Editorial

Phenomenal advances in computer technology together with progress in the areas of mathematical modelling in recent decades have made simulation procedures very powerful tools for the analysis and design of almost all types of industrial and economic processes. Accurate and reliable predictions about the outcome of complex natural transport processes and performance of novel designs for industrial equipments are routinely made using modern simulation methodologies. Annually held international Industrial Simulation Conferences (ISC), organised and run by the EUROSIS in conjunction with various organisations, provides an important forum for the presentation and exchange of new ideas related to the development and application of computer simulation techniques in a very diverse and wide ranging area of industrial relevance

Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities

An artificial neural network (ANN) is presented for computing a parameter of dynamic two-phase flow in porous media with water as wetting phase, namely, dynamic coefficient (τ), by considering micro-heterogeneity in porous media as a key parameter. τ quantifies the dependence of time derivative of water saturation on the capillary pressures and indicates the rates at which a two-phase flow system may reach flow equilibrium. Therefore, τ is of importance in the study of dynamic two-phase flow in porous media. An attempt has been made in this work to reduce computational and experimental effort by developing and applying an ANN which can predict the dynamic coefficient through the “learning” from available data. The data employed for testing and training the ANN have been obtained from computational flow physics-based studies. Six input parameters have been used for the training, performance testing and validation of the ANN which include water saturation, intensity of heterogeneity, average permeability depending on this intensity, fluid density ratio, fluid viscosity ratio and temperature. It is found that a 15 neuron, single hidden layer ANN can characterize the relationship between media heterogeneity and dynamic coefficient and it ensures a reliable prediction of the dynamic coefficient as a function of water saturation

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work

Network-level accident-mapping: distance based pattern matching using artificial neural network

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily ‘transferable’ as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in “learning” the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that the accuracy is much better than other methods

Implementation and evaluation of consistent online backup in transactional file systems

A consistent backup which preserves data integrity across files in a file system is of utmost importance for the purpose of correctness and minimizing system downtime during the process of data recovery. With present day demand for continuous access to data, backup has to be taken of an active file system, putting the consistency of the backup copy at risk. In order to address this issue, we propose a scheme which is referred to as mutual serializability assuming that the file system support transactions. Mutual serializability captures a consistent backup of an active file system by ensuring that the backup transaction is mutually serializable with every other transaction individually. This mutually serializable relationship is established considering an extended set of conflicting operations which include read-read conflicts. User transactions serialize within themselves using some standard concurrency control protocol such as the Strict 2PL and a set of conflicting operations that only include the traditional read-write, write-write and write-read conflicts. The proposed scheme has been implemented on top of a transactional file system and workloads exhibiting a wide range of access patterns were used as inputs to conduct experiments in two scenarios, one with the mutual serializability protocol enabled (thus capturing a consistent backup) and one without (thus capturing an inconsistent backup). The results obtained from the two scenarios were then compared to determine the overhead incurred while capturing a consistent backup. The performance evaluation shows that for workloads resembling most present day real workloads exhibiting low intertransactional sharing and actively accessing only a small percentage of the entire file system space, the proposed scheme has very little overhead (a 5.7% increase in backup time and a user transaction throughput reduction of 3.68%). Noticeable performance improvement is recorded when using performance enhancing heuristics which involve diversion of the backup transaction to currently “colder” regions of the file system hierarchy on detecting conflicts with user transactions