Paweł M. Rowiński

Statistical sand wave dynamics in one-directional water flows

Moving sand waves and the overlying tubulent flow were measured on the Wilga River in Poland, and the Tirnava Mica and Buzau Rivers in Romania. Bottom elevations and flow velocities were measured at six points simultaneously by multi-channel measuring systems. From these data, the linear and two-dimensional sections of the three-dimensional correlation and structure functions and various projections of sand wave three-dimensional spectra were investigated. It was found that the longitudinal wavenumber spectra of the sand waves in the region of large wavenumbers followed Hinos −3 law ( S ( K x ) ∝ K −3 x ) quite satisfactorily, confirming the theoretical predictions of Hino (1968) and Jain & Kennedy (1974). However, in contrast to Hino (1968), the sand wave frequency spectrum in the high-frequency region was approximated by a power function with the exponent −2, while in the lower-frequency region this exponent is close to −3. A dispersion relation for sand waves has been investigated from analysis of structure functions, frequency spectra and the cross-correlation functions method. For wavelengths less than 0.15–0.25 of the flow depth, their propagation velocity C is inversely proportional to the wavelength λ. When the wavelengths of spectral components are as large as 3–4 times the flow depth, no dispersion occurs. These results proved to be in good qualitative agreement with the theoretical dispersion relation derived from the potential-flow-based analytical models (Kennedy 1969; Jain & Kennedy 1974). We also present another, physically-based, explanation of this phenomenon, introducing two types of sand movement in the form of sand waves. The first type (I) is for the region of large wavenumbers (small wavelengths) and the second one (II) is for the region of small wavenumbers (large wavelengths). The small sand waves move due to the motion of individual sand particles (type I, C ∝λ −1 ) while larger sand waves propagate as a result of the motion of smaller waves on their upstream slopes (type II, C ∝λ 0 ). Like the sand particles in the first type, these smaller waves redistribute sand from upstream slopes to downstream ones. Both types result in sand wave movement downstream but with a different propagation velocity. The main characteristics of turbulence, as well as the quantitative values characterizing the modulation of turbulence by sand waves, are also presented.

Properties of the dead-zone model of longitudinal dispersion in rivers

The dead zone equations were solved with the use of the Laplace transform technics. The solution was a base to derive the three moments of the pollution concentration distribution in a river. They differ from the moments published so far, because they were calculated from the solution of the pure boundary problem. This approach is easier to apply in calculations of the pollution concentration distributions and it covers a wider range of cases when we deal with natural field data. Main features of the model equations were analysed from the point of view of the theory of dynamic systems. The transfer function was derived and analysed as well as the frequency-response function. The dispersion relation for the dead zone equations was also obtained and analysed for different parameters of the model.

3D Lagrangian modelling of saltating particles diffusion in turbulent water flow

A 3D Lagrangian model of the saltation of solid spherical particles on the bed of an open channel flow, accounting for turbulence-induced mechanisms, is proposed and employed as the key tool of the study. The differences between conventional 2D models and a proposed 3D saltation model are discussed and the advantages of the 3D model are highlighted. Particularly, the 3D model includes a special procedure allowing generation of 3D flow velocity fields. This procedure is based on the assumption that the spectra of streamwise, vertical and transverse velocity components are known at any distance from the bed. The 3D model was used to identify and quantify effects of turbulence on particle entrainment and saltation. The analysis of particle trajectories focused on their diffusive nature, clarifying: (i) the effect of particle mobility parameter; (ii) the effect of bed topography; and (iii) the effect of turbulence. Specifically, the results of numerical simulations describing the abovementioned effects on the change in time of the variance are presented. In addition, the change in time of the skewness and kurtosis, which are likely to reflect the turbulence influence on the spread of particles, are also shown. Two different diffusion regimes (local and intermediate) for each of the investigated flow conditions are confidently identified.

Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers

This study presents the comparison of various evolutionary computation (EC) optimization techniques applied to train the noise-injected multi-layer perceptron neural networks used for estimation of longitudinal dispersion coefficient in rivers. The special attention is paid to recently developed variants of Differential Evolution (DE) algorithm. The most commonly used gradient-based optimization methods have two significant drawbacks: they cannot cope with non-differentiable problems and quickly converge to local optima. These problems can be avoided by the application of EC techniques. Although a great amount of various EC algorithms have been proposed in recent years, only some of them have been applied to neural network training - usually with no comparison to other methods. We restrict our comparison to the regression problem with limited data and noise injection technique used to avoid premature convergence and to improve robustness of the model. The optimization methods tested in the present paper are: Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization, Efficient Population Utilization Strategy Particle Swarm Optimization and Covariance Matrix Adaptation - Evolution Strategy.

Response to the slug injection of a tracer—a large-scale experiment in a natural river

Abstract A unique, large-scale tracer test performed along a 90-km reach of a natural river is presented. This method was crucial for evaluating the impact of a retention reservoir on protected areas of the river downstream, and to assess the threats due to potentially catastrophic releases of toxic substances into that river. The response to the slug injection of a soluble tracer is assumed to imitate the characteristics of a soluble pollutant, so an understanding of how tracers mix and disperse in a stream is essential to understanding the processes of pollution transport. The procedure applied during this experiment consisted of the instantaneous injection of a known quantity of Rhodamine WT into the stream and the determination of the temporal variation in concentration of the tracer at sites as it moved downstream. The results were analysed from the perspective of a transient storage model. Relevant model parameters were evaluated by fitting the computed breakthrough curves to the observed ones on a reach-by-reach basis.

How novel is the novel black hole optimization approach

Due to abundance of novel optimization algorithms in recent years, the problem of large similarities among methods that are named differently is becoming troublesome and general. The question arises if the novel source of inspiration is sufficient to breed an optimization algorithm with a novel name, even if its search properties are almost the same as, or are even a simplified variant of, the search properties of an older and well-known method. In this paper it is rigidly shown that the recently proposed heuristic approach called the black hole optimization is in fact a simplified version of Particle Swarm Optimization with inertia weight. Additionally, because a large number of metaheuristics developed during the last decade is claimed to be nature-inspired, a short discussion on inspirations of optimization algorithms is presented.

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?

Abstract Abstract Accurate application of the longitudinal dispersion model requires that specially designed experimental studies are performed in the river reach under consideration. Such studies are usually very expensive, so in order to quantify the longitudinal dispersion coefficient, as an alternative approach, various researchers have proposed numerous empirical formulae based on hydraulic and morphometric characteristics. The results are presented of the application of artificial neural networks as a parameter estimation technique. Five different cases were considered with the network trained for different arrangements of input nodes, such as channel depth, channel width, cross-sectionally averaged water velocity, shear velocity and sinuosity index. In the case where the sinuosity index is included as an input node, the results turned out to be better than those presented by other authors.

Water quality hazards and dispersion of pollutants

Mixing Models for Water Quality Management in Rivers: Continuous and Instantaneous Pollutant Releases.- Three-Dimensional Model of Flow and Mixing Processes in Open Channels.- Horizontal Mixing in Shallow Flows.- On the Theoretical Prediction of Longitudinal Dispersion Coefficients in a Compound Channel.- Application of a Transient Storage Model to Meandering Channel Studies of Solute Transport and Dispersion.- Experimental Study of Travel Times in a Small Stream.- Migration of Floating Particles in a Compound Channel.- Persistence of Skewness of Concentration Distribution.- Moments and Analytical Solution of Coupled Equation Describing Transport of Pollutants in Rivers.- Influence of Hyporheic Exchange on Solute Transport in a Highly Hydropower Regulated River.- Models of Hyporheic Contamination by Non Reactive Solutes, Metals and Colloids.- The Flow in Groyne Fields.

Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river

Nature-inspired metaheuristics found various applications in different fields of science, including the problem of artificial neural networks (ANN) training. However, very versatile opinions regarding the performance of metaheuristics applied to ANN training may be found in the literature.Both nature-inspired metaheuristics and ANNs are widely applied to various geophysical and environmental problems. Among them the water temperature forecasting in a natural river, especially in colder climate zones where the seasonality plays important role, is of great importance, as water temperature has strong impact on aquatic life and chemistry. As the impact of possible future climate change on water temperature is not trivial, models are needed to allow projection of streamwater temperature based on simple hydro-meteorological variables.In this paper the detailed comparison of the performance of nature-inspired optimization methods and Levenberg-Marquardt (LM) algorithm in ANNs training is performed, based on the case study of water temperature forecasting in a natural stream, namely Biala Tarnowska river in southern Poland. Over 50 variants of 22 various metaheuristics, including a large number of Differential Evolution, as well as some Particle Swarm Optimization, Evolution Strategies, multialgorithms and Direct Search methods are compared with LM algorithm on ANN training for the described case study. The impact of population size and some control parameters of particular metaheuristics on the ANN training performance are verified. It is found that despite widely claimed large improvement in nature-inspired methods during last years, the vast majority of them are still outperformed by LM algorithm on the selected problem. The only methods that, based on this case study, seem competitive to LM algorithm in terms of the final performance (but not speed) are Differential Evolution algorithms that benefit from the concept of Global and Local neighborhood-based mutation operators. The streamwater forecasting performance of the neural networks is adequate, the major prediction errors are related to the river freezing and melting processes that occur during winter in the mountainous catchment under study. The applicability of metaheuristics to neural networks training is verified.Levenberg-Marquardt and DEGL algorithms outperform other training methods.In case of Differential Evolution methods population size is crucial.Neural networks appear to be useful for water temperature predictions in rivers.

Scenarios of the spread of a waste heat discharge in a river — Vistula River case study

The problem of two-dimensional mathematical modelling of heated cooling water discharges into running waters is considered in the paper. Two models — one for the evaluation of 2D turbulent velocity field and the other, developed by authors of the study, for 2D heat transport in open-channels — were used in the calculations. Relevant scenarios of the spread of heated water discharged from a designed gas-stem power plant to be constructed at the Vistula River were presented. Environmentally most friendly variant of the discharge of the thermal pollution was selected from among four various variants.