show that the force chain in deeper regions of the ballast bed is less affected by $$\text {USP}$$USP for the two types of ballast. Conversely, dense lateral forces near to the box bottom were seen with $$\text {UBM}$$UBM. The findings are in good (qualitative) agreement with the experimental observations. Thus, DEM simulations can aid to better understand the micro-macro phenomena for railway ballast. This can help to improve the track components and track design based on simulation models taking into account the physical behavior of ballast. Graphical Abstract

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Flow Analysis of Spray Jet and Direct Jet Nozzle for Fire Water Monitor

2010 ◽  
Vol 139-141 ◽  
pp. 913-916 ◽  
Author(s):  
Guo Liang Hu ◽  
Wei Gang Chen ◽  
Zhi Gang Gao
Keyword(s):  
Flow Analysis ◽  
Flow Characteristics ◽  
Flow Simulation ◽  
Jet Flow ◽  
Extended Length ◽  
Jet Nozzle ◽  
Simulation Results ◽  
Steady Velocity ◽  
Nozzle Structure

In order to investigate the influence rules between the jet nozzle of fire water monitor and the jet performances, two typical jet nozzle, the spray jet and direct jet nozzle was designed to analysis the jet flow characteristics. Flow simulation of the jet nozzle was completed using fluent kits. The outlet velocity of the spray jet nozzle and direct jet nozzle were investigated in detail, and the influence rules of the nozzle structure on the outlet velocity was also discussed. The simulation results show that the steady velocity of the jet nozzle is about 34m/s that coinciding the contour magnitude, and the better extended length of the direct jet nozzle is about 50mm length that can improve the jet performances. The results can verify the reasonableness of the designed nozzle, it also can optimize the nozzle structure and increase the jet performance of the fire water monitor.

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