Development of a Computationally Efficient Fabric Model for Optimization of Gripper Trajectories in Automated Composite Draping

An automated prepreg fabric draping system is being developed which consists of an array of actuated grippers. It has the ability to pick up a fabric ply and place it onto a double-curved mold surface. A previous research effort based on a nonlinear Finite Element model showed that the movements of the grippers should be chosen carefully to avoid misplacement and induce of wrinkles in the draped configuration. Thus, the present study seeks to develop a computationally efficient model of the mechanical behavior of a fabric based on 2D catenaries which can be used for optimization of the gripper trajectories. The model includes bending stiffness, large deflections, large ply shear and a simple contact formulation. The model is found to be quick to evaluate and gives very reasonable predictions of the displacement field

X-ray ptychography on low-dimensional hard-condensed matter materials

Tailoring structural, chemical, and electronic (dis-)order in heterogeneous media is one of the transformative opportunities to enable new functionalities and sciences in energy and quantum materials. This endeavor requires elemental, chemical, and magnetic sensitivities at the nano/atomic scale in two- and three-dimensional space. Soft X-ray radiation and hard X-ray radiation provided by synchrotron facilities have emerged as standard characterization probes owing to their inherent element-specificity and high intensity. One of the most promising methods in view of sensitivity and spatial resolution is coherent diffraction imaging, namely, X-ray ptychography, which is envisioned to take on the dominance of electron imaging techniques offering with atomic resolution in the age of diffraction limited light sources. In this review, we discuss the current research examples of far-field diffraction-based X-ray ptychography on two-dimensional and three-dimensional semiconductors, ferroelectrics, and ferromagnets and their blooming future as a mainstream tool for materials sciences

On Timing Model Extraction and Hierarchical Statistical Timing Analysis

In this paper, we investigate the challenges to apply Statistical Static Timing Analysis (SSTA) in hierarchical design flow, where modules supplied by IP vendors are used to hide design details for IP protection and to reduce the complexity of design and verification. For the three basic circuit types, combinational, flip-flop-based and latch-controlled, we propose methods to extract timing models which contain interfacing as well as compressed internal constraints. Using these compact timing models the runtime of full-chip timing analysis can be reduced, while circuit details from IP vendors are not exposed. We also propose a method to reconstruct the correlation between modules during full-chip timing analysis. This correlation can not be incorporated into timing models because it depends on the layout of the corresponding modules in the chip. In addition, we investigate how to apply the extracted timing models with the reconstructed correlation to evaluate the performance of the complete design. Experiments demonstrate that using the extracted timing models and reconstructed correlation full-chip timing analysis can be several times faster than applying the flattened circuit directly, while the accuracy of statistical timing analysis is still well maintained

Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application

The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil

Multiscale structural, thermal and thermo-structural optimization towards three-dimensional printable structures

This thesis develops a robust framework for the multiscale design of three-dimensional lattices with macroscopically tailored structural and thermal characteristics. The work exploits the high process flexibility and precision of additive manufacturing to the physical realization of complex microstructure of metamaterials by developing and implementing a multiscale approach. Structures derived from such metamaterials exhibit properties which differ from that of the constituent base material. Inspired by the concept of Free Material Optimization (FMO), a periodic microscale model is developed whose geometric parameterization enables smoothly changing properties and for which the connectivity of neighbouring microstructures in the large-scale domain is guaranteed by slowly changing large-scale descriptions of the lattice parameters. The microscale model is evaluated at full factorial design points to discretely populate material property spaces. A property point is fully defined for a micro-architecture when its elasticity matrix, thermal conductivity matrix and volume fraction is determined. The process of property-space population is facilitated by leveraging the existence of micro-architecture symmetries so that there exists a 95% reduction in the simulations required despite a full-factorial design of experiments. The discrete property evaluations are converted to continuous functions by response surface modelling so that the properties exist as continuous functions of the micro-architecture geometry parameters. A lattice-based functional grading of material is derived using the finite element method. The optimization is driven by a chain-rule combination of sensitivities derived by the adjoint method and sensitivities derived from explicit material property expressions. The novelty of the work lies in the use of multiple geometry-based small-scale design parameters for optimization problems in three-dimensional real space. The approach is demonstrated by solving structural, thermal and thermo-structural optimization problems. The results show designs with improved optimality compared to commonly implemented optimization methodologies. The optimal designs obtained are physically realizable by additive manufacturing techniques.Open Acces

Facies discrimination with electrical resistivity tomography using a probabilistic methodology: Effect of sensitivity and regularization

peer reviewedElectrical resistivity tomography (ERT) has become a standard geophysical method in the field of hydrogeology, as it has the potential to provide important information regarding the spatial distribution of facies. However, inverted ERT images tend to be grossly smoothed versions of reality because of the regularization of the inverse problem. In this study, we use a probabilistic methodology based upon co-located measurements to assess the utility of ERT to identify hydrofacies in alluvial aquifers. With this methodology, ERT images are interpreted in terms of the probability of belonging to pre-defined hydrofacies. We first analyze through a synthetic study the ability of ERT to discriminate between different facies. As ERT data suffer from a loss of sensitivity with depth, we find that low sensitivity regions are more affected by misclassification. To counteract this effect, we adapt the probabilistic framework to include the spatially varying data sensitivity. We then apply our learning to a field case. For the latter, we consider two different regularization procedures. In contrast to the data sensitivity which affects the facies probability to a limited amount, the regularization can affect the probability maps more considerably because it has a strong influence on the spatial distribution of inverted resistivity. We find that a regularization strategy based on the most realistic prior information tends to offer the most reliable discrimination of facies. Our results confirm the ability of ERT surveys, when properly designed, to detect facies variations in alluvial aquifers. The method can be easily extended to other contexts

Automatic synthesis of analog layout : a survey

A review of recent research in the automatic synthesis of physical geometry for analog integrated circuits is presented. On introduction, an explanation of the difficulties involved in analog layout as opposed to digital layout is covered. Review of the literature then follows. Emphasis is placed on the exposition of general methods for addressing problems specific to analog layout, with the details of specific systems only being given when they surve to illustrate these methods well. The conclusion discusses problems remaining and offers a prediction as to how technology will evolve to solve them. It is argued that although progress has been and will continue to be made in the automation of analog IC layout, due to fundamental differences in the nature of analog IC design as opposed to digital design, it should not be expected that the level of automation of the former will reach that of the latter any time soon