Synthetic Turbulence, Fractal Interpolation and Large-Eddy Simulation

Fractal Interpolation has been proposed in the literature as an efficient way to construct closure models for the numerical solution of coarse-grained Navier-Stokes equations. It is based on synthetically generating a scale-invariant subgrid-scale field and analytically evaluating its effects on large resolved scales. In this paper, we propose an extension of previous work by developing a multiaffine fractal interpolation scheme and demonstrate that it preserves not only the fractal dimension but also the higher-order structure functions and the non-Gaussian probability density function of the velocity increments. Extensive a-priori analyses of atmospheric boundary layer measurements further reveal that this Multiaffine closure model has the potential for satisfactory performance in large-eddy simulations. The pertinence of this newly proposed methodology in the case of passive scalars is also discussed

Biological Role of Aldo–Keto Reductases in Retinoic Acid Biosynthesis and Signaling

Several aldo–keto reductase (AKR) enzymes from subfamilies 1B and 1C show retinaldehyde reductase activity, having low Km and kcat values. Only AKR1B10 and 1B12, with all-trans-retinaldehyde, and AKR1C3, with 9-cis-retinaldehyde, display high catalytic efficiency. Major structural determinants for retinaldehyde isomer specificity are located in the external loops (A and C for AKR1B10, and B for AKR1C3), as assessed by site-directed mutagenesis and molecular dynamics. Cellular models have shown that AKR1B and 1C enzymes are well suited to work in vivo as retinaldehyde reductases and to regulate retinoic acid (RA) biosynthesis at hormone pre-receptor level. An additional physiological role for the retinaldehyde reductase activity of these enzymes, consistent with their tissue localization, is their participation in β-carotene absorption. Retinaldehyde metabolism may be subjected to subcellular compartmentalization, based on enzyme localization. While retinaldehyde oxidation to RA takes place in the cytosol, reduction to retinol could take place in the cytosol by AKRs or in the membranes of endoplasmic reticulum by microsomal retinaldehyde reductases. Upregulation of some AKR1 enzymes in different cancer types may be linked to their induction by oxidative stress and to their participation in different signaling pathways related to cell proliferation. AKR1B10 and AKR1C3, through their retinaldehyde reductase activity, trigger a decrease in the RA biosynthesis flow, resulting in RA deprivation and consequently lower differentiation, with an increased cancer risk in target tissues. Rational design of selective AKR inhibitors could lead to development of novel drugs for cancer treatment as well as reduction of chemotherapeutic drug resistance

Linear stability analysis of wind turbine wakes performed on wind tunnel measurements

Wind tunnel measurements were performed for the wake produced by a three-bladed wind turbine immersed in uniform flow. These tests show the presence of a vorticity structure in the near-wake region mainly oriented along the streamwise direction, which is denoted as the hub vortex. The hub vortex is characterized by oscillations with frequencies lower than that connected to the rotational velocity of the rotor, which previous works have ascribed to wake meandering. This phenomenon consists of transversal oscillations of the wind turbine wake, which might be excited by the vortex shedding from the rotor disc acting as a bluff body. In this work, temporal and spatial linear stability analyses of a wind turbine wake are performed on a base flow obtained with time-averaged wind tunnel velocity measurements. This study shows that the low-frequency spectral component detected experimentally matches the most amplified frequency of the counter-winding single-helix mode downstream of the wind turbine. Then, simultaneous hot-wire measurements confirm the presence of a helicoidal unstable mode of the hub vortex with a streamwise wavenumber roughly equal to that predicted from the linear stability analysi

A scale-dependent dynamic model for large-eddy simulation: application to a neutral atmospheric boundary layer

A scale-dependent dynamic subgrid-scale model for large-eddy simulation of turbulent flows is proposed. Unlike the traditional dynamic model, it does not rely on the assumption that the model coefficient is scale invariant. The model is based on a second test-filtering operation which allows us to determine from the simulation how the coefficient varies with scale. The scale-dependent model is tested in simulations of a neutral atmospheric boundary layer. In this application, near the ground the grid scale is by necessity comparable to the local integral scale (of the order of the distance to the wall). With the grid scale and/or the test-filter scale being outside the inertial range, scale invariance is broken. The results are compared with those from (a) the traditional Smagorinsky model that requires specification of the coefficient and of a wall damping function, and (b) the standard dynamic model that assumes scale invariance of the coefficient. In the near-surface region the traditional Smagorinsky and standard dynamic models are too dissipative and not dissipative enough, respectively. Simulations with the scale-dependent dynamic model yield the expected trends of the coefficient as a function of scale and give improved predictions of velocity spectra at different heights from the ground. Consistent with the improved dissipation characteristics, the scale-dependent model also yields improved mean velocity profiles

Revisiting the Local Scaling Hypothesis in Stably Stratified Atmospheric Boundary Layer Turbulence: an Integration of Field and Laboratory Measurements with Large-eddy Simulations

The `local scaling' hypothesis, first introduced by Nieuwstadt two decades ago, describes the turbulence structure of stable boundary layers in a very succinct way and is an integral part of numerous local closure-based numerical weather prediction models. However, the validity of this hypothesis under very stable conditions is a subject of on-going debate. In this work, we attempt to address this controversial issue by performing extensive analyses of turbulence data from several field campaigns, wind-tunnel experiments and large-eddy simulations. Wide range of stabilities, diverse field conditions and a comprehensive set of turbulence statistics make this study distinct

Atmospheric stability effect on subgrid scale physics for large-eddy simulation

Field measurements in the atmospheric boundary layer were carried out to identify the effect of atmospheric stability on subgrid-scale physics for large-eddy simulation. The basic instrumentation setup consisted of 12 three-dimensional sonic anemometers arranged in two parallel horizontal arrays (seven sensors in the lower array and five sensors in the upper array). Data from this setup are used to compute the subgrid-scale (SGS) heat fluxes and SGS dissipation of the temperature variance under stable and unstable stability conditions. The relative contribution of the SGS vertical flux to the total turbulent flux increases when going from unstable to stable conditions. The relative importance of negative SGS dissipation (backscatter) events becomes larger under stable conditions. The model coefficients for two well-known SGS models (eddy-viscosity and non-linear) are computed. Model coefficients are found to depend strongly on stability. Under both stable and unstable conditions, large negative SGS dissipation is associated with the onset of ejection events while large positive SGS dissipation tends to occur during the onset of sweep events. These findings are also supported by conditionally sampled 2D velocity and temperature fields obtained using the 12 anemometers placed in a vertical array

Dealing with paralogy in RADseq data: in silico detection and single nucleotide polymorphism validation in Robinia pseudoacacia L.

peer reviewedThe RADseq technology allows researchers to efficiently develop thousands of polymorphic loci across multiple individuals with little or no prior information on the genome. However, many questions remain about the biases inherent to this technology. Notably, sequence misalignments arising from paralogy may affect the development of single nucleotide polymorphism (SNP) markers and the estimation of genetic diversity. We evaluated the impact of putative paralog loci on genetic diversity estimation during the development of SNPs from a RADseq dataset for the nonmodel tree species Robinia pseudoacacia L. We sequenced nine genotypes and analyzed the frequency of putative paralogous RAD loci as a function of both the depth of coverage and the mismatch threshold allowed between loci. Putative paralogy was detected in a very variable number of loci, from 1% to more than 20%, with the depth of coverage having a major influence on the result. Putative paralogy artificially increased the observed degree of polymorphism and resulting estimates of diversity. The choice of the depth of coverage also affected diversity estimation and SNP validation: A low threshold decreased the chances of detecting minor alleles while a high threshold increased allelic dropout. SNP validation was better for the low threshold (4×) than for the high threshold (18×) we tested. Using the strategy developed here, we were able to validate more than 80% of the SNPs tested by means of individual genotyping, resulting in a readily usable set of 330 SNPs, suitable for use in population genetics applications