10,832 research outputs found

    Development of Porous Rubber Pavement for the Canadian Climate

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    Permeable pavement usage in North America has increased over the last decade as a viable stormwater management system. Porous Rubber Pavement (PRP) is a new material in this category which has been currently utilized as a pavement surface material for low-traffic areas and pedestrian walkways. This material consists of recycled crumb rubber aggregates, granite aggregates and polyurethane as a binder and is proportioned to attain a very high percentage of interconnected air voids (up to 45%). As a new pavement material in North America, the properties and performance of PRP are not thoroughly understood for cold climate conditions. This research aimed to understand the properties and performance of PRP and improve its performance as a pavement surface material for the Canadian climate. This objective is achieved through an evaluation of existing sites and mixes, developing new mixes through an experimental design process, and evaluating new mixes in the laboratory facilities. Some of the mixes were selected to apply in the trial section to assess field performance. Finally, recommendations and guidelines are developed for this climatic zone. Through the experimental design, four new mixes were developed using different proportions of stone aggregates, rubber aggregates and polyurethane binder. Also, using the proportion of the Control Mix, four polyurethane binders were used to make four different mixes to determine the different binder effects in PRPs. In the next stage of research, two trial sections were constructed using selected mixes along with the Control Mix. In addition, samples were also prepared from the field mixes to test their properties in the laboratory. Then the field performance of the various mixes was evaluated over a series of months. They were initially tested immediately following construction before fully opening for traffic. Then three weeks after construction and after seven months when the sections had experienced their first winter. Preliminary field investigations showed that with the current commercial mix, the achieved elastic modulus of PRP surfaces ranged between 37 MPa and 33 MPa. Besides, frictional values ranged between 57 BPN and 74 BPN. Higher IRI values were calculated for both sites, ranging between 7.56 m/km to 15.77 m/km. The average infiltration rate for the pavement surface areas was found to be 30836 mm/hr. The mechanical properties and durability of the Control Mix and newly developed mixes were investigated. The tensile and compressive strength of the mixes were found to be higher when the percentages of stone aggregates and binders were increased in the mixes. Additionally, an increase in air voids in the samples reduced the materials' tensile and compressive strength. Concerning the types of binder and sources, the obtained results showed no considerable influence of different types of binder in compressive strength test results, whereas binder sources influenced the tensile strength of the PRP materials. PRP samples with varying compositions retained more than 70% of their tensile strength after conditioning with five freeze-thaw cycles. However, due to the variety of binders used, retained tensile strength for PRP samples varied, and some showed retained tensile strength below 70%. The durability study showed that the granite stones that were used for all the sample preparation were not strong enough to withstand higher abrasion loss. However, PRPs with different compositions showed good rutting resistance, ranging from 0.3mm to 2.8mm in different mixes. Moisture-induced damage, stripping related abrasion was also found to be very small in PRP mixes, ranging from 2.6% to 0.1%. Also, the use of different binders from different sources showed that the B2—aliphatic binder could withstand more rutting than other binders. A Los Angeles abrasion tester tested unconditioned and conditioned samples to determine the materials' ravelling resistance. The result showed that abrasion loss increased in the samples after conditioning with five freeze-thaw cycles. However, it was consistent with the mix types. On the other hand, abrasion loss of samples with different binders occurred differently in the conditioned and unconditioned situations and was inconsistent in the mixes. Subgrade samples were collected from sites A and B during the trial section construction. The bearing capacity of subgrade soil for Site B was found to be higher than that of Site A. Subsequently, the performance of the mixes in the sections was evaluated through a series of field testing. The LWD results showed that the stiffness modulus differed for the same mixes at Site A and Site B. Besides, all the mixes showed higher stiffness in the 2nd field test than the 1st since compaction occurred on the pavement after opening for traffic. Nevertheless, after experiencing their first winter, a reduction in stiffness was observed for all mixes in the 3rd test. The BPT test revealed that a higher frictional value of PRP mixes was associated with a higher percentage of rubber aggregates and a lower percentage of binder in the mixes. At the same time, a reduction in BPN values was observed in the 2nd test than in the 1st since the sections were further compacted and polished after opening for traffic. In addition, the surface ravelling and transported loose particles affected the frictional values in the 3rd test, increasing the BPN numbers. Initial rut depths on Site A for different mixes ranged from -7.0 mm to -8.7mm, and the range was -5.8 mm to -10.7mm for Site B. However, after fully opening for traffic, greater rut depths were observed on each section due to the additional compaction under the wheel paths. The permeability of the PRP sections ranged from 28368 mm/h to 45605 mm/h, which is higher than the highest rainfall rate in Canada (298.8 mm/h). However, most of the sections showed higher permeability in the 2nd test. After the first winter, the permeability of some of the sections was found to be further increased, whereas others were found to be decreased due to the influence of site surroundings. In the 1st and 2nd field tests, no visible surface distress was observed at Site A and Site B. A small amount of surface distress was observed after the first winter (seven months after the construction), which included a slight loss of coarse aggregate, minor ravelling, small cracking, and rutting. Throughout the trial section construction process, it was also observed that by improving the construction methods and making slight modifications during the construction process (like increased compaction), the performance of PRPs could be further enhanced. Finally, a set of recommendations and guidelines were developed for using the PRP in the Canadian climate

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

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    Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.Comment: 80 pages, 11 figure

    Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

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    Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively

    Outdoor Insulation and Gas Insulated Switchgears

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    This book focuses on theoretical and practical developments in the performance of high-voltage transmission line against atmospheric pollution and icing. Modifications using suitable fillers are also pinpointed to improve silicone rubber insulation materials. Very fast transient overvoltage (VFTO) mitigation techniques, along with some suggestions for reliable partial discharge measurements under DC voltage stresses inside gas-insulated switchgears, are addressed. The application of an inductor-based filter for the protective performance of surge arresters against indirect lightning strikes is also discussed

    Electrical and Optical Modeling of Thin-Film Photovoltaic Modules

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    Heutzutage ist durch viele wissenschaftliche Studien nachgewiesen, dass die Erde längst dem Klimawandel unterworfen ist. Daher muss die gesamte Menschheit vereint handeln, um die schlimmsten Katastrophenszenarien zu verhindern. Ein vielversprechender Ansatz - wenn nicht sogar der vielversprechendste überhaupt - um diese angesprochene, größte Herausforderung in der Geschichte der Menschheit zu bewältigen, ist es, den Energiehunger der Menschheit durch die Erzeugung erneuerbarer und unerschöpflicher Energie zu sättigen. Die Photovoltaik (PV)-Technologie ist ein vielversprechender Anwärter, die leistungsstärkste erneuerbare Energiequelle zu stellen, und spielt aufgrund ihrer direkten Umwandlung des Sonnenlichtes und ihrer skalierbaren Anwendbarkeit in Form von großflächigen Solarmodulen bereits jetzt eine große Rolle bei der Erzeugung erneuerbarer Energie. Im PV-Sektor sind Solarmodule aus Siliziumwafern die derzeit vorherrschende Technologie. Neu aufkommende PV-Technologien wie die Dünnschichttechnologie haben jedoch vorteilhafte Eigenschaften wie einen sehr geringen Kohlenstoffdioxid (CO2)-Fußabdruck, eine kurze energetische Amortisierungszeit und das Potenzial für eine kostengünstige monolithische Massenproduktion, obwohl diese derzeit noch nicht final ausgereift ist. Um die Dünnschichttechnologie jedoch gezielt in Richtung einer breiten Marktreife zu entwickeln, sind numerische Simulationen eine wichtige Säule für das wissenschaftliche Verständnis und die technologische Optimierung. Während sich traditionelle Simulationsliteratur häufig mit materialspezifischen Herausforderungen befasst, konzentriert sich diese Arbeit auf industrieorientierte Herausforderungen auf Modulebene, ohne die zugrundeliegenden Materialparameter zu verändern. Um ein allumfassendes, digitales Modell eines Solarmoduls zu erstellen, werden in dieser Arbeit mehrere Simulationsansätze aus verschiedenen physikalischen Bereichen kombiniert. Zur Abbildung elektrischer Effekte, einschließlich der räumlichen Spannungsvariation innerhalb des Moduls, wird eine Finite Elemente Methode (FEM) zur Lösung der räumlich quantisierten Poisson-Gleichung verwendet. Um optische Effekte zu berücksichtigen, wird eine generalisierte Transfermatrix-Methode (TMM) verwendet. Alle Simulationsmethoden sind in dieser Arbeit von Grund auf neu programmiert worden, um eine Verknüpfung aller Simulationsebenen mit dem höchstmöglichen Grad an Anpassung und Verknüpfung zu ermöglichen. Die Simulation und die Korrektheit der Parameter wird durch externe Quanteneffizienz (EQE)-Messungen, experimentelle Reflexionsdaten und gemessene Strom-Spannungs (I-U)-Kennlinien verifiziert. Der Kernpunkt der Vorgehensweise dieser Arbeit ist eine ganzheitliche Simulationsmethodik auf Modulebene. Dies ermöglicht es, die Lücke zwischen der Simulation auf Materialebene über die Berechnung von Laborwirkungsgraden bis hin zur Bestimmung der von zahlreichen Umweltfaktoren beeinflusste Leistung der Module im Freifeld zu überbrücken. Durch diese Verknüpfung von Zellsimulation und Systemdesign ist es lediglich aus Laboreigenschaften möglich, das Freifeldverhalten von Solarmodulen zu prognostizieren. Sogar das Zurückrechnen von experimentellen Messungen zu Materialparameter ist mittels des in dieser Arbeit entwickelten Verfahrens des Reverse Engineering Fittings (REF) möglich. Das in dieser Arbeit entwickelte numerische Verfahren kann für mehrere Anwendungen genutzt werden. Zunächst können durch die Kombination von elektrischen und optischen Simulationen ganzheitliche Top-Down-Verlustanalysen durchgeführt werden. Dies ermöglicht eine wissenschaftliche Einordnung und einen quantitativen Vergleich aller Verlustleistungsmechanismen auf einen Blick, was die zukünftige Forschung und Entwicklung in Richtung von technologischen Schwachstellen von Solarmodulen lenkt. Darüber hinaus ermöglicht die Kombination von Elektrik und Optik die Detektion von Verlusten, die auf dem nichtlinearen Zusammenspiel dieser beiden Ebenen beruhen und auf eine räumliche Spannungsverteilung im Solarmodul zurückzuführen sind. Diese Arbeit verwendet die entwickelten numerischen Modelle ebenfalls für Optimierungsprobleme, die an digitalen Modellen realer Solarmodule durchgeführt werden. Häufig auftretende Fragestellungen bei der Entwicklung von Solarmodulen sind beispielsweise die Schichtdicke des vorderen optisch transparenten, elektrisch leitfähigen Oxids (TCO) oder die Breite von monolithisch verschalteten Zellen. Die Bestimmung des Optimums dieser mehrdimensionalen Abwägungen zwischen optischer Transparenz, elektrischer Leitfähigkeit und geometrisch inaktiver Fläche zwischen den einzelnen Zellen ist ein Hauptmerkmal der Methodik dieser Arbeit. Mittels des FEM-Ansatzes dieser Arbeit ist es möglich, alle gegenseitigen Wechselwirkungen über verschiedene physikalische Ebenen hinweg zu berücksichtigen und ein ganzheitlich optimiertes Moduldesign zu finden. Auch topologisch komplexere Probleme, wie das Finden eines geeigneten Designs für das Metallisierungsgitter, können auf Grundlage der Simulation mittels der Methode der Topologie-Optimierung (TO) gelöst werden. In dieser Arbeit wurde das TO-Verfahren zum ersten Mal für monolithisch integrierte Zellen eingesetzt. Darüber hinaus wurde gezeigt, dass sowohl einfache Optimierungen der TCO-Schichtdicken als auch Topologie-Optimierungen stark von den vorherrschenden Beleuchtungsverhältnissen abhängen. Daher ist eine Optimierung auf den Jahresertrag anstelle des Laborwirkungsgrades für industrienahe Anwendungen wesentlich sinnvoller, da die mittleren Jahreseinstrahlungen deutlich von den Laborbedingungen abweichen. Mit Hilfe dieser Ertragsoptimierung wurde in dieser Arbeit für die Kupfer-Indium-Gallium-Diselenid CuIn1x_{1-x}Gax_xSe2_2 (CIGS)-Technologie ein Leistungsgewinn von über 1 % im Ertrag für einige geografische Standorte und gleichzeitig eine Materialeinsparung für die Metallisierungs- und TCO-Schicht von bis zu 50 % errechnet. Mit Hilfe der numerischen Simulationen dieser Arbeit können alle denkbaren technologischen Verbesserungen auf Modulebene in das Modell eingebracht werden. Auf diese Weise wurde das aktuelle technologische Limit für CIGS-Dünnschicht-Solarmodule berechnet. Unter Verwendung der Randbedingungen der derzeit verfügbaren Materialien, Technologie- und Fertigungstoleranzen und des derzeit besten in der Literatur veröffentlichten CIGS-Materials ergibt sich ein theoretisches Wirkungsgradmaximum von 24 % auf Modulebene. Das derzeit beste veröffentlichte Modul mit den gegebenen Restriktionen weist einen Wirkungsgrad von 19,2 % auf [1]. Verbessert sich der CIGS-Absorber vergleichbar mit jenem von Galliumarsenid (GaAs) im Hinblick auf dessen Rekombinationsrate, ergibt sich ein erhöhtes Wirkungsgradlimit von etwa 28 %. Im Falle eines idealen CIGS-Absorbers ohne intrinsische Rekombinationsverluste wird in dieser Arbeit eine maximale Effizienzobergrenze von 29 % berechnet

    Investigation of Inorganic Salt Hydrate Phase Change Materials for Thermal Energy Storage Integrated into Heat Pump Systems

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    Thermal energy storage (TES) is a promising technology for the Energy Transition. Low grade heat is valuable for many everyday applications: indoor heating and cooling, hot water, refrigeration, etc. Heat pumps (HPs) move heat up a thermal gradient (from cold to hot) with an input of energy. Integrating TES into a HP grants flexibility to dispatch the stored heat as needed. When operating a HP against a fluctuating temperature body (i.e., outdoor ambient air temperature), TES provides an isothermal heat source that enables more efficient HP operation to its reduce energy consumption without sacrificing thermal comfort. This work explores the thermodynamic limits of HP-TES and it was found that TES temperatures equal to the application temperature leads to the highest potential for energy savings and peak demand reduction. This HP-TES system was then modeled in a building thermal energy simulation where the same findings emerge: a TES temperature near the application temperature shows the highest potential. A common method of achieving an isothermal TES is to incorporate phase change materials (PCMs) that store heat through the enthalpy of phase change. Salt hydrates are valued for their high volumetric storage density and low cost. This work explores the Brunauer-Emmett-Teller method to model sodium sulfate, but this salt was found to be incompatible with this reduced order method. Salt hydrates also exhibit low thermal conductivity which limits their direct use in TES. This work develops salt hydrate-graphite composite PCMs with improved thermal conductivity, however a tradeoff between energy storage capacity and thermal power density was seen. The composite PCMs were experimentally tested in a TES device and the improved thermal properties demonstrate their potential for use in simple TES architectures. Overall, this work evaluated TES systems from a holistic perspective, spanning several orders of magnitude, both energetically and spatially.Ph.D

    Active Commuting and Active Transportation

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    This book focuses on active transport as a way to increase physical activity levels. Active commuting and active transportation on foot or by bicycle create opportunities for physical activity, provide transportation options for those without a car, encourage social cohesion, and reduce contributions to air pollution

    Stochastic Transport in Upper Ocean Dynamics

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    This open access proceedings volume brings selected, peer-reviewed contributions presented at the Stochastic Transport in Upper Ocean Dynamics (STUOD) 2021 Workshop, held virtually and in person at the Imperial College London, UK, September 20–23, 2021. The STUOD project is supported by an ERC Synergy Grant, and led by Imperial College London, the National Institute for Research in Computer Science and Automatic Control (INRIA) and the French Research Institute for Exploitation of the Sea (IFREMER). The project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. It will provide decision makers a means of quantifying the effects of local patterns of sea level rise, heat uptake, carbon storage and change of oxygen content and pH in the ocean. Its multimodal monitoring will enhance the scientific understanding of marine debris transport, tracking of oil spills and accumulation of plastic in the sea. All topics of these proceedings are essential to the scientific foundations of oceanography which has a vital role in climate science. Studies convened in this volume focus on a range of fundamental areas, including: Observations at a high resolution of upper ocean properties such as temperature, salinity, topography, wind, waves and velocity; Large scale numerical simulations; Data-based stochastic equations for upper ocean dynamics that quantify simulation error; Stochastic data assimilation to reduce uncertainty. These fundamental subjects in modern science and technology are urgently required in order to meet the challenges of climate change faced today by human society. This proceedings volume represents a lasting legacy of crucial scientific expertise to help meet this ongoing challenge, for the benefit of academics and professionals in pure and applied mathematics, computational science, data analysis, data assimilation and oceanography

    Future Trends in Advanced Materials and Processes

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    The Special Issue “Future Trends in Advanced Materials and Processes” contains original high-quality research papers and comprehensive reviews addressing the relevant state-of-the-art topics in the area of materials focusing on relevant or innovative applications such as radiological hazard evaluations of non-metallic materials, composite materials' characterization, geopolymers, metallic biomaterials, etc
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