1,721,079 research outputs found
Data-Driven Modeling for Transonic Aeroelastic Analysis
Get PDFAeroelasticity in the transonic regime is challenging because of the strongly nonlinear phenomena involved in the formation of shock waves and flow separation. In this work, we introduce a computationally efficient framework for accurate transonic aeroelastic analysis. We use dynamic mode decomposition with control to extract surrogate models from high-fidelity computational fluid dynamics (CFD) simulations. Instead of identifying models of the full flowfield or focusing on global performance indices, we directly predict the pressure distribution on the body surface. The learned surrogate models provide information about the system’s stability and can be used for control synthesis and response studies. Specific techniques are introduced to avoid spurious instabilities of the aerodynamic model. We use the high-fidelity CFD code SU2 to generate data and test our method on the benchmark supercritical wing. Our Python-based software is fully open source and will be included in the SU2 package to streamline the workflow from defining the high-fidelity aerodynamic model to creating a surrogate model for flutter analysis
Multi-fidelity reduced-order surrogate modelling
No full textHigh-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated. Multi-fidelity surrogate modelling aims to leverage less accurate, lower-fidelity models that are computationally inexpensive in order to enhance predictive accuracy when high-fidelity data are scarce. However, low-fidelity models, while often displaying the qualitative solution behaviour, fail to accurately capture fine spatio-temporal and dynamic features of high-fidelity models. To address this shortcoming, we present a data-driven strategy that combines dimensionality reduction with multifidelity neural network surrogates. The key idea is to generate a spatial basis by applying proper orthogonal decomposition (POD) to high-fidelity solution snapshots, and approximate the dynamics of the reduced states—time-parameter-dependent expansion coefficients of the POD basis—using a multi-fidelity long short-term memory network. By mapping low-fidelity reduced states to their high-fidelity counterpart, the proposed reduced-order surrogate model enables the efficient recovery of full solution fields over time and parameter variations in a non-intrusive manner. The generality of this method is demonstrated by a collection of PDE problems where the low-fidelity model can be defined by coarser meshes and/or time stepping, as well as by misspecified physical features
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
An exploration of data-driven system identification and machine learning for plasma physics
Get PDFThesis (Ph.D.)--University of Washington, 2021Plasma is the most common state of visible matter in the universe and provides a myriad of scientific and engineering applications. However, the complexity of these systems poses a significant challenge for understanding and controlling plasmas. Fortunately, machine learning is increasingly used to handle complex, nonlinear systems, and the field of machine learning is advancing at an unprecedented pace, propelled forward by advances in sensing technology and computing power. This thesis summarizes work towards applying modern machine learning algorithms for fluid and plasma physics applications, with a focus on the understanding and control of magnetohydrodynamic (MHD) phenomena and fusion-relevant plasmas. Although this work is primarily focused on machine learning, first conventional numerical techniques are used to implement a two-temperature Hall-MHD model into the 3D PSI-Tet code, followed by an investigation of the plasma dynamics in the HIT-SI experiment. These simulations agree well with experimental measurements, and indicate that low-densities are required for significant closed flux surfaces - a recommendation that is now helping to guide the next generation of experimental design. Next, plasma modeling with machine learning is discussed in the context of the hierarchy of plasma models and it is illustrated that there is "plenty of room at the bottom" for physics-constrained reduced order models that approximate more complex MHD or kinetic plasma models. Variants of the dynamic mode decomposition are explored on experimental data and simulations of the HIT-SI plasma device and indicate promise for magnetic mode spectroscopy and forecasting diagnostic measurements. Continuing, analytic reduced-order modeling methods are extended using techniques in system identification for extracting reduced-order models directly from data. In the process, new methods are invented to enforce physical constraints and stability in data-driven fluid and plasma models. For instance, the ability to build data-driven models that obey global conservation of energy or global conservation of cross-helicity is demonstrated, with promise for efficient simulations of ideal and resistive MHD turbulence. With the new functionality implemented into the open-source PySINDy code as part of this work, advanced system identification methods that can robustly extract dynamical equations from data are available to the larger scientific community.In total, this work illustrates that new machine learning methods can be directly tied with known physical laws in plasma physics, have promise to significantly impact much of the plasma physics and nonlinear systems fields, and can provide complementary, interpretable methods to the relatively black-box deep learning techniques that are frequently used in the plasma physics field for extracting diagnostic information, building reduced-order models, and performing real-time control
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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