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Given the importance of reducing energy bills in the building sector, especially for schools located in rural areas, where detachment from the grid electricity is recommended, achieving energy self-sufficiency is crucial to provide a conducive indoor environment for students while minimizing energy costs. Therefore, this paper presents a comprehensive methodology aimed at enhancing building energy efficiency, indoor thermal comfort, and achieving net zero energy self-sufficiency for a rural school building, by developing a climate-responsive architectural paradigm for rural schools, ensuring adaptability to diverse environmental conditions while striving for energy independence through passive design strategies. Employing multi-objective optimization with the NSGA-II genetic algorithm, passive design parameters such as construction type, glazing type, insulation specifications, roof vegetation, window overhang, and outdoor shading structures were evaluated across six distinct climatic zones in Morocco. Integration of EnergyPlus, jEPlus, and jEPlus+EA software facilitated the optimization process. Pareto fronts of optimal solutions were generated, prioritizing the minimization of heating and cooling energy consumption alongside discomfort hours. Results demonstrate that the optimized solutions effectively enhance building energy efficiency and indoor thermal comfort while achieving net zero energy status across all studied climatic zones. Optimal solutions enhanced building energy efficiency by 18.6 % - 35.6 %, tailored to climate and school design.
All Copper NICE Modules
(2018)
In this work the first All Copper NICE (New Industrial Solar Cell Encapsulation) modules with solder-freeribbon to finger interconnections are presented. Experiments were conducted to investigate if silver can be fully omitted from solar cells with copper plated fingers in combination with the NICE module technology. We could show that silver flash plating, front and rear silver busbars or pads can be omitted without compromising series resistance or module performance. All Copper NICE modules were manufactured with fillfactors up to 76.8 %.
A digital twin of an automotive component was created by fusing space-resolved data. The benefit of fused data from different virtual and experimental sources like CAD, FEM, DIC, IR and laser scanners is demonstrated with a recyclable car-seat back-shell and its mechanical impact testing. Component properties depend on its shape, its material conditions and its manufacturing process. Along the whole product life cycle nowadays a huge amount of data accrues. The aim is the use of this information to improve products and save resources. To understand the mechanics of materials in use and forecast components' behavior in simulations, the MaterialDataFusion (MDF) tool was created. MDF correlates information geometrically and in time to one file of fused data. Within the virtual geometry of a component, all available data are registered in a common coordinate system with local accuracy. MDF is able to correlate all data on a common grid. The grid can be created within the tool by using standard shells or solids Finite Element (FE) meshes. The local FE size can be adjusted dependent on the spatial resolution of the data. Across all scales, data on different levels brought together. To use the accumulated information for the parametrization or validation of material models, the correlation process also can be performed using predefined meshes from usual FE pre-processors imported in MDF. These meshes are used within the correlation as an information carrier. The correlated information grid is than exported as a data frame, which contains the local available static or time dependent information on each node. One can include porosity, fiber orientation, heat treatment history, microstructure information, deformation and heat development in mechanical tests and all other data measured or simulated on components. As an example, along the life cycle of a recyclable car-seat back-shell, consisting of basalt fibers laminates and molded polylactic acid (PLA), different data from multiple institutions where fused in MDF and used to validate a complex hybrid material model. Whereat, the demonstrated analyses on the correlated back-shell data file are just a small inside into the possibilities of digital twins, created by MDF.
In dieser Arbeit wird eine Methode zur Kalibrierung einer Standard-LS-DYNA-Materialkarte für ein langfaserverstärktes thermoplastisches Material anhand von virtuellen Messungen vorgestellt. Diese Messungen werden durch ein Mikroskalenmodell gewonnen, das mit einfachen Messungen am Matrixmaterial kalibriert und durch wenige Messungen am Verbundwerkstoff validiert wird. Die resultierende Materialkarte kann das Materialverhalten des Verbundwerkstoffes sowohl für Zug- als auch für Scherbelastungen sowie für einen Durchstoßversuch mit guter Genauigkeit vorhersagen.
Anisotropic attenuation of GHz-frequency acoustic phonons and the Grüneisen tensor in MgO crystal
(2024)
We report on measurements of the anisotropy of velocities and attenuation of GHz-frequency acoustic phonons in a cubic MgO crystal at room temperature. They are used to quantify the strong anisotropy of the Grüneisen parameter and calculate the attenuation anisotropy for Rayleigh surface acoustic waves. These observations constitute important building blocks for better understanding of ultrafast laser-based magneto-acoustic and nonlinear acoustic experiments at ultrahigh frequencies.
The robust generalization of models to rare, in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is one of the major challenges of current deep learning methods. For image classification, this man-ifests in the existence of adversarial attacks, the performance drops on distorted images, and a lack of generalization to concepts such as sketches. The current under-standing of generalization in neural networks is very lim-ited, but some biases that differentiate models from human vision have been identified and might be causing these lim-itations. Consequently, several attempts with varying success have been made to reduce these biases during training to improve generalization. We take a step back and sanity-check these attempts. Fixing the architecture to the well-established ResNet-50, we perform a large-scale study on 48 ImageNet models obtained via different training methods to understand how and if these biases - including shape bias, spectral biases, and critical bands - interact with generalization. Our extensive study results reveal that contrary to previous findings, these biases are insufficient to accu-rately predict the generalization of a model holistically.
Die Vogesen sind geografisch von scharfen Klimawechseln geprägt. Die Gebirgskette ist geografisch das erste große Hindernis für feuchte Luftmassen vom Atlantik. Hohe Niederschläge führten in der Eiszeit zu starker Gletscherbildung.
Als diese schmolzen, brachte die Sedimentation des Granitgesteins die charakteristische Morphologie der Landschaft hervor. Die Rundkuppen der Berge sind so durch fortlaufende Erosionen entstanden. Das halbkreisförmige Plateau am Südwestauslauf des Grand Ballon wurde von einem Kargletscher ausgeschürft. Dort liegt das Dorf Geishouse mit heute 500 Einwohnern.
The integration of web servers into embedded systems offers a dividend at many levels. Operationally it can place industrial control, diagnostics and maintenance half a world away from the actual plant location – in fact anywhere with access to a reliable Internet connection.The ease and economy with which web-based architecture can integrate the activities of geographically diverse plant locations alone justifies use of the technology:The low cost and ubiquitous nature of Web Services components makes the case unassailable. Using public networks as a transmission layer for industrial architecture raises a legitimate concerns about security. But Virtual Private Networks can now deal effectively with the issue say Axel Sikora and Peter Brügger.
The last several years have witnessed a paradigm shift in industry that has now ushered in the fourth industrial revolution era also referred to as Industry 4.0. This new technology promises major improvements in the industrial processes by connecting local and global networks for information exchange amongst smart machinery while integrating possibly all stages of the value chain. However, small and medium-sized enterprises (SMEs) still have many concerns about Industry 4.0 and about its potential benefits. This is generally as a result of high investment and conversion costs. An evaluation platform to test Industry 4.0 applications for enabling engineers and managers in identifying the potential benefits before making expensive decisions is attractive. With this aim, the authors present an extensible and customizable open source toolkit for the evaluation of Industry 4.0 applications by providing a complete set of capabilities from sensing of data at the shop floor to monitoring at the upper level of the enterprise.
Generative machine learning models for creative purposes play an increasingly prominent role in the field of dance and technology. A particularly popular approach is the use of such models for generating synthetic motions. Such motions can either serve as source of ideation for choreographers or control an artificial dancer that acts as improvisation partner for human dancers. Several examples employ autoencoder-based deep-learning architectures that have been trained on motion capture recordings of human dancers. Synthetic motions are then generated by navigating the autoencoder's latent space. This paper proposes an alternative approach of using an autoencoder for creating synthetic motions. This approach controls the generation of synthetic motions on the level of the motion itself rather than its encoding. Two different methods are presented that follow this principle. Both methods are based on the interactive control of a single joint of an artificial dancer while the other joints remain under the control of the autoencoder. The first method combines the control of the orientation of a joint with iterative autoencoding. The second method combines the control of the target position of a joint with forward kinematics and the application of latent difference vectors. As illustrative example of an artistic application, this latter method is used for an artificial dancer that plays a digital instrument. The paper presents the implementation of these two methods and provides some preliminary results.