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Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks

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arXiv 2020年

作者： Papamarkou, Theodore Guy, Hayley Kroencke, Bryce Miller, Jordan Robinette, Preston Schultz, Daniel Hinkle, Jacob Pullum, Laura Schuman, Catherine Renshaw, Jeremy Chatzidakis, Stylianos Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Mathematics North Carolina State University RaleighNC United States Department of Computer Science University of California DavisCA United States Center for Cognitive Ubiquitous Computing Arizona State University TempeAZ United States Presbyterian College ClintonSC United States Innovative Computing Laboratory University of Tennessee KnoxvilleTN United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN United States Electric Power Research Institute Palo AltoCA United States Reactor and Nuclear Systems Division Oak Ridge National Laboratory Oak RidgeTN United States

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

On the incorporation of obstacles in a fluid flow problem using a Navier-Stokes-Brinkman penalization approach

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arXiv 2020年

作者： Fuchsberger, Jana Karabelas, Elias Aigner, Philipp Niederer, Steven Plank, Gernot Schima, Heinrich Haase, Gundolf Institute of Mathematics and Scientific Computing University of Graz Heinrichstraße 36 GrazA-8010 Austria Gottfried Schatz Research Center for Cell Signaling Metabolism and Aging Biophysics Medical University of Graz Neue Stiftingtalstraße 6/D04 GrazA-8010 Austria Department of Biomedical Engineering School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom Center for Medical Physics and Biomedical Engineering Medical University Vienna Währinger Gürtel 18-20 ViennaA-1090 Austria Ludwig Boltzmann Institute for Cardiovascular Research Währinger Guertel 18-20 ViennaA-1090 Austria BioTechMed-Graz Graz Austria

Simulating the interaction of fluids with immersed moving solids is playing an important role for gaining a better quantitative understanding of how fluid dynamics is altered by the presence of obstacles and, vice versa, which forces are exerted on the solids by the moving fluid. Such problems appear in various contexts, ranging from numerous technical applications such as e.g. turbines to medical problems such as the regulation of cardiovascular hemodyamics by valves. Typically, the numerical treatment of such problems is posed within a fluid structure interaction (FSI) framework. General FSI models are able to capture bidirectional interactions, but are challenging to solve and computationally expensive. Simplified methods offer a possible remedy by achieving better computational efficiency to broaden the scope to demanding application problems with focus on understanding the effect of solids on altering fluid dynamics. In this study we report on the development of a novel method for such applications. In our method rigid moving obstacles are incorporated in a fluid dynamics context using concepts from porous media theory. Based on the Navier-Stokes-Brinkman equations which augments the Navier-Stokes equation with a Darcy drag term our method represents solid obstacles as time-varying regions containing a porous medium of vanishing permeability. Numerical stabilization and turbulence modeling is dealt with by using a residual based variational multiscale (RBVMS) formulation. The additional Darcy drag term and its respective stabilization are easily accomodated in any existing finite-element based Navier-Stokes solver. The key advantages of our approach - computational efficiency and ease of implementation - are demonstrated by solving a standard benchmark problem of a rotating blood pump posed by the Food and Drug Administration Agency (FDA). Validity is demonstrated by conducting a mesh convergence study and by comparison against the extensive set of experimental

关键词： Large eddy simulation

Impact of subdominant modes on the interpretation of gravitational-wave signals from heavy binary black hole systems

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arXiv 2019年

作者： Shaik, Feroz H. Lange, Jacob Field, Scott E. O'Shaughnessy, Richard Varma, Vijay Kidder, Lawrence E. Pfeiffer, Harald P. Wysocki, Daniel Department of Physics Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth DartmouthMA02747 United States Center for Computational Relativity and Gravitation Rochester Institute of Technology RochesterNY14623 United States Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth DartmouthMA02747 United States Theoretical Astrophysics California Institute of Technology PasadenaCA91125 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Am Mühlenberg 1 Potsdam14476 Germany

Over the past year, a handful of new gravitational wave models have been developed to include multiple harmonic modes thereby enabling for the first time fully Bayesian inference studies including higher modes to be performed. Using one recently-developed numerical relativity surrogate model, NRHybSur3dq8, we investigate the importance of higher modes on parameter inference of coalescing massive binary black holes. We focus on examples relevant to the current three-detector network of observatories, with detector-frame masses and signal amplitudes consistent with plausible candidates for the next few observing runs. We show that for such systems the higher mode content will be important for interpreting coalescing binary black holes, reducing systematic bias, and computing properties of the remnant object. For asymmetric binaries with mass ratios q > 1, higher modes are critical, and their omission usually produces substantial parameter biases. Even for comparable-mass binaries and at low signal amplitude, the omission of higher modes can influence posterior probability distributions. We discuss the impact of our results on source population inference and self-consistency tests of general relativity. Copyright © 2019, The Authors. All rights reserved.

关键词： Stars

A Family of-Stable Optimized Hybrid Block Methods for Integrating Stiff Differential Systems

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Mathematical Problems in engineering 2022年第1期2022卷

作者： Rajat Singla Gurjinder Singh Higinio Ramos V. Kanwar Department of Mathematical Sciences I. K. Gujral Punjab Technical University Jalandhar Main Campus Kapurthala-144603 Punjab India Department of Mathematics Akal University Bathinda-151302 India Scientific Computing Group Universidad de Salamanca Plaza de la Merced 37008 Salamanca Spainusal.es Escuela Politécnica Superior de Zamora Campus Viriato 49022 Zamora Spain University Institute of Engineering and Technology Panjab University Chandigarh-160014 Indiapuchd.ac.in

In this article, a family of one-step hybrid block methods having two intrastep points is developed for solving first-order initial value stiff differential systems that occur frequently in science and engineering. In each method of the family, an intrastep point controls the order of the main method and a second one has a control over the stability features of the method. The approach used to develop the class of -stable methods is based on interpolation and collocation procedures. The methods exhibit hybrid nature and produce numerical solutions at several points simultaneously. These methods can also be formulated as Runge-Kutta (RK) methods. Comparisons between the RK and block formulations of the proposed methods reveal a better performance of the block formulation in terms of computational efficiency. Furthermore, the efficiency of the methods is improved when they are formulated as adaptive step-size solvers using an error-control approach. Some methods of the proposed class have been tested to solve some well-known stiff differential systems. The numerical experiments show that the proposed family of methods performs well in comparison with some of the existing methods in the scientific literature.

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Electron spin mediated distortion in metallic systems

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arXiv 2019年

作者： Anand, G. Eisenbach, Markus Goodall, Russell Freeman, Colin L. Department of Metallurgy and Materials Engineering Indian Institute of Engineering Science and Technology-Shibpur Howrah WB India Department of Materials Science and Engineering University of Sheffield United Kingdom Warwick Centre for Predictive Modelling University of Warwick United Kingdom Scientific Computing Group Centre for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States

The deviation of positions of atoms from their ideal lattice sites in crystalline solid state systems causes distortion and can lead to variation in structural [1] and functional properties [2]. Distortion in molecular systems has been traditionally understood in the term of Jahn-Teller distortion [3], while for the one-dimensional chain of metals, the Peierls distortion mechanism has been proposed [4]. In real three dimensional metallic systems, a fundamental description of the distortion is missing, which we need to design alloys with attractive structural properties. The present investigation presents the evolution of distortion in metallic systems in terms of magnetovolume effects arising due to magnetic ground-state of the system. Particularly the significant distortion in the case of Cr due to presence of other transition metals is seen. Cr with transition metals as a candidate system to study the correlation between charge disproportion, spin-flip, magnetovolume effects and Fermi surface nesting. This study provides a novel explanation of strengthening effect of Cr observed in alloys due to its unique magnetic properties. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic moments

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

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arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Rajendra Acharya, U. Gorriz, Juan M. The Data Science and Computational Intelligence Institute University of Granada Spain The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Deakin University VIC3217 Australia The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran System Administrator at Dibrugarh University Assam786004 India Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran The School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Dept. of Psychiatry University of Cambridge United Kingdom

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed. © 2020, CC BY-NC-ND.

关键词： COVID-19

A lowest-order mixed finite element method for the elastic transmission eigenvalue problem

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arXiv 2018年

作者： Xi, Yingxia Ji, Xia School of Science Nanjing University of Science and Technology Nanjing210094 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China

The goal of this paper is to develop numerical methods computing a few smallest elastic interior transmission eigenvalues, which are of practical importance in inverse elastic scattering theory. The problem is challenging since it is nonlinear, non-self-adjoint, and of fourth order. In this paper, we construct a lowest-order mixed finite element method which is close to the Ciarlet-Raviart mixed finite element method. This scheme is based on Lagrange finite elements and is one of the less expensive methods in terms of the amount of degrees of freedom. Due to the non-self-adjointness, the discretization of elastic transmission eigenvalue problem leads to a non-classical mixed method which does not fit into the framework of classical theoretical analysis. In stead, we obtain the convergence analysis based on the spectral approximation theory of compact operators. Numerical examples are presented to verify the theory. Both real and complex eigenvalues can be *** Codes 65N25, 65N30, 47B07 Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

A foundation model for atomistic materials chemistry

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arXiv 2023年

作者： Batatia, Ilyes Benner, Philipp Chiang, Yuan Elena, Alin M. Kovács, Dávid P. Riebesell, Janosh Advincula, Xavier R. Asta, Mark Avaylon, Matthew Baldwin, William J. Berger, Fabian Bernstein, Noam Bhowmik, Arghya Blau, Samuel M. Cărare, Vlad Darby, James P. De, Sandip Pia, Flaviano Della Deringer, Volker L. Elijošius, Rokas El-Machachi, Zakariya Falcioni, Fabio Fako, Edvin Ferrari, Andrea C. Genreith-Schriever, Annalena George, Janine Goodall, Rhys E.A. Grey, Clare P. Grigorev, Petr Han, Shuang Handley, Will Heenen, Hendrik H. Hermansson, Kersti Holm, Christian Hofmann, Stephan Jaafar, Jad Jakob, Konstantin S. Jung, Hyunwook Kapil, Venkat Kaplan, Aaron D. Karimitari, Nima Kermode, James R. Kroupa, Namu Kullgren, Jolla Kuner, Matthew C. Kuryla, Domantas Liepuoniute, Guoda Margraf, Johannes T. Magdău, Ioan-Bogdan Michaelides, Angelos Harry Moore, J. Naik, Aakash A. Niblett, Samuel P. Norwood, Sam Walton O’Neill, Niamh Ortner, Christoph Persson, Kristin A. Reuter, Karsten Rosen, Andrew S. Schaaf, Lars L. Schran, Christoph Shi, Benjamin X. Sivonxay, Eric Stenczel, Tamás K. Svahn, Viktor Sutton, Christopher Swinburne, Thomas D. Tilly, Jules van der Oord, Cas Vargas, Santiago Varga-Umbrich, Eszter Vegge, Tejs Vondrák, Martin Wang, Yangshuai Witt, William C. Zills, Fabian Csányi, Gábor Engineering Laboratory University of Cambridge Trumpington St and JJ Thomson Ave Cambridge United Kingdom Berlin Germany Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Mathematics Department University of British Columbia 1984 Mathematics Rd VancouverBCV6T 1Z2 Canada Institute of Condensed Matter Theory and Solid State Optics Friedrich Schiller University Jena Germany Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Bayreuth Germany Fritz-Haber-Institute of the Max-Planck-Society Berlin Germany Energy Technologies Area Lawrence Berkeley National Laboratory BerkeleyCA94720 United States U. S. Naval Research Laboratory WashingtonDC20375 United States Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road Cambridge United Kingdom Cavendish Laboratory University of Cambridge J. J. Thomson Ave Cambridge United Kingdom Department of Materials Science and Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Chemix Inc. SunnyvaleCA94085 United States Inorganic Chemistry Laboratory Department of Chemistry University of Oxford OxfordOX1 3QR United Kingdom Scientific Computing Department Science and Technology Facilities Council Daresbury Laboratory Keckwick Lane DaresburyWA4 4AD United Kingdom BASF SE Carl-Bosch-Straße 38 Ludwigshafen67056 Germany Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Department of Chemistry and Biochemistry University of South Carolina South Carolina29208 United States Lennard-Jones Centre University of Cambridge Trinity Ln CambridgeCB2 1TN United Kingdom Institute for Computational Physics University of Stuttgart Stuttgart70569 Germany Department of Chemistry–Ångström Uppsala University Box 538 Uppsala

Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest;and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials. We demonstrate the power of the MACE-MP-0 model — and its qualitative and at times quantitative accuracy — on a diverse set problems in the physical sciences, including the properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximation

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arXiv 2019年

作者： Tang, Shanshan Li, Bo Yu, Haijun Software Development Center Industrial and Commercial Bank of China No. 16 Building of ZhongGuanCun Software Park Haidian District Beijing100193 China Huawei Technologies Co. Ltd Bai Ruida Apartment Bantian Street Longgang District Shenzhen518129 China NCMIS & LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

In a previous study [B. Li, S. Tang and H. Yu, Commun. Comput. Phy. 27(2):379-411, 2020], it is shown that deep neural networks built with rectified power units (RePU) as activation functions can give better approximation for sufficient smooth functions than those built with rectified linear units, by converting polynomial approximations using power series into deep neural networks with optimal complexity and no approximation error. However, in practice, power series approximations are not easy to obtain due to the associated stability issue. In this paper, we propose a new and more stable way to construct RePU deep neural networks based on Chebyshev polynomial approximations. By using a hierarchical structure of Chebyshev polynomial approximation in frequency domain, we obtain efficient and stable deep neural network construction, which we call ChebNet. The approximation of smooth functions by ChebNets is no worse than the approximation by deep RePU nets using power series. On the same time, ChebNets are much more stable. Numerical results show that the constructed ChebNets can be further fine-tuned to obtain much better results than those obtained by tuning deep RePU nets constructed by power series approach. As spectral accuracy is hard to obtain by direct training of deep neural networks, ChebNets provide a practical way to obtain spectral accuracy, it is expected to be useful in real applications that require efficient approximations of smooth functions. © 2019, CC BY-NC-ND.

关键词： Chebyshev polynomials