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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.
Anti-Gewalttrainings, Gewaltwissen und die institutionelle Erzeugung gewaltbefreiter Subjekte
(2023)
Dieser Beitrag fragt danach, wie Wissen über Gewalt in Anti-Gewalttrainings produziert, vermittelt und sozial wirkmächtig wird. Auf Basis des kommunikativen Konstruktivismus werden diese Kurse als gewaltbezogene Institutionen begriffen, in denen eine Wissensordnung der Gewalt stabilisiert wird. Sie sollen Abweichungen von institutionalisierten Wirklichkeitsvorstellungen in Bezug auf Gewalt entgegenwirken. Dabei lassen sie sich als Selbsttechniken begreifen, durch welche die Kursteilnehmer*innen eine spezifische Subjektposition einüben, nämlich die des gewaltbefreiten Subjekts. Vor diesem Hintergrund wird anschließend zwischen konditionalen und konzessiven Anti-Gewalttrainings unterschieden. Erstere wenden sich an Personen, die tatsächlich von der institutionalisierten Wirklichkeitsvorstellung abgewichen sind und somit gesellschaftlich als Gewalttäter*innen eingestuft werden, während Letztere auf eine Klientel zielen, die potenziell von den gängigen Normen abweichen könnte, obwohl noch keine Gewalt aufgetreten sein muss. Abschließend wird gezeigt, dass den Kursleiter*innen eine wichtige Rolle im Subjektivierungsprozess und der Wissenskommunikation über Gewalt zukommt. Neben Wissen über Gewalt werden durch sie auch Wertbindungen, Legitimationen und Weltbilder vermittelt.
Die d - Dirac - Funktion d(x-xi) und Heviside - Funktion wurden vor allem zur Beschreibung diskontinuierlicher Belastungsfunktionen von gebogenen Balken angewendet. Im Referat werden die mathematischen Operationen auf den erwähnten Funktionen dargestellt. Darüber hinaus wird die Möglichkeit eindeutiger Aufschreibung von belibigen Belestungsfunktionen vorgeschlagen, die fast alle Fälle der in der Ingenieurpraxis auftretenden Belastungen umfassen. Die vorgeschlagenen Lösungen geben die Möglichkeit leichter Aufschreibung im Form eines Algorithmus, das die Ausnutzung von Komputertechnik in statischen Berechnungen ermöglicht. Am Ende des Referates werden die Zahlebeispiele dargestellt, die die Möglichkeiten praktischer Anwendungen der besprochenen Funktionen darstellen.
This paper presents an agent-based software, Virtual Administrator System (VAS) for the smallscale maintenance of school buildings. VAS is capable of handling a heavy load of routine, lowtech maintenance jobs. It assigns a different priority to each job application according to its significance and urgency, and automatically adjusts schedules for maintenance engineers when on-site supervision is needed. The system can help ease off the burden of routine small-scale maintenance work, making it more cost-effective and efficient in the overall management of school building maintenance. VAS posts jobs on the Web in a multi-media format and classified all applications into four categories: the on-call maintenance contract, the term maintenance contract, the guaranty maintenance contract, and the regular maintenance contract. It then estimates their urgency level and passes the information to maintenance engineers who will decide whether on-site inspection is needed. Based on the engineers’ feedback, VAS automatically implements the scheduling for inspection as well as sends out real-time or batch notifications to contractors. All these activities are recorded in a database to allow continuous research and data mining and the analysis and diagnosis of specific jobs for followup maintenance plans.
This paper presents the combination of two different parallelization environments, OpenMP and MPI, in one numerical simulation tool. The computation of the system matrices and vectors is parallelized with OpenMP and the solution of the system of equations is done with the MPIbased solver MUMPS. The efficiency of both algorithms is shown on several linear and nonlinear examples using the Finite Element Method and a meshless discretization technique.
The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis.
Creation of hierarchical sequence of the plastic and viscoplastic models according to different levels of structure approximations is considered. Developed strategy of multimodel analysis, which consists of creation of the inelastic models library, determination of selection criteria system and caring out of multivariant sequential clarifying computations, is described. Application of the multimodel approach in numerical computations has demonstrated possibility of reliable prediction of stress-strain response under wide variety of combined nonproportional loading.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
Wissenschaftliches Kolloquium vom 24. bis 27. April 2003 in Weimar an der Bauhaus-Universität zum Thema: ‚MediumArchitektur - Zur Krise der Vermittlung'
Architecture and Air
(2003)
Wissenschaftliches Kolloquium vom 24. bis 27. April 2003 in Weimar an der Bauhaus-Universität zum Thema: ‚MediumArchitektur - Zur Krise der Vermittlung'
Architecture and association
(2003)
Wissenschaftliches Kolloquium vom 24. bis 27. April 2003 in Weimar an der Bauhaus-Universität zum Thema: ‚MediumArchitektur - Zur Krise der Vermittlung'
Wissenschaftliches Kolloquium vom 24. bis 27. April 2003 in Weimar an der Bauhaus-Universität zum Thema: ‚MediumArchitektur - Zur Krise der Vermittlung'
Wissenschaftliches Kolloquium vom 19. bis 22. April 2007 in Weimar an der Bauhaus-Universität zum Thema: ‚Die Realität des Imaginären. Architektur und das digitale Bild'
Wissenschaftliches Kolloquium vom 24. bis 27. April 2003 in Weimar an der Bauhaus-Universität zum Thema: ‚MediumArchitektur - Zur Krise der Vermittlung'
Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universität zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien'
Wissenschaftliches Kolloquium vom 19. bis 22. April 2007 in Weimar an der Bauhaus-Universität zum Thema: ‚Die Realität des Imaginären. Architektur und das digitale Bild'
Wissenschaftliches Kolloquium vom 19. bis 22. April 2007 in Weimar an der Bauhaus-Universität zum Thema: ‚Die Realität des Imaginären. Architektur und das digitale Bild'
Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universität zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien'
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'
Architekturen der Macht
(1992)
Architekturen des Films
(2008)
Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universität zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien'
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'
Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique
(2016)
Finding a method to evaluate people’s emotional responses to urban spaces in a valid and objective way is fundamentally important for urban design practices and related policy making. Analysis of the essential qualities of urban space could be made both more effective and more accurate using innovative information techniques that have become available in the era of big data. This study introduces an integrated method based on geographical information systems (GIS) and an emotion-tracking technique to quantify the relationship between people’s emotional responses and urban space. This method can evaluate the degree to which people’s emotional responses are influenced by multiple urban characteristics such as building shapes and textures, isovist parameters, visual entropy, and visual fractals. The results indicate that urban spaces may influence people’s emotional responses through both spatial sequence arrangements and shifting scenario sequences. Emotional data were collected with body sensors and GPS devices. Spatial clustering was detected to target effective sampling locations; then, isovists were generated to extract building textures. Logistic regression and a receiver operating characteristic analysis were used to determine the key isovist parameters and the probabilities that they influenced people’s emotion. Finally, based on the results, we make some suggestions for design professionals in the field of urban space optimization.
One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel.
To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.
Strain measurement is important in mechanical testing. A wide variety of techniques exists for measuring strain in the tensile test; namely the strain gauge, extensometer, stress and strain determined by machine crosshead motion, Geometric Moire technique, optical strain measurement techniques and others. Each technique has its own advantages and disadvantages. The purpose of this study is to quantitatively compare the strain measurement techniques. To carry out the tensile test experiments for S 235, sixty samples were cut from the web of the I-profile in longitudinal and transverse directions in four different dimensions. The geometry of samples are analysed by 3D scanner and vernier caliper. In addition, the strain values were determined by using strain gauge, extensometer and machine crosshead motion. Three techniques of strain measurement are compared in quantitative manner based on the calculation of mechanical properties (modulus of elasticity, yield strength, tensile strength, percentage elongation at maximum force) of structural steel. A statistical information was used for evaluating the results. It is seen that the extensometer and strain gauge provided reliable data, however the extensometer offers several advantages over the strain gauge and crosshead motion for testing structural steel in tension. Furthermore, estimation of measurement uncertainty is presented for the basic material parameters extracted through strain measurement.