Stochastic Modelling of Turbulent Flows for Numerical Simulations

Cintolesi, Carlo; Mémin, Étienne

doi:10.3390/fluids5030108

Cited by 12 publications

(6 citation statements)

References 46 publications

(73 reference statements)

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“…The European Environmental Protection Agency has collected more than 150 different models that allow us to predict the spread of impurities by interpolation and extrapolation of measured data. Most models are based on the theory of atmospheric diffusion of pollutants [13][14][15] and empirical-statistical analysis of the distribution of impurities using Gaussian interpolation models [16][17][18]. Gaussian models have been most widely used because of the simplicity and high accuracy of the calculation in the presence of minimal information about the state of the surface layer of the atmosphere.…”

Section: The Study Materials and Methodsmentioning

confidence: 99%

Assessing risk caused by atmospheric air pollution from motor vehicles to the health of population in urbanized areas

Mislyuk

Khomenko

Єгорова

et al. 2023

EEJET

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The object of this study is a regional center with a developed industry and a significant transport load. The study assesses the impact of car emissions on public health. It is shown that on average in the city, emissions from vehicles are 0.341 mg/m∙s and vary from 0.038 to 1.012 mg/m∙s. The main pollutants are nitrogen dioxide (from 39.3 to 58.5 % of the total emission, the average value is 50.9 %) and carbon monoxide (from 39.5 to 47.7 %, the average value is 38.7 %). There is an excess of the maximum permissible concentrations (MPC) by the content of NO2 (1.5‒4.5 MPC with an average value of 2.7 MPC), formaldehyde (4.3‒4.4 MPC ), hydrocarbons (1.2 MPC), soot (1.3 MPC). The level of carcinogenic danger for adults (R=1.9∙10-4) and children (R=3.8∙10-5‒9.8∙10-5) is rated as average. Soot (61.0 %) and formaldehyde (38.9 %) account for the largest contribution. Behind a non-carcinogenic risk, there is a significant danger of increasing morbidity of the population (HI=9.9‒14.0 with an average value of 11.1). Principal pollutants are formaldehyde (37 %), NO2 (23 %), hydrocarbons (17 %). The most vulnerable are the respiratory system (НІ=8.7) and the immune system (НІ=4.3). A significant health hazard is due to low fuel quality, technical and operational characteristics of cars, adverse natural conditions for the dispersion of impurities. The method of interpolation and extrapolation of the results of full-scale observations was used to assess the effect of gaseous and dust components from vehicles on humans. The current study makes it possible to assess the combined impact of pollutants on the risks of disease and premature death of the population, to identify the danger and zoning the territory by the level of danger. The results obtained can be important for making effective management decisions in the field of environmental protection and public health

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Section: The Study Materials and Methodsmentioning

confidence: 99%

Assessing risk caused by atmospheric air pollution from motor vehicles to the health of population in urbanized areas

Mislyuk

Khomenko

Єгорова

et al. 2023

EEJET

View full text Add to dashboard Cite

show abstract

“…with ∆ the cut-off local cell length size, f d the Van Driest damping function for no-slip wall boundary condition and C S the Smagorinsky constant. This model is known to be highly robust but dissipative and a damping process using the closure relation for the turbulent stress tensor to compute the eddy viscosity in the core region with the dynamic Smagorinsky turbulence subgrid scale model is needed to reach a more accurate estimation of the dissipative scales correlated to the local level of turbulence [36,37]. Finally, filtered equations are solved using finite element approach with 2nd order numerical schemes.…”

Section: Computational Set-upmentioning

confidence: 99%

Computational Analysis of Actuation Techniques Impact on the Flow Control around the Ahmed Body

Edwige

Gilotte

Mortazavi

2022

Fluids

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Active flow control with jet devices is a promising approach for vehicle aerodynamics control. In this work an extended computational study is performed comparing three different actuation strategies for active flow control around the square back Ahmed body at Reynolds number 500,000 (based on the vehicle height). Numerical simulations are run using a Large Eddy Simulation (LES) approach, well adapted to calculate the unsteady high Reynolds number flow control using periodic jet devices. computations are validated comparing to in-house experiments for uncontrolled and some controlled cases. The novelty of this investigation is mainly related to the in-depth study of the base flow and actuation approaches by an accurate LES method and their comparison to experiments. Here, several simulations are performed to estimate the effect of active controls on the flow topology and the drag reduction. Beside the continuous blowing jet, three periodic actuation techniques including periodic blowing and suction as well as the zero flux synthetic jet devices are explored. The slots are implemented discontinuously in order to achieve a better control efficiency linked to vortex generation. In this framework, spectral analyses on global aerodynamical quantities, rear pressure/drag coefficient behavior examination as well as wake structure investigations are performed in order to compare these jet actuations. As a result, shear layer variations are observed during the blowing phase, but the main flow topology change occurs with suction and synthetic jets. Rear back pressure is therefore substantially increased.

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“…[ 1 ] In physical sciences, SDEs are used for describing quantum dynamics, [ 2 ] thermal effects, [ 3 ] molecular dynamics, [ 4 ] and they lie at the core of stochastic fluid dynamics. [ 5,6 ] In biology SDEs help in the studies of population dynamics [ 7 ] and epidemiology. [ 8,9 ] They can also help to detect anomalies.…”

Section: Introductionmentioning

confidence: 99%

Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time‐Series

Paine

Elfving²,

Kyriienko

2023

Adv Quantum Tech

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A quantum algorithm is proposed for sampling from a solution of stochastic differential equations (SDEs). Using differentiable quantum circuits (DQCs) with a feature map encoding of latent variables, the quantile function is represented for an underlying probability distribution and samples extracted as DQC expectation values. Using quantile mechanics the system is propagated in time, thereby allowing for time‐series generation. The method is tested by simulating the Ornstein‐Uhlenbeck process and sampling at times different from the initial point, as required in financial analysis and dataset augmentation. Additionally, continuous quantum generative adversarial networks (qGANs) are analyzed, and the authors show that they represent quantile functions with a modified (reordered) shape that impedes their efficient time‐propagation. The results shed light on the connection between quantum quantile mechanics (QQM) and qGANs for SDE‐based distributions, and point the importance of differential constraints for model training, analogously with the recent success of physics informed neural networks.

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Stochastic Modelling of Turbulent Flows for Numerical Simulations

Cited by 12 publications

References 46 publications

Assessing risk caused by atmospheric air pollution from motor vehicles to the health of population in urbanized areas

Assessing risk caused by atmospheric air pollution from motor vehicles to the health of population in urbanized areas

Computational Analysis of Actuation Techniques Impact on the Flow Control around the Ahmed Body

Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time‐Series

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