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Slipping rib syndrome (SRS) is an uncommon condition, typically affecting young adults and adolescents. The syndrome is mainly characterized by chronic, restrictive pain, caused by the impingement of the loose false rib tips to the adjacent intercostal nerves. The most commonly involved ribs are the 9th, 10th, and 11th ribs. There are several reports and case series reporting the efficacy of surgical interventions for the treatment of SRS. Costal cartilage excision, rib resection, and rib stabilization-plating procedure are successful surgical approaches for the management of slipping rib syndrome.

A data-driven two-branch deep neural network (DNN), to be referred to as $$\lambda $$ -DNN, used to predict scalar fields is presented. The network architecture consists of two separate branches (input layers) connected to the main one towards its output. In multi-disciplinary shape optimization problems, such as those this paper is dealing with, the input to the $$\lambda $$ -DNN contains data relevant to the geometrical shape and the case itself. Herein, the $$\lambda $$ -DNN is used in conjugate heat transfer (CHT) analysis and shape optimization problems, synergistically with codes simulating flows over the fluid domain and solving the heat conduction equations over the solid one. It is used to optimize a duct and an internally cooled turbine blade-airfoil surrounded by hot gas. The $$\lambda $$ -DNNs, after being trained on fields computed using the CHT solver, are used as surrogates for either the heat conduction equation solver of the solid domain, replicating either one out of the two disciplines of the problem or the coupled CHT solver.

This paper presents an adjoint-based shape optimization framework and its demonstration in a conjugate heat transfer problem in a turbine blading. The gradient of the objective function is computed based on the continuous adjoint method, which also includes the adjoint to the turbulence model. Differences in the gradient resulting from making the frozen turbulence assumption are discussed. The developed software was used to optimize both the blade shape of the internally cooled linear C3X turbine blade and the position of cooling channels aiming at (a) minimum total pressure drop of the hot gas flow and (b) minimum highest temperature within the blade. A two-step optimization procedure was used. A free-form parameterization tool, based on volumetric NURBS, controls the blade airfoil contour, while the cooling channels are free to move following changes in the coordinates of their centers. Geometric and flow constraints are included in the performed optimizations, keeping the cooling channels away from the airfoil sides and retaining the turbine inlet capacity and flow turning.

A fully-connected Deep Neural Network (DNN) architecture, to be referred to as \(\lambda \)-DNN, used to predict 2D/3D scalar fields is presented. In aerodynamics, the \(\lambda \)-DNN is firstly trained on fields computed using a Computational Fluid Dynamics (CFD) software. Then, it can be incorporated into engineering processes in various ways. One possibility is to use them in optimization problems solved by stochastic population-based methods, in which the \(\lambda \)-DNN may act as the surrogate evaluation model, replacing calls to the CFD tool. Another possibility is in multi-disciplinary problems, to replicate the numerical solver for any of the disciplines. This small list of possible usages is not exhaustive and, of course, different usages can be combined. The input to each DNN contains information to identify the geometrical shape and case-related data, nodal coordinates and, in multi-disciplinary problems, interfacing data connecting solvers for different disciplines on adjacent domains. In this paper, the \(\lambda \)-DNN is firstly used in the aerodynamic shape optimization of a wing using evolutionary algorithms, in which it replicates the CFD solver. Then, it is used in a conjugate heat transfer problem dealing with a solid domain in contact with a flow within a duct. In this problem, the \(\lambda \)-DNN acts as a surrogate to the solver of the heat conduction equation on the solid domain, by interfacing with a CFD solver of the fluid domain.

A simple and selective HPLC method, using a monolithic column, was developed for the simultaneous determination of the histamine H2-receptor antagonists: famotidine, cimetidine and nizatidine, in the presence of sulfadimethoxine as internal standard. The separation was performed on a Chromolith Performance RP-18 column (100 mm x 4.6 mm i.d.) with an isocratic mobile phase consisting of 0.05 mol/L acetate buffer (adjusted to pH 6.5 with triethylamine)/methanol/acetonitrile (85:10:5, v/v/v). The wavelength was set at 230 nm. Linearity was obtained for concentrations between 0.2 to 50 μg/mL and limits of detection were in the range 0.07-0.17 μg/mL. Full validation with respect to linearity, selectivity, detection and quantification limits, accuracy, precision and robustness, the latter using the Youden’s test, was carried out. The method was successfully applied to the determination of the drugs in human serum and urine following solid phase extraction. Average recoveries between 88.0 to 104.4% and 88.0 to 108.0% in serum and urine samples, respectively, were obtained.