Joseph Morlier

Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO) | ISAE · ICA

Surrogate models are of high interest for many engineering applications, serving as cheap-to-evaluate time-efficient approximations of black-box functions to help engineers and practitioners make decisions and understand complex systems. As such, the need for explainability methods is rising and many studies have been performed to facilitate knowle...

This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for different flow conditions, handle non-parametric 3D geometric variations, and generalize to unseen shapes a...

This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for different flow conditions, handle non-parametric 3D geometric variations, and generalize to unseen shapes a...

--- ODAS 2024: 24th joint ONERA-DLR Aerospace Symposium --- For developing innovative systems architectures, modeling and optimization techniques have been central to frame the architecting process and define the optimization and modeling problems. In this context, for system-of-systems the use of efficient dedicated approaches (often physics-based...

In an increasingly competitive and digital industrial environment, the optimization of structures is a key point not only to reduce costs but also to reduce the consumption of natural resources. To this end, different approaches have emerged throughout history based on the tools available at the time. With the current rise of artificial intelligenc...

Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be used to build the mixed-categorical GP. Many of these approaches involve a high number of hyperparameters; in fact, the more general and precise the strategy used to build...

Distributed electric propulsion in aircraft design is a concept that involves placing multiple electric motors across the aircraft’s airframe. Such a system has the potential to contribute to sustainable aviation by significantly reducing greenhouse gas emissions, minimizing noise pollution, improving fuel efficiency, and encouraging the use of cle...

In this paper, a new approach to design ultralight structures is developed based on a previous work called Efficient Multiscale Topology Optimization. A parameterized (or explicit) truss-based cell is introduced to generate intrinsically well-connected microstructures and to get clear interpretable optimal multiscale structures. The method uses a p...

Truss lattices are potential candidates for the design of innovative aerostructures, thanks to their high stiffness-to-weight ratio, modularity, and aeroelastic properties. However, when designing ultralight structures, multiple mechanical constraints, such as maximum internal stress or local buckling constraints, must be taken into account since t...

The surrogate model is an essential part of modern design optimization and exploration. In some cases, exploration of design space in multi-objective problems is important to reveal useful design insight and guidelines that will be useful for engineers. However, most surrogate models are black boxes, making interpretation difficult. This paper inve...

The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixe...

Multidisciplinary design optimization (MDO) methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer, and categorical variables might arise during the optimization process, and practical applications involve a significa...

The continuous growth of the small launcher market and the emergence of many new concepts in the past years, combined with the growing need for sustainability in the space sector, raises the question of their environmental impact. The sustainability of space activities is becoming a significant constraint on future space applications. There is a ne...

Multidisciplinary Design Optimization (MDO) enables one to reach a better solution than by optimizing each discipline independently. In particular, the optimal structure of a drone varies depending on the selected material. The [Formula: see text] footprint of a solar-powered High Altitude Long Endurance (HALE) drone is optimized here, where the st...

This work aims at developing new methodologies to optimize computational costly complex systems (e.g., aeronautical engineering systems). The proposed surrogate-based method (often called Bayesian optimization) uses adaptive sampling to promote a trade-off between exploration and exploitation. Our in-house implementation, called SEGOMOE, handles a...

As sustainability becomes one of the main challenges of the aerospace industry , we need to find new ways to integrate it into the design phase of aerospace systems. The Multidisciplinary Analysis and Optimization (MDAO) framework is a great host for an environmental discipline thanks to its modularity. However current Life Cycle Assessment (LCA) s...

The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixe...

We propose an innovative approach to minimize the greenhouse gas impacts of additive manufactured structures over their entire life cycle. The novelty of our method lies in its simultaneous optimization of material selection, process selection, and design optimization. To fully leverage the potential benefits of additive manufacturing, we use topol...

A low-computational-cost method is proposed in this paper to predict steady-state nonlinear aeroelastic response for high-aspect-ratio wings. This fast nonlinear static aeroelasticity method relies on a simple but robust mathematical approach. The work presented in this paper evaluates the accuracy of a nonlinear aerodynamic method on a large range...

The introduction of suborbital vehicles for tourism by companies such as Virgin Galactic or Blue Origin, combined with the growing need for sustainability in the space sector, raises the question of their environmental impact. There is a need to integrate sustainability in the framework of vehicle design since the preliminary phase. The objective o...

Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of d...

Selecting the optimal material for a part designed through topology optimization is a complex problem. The shape and properties of the Pareto front plays an important role in this selection. In this paper we show that the compliance-volume fraction Pareto fronts of some topology optimization problems in linear elasticity share some useful propertie...

Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this...

Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this...

Selecting the optimal material for a part designed through topology optimization is a complex problem. The shape and properties of the Pareto front plays an important role in this selection. In this paper we show that the compliance-volume fraction Pareto fronts of some topology optimization problems in linear elasticity share some useful propertie...

Machine learning has promoted advances in aerodynamic design optimization in multiple aspects such as aerodynamic modeling, shape parameterization, optimization architectures, etc. In order to provide our community with a briefing on the state-of-the-art and future directions, we organize this special issue to collect relevant studies applied to th...

Multidisciplinary Design Optimization (MDO) makes it possible to reach a better solution than by optimizing each discipline independently. In particular, the optimal structure of a drone won't be the same depending on the material used. The CO2 footprint of a solar-powered High Altitude Long Endurance (HALE) drone is optimized here, the structural...

In this paper, mixed categorical structural optimization problems are investigated. The aim is to minimize the weight of a truss structure with respect to cross-section areas, materials, and cross-section type. The proposed methodology consists of using a bi-level decomposition involving two problems: master and slave. The master problem is formula...

In this paper, mixed categorical structural optimization problems are investigated. The aim is to minimize the weight of a truss structure with respect to cross-section areas, materials and cross-section type. The proposed methodology consists of using a bi-level decomposition involving two problems: master and slave. The master problem is formulat...

Bayesian optimization is an advanced tool to perform efficient global optimization. It consists on enriching iteratively surrogate Kriging models of the objective and the constraints (both supposed to be computationally expensive) of the targeted optimization problem. Nowadays, efficient extensions of Bayesian optimization to solve expensive multi-...

Recently, there has been a growing interest for mixed categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies. Among the recently developed methods, we could cite: GP models built using continuous relaxation of the variables, Gower distance based models or GP models de...

In order to mitigate the impact of the transportation sector on climate change, light and ecological parts must be designed. A lifecycle oriented design methodology with CO2 footprint minimization of parts used in various transports is presented in this work. Only material production and use phase are considered in this work, to have a better under...

Surrogate models are an essential engineering tool and their popularity has increased recently due to the high computational cost of evaluating real-world simulations. However, most of these functions are described by mixed variables (continuous and categorical), which makes it harder to create accurate interpolation functions. This work builds a s...

Recently, there has been a growing interest for mixed categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies. Among the recently developed methods, we could cite: continuous relaxation of the variables, Gower distance based model or GP model based on direct estimation...

Aeroelastic scaling theory shows that the design problem of aeroelastically equivalent scaled aircraft can be treated as a structural-only design problem if the aerodynamic shape and airflow properties of the full scale aircraft are preserved. In that case, the theory shows that it is sufficient to match the scaled natural mode shapes, frequencies...

Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of d...

View Video Presentation: https://doi.org/10.2514/6.2022-2243.vid Co-design is a multi-disciplinary optimization which can be used to concurrently optimize structural thickness together with the control law. In this work, a co-design method is applied to include a flutter suppression law design in the sizing of an aeroelastic wing. A nested or multi...

View Video Presentation: https://doi.org/10.2514/6.2022-2538.vid Time domain rapid methodologies for dynamic fluid-structure interactions for gust loads computation are presented in this work. In order to reduce the CPU cost of the high-fidelity simulations, different alternatives are compared: an unsteady strip theory, a modification of Wagner lif...

The paper has been renamed: Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design. More: https://arc.aiaa.org/doi/10.2514/6.2022-0082

Multi-scale topology optimization (a.k.a. micro-structural topology optimization, MTO) consists in optimizing macro-scale and micro-scale topology simultaneously. MTO could improve structural performance of products significantly. However, a few issues related to connectivity between micro-structures and high computational cost have to be addressed...

In recent years, Topology Optimization (TO) gained interest in the scientific community. It assists in finding the best arrangement of material in a design volume. The classical approach named ”Solid Isotropic Material with Penalization” (SIMP) associates a fictitious density to each finite element in the domain. While SIMP is described as an impli...

The traditional approach for the design of aeroelastically scaled models assumes that either there exists flow similarity between the full-size aircraft and the model, or that flow non-similarities have a negligible effect. However, when trying to reproduce the behavior of an airliner that flies at transonic conditions using a scaled model that fli...

View Video Presentation: https://doi.org/10.2514/6.2021-3099.vid A multifidelity approach for the solution of aeroelastic optimization problems is developed using open-source tools. The Multidisciplinary Optimization problem is managed via OpenMDAO and a dedicated python library. Aerodynamics are solved with PANAIR, with a detailed mesh for the Hig...

The repetitive nature of cellular lattice structures brings various interesting features among which fast assembly and repair time, reduced tooling, and manufacturing costs are major advantages. Additionally, as the mechanical performances of the structure are heavily influenced by the topology and materials of the cell, the designers can optimize...

Multidisciplinary Design Optimization (MDO) methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Among MDO architectures, various ones are considering the resolution of the Multidisciplinary Design Analysis (MDA). In our study, the system of interest being an ai...

Detect and prevent an aircraft instability condition is extremely important, especially for flight control, and morphing airfoils can be used for this purpose. This work proposes the determination of a digital morphing airfoil, using a deep learning approach, to avoid an unstable aeroelastic condition in a 2D wing model. To parametrize the airfoil'...

Following the renewed interest in Moon exploration and in-situ resources exploitation, this study analyses new optimal ascent and descent trajectories from the Moon surface to Low Lunar Orbits (LLO) and Near Rectilinear Halo Orbits (NRHO). The planar, restricted two-body dynamics is employed to describe the motion of a unitary-mass spacecraft subje...

With renewed interest in lunar exploration and the upcoming deployment of the lunar space station, the Lunar Orbital Platform-Gateway (LOP-G), a scientific community, is focusing on the design of a lander to bring people back to the lunar surface. This work focuses on optimizing two aspects of the lunar lander concurrently: the mission architecture...

Structural optimization (topology, shapes, sizing) is an important tool for facilitating the emergence of new concepts in structural design. Normally, topology optimization is carried out at the early stage of design and then shape and sizing design are performed sequentially. Unlike traditional topology optimization method, explicit methodologies...

High-dimensional constrained Bayesian optimization with mixed integer variables using continuous relaxation, supper efficient global optimization and kriging with partial least squares for multidisciplinary aircraft optimization.

The classical aeroelastic scaling theory used to design scaled models is based on the assumption that complete flow similarity exists between the full aircraft and the scaled model. When this condition is satisfied, the scaling problem of the model can be treated as a structural design problem only, where the scaled aerodynamic shape is preserved....

Co-design is a multi-disciplinary optimization which aims to concurrently optimize structure and control law. In this work, a co-design method is applied to include a flutter suppression law design in an aeroelastic wing sizing exercise. A nested architecture is used. The outer loop optimizes a beam cross-section and the inner loop is a structured...

Isogeometric shape optimization has been now studied for over a decade. This contribution aims at compiling the key ingredients within this promising framework, with a particular attention to sensitivity analysis. Based on all the researches related to isogeometric shape optimization, we present a global overview of the process which has emerged. T...