Using Shannon entropy to model turbulence-induced flocculation of cohesive sediment in water.

Environ Sci Pollut Res Int

College of Water Sciences, Beijing Normal University, Beijing, 100875, China.

Published: January 2019

Turbulence-induced flocculation of cohesive fine-grained sediment plays an important role in the transport characteristics of pollutants and nutrients absorbed on the surface of sediment in estuarine and coastal waters via the complex processes of sediment transport, deposition, resuspension and consolidation. In this study, the concept of Shannon entropy based on probability is applied to modelling turbulence-induced flocculation of cohesive sediment in water. Using the hypothesis regarding the cumulative distribution function, the function of floc size with flocculation time is derived by assuming a characteristic floc size as a random variable and maximizing the Shannon entropy, subject to certain constraints. The Shannon entropy-based model is capable of modelling the variation in floc size as the flocculation time progresses from zero to infinity. The model is tested against some existing experimental data from the literature and against a few deterministic mathematical models. The model yields good agreement with the observed data and yields better prediction accuracy than the other models. The parameter that has been incorporated into the model exhibits an empirical power-law relationship with the flow shear rate. An empirical model formulation is proposed, and it exhibits high prediction accuracy when applied to existing experimental data.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11356-018-3462-4DOI Listing

Publication Analysis

Top Keywords

shannon entropy
12
turbulence-induced flocculation
12
flocculation cohesive
12
floc size
12
cohesive sediment
8
sediment water
8
size flocculation
8
flocculation time
8
existing experimental
8
experimental data
8

Similar Publications

Mechanized tunneling in harsh environments faces many hazards, which can stop tunneling operations for a long time. Due to the high investment volume in tunneling projects, it is imperative to predict and assess the geotechnical hazards. This research has tried to evaluate and introduce the most dangerous section of the Kerman water conveyance tunnel (KWCT) using multi-index decision-making techniques including PROMETHEE II, WASPAS, and CoCoSo models.

View Article and Find Full Text PDF

The intersection of quantum chemistry and quantum computing has led to significant advancements in understanding the potential of using quantum devices for the efficient calculation of molecular energies. Simultaneously, this intersection enhances the comprehension of quantum chemical properties through the use of quantum computing and quantum information tools. This paper tackles a key question in this relationship: Is the nature of the orbital-wise electron correlations in wavefunctions of realistic prototypical cases classical or quantum? We address this question with a detailed investigation of molecular wavefunctions in terms of Shannon and von Neumann entropies, common tools of classical and quantum information theory.

View Article and Find Full Text PDF

Background: Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder, affecting between 5% and 7% of school-age children. ADHD is typically characterized by persistent patterns of inattention or hyperactivity-impulsivity, and it is diagnosed on the basis of the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, through subjective observations and information provided by parents and teachers. Diagnosing ADHD in children is challenging, despite several assessment tools, such as the Swanson, Nolan, and Pelham questionnaire, being widely available.

View Article and Find Full Text PDF

This study investigates the impact of recent Artificial Intelligence (AI)-driven technological innovations on carbon prices across different quantiles, assessing the influence of AI stock prices on energy prices based on European carbon allowances while controlling for other macroeconomic factors. Using robust methods such as quantile-on-quantile regression, wavelet analysis, and transfer entropy, the research quantifies the information flow between the AI market and carbon allowances. Using daily data with four alternative AI stock prices from September 14, 2016, to December 29, 2023, the findings reveal a strong effect of AI returns on carbon prices, with significant fluctuations across price quantiles and consistent long-term average growth in market returns.

View Article and Find Full Text PDF

Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need for innovative detection methods. This study aimed to assist in the advancement of an accurate and efficient fall detection system using electroencephalogram (EEG) data to recognize the reaction to a postural disturbance.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!