Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

The problem of space layout in architecture: A survey and reflections

Abstract: Resumo This work is about the problem of Space Layout Planning (SLP) applied to Ar chitectural Design. The creation of floor-plan layouts is key to architecture, and it is a problem that, because of its complexity, does not have a precise general method for its resolution. An overview and a wide description of the “Architectural Design” process are presented to understand this phenomenon. Secondly we present a brief history and development of the SLP field, and then a description of the state-of-the-art (techniques, approaches and strategies) to implement layout solutions. Concepts such a s optimization, generative systems, artificial intelligence, genetic algorithms, physically-based modelling are reviewed and discussed. Commercial CAAD and BIM software is also reviewed. Conclusions and a discussion about the results and absence of this ty pe of software for architectural practice are presented. O artigo trata do problema da distribuicao espacial em planta (SLP, por sua sigla em Ingles) aplicada ao projeto arquitetonico. A criacao de desenhos de plantas e uma questao fundamental e, devido a sua complexidade, nenhum metodo preciso para ser resolvido. Apresentamos uma visao geral e uma descricao detalhada do processo de projeto arquitetonico para compreender esse fenomeno. Em segundo lugar, apresentamos uma breve historia e o desenvolvimento do SLP. Em seguida, trataremos sobre o estado da arte (tecnicas, abordagens e estrategias) para implementar solucoes de design. Os conceitos de otimizacao, sistemas generativos, inteligencia artificial, algoritmos geneticos e modelagem com base na fisica t ambem sao revistos e discutidos. Tambem sao analisados os softwares comerciais CAAD e BIM. Ao final, apresentamos as conclusoes e uma discussao sobre os resultados e o impacto deste tipo de software na pratica da arquitetura.

Theoretical Investigation: 2D N-Graphdiyne Nanosheets as Promising Anode Materials for Li/Na Rechargeable Storage Devices.

Abstract: N-graphdiyne monolayers, a set of carbon-nitride nanosheets, have been synthesized recently through the polymerization of triazine- and pyrazine-based monomers. Since the two-dimensional nano-structures are mainly composed of light-weight nonmetallic elements including carbon and nitrogen, they might be able to provide high storage capacities for rechargeable cells. In this study, we used extensive first principle calculations such as electronic density of states, band structure, adsorption energy, open-circuit voltage, nudged-elastic band and charge analyses to investigate the application of the newly fabricated N-graphdiyne monolayers as the anode material for Li/Na/Mg ion batteries. Our calculations suggest that while Mg foreign atoms poorly interact with monolayers, Li and Na adatoms illustrate outstanding anodic characteristics for rechargeable storage cells. Electronic density of states calculations indicate that the insertion of Li/Na into the novel N-graphdiyne materials enhances the electrical conductivity of nanosheets. Adsorption energy and open-circuit voltage calculations predict that the nanosheets can provide a high storage capacity spectrum of 623-2180 mAh/g which is higher than that for most recently discovered 2D materials (e.g. phosphorene, borophane, and germanene involve Li binding capacities of 433, 504, and 369 mAh/g, respectively) and it is also significantly greater than the capacity of commercial anode materials (e.g. graphite contains a capacity of 372 mAh/g). This study provides valuable insights about the electronic characteristics of newly fabricated N-graphdiyne nanomaterials, rendering them as promising candidates to be used in the growing industry of rechargeable storage devices.

Humidity controlled calorimetric investigation of the hydration of MgSO4 hydrates

Abstract: The isothermal heat of hydration of MgSO4 hydrates was studied by humidity controlled calorimetry. Two hydrates, starkeyite (MgSO4·4H2O) and a mixture of MgSO4 hydrates with summary 1.3 mol H2O were investigated. The solid-gas reactions were initiated at 30°C and 85% relative humidity. The heat of hydration was determined in a circulation cell in the calorimeter C80 (Setaram). The crystal phases formed after the hydration process were analyzed by thermogravimetry (TG) and X-ray powder diffraction (XRD). Starkeyite reacted with the water vapour to the thermodynamic stable epsomite and the MgSO4 hydrate mixture with 1.3 mol water to hexahydrite. The hydration heats of starkeyite and the mixture were determined to be −169±3 and −257±5 kJmol−1, respectively.

A Flexible Multi-Volume Shader Framework for Arbitrarily Intersecting Multi-Resolution Datasets

Abstract: We present a powerful framework for 3D-texture-based rendering of multiple arbitrarily intersecting volumetric datasets. Each volume is represented by a multi-resolution octree-based structure and we use out-of-core techniques to support extremely large volumes. Users define a set of convex polyhedral volume lenses, which may be associated with one or more volumetric datasets. The volumes or the lenses can be interactively moved around while the region inside each lens is rendered using interactively defined multi-volume shaders. Our rendering pipeline splits each lens into multiple convex regions such that each region is homogenous and contains a fixed number of volumes. Each such region is further split by the brick boundaries of the associated octree representations. The resulting puzzle of lens fragments is sorted in front-to-back or back-to-front order using a combination of a view-dependent octree traversal and a GPU-based depth peeling technique. Our current implementation uses slice-based volume rendering and allows interactive roaming through multiple intersecting multi-gigabyte volumes.

Faster discovery of faster system configurations with spectral learning

Abstract: Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT's innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors--less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2---10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.