Sharif Digital Repository

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-Error-Propagation (BEP) learning algorithm to provide a versatile SAGD forecasting and analysis framework. The constructed neural network architectures are capable of estimating the recovery factors of the SAGD production as an enhanced oil recovery method satisfactorily. Rigorous studies regarding the hybrid... 

Abstract ORM (Object Role Modeling) is a rich and popular conceptual modeling method. ORM has been used for data modeling, ontology engineering, modeling business rules, XML-Schemes and data warehouses, requirements engineering and web forms. Automated reasoning like satisfiability testing allows developers to detect modeling mistakes in the early stages of development. In this paper we propose a lightweight formalization of the ORM meta-model in Alloy. Using this meta-model as a toolkit one can easily specify ORM models in Alloy and verify various properties on them using the Alloy Analyzer. In order to achieve scalability, we use the cardinality of concepts to model their population. This... 

The advent of agile methodologies, though contributing much to software development processes, had a more profound impact on project management processes. Through supporting adaptability in their process frameworks, agile methodologies deviated from conventional project management approaches. This novel attitude has resulted in the emergence of an agile framework for project management. The Agile Project Management Framework (APMF) consists of fine-grained project management practices applied in agile methodologies, and is fast emerging as an alternative to the conventional project management framework. However, there are deficiencies in both frameworks that prevent developers from enhancing... 

Optimal tolerance design is a time-consuming and multi-disciplinary procedure and involves several aspects of design, manufacturing, quality and cost problems. In addition, the quality of assemblies can be significantly affected by the flexibility of components which has not been considered in most of the previous research. In this paper, a new method is proposed for multi-objective optimal tolerance design of compliant assemblies based on an integrated Kriging meta-modelling–NSGA-II–Shannon’s Entropy TOPSIS algorithm. The tolerance propagation of flexible components in the assembly process is modelled through the enhanced Method of Influence Coefficients (MIC). Geometrical variations of key... 

Optimal tolerance design is a time-consuming and multi-disciplinary procedure and involves several aspects of design, manufacturing, quality and cost problems. In addition, the quality of assemblies can be significantly affected by the flexibility of components which has not been considered in most of the previous research. In this paper, a new method is proposed for multi-objective optimal tolerance design of compliant assemblies based on an integrated Kriging meta-modelling–NSGA-II–Shannon’s Entropy TOPSIS algorithm. The tolerance propagation of flexible components in the assembly process is modelled through the enhanced Method of Influence Coefficients (MIC). Geometrical variations of key... 

This paper presents artificial neural network (ANN) meta-models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta-models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non-linear nature of the ANN, the SO problem is iteratively approximated via non-linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane-relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition... 

In this article, a new algorithm for rather expensive simulation problems is presented, which consists of two phases. In the first phase, as a model-based algorithm, the simulation output is used directly in the optimization stage. In the second phase, the simulation model is replaced by a valid metamodel. In addition, a new optimization algorithm is presented. To evaluate the performance of the proposed algorithm, it is applied to the (s,S) inventory problem as well as to five test functions. Numerical results show that the proposed algorithm leads to better solutions with less computational time than the corresponding metamodel-based algorithm. © 2017 Taylor & Francis Group, LLC  

Natural gas hydrate, a crystalline solid existing under high-pressure and low-temperature conditions, has been regarded as a potential alternative energy resource. It is globally widespread and occurs mainly inside the pores of deepwater sediments and sediments under permafrost area. Hydrate production via well depressurization is deemed well-suited to existing technology, in which the pore pressure is lowered, the natural gas hydrate is dissociated into water and gas, and the water and gas are produced from well. This method triggers multiphysics processes such as fluid flow, heat transfer, energy adsorption, chemical reaction and sediment deformation, all of which are dependent on the... 

Various agile software development methodologies, practices, and techniques have been proposed in the last decade; some present novel ideas, while many are simply made up of tasks and techniques borrowed from prominent agile methodologies. Each of these methodologies prescribes a set of practices and techniques which are deemed appropriate for application in a specific context. However, there exists no single method which fits all project situations. This has resulted in the advent of Situational Method Engineering (SME) approaches, which are used for developing software methodologies that are tailored to fit the specific circumstances of the project situation at hand. Since tool support has... 

A flexible meta modelling approach is presented to predictive control of a drying process using Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Partial Least Squares (PLS) analysis. In the proposed approach, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modelling with aim at predicting the granule particle size and executing by ANFIS and ANN. ANN hold the promise of being capable of producing non-linear models, being able to work under noise conditions and being fault tolerant to the loss of neurons or connections. Also, the... 

The relative vulnerability of spinal motion segments to different loading combinations remains unknown. The meta-analysis described here using the results of a validated L2-L3 nonlinear viscoelastic finite element model was designed to investigate the critical loading and its effect on the internal mechanics of the human lumbar spine. A Box-Behnken experimental design was used to design the magnitude of seven independent variables associated with loads, rotations and velocity of motion. Subsequently, an optimization method was used to find the primary and secondary variables that influence spine mechanical output related to facet forces, disc pressure, ligament forces, annulus matrix...