The continuous increase in the number of flights in the last decades caused a steepgrowth of aviation-related pollution to the point that the aviation sector is responsible for3% of the global greenhouse gas emissions. Regulators have been slow at catching up withthis problem, and stringent emission targets have been put in place only very recently. Asa consequence, innovative solutions to make airplanes comply with regulations must besought in a short time span. However, the aviation industry is known to be risk-averse andslow at incorporating innovation, especially when it comes to new aerodynamic designs.A decisive acceleration to the design development process has been given by the in-troduction of aerodynamic shape optimization techniques (ASO), using a computationalfluid dynamics (CFD) code to optimize the shape of a part of an aircraft in order to re-duce its aerodynamic drag and, consequently, its overall carbon footprint. The first partof this dissertation focuses on the optimization of the wing-fuselage junction, a regionwhere complex flow phenomena significantly contribute to the total drag of the aircraft.The ASO discovers an innovative shape of the fuselage that reduces drag by activating apropulsive pressure force that would otherwise be null.However, the CFD code used for the ASO is subject to uncertainties and errors and sois the complex experiment carried out to validate the optimized design. As a consequence,the confidence in the results of Reynolds-averaged Navier-Stokes (RANS) simulationsand wind-tunnel experiments of complex flow phenomena is often limited. Hence, thesecond part of the dissertation explores ways to reduce these errors by developing twovariational data assimilation (DA) techniques that inject sparse experimental data into aRANS code in order to correct the Reynolds stress tensor (RST) computed by a lineareddy viscosity turbulence model, one of the largest sources of errors in a CFD simulation.The DA problem is formulated in a way to guarantee the physical realizability of theRST and the results demonstrate an excellent ability to reconstruct complex flow fields.Finally, the DA methodology is extended to incorporate corrections to the experimentalangle of attack and Mach number, thus being able to simultaneously correct turbulencemodeling and wind-tunnel wall interference errors. The methodology is validated on 2Dand 3D test cases, showing that different corrections for the angle of attack and Machnumber than those from conventional correction techniques are needed for an optimalreconstruction of the flow field around the test object.@en

This paper presents a novel approach for correcting wind-tunnel wall interference in the nonlinear flow regime, that is, in the presence of phenomena such as flow separation and shocks. The methodology uses a gradient-based optimization to minimize the difference between experimental measurements and a Favre-averaged Navier–Stokes (FANS) simulation. The aim is to exploit the high-fidelity experimental data to correct turbulence-modeling errors in the FANS simulations, as well as to use the accurate angle of attack and Mach number from the FANS simulations to correct the in-tunnel flow conditions. The optimization is carried out directly in free air, thus avoiding the requirement to mesh the wind-tunnel walls and/or to model the ventilated-wall boundary condition. A byproduct of this method is the availability of flow information everywhere around the test object, which augments and complements the experimental data. The methodology is tested on two-dimensional and three-dimensional flow cases, demonstrating a significant improvement in the agreement between experimental and numerical data.

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An anti-fairing is a concave deformation of the wall around a wing-body junction that can decrease the aerodynamic drag through the activation of a propulsive force generated by the interaction of the curved concave shape and the high-pressure region in proximity of the wing leading-edge. Although this mechanism is well understood, the dynamics of the interaction between the anti-fairing and the junction flow remain largely unexplored. This work brings together all the numerical and experimental studies of the anti-fairing to investigate its effect on turbulent quantities and the robustness of its design to changes to the incoming flow parameters, and to estimate the drag change with respect to a normal wing/flat-plate configuration. It is found that the interaction of the streamwise pressure gradient generated by the anti-fairing with the incoming boundary layer substantially reduces the shear responsible for viscous drag. Furthermore, no significant influence of the incoming boundary layer thickness on the anti-fairing performance is observed. However, a direct drag measurement with a force balance casts some doubts on the possibility to achieve large drag reductions.

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This paper presents two novel data assimilation (DA) techniques for reconstructing steady turbulent flows at high Reynolds numbers by introducing perturbations to the Reynolds stress tensor computed by the turbulence model of a Favre-averaged Navier–Stokes (FANS) code. These techniques minimize the least-squares difference between an experimentally measured mean flow quantity and the corresponding quantity as computed by the FANS code. The two DA methods differ from each other in the choice of the control parameters: one perturbs the eigenvalues and eigenvectors of a baseline Reynolds stress, whereas the other perturbs the components of a baseline realizable Reynolds stress such that the perturbed result is still realizable. For the optimization procedure, a gradient-based algorithm is used in combination with a discrete adjoint methodology. The DA methods are applied to high-Reynolds-number problems, and their results compared with a reference technique. The results show that the approaches developed in this work are more effective at reconstructing the turbulent flowfield than standard techniques, but are more computationally expensive due to the high dimensionality of the optimization problem. Furthermore, it appears that only small perturbations to the control parameters are necessary to obtain significant improvements over the baseline results.@en

Despite a steady decrease in the overall usage of wind tunnels due to the increasing reliability and speed of computational fluid dynamics (CFD), ground testing remains a fundamental part of the development of an air vehicle. In this context, one of the new trends is represented by data assimilation (DA), whereby experimental and numerical measurements are combined together in an attempt to extract more complete and accurate information from the available data. This work presents an assessment of a variational data assimilation framework for the problem of wind tunnel wall interference corrections. The methodology minimizes the discrepancy between experimental measurements and the corresponding values from a Reynolds-averaged Navier-Stokes (RANS) simulation by optimally tuning the free-stream angle of attack and Mach number, as well as a corrective field for the turbulence model. The framework is first validated on synthetic data, and then tested on two cases at high Mach number and low angle of attack using pressure coefficients on the model’s surface as the reference measurements. The assimilated pressure coefficients match the reference ones better than those obtained from a linear correction technique. Furthermore, assimilating also a corrective term in the turbulence model improves the quality of the results with respect to assimilating only the angle of attack and Mach number. @en

Junction flows occur when a boundary layer develops on a wall and encounters an obstacle protruding from this surface. When the obstacle generates enough of an adverse pressure gradient to separate the flow, the aerodynamic drag is increased. In this paper, aerodynamic shape optimization (ASO) is employed to optimize a wing/body junction geometry at a chordReynolds number ofReC = 9.7 105,where thewing is theprotrusionandthebodyis representedby a flat plate. In contrast to conventional ASOs, thewing shape is kept fixed and only deformations of the body are allowed in order to study its influence on the junction drag. The obtained optimized design is characterized by a concave shape similar to a dent in the junction area and differentiates itself from the traditional convex fairings. For this reason, it is named the anti-fairing. Wind-tunnel experiments using stereoscopic particle image velocimetry in the wake of the junction area and a new set of Reynolds-averaged Navier-Stokes simulations with a finermesh than that used in the optimization are performed in order to validate the optimization, estimate the drag reduction with respect to the baseline geometry and two different leading-edge fairings, and investigate the mechanism by which drag is reduced. The anti-fairing is shown to systematically reduce drag and outperform leading-edge fairings thanks to the interaction between the wing and the front part of the concavity, generating a pressure force in the direction opposite to the drag force.

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Among all the interference effects contaminating wind-tunnel experiments, those due to the presence of the walls are particularly challenging to account for. Their influence becomes hard to quantify in the non-linear flow regime, for which classical linear correction methodologies are inadequate. This work proposes to use a variational data assimilation framework to tackle the problem of wall interference corrections in the presence of non-linear flow phenomena. The main idea is to use a gradient-based optimization technique to minimize the difference between an experimentally measured quantity of interest and the same quantity obtained by means of a RANS simulation. The control parameters contain the free-stream angle of attack and Mach number, and terms correcting the approximations introduced by eddy viscosity turbulence models. The former return the corrections to the angle of attack and Mach number, while the latter treat the main source of error in RANS simulations. Two parameterizations of the turbulence model are presented in this paper, each one correcting the error introduced by the turbulence model in its own way. One re-calibrates the balance of terms within the turbulent transport equations, while the other directly assimilates the Reynolds stress anisotropy tensor, thus bypassing the limitations introduced by the Boussinesq hypothesis. The two methodologies are tested on transonic 2D cases, and are shown to yield more accurate corrections than traditional methods. @en

Two methods of flow measurement in stacks are investigated to determine their errors in presence of cyclonic flow. One method – based on velocity measurements with a Pitot tube in a grid of points – is the standard reference method according to EN ISO 16911-1. The second method – ultrasonic flow measurement – is often used as the automated measurement system in stacks according to EN ISO 16911-2. Several typical stack configurations are considered and the flow field in the stacks is obtained using validated computational fluid dynamics (CFD) modelling with OpenFoam software. We show that possible errors of the standard reference method due to the cyclonic flow are significant compared to the requirements of the EU's Emissions Trading System. For the ultrasonic flow meter we compare various configurations (number, orientation, position) of the ultrasound beams and we demonstrate the flow profile pre-investigation by CFD as prescribed in section 8.3 of EN ISO 16911-2.

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This paper presents an approach for updating the epistemic uncertainty of ultrasonic flow meter measurements under non-ideal flow conditions. Instead of re-calibrating the instrument to correct its behavior in these difficult working conditions, a Bayesian calibration of a computer model of the real process is used. The numerical model is based on computational fluid dynamics (CFD) and a surrogate model is constructed from a limited number of CFD calculations using kriging. The computer model predicts the flow rate dependent on certain parameters including the bulk Reynolds number - which carries information about the true speed of the flow, and is measured only approximately by an ultrasonic flow meter. Bayesian calibration is applied, and the posterior of the true speed can be derived from the marginal posterior of the Reynolds number. This pdf has a smaller uncertainty with respect to the observed data used to fit the model on the condition that sufficiently informative data are available. If this is the case, the proposed approach is capable of reducing not only the uncertainty but also the error associated with the flow meter measurements in non-ideal conditions.

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