4,665 research outputs found

    Non-invasive and non-intrusive diagnostic techniques for gas-solid fluidized beds – A review

    Get PDF
    Gas-solid fluidized-bed systems offer great advantages in terms of chemical reaction efficiency and temperature control where other chemical reactor designs fall short. For this reason, they have been widely employed in a range of industrial application where these properties are essential. Nonetheless, the knowledge of such systems and the corresponding design choices, in most cases, rely on a heuristic expertise gained over the years rather than on a deep physical understanding of the phenomena taking place in fluidized beds. This is a huge limiting factor when it comes to the design, the scale-up and the optimization of such complex units. Fortunately, a wide array of diagnostic techniques has enabled researchers to strive in this direction, and, among these, non-invasive and non-intrusive diagnostic techniques stand out thanks to their innate feature of not affecting the flow field, while also avoiding direct contact with the medium under study. This work offers an overview of the non-invasive and non-intrusive diagnostic techniques most commonly applied to fluidized-bed systems, highlighting their capabilities in terms of the quantities they can measure, as well as advantages and limitations of each of them. The latest developments and the likely future trends are also presented. Neither of these methodologies represents a best option on all fronts. The goal of this work is rather to highlight what each technique has to offer and what application are they better suited for

    Study of the tracking performance of a liquid Argon detector based on a novel optical imaging concept

    Get PDF
    The Deep Underground Neutrino Experiment (DUNE) is a long-baseline accelerator experiment designed to make a significant contribution to the study of neutrino oscillations with unprecedented sensitivity. The main goal of DUNE is the determination of the neutrino mass ordering and the leptonic CP violation phase, key parameters of the three-neutrino flavor mixing that have yet to be determined. An important component of the DUNE Near Detector complex is the System for on-Axis Neutrino Detection (SAND) apparatus, which will include GRAIN (GRanular Argon for Interactions of Neutrinos), a novel liquid Argon detector aimed at imaging neutrino interactions using only scintillation light. For this purpose, an innovative optical readout system based on Coded Aperture Masks is investigated. This dissertation aims to demonstrate the feasibility of reconstructing particle tracks and the topology of CCQE (Charged Current Quasi Elastic) neutrino events in GRAIN with such a technique. To this end, the development and implementation of a reconstruction algorithm based on Maximum Likelihood Expectation Maximization was carried out to directly obtain a three-dimensional distribution proportional to the energy deposited by charged particles crossing the LAr volume. This study includes the evaluation of the design of several camera configurations and the simulation of a multi-camera optical system in GRAIN

    A study of particles-flow interactions based on the numerical solution of the Boltzmann equation

    Get PDF

    Modern meat: the next generation of meat from cells

    Get PDF
    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    The Forward Physics Facility at the High-Luminosity LHC

    Get PDF
    High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF's physics potential

    The Forward Physics Facility at the High-Luminosity LHC

    Get PDF

    Tools for Quantifying Bacterial Motility Using Digital Holographic Microscopy as Applied to Studying the Simulated Microgravity Environment

    Get PDF
    Digital holographic microcopy (DHM) is a label-free technique that has gained attention in recent years as a tool for volumetric imaging. One application of DHM is for the study of microbial motility with the advantage being that organisms may freely move within their environment. Images created from DHM are in the form of holograms. Holograms are time recordings showing XY information with the Z information contained within. Z information can be retrieved from the holograms directly through a variety of numerical techniques or through reconstruction. Datasets generated from DHM are large and processing remains a challenging task. Here, we show how following reconstruction, the refocusing method can be used to locate particles manually through Z. We note the difference between the lateral and axial resolutions and show the impact of the point-spread functions on resolving data. We show that 2D tracking of organisms is generally sufficient for quantifying motility though specific applications such as surface behavior still require 3D information. With this understanding, we shift to studying the microgravity environment. The microgravity environment is the weightless environment of the space station. It is difficult to conduct experiments on the space station, so we simulate certain characteristics of that environment on Earth by using simulated microgravity devices. We review bacterial responses to microgravity and the simulated microgravity environment with an emphasis on motility and chemotaxis. Finally, we apply the techniques developed in this thesis to study the simulated microgravity environment by examining the motility and chemotaxis of Vibrio alginolyticus. We show that while there was little change in motility between simulated microgravity and normal gravity, there is a statistically significant difference in cloud sizes. Future work would involve comparing these responses with the actual microgravity environment

    University of Windsor Graduate Calendar 2023 Winter

    Get PDF
    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    Synergies between Numerical Methods for Kinetic Equations and Neural Networks

    Get PDF
    The overarching theme of this work is the efficient computation of large-scale systems. Here we deal with two types of mathematical challenges, which are quite different at first glance but offer similar opportunities and challenges upon closer examination. Physical descriptions of phenomena and their mathematical modeling are performed on diverse scales, ranging from nano-scale interactions of single atoms to the macroscopic dynamics of the earth\u27s atmosphere. We consider such systems of interacting particles and explore methods to simulate them efficiently and accurately, with a focus on the kinetic and macroscopic description of interacting particle systems. Macroscopic governing equations describe the time evolution of a system in time and space, whereas the more fine-grained kinetic description additionally takes the particle velocity into account. The study of discretizing kinetic equations that depend on space, time, and velocity variables is a challenge due to the need to preserve physical solution bounds, e.g. positivity, avoiding spurious artifacts and computational efficiency. In the pursuit of overcoming the challenge of computability in both kinetic and multi-scale modeling, a wide variety of approximative methods have been established in the realm of reduced order and surrogate modeling, and model compression. For kinetic models, this may manifest in hybrid numerical solvers, that switch between macroscopic and mesoscopic simulation, asymptotic preserving schemes, that bridge the gap between both physical resolution levels, or surrogate models that operate on a kinetic level but replace computationally heavy operations of the simulation by fast approximations. Thus, for the simulation of kinetic and multi-scale systems with a high spatial resolution and long temporal horizon, the quote by Paul Dirac is as relevant as it was almost a century ago. The first goal of the dissertation is therefore the development of acceleration strategies for kinetic discretization methods, that preserve the structure of their governing equations. Particularly, we investigate the use of convex neural networks, to accelerate the minimal entropy closure method. Further, we develop a neural network-based hybrid solver for multi-scale systems, where kinetic and macroscopic methods are chosen based on local flow conditions. Furthermore, we deal with the compression and efficient computation of neural networks. In the meantime, neural networks are successfully used in different forms in countless scientific works and technical systems, with well-known applications in image recognition, and computer-aided language translation, but also as surrogate models for numerical mathematics. Although the first neural networks were already presented in the 1950s, the scientific discipline has enjoyed increasing popularity mainly during the last 15 years, since only now sufficient computing capacity is available. Remarkably, the increasing availability of computing resources is accompanied by a hunger for larger models, fueled by the common conception of machine learning practitioners and researchers that more trainable parameters equal higher performance and better generalization capabilities. The increase in model size exceeds the growth of available computing resources by orders of magnitude. Since 20122012, the computational resources used in the largest neural network models doubled every 3.43.4 months\footnote{\url{https://openai.com/blog/ai-and-compute/}}, opposed to Moore\u27s Law that proposes a 22-year doubling period in available computing power. To some extent, Dirac\u27s statement also applies to the recent computational challenges in the machine-learning community. The desire to evaluate and train on resource-limited devices sparked interest in model compression, where neural networks are sparsified or factorized, typically after training. The second goal of this dissertation is thus a low-rank method, originating from numerical methods for kinetic equations, to compress neural networks already during training by low-rank factorization. This dissertation thus considers synergies between kinetic models, neural networks, and numerical methods in both disciplines to develop time-, memory- and energy-efficient computational methods for both research areas
    • …
    corecore