Tracer distributions of line advected thermals

A Light Attenuation system is employed to study the behaviour of strongly advected buoyant jets, focusing in particular on line advected thermals. A double-Gaussian approximation is employed to characterize the cross-sectional form of the tracer distributions. The new data highlights distinct differences in the form of line advected thermals, when compared to advected line momentum puffs. Thermals have a wider peak separation and a relatively large cross-sectional distortion that decays as the flow becomes fully established. It is therefore possible to improve predictions of mean minimum dilution, spread and flow path through separate parameterization of these strongly-advected flows. However, there are notable inconsistencies in recently obtained trajectory data, which require further investigation before such improvements can be made.     

k-ε Predictions of the initial mixing of desalination discharges

Predictions from a k-ε model are compared with recently acquired experimental data from inclined negatively buoyant discharges. The k-ε model is part of a standard computational fluid dynamics package (CFX). Two approaches are taken when implementing the model. One involves using an essentially standard form of the model to predict flow behaviour. The other approach involves calibrating the model, through adjustment of the turbulent Schmidt number in the tracer transport equation, to achieve reasonable predictions for positively buoyant vertical discharges and then applying it to inclined negatively buoyant discharges. While the calibrated approach improves the predictions of some bulk parameters (notably the tracer spread and dilution) when compared to predictions from the standard model, the overall effect on the quality of the predictions is small. Comparisons with experimental data indicate that predictions from both the standard and calibrated simulations compare favourably with trajectory data, but integrated dilution predictions at the centreline maximum height are conservative (mean-integrated concentrations are over-predicted). The standard and calibrated k-ε predictions confirm the importance of buoyant instabilities on the lower (inner) side of the flow, the effects of which are clearly evident in the mean concentration profiles. However, these simulations have a tendency to overestimate the influence of stabilizing density gradients on the upper (outer) side of the flow and are unable to effectively predict the cross-sectional distribution of a tracer. In contrast to a previous study, the above comparisons indicate that predictions of bulk parameters from such models can be poor and indeed are no better than those obtained from relatively simple analytical solutions.     

Integral Model for Turbulent Buoyant Jets in Unbounded Stratified Flows. Part I: Single Round Jet

The mechanics of buoyant jet flows issuing with a general three-dimensional geometry into an unbounded ambient environment with uniform density or stable density stratification and under stagnant or steady sheared current conditions is investigated. An integral model is formulated for the conservation of mass, momentum, buoyancy and scalar quantities in the turbulent jet flow. The model employs an entrainment closure approach that distinguishes between the separate contributions of transverse shear (leading to jet, plume, or wake internal flow dynamics) and of azimuthal shear mechanisms (leading to advected momentum puff or thermal flow dynamics), respectively. Furthermore, it contains a quadratic law turbulent drag force mechanism as suggested by a number of recent detailed experimental investigations on the dynamics of transverse jets into crossflow. The model is validated in several stages: First, comparison with basic experimental data for the five asymptotic, self-similar stages of buoyant jet flows, i.e., the pure jet, the pure plume, the pure wake, the advected line puff, and the advected line thermal, support the choice and magnitude of the turbulent closure coefficients contained in the entrainment formulation. Second, comparison with many types of non-equilibrium flows support the proposed transition function within the entrainment relationship, and also the role of the drag force in the jet deflection dynamics. Third, a number of spatial limits of applicability have been proposed beyond which the integral model necessarily becomes invalid due to its parabolic formulation. These conditions, often related to the breakdown of the boundary layer nature of the flow, describe features such as terminal layer formation in stratification, upstream penetration in jets opposing a current, or transition to passive diffusion in a turbulent ambient shear flow. Based on all these comparisons, that include parameters such as trajectories, centerline velocities, concentrations and dilutions, the model appears to provide an accurate and reliable representation of buoyant jet physics under highly general flow conditions.     

Prediction of the upper tail of concentration distributions of a continuous point source release in urban environments

The peak values observed in a measured concentration time series of a dispersing gaseous pollutant released continuously from a point source in urban environments, and the hazard level associated with them, demonstrate the necessity of predicting the upper tail of concentration distributions. For the prediction of concentration distributions statistical models are preferably employed which provide information about the probability of occurrence. In this paper a concentration database pertaining to a field experiment is used for the selection of the statistical distribution. The inverses of the gamma cumulative distribution function (cdf) for 75th–99th percentiles of concentration are found to be more consistent with the experimental data than those of the log-normal distribution. The experimental values have been derived from measured high frequency time series by sorting first the concentrations and then finding the concentration which corresponds to each probability. Then the concentration mean and variance that are predicted with Computational Fluid Dynamics-Reynolds Averaged Navier–Stokes (RANS) methodology are used to construct the gamma distribution. The proposed model (“RANS-gamma”) is included in the framework of a computational code (ADREA-HF) suitable for simulating the dispersion of airborne pollutants over complex geometries. The methodology is validated by comparing the inverses of the model cdfs with the observed ones from two wind tunnel experiments. The evaluation is performed in the form of validation metrics such as the fractional bias, the normalized mean square error and the factor-of-two percentage. From the above comparisons it is concluded that the overall model performance for the present cases is satisfactory.     

Influence of the secondary motions on pollutant mixing in a meandering open channel flow

This paper presents large eddy simulation of turbulent flow in a meandering open channel with smooth wall and rectangular cross-section. The Reynolds number based on the channel height is 40,000 and the aspect ratio of the cross-section is 4.48. The depth-averaged mean stream-wise velocity agree well to experimental measurements. In this specific case, two interacting cells are formed that swap from one bend to the other. Transport and mixing of a pollutant is analysed using three different positions of release, e.g. on the inner bank, on the outer bank and on the centre of the cross section. The obtained depth-average mean concentration profiles are reasonably consistent with available experimental data. The role of the secondary motions in the mixing processes is the main focus of the discussion. It is found that the mixing when the scalar is released on the centre of the cross-section is stronger and faster than the mixing of the scalar released on the sides. When the position of release is close to a bank side, the mixing is weaker and a clear concentration of scalar close to the corresponding side-wall can be observed in both cases.     

Tsunami generation by submarine landslides: comparison of physical and numerical models

An idealised two-dimensional laboratory model of tsunamis generated by submarine landslides is described. The experimental configuration corresponds to the benchmark configuration suggested by other researchers in the international tsunami community. It comprises a semi-elliptical rigid landslide with a height to length ratio of 0.052 sliding down a 15° slope. The initial landslide submergence and specific gravity are varied, the second of which primarily determines the initial landslide acceleration. In these experiments the landslide motion is generally well approximated as consisting of two periods of constant acceleration. The first phase of positive acceleration finishes as the landslide reaches the base of the slope, while the second period of a slower deceleration continues until the landslide comes to rest along the horizontal base of the tank. A novel experimental technique, which utilises laser-induced fluorescence (LIF), is employed to measure the free surface displacement over the entire space and time domains. This enables the wave potential energy field to be computed directly and provides a vivid picture of the wave generation and development process. Particle tracking velocimetry provides detailed information on the landslide motion and also some data on the sub-surface velocity field. Experimental runs require multiple repeats (typically 35–50) of the same setup in order to capture the entire wave field with the desired resolution. Thus high level experimental repeatability is required, and this is demonstrated. A range of parameters relevant to hazard management are presented and discussed. Maximum crest and trough amplitudes of the offshore propagating waves are shown to be approximately proportional to the initial landslide acceleration and somewhat less strongly dependent on the initial landslide submergence. The maximum wave run-up experienced at the shoreline is shown to depend almost linearly on the magnitude of a high deceleration that occurs for a short period when the landslide nears the toe of the slope. The initial submergence and initial acceleration do not directly determine the maximum wave run-up, although for these experiments they impact indirectly on the magnitude of the deceleration. The efficiency of the energy transfer from the landslide potential energy to the wave field potential energy reaches values of up to 6% and is found to be strongly dependent on the initial submergence. However because of the link between the landslide mass and its acceleration, this efficiency is almost completely independent of the initial acceleration. The results from a numerical model based on linear, inviscid and irrotational wave theory, and solved with the boundary element method, are compared with the data from the experimental program. The numerical model accurately produces the generated sequence of wave crests and troughs, but slightly overpredicts their phase speed by between 2 and 4%. For all other parameters the numerical model predictions are within 25% of the experimental values, although this includes both under- and overprediction for the range of independent parameters covered.     

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.     

DNA Registers of Legally Obtained Wildlife and Derived Products as Means to Identify Illegal Takes

Abstract:  The exploitation and sale of wildlife species that are endangered in only part of their range present regulators with the critical challenge of separating legal from illegal takes. Wildlife DNA registers created from tissue samples of legally obtained individual wildlife specimens can address this problem by allowing managers to identify unregistered (presumably illegally obtained) specimens. We tested the effectiveness of the only current, fully operational wildlife DNA register of individual genetic profiles collected from legally caught minke whales ( Balaenoptera acutorostrata ). Twenty minke whale tissue samples collected at markets in Norway and 2 additional samples collected from beached minke whales in Denmark were genotyped at 12 loci used by the Norwegian minke whale DNA register. Genetic profiles of these samples then were compared against the 2676 individual profiles deposited in the Norwegian register. The high number of genetic markers used to identify individuals in our study allowed consistent matching of sample and reference profiles despite an overall error rate (due to experimental and interlaboratory data standardization) estimated at 0.015 per locus. Of the 22 test samples only the 2 Danish samples failed to match an existing profile in the Norwegian minke whale DNA register. Our results show that the basic principle of wildlife DNA registers can work in a real-life situation. The strength of wildlife DNA registers lies in their ability to unambiguously identify unregistered specimens with the aid of sensitive genetic methods that enable analysis of highly processed or degraded tissue samples. Our study also highlights a number of methodological problems such as laboratory errors and interlaboratory data standardization, which need be addressed to ensure a successful implementation of wildlife DNA registers.     

Suspended particulate matter dynamics in a particle framework

Suspended particulate matter (SPM) dynamics in ocean models are usually treated with an advection–diffusion equation for one or more sediment size classes coupled to the hydrodynamical part of the model. Numerical solution of these additional partial differential equations unavoidably introduces numerical diffusion, i.e. in the case of sharp gradients the possible occurrence of artificial oscillations and non-positivity. A Lagrangian particle-tracking model has been developed to simulate short-term SPM dynamics. Modelling individual sediment particles allows a straightforward physical interpretation of the processes. The tracking of large numbers of individual and independent particles (up to 25 million in total in a single sediment class) can be achieved on high performance computer clusters, due to efficient parallelisation of particle tracking. The movement of the particles is described by a stochastic differential equation, which is consistent with the advection–diffusion equation. Here, the concentration profile is represented by a set of independent moving particles, which are advected according to the 3D velocity field, while the diffusive displacements of the particles are sampled from a random distribution, which is related to the eddy diffusivity field. To account for erosion a new parameterisation is proposed. Three numerical particle tracking schemes (EULER, MILSTEIN and HEUN) are presented and validated in idealised test cases. Finally, the particle tracking algorithms are applied to a realistic scenario, a severe winter storm in the East Frisian Wadden Sea (southern North Sea). The comparison with observations and an Eulerian SPM transport model seems to indicate a somewhat better fidelity of the Lagrangian approach.     

Mixing and boundary interactions of 30° and 45° inclined dense jets

60° inclined dense jets had been recommended for brine discharges from desalination plants to achieve a maximum mixing efficiency. However, the terminal rise associated with 60° is relatively high and thus the angle may be too large for disposal in shallow coastal wasters. In this study, we investigate the mixing behavior of dense jets discharging at smaller angles of 30° and 45° in a stationary ambient. Combined Particle Image Velocimetry (PIV) and Planar Laser Induced Fluorescence (PLIF) were used as the measurement approaches that captured the velocity and concentration fields, respectively. Based on the experimental results, the characteristic geometrical features of the inclined dense jets, including the location of the centerline peak and the return point where the dense jet returns to the source level, etc., are quantified. The mixing and diluting behaviors are also revealed through the analysis of the axial and cross-sectional velocity and concentration profiles. In addition to the free inclined discharges, the present study also examines the effect of the proximity to the bed. Through the comparison of the results between two experimental series with distinct z ₀/D but overlapping z ₀/L _M , the latter is identified as the deciding factor for the boundary influence.     

Effect of initial excess density and discharge on constant flux gravity currents propagating on a slope

The effect of the upstream conditions on propagation of gravity current over a slope is investigated using three-dimensional numerical simulations. The current produced by constant buoyancy flux, is simulated using a large eddy simulation solver. The dense saline solution used at the inlet is the driving force of the flow. Higher replenishment of the current is possible either by a high inflow discharge or high initial fractional density excess. In the simulations, it is observed that these two parameters affect the flow in different ways. Results show that the front speed of the descending current is proportional to the cube root of buoyancy flux, $(g_o^{\prime } Q)^{1/3}$ , which agrees with the previous experimental and numerical observations. The height of the tail of the current grows linearly in the streamwise direction. Formation of a strong shear layer at the boundary of mixed upper layer and dense lower layer is observed within the body and the tail of the current. Over the tail of the current far enough from the inlet, the vertical velocity and density profiles are compared to the ones from an experimental study. Distance from the bed to the point of maximum velocity increases with an increase in inflow discharge, while it remains practically unchanged with increasing initial fractional excess density in the simulations. Even though the velocity profiles are in good agreement, some discrepancies are observed in fractional excess density profiles among experimental and numerical results. Possible reasons for these discrepancies are discussed. Generally, gravity current type of flows could be expressed in layer-integrated formulation of governing equations. However, layer integration introduces several constants, commonly known as shape factors, to the equations of motion. The values of these shape factors are calculated based on simulation results and compared to the values from experiments and to the favorably used ‘top hat’ assumption.     

Prediction of mass and momentum transport in turbulent plane wall jets over smooth and transitionally rough surfaces

This paper is concerned with the prediction of mass and momentum transport in turbulent wall jets developing over smooth and transitionally rough plane walls. The ability to accurately predict the resulting wall shear stresses and vertical profiles of the Reynolds stresses in these flows is prerequisite to the accurate prediction of bed scour, sediment re-suspension and transport by turbulent diffusion. The computations were performed by solving the Reynolds-averaged forms of the equations describing conservation of mass, momentum and concentration. The unknown correlations that arise from the averaging process (the Reynolds stresses in the case of the momentum equation, and the turbulent mass fluxes in the case of concentration) were obtained from the solution of modeled differential equations that describe their conservation. Since these models are somewhat more complex than those typically used in practice, their benefits are demonstrated by comparisons with results obtained from simpler, eddy-viscosity based closures. Comparisons with experimental data show that results of acceptable accuracy can be obtained only by using the appropriate combination of models for the turbulent fluxes of mass and momentum that properly account for the reduction of the Reynolds stresses due to wall damping effects, and for the modification of the mass transfer rates due to interactions with the mean rates of strain.     

An Inverse Gaussian distribution of tree frequencies based on tree size

Forest stand management often depends on data from a single fixed area inventory plot located at random in a forest stand. The plot provides detailed information about tree size distribution but not about per unit area tree frequency distribution unless one assumes a Poisson (POI) distribution. The POI assumption ignores any relationship between a tree's size and its demand for growing space. This study argues for the Inverse Gaussian (IG) distribution as a more realistic model. Maximum likelihood estimates of the IG parameters are obtained from a transformation of tree size data (diameter) to proxies of tree counts. Data from two stands indicated that an IG model was better at predicting the tree frequency distribution than a POI model.     

HO ‐ initiated oxidation of acridine and aminacrine in aqueous media: Kinetic model

The photodecomposition of diluted aqueous solutions of acridine and aminacrine in the presence of hydrogen peroxide was studied. Irradiation was carried out with a low pressure mercury vapour lamp. The kinetic model describes the photodegradation rate of the organic compound with respect to the technological parameters of the reactor and provides the reaction rate constants of hydroxyl radicals towards these two molecules. This model was extented to high hydrogen peroxide concentrations ([H₂O₂] > 200 μmol/l) by considering the reactivity of hydroxyl radicals towards hydrogen peroxide. This assumption allows us to define an optimal hydrogen peroxide concentration.     

Fitting complex population models by combining particle filters with Markov chain Monte Carlo

We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.     

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.     

Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? Most modeling applications of soil organic carbon (SOC) time series in agricultural field trial datasets have been conducted without accounting for model parameter uncertainty. There have been recent advances with Monte Carlo-based uncertainty analyses in the field of hydrological modeling that are applicable, relevant and potentially valuable in modeling the dynamics of SOC. Here we employed a Monte Carlo method with threshold screening known as Generalized Likelihood Uncertainty Estimation (GLUE) to calibrate the Introductory Carbon Balance Model (ICBM) to long-term field trail data from Ultuna, Sweden and Machang’a, Kenya. Calibration results are presented in terms of parameter distributions and credibility bands on time series simulations for a number of case studies. Using these methods, we demonstrate that widely uncertain model parameters, as well as strong covariance between inert pool size and rate constant parameters, exist when root mean square simulation errors were within uncertainties in input estimations and data observations. We show that even rough estimates of the inert pool (perhaps from chemical analysis) can be quite valuable to reduce uncertainties in model parameters. In fact, such estimates were more effective at reducing parameter and predictive uncertainty than an additional 16 years time series data at Ultuna. We also demonstrate an effective method to jointly, simultaneously and in principle more robustly calibrate model parameters to multiple datasets across different climatic regions within an uncertainty framework. These methods and approaches should have benefits for use with other SOC models and datasets as well.     

Advanced integral model for groups of interacting round turbulent buoyant jets

An integral model that combines all advantages of Superposition Method (SM), Entrainment Restriction Approach (ERA) and Second Order Approach (SOA) is proposed to predict the mean axial velocity and concentration fields of a group of N interacting vertical round turbulent buoyant jets. SM is successful in predicting the fields of mean axial velocity and mean concentration for a group of N interacting jets or plumes and ERA is advantageous in predicting the above fields for either two or large number (N → ∞) of interacting buoyant jets in the whole range of buoyancy. SOA takes into consideration in a dynamic way the turbulent contribution to the momentum and buoyancy fluxes and provides better accuracy than the usual procedures. A novelty of the proposed model is the production and utilisation of advanced profile distributions, convenient for the mean axial velocities and concentrations in a cross-section of the entire group of buoyant jets. These profiles are developed on the basis of flux conservation of momentum, buoyancy and kinetic energy for the mean motion. They enhance dynamic adaptation of the individual buoyant jet axes to the group centreline. Due to these profile distributions, the present model owns generality of application and better accuracy of predictions compared to usual integral models using simple Gaussian or top-hat profiles; thus it conferred the name Advanced Integral Model (AIM). AIM is herein applied to predict the mean flow properties of two different arrangement types of any number of buoyant jets: (a) linear diffusers and (b) rosette-type risers. Present results are compared to available experimental data and traditional solutions based on Gaussian profiles. Findings may be useful for design purposes and environmental impact assessment.