Evaluation of the approximated diffusion flamelet concept using fuels with different chemical complexity

The ability of flamelet models to reproduce turbulent combustion in devices such as diesel engines or gas turbines has enhanced the usage of these approaches in Computational Fluid Dynamics (CFD) simulations. The models based on turbulent look-up tables generated from counterflow laminar diffusion flames (DF model) permit drastic reduction of the computational cost of the CFD calculation. Nevertheless, for complex molecular fuels, such as n-heptane, the oxidation process involves hundreds of species and the calculation of the transport equations together with the ODE system that models the chemical kinetics for the DF solution becomes unaffordable for industrial devices where hundreds of flamelets are required. In this context, new hypotheses have to be introduced in order to reduce the computational cost maintaining the coherence of the combustion process. Recently, a new model known as Approximated Diffusion Flamelet (ADF) has been proposed with the aim of solving the turbulent combustion for complex fuels in a reduced time. However, the validity of this model is still an open question and has to be verified in order to justify subsequent CFD calculations. This work assesses the ADF model and its ability to reproduce accurately the combustion process and its main parameters for three fuels with different chemical complexity and boundary conditions by its comparison with the DF model. Results show that although some discrepancies arise, the ADF model has the ability to correctly describe the ignition delay and the combustion structure in the auto-ignition zone that is the most relevant one for industrial processes.     

PROMOT: A Modeling Tool for Chemical Processes

The novel process modeling tool PROMOT supports the object-oriented modeling of chemical processes for the simulation environment DIVA. In PROMOT, differential-algebraic process models can be built by aggregating structural and behavioral modeling entities that represent the topological structure or the dynamic and steady-state behavior, respectively, of the investigated chemical processes. Process models and their modeling entities may be defined either in an object-oriented modeling language or with a graphical user interface. This paper discusses the modeling concept, the modeling language, the knowledge representation aspects, and the implementation of PROMOT.     

Recent applications of CFD modelling in the power generation and combustion industries

Computational fluid dynamics (CFD) modelling is now widely applied as an industrial plant development and process optimisation tool. The steady increase in computer power over recent years has enabled process engineers to model reacting multi-phase flows in a realistic geometry with good mesh resolution. As a result, the number of applications of CFD to industrial processes is also growing rapidly and increasing in sophistication. This paper reviews some of the recent applications of the CFX-4 code [CFX-4.3: Solver Manual, AEA Technology Engineering Software, 1999] to the power generation and combustion industries. The aim is to illustrate what can be done and also to identify trends and those areas where further work is needed. Examples include coal-fired low-NO_x burner design, furnace optimisation, over-fire air, gas reburn, and laminar flames. It is argued that the trend is for CFD models to become more comprehensive and accessible by being coupled to other process models and embedded in automated information and process control systems.     

CFD modelling of thickeners at Worsley Alumina Pty Ltd

Computational fluid dynamics (CFD) modelling of process unit operations is a tool that is being used increasingly by mineral processing industries to reduce operating and capital costs and increase throughputs.Worsley Alumina first became involved with CFD modelling through support of the AMIRA Thickener Technology Project 266A in 1994, and subsequent extension projects in which CSIRO has been developing validated CFD models of thickener operation. The CSIRO Division of Minerals has been involved with CFD modelling since 1984 mainly on high temperature furnace applications and has been developing multi-phase thickener models since 1992. The benefits of obtaining a better understanding of flow patterns in thickeners using this modelling method became obvious and projects commenced in the third quarter of 1995 to utilise the CSIRO expertise. Projects have been ongoing almost continuously since that time. The CFD modelling was verified using tracers to measure actual flow patterns in a settler. Once verification had been achieved the CFD model was used to test innovative changes in design aimed at achieving higher throughputs and improved operation. These innovative changes when implemented on the full plant gave results similar to the CFD model predictions and resulted in improved process stabilisation, reduced chemical costs and very large savings in capital requirements for our major expansion that has just been completed. There are a number of assumptions made in the CFD model and these are discussed in detail in the paper together with details of individual CFD modelling projects and cost benefits achieved from completed projects.     

Developments in modelling of gas injection and slag foaming

Gas injection into metallurgical ladles has been an active area of CFD modelling for many years. Recent work with both Eulerian and Lagrangian frameworks is presented for bottom stirring in ladle and steelmaking electric furnace configurations. Comparison with water and liquid metal results shows that the Lagrangian models provide a better representation of the systems. Slag foaming is an important phenomenon in smelting–reduction processes and electric furnace steelmaking. The void fraction in the foam is generally greater than 0.9, a regime that has received considerably less attention than bottom stirring where the local void fraction is less than 0.1. Again, it was found, by comparison with experimental data, that Lagrangian models were generally preferable over Eulerian models.     

Process planning in a fuzzy environment

Mixed-integer optimization models for chemical process planning typically assume that model parameters can be accurately predicted. As precise forecasts are difficult to obtain, process planning usually involves uncertainty and ambiguity in the data. This paper presents an application of fuzzy programming to process planning. The forecast parameters are assumed to be fuzzy with a linear or triangular membership function. The process planning problem is then formulated in terms of decision making in a fuzzy environment with fuzzy constraints and fuzzy net present value goals. The model is transformed to a deterministic mixed-integer linear program or mixed-integer nonlinear program depending on the type of uncertainty involved in the problem. For the nonlinear case, a global optimization algorithm is developed for its solution. This algorithm is applicable to general possibilistic programs and can be used as an alternative to the commonly used bisection method. Illustrative examples and computational results for a petrochemical complex with 38 processes and 24 products illustrate the applicability of the developed models and algorithms.     

Optimising the design of fume extraction hoods using a combination of engineering and CFD modelling

Fume and hygiene hoods are widely used to prevent fugitive emissions from charge ports, tap holes and many other openings in mineral processing and smelting vessels. The highly buoyant nature of the fume combined with often complex geometries make the design of these hoods difficult with traditional engineering tools. However, by combining the traditional engineering approach with computational fluid dynamics (CFD) techniques, a clear understanding of the shortfalls of an existing system can be obtained, and an optimised hood design can be achieved. This paper reports on a combined engineering and CFD analysis of a fume extraction system for a zinc slag fumer charge port. The engineering model revealed that the existing plant components (bag house and fan) were not capable of capturing the required amount of fume, and that the original hood design was flawed. The CFD model was then used to predict the fume capture and emission from the existing hood. CFD model predictions showed that increasing the draft flow rate by an order of magnitude would only give a marginal improvement in fume capture. Using findings of both the models enabled a new fume capture hood to be designed. CFD analysis of the new hood revealed that a significant improvement in fume capture is possible. Construction and installation of the hood has been performed and a 65% reduction in fume emission was achieved, thus significantly mitigating a long-standing emission problem.     

Towards Modeling the Curing Processes of Thermosets

This contribution presents a newly developed phenomenological model to describe the curing processes of thermosets undergoing small strain deformations [1]. The governing equations are derived from a number of physical and chemical assumptions. Some numerical examples demonstrate the model's capability to correctly represent the evolution of elastic and inelastic material properties as well as the volume shrinkage taking place during the curing process. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Implementation of a finite‐volume method for the determination of effective parameters in fissured porous media

Many studies have proposed one‐equation models to represent transport processes in heterogeneous porous media. This approach is based on the assumption that dependent variables such as pressure, temperature, or concentration can be expressed in terms of a single large‐scale averaged quantity in regions having very different chemical and/or mechanical properties. However, one can also develop large‐scale averaged equations that apply to the distinct regions that make up a heterogeneous porous medium. This approach leads to region‐averaged equations that contain traditional convective and dispersive terms, in addition to exchange terms that account for the transfer between the different media. In our approach, the fissures represent one region, and the porous media blocks represent the second region. The analysis leads to upscaled equations having a domain of validity that is clearly identified in terms of time and length‐scale constraints. Closure problems are developed that lead to the prediction of the effective coefficients that appear in the region averaged equations, and the main purpose of this article is to provide solutions to those closure problems. The method of solution makes use of an unstructured grid and a joint element method in order to take care of the special characteristics of the fissured network. This new numerical method uses the theory developed by Quintard and Whitaker and is applied on considerably more complex geometries than previously published results. It has been tested for several special cases such as stratified systems and “sugarbox” media, and we have compared our calculations with other computational methods. © 2000 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 16: 237–263, 2000     

Statistical analysis of first-order MARMA processes

In this paper, we consider processes of the MARMA type that can be derived from the classical ARMA processes by replacing summation by the maximum operation. It is assumed that the innovations and the values of the process have the standard Fréchet distribution. For simple MARMA processes of first order, certain numerical characteristics are calculated. Sign tests and rank statistical methods for parameter estimation are developed. The characterization relations that can be used for the identification of models are justified.     

Validation of the mission-based approach to representing command and control in simulation models of conflict

There is a need to represent military command and control in closed-form simulation models of conflict, in order to compare investment in such capability with alternative defence investments. This paper considers such representation of military command and control in the context of embodied cognitive science. This means that we represent such processes in terms of both decision-making and resultant behaviour. Previous work leads to the view that such a representation can be captured by a combination of deliberate (top down) planning and rapid (bottom up) planning. We have developed an approach on these lines as a way of representing human decision-making and behaviour in conflict. Here we show, by comparing simulation model results with real conflict situations, that our approach yields emergent force behaviour which is valid and representative. This thus increases our confidence that our representation of command and control in such simulation models is sufficient for our requirements.     

Statistical modelling and repeatable structures: purpose,process and prediction

Children have limited exposure to statistical concepts and processes, yet researchers have highlighted multiple benefits of experiences in which they design and/or engage informally with statistical modelling. A study was conducted with a classroom in which students developed and utilised data-based models to respond to the inquiry question, Which origami animal jumps the furthest? The students used hat plots and box plots in Tinkerplots to make sense of variability in comparing distributions of their data and to support them to write justified conclusions of their findings. The study relied on classroom video and student artefacts to analyse aspects of the students’ modelling experiences which exposed them to powerful statistical ideas, such as key repeatable structures and dispositions in statistics. Three principles—purpose, process and prediction—are highlighted as ways in which the problem context, statistical structures and inquiry dispositions and cycle extended students’ opportunities to reason in sophisticated ways appropriate for their age. The research question under investigation was, How can an emphasis on purpose, process and prediction be implemented to support children’s statistical modelling? The principles illustrated in the study may provide a simple framework for teachers and researchers to develop statistical modelling practices and norms at the school level.     

A numerical model for predicting bubble formation in a 3D fluidized bed

Fluidized bed systems have the potential to be widely used in the power generation, mineral processing and chemical industries. One factor limiting their increased use is the lack of adequate design techniques for scaling such systems. A model has been developed for simulating gas–solid fluidized bed plant. The model uses a multiphase Eulerian–Eulerian technique to predict the transient behaviour of fluidized bed systems. The commercial CFD code CFX is used as the computational framework for solving the discretized equations. To overcome the problem of accurate geometrical representation experienced in previous models a body fitted grid system is employed. The model is used to predict isothermal flow in a three-dimensional bubbling fluidized bed. Predictions of the three-dimensional model show bubble formation with gas bubbles or voids preferentially moving along the centre of the bed. Predicted behaviour is qualitatively consistent with experimental observations.     

Coupling methods and exponential ergodicity for two-factor affine processes

In this paper, by invoking the coupling approach, we establish exponential ergodicity under the L¹-Wasserstein distance for two-factor affine processes. The method employed herein is universal in a certain sense so that it is applicable to general two-factor affine processes, which allow that the first component solves a general Cox-Ingersoll-Ross (CIR) process, and that there are interactions in the second component, as well as that the Brownian noises are correlated; and even to some models beyond two-factor processes.     

Modelling and computation of acrylic bone cement injection and curing within the framework of vertebroplasty

This contribution deals with the simulation based investigation of processes related to the surgical treatment of vertebroplasty. In this regard, a simulation framework has been developed, which includes the generation of microstructural computer models of cancellous bone structures, the simulation of bone cement injection by computational fluid dynamics (CFD) methods and finite element (FE) simulations of bone cement curing processes. The modelling and computation strategy is illustrated and different material modelling approaches for the representation of acrylic bone cements as a non-linear fluid and a non-linear viscoelastic solid with curing dependent properties are outlined. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Carr's randomization for American options in regime-switching models

In the paper, we solve the pricing problem for American put-like options in Markov-modulated Lévy models. The early exercise boundaries and prices are calculated using a generalization of Carr's randomization for regime-switching models. An efficient iteration pricing procedure is developed. The computational time is of order m², where m is the number of states, and of order m, if the parallel computations are allowed. The payoffs, riskless rates and class of Lévy processes may depend on a state. Special cases are stochastic volatility models and models with stochastic interest rate; both must be modelled as finite-state Markov chains. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

State-space stochastic volatility models: A review of estimation algorithms

Stochastic volatility models (SVMs) represent an important framework for the analysis of financial time series data, together with ARCH-type models; but unlike the latter, the former, at least from the statistical point of view, cannot rely on the possibility of obtaining exact inference, in particular with regard to maximum likelihood estimates for the parameters of interest. For SVMs, usually only approximate results can be obtained, unless particularly sophisticated estimation strategies like exact non-gaussian filtering methods or simulation techniques are employed. In this paper we review SVM and present a new characterization for them, called ‘generalized bilinear stochastic volatility’. © 1996 John Wiley & Sons, Ltd.     

An anisotropic enhanced thermal conductivity approach for modelling laser melt pools for Ni-base super alloys

Ni-base super alloys are extensively used in high temperature gas turbine engines and energy industries. Due to the high replacement costs of these components, there are huge economic benefits of repairing these components. Laser direct metal deposition processes (LDMD) based on laser cladding, laser fusion welding, and laser surface melting are some of the processes which are used to repair these high value components. Precise control of these processes is important to achieve the desired microstructure, stress distribution, distortions due to thermal stresses and other important output variables. Modelling of these processes is therefore an extremely important activity for achieving any degree of control/optimisation. However, modelling of these processes is not straight-forward due to melt pool flows dominated by Marangoni and buoyancy driven convection. Detailed CFD models are required for accurate prediction of melt pool geometry. But these models are computationally expensive and require greater expertise. To simplify and speed up the modelling process, many researchers have used the isotropic enhanced thermal conductivity approach to account for melt pool convection. A recent study on mild steel has highlighted that isotropic enhanced thermal conductivity approach is not able to accurately predict the melt pool geometry. Based on these findings a new approach namely anisotropic enhanced thermal conductivity approach has been developed. This paper presents an analysis on the effectiveness of the isotropic and anisotropic enhanced thermal conductivity approaches for laser melting of Inconel 718 using numerical technique. Experimental melt pool geometry has been compared with modelling results. It has been found that the isotropic enhanced thermal conductivity approach is not able to accurately predict the melt pool geometry, whereas anisotropic enhanced thermal conductivity approach gives good agreement with the experimental results.