A finite element model capable of predicting resin pockets for arbitrary inclusions in composite laminates

This work presents the progress in the development of a finite element model capable of predicting resin pockets occurring in composite structures with embedded sensors. The F.E.- model is built using standard tools in ABAQUS software, avoiding the need of specialized coding. Both progresses in material characterization as well as finite element modeling are shown. The model will eventually be used to optimize the shape of an embedded optical fibre interrogator used within the FP7 ‘SmartFiber’ projec

Statistics of populations of images and its embedded objects: driving applications in neuroimaging

journal articleWork in progress towards modeling shape statistics of multi-object complexes is presented. Constraints defined by the set of objects such as a compact representation of object shape relationships and correlation of shape changes might have advantages for automatic segmentation and group discrimination. We present a concept for statistical multi-object modeling and discuss the major challenges which are a reduction to a small set of descriptive features, calculation of mean and variability via curved statistics, the choice of aligning sets of multiple objects, and the problem of describing the statistics of object pose and object shape and their interrelationship. Shape modeling and analysis is demonstrated with an application to a longitudinal autism study, with shape modeling of sets of 10 subcortical structures in a population of 20 subjects

Sketch-based modeling with a differentiable renderer

© 2020 The Authors. Computer Animation and Virtual Worlds published by John Wiley & Sons, Ltd. Sketch-based modeling aims to recover three-dimensional (3D) shape from two-dimensional line drawings. However, due to the sparsity and ambiguity of the sketch, it is extremely challenging for computers to interpret line drawings of physical objects. Most conventional systems are restricted to specific scenarios such as recovering for specific shapes, which are not conducive to generalize. Recent progress of deep learning methods have sparked new ideas for solving computer vision and pattern recognition issues. In this work, we present an end-to-end learning framework to predict 3D shape from line drawings. Our approach is based on a two-steps strategy, it converts the sketch image to its normal image, then recover the 3D shape subsequently. A differentiable renderer is proposed and incorporated into this framework, it allows the integration of the rendering pipeline with neural networks. Experimental results show our method outperforms the state-of-art, which demonstrates that our framework is able to cope with the challenges in single sketch-based 3D shape modeling

Dynamic modeling of the motions of variable-shape wave energy converters

In the recently introduced Variable-Shape heaving wave energy converters, the buoy changes its shape actively in response to changing incident waves. In this study, a Lagrangian approach for the dynamic modeling of a spherical Variable-Shape Wave Energy Converter is described. The classical bending theory is used to write the stress-strain equations for the flexible body using Love's approximation. The elastic spherical shell is assumed to have an axisymmetric vibrational behavior. The Rayleigh-Ritz discretization method is adopted to find an approximate solution for the vibration model of the spherical shell. A novel equation of motion is presented that serves as a substitute for Cummins equation for flexible buoys. Also, novel hydrodynamic coefficients that account for the buoy mode shapes are proposed. The developed dynamic model is coupled with the open-source boundary element method software NEMOH. Two-way and one-way Fluid-Structure Interaction simulations are performed using MATLAB to study the effect of using a flexible shape buoy in the wave energy converter on its trajectory and power production. Finally, the variable shape buoy was able to harvest more energy for all the tested wave conditions.Comment: 29 pages, 13 figures; Renewable and Sustainable Energy Reviews, v173 (2023) in progress. arXiv admin note: substantial text overlap with arXiv:2201.0894

NOx emissions modelling in biomass combustion grate furnaces

A new flamelet combustion model is developed for the modeling of NOx emissions in biomass grate furnaces. The model describes the combustion chemistry using premixed flamelets. The chemical system is mapped on two controlling variables: the mixture fraction and a reaction progress variable. The species mass fractions and temperature are tabulated as functions of the controlling variables in a pre-processing step to speed up the numerical calculations. The turbulence-chemistry interaction is described by an assumed shape PDF approach. Transport equations are solved for mean and variance of mixture fraction and progress variable. For the accurate prediction of NO formation, an extra transport equation is solved for the mean NO mass fraction. The model is validated for a well documented flame, Sandia Flame D. Good agreement between predictions and experimental data is found. The model is also applied to a 2D biomass grate furnace. The preliminary results are encouraging

PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition

As two fundamental representation modalities of 3D objects, 3D point clouds and multi-view 2D images record shape information from different domains of geometric structures and visual appearances. In the current deep learning era, remarkable progress in processing such two data modalities has been achieved through respectively customizing compatible 3D and 2D network architectures. However, unlike multi-view image-based 2D visual modeling paradigms, which have shown leading performance in several common 3D shape recognition benchmarks, point cloud-based 3D geometric modeling paradigms are still highly limited by insufficient learning capacity, due to the difficulty of extracting discriminative features from irregular geometric signals. In this paper, we explore the possibility of boosting deep 3D point cloud encoders by transferring visual knowledge extracted from deep 2D image encoders under a standard teacher-student distillation workflow. Generally, we propose PointMCD, a unified multi-view cross-modal distillation architecture, including a pretrained deep image encoder as the teacher and a deep point encoder as the student. To perform heterogeneous feature alignment between 2D visual and 3D geometric domains, we further investigate visibility-aware feature projection (VAFP), by which point-wise embeddings are reasonably aggregated into view-specific geometric descriptors. By pair-wisely aligning multi-view visual and geometric descriptors, we can obtain more powerful deep point encoders without exhausting and complicated network modification. Experiments on 3D shape classification, part segmentation, and unsupervised learning strongly validate the effectiveness of our method. The code and data will be publicly available at https://github.com/keeganhk/PointMCD