Installed performance of air-augmented nozzles based on analytical determination of internal ejector characteristics

Procedures for matching intake and ejector pumping characteristics of air-augmented nozzle

Investigating the effects of inter-annual weather variation (1968- 2016) on the functional response of cereal grain yield to applied nitrogen, using data from the Rothamsted Long-Term experiments

The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat (Triticum aestivvum L.) and spring barley (Hordeum vulgare L.) was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rothamsted Research. Grain yields of crops from 1968 to 2016 were modelled as a function of N rates using a linear-plus-exponential (LEXP) function. The extent to which inter-annual variation in the parameters of these responses was explained by variations in weather (monthly summarized temperatures and rainfall), and by changes in the cultivar grown, was assessed. The inter-annual variability in rainfall and underlying temperature influenced the crop N response and hence grain yields in both crops. Asymptotic yields in wheat were particularly sensitive to mean temperature in November, April and May, and to total rainfall in October, February and June. In spring barley asymptotic yields were sensitive to mean temperature in February and June, and to total rainfall in April to July inclusive and September. The method presented here explores the separation of agronomic and environmental (weather) influences on crop yield over time. Fitting N response curves across multiple treatments can support an informative analysis of the influence of weather variation on the yield variability. Whilst there are issues of the confounding and collinearity of explanatory variables within such models, and that other factors also influence yields over time, our study confirms the considerable impact of weather variables at certain times of the year. This emphasizes the importance of including weather temporal variation when evaluating the impacts of climate change on crops

RANS and DES Computations for a Wing with Ice Accretion

A computational investigation was performed to assess the effectiveness of Detached Eddy Simulation (DES) as a tool for predicting icing effects. The AVUS code, a public domain flow solver, was employed to compute solutions for an iced wing configuration using DES and steady Reynolds Averaged Navier-Stokes (RANS) equation methodologies. The wing section considered here was a business jet airfoil (GLC305) with a 22.5-minute glaze ice accretion (944-ice shape). The section was extruded to form a rectangular planform. The model was mounted between two walls so no tip effects were considered. The numerical results were validated by comparison with experimental data for the same configuration. The time-averaged DES computations showed some improvement in lift and drag results near stall when compared to steady RANS results. However, comparisons of the flow field details did not show the level of agreement suggested by the integrated quantities. More validation is needed to determine what role DES can play as part of an overall icing effects prediction strategy

Analysis of free turbulent shear flows by numerical methods

Studies are described in which the effort was essentially directed to classes of problems where the phenomenologically interpreted effective transport coefficients could be absorbed by, and subsequently extracted from (by comparison with experimental data), appropriate coordinate transformations. The transformed system of differential equations could then be solved without further specifications or assumptions by numerical integration procedures. An attempt was made to delineate different regimes for which specific eddy viscosity models could be formulated. In particular, this would account for the carryover of turbulence from attached boundary layers, the transitory adjustment, and the asymptotic behavior of initially disturbed mixing regions. Such models were subsequently used in seeking solutions for the prescribed two-dimensional test cases, yielding a better insight into overall aspects of the exchange mechanisms

Social and Environmental Factors Associated with Preschoolers' Non-sedentary Physical Activity

The two-fold purpose of the investigation was (1) to describe with direct observation data the physical activity behaviors and the accompanying social and environmental events of those behaviors for children in preschools; and (2) to determine which contextual conditions were predictors of moderate-to-vigorous physical activity (MVPA) and non-sedentary physical activity (i.e., light activity + MVPA) for 3-, 4-, and 5-year-old children during their outdoor play. The results indicate that preschoolers' physical activity is characterized as sedentary in nature throughout their preschool day (i.e., 89% sedentary, 8% light activity, 3% MVPA). During outdoor play periods, when children are most likely to be physically active, some contextual and social circumstances better predict their physical activity. Implications for policymakers, practitioners, and researchers are discussed. Originally published Child Development, Vol. 80, No. 1, Jan/Feb 200

Simulation-based model selection for dynamical systems in systems and population biology

Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that allow us to choose rationally between different mechanistic models of e.g. signal transduction or gene regulation networks. This is particularly challenging in systems biology where only a small number of molecular species can be assayed at any given time and all measurements are subject to measurement uncertainty. Here we develop such a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling. We show that our approach can be applied across a wide range of biological scenarios, and we illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway. Bayesian model selection strikes a balance between the complexity of the simulation models and their ability to describe observed data. The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable.Comment: This article is in press in Bioinformatics, 2009. Advance Access is available on Bioinformatics webpag