393 research outputs found

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    GAC-MAC-SGA 2023 Sudbury Meeting: Abstracts, Volume 46

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    Modeling and Simulation in Engineering

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    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    Scaling up integrated photonic reservoirs towards low-power high-bandwidth computing

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    Machine Learning and Its Application to Reacting Flows

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    This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

    A POD-Based Reduced Order Model for Offshore Wind Applications, using LiDAR Measurements and WRF-PALM Simulations

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    Wind flow fields within a wind farm, including wake dynamics, are a complex system of large degrees of freedom. The wake shape and velocity deficit downstream of a turbine may be calculated using numerical modeling, such as Large Eddy Simulations (LES). However, these estimations are computationally expensive and time-consuming. On the other hand, analytical models may provide efficient computations but generally exclude several features of the wake dynamics. Reducing the complexity of the numerical models and improving the details of the analytical models are highly relevant issues for today’s wind farm controlling and layout designing. This work proposes a methodology applicable to the industry to conduct wind farm flow field calculations by investigating the wake dynamics based on applying Proper Orthogonal Decomposition (POD) to Light Detection And Ranging (LiDAR) measurements and Weather Research and Forecasting (WRF) data using a Parallelized Large Eddy Simulation Model (PALM). Both data sets comprise a complex, high-dimensional system consisting of an area of Germany’s first offshore wind farm, Alpha Ventus, located in the North Sea. 10 days of wind speed and direction data are retrieved from the radial velocity measured by the LiDAR at the FINO1 platform, located in close proximity to the wind farm Alpha Ventus, between September and October 2016, during varying atmospheric forcing conditions. The WRF-PALM data were simulated for one hour on the 21st of September 2015, using ERA5 data as input, during unstable conditions. Reduced Order Models (ROMs) are built separately for both the LiDAR and WRFPALM data, by decomposing them into a number of time-dependent, truncated weighting coefficients and spatial orthogonal basis functions. Proper Orthogonal Decomposition (POD) is shown to reconstruct selected wind fields in a reduced manner while preserving the global patterns of the wind fields for both LiDAR and WRF-LES data. The study has further investigated the ability of the Gaussian Process (GP) to incorporate unresolved small-scale wake structures in the reconstruction that are excluded by the truncated ROM. A sensitivity study for a variety of kernels accompanied by hyperparameters is conducted. By replacing the temporal weighting coefficients obtained for the POD with stochastic weights obtained from the GP, the study has shown that the reconstruction is sensitive to kernel selections. By reconstructing the field using both the weighting coefficients from the standard POD and those obtained using GP, both for the retrieved and WRF-PALM data, the performance of the methods has been evaluated based on visual inspection, energetic contribution, and Root Mean Square Error (RMSE).Masteroppgave i energiENERGI399MAMN-ENER

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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