Modeling preferential zone-regulated freshwater-saltwater mixing zone

2010 ◽  
pp. 87-92
Keyword(s):  
Mixing Zone

Modelling of water quality-based emission limits for industrial discharges in rivers

1999 ◽  
Vol 39 (4) ◽  
pp. 185-192 ◽  
Author(s):  
A. M. J. Ragas ◽  
R. S. E. W. Leuven
Keyword(s):  
Water Quality ◽  
United States Of America ◽  
Input Data ◽  
The United States ◽  
Mixing Zone ◽  
Pollutant Loads ◽  
The United Kingdom ◽  
Emission Limits ◽  
Modelling Of Water Quality ◽  
Upstream Flow

Water authorities apply a diversity of models and input data to set water quality-based emission limits in discharge permits. To illustrate the consequences of model and data selection, two complete mixing models and four mixing zone models used in Germany, the United Kingdom (UK), the Netherlands and the United States of America (USA) were selected and applied to various discharges of cadmium. The maximum allowable annual cadmium load was calculated for each model and diverging input data for upstream flow, upstream concentration, effluent flow and effluent concentration. Due to model selection, differences in pollutant loads amounted to a factor 3. Harmonisation of the derivation of water quality-based emission limits is necessary to prevent widely divergent pollutant loads under comparable environmental conditions.

scholarly journals Turbulent transition of a gaseous mixing zone induced by the Richtmyer-Meshkov instability

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Yannick Bury ◽  
Pierre Graumer ◽  
Stéphane Jamme ◽  
Jérôme Griffond
Keyword(s):  
Mixing Zone ◽  
Turbulent Transition

Boundary Schematization in Regulatory Mixing Zone Analysis

2002 ◽  
Vol 128 (1) ◽  
pp. 46-56 ◽  
Author(s):  
Robert L. Doneker ◽  
Gerhard H. Jirka
Keyword(s):  
Mixing Zone

scholarly journals Supervised committee machine with artificial intelligence for prediction of fluoride concentration

2013 ◽  
Vol 15 (4) ◽  
pp. 1474-1490 ◽  
Author(s):  
Ata Allah Nadiri ◽  
Elham Fijani ◽  
Frank T.-C. Tsai ◽  
Asghar Asghari Moghaddam
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Ground Water ◽  
Intelligent System ◽  
Fluoride Concentration ◽  
Mixing Zone ◽  
Ann Model ◽  
Committee Machine ◽  
Hydrogeological Characteristics ◽  
Model Predictions

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.

scholarly journals Silicified serpentinite – a residuum of a Tertiary palaeo-weathering surface in the United Arab Emirates

2012 ◽  
Vol 150 (3) ◽  
pp. 385-395 ◽  
Author(s):  
ALICJA M. LACINSKA ◽  
MICHAEL T. STYLES
Keyword(s):  
United Arab Emirates ◽  
Middle Eocene ◽  
Temperate Climate ◽  
Mixing Zone ◽  
Saturated Zone ◽  
Near Surface ◽  
Silica Precipitation ◽  
Surface Mixing ◽  
Ph Conditions ◽  
Mineralizing Fluids

AbstractMineralogical studies of a silicified serpentinite from the United Arab Emirates throw light on the formative processes. The silicified serpentinite is a residuum of a palaeo-weathering surface that probably developed in a temperate climate with alternating wet and dry periods during middle Eocene to late Miocene times. The rock textures indicate that silicification occurred in a fluid-saturated zone. Silica precipitation is favoured at near-neutral pH. In this study we infer that these pH conditions of the mineralizing fluids could arise in a near-surface mixing zone where acidic meteoric and hyperalkaline groundwater fluids are mingled. This mingling is believed to have resulted from alternating processes of evaporation and precipitation that prevailed during dry and wet seasons, respectively. The silicified serpentinite is composed of > 95% quartz and exhibits a ghost texture of the protolith serpentinite. Preservation of the textures indicates an iso-volumetric grain-by-grain replacement by dissolution of Mg-silicate and simultaneous precipitation of either opal or microquartz as siliceous seeds. These were subsequently overgrown by silica that was probably remobilized from deeply weathered regolith elsewhere.

Dynamical behavior of the Richtmyer–Meshkov instability-induced turbulent mixing under multiple shock interactions

2017 ◽  
Vol 95 (8) ◽  
pp. 671-681 ◽  
Author(s):  
Tao Wang ◽  
Gang Tao ◽  
Jingsong Bai ◽  
Ping Li ◽  
Bing Wang ◽  
...  
Keyword(s):  
Turbulent Mixing ◽  
Compression Wave ◽  
Dynamical Behavior ◽  
Characteristic Scale ◽  
Mixing Zone ◽  
Shock Interactions ◽  
Scale Parameters ◽  
Eddy Simulation ◽  
Negative Exponential ◽  
Multiple Shock

The dynamical behavior of Richtmyer–Meshkov instability-induced turbulent mixing under multiple shock interactions is investigated by large-eddy simulation. After the initial shockwave–interface interaction, the transmitted wave reverberates between the accelerated interface and the end-wall of the shock tube to form a process of multiple shock interactions. The turbulent mixing zone grows in a different manner under each of the impingements. After the initial shock, it grows as a power law of time. After the reshock and the impingement of the reflected rarefaction wave, it grows with time as a different negative exponential law. When the impingement of the reflected compression wave completes, it grows approximately in a linear fashion. The statistical quantities in the turbulent mixing zone evolve with time in a similar way under multiple impingements, and after the impingement of the reflected compression wave, they all decay asymptotically. Therefore, the turbulent mixing zone behaves in a statistically self-similar pattern. Even though the impingements of different waves result in different abrupt changes of the characteristic scale parameters of mixing turbulence, as a whole, the characteristic scales present a feature of growth, and the characteristic-scale Reynolds numbers present a feature of decay. The mixing flow is continuously anisotropic, yet the anisotropy weakens gradually. Therefore the development of turbulent mixing presents a trend of isotropy.

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