Pore scale network modelling of residual oil saturation in mixed-wet systems

The prediction of residual oil saturation (Sor) and relative permeabilities after waterflooding in mixed-wet systems is a very challenging task. These are important parameters which must be estimated for a full field simulation of waterflooding. The Sor also defines the target oil for any proposed EOR process after an initial waterflood. Pore-scale network modelling can be used to estimate relative permeabilities, and the amount and nature of the trapped residual oil if the correct physics of oil displacement are properly included. During the waterflooding of mixed-wet systems, oil may drain down to relatively low residual saturations. Such Sor levels can only be calculated correctly when oil layers in pore corners are included in the pore-scale modelling. van Dijke and Sorbie (J. Coll. Int. Sci. 293 (2006) 455) obtained an accurate thermodynamically derived criterion for oil layers’ existence in pores with non-uniform wettability caused by ageing, which is more restrictive than the previously used geometrical layer existence criterion. This thermodynamic criterion has been included in a newly developed two-phase pore network model to calculate realistic Sor values for mixed-wet sandstones. A new ncornered star pore shape characterization technique has also been implemented in this model since the precise description of the pore shape was found to be important. Two unstructured networks, derived from Berea sandstone have been used for a number of sensitivities of the Sor and relative permeabilities with respect to wettability conditions. It is shown that Sor is lower for the more strongly oil-wet cases, while the water relative permeability curves increase gradually with oil-wetness at the higher water saturations. It has also been shown that pore shape approximations and oil layers collapse criterion have a significant impact on the Sor and the relative permeabilities. In particular, the thermodynamic oil layer existence criterion gives higher and more realistic Sor compared to previously used geometrical criterion. The network modelling has been used to match experimental data for water-wet and mixed-wet systems. In particular, the good agreement with mixed-wet systems strongly indicates that using the correct oil layer existence criteria is a significant step forward in the reliable prediction of Sor

TUNING TO ROAD AND LOAD PASSIVE SUSPENSIONS MULTI-MODELLING AND OPTIMISATION

This work explores the ability to apply multi-modeling technique of new suspension system based on a shock absorber model VZN (European Patent 1190184/20052). Here are presented the results of their own scientific research on the multi-modelling a auto vehicles suspension systems based on passive hydraulic shock absorbers with variable damping characteristics depending on the position of the sprung mass and road conditions. For such a system was proposed and verified by in Matlab-Simulink simulation, a procedure for optimizing the damping characteristics of the road conditions and load given. Suspension system is represented by a quarter-car multi-model with one degree of freedom and representative way perturbation by white noise. Proposed new criterion function in optimisation self-adaptive passive suspension

Using Key Performance Indicators for multi-criteria traffic management strategic decisions

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. The first stage of the extension focused on pollution reduction, and a novel decision support tool (CONDUITS_DST) integrating the respective KPI with micro-simulation modelling was developed. Case studies executed in Brussels and Zurich demonstrated the usability and viability of the tool. This paper takes the development one step further and reports on the extension of the approach, which moves from single-criterion to a multi-criteria decision support tool through the inclusion of the KPI on traffic efficiency, again based onmicro-simulation modelling outputs

Analysis of nonlinear oscillators in the frequency domain using volterra series Part II : identifying and modelling jump Phenomenon

In this the second part of the paper, a common and severe nonlinear phenomenon called jump, a behaviour associated with the Duffing oscillator and the multi-valued properties of the response solution, is investigated. The new frequency domain criterion of establishing the upper limits of the nonlinear oscillators, developed in Part I of this paper, is applied to predict the onset point of the jump, and the Volterra time and frequency domain analysis of this phenomenon are carried out based on graphical and numerical techniques

CFD modelling of mono-component and binary gas-solid fluidized beds with application to industrial materials

Fluidized bed technology is employed in a wide range of industrial applications, covering the pharmaceutical, food, chemical and petrochemical industries as well as the mining and power generation industries. However in most industrial applications of fluidization, the suspension consists of non-spherical particles of different diameters and sometimes-different densities. Computational Fluid Dynamics modelling has been recognized by several in academia and industry as an indepensible tool to study multiphase systems including fluidization. CFD models that describe gas solid flow systems can be formulated at different levels of mathematical detail. The use of the Eulerian-Eulerian approach has been considered as the highest possible level of continuum modelling where both the fluid and particle phase are treated as interpenetrating continua and mass and momentum conservation equations are solved for each phase. The Eulerian-Eulerian approach has been successfully used by many researchers for tackling problems relating to the modelling of gas-solid fluidized beds coupled with using the kinetic theory of granular flow for the description of the solid phase as derived from the kinetic theory of gases. However, most of the CFD investigations carried out to date have been limited to the study of the fluidization behaviour of mono-component gas-solid systems of modelling type materials (e.g. Ballotini). The aim of this thesis is to address the computational modelling of mono-component and binary gas-solid fluidized beds with particular focus placed on industrial materials. This work, sponsored by Huntsman Tioxide, is concerned with the titanium refining industry where a bubbling fluidized bed is used for extracting titanium from naturally occurring ore. The refining process begins in a fluidized bed with the chlorination of titanium rich rutile ore which is composed of many constituents. Due to the size and density differences of all the feedstock components used in the process, there are industrial concerns about the pervasiveness of dead zones within the fluidized bed as a result of feed stock segregation. Thus, the objective of the work is to develop a model capable of predicting the degree of mixing and segregation in the fluid bed system. To this end, the following powders, slag, natural and synthetic rutile, belonging to the Geldart Group B classification and used as feedstock in the Huntsman Tioxide chlorination process, were provided for the experimental and computational investigations in this project. This work presents a new hydrodynamic model for the CFD simulations of the mono-component and binary industrial materials using a commercial code (CFX4.4). The modelling development allowed the assessment of suitable governing equations for the description of the internal stress relevant to the solid phase(s), the fluid-particle and particle-particle interphase exchange terms. For the mono-component systems, a new expression for the fluid-particle interaction term has been developed based on the fluid bed elasticity concept originally proposed by Wallis (1969). Consequently, the procedure followed to obtain a stability criterion was re-examined analytically and subsequently numerical simulations were performed to validate the ability of the model to predict the fluidization behavior of the materials investigated. As part of the development, a comparison was conducted between the model proposed in this thesis and the granular kinetic theory model in order to assess the impact of the collisional stresses on the numerical predictions. The new modelling approach was subsequently extended to the modelling of binary systems using the three fluid approach, where a separate momentum equation is solved for the fluid and each solid phase. This part of the study also assessed the effect of the particle- particle drag force on the dynamics of the binary system by comparing three different closures available in literature and catering for this contribution against a reference test case where such contribution was not accounted for. Similar to the approach followed for the mono-component systems, a sensitivity analysis on the effect of the collisional stress on the simulations of the binary systems was also performed. Furthermore, a sensitivity analysis on grid and time step resolution was also carried out. Results of these analyses enabled the qualitative and quantitative numerical investigation into the mixing and segregation behaviour of the binary mixture of the industrial materials provided for this project. In this investigation, three different average compositions, corresponding to the average mass fraction of jetsam particles of 0.25, 0.50, 0.75 in the bed were considered, so that the hydrodynamic behavior of three binary mixtures in all was studied. In addition, a new fluid-particle interaction force closure for well mixed binary systems based on the two-fluid approach, where mixture continuity and momentum equations are employed in the description of the solid phases, was also derived and corresponding CFD simulations are carried out to assess the reliability of the derived mixture models

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

Improved model identification for nonlinear systems using a random subsampling and multifold modelling (RSMM) approach

In nonlinear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of ‘hold-out’ or ‘split-sample’ data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. Firstly, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

A comparative study on global wavelet and polynomial models for nonlinear regime-switching systems

A comparative study of wavelet and polynomial models for non-linear Regime-Switching (RS) systems is carried out. RS systems, considered in this study, are a class of severely non-linear systems, which exhibit abrupt changes or dramatic breaks in behaviour, due to RS caused by associated events. Both wavelet and polynomial models are used to describe discontinuous dynamical systems, where it is assumed that no a priori information about the inherent model structure and the relative regime switches of the underlying dynamics is known, but only observed input-output data are available. An Orthogonal Least Squares (OLS) algorithm interfered with by an Error Reduction Ratio (ERR) index and regularised by an Approximate Minimum Description Length (AMDL) criterion, is used to construct parsimonious wavelet and polynomial models. The performance of the resultant wavelet models is compared with that of the relative polynomial models, by inspecting the predictive capability of the associated representations. It is shown from numerical results that wavelet models are superior to polynomial models, in respect of generalisation properties, for describing severely non-linear RS systems