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Multi-objective optimization of glazing and shading configurations for visual, thermal, and energy performance of cooling dominant climatic regions of India
Get PDFClimate adaptive passive design features such as glazing and shading configurations, often exhibit conflicting behaviour to maintaining indoor visual and thermal environment while minimizing energy consumption. This study employed the MOO approach through SPEA-II and HypE algorithms to find the efficient glazing and shading configurations in four cooling dominant climatic zones of India such as hot and dry, composite, warm and humid, and temperate. A residential building situated in Jaipur city (India) was chosen for the analysis and to demonstrate the effectiveness and reliability of the optimization process. Twenty-six design variables including wall window ratio, louvers depth, louvers count, fin depth and fins count of each orientation, window height, sill height, glazing U-value, solar heat gain coefficient, and visual light transmittance were imported into the Octopus to investigate their interactive impact on the Useful Daylight Illuminance (UDI), Thermal Discomfort Percentage (TDP) and Energy Use Intensity (EUI). The optimized trade-off solutions represented a considerable improvement; UDI increased by 24.61%, 21.90%, 14.91% & 26.41%, and TDP reduced by 10.38%, 1.5%, 13.95%, & 17.68%, and EUI decreased by 10.10%, 1.9%, 15.82%, & 19.19% compared to initially generated solutions for Ahmedabad, Delhi, Mumbai and Bangalore cities respectively. Finally, the Bayesian machine learning technique has been used for sensitivity analysis to identify the correlation between the design variables and performance objectives. The outcomes of this research can help architects in selecting optimal glazing and shading features for both new building design and retrofitting of existing buildings in the tropical climatic conditions of India
3D‐Printing with Steel ‐ Additive Manufacturing Connections and Structures
Get PDFAutomated production is finding its way into fabrication of structural steel. One robot holds attachments (stiffeners, head plates, etc.) to a steel beam or column and another robot produces weld seams. However, welding robots can also be used for additive manufacturing (Wire + Arc Additive Manufacturing, WAAM). The wire electrode serves as printing material. The Institute for Steel Construction and Materials Mechanics in Darmstadt is investigating how typical connecting elements of steel construction can be printed directly on steel beams using Additive Manufacturing with arc welding and robots. Furthermore structural elements like nodal points are printed and even complete structures like columns and a little bridge have been manufactured additively already. The main focus is on determining suitable welding and process parameters. In addition, topology optimization is used to find good structures using a low amount of material. This is possible due to the free design prospects of 3D‐printing. This opens for novel design and production strategies
Scalable engine and the performance of different LLM models in a SLURM based HPC architecture
No full textHerabrechnen von Oberflächenwellen durch vorbeifahrende Züge auf beliebige Gründungsebenen für Prognosen – Teil 1
No full textInfluence of a Standard Heat Treatment on the Microstructure and Properties of Inconel X‐750 Produced by Laser Powder Bed Fusion
Get PDFThe production of nickel‐base superalloys through powder bed fusion using a laser beam (PBF‐LB) offers a promising method for reducing the cost of aeronautical components, especially those with complex geometries. However, when these materials are processed using PBF‐LB, the standard heat treatments developed for conventional manufacturing routes do not consistently yield predictable microstructures or mechanical properties. Thus, this study analyzes how PBF‐LB affects the microstructure and mechanical behavior of Inconel alloy X‐750 when compared to a hot‐rolled counterpart after undergoing a standard precipitation‐hardening heat treatment (solution annealing, stabilization, and precipitation). Microstructure is examined using analytical electron microscopy and high‐energy X‐ray diffraction, while mechanical behavior is assessed through microhardness. Significant differences between both manufacturing routes are detected, so that PBF‐LB processing led to an increase in grain size, anisotropy, fraction of low‐angle grain boundaries, mean angle of misorientation, and dislocation density. Moreover, PBF‐LB promoted the formation of γ′‐Ni3(Al,Ti) and η‐Ni3Ti phases during solution annealing and stabilization, respectively. Notably, the formation of γ′ contributes to an anomalous microhardness increase observed between these steps. These findings suggest that PBF‐LB may inhibit recrystallization and facilitate precipitation during heat treatment, thereby affecting the performance of components produced using this technology
Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)
No full textReconstruction-based visual anomaly detection in wound rotor synchronous machine production using convolutional autoencoders and structural similarity
Get PDFManufacturing wound rotor synchronous machines (WRSMs) for electric vehicle traction systems necessitates rigorous quality inspection to ensure optimal product performance and efficiency. This paper presents a novel visual anomaly detection method for monitoring the needle winding process of WRSMs, utilizing unsupervised learning with convolutional autoencoders (CAEs) and the structural similarity index measure (SSIM). The method identifies deviations from the desired orthocyclic winding pattern during each stage of the winding process, enabling early detection of winding errors and preventing resource wastage and potential damage to the product or winding machinery. Trajectory-synchronized frame extraction aligns the visual inspection system with the winding trajectory, ensuring precise monitoring traceable to a specific point in the winding process. We present the comprehensive Winding Anomaly Dataset (WAD), which comprises images of WRSM rotor prototypes with and without winding faults recorded in different lighting conditions. The proposed reconstruction-based anomaly detection technique is trained on fault-free data only and utilizes the introduced masked mean structural dissimilarity index measure (MMSDIM) to focus on the relevant sections of the winding during inference. Comprehensive comparative analysis reveals that the CAE with unregularized latent space and the maximum mean discrepancy Wasserstein autoencoder (MMD-WAE) outperform the beta variational autoencoder (beta-VAE) in terms of anomaly detection performance, with the CAE and WAE delivering comparable results. Extensive testing confirms the approach’s effectiveness, achieving 95.6 % recall at 100 % precision, an AUROC of 99.9 %, and an average precision of 99.1 % on the challenging WAD, considerably outperforming state-of-the-art visual anomaly detection models. This work thus offers a robust solution for WRSM production quality monitoring and promotes the incorporation of visual inspection in electric drive manufacturing systems