M. J. Cleary

Developing an efficient and reliable dry powder inhaler for pulmonary drug delivery - A review for multidisciplinary researchers

Pulmonary drug delivery is the focus of much research and development because of its great potential to produce maximum therapeutic benefit. Among the available options the dry powder inhaler (DPI) is the preferred device for the treatment of an increasingly diverse number of diseases. However, as drug delivery from a DPI involves a complicated set of physical processes and the integration of drug formulations, device design and patient usage, the engineering development of this medical technology is proving to be a great challenge. Currently there is large range of devices that are either available on the market or under development, however, none exhibit superior clinical efficacy. A major concern is the inter- and intra-patient variability of the drug dosage delivered to the deep lungs. The extent of variability depends on the drug formulation, the device design and the patients inhalation profile. This article reviews recent advances in DPI technology and presents the key factors which motivate and constrain the successful engineering of a universal, patient-independent DPI that is capable of efficient, reliable and repeatable drug delivery. A strong emphasis is placed on the physical processes of drug powder aerosolisation, deagglomeration, and dispersion and on the engineering of formulations and inhalers that can optimise these processes.

A detailed quantitative analysis of sparse-Lagrangian filtered density function simulations in constant and variable density reacting jet flows

Sparse-Lagrangian filtered density function (FDF) simulations using a generalized multiple mapping conditioning mixing model and density coupling via a conditional form of the equivalent enthalpy method are performed for both constant density and variable density turbulent jet diffusion flames. The consistency between the sparse-Lagrangian FDF for the reactive species and the Eulerian large eddy simulation (LES) for velocity along with the accuracy of the reactive species predictions relative to the exact equilibrium solution are presented in detail. The sensitivity to the number of particles used in the simulations, the mixing localization structure, chemistry and numerical time step are all investigated. The analysis shows that consistency between the FDF and LES fields is relatively insensitive to the sparseness of the particle distributions and other model parameters but that the reactive species are strongly dependent on the degree of mixing localization in the LES mixture fraction space. An algorith...

Prediction of carbon monoxide in fires by conditional moment closure

Carbon monoxide is the chief killer in fires. Dangerous levels of CO can occur when reacting combustion gases are quenched by heat transfer, or by mixing of the fire plume in a cooled under- or overventilated upper layer. In this paper, carbon monoxide predictions for enclosure fires are modeled by the conditional moment closure (CMC) method and are compared with laboratory data. The modeled fire situation is a buoyant, turbulent, diffusion flame burning under a hood. The fire plume entrains fresh air, and the postflame gases are cooled considerably under the hood by conduction and radiation, emulating conditions which occur in enclosure fires and lead to the freezing of CO burnout. Predictions of CO in the cooled layer are presented in the context of a complete computational fluid dynamics solution of velocity, temperature, and major species concentrations. A range of underhood equivalence ratios, from rich to lean, are investigated. The CMC method predicts CO in very good agreement with data. In particular, CMC is able to correctly predict CO concentrations in lean cooled gases, showing its capability in conditions where reaction rates change considerably.

Conditional moment closure and transient flamelet modelling for detailed structure and NOx formation characteristics of turbulent nonpremixed jet and recirculating flames

This study has been mainly motivated to assess computationally and theoretically the conditional moment closure (CMC) model and the transient flamelet model for the simulation of turbulent nonpremixed flames. These two turbulent combustion models are implemented into the unstructured grid finite volume method that efficiently handles physically and geometrically complex turbulent reacting flows. Moreover, the parallel algorithm has been implemented to improve computational efficiency as well as to reduce the memory load of the CMC procedure. Example cases include two turbulent CO/H2/N2 jet flames having different flow timescales and the turbulent nonpremixed H2/CO flame stabilized on an axisymmetric bluff-body burner. The Lagrangian flamelet model and the simplified CMC formulation are applied to the strongly parabolic jet flame calculation. On the other hand, the Eulerian particle flamelet model and full conservative CMC formulation are employed for the bluff-body flame with flow recirculation. Based on the numerical results, a detailed discussion is given for the comparative performances of the two combustion models in terms of the flame structure and NO x formation characteristics.

Modeling of scalar mixing in turbulent jet flames by multiple mapping conditioning

Multiple mapping conditioning (MMC) combines the probability density function (PDF) and the conditional moment closure (CMC) methods via the application of a generalized mapping function to a prescribed reference space. Stochastic and deterministic formulations of MMC exist, and the deterministic implementation has been applied here to a piloted jet diffusion flame (Sandia Flame D). This paper focuses on the feasibility of MMC and its closures for real (laboratory) flames and a relatively simple one-dimensional reference space that represents mixture fraction has been used. The remaining chemically reactive species are implicitly conditioned on mixture fraction and their fluctuations around the conditional mean are neglected. This work primarily evaluates the ability of the deterministic form of MMC to provide accurate and consistent closures for the mixture fraction PDF and the conditional scalar dissipation which do not rely on presumed shape functions for the PDF such as the commonly used β-PDF. Comput...

Multiple Mapping Conditioning: A New Modelling Framework for Turbulent Combustion

Multiple mapping conditioning (MMC) is a relatively new addition to the list of models for turbulent combustion that unifies the features of the probability density function, conditional moment closure and mapping closure models. This chapter presents the major concepts and theory of MMC without the detailed derivations which can be found in the cited literature. While the fundamental basis remains the same, MMC ideas have undergone considerable evolution since they were first proposed and the result is a generalised combustion modelling framework which can more transparently and simply incorporate the major turbulence models which have been developed over the past decades including LES. A significant part of this chapter is devoted to a review of the published MMC applications comparing model predictions with DNS and experimental flame databases. Finally, the chapter concludes with a list of some of the advances in MMC methodology that we can expect to see in the coming years.

A direct approach to generalised multiple mapping conditioning for selected turbulent diffusion flame cases

This work presents a direct and transparent interpretation of two concepts for modelling turbulent combustion: generalised Multiple Mapping Conditioning (MMC) and sparse-Lagrangian Large Eddy Simulation (LES). The MMC approach is presented as a hybrid between the Probability Density Function (PDF) method and approaches based on conditioning (e.g. Conditional Moment Closure, flamelet, etc.). The sparse-Lagrangian approach, which allows for a dramatic reduction of computational cost, is viewed as an alternative interpretation of the Filtered Density Function (FDF) methods. This work presents simulations of several turbulent diffusion flame cases and discusses the universality of the localness parameter between these cases and the universality of sparse-Lagrangian FDF methods with MMC.

A multiple mapping conditioning model for differential diffusion

This work introduces modeling of differential diffusion within the multiple mapping conditioning (MMC) turbulent mixing and combustion framework. The effect of differential diffusion on scalar variance decay is analyzed and, following a number of publications, is found to scale as Re−1/2. The ability to model the differential decay rates is the most important aim of practical differential diffusion models, and here this is achieved in MMC by introducing what is called the side-stepping method. The approach is practical and, as it does not involve an increase in the number of MMC reference variables, economical. In addition we also investigate the modeling of a more refined and difficult to reproduce differential diffusion effect – the loss of correlation between the different scalars. For this we develop an alternative MMC model with two reference variables but which also makes use of the side-stepping method. The new models are successfully validated against DNS results available in literature for homoge...

Coupling the porous conditional moment closure with the random pore model: applications to gasification and CO 2 capture

Gasification of coal or biomass with in situ CO2 capture simultaneously allows production of clean hydrogen at relatively low cost and reduced emission of CO2 into the atmosphere. Clearly, this technology has a great potential for a future carbon constrained economy. Therefore, the development of a comprehensive, physically-based gasifier model is important. The submodels that describe reactive transport processes in coal particles as well as in particles of CO2 sorbent material are among the key sub-models, which provide a necessary input for an overall gasifier model. Both coal and sorbent are materials that have complicated pore structures. The porous conditional moment closure (PCMC) model proves to be adequate for modeling reactive transport through porous media with fixed pore structure. Consumption of coal in the heterogeneous gasification reaction, however, widens the pores and reduces the surface area available for this reaction. At the same time, formation of a carbonate layer narrows the pores in the sorbent material and reduces the reaction rate of CO2 sorption. In both cases the pore structures are affected. Such changes are not taken into account in the existing PCMC model. In this study, we obtain the parameters of the diffusive tracer distribution based on the pore size distribution given by the widely applied random pore model (RPM), while coupling PCMC with RPM. Such coupling allows taking into account changes in pore structure caused by heterogeneous reactions and thus improves the accuracy of these key sub-models.

General approach for modelling of reactive transport in porous media

This work presents a relatively new approach designed for modelling reacting flows in porous media by using conditional expectations. Similar methods, aimed at obtaining, closing and using conditional expectations in reacting fluid flows, were previously developed for and successfully used in turbulent combustion (e.g. conditional moment closure or CMC) are now generalised and adapted to perform simulations of reacting flows in porous media. Different versions of the porous models PCMC (porous CMC) variations of PDCMC (distance conditioned moment closure) have been proposed and are now summarised in this work. These approaches utilise single-conditioned expectations and the closure of the equations is obtained by using diffusion approximations conventional in CMC. Fractal properties of a porous medium can be used to evaluate the coefficients of the conditional equations. A new approach for investigating transport phenomena in irregularly-connected pore networks and obtaining corresponding transport coefficients has also been suggested. This approach combines a generalised effective medium approximation with a macroscopic continuum model and allows us to explicitly obtain analytical expressions for the transport coefficients for both unconditional and conditional models. As demonstrated, the proposed general approach is capable of emulating various regimes of reactive transport in porous media, while permitting accurate reproduction of the experimental results.