88 research outputs found

    25th Annual Computational Neuroscience Meeting: CNS-2016

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    Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

    Metamodel-based uncertainty quantification for the mechanical behavior of braided composites

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    The main design requirement for any high-performance structure is minimal dead weight. Producing lighter structures for aerospace and automotive industry directly leads to fuel efficiency and, hence, cost reduction. For wind energy, lighter wings allow larger rotor blades and, consequently, better performance. Prosthetic implants for missing body parts and athletic equipment such as rackets and sticks should also be lightweight for augmented functionality. Additional demands depending on the application, can very often be improved fatigue strength and damage tolerance, crashworthiness, temperature and corrosion resistance etc. Fiber-reinforced composite materials lie within the intersection of all the above requirements since they offer competing stiffness and ultimate strength levels at much lower weight than metals, and also high optimization and design potential due to their versatility. Braided composites are a special category with continuous fiber bundles interlaced around a preform. The automated braiding manufacturing process allows simultaneous material-structure assembly, and therefore, high-rate production with minimal material waste. The multi-step material processes and the intrinsic heterogeneity are the basic origins of the observed variability during mechanical characterization and operation of composite end-products. Conservative safety factors are applied during the design process accounting for uncertainties, even though stochastic modeling approaches lead to more rational estimations of structural safety and reliability. Such approaches require statistical modeling of the uncertain parameters which is quite expensive to be performed experimentally. A robust virtual uncertainty quantification framework is presented, able to integrate material and geometric uncertainties of different nature and statistically assess the response variability of braided composites in terms of effective properties. Information-passing multiscale algorithms are employed for high-fidelity predictions of stiffness and strength. In order to bypass the numerical cost of the repeated multiscale model evaluations required for the probabilistic approach, smart and efficient solutions should be applied. Surrogate models are, thus, trained to map manifolds at different scales and eventually substitute the finite element models. The use of machine learning is viable for uncertainty quantification, optimization and reliability applications of textile materials, but not straightforward for failure responses with complex response surfaces. Novel techniques based on variable-fidelity data and hybrid surrogate models are also integrated. Uncertain parameters are classified according to their significance to the corresponding response via variance-based global sensitivity analysis procedures. Quantification of the random properties in terms of mean and variance can be achieved by inverse approaches based on Bayesian inference. All stochastic and machine learning methods included in the framework are non-intrusive and data-driven, to ensure direct extensions towards more load cases and different materials. Moreover, experimental validation of the adopted multiscale models is presented and an application of stochastic recreation of random textile yarn distortions based on computed tomography data is demonstrated

    DEEP-LEARNING-ENHANCED MULTIPHYSICS FLOW COMPUTATIONS FOR PROPULSION APPLICATIONS

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    Numerical simulation is a critical part of research into and development of engineering systems. Engineers often use simulation to explore design settings both analytically and numerically before prototypes are built and tested. Even with the most advanced high performance computing facility, however, high-fidelity numerical simulations are extremely costly in time and resources. For example, a survey of the design parameter space for a single-element injector for a propulsion application (such as the RD-170 rocket engine) using the large eddy simulation technique may require several tens of millions of CPU-hours on a major computer cluster. This is because the flowfields can only be fully characterized by resolving a multitude of strongly coupled fluid dynamic, thermodynamic, transport, multiphase, and combustion processes. The cost is further increased by grid resolution requirements and by the effects of turbulence and high-pressure phenomena, which require treatment of real-fluid physics at supercritical conditions. If such models are used for statistical analysis or design optimization, the total computation time and resource requirements may render the work unfeasible. Recent developments in deep learning techniques offer the possibility of significant advances in dealing with these challenges and significant shortening of the time-to-solution. The general scope of this thesis research is to set the foundations for new paradigms in modeling, simulation, and design by applying deep learning techniques to recent developments in computational science. More specifically, the research aims at developing an integrated suite of data-driven surrogate modeling approaches and software for large-scale simulation problems. The techniques to be put into practice include: (1) deep neural networks for function approximation and solver acceleration, (2) deep autoencoders for nonlinear dimensionality reduction, and (3) spatiotemporal emulators based on multi-level neural networks for simulator approximation and rapid exploration of design spaces. A hierarchy of benchmark cases has been studied to generate databases to enable and support the development and verification of the proposed approaches. Emphasis is placed on canonical examples, as well as on engineering problems for aerospace and automotive applications, including supercritical turbulent flows in a rocket-engine swirl injector, and multiphase cavitating flows in a diesel engine injector.Ph.D

    Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

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    The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

    Quantification du risque incendie par métamodélisation de la propagation de feux de forêt

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    This work addresses the quantification of wildfire risk by relying on simulations of fire spread. The objectives are to compute the probability distribution of burned surfaces that could result from wildfire ignition and quickly generate maps to assess which areas should receive focused protection against wildfires. This probability distribution should represent the uncertainty in the simulations. First, an ensemble of wildland fire spread simulations accounting for sources of uncertainty is generated following a Monte Carlo approach, and probabilistic evaluation of the predictions with observations is carried out. Then, the underlying probability distributions are calibrated based on the observations by adapting the Wasserstein distance to the comparison of burned surfaces to improve prediction performance in presence of uncertainty. Subsequently, a deep learning approach is followed to train a ``hybrid'' neural network with a convolutional part, thus building an emulator of ``potential'' fire size simulated by the fire spread model allowing to considerably reduce the computational time implied by the large amount of simulations required for high-resolution maps. Eventually, this emulator is applied to derive fire danger mapping from daily weather forecasts and applied to assess relatively large fire events.Ce travail porte sur la quantification du risque incendie en se fondant sur des simulations de propagation de feux de forêt. Les objectifs sont de calculer la distribution de probabilité des surfaces brûlées pouvant résulter d'un départ de feu et de générer des cartes permettant d'estimer quelles zones doivent être protégées en priorité. Les simulations pouvant donner lieu à des erreurs de prévision, la distribution de probabilité en question doit représenter l'incertitude associée aux simulations. Dans un premier temps, un ensemble de simulations de propagation de feux de forêt prenant en compte les sources d'incertitude est généré selon une approche Monte Carlo, et les prévisions, probabilistes, sont comparées à des observations selon des critères adaptés. Ensuite, les distributions de probabilité sous-jacentes sont calibrées à partir des observations en adaptant la distance de Wasserstein à la comparaison de surfaces brûlées afin d'améliorer la qualité des prévisions, tout en tenant compte de l'incertitude. Par la suite, une approche d'apprentissage profond est mise en œuvre pour entraîner un réseau de neurones ``hybride'' avec une partie convolutionnelle, élaborant ainsi un émulateur de taille de feu ``potentielle'' simulée par le modèle de propagation afin de diminuer considérablement le temps de calcul associé au grand nombre de simulations nécessaires à l'élaboration de cartes à haute résolution. Enfin, l'émulateur est utilisé pour générer des cartes de danger incendie à partir de vraies prévisions météorologiques générées pour des jours où des feux relativement grands ont eu lieu

    Neuromodulatory Supervised Learning

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    27th Annual European Symposium on Algorithms: ESA 2019, September 9-11, 2019, Munich/Garching, Germany

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