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Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling.
The concept of isogeometric analysis, where functions that are used to describe geometry in CAD software are used to approximate the unknown fields in numerical simulations, has received great attention in recent years. The method has the potential to have profound impact on engineering design, since the task of meshing, which in some cases can add significant overhead, has been circumvented. Much of the research effort has been focused on finite element implementations of the isogeometric concept, but at present, little has been seen on the application to the Boundary Element Method. The current paper proposes an Isogeometric Boundary Element Method (BEM), which we term IGABEM, applied to two-dimensional elastostatic problems using Non-Uniform Rational B-Splines (NURBS). We find it is a natural fit with the isogeometric concept since both the NURBS approximation and BEM deal with quantities entirely on the boundary. The method is verified against analytical solutions where it is seen that superior accuracies are achieved over a conventional quadratic isoparametric BEM implementation.
Der vorliegende Beitrag ist in zwei thematische Teilebereiche gegliedert. Der erste Teil beschäftigt sich mit der Analyse von Graphen, insbesondere von Graphen, die Straßennetzwerke repräsentieren. Hierzu werden Methoden aus der Graphentheorie angewendet und Kenngrößen aus der Space Syntax Methode ausgewertet. Ein Framework, welches basierend auf der Graphentheorie in Architektur und Stadtplanung Einzug gehalten hat, ist die Space Syntax Methode. Sie umfasst die Ableitung unterschiedlicher Kenngrößen eines Graphen bzw. Netzwerkes, wodurch eine Analyse für architektonische und stadtplanerische Zwecke ermöglicht wird.
Der zweite Teil dieses Berichts beschäftigt sich mit der Generierung von Graphen, insbe-sondere der von Straßennetzwerkgraphen. Die generativen Methoden basieren zum Teil auf den gewonnenen Erkenntnissen der Analyse von Straßennetzwerken. Es werden unterschiedliche Ansätze untersucht, um verschiedene Parameterwerte zur Generierung von Straßengraphen festzulegen. Als Ergebnis der Arbeiten ist ein Softwaretool entstanden, welches es erlaubt, auf Grundlage einer Voronoi-Tesselierung realistische Straßennetzwerkgraphen zu erzeugen.
Individual views on a building product of people involved in the design process imply different models for planning and calculation. In order to interpret these geometrical, topological and semantical data of a building model we identify a structural component graph, a graph of room faces, a room graph and a relational object graph as aids and we explain algorithms to derive these relations. The application of the technique presented is demonstrated by the analysis and discretization of a sample model in the scope of building energy simulation.
In this work, we present a deep collocation method (DCM) for three-dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations.
Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1989 in Weimar an der Hochschule für Architektur und Bauwesen zum Thema: ‚Produktivkraftentwicklung und Umweltgestaltung. Sozialer und wissenschaftlich-technischer Fortschritt in ihren Auswirkungen auf Architektur und industrielle Formgestaltung in unserer Zeit. Zum 100. Geburtstag von Hannes Meyer'
The Carbon journal is pleased to introduce a themed collection of recent articles in the area of computational carbon nanoscience. This virtual special issue was assembled from previously published Carbon articles by Guest Editors Quan Wang and Behrouz Arash, and can be accessed as a set in the special issue section of the journal website homepage: www.journals.elsevier.com/carbon. The article below by our guest editors serves as an introduction to this virtual special issue, and also a commentary on the growing role of computation as a tool to understand the synthesis and properties of carbon nanoforms and their behavior in composite materials.