Section: New Results

Optimum design and control in fluid dynamics and its couplings

In computational sciences for physics and engineering, Computational Fluid Dynamics (CFD) are playing one of the major roles in the scientific community to foster innovative developments of numerical methodologies. Very naturally, our expertise in compressible CFD has led us to give our research on numerical strategies for optimum design a particular, but not exclusive focus on fluids.

The framework of our research aims to contribute to numerical strategies for PDE-constrained multi-objective optimization, with a particular emphasis on CPU-demanding computational applications in which the different criteria to be minimized (or reduced) originate from different physical disciplines that share the same set of design variables. These disciplines are often fluids, as a primary focus, coupled with some other discipline, such as structural mechanics.

Our approach to competitive optimization is based on a particular construction of Nash games, relying on a split of territory in the assignment of individual strategies. A methodology has been proposed for the treatment of two-discipline optimization problems in which one discipline, the primary discipline, is preponderant, or fragile. Then, it is recommended to identify, in a first step, the optimum of this discipline alone using the whole set of design variables. Then, an orthogonal basis is constructed based on the evaluation at convergence of the Hessian matrix of the primary criterion and constraint gradients. This basis is used to split the working design space into two supplementary subspaces to be assigned, in a second step, to two virtual players in competition in an adapted Nash game, devised to reduce a secondary criterion while causing the least degradation to the first. The formulation has been proved to potentially provide a set of Nash equilibrium solutions originating from the original single-discipline optimum point by smooth continuation, thus introducing competition gradually. This approach has been demonstrated over a test-case of aero-structural aircraft wing shape optimization, in which the eigensplit-based optimization reveals clearly superior [38] .

While the two-discipline method is currently being applied to various complex physical multi-objective situations (see in particular 6.2.4 , 6.2.5 , 6.2.6 ), the method has been extended to situations involving more than two objectives when the initial point is Pareto-optimal. Then, a particular convex combination of the criteria is locally stationary, and the two-discipline strategy can be applied using this combination as preponderant criterion, and a particular other criterion as secondary one. Whence, the proposed split of territory produces a continuum of Nash equilibrium points tangent to the Pareto set. This theoretical result has been illustrated in the context of a simpler numerical experiment by E. Baratchart during his internship [4] , see Fig. 2 .

Our approach to cooperative optimization is based on a result of convex analysis established for a general unconstrained mult-iobjective problem in which all the gradients are assumed to be known. The theorem [39] states that in the convex hull of the gradients, there exists a unique vector of minimal norm, $\omega$; if it is nonzero, the vector $\omega$ is a descent direction common to all criteria; otherwise, the current design point is Pareto-optimal. This result led us to generalize the classical steepest-descent algorithm by using the vector $\omega$ as search direction. We refer to the new algorithm as the multiple-gradient descent algorithm (MGDA). The MGDA yields to a point on the Pareto set, at which a competitive optimization phase can possibly be launched on the basis of the local eigenstructure of the different Hessian matrices. This general formulation fosters several connected studies detailed in 6.2.1 .

Multiple-Gradient Descent Algorithm (MGDA)

Participants : Jean-Antoine Désidéri, Régis Duvigneau, Matteo Giacomini, Adrien Zerbinati.

Theory and numerical experimentation of the MGDA construction

In multi-objective optimization, the knowledge of the Pareto set provides valuable information on the reachable optimal performance. A number of evolutionary strategies (PAES, NSGA-II, etc), have been proposed in the literature and proved to be successful to identify the Pareto set. However, these derivative-free algorithms are very demanding in terms of computational time. Today, in many areas of computational sciences, codes are developed that include the calculation of the gradient, cautiously validated and calibrated.

In the original report [14] , and in [39] , we have introduced the notion of Pareto-stationarity, and given a first proof that it was the natural necessary condition for Pareto-optimality when the objective-functions are locally smooth in some open domain about the design-point. This report has been revised to provide a more rigorous, and extended proof. In particular, in the revised version [14] (version 3, 2012), the number of objective-functions $n$ and the dimension of the design space compare arbitrarily. The objective-functions are assumed to be locally convex.

Additionally, we had established that MGDA converges to Pareto-stationary design-points. This had been confirmed by numerical experiments in which MGDA had been tested over a number of classical multi-objective optimization test-cases, and found successful to converge to Pareto-optimal solutions in situations of either convex or concave Pareto sets. Additionally, MGDA [57] and PAES [69] were found to have complementary merits, making a hybrid method promising.

The method was tested successfully in a domain partition model problem in which the sub-solutions to the Poisson equation are matched at the interfaces by minimization of the integral along the interface of the squared normal-derivative jump. This academic exercise has permitted to illustrate the importance of applying an appropriate scaling to the gradients prior to calculating the descent direction [61] [47] . This has led us to define, a novel form of MGDA, consisting of a direct algorithm [62] based on a Gram-Schmidt orthogonalization conducted with a special normalization. The direct method was found more accurate and more efficient. Subsequently, we proposed two enhancements [63] , the first to define the order in which the gradients are introduced in the Gram-Schmidt process uniquely and to interrupt the process as soon as the current estimate of the search direction is proved to satisfy the descent property, and the second to optimally scale the gradients when the Hessians are known, or approximated (e.g. by the BFGS estimate).

Meta-model-assisted CFD optimization by MGDA

Using MGDA in a multi objective optimization problem requires the evaluation of a large number of points with regard to criteria, and their gradients. In the particular case of a CFD problems, each point evaluation is very costly since it involves a flow computation, possibly the solution of an adjoint-equation. To alleviate this difficulty, we have proposed to construct meta-models of the functionals of interest (lift, drag, etc) and to calculate approximate gradients by local finite differences. These meta-models are updated throughout the convergence process to the evaluation of the new design points by the high-fidelity model, here the 3D compressible Euler equations.

This variant of MGDA has been tested successfully over a problem of external aerodynamic optimum-shape design of an aircraft wing consisting of reducing wave-drag, and augmenting lift. After only a few cycles of database updates, the Pareto front visibly forms, and this result is achieved at a very moderate computational cost. This variant has been extended successfully to an internal flow optimization problem related to an automobile air-conditioning system and governed by the Navier-Stokes equations [55] . This more difficult problem has been proposed by Renault within the OMD2 ANR project.

Exact shape gradients

MGDA has successfully been tested over a two-objective optimization problem governed by two-dimensional elasticity. The deformation of a plate is calculated using an isogeometric approximation (see 6.6 ) and compliance derived from it. The exact parametric shape gradient is calculated, yielding the gradient of the objective function in two antagonistic situations differing by the loading. Pareto-fronts are thus identified.

Perspectives

MGDA offers the possibility to handle in a rational way several objective-functions for which gradients are known or approximated concurrently. This potential opens methodological paths to several themes of interest in high-fidelity simulation-based optimization: optimization of complex systems whose performance is evaluated w.r.t. several criteria originating from different, coupled disciplines; optimization under uncertainties, by introducing sensitivities as additional objectives; optimization of time-dependent systems, such as optimization of flow-control devices that generate a periodic flow (see next subsection), by converting the problem into a multi-point problem by time-discretization of the time and parameter-dependent functional; etc.

Flow control

Participants : Jean-Antoine Désidéri, Régis Duvigneau, Jérémie Labroquère.

Shape optimization methods are not efficient to improve the performance of fluid systems, when the flow is characterized by a strong unsteadiness related to a massive detachment. This is typically the case for the flow around an automotive body or a wing in stall condition. To overcome this difficulty, flow control strategies are developed, that aim at manipulating vortex dynamics by introducing some active actuators, such as periodic blowing/suction jets. In this context, the choice of the control parameters (location, amplitude, frequency) is critical and not straightforward. Therefore, a numerical study is conducted to i) improve the understanding of controlled flows ii) develop a methodology to determine optimal control parameters by coupling the controlled flow simulation with optimization algorithms. Two research axes have been considered :

• the resolution of the unsteady sensitivity equations derived from the state equations, to exhibit the dependency of the flow dynamics with respect to the control ;

• the optimization of control parameters using a statistical metamodel-based strategy[37] .

In this perspective, unsteady Reynolds Averaged Navier-Stokes equations are considered, with the Spalart-Allmaras turbulence closure. A numerical model for synthetic jets has been implemented to simulate the actuation[48] , based on imposed velocity boundary conditions. Particular developments have then be carried out to include a noise term into Gaussian Process metamodels, which is used to filter errors arising from unsteady simulations/citelabroquere:hal-00742940. First results have demonstrated the feasibility of the proposed method. A systematic assessment of modeling and numerical errors is in progress, for a backward facing step test-case, with the objective of controlling the re-attachment point location.

This activity is conducted in collaboration with the CFD team of Ecole Centrale de Nantes.

Robust design

Participants : Jean-Antoine Désidéri, Régis Duvigneau, Daïgo Maruyama.

This work aims to develop robust design tools for aircraft design w.r.t. aerodynamic performance subject to uncertainties arising from geometrical features and fluctuations of inflow conditions. The robust design process is considered as a multi-objective optimization problem consisting of minimizing statistical quantities such as mean and variance of a cost function, typically the drag coefficient under lift constraint. MGDA is used for this purpose.

At present, analytical test cases have been tested, confirming the validity of our approach to identify the Pareto set.

One aspect of the problem is that the evaluation of these statistics and performing their optimization is very cost demanding. One solution could be, for aerodynamic design, to identify the most important variables to be treated as uncertain, possibly by the ANOVA approach, and construct adequate meta-models.

Aero-structural optimization

Participants : Gérald Carrier [Research Engineer, ONERA/DAAP] , Jean-Antoine Désideri, Imane Ghazlane.

In industry, aircraft wings are designed by accounting for several multidisciplinary couplings. Certainly of greatest importance is the coupling, or concurrency, between aerodynamic optimization and structural design. At ONERA, in the former thesis of M. Marcelet, the aerodynamic gradient has been extended to account for (the main terms of) static fluid-structure interaction, commonly referred to as the “aeroelastic gradient”.

In her thesis, I. Ghazlane has extended M. Marcelet's work to take into account, in the aeroelastic gradient, the terms originating from the differentiation of the wing-structural model. In this development, the wing structure is treated as an equivalent Euler-Bernoulli beam. These formal extensions have been validated by an extensive experimentation. Additionally, special post-processing procedures have been set up to evaluate accurately the various physical contributions to drag. As a result, a realistic aircraft wing optimization has been conducted using a configuration provided by Airbus France as initial design. I. Ghazlane defended successfully her doctoral thesis thesis in December 2012 [34] .

Besides, I. Ghazlane has realized a two-objective optimization (drag and mass reduction) via a Nash game using our optimization platform FAMOSA. These results will be included in a common publication on Nash games in preparation.

Sonic boom reduction

Participants : Gérald Carrier [Research Engineer, ONERA/DAAP] , Jean-Antoine Désideri, Andrea Minelli, Itham Salah El Din [Research Engineer, ONERA/DAAP] .

When an aircraft flies at supersonic speed, it generates at ground level an N-shaped shock structure which can cause serious environmental damage (“sonic boom”). Thus a problem of interest in aerodynamic optimization is to design such an aircraft to reduce the intensity of the sonic boom while maintaining the aerodynamic performance (drag minimization under lift constraint). Andrea Minelli aimed at contributing to this two-discipline optimization problem. In the first part of his work, an inverse problem has been formulated and solved for “shaped sonic boom” and found in excellent agreement with the George-Seebass-Darden theory [68] for the calculation of the Whitham function corresponding to the lowest-boom (axisymmetric) shape. Method and results for more general geometries have been presented internationally in [50] .

Besides, aero-acoustic optimizations have been realized successfully by coupling the aerodynamic optimizer (based on Euler calculations by the elsA software) with the sonic-boom computation in a Nash game formulation. These experiments, conducted with our optimization platform FAMOSA, have demonstrated that starting from the shape optimized aerodynamically, one could retrieve smoothly a shape corresponding to nearly-optimal sonic-boom reduction. These results will be included in a common publication on Nash games in preparation.

Helicopter rotor blade optimization in both situations of hovering and forward flight

Participants : Michel Costes [Research Engineer, ONERA/DAAP] , Jean-Antoine Désideri, Arnaud Le Pape [Research Engineer, ONERA/DAAP] , Enric Roca Leon.

E. Roca Leon is conducting a CIFRE thesis supported by EUROCOPTER (Marignane) at ONERA DAAP. This thesis follows the doctoral thesis of A. Dumont in which the adjoint-equation approach was used to optimize a rotor blade in hovering flight. The goal of this new thesis is to solve a two-objective optimization problem in which the hovering-flight criterion is considered preponderant, but a new criterion that takes into account the forward-flight situation is also introduced, concurrently. The second criterion is the power necessary to maintain the forward motion. The first phase of thesis work has been devoted to the set up of a hierarchy of models from low to high fidelity, in order to calibrate appropriate functional criteria. In the current work, actual two-objective optimizations are conducted via our Nash game approach to competitive optimization with territory splitting based on reduced Hessian diagonalization. A first successful experiment has been realized in which the twist angle along the wing is optimized to reduce the power in forward motion while maintaining sub-optimality of the drag in hover. These results have been accepted for presentation at a forthcoming AIAA Conference, and will also contribute to a common publication on Nash games in preparation.

Optimum design in naval hydrodynamics

Participants : Régis Duvigneau, Louis Blanchard.

Naval hydrodynamics field has recently shown a growing interest for optimum design methods. The computational context is especially complex because it implies unsteady two-phase turbulent flows, with possibly very high Reynolds number (up to ${10}^9$). The use of automated design optimization methods for such problems requires new developments to take into account the large CPU time necessary for each simulation and the specificity of the geometries considered.

In collaboration with GALAAD Project-Team, some developments have been initiated on the geometrical modelling of hull shapes by parametric surfaces. The objective was to be able to modify existing hull shapes by controlling a small number of parameters, that are meaningful for naval architects. We have considered as test-case the bow shape for trawler ships[58] . As a second step, an optimum shape procedure has been set up, based on a metamodel-based optimizer, the developed CAD model and the simulation tool for free-surface flows provided by K-Epsilon company. The objective was to reduce the wave drag of a trawler ship by adding a bow, whose parameters are optimized.