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Computational fluid dynamics (CFD) is a simulation technique widely used in chemical and process engineering applications. However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. In this research, the kriging meta-modelling approach (also termed Gaussian process) was coupled with expected improvement (EI) to address this challenge. A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. The new EI measure is to suggest the CFD model parameter to simulate with, hence minimising SSE and improving match between simulation and experiments. The usefulness of the developed method was demonstrated through a case study of a single-phase flow in both a straight-type and a convergent-divergent-type annular jet pump, where a single model parameter was calibrated with experimental data.Keywords: Calibration, computational fluid dynamics, expected improvement, Gaussian process, parameter estimation Correspondence concerning this article should be addressed to Tao Chen at t.chen@surrey.ac.uk. 2 IntroductionComputational fluid dynamics (CFD) simulations are usually large complex computer programs representing real life fluid systems. The complexity of such simulations can make them quite a herculean task to solve, with the run time associated with the simulation being of great concern. Such simulation run time could be for hours or days.1,2 In addition, large scale or complex systems usually place a great demand on computer memory. 3 Computation is especially a problem when the CFD simulation is required to run for a large number of times, for example when sensitivity analysis is carried out 1,4 , or the models are used within a certain optimisation problem. 4,5 Of particular interest in this research project is calibration of CFD models (also known as model parameter estimation), which can be cast into an optimisation problem.CFD model calibration can be defined as the process of adjusting numerical or physical parameters in the CFD model which, on obtaining the optimal values 6 , helps to improve agreement with experimental data. 7,8,9 The parameters in this context are typically turbulence model coefficients or turbulence model choice. The default values given by CFD software providers for turbulence parameters may apply only in standard circumstances. In this paper, calibration is formulated as an optimisation problem to minimise the mismatch between simulated and experimental outputs (responses). The calibration process may need tens to hundreds of simulation runs to evaluate the objective function, the sum of squared errors (where the errors quantify the differences between the simulated and the experimental outputs) over the region of interest, a requirement that is often infeasible for CFD.These difficulties faced by CFD simulations have led to the application of meta-modelling, whi...

An experimental study was carried out to explore viscous effects on the flow pattern and bubble behavior in a small diameter bubble column using silicone oil fluids of viscosities 5, 100, 1000, and 5000 mPa s. The flow pattern was obtained from the probability density function using the time-averaged void fraction traces of the real-time qualitative and quantitative measurements from Electrical Capacitance Tomography (ECT). This was confirmed from the high-speed camera and ECT images which also gave a vivid description of the bubble behavior. Further confirmation of the observed flow pattern was obtained using the diameter ratio, λ, where for slug flow λ > 0.6. The flow pattern was observed to vary from spherical cap bubbles to developing slug and slug flow within the gas superficial velocities considered (0.02 ≤ Ugs ≤ 0.361 m/s). As viscosity increases, the appearance of spherical cap bubbles decreases, while slug flow tendency increases. The observed flow patterns were compared with previous work. The developing slugs in liquid viscosities of 5 and 100 mPa s were observed to be deformed. The Taylor bubble obtained from 1000 mPa s is called prolate spheroid, while that obtained from 5000 mPa s is called oblate spheroid. In addition, as the superficial gas velocity increases, the length of Taylor bubbles increases, while that of slug flow decreases. The bubble behavior was further characterised using the inverse dimensionless viscosity and Eotovos number. The process of leading and trailing bubble merging to form Taylor bubbles via coalescence was also captured and explained using the high-speed camera video.

Abstract

Background A large number of studies have assessed risk factors for infection with soil-transmitted helminths (STH), but few have investigated the interactions between the different parasites or compared these between host species across hosts. Here, we assessed the associations between Ascaris, Trichuris, hookworm, strongyle and Toxocara infections in the Philippines in human and animal hosts. Methods Faecal samples were collected from humans and animals (dogs, cats and pigs) in 252 households from four villages in southern Philippines and intestinal helminth infections were assessed by microscopy. Associations between worm species were assessed using multiple logistic regression. Results Ascaris infections showed a similar prevalence in humans (13.9%) and pigs (13.7%). Hookworm was the most prevalent infection in dogs (48%); the most prevalent infection in pigs was strongyles (42%). The prevalences of hookworm and Toxocara in cats were similar (41%). Statistically significant associations were observed between Ascaris and Trichuris and between Ascaris and hookworm infections in humans, and also between Ascaris and Trichuris infections in pigs. Dual and triple infections were observed, which were more common in dogs, cats and pigs than in humans. Conclusions Associations are likely to exist between STH species in humans and animals, possibly due to shared exposures and transmission routes. Individual factors and behaviours will play a key role in the occurrence of co-infections, which will have effects on disease severity. Moreover, the implications of co-infection for the emergence of zoonoses need to be explored further.