Numerical Investigation of Boundary Layers in Wet Steam Nozzles

Condensing nozzle flows have been used extensively to validate wet steam models. Many test cases are available in the literature, and in the past, a range of numerical studies have dealt with this challenging task. It is usually assumed that the nozzles provide a one- or two-dimensional flow with a fully turbulent boundary layer (BL). The present paper reviews these assumptions and investigates numerically the influence of boundary layers on dry and wet steam nozzle expansions. For the narrow nozzle of Moses and Stein, it is shown that the pressure distribution is significantly affected by the additional blockage due to the side wall boundary layer. Comparison of laminar and turbulent flow predictions for this nozzles suggests that laminar–turbulent transition only occurs after the throat. Other examples are the Binnie and Green nozzle and the Moore et al. nozzles for which it is known that sudden changes in wall curvature produce expansion and compression waves that interact with the boundary layers. The differences between two- and three-dimensional calculations for these cases and the influence of laminar and turbulent boundary layers are discussed. The present results reveal that boundary layer effects can have a considerable impact on the mean nozzle flow and thus on the validation process of condensation models. In order to verify the accuracy of turbulence modeling, a test case that is not widely known internationally is included within the present study. This experimental work is remarkable because it includes boundary layer data as well as the usual pressure measurements along the nozzle centerline. Predicted and measured boundary layer profiles are compared, and the effect of different turbulence models is discussed. Most of the numerical results are obtained with the in-house wet steam Reynolds-averaged Navier–Stokes (RANS) solver, Steamblock, but for the purpose of comparison, the commercial program ansys cfx is also used, providing a wider range of standard RANS-based turbulence models.Engineering and Physical Sciences Research CouncilThis is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the American Society of Mechanical Engineers (ASME)

Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research

INTRODUCTION: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. METHODS: Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. RESULTS: Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). CONCLUSION: TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science's translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the "AI chasm" continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice

Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research

2023 Hogg, Al-Zubaidy, Keane, Hughes, Beyer and Maniatopoulos.Introduction: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. Methods: Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. Results: Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). Conclusion: TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science\u27s translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the “AI chasm” continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice

Nucleation and wake-chopping in low pressure steam turbines

While wetness formation in steady flows such as nozzles and cascades is well understood, predicting the polydispersed droplet spectra observed in turbines remains challenging. The characteristics of wetness formation are affected by the expansion rate at the Wilson point. Because the expansion rate varies substantially both axially and circumferentially within steam turbines, the location of the Wilson point within a blade row is a primary factor determining the droplet spectrum and phase change losses. This effect is first investigated using a single streamline with a varying expansion rate, and it is shown that the phase change losses during spontaneous condensation are highest when a large region of high subcooling precedes the Wilson point. The conditions resulting in the highest wetness loss in the nucleation zone do not correspond to those that produce the largest downstream droplets. The effect of nucleation location is then assessed using a non-equilibrium RANS calculation of a realistic low pressure (LP) steam turbine geometry. A quasi-three dimensional (Q3D) flow domain is used to simplify the analysis, which is performed both steadily and unsteadily to isolate the effects of wake-chopping. The inlet temperature is varied to investigate the impact of the Wilson point location on the steady and unsteady wetness loss and droplet spectra. The trends observed in the 1D analysis are repeated in the steady RANS results. The unsteady results show that the Wilson zone is most sensitive to wake-chopping when located near a blade trailing edge and the following inter-row gap. The predicted wetness losses are compared to those predicted by the Baumann rule. The first author, FRH, is grateful for the support of a Cambridge International Scholarship provided by the Cambridge Commonwealth, European, and International Trust in collaboration with ALSTOM. All authors wish to acknowledge generous support from ALSTOM Power (Steam Turbines Division)

Comets, historical records and vedic literature

A verse in book I of Rigveda mentions a cosmic tree with rope-like aerial roots held up in the sky. Such an imagery might have ensued from the appearance of a comet having `tree stem' like tail, with branched out portions resembling aerial roots. Interestingly enough, a comet referred to as `heavenly tree' was seen in 162 BC, as reported by old Chinese records. Because of weak surface gravity, cometary appendages may possibly assume strange shapes depending on factors like rotation, structure and composition of the comet as well as solar wind pattern. Varahamihira and Ballala Sena listed several comets having strange forms as reported originally by ancient seers such as Parashara, Vriddha Garga, Narada and Garga. Mahabharata speaks of a mortal king Nahusha who ruled the heavens when Indra, king of gods, went into hiding. Nahusha became luminous and egoistic after absorbing radiance from gods and seers. When he kicked Agastya (southern star Canopus), the latter cursed him to become a serpent and fall from the sky. We posit arguments to surmise that this Mahabharata lore is a mythical recounting of a cometary event wherein a comet crossed Ursa Major, moved southwards with an elongated tail in the direction of Canopus and eventually went out of sight. In order to check whether such a conjecture is feasible, a preliminary list of comets (that could have or did come close to Canopus) drawn from various historical records is presented and discussed.Comment: This work was presented in the International Conference on Oriental Astronomy held at IISER, Pune (India) during November, 201

TVL₁ Planarity Regularization for 3D Shape Approximation

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy

aeGEPUCI: a database of gene expression in the dengue vector mosquito, Aedes aegypti

<p>Abstract</p> <p>Background</p> <p><it>Aedes aegypti </it>is the principal vector of dengue and yellow fever viruses. The availability of the sequenced and annotated genome enables genome-wide analyses of gene expression in this mosquito. The large amount of data resulting from these analyses requires efficient cataloguing before it becomes useful as the basis for new insights into gene expression patterns and studies of the underlying molecular mechanisms for generating these patterns.</p> <p>Findings</p> <p>We provide a publicly-accessible database and data-mining tool, aeGEPUCI, that integrates 1) microarray analyses of sex- and stage-specific gene expression in <it>Ae. aegypti</it>, 2) functional gene annotation, 3) genomic sequence data, and 4) computational sequence analysis tools. The database can be used to identify genes expressed in particular stages and patterns of interest, and to analyze putative <it>cis</it>-regulatory elements (CREs) that may play a role in coordinating these patterns. The database is accessible from the address <url>http://www.aegep.bio.uci.edu</url>.</p> <p>Conclusions</p> <p>The combination of gene expression, function and sequence data coupled with integrated sequence analysis tools allows for identification of expression patterns and streamlines the development of CRE predictions and experiments to assess how patterns of expression are coordinated at the molecular level.</p

The extraordinary evolutionary history of the reticuloendotheliosis viruses

The reticuloendotheliosis viruses (REVs) comprise several closely related amphotropic retroviruses isolated from birds. These viruses exhibit several highly unusual characteristics that have not so far been adequately explained, including their extremely close relationship to mammalian retroviruses, and their presence as endogenous sequences within the genomes of certain large DNA viruses. We present evidence for an iatrogenic origin of REVs that accounts for these phenomena. Firstly, we identify endogenous retroviral fossils in mammalian genomes that share a unique recombinant structure with REVs—unequivocally demonstrating that REVs derive directly from mammalian retroviruses. Secondly, through sequencing of archived REV isolates, we confirm that contaminated Plasmodium lophurae stocks have been the source of multiple REV outbreaks in experimentally infected birds. Finally, we show that both phylogenetic and historical evidence support a scenario wherein REVs originated as mammalian retroviruses that were accidentally introduced into avian hosts in the late 1930s, during experimental studies of P. lophurae, and subsequently integrated into the fowlpox virus (FWPV) and gallid herpesvirus type 2 (GHV-2) genomes, generating recombinant DNA viruses that now circulate in wild birds and poultry. Our findings provide a novel perspective on the origin and evolution of REV, and indicate that horizontal gene transfer between virus families can expand the impact of iatrogenic transmission events

The pseudo McMillan degree of implicit transfer functions of RLC networks

We study the structure of a given RLC network without sources. Since the McMillan degree of the implicit transfer function is not a suitable measure of complexity of the network, we introduce the pseudo McMillan degree to overcome these shortcomings.Using modified nodal analysis models, which are linked directly to the natural network topology, we shaw that the pseudo-McMillan degree equals the sum of the number of capacitors and inductors minus the number of fundamental loops of capacitors and fundamental cutsets of inductors; this is the number of independent dynamic elements in the network. Exploiting this representation we derive a minimal realization of the given RLC network, that is one where the number of independent differential equations equals the pseudo McMillan degree