On acceleration of Krylov-subspace-based Newton and Arnoldi iterations for incompressible CFD: replacing time steppers and generation of initial guess

We propose two techniques aimed at improving the convergence rate of steady state and eigenvalue solvers preconditioned by the inverse Stokes operator and realized via time-stepping. First, we suggest a generalization of the Stokes operator so that the resulting preconditioner operator depends on several parameters and whose action preserves zero divergence and boundary conditions. The parameters can be tuned for each problem to speed up the convergence of a Krylov-subspace-based linear algebra solver. This operator can be inverted by the Uzawa-like algorithm, and does not need a time-stepping. Second, we propose to generate an initial guess of steady flow, leading eigenvalue and eigenvector using orthogonal projection on a divergence-free basis satisfying all boundary conditions. The approach, including the two proposed techniques, is illustrated on the solution of the linear stability problem for laterally heated square and cubic cavities

Dissipation-based proper orthogonal decomposition of turbulent Rayleigh-Bénard convection flow

We present a formulation of proper orthogonal decomposition (POD) producing a velocity-temperature basis optimized with respect to an H1 dissipation norm. This decomposition is applied, along with a conventional POD optimized with respect to an L2 energy norm, to a data set generated from a direct numerical simulation of Rayleigh-Bénard convection in a cubic cell (Ra=107, Pr=0.707). The data set is enriched using symmetries of the cell, and we formally link symmetrization to degeneracies and to the separation of the POD bases into subspaces with distinct symmetries. We compare the two decompositions, demonstrating that each of the 20 lowest dissipation modes is analogous to one of the 20 lowest energy modes. Reordering of modes between the decompositions is limited, although a corner mode known to be crucial for reorientations of the large-scale circulation is promoted in the dissipation decomposition, indicating suitability of the dissipation decomposition for capturing dynamically important structures. Dissipation modes are shown to exhibit enhanced activity in boundary layers. Reconstructing kinetic and thermal energy, viscous and thermal dissipation, and convective heat flux, we show that the dissipation decomposition improves overall convergence of each quantity in the boundary layer. Asymptotic convergence rates are nearly constant among the quantities reconstructed globally using the dissipation decomposition, indicating that a range of dynamically relevant scales are efficiently captured. We discuss the implications of the findings for using the dissipation decomposition in modeling, and argue that the H1 norm allows for a better modal representation of the flow dynamics

Endocrine disruptors characterized from a complex matrix using bioanalytical methods : ER affinity columns and LC-HRMS

Complex mixtures of contaminants with potential adverse effects for human health are found in the environment and in the food chain, including natural and synthetic molecules able to act as endocrine disruptors. The structural variety of these compounds, but also their biotic or abiotic transformation, render these mixtures even more complex (parent compounds +- metabolites). Endocrine Disruptor Chemicals can interfere with a variety of nuclear receptors (NR), urging the need to identify the structure of known and unknown NR ligands present in complex matrices. New strategies need to be developed in order to address this question. We have previously shown that NR-based affinity columns are a useful tool for the isolation and characterization of bioactive compounds from complex food matrices such as infant formulas [1]. Here, we extended this approach with the aim to develop and validate the possibility to use NR-based affinity columns, to highlight the presence of known or unknown bioactive molecules (i.e. endocrine disruptors) out of different complex mixtures such as environmental samples. We used recombinant estrogen receptor alpha (ER?)-based affinity columns, for the isolation and characterization of estrogenic substances present in surface sediment samples collected in a French river under mixed anthropogenic pressure. We combined biological, biochemical and analytical techniques (HPLC, LC-MS) as well as High Resolution Mass Spectrometry (HRMS), to characterize the structure of ligands retained on the NR, and demonstrated by LC-HRMS the presence of several active molecules, including bisphenol A, octylphenol, paraben and hydroxymethylbenzofuranone. ER? affinity columns can be used for the isolation, purification and identification of known and unknown estrogenic contaminants present in various complex matrices

Nuclear receptor-based affinity columns as an innovative tool for the isolation and the identification of estrogenic substances present in complex matrices

National audienc

Characterization of endocrine disruptors from a complex matrix using estrogen receptor affinity columns and high performance liquid chromatography-high resolution mass spectrometry

International audienceComplex mixtures of contaminants with potential adverse effects on human health and wildlife are found in the environment and in the food chain. These mixtures include numerous anthropogenic compounds of various origins and structures, which may behave as endocrine disruptors. Mixture's complexity is further enhanced by biotic and abiotic transformations. It is therefore necessary to develop new strategies allowing the identification of the structure of known, as well as unknown, nuclear receptor (NR) ligands present in complex matrices. We explored the possibility to use NR-based affinity columns to characterize the presence of bioactive molecules in environmental complex mixtures. Estrogen receptor alpha (ERalpha)-based affinity columns were used to trap and purify estrogenic substances present in surface sediment samples collected in a French river under mixed anthropogenic pressure. We combined biological, biochemical and analytical approaches to characterize the structure of ligands retained on columns and demonstrate the presence of known active molecules such as bisphenol A and octylphenol, but also of unexpected ERalpha ligands (n-butylparaben, hydroxyl-methyl-benzofuranone). High resolution mass spectrometry results demonstrate that ERalpha affinity columns can be used for the isolation, purification and identification of known as well as unknown estrogenic contaminants present in complex matrices