8 research outputs found

    Adaptive construction of surrogate functions for various computational mechanics models

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    In most science and engineering fields, numerical simulation models are often used to replicate physical systems. An attempt to imitate the true behavior of complex systems results in computationally expensive simulation models. The models are more often than not associated with a number of parameters that may be uncertain or variable. Propagation of variability from the input parameters in a simulation model to the output quantities is important for better understanding the system behavior. Variability propagation of complex systems requires repeated runs of costly simulation models with different inputs, which can be prohibitively expensive. Thus for efficient propagation, the total number of model evaluations needs to be as few as possible. An efficient way to account for the variations in the output of interest with respect to these parameters in such situations is to develop black-box surrogates. It involves replacing the expensive high-fidelity simulation model by a much cheaper model (surrogate) using a limited number of the high-fidelity simulations on a set of points called the design of experiments (DoE). The obvious challenge in surrogate modeling is to efficiently deal with simulation models that are expensive and contains a large number of uncertain parameters. Also, replication of different types of physical systems results in simulation models that vary based on the type of output (discrete or continuous models), extent of model output information (knowledge of output or output gradients or both), and whether the model is stochastic or deterministic in nature. All these variations in information from one model to the other demand development of different surrogate modeling algorithms for maximum efficiency. In this dissertation, simulation models related to application problems in the field of solid mechanics are considered that belong to each one of the above-mentioned classes of models. Different surrogate modeling strategies are proposed to deal with these models and their performance is demonstrated and compared with existing surrogate modeling algorithms. The developed algorithms, because of their non-intrusive nature, can be easily extended to simulation models of similar classes, pertaining to any other field of application

    Deep material networks for efficient scale-bridging in thermomechanical simulations of solids

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    We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations

    Advanced Theoretical and Computational Methods for Complex Materials and Structures

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    The broad use of composite materials and shell structural members with complex geometries in technologies related to various branches of engineering has gained increased attention from scientists and engineers for the development of even more refined approaches and investigation of their mechanical behavior. It is well known that composite materials are able to provide higher values of strength stiffness, and thermal properties, together with conferring reduced weight, which can affect the mechanical behavior of beams, plates, and shells, in terms of static response, vibrations, and buckling loads. At the same time, enhanced structures made of composite materials can feature internal length scales and non-local behaviors, with great sensitivity to different staking sequences, ply orientations, agglomeration of nanoparticles, volume fractions of constituents, and porosity levels, among others. In addition to fiber-reinforced composites and laminates, increased attention has been paid in literature to the study of innovative components such as functionally graded materials (FGMs), carbon nanotubes (CNTs), graphene nanoplatelets, and smart constituents. Some examples of smart applications involve large stroke smart actuators, piezoelectric sensors, shape memory alloys, magnetostrictive and electrostrictive materials, as well as auxetic components and angle-tow laminates. These constituents can be included in the lamination schemes of smart structures to control and monitor the vibrational behavior or the static deflection of several composites. The development of advanced theoretical and computational models for composite materials and structures is a subject of active research and this is explored here for different complex systems, including their static, dynamic, and buckling responses; fracture mechanics at different scales; the adhesion, cohesion, and delamination of materials and interfaces

    Optimisation Approaches for Energy Supply Chains

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    This research presents decision-support tools for the assessment of energy systems development at national and regional scales. For this purpose, mathematical frameworks for the design and optimisation of energy systems are developed. A methodology is proposed as a preliminary assessment of shale gas development. For this purpose, economic and environmental metrics are proposed to address different aspects of well-pad designs such as productivity and water intensity. The outcome of this methodology is included in a comprehensive optimisation-based decision-support tool developed to address the design of shale gas supply chains along with water management strategies. In this framework, the optimisation of well-pad designs is regarded as a critical decision variable. Next, implications of water scarcity, the role of economy of scales, and the impact of wastewater quality are addressed through a case study focusing on the development of shale gas supply chains in Colombia. The production of synthetic natural gas is studied as a possible substitute of natural gas. In this case, an optimisation approach is proposed to address decisions such as feedstock procurement, transportation and optimal production schemes of BioSNG and power. The mathematical framework can be implemented to investigate policies that encourage the development of renewable energy sources. The impact of uncertainty in input data is addressed through a global sensitivity analysis (GSA). The implementation of GSA assists not only in the identification of key parameters in the design of BioSNG supply chains, but also in revealing recurring trends in light of uncertainty. Finally, the development of BioSNG supply chains in the UK is investigated through the implementation of the proposed mathematical framework
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