Summary We introduce a new flexible mesh adaptation approach to efficiently compute a quantity of interest by the finite element method. Efficiently, we mean that the method provides an evaluation of that quantity up to a predetermined accuracy at a lower computational cost than other classical methods. The central pillar of the method is our scalar error estimator based on sensitivities of the quantity of interest to the residuals. These sensitivities result from the computation of a continuous adjoint problem. The mesh adaptation strategy can drive anisotropic mesh adaptation from a general scalar error contribution of each element. The full potential of our error estimator is then reached. The proposed method is validated by evaluating the lift, the drag, and the hydraulic losses on a 2D benchmark case: the flow around a cylinder at a Reynolds number of 20.

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The COVID-19 pandemic led to large increases in telemedicine activity worldwide. This rapid growth, however, may have impacted the quality of care where compliance with guidelines and best practices are concerned. The aim of this study was to describe the recent practices of a telemedicine activity (teleconsultations) and the breaches of best practice guidelines committed by general practitioners (GPs) in the Greater Eastern Region of France. A cross-sectional study was conducted using a 33-item questionnaire and was provided to the Regional Association of Healthcare Professionals, Union Régionale des Professionnels de Santé (URPS) to be shared amongst the GPs. Between April and June 2021, a total of 233 responses were received, showing that (i) by practicing telemedicine in an urban area, (ii) performing a teleconsultation at the patient’s initiative, and (iii) carrying out more than five teleconsultations per week were factors associated with a significantly higher level of best practices in telemedicine. All in all, roughly a quarter of GPs (25.3%, n = 59) had a self-declared good telemedicine practice, and the rules of good practice are of heterogeneous application. Despite the benefits of learning on the job for teleconsultation implementation during the COVID-19 lockdowns, there may be a clear need to develop structured and adapted telemedicine training programs for private practice GPs.

Summary This work presents a new methodology in finite element to simulate, according to a controlled precision on an engineering value, steady turbulent flows. First, we developed a new implementation of Reynolds‐averaged Navier‐Stokes equations combined with k−ω SST turbulence model and automatic wall treatment. Then, to simulate these complex multiscale flows, spatial discretization is critical. It is still common for expert users to generate meshes manually since they can roughly anticipate the physics of the flow. However, this remains a difficult task, especially for a neophyte. A recent mesh adaptation methodology based on an adjoint sensitivity analysis allows generating automatically appropriate meshes for analysis of steady laminar flows. Here, we extended this work to turbulent flows. The presentation is limited to two‐dimensional (2D) to demonstrate the effectiveness of the approach without getting unnecessarily entangled in the implementation details. The methodology is validated on the classic 2D zero pressure gradient flat plate verification case at Re = 5 · 106. Then, a more complex example is also presented: flow around multicomponent airfoil (30P30N, α=16.21∘) at Re = 9 · 106.

In this work, a sensitivity calculation approach called the adjoint method is explored to estimate and control the discretization error in computational fluid dynamic (CFD). This paper describes how the adjoint is applied to incompressible RANS simulations to approach the continuous solution which is unknown. While the adjoint method is already widespread in aerodynamics to optimize designs, here it is used as an error estimator. The error is thus calculated from the scalar product of sensitivities to an objective function selected by the user (efficiency, power, losses, etc.) with respect to residuals of governing equations. The key point is that adjoint method pinpoints the sensitive areas of the simulation. By adapting the mesh accordingly, it is possible to improve the numerical accuracy while keeping the mesh size manageable.