On the influence of inlet perturbations on slug dynamics in horizontal multiphase flow—a computational study

When multiphase flows are modeled numerically, complex geometrical and operational features of the experiments, such as the phase mixing section, are often not resolved in detail. Rather simplified boundary conditions are prescribed, which usually cause less irregular dynamics in the system than present in reality. In this paper, a perturbation that randomly disturbs the secondary components of the velocity vector at the inlet is proposed in order to capture the experimentally observed instabilities at the interface between the phases. This in particular enhances the formation of slugs in the pipe. Different amplitudes of the perturbation are investigated. One observes that, the higher the perturbation amplitude, the earlier the slugs occur. On the other hand, sufficiently far away from the inlet, the flow pattern shows the same dynamics for different perturbation amplitudes. Hence, no specific frequency is imposed by the prescribed perturbation. The simulation results are validated by comparison with liquid level data from a corresponding experiment

Validation of simulations in multiphase flow metrology by comparison with experimental video observations

One important task in flow metrology is to evaluate the uncertainty in multiphase flow metering. A first important step towards this goal is to establish an accurate computational fluid dynamics (CFD) model of multiphase flows. In this contribution, results of multiphase flow simulations are validated by comparison with experimental data. For the evaluation and quantification of experimental observations, a tool for video analysis has been implemented. This tool extracts the liquid level over time, which is then used for further analysis and comparison with simulation data. Additional relevant parameters are obtained by frequency analysis, which is applied to both, experimental and simulation data. A comparison of the results shows good agreement between experiment and simulation

Deep learning based liquid level extraction from video observations of gas-liquid flows

The slug flow pattern is one of the most common gas–liquid flow patterns in multiphase transportation pipelines, particularly in the oil and gas industry. This flow pattern can cause severe problems for industrial processes. Hence, a detailed description of the spatial distribution of the different phases in the pipe is needed for automated process control and calibration of predictive models. In this paper, a deep-learning based image processing technique is presented that extracts the gas–liquid interface from video observations of multiphase flows in horizontal pipes. The supervised deep learning model consists of a convolutional neural network, which was trained and tested with video data from slug flow experiments. The consistency of the hand-labelled data and the predictions of the trained model have been evaluated in an inter-observer reliability test. The model was further tested with other data sets, which also included recordings of a different flow pattern. It is shown that the presented method provides accurate and reliable predictions of the gas–liquid interface for slug flow as well as for other separate flow patterns. Moreover, it is demonstrated how flow characteristics can be obtained from the results of the deep-learning based image processing technique

Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations

2-(4-Fluorophenyl)-3-methyl-1H-indole

Funding: University of St Andrews and the Engineering and Physical Science Research Council (EPSRC, UK).The indole N-H hydrogen in the title compound, C15H12FN, does not display classical hydrogen bonding. Rather it forms an interaction with the pi system of an adjacent indole, resulting in weakly interacting chains along the [001] direction.Publisher PDFPeer reviewe

Apoptosis, A Protective Mechanism for Pathogens and Their Hosts

In this review we summarize the great amount of recent information on the apoptosis in aspeets of the host-parasite interaction. Although apoptosis is a form of programmed cell death which plays a pivotal role in normal tissue development a plethora of pathogens including parasitic protista and helminths are able to modulate host apoptosis pathways to their own advantage. Here in we present and discuss new research data and results describing the phenomenon as a process have been controlled by gene expression, biochemical reactions and receptor-ligand interactions at the cell membrane surface. Section | describes apoptosis as ongoing process in normal tissue development. Section 2 analyzes the role of apoptosis in outcome of infection and pathogenesis of several disorders evoked by viruses and bacteria. The cellular mechanisms of cell death during infection with unicellular parasites such as Leishmania sp. and Plasmodium sp. are described in Section 3. In the next paragraph the potency of parasitic protista and helmiths for modulation host apoptosis pathways to their own advantage is discussed. The involvement of apoptosis in immunoregulation of the host immune function was proposed as a one of possible mechanism in creation of the host-parasite relationship. The molecular and cellular mechanisms of parasite-induced immune response via apoptosis pathways are discussed. We conclude that novel strategies for the management of the host-parasite relationships need to be explained into the mechanisms by which parasites induced apoptosis in contribution to the activity of immune system of the host

Nematode infections in mice - an experimental model of immunoregulation

There has been a substantial increase in the incidence of autoimmune and allergic diseases in Western countries in the past few decades. However, in the geographic area endemic for parasitic helminth infections, such diseases remain relatively rare. It has been hypothesized that helminths may protect against immune disorders that have been observed in urbanized area. Studies on rodents infected with nematodes Trichinella spiralis, Heligmosomoides polygyrus, Nippostrongylus brasiliensis and Trichuris muris have provided considerable information about immune mechanisms in aspects of host-parasite interaction and immunoregulation. Helminths inducing a long-lasting asymptomatic infection are regarded as major modifiers of the host immune system. Parasitic worms can establish and reproduce in mammalian hosts switching off inflammation and inducing a tolerant response to parasitic antigens. In this review we summarized recent information on the immunoregulation during nematode infection and mechanisms used by nematodes, including the induction of regulatory T cells and apoptosis in the host. The innate immune response seems to determine the different sensitivity of mice to nematode infection. In this review we also discuss results of our own studies on H. polygyrus, demonstrating that it induces different mechanisms in different strains of mice which might play important role in the modulation of immune response. In the slow responder mice apoptosis would play a key role in the outcome of immune response. Contrary to that, in fast responder mice a defensive inflammatory response is mostly down-regulated via endogenous opioids pathway. Understanding the molecular mechanisms that mediate the effects that helminths have on the immune system will provide information that can be exploited to prevent inflammatory diseases

Accounting for Uncertainty in Cumulative Sediment Transport Using a Bayesian Approach

That sediment transport estimates have large uncertainty is widely acknowledged. When these estimates are used as the basis for a subsequent analysis, such as cumulative sediment loads or budgets, treatment of uncertainty requires careful consideration. The propagation of uncertainty is a problem that has been studied in many other scientific disciplines. In recent years, Bayesian statistical methods have been successfully used to this end in hydrology, ecology, climate science, and other disciplines where uncertainty plays a major role—their applications in sediment transport, however, have been few. Previous work demonstrated how deterministic sediment transport equations can be brought into a probabilistic framework using Bayesian methods. In this paper, we extend this basic model and apply it to sediment transport observations collected on the Snake River in Wyoming, USA. These data were used previously to develop a 50-year sediment budget below Jackson Lake dam. We revisit this example to demonstrate how viewing sediment transport probabilistically can help better characterize the propagation of uncertainty in the calculation of cumulative sediment transport. We present the development of probabilistic sediment rating curves that rely on deterministic sediment transport equations and then show how these can be used to compute the distribution of sediment input and output for each year from 1958 to 2007. The Bayesian approach described provides a robust way to quantify uncertainty and then propagate it through to subsequent analyses. Results show that transport uncertainty is quantified naturally in the Bayesian approach, making it unnecessary for modelers to assume some specified error rate (e.g., ± 5%) when developing estimates of cumulative transport. Further, we demonstrate that a Bayesian approach better constrains uncertainty and allows sediment deficit and surplus to be examined in terms of quantified risk