This thesis presents advances in reduced-order modeling based on proper orthogonal decomposition (POD) for single and two-phase flows. Reduced-order models (ROMs) are generated for two-phase gas-solid flows. A multiphase numerical flow solver, MFIX, is used to generate a database of solution snapshots for proper orthogonal decomposition. Time-independent basis functions are extracted using POD from the data, and the governing equations of the MFIX are projected onto the basis functions to generate the multiphase POD-based ROMs. Reduced-order models are constructed to simulate multiphase two-dimensional non-isothermal flow and isothermal flow particle kinetics and three-dimensional isothermal flow. These reduced-order models are applied to three reference cases. The results of this investigation show that the two-dimensional reduced-order models are capable of producing qualitatively accurate results with less than 5 percent error with at least an order of magnitude reduction of computational costs. The three-dimensional ROM shows improvements in computational costs. This thesis also presents an algorithm based on mathematical morphology used to extract discontinuities present in quasi-steady and unsteady flows for POD basis augmentation. Both MFIX and a Reynolds Average Navier-Stokes (RANS) flow solver, UNS3D, are used to generate solution databases for feature extraction. The algorithm is applied to bubbling uidized beds, transonic airfoils, and turbomachinery seals. The results of this investigation show that all of the important features are extracted without loss in accuracy.

abstract: The focus of this investigation is on the renewed assessment of nonlinear reduced order models (ROM) for the accurate prediction of the geometrically nonlinear response of a curved beam. In light of difficulties encountered in an earlier modeling effort, the various steps involved in the construction of the reduced order model are carefully reassessed. The selection of the basis functions is first addressed by comparison with the results of proper orthogonal decomposition (POD) analysis. The normal basis functions suggested earlier, i.e. the transverse linear modes of the corresponding flat beam, are shown in fact to be very close to the POD eigenvectors of the normal displacements and thus retained in the present effort. A strong connection is similarly established between the POD eigenvectors of the tangential displacements and the dual modes which are accordingly selected to complement the normal basis functions. The identification of the parameters of the reduced order model is revisited next and it is observed that the standard approach for their identification does not capture well the occurrence of snap-throughs. On this basis, a revised approach is proposed which is assessed first on the static, symmetric response of the beam to a uniform load. A very good to excellent matching between full finite element and ROM predicted responses validates the new identification procedure and motivates its application to the dynamic response of the beam which exhibits both symmetric and antisymmetric motions. While not quite as accurate as in the static case, the reduced order model predictions match well their full Nastran counterparts and support the reduced order model development strategy. / Dissertation/Thesis / M.S. Mechanical Engineering 2011

Les phases de conception et de validation de pièces mécaniques nécessitent des outils de calculs rapides et fiables, permettant de faire des choix technologiques en un temps court. Dans ce cadre, il n'est pas possible de calculer la réponse exacte pour l'ensemble des configurations envisageables. Les métamodèles sont alors couramment utilisés mais nécessitent un grand nombre de réponses, notamment dans le cas où celles-ci sont non-linéaires. Une solution est alors d'exploiter plusieurs sources de données de qualité diverses pour générer un métamodèle multi-fidélité plus rapide à calculer pour une précision équivalente. Ces données multi-fidélité peuvent être extraites de modèles réduits.Les travaux présentés proposent une méthode de génération de métamodèles multi-fidélité pour l'optimisation de structures mécaniques par la mise en place d'une stratégie d'enrichissement adaptatif des informations sur la réponse de la structure, par utilisation de données issues d'un solveur LATIN-PGD permettant de générer des données de qualités adaptées, et d'accélérer le calcul par la réutilisation des données précédemment calculées. Un grand nombre de données basse-fidélité sont calculées avant un enrichissement intelligent par des données haute-fidélité.Ce manuscrit présente les contributions aux métamodèles multi-fidélité et deux approches de la méthode LATIN-PGD avec la mise en place d'une stratégie multi-paramétrique pour le réemploi des données précédemment calculées. Une implémentation parallèle des méthodes a permis de tester la méthode sur trois cas-tests, pour des gains pouvant aller jusqu'à 37x. / Engineering simulation provides the best design products by allowing many design options to be quickly explored and tested, but fast-time-to-results requirement remains a critical factor to meet aggressive time-to-market requirements. In this context, using high-fidelity direct resolution solver is not suitable for (virtual) charts generation for engineering design and optimization.Metamodels are commonly considered to explore design options without computing every possibility, but if the behavior is nonlinear, a large amount of data is still required. A possibility is to use further data sources to generate a multi-fidelity surrogate model by using model reduction. Model reduction techniques constitute one of the tools to bypass the limited calculation budget by seeking a solution to a problem on a reduced order basis (ROB).The purpose of the present work is an online method for generating a multi-fidelity metamodel nourished by calculating the quantity of interest from the basis generated on-the-fly with the LATIN-PGD framework for elasto-viscoplastic problems. Low-fidelity fields are obtained by stopping the solver before convergence, and high-fidelity information is obtained with converged solution. In addition, the solver ability to reuse information from previously calculated PGD basis is exploited.This manuscript presents the contributions to multi-fidelity metamodels and the LATIN-PGD method with the implementation of a multi-parametric strategy. This coupling strategy was tested on three test cases for calculation time savings of more than 37x.

This work proposes a new method for using reduced order models in lieu of high fidelity analysis during the sensitivity analysis step of gradient based design optimization. The method offers a reduction in the computational cost of finite difference based sensitivity analysis in that context. The method relies on interpolating reduced order models which are based on proper orthogonal decomposition. The interpolation process is performed using radial basis functions and Grassmann manifold projection. It does not require additional high fidelity analyses to interpolate a reduced order model for new points in the design space. The interpolated models are used specifically for points in the finite difference stencil during sensitivity analysis. The proposed method is applied to an airfoil shape optimization (ASO) problem and a transport wing optimization (TWO) problem. The errors associated with the reduced order models themselves as well as the gradients calculated from them are evaluated. The effects of the method on the overall optimization path, computation times, and function counts are also examined. The ASO results indicate that the proposed scheme is a viable method for reducing the computational cost of these optimizations. They also indicate that the adaptive step is an effective method of improving interpolated gradient accuracy. The TWO results indicate that the interpolation accuracy can have a strong impact on optimization search direction.

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Quadrupedal robots play a vital role in various applications, from search and rescue operations to exploration in challenging terrains. However, locomotion tasks involving unknown payload transportation on rough terrains pose significant challenges, requiring adaptive control strategies to ensure stability and performance. This dissertation contributes to the advancement of adaptive motion planning and control solutions that enable quadrupedal robots to traverse unknown rough environments while tasked with transporting unknown payloads. In the first project, a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots is developed. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law applied to reduced-order locomotion (i.e., template) models. At the high level of the control hierarchy, an indirect adaptive law estimates unknown parameters of the reduced-order locomotion model under varying payloads, ensuring stability during trajectory planning. The optimal trajectories generated by the AMPC are then passed to a low-level and full-order nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations and hardware experiments on the A1 quadru[pedal robot validate the framework's capabilities, showcasing significant improvements in payload transportation on both flat and rough terrains compared to conventional MPC strategies. Specifically, the robot demonstrates proficiency in transporting unmodeled, unknown static payloads up to 109% of its own mass in experiments on flat terrains and 91% on rough experimental terrains. Moreover, the robot successfully manages dynamic payloads with 73% of its mass on rough terrains. Adaptive controllers must also address external disturbances inherent in real-world environments. Therefore, the second project introduces a hierarchical planning and control scheme with an adaptive L1 nonlinear model predictive control (ANMPC) at the high level, which integrates nonlinear MPC (NMPC) with an L1 adaptive controller. The prescribed optimal state and control input profiles generated by the ANMPC are then fed to the low-level nonlinear WBC. This approach aims to stabilize locomotion gaits in the presence of parametric uncertainties and external disturbances. The proposed controller is analyzed to accommodate uncertainties and external disturbances. Comprehensive numerical simulations and experimental validations on the A1 quadrupedal robot demonstrate its effectiveness on rough terrains. Numerical results suggest that ANMPC significantly improves the stability of the gaits in the presence of uncertainties and external disturbances compared to NMPC and AMPC. The robot can carry payloads up to 109% of its own mass on its trunk on flat and rough terrains. Simulation results show that the robot achieves a maximum payload capacity of 26.3 (kg), which is equivalent to 211% of its own mass on rough terrains with uncertainties and disturbances. / Doctor of Philosophy / In the rapidly advancing domain of robotics, there is a growing demand for intelligent robotic systems capable of adeptly addressing novel and unforeseen scenarios, such as uneven paths or external forces applied to the robots, like kicks and hits. This necessitates robots with the capability to handle diverse tasks with precision, particularly in the domains of object transportation and navigation through unknown terrains in applications such as search and rescue operations or cargo handling. This dissertation introduces innovative motion planning and control frameworks designed to imbue robots with adaptive capabilities, enabling them to adapt to real-world unanticipated scenarios and uncertainties during their movement, particularly when carrying unknown payloads. In the first project, a new framework is developed to enhance payload transportation by quadrupedal robots. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law. Through extensive experiments and simulations, the framework shows remarkable improvements in payload transportation on both flat and rough terrains. The robot successfully transports payloads exceeding its own mass by up to 109% on flat terrains and 91% on rough terrains. Recognizing the need to address uncertainties in real-world environments, the second project introduces a hierarchical planning and control scheme with adaptive L1 nonlinear model predictive control (ANMPC). This approach stabilizes legged locomotion in the presence of uncertainties and disturbances. Results demonstrate that ANMPC significantly improves gait stability compared to existing methods. The robot achieves a payload capacity of up to 109% of its own mass on both experimental flat and rough terrains and reaches a maximum of 26.3 kg (around 212% of its own mass) on rough terrain simulations with uncertainties and disturbances.

L’ablation partielle du foie est une chirurgie qui intervient dans le traitement des lésions du foie et lors d’une transplantation partielle de foie. Les relations entre l’hémodynamique du foie, son volume et ses fonctions restent à élucider pour mieux comprendre les causes des complications de ces chirurgies. Lors de la chirurgie, l’hémodynamique du foie est altérée suite à l’augmentation de la résistance au flux sanguin de l’organe. La régénération du foie semble dépendante des changements de débit et de pression dans la veine porte. D’autre part, comme le foie reçoit 25% du débit cardiaque, la chirurgie impacte la circulation sanguine globale.
 Dans ce contexte, le premier objectif est de mieux comprendre, grâce à des modèles mathématiques, l’influence de l’hépatectomie sur l’hémodynamique. Le second objectif est l’analyse de la perfusion et de la fonction du foie. Premièrement, la procédure chirurgicale, les conditions expérimentales ainsi que les mesures obtenues sont détaillées.
 Ensuite, les valeurs moyennes mesurées lors de douze chirurgies sont reproduites par un modèle de circulation entière, basé sur des équations différentielles ordinaires. Lors des différentes hépatectomies, des changements de forme de courbe sont observés. Un modèle de circulation entière, basée sur des équations 1D et 0D est proposé pour analyser ces changements. Ce travail pourrait permettre une meilleure compréhension des changements d’architecture du foie induits par l’hépatectomie.
 Puis, le transport dans le sang d’un composé ainsi que son traitement par le foie sont modélisés. Un modèle pharmacocinétique est développé et grâce aux mesures, les paramètres du modèle sont estimés. / Major liver resection is being performed to treat liver lesions or for adult-to-adult living donor liver transplantation. Complications of these surgeries are related to a poor liver function. The links between liver hemodynamics, liver volume and liver function remain unclear and are important to better understand these complications. The surgery increases the resistance to blood flow in the organ, therefore it modifies liver hemodynamics. Large modifications of the portal vein hemodynamics have been associated with poor liver regeneration. Moreover the liver receives 25% of the cardiac outflow, therefore liver surgery may impact the whole blood circulation. In this context, the first goal is to investigate with mathematical models the impact of liver surgery on liver hemodynamics. The second goal is to study the liver perfusion and function with mathematical models. The first part describes the experimental conditions and reports the measurements recorded. Then, the second part focuses on the liver hemodynamics during partial hepatectomy. On one hand, the hemodynamics during several surgeries is quantitatively reproduced and explained by a closed-loop model based on ODE. On the other hand, the change of waveforms observed after different levels of liver resection is reproduced with a model of the global circulation, including 0D and 1D equations. This may contribute to a better understanding of the change of liver architecture induced by hepatectomy. Next, the transport in blood of a compound is studied. And a pharmacokinetics model and its parameter identification are developed to quantitatively analyze indocyanine green fluorescence dynamics in the liver tissue.

Dans les centrales nucléaires et thermiques, certaines installations sont sujettes à des couplages acousto-mécaniques pouvant nuire fortement à leur bon fonctionnement. La compréhension et la prédiction de ces couplages multi-physiques nécessitent le développement de modèles numériques de très grande précision. Ces modèles sont si coûteux à résoudre qu’il n’est pas envisageable de les utiliser dans des boucles de contrôle ou encore d’optimisation paramétrique. Dans ce manuscrit de thèse, le but est d’exploiter un nombre limité de calculs coûteux pour construire un modèle numérique qui soit de très faible dimension. Ces modèles numériques réduits doivent être capables, en temps réel, de reproduire ces calculs haute-fidélité mais aussi d’extrapoler ces résultats à d’autres points de fonctionnement plus ou moins proches. L’évolution dé petites perturbations compressibles au sein d’un écoulement complexe moyenné est modélisée à partir des équations d’Euler linéarisées dont la nature hyperbolique complique l’application des méthodes de réduction classiques. Les principales problématiques théoriques et numériques qui émergent lors de la construction du système réduit par méthode de projection sont alors exposées. En particulier, les problèmes fondamentaux de la préservation de la stabilité et du contrôle de l’énergie des systèmes réduits sont largement développés et une nouvelle méthode de stabilisation est proposée. Leur sensibilité paramétrique est aussi discutée. Les modèles réduits stables sont ensuite intégrées dans un code de calcul industriel pour prendre en compte des géométries complexes. De plus, la présence de solides dont les parois peuvent être fixes ou mobiles est abordée. En particulier, les petits déplacements de paroi sont modélisés avec une loi de transpiration. Cette condition aux limites est intégrée dans le formalisme du contrôle de façon à lever la difficulté induite par sa non homogénéité. Finalement, les modèles réduits sont exploités pour prédire en temps réel la réponse des systèmes à une loi de contrôle arbitraire. Par exemple, la fréquence et l’amplitude du chargement peuvent varier. Le code de calcul réduit ainsi développé a pour principale vocation de rendre possible des expertises aéroélastiques à faible coût. / In nuclear and thermal power stations, some installations produce acoustics/mechanics coupling which may cause important damage and bad operating performances. Prediction and understanding of these physical phenomena need the development of high-fidelity numerical models which are prohibitive to solve. Therefore, these models cannot be used for control or even parametric optimization applications. In this work, the goal is to use some high-fidelity solutions for building reduced-order models which are able to calculate again these solutions but in real-time, and also to predict solutions for other close configurations. Modelling of compressible disturbances in a complex mean flow is given by hyperbolic linearized Euler equations which create some difficulties to perform classical reduction methods. Theoretical and numerical problems are then introduced when a projection method is applied. In particular, the conservation of stability and the control of energy of reduced-order models are studied and a new stabilization procedure is proposed. Parametric sensitivity is also discussed. Afterwards, stable reduced-order models are developed in an industrial code to consider complex geometries. Furthermore, modelling of solids with fixed or vibrating walls are taken into account. Particularly, small vibrations are modelled thanks to a transpiration law. This boundary condition is implemented in the framework of linear control theory to apply reduction methods. Finally, reduced-order models are tested to predict solutions in real time. For instance, frequency and amplitude of the loading can change. The developed reduced order model should be used for aeroelastic industrial problems with more realistic costs.

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