Influence of Microscale Turbulent Droplet Clustering on Radar Cloud Observations

Abstract This study investigates the influence of microscale turbulent clustering of cloud droplets on the radar reflectivity factor and proposes a new parameterization to account for it. A three-dimensional direct numerical simulation of particle-laden isotropic turbulence is performed to obtain turbulent clustering data. The clustering data are then used to calculate the power spectra of droplet number density fluctuations, which show a dependence on the Taylor microscale-based Reynolds number (Reλ) and the Stokes number (St). First, the Reynolds number dependency of the turbulent clustering influence is investigated for 127 < Reλ < 531. The spectra for this wide range of Reλ values reveal that Reλ = 204 is sufficiently large to be representative of the whole wavenumber range relevant for radar observations of atmospheric clouds. The authors then investigate the Stokes number dependency for Reλ = 204 and propose an empirical model for the turbulent clustering influence assuming power laws for the number density spectrum. For Stokes numbers less than 2, the proposed model can estimate the influence of turbulence on the spectrum with an RMS error less than 1 dB when calculated over the wavenumber range relevant for radar observations. For larger Stokes number droplets, the model estimate has larger errors, but the influence of turbulence is likely negligible in typical clouds. Applications of the proposed model to two idealized cloud observing scenarios reveal that microscale turbulent clustering can cause a significant error in estimating cloud droplet amounts from radar observations with microwave frequencies less than 13.8 GHz.

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On the transition between turbulence regimes in particle-laden channel flows

Journal of Fluid Mechanics ◽

10.1017/jfm.2018.259 ◽

2018 ◽

Vol 845 ◽

pp. 499-519 ◽

Cited By ~ 22

Author(s):

Jesse Capecelatro ◽

Olivier Desjardins ◽

Rodney O. Fox

Keyword(s):

Reynolds Number ◽

Fluid Phase ◽

Stokes Number ◽

Channel Flows ◽

High Mass ◽

Inertial Particles ◽

Mass Loading ◽

Two Phase ◽

Phase Dynamics ◽

Wide Range

Turbulent wall-bounded flows exhibit a wide range of regimes with significant interaction between scales. The fluid dynamics associated with single-phase channel flows is predominantly characterized by the Reynolds number. Meanwhile, vastly different behaviour exists in particle-laden channel flows, even at a fixed Reynolds number. Vertical turbulent channel flows seeded with a low concentration of inertial particles are known to exhibit segregation in the particle distribution without significant modification to the underlying turbulent kinetic energy (TKE). At moderate (but still low) concentrations, enhancement or attenuation of fluid-phase TKE results from increased dissipation and wakes past individual particles. Recent studies have shown that denser suspensions significantly alter the two-phase dynamics, where the majority of TKE is generated by interphase coupling (i.e. drag) between the carrier gas and clusters of particles that fall near the channel wall. In the present study, a series of simulations of vertical particle-laden channel flows with increasing mass loading is conducted to analyse the transition from the dilute limit where classical mean-shear production is primarily responsible for generating fluid-phase TKE to high-mass-loading suspensions dominated by drag production. Eulerian–Lagrangian simulations are performed for a wide range of particle loadings at two values of the Stokes number, and the corresponding two-phase energy balances are reported to identify the mechanisms responsible for the observed transition.

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Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers

10.26434/chemrxiv.5446564 ◽

2017 ◽

Author(s):

Mohammad Atif Faiz Afzal ◽

Chong Cheng ◽

Johannes Hachmann

Keyword(s):

First Principles ◽

Data Modeling ◽

Accurate Prediction ◽

Index Of Refraction ◽

Structure Theory ◽

Organic Polymers ◽

Number Density ◽

Lorenz Equation ◽

In Silico Studies ◽

Wide Range

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3–1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that an guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and highly economical path to determining the RI values for a wide range of organic polymers.

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Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽

10.3390/en14040930 ◽

2021 ◽

Vol 14 (4) ◽

pp. 930

Author(s):

Fahimeh Hadavimoghaddam ◽

Mehdi Ostadhassan ◽

Ehsan Heidaryan ◽

Mohammad Ali Sadri ◽

Inna Chapanova ◽

...

Keyword(s):

Machine Learning ◽

Viscosity Data ◽

Oil Viscosity ◽

Pvt Data ◽

Proposed Model ◽

Wide Range ◽

Engineering Problems ◽

Functional Forms ◽

Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

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Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Remote Sensing ◽

10.3390/rs11060608 ◽

2019 ◽

Vol 11 (6) ◽

pp. 608 ◽

Cited By ~ 2

Author(s):

Yun-Jia Sun ◽

Ting-Zhu Huang ◽

Tian-Hui Ma ◽

Yong Chen

Keyword(s):

Remote Sensing ◽

Real Data ◽

Total Variation Regularization ◽

Joint Sparsity ◽

Alternating Direction ◽

Proposed Model ◽

Comparison Results ◽

Wide Range ◽

Image Edges ◽

Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

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The Effect of Pulsed Blowing on the Boundary Layer of a Highly Loaded Low Pressure Turbine Blade

Volume 6A: Turbomachinery ◽

10.1115/gt2013-94566 ◽

2013 ◽

Cited By ~ 3

Author(s):

Marion Mack ◽

Roland Brachmanski ◽

Reinhard Niehuis

Keyword(s):

Boundary Layer ◽

Reynolds Number ◽

Mach Number ◽

Suction Side ◽

Reynolds Numbers ◽

Low Pressure ◽

Trailing Edge ◽

Low Pressure Turbine ◽

Wide Range ◽

Highly Loaded

The performance of the low pressure turbine (LPT) can vary appreciably, because this component operates under a wide range of Reynolds numbers. At higher Reynolds numbers, mid and aft loaded profiles have the advantage that transition of suction side boundary layer happens further downstream than at front loaded profiles, resulting in lower profile loss. At lower Reynolds numbers, aft loading of the blade can mean that if a suction side separation exists, it may remain open up to the trailing edge. This is especially the case when blade lift is increased via increased pitch to chord ratio. There is a trend in research towards exploring the effect of coupling boundary layer control with highly loaded turbine blades, in order to maximize performance over the full relevant Reynolds number range. In an earlier work, pulsed blowing with fluidic oscillators was shown to be effective in reducing the extent of the separated flow region and to significantly decrease the profile losses caused by separation over a wide range of Reynolds numbers. These experiments were carried out in the High-Speed Cascade Wind Tunnel of the German Federal Armed Forces University Munich, Germany, which allows to capture the effects of pulsed blowing at engine relevant conditions. The assumed control mechanism was the triggering of boundary layer transition by excitation of the Tollmien-Schlichting waves. The current work aims to gain further insight into the effects of pulsed blowing. It investigates the effect of a highly efficient configuration of pulsed blowing at a frequency of 9.5 kHz on the boundary layer at a Reynolds number of 70000 and exit Mach number of 0.6. The boundary layer profiles were measured at five positions between peak Mach number and the trailing edge with hot wire anemometry and pneumatic probes. Experiments were conducted with and without actuation under steady as well as periodically unsteady inflow conditions. The results show the development of the boundary layer and its interaction with incoming wakes. It is shown that pulsed blowing accelerates transition over the separation bubble and drastically reduces the boundary layer thickness.

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Influence from cosmological uncertainties on galaxy number count at faint limit

Modern Physics Letters A ◽

10.1142/s0217732315501394 ◽

2015 ◽

Vol 30 (28) ◽

pp. 1550139

Author(s):

Keji Shen ◽

Qiang Zhang ◽

Xin-He Meng

Keyword(s):

Galaxy Evolution ◽

Number Density ◽

Number Count ◽

Density Prediction ◽

Evolution Pattern ◽

Wide Range ◽

Different Types ◽

Physical Cosmology ◽

Cosmology Observations ◽

Near Future

Counting galaxy number density with wide range sky surveys has been well adopted in researches focusing on revealing evolution pattern of different types of galaxies. As understood intuitively the astrophysics environment physics is intimately affected by cosmology priors with theoretical estimation or vice versa, or simply stating that the astrophysics effect couples the corresponding cosmology observations or the way backwards. In this paper, we try to quantify the influence on galaxy number density prediction at faint luminosity limit from the uncertainties in cosmology, and how much the uncertainties blur the detection of galaxy evolution, with the hope that this trying may indeed help for precise and physical cosmology study in near future or vice versa.

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A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

Evolutionary Computation ◽

10.1162/evco_a_00107 ◽

2014 ◽

Vol 22 (1) ◽

pp. 159-188 ◽

Cited By ~ 5

Author(s):

Mikdam Turkey ◽

Riccardo Poli

Keyword(s):

Population Dynamics ◽

Dynamic Behaviour ◽

Fitness Landscape ◽

Population Based ◽

Levels Of Abstraction ◽

Collective Dynamic ◽

Model Studies ◽

Proposed Model ◽

Wide Range ◽

Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

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An Enhanced One-Layer Passive Microfluidic Mixer With an Optimized Lateral Structure With the Dean Effect

Journal of Fluids Engineering ◽

10.1115/1.4030288 ◽

2015 ◽

Vol 137 (9) ◽

Cited By ~ 28

Author(s):

Teng Zhou ◽

Yifan Xu ◽

Zhenyu Liu ◽

Sang Woo Joo

Keyword(s):

Reynolds Number ◽

Topology Optimization ◽

Optimization Method ◽

Mixing Efficiency ◽

Boolean Operation ◽

Microfluidic Mixer ◽

Wide Range ◽

The One ◽

Lateral Structure ◽

Topology Optimization Method

Topology optimization method is applied to a contraction–expansion structure, based on which a simplified lateral flow structure is generated using the Boolean operation. A new one-layer mixer is then designed by sequentially connecting this lateral structure and bent channels. The mixing efficiency is further optimized via iterations on key geometric parameters associated with the one-layer mixer designed. Numerical results indicate that the optimized mixer has better mixing efficiency than the conventional contraction–expansion mixer for a wide range of the Reynolds number.

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Evolution of clusters of sedimenting low-Reynolds-number particles with Oseen interactions

Journal of Fluid Mechanics ◽

10.1017/s0022112008000797 ◽

2008 ◽

Vol 603 ◽

pp. 63-100 ◽

Cited By ~ 28

Author(s):

G. SUBRAMANIAN ◽

DONALD L. KOCH

Keyword(s):

Reynolds Number ◽

Velocity Field ◽

Aspect Ratio ◽

Evolution Equation ◽

Similarity Solution ◽

Initial Conditions ◽

Radial Distance ◽

Two Dimensions ◽

Number Density ◽

Cluster Radius

A theoretical framework is developed to describe, in the limit of small but finite Re, the evolution of dilute clusters of sedimenting particles. Here, Re =aU/ν is the particle Reynolds number, where a is the radius of the spherical particle, U its settling velocity, and ν the kinematic viscosity of the suspending fluid. The theory assumes the disturbance velocity field at sufficiently large distances from a sedimenting particle, even at small Re, to possess the familiar source--sink character; that is, the momentum defect brought in via a narrow wake behind the particle is convected radially outwards in the remaining directions. It is then argued that for spherical clusters with sufficiently many particles, specifically with N much greater than O(R0U/ν), the initial evolution is strongly influenced by wake-mediated interactions; here, N is the total number of particles, and R0 is the initial cluster radius. As a result, the cluster first evolves into a nearly planar configuration with an asymptotically small aspect ratio of O(R0U/N ν), the plane of the cluster being perpendicular to the direction of gravity; subsequent expansion occurs with an unchanged aspect ratio. For relatively sparse clusters with N smaller than O(R0U/ν), the probability of wake interactions remains negligible, and the cluster expands while retaining its spherical shape. The long-time expansion in the former case, and that for all times in the latter case, is driven by disturbance velocity fields produced by the particles outside their wakes. The resulting interactions between particles are therefore mutually repulsive with forces that obey an inverse-square law. The analysis presented describes cluster evolution in this regime. A continuum representation is adopted with the clusters being characterized by a number density field (n(r, t)), and a corresponding induced velocity field (u (r, t)) arising on account of interactions. For both planar axisymmetric clusters and spherical clusters with radial symmetry, the evolution equation admits a similarity solution; either cluster expands self-similarly for long times. The number density profiles at different times are functions of a similarity variable η = (r/t1/3), r being the radial distance away from the cluster centre, and t the time. The radius of the expanding cluster is found to be of the form Rcl (t) = A (ν a)1/3N1/3t1/3, where the constant of proportionality, A, is determined from an analytical solution of the evolution equation; one finds A = 1.743 and 1.651 for planar and spherical clusters, respectively. The number density profile in a planar axisymmetric cluster is also obtained numerically as a solution of the initial value problem for a canonical (Gaussian) initial condition. The numerical results compare well with theoretical predictions, and demonstrate the asymptotic stability of the similarity solution in two dimensions for long times, at least for axisymmetric initial conditions.

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Axial Turbine Performance Evaluation. Part B—Optimization With and Without Constraints

Journal of Engineering for Power ◽

10.1115/1.3609212 ◽

1968 ◽

Vol 90 (4) ◽

pp. 349-359 ◽

Cited By ~ 3

Author(s):

O. E. Balje´ ◽

R. L. Binsley

Keyword(s):

Reynolds Number ◽

Performance Evaluation ◽

Reference Value ◽

Tip Clearance ◽

Strong Function ◽

Turbine Performance ◽

Wide Range ◽

Leakage Area ◽

Partial Admission ◽

Specific Speed

The maximum obtainable efficiency and associated geometry have been calculated based on the use of generalized loss correlations from Part A and are presented for full and partial admission turbines over a wide range of specific speeds. The calculated effects of varying values of Reynolds number, tip clearance, and trailing edge thickness on turbine performance are presented. Because of the anticipated difficulty in fabricating some of the optimum geometries calculated, the effects of using nonoptimum values of geometric parameters on attainable efficiency have also been investigated. The derating factor for machine Reynolds number is shown to be a strong function of specific speed, varying from 0.96 at a specific speed of 100, to 0.6 at a specific speed of 3, when Reynolds number is 105 compared to a reference value of 106. The derating factor for tip clearance is shown to be similar to what would be expected if the clearance area were considered as a leakage area. The use of blade heights, blade numbers, rotor exit angles, and degrees of reaction varying from the optimum by 25 percent produce maximum derating factors of 0.99, 0.98, 0.985, and 0.97, respectively, when compared to full optimum values.

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