3D digital modelling, fabrication and installation for understanding space and place

Traditionally the teaching of history or theory on art and design courses often takes place in a lecture theatre. Space and place theory is integral to informing the practice led and practice-based experiences in architecture, interior and the built environment. The research team has investigated how digital modeling, fabrication and population tools can enhance the understanding of current theoretical debates surrounding space and place. The aim is to integrate inter-disciplinary practice allowing us to address key research questions relating to the emergence of digital fabrication and its potential impact upon art and design education. The purpose is to provide an engaging and informative situated display, offering an experiential and intuitive frame of reference for constructing and placing objects, activities or events into their spatial context. The research has potential to act as an integrative experiential framework through which we can learn more about different contexts or connections between themes or theories which provides a deeper understanding of space or place. In this new work with Taylor, Benincasa, and Unver evolve their practice through translating 3D research data for a series of new digital and physical experiments intended for enhancing or informing teaching and learning in art, design & architecture. The researchers experimented with a range of 3D software and the functionality of different tool parameters. Fabrication apps and 3D crowd simulation animation tools were used for the first time in this research to explore digital fabrication using cardboard in order to compose and construct 2D and 3D physical simulations of this well-known built environment in the landscape. The fabricated physical cardboard models we produced were located in studio spaces and 3D visual projection live drawing experiences were tested with students and staff working together. The 2D and 3D simulations that the team envisioned are both digital and real; and when installed facilitate a more kinesthetic experience of learning as students are able to create together, and interact with fabricated structures. This evolving research demonstrates how these 3D models, animations and fabrications have the potential to be used together as a catalyst to explore multiple projections of space, place identities, historical and cultural built environment concepts for art, design and architecture students at undergraduate and postgraduate level

Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model shape correspondence as a labelling problem, where each point of a query shape receives a label identifying a point on some reference domain; the correspondence is then constructed a posteriori by composing the label predictions of two input shapes. We propose a paradigm shift and design a structured prediction model in the space of functional maps, linear operators that provide a compact representation of the correspondence. We model the learning process via a deep residual network which takes dense descriptor fields defined on two shapes as input, and outputs a soft map between the two given objects. The resulting correspondence is shown to be accurate on several challenging benchmarks comprising multiple categories, synthetic models, real scans with acquisition artifacts, topological noise, and partiality.Comment: Accepted for publication at ICCV 201

A 3d multidisciplinary automated design optimization toolbox for wind turbine blades based on ns solver and experimental data

This thesis attempts to develop a framework to optimize wind turbine blades automatically by a multidisciplinary 3D modeling and simulation methods. The original NREL Phase VI wind turbine blade and its experimental measurements are used to validate the Computational Fluid Dynamics (CFD) model developed in ANSYS Fluent and based on the 3D Navier-Stokes (NS) solver with a realizable k-epsilon turbulence model, which is later used in the automation process. The automated design optimization process involves multiple modeling and simulation methods using Solidworks and ANSYS Mesher and ANSYS Fluent NS solver, which are integrated and controlled through Matlab by implementing the scripting capabilities of each software package. Then all scripts are integrated into one optimization cycle, with its optimization objective being the highest mean value of 3D Lift/Drag ratio (3DLDR) across the blade. A 3DLDR distribution across the blade can be calculated by the Inverse Blade Element Momentum (IBEM) Method based on experimental measurements. The optimization process is performed to find optimized twist angles across the blade using the Angle of Attack (AOA) with the highest 3DLDR as a reference, in order to 3 achieve the optimization objective. Therefore, the automatic optimization framework is based on 3D solid modeling and 3D aerodynamic simulation and guided by IBEM and experimental data. Thus the design tool is capable of exploiting the 3D stall delay of blades designed by the traditional 2D BEM method to enhance their performances. It is found that this automated framework can result in optimized blade geometries with the improvement of performance parameters compared to the original ones

Navigation domain representation for interactive multiview imaging

Enabling users to interactively navigate through different viewpoints of a static scene is a new interesting functionality in 3D streaming systems. While it opens exciting perspectives towards rich multimedia applications, it requires the design of novel representations and coding techniques in order to solve the new challenges imposed by interactive navigation. Interactivity clearly brings new design constraints: the encoder is unaware of the exact decoding process, while the decoder has to reconstruct information from incomplete subsets of data since the server can generally not transmit images for all possible viewpoints due to resource constrains. In this paper, we propose a novel multiview data representation that permits to satisfy bandwidth and storage constraints in an interactive multiview streaming system. In particular, we partition the multiview navigation domain into segments, each of which is described by a reference image and some auxiliary information. The auxiliary information enables the client to recreate any viewpoint in the navigation segment via view synthesis. The decoder is then able to navigate freely in the segment without further data request to the server; it requests additional data only when it moves to a different segment. We discuss the benefits of this novel representation in interactive navigation systems and further propose a method to optimize the partitioning of the navigation domain into independent segments, under bandwidth and storage constraints. Experimental results confirm the potential of the proposed representation; namely, our system leads to similar compression performance as classical inter-view coding, while it provides the high level of flexibility that is required for interactive streaming. Hence, our new framework represents a promising solution for 3D data representation in novel interactive multimedia services

Computer Aided Aroma Design. II. Quantitative structure-odour relationship

Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties, which can be difficult when complex molecular structures like odours are sought and their odour quality are definitely subjective or their odour intensity are partly subjective as stated in Rossitier’s review (1996). The CAAD methodology and a novel molecular framework were presented in part I. Part II focuses on a classification methodology to characterize the odour quality of molecules based on Structure – Odour Relation (SOR). Using 2D and 3D molecular descriptors, Linear Discriminant Analysis (LDA) and Artificial Neural Network are compared in favour of LDA. The classification into balsamic / non balsamic quality was satisfactorily solved. The classification among five sub notes of the balsamic quality was less successful, partly due to the selection of the Aldrich’s Catalog as the reference classification. For the second case, it is shown that the sweet sub note considered in Aldrich’s Catalog is not a relevant sub note, confirming the alternative and popular classification of Jaubert et al., (1995), the field of odours

An Interactive Product Customization Framework for Freeform Shapes

Additive Manufacturing (AM) enables the fabrication of three-dimensional (3D) objects with complex shapes without additional tools and refixturing. However, it is difficult for user to use traditional computer-aided design tools to design custom products. In this paper, we presented a design system to help user design custom 3D printable products on top of some freeform shapes. Users can define and edit styling curves on the reference model using our interactive geometric operations for styling curves. Incorporating with the reference models, these curves can be converted into 3D printable models through our fabrication interface. We tested our system with four design applications including a hollow-patterned bicycle helmet, a T-rex with skin frame structures, a face mask with Voronoi patterns, and an AM-specific night dress with hollow patterns. The executable prototype of the presented design framework used in the customization process is publicly available

High-order discrete ordinate transport in hexagonal geometry: A new capability in ERANOS

This paper presents the implementation of an arbitrary order discontinuous Galerkin scheme within the framework of a discrete ordinate solver of the neutron transport equation for nuclear reactor calculations. More precisely, it deals with non-conforming spatial meshes for the 2D and 3D modeling of core geometries based on hexagonal assemblies. This work aims at improving the capabilities of the ERANOS code system dedicated to fast reactor analysis and design. Both the angular quadrature and spatial scheme peculiarities for hexagonal geometries are presented. A particular focus is set on the spatial non-conforming mesh and variable order capabilities of this scheme in anticipation to the development of spatial adaptiveness algorithms. These features are illustrated on a 3D numerical benchmark with comparison to a Monte Carlo reference and a 2D benchmark that shows the potential of this scheme for both h- and p-adaptation

Scalable Remote Rendering using Synthesized Image Quality Assessment

Depth-image-based rendering (DIBR) is widely used to support 3D interactive graphics on low-end mobile devices. Although it reduces the rendering cost on a mobile device, it essentially turns such a cost into depth image transmission cost or bandwidth consumption, inducing performance bottleneck to a remote rendering system. To address this problem, we design a scalable remote rendering framework based on synthesized image quality assessment. Specially, we design an efficient synthesized image quality metric based on Just Noticeable Distortion (JND), properly measuring human perceived geometric distortions in synthesized images. Based on this, we predict quality-aware reference viewpoints, with viewpoint intervals optimized by the JND-based metric. An adaptive transmission scheme is also developed to control depth image transmission based on perceived quality and network bandwidth availability. Experiment results show that our approach effectively reduces transmission frequency and network bandwidth consumption with perceived quality on mobile devices maintained. A prototype system is implemented to demonstrate the scalability of our proposed framework to multiple clients