Publications

Superhydrophobic surfaces dramatically reduce the skin friction of overlying liquid flows, providing a lubricating layer of gas bubbles trapped within their surface nano-sculptures. Under wetting-stable conditions, different models can be used to numerically simulate their effect on the overlying flow, ranging from spatially homogeneous slip conditions at the wall, to spatially heterogeneous slip–no-slip conditions taking into account or not the displacement of the gas–water interfaces. These models provide similar results in both laminar and turbulent regimes, but their effect on transitional flows has not been investigated yet. In this work we study, by means of numerical simulations and global stability analyses, the influence of the modelling of superhydrophobic surfaces on laminar–turbulent transition in a channel flow. For the K-type scenario, a strong transition delay is found using spatially homogeneous or heterogeneous slippery boundaries with flat, rigid liquid–gas interfaces. Whereas, when the interface dynamics is taken into account, the time to transition is reduced, approaching that of a no-slip channel flow. It is found that the interface deformation promotes ejection events creating hairpin heads that are prone to breakdown, reducing the transition delay effect with respect to flat slippery surfaces. Thus, in the case of modal transition, the interface dynamics must be taken into account for accurately estimating transition delay. Contrariwise, non-modal transition triggered by a broadband forcing is unaffected by the presence of these surfaces, no matter the surface modelling. Thus, superhydrophobic surfaces may or not influence transition to turbulence depending on the interface dynamics and on the considered transition process.

Understanding the mechanisms at the microstructure scale is of great importance for modeling the behavior of materials at different scales. To this end, digital image correlation (DIC) is an effective measurement method for evaluating the strains generated by various loading conditions. The objective of this paper is to describe the experimental setup and the use of high resolution digital image correlation (HRDIC) during in situ Scanning Electron Microscope (SEM) tests in order to provide a coupling between polycrystalline modeling and experiment in the near future. The HRDIC technique is used to evaluate the tensile behavior of a pure copper polycrystal at room temperature. Several magnitudes are investigated in order to discuss the representativeness of the results with respect to the macroscopic scale. The selected image correlation parameters are discussed regarding the ability of the technique to define inter- and intra- granular strain heterogeneities. Finally, based on EBSD analyzes, the impact of grain orientation on the mechanical behavior is discussed. The Schmid factor, calculated from a macroscopic stress, appears to be the determining factor concerning the orientation of the location bands. On the other hand, it is not sufficient to define the mean strains in the grains.

Shot peening of turbine disk engines is performed in the aerospace industry in order to enhance fatigue life. This surface enhancement method generates beneficial modifications like superficial compressive residual stresses that are known to delay crack initiation and propagation. In the same way, work hardening is also introduced at the surface of the part during shot peening and can have a significant influence on fatigue crack initiation. Taking this parameter into account in the fatigue design of parts, in addition to the residual stresses, is a real challenge to be the most predictive. One possibility for this is to be able to predict it during the modeling of the shot peening process. In the present work, various peening conditions are considered in order to be able to propose a model able to account for the influence of coverage and Almen intensity on residual stresses and work hardening. The studied material is Inconel 718, commonly used for aeronautical parts. The X-ray diffraction method is used to obtain the in-depth residual stress and work hardening profiles. A three-dimensional numerical model is proposed to predict these quantities. Efforts are made to consider all recent advances in three-dimensional simulation of the process, in terms of coverage assessment, shot and treated part modeling. The numerical results are compared to the experimentally measured residual stresses and work hardening.

Nowadays the remediation of DNAPL contaminated zones near groundwater has gained great prominence in environmental fields due to the high importance of water resources. In this work, we suggest injecting a densified polymer suspension by adding barite particles to displace DNAPL. To evaluate the efficiency of the densification of polymer suspensions on the displacement of DNAPL, various densities of barite-polymer suspension; lower, equal, and higher than the density of DNAPL were prepared and their rheological behavior was analyzed. Then flow experiments were performed using a decimetric-scale 2D tank. The displacement procedure was monitored with an imaging technique and the production and injection process data were recorded by mass balance interpretation. It was shown that the densification of the polymer suspension could improve the displacement efficiency of DNAPL up to four times. The clogging behavior of barite-polymer suspension was assessed in a 1D column. Generalized Darcy’s law and the continuity equation were used to numerically simulate the experimental two-phase flow. To take into account the clogging behavior of the suspension, the transport equation of diluted species was implemented into the model. The simulation results show that the model can properly predicts the experimental consequences.

A novel Sparse Bayesian Learning (SBL) framework is introduced for generating parsimonious stochastic algebraic stress closures for the Reynolds-Averaged Navier–Stokes (RANS) equations from high-fidelity data. The models are formulated as physically-interpretable frame-invariant tensor polynomials and built from a library of candidate functions. By their stochastic formulation, the learned model coefficients are described by probability distributions and are therefore equipped with an intrinsic measure of uncertainty. The SBL framework is used to derive customized stochastic closure models for three separated flow configurations, characterized by different geometries but similar Reynolds number. The resulting SBL models are then propagated through a CFD solver for all three configurations. The results show significantly improved predictions of velocity profiles and friction coefficient in the separation / reattachment region in comparison with a baseline LEVM (namely, k-ω SST model), for training as well as for test cases. In all cases, the computed uncertainty intervals encompass reasonably well the reference data. Furthermore, the stochastic outputs enable a global sensitivity analysis with respect to the model terms selected by the algorithm, thus providing insights in view of further improvements of EARSM-type corrections.

The Reynolds-Averaged Navier-Stokes (RANS) equations represent the computational workhorse for engineering design, despite their numerous flaws. Improving and quantifying the uncertainties associated with RANS models is particularly critical in view of the analysis and optimization of complex turbomachinery flows. In this work, we use Bayesian inference for assimilating data into RANS models for the following purposes: (i) updating the model closure coefficients for a class of turbomachinery flows, namely a compressor cascade; (ii) quantifying the parametric uncertainty associated with closure coefficients of RANS models and (iii) quantifying the uncertainty associated with the model structure and the choice of the calibration dataset based on an ensemble of concurrent models and calibration scenarios. Inference of the coefficients of three widely employed RANS models is carried out from high-fidelity LES data for the NACA65 V103 compressor cascade [1, 2]. Posterior probability distributions of the model coefficients are collected for various calibration scenarios, corresponding to different values of the flow angle at inlet. The Maximum A Posteriori estimates of the coefficients differ from the nominal values and depend on the scenario. A recently proposed Bayesian mixture approach, namely, Bayesian Model-Scenario Averaging (BMSA) [3, 4], is used to build a prediction model that takes into account uncertainties associated with alternative model forms and with sensitivity to the calibration scenario. Stochastic predictions are presented for the turbulent flow around the NACA65 V103 cascade at mildly and severe off-design conditions. The results show that BMSA generally yields more accurate solutions than the baseline RANS models and succeeds well in providing an estimate for the predictive uncertainty intervals, provided that a sufficient diversity of scenarios and models is included in the mixture.

Reynolds-stress models (EARSM) from high-fidelity data is developed building on the frozen-training SpaRTA algorithm of [1]. Corrections for the Reynolds stress tensor and the production of transported turbulent quantities of a baseline linear eddy viscosity model (LEVM) are expressed as functions of tensor polynomials selected from a library of candidate functions. The CFD-driven training consists in solving a blackbox optimization problem in which the fitness of candidate EARSM models is evaluated by running RANS simulations. The procedure enables training models against any target quantity of interest, computable as an output of the CFD model. Unlike the frozen-training approach, the proposed methodology is not restricted to data sets for which full fields of high-fidelity data, including second flow order statistics, are available. However, the solution of a high-dimensional expensive blackbox function optimization problem is required. Several steps are then undertaken to reduce the associated computational burden. First, a sensitivity analysis is used to identify the most influential terms and to reduce the dimensionality of the search space. Afterwards, the Constrained Optimization using Response Surface (CORS) algorithm, which approximates the black-box cost function using a response surface constructed from a limited number of CFD solves, is used to find the optimal model parameters. Model discovery and cross-validation is performed for three configurations of 2D turbulent separated flows in channels of variable section using different sets of training data to show the flexibility of the method. The discovered models are then applied to the prediction of an unseen 2D separated flow with higher Reynolds number and different geometry. The predictions of the discovered models for the new case are shown to be not only more accurate than the baseline LEVM, but also of a multi-purpose EARSM model derived from purely physical arguments. The proposed deterministic symbolic identification approach constitutes a promising candidate for building accurate and robust RANS models customized for a given class of flows at moderate computational cost. ©2022 Elsevier Inc. All rights reserved.

Introduction X-linked hypophosphatemia (XLH) rickets mainly causes leg deformities in children that can get worse as they grow. We hypothesized that quantifying the bone parameters will help to document and monitor these deformities in children with XLH. Methods Thirty-five growing children affected by XLH were included in this cross-sectional study. Biplanar radiographs were taken with an EOS system allowing 3D reconstructions of the pelvis and legs. Sixteen geometric parameters were calculated for the legs and pelvis. A control group of 40 age-matched patients was used to define the reference values for these geometric parameters. Results For the legs, significant differences (p < 0.05) appeared between the XLH patients and the control group in the neck-shaft angle, femur/tibia length ratio and HKS. Among the 70 legs in the XLH group, 23 were in genu varum, 25 were in genu valgum and 22 were straight. There were significant differences between the genu varum and genu valgum subgroups in the femoral mechanical angle and the HKS. A strong correlation was found between the femoral mechanical angle and tibiofemoral angle (r² = 0.73) and between the femoral mechanical angle and HKS (r²=0.69) The sacral slope and acetabular anteversion were significant different from the reference values. Discussion Quantitative radiological parameters derived from 3D reconstructions show that the deformities in XLH patients are 1) mainly in but not limited to the femoral shaft; 2) highly variable from one person to another. Some of these radiological parameters may be useful for the diagnosis and monitoring of XLH patients.

Purpose. The purpose of this study was to analyze the impact of postural alignment changes with age on vertebral strength using finite element analysis and barycentremetry. Methods. A total of 117 subjects from 20 to 83 years were divided in three age groups: young, (20 to 40 years, 62 subjects), intermediate (40 to 60 years, 26 subjects) and elderly (60 years and over, 29 subjects). EOS biplane radiographs were acquired, allowing 3D reconstruction of the spine and body envelope as well as spinal, pelvic and sagittal alignment parameters measurements. A barycentremetry method allowed estimating of the mass and center of mass (CoM) position of the upper body above L1, relatively to the center of the L1 vertebra (lever arm). To investigate the effect of this lever arm, vertebral strength of a generic finite element models (with constant geometry and mechanical properties for all subjects) was successively computed applying the personalized lever arm of each subject. Results. A combination of an increase in thoracic kyphosis, cervical lordosis and pelvic tilt with a loss of lumbar lordosis was observed between the young and the older groups. Sagittal alignment parameters indicated a more forward position as age increased. The lever arm of the CoM above L1 varied from an average of 1 mm backward for the young group, to averages of 7 and 24 mm forward, respectively for the intermediate and elderly group. As a result, vertebral strength decreased from 2527 N for the young group to 1820 N for the elderly group. Conclusion. The global sagittal alignment modifications observed with age were consistent with the literature. Posture alteration with age reduced vertebral strength significantly in this simplified loading model. Postural alignment seems essential to be considered in the evaluation of osteoporotic patients.

The influence of dense gas effects on compressible turbulence is investigated by means of numerical simulations of the decay of compressible homogeneous isotropic turbulence (CHIT) and of supersonic turbulent flows through a plane channel (TCF). For both configurations, a parametric study on the Mach and Reynolds numbers is carried out. The dense gas considered in these parametric studies is PP11, a heavy fluorocarbon. The results are systematically compared to those obtained for a diatomic perfect gas (air). In our computations, the thermodynamic behaviour of the dense gases is modelled by means of the Martin-Hou equation of state. For CHIT cases, initial turbulent Mach numbers up to 1 are analyzed using mesh resolutions up to 5123. For TCF, bulk Mach numbers up to 3 and bulk Reynolds numbers up to 12000 are investigated. Average profiles of the thermodynamic quantities exhibit significant differences with respect to perfect-gas solutions for both of the configurations. For high-Mach CHIT, compressible structures are modified with respect to air, with weaker eddy shocklets and stronger expansions. In TCF, the velocity profiles of dense gas flows are much less sensitive to the Mach number and collapse reasonably well in the logarithmic region without any special need for compressible scalings, unlike the case of air, and the overall flow behaviour is midway between that of a variable-property liquid and that of a gas.