Albany is a parallel C++ finite element library for solving forward and inverse problems involving partial differential equations (PDEs). In this paper we introduce PyAlbany, a newly developed python interface to the ...
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Albany is a parallel C++ finite element library for solving forward and inverse problems involving partial differential equations (PDEs). In this paper we introduce PyAlbany, a newly developed python interface to the Albany library. PyAlbany can be used to effectively drive Albany enabling fast and easy analysis and post-processing of applica-tions based on PDEs that are pre-implemented in Albany. PyAlbany relies on the library PyBind11 to bind python with C++ Albany code. Here we detail the implementation of PyAlbany and showcase its capabilities through a number of examples targeting a heat -diffusion problem. In particular we consider (1) the generation of samples for a Monte Carlo application, (2) a scalability study, (3) a study of parameters on the performance of a linear solver, and (4) a tool for performing eigenvalue decompositions of matrix-free operators for a Bayesian inference application.(c) 2023 Elsevier B.V. All rights reserved.
Data-driven tools and techniques have proved their effectiveness in many engineering applications. Machine-learning has gradually become a paradigm to explore innovative designs in turbomachinery. However, industrial ...
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Data-driven tools and techniques have proved their effectiveness in many engineering applications. Machine-learning has gradually become a paradigm to explore innovative designs in turbomachinery. However, industrial computational fluid dynamics (CFD) experts are still reluctant to embed similar approaches in standard practice and very few solutions have been proposed so far. The aim of the work is to prove that standard wall treatments can obtain serious benefits from machine-learning modeling. Turbomachinery flow modeling lives in a constant compromise between accuracy and the computational costs of numerical simulations. One of the key factors of the process is defining a proper wall treatment. Many works point out how insufficient resolutions of boundary layers may lead to incorrect predictions of turbomachinery performances. Wall functions are universally exploited to replicate the physics of boundary layers where grid resolution does not suffice. Widespread wall functions were derived by the observation of a few canonical flows, further expressed as a simple polynomial of Reynolds number and turbulent kinetic energy. Despite their popularity, these functions are frequently applied in flows where the ground assumptions cease to be true, such as rotating passages or swirled flows. In these flows, the mathematical formulations of wall functions do not account for the distortion on the boundary layer due to the combined action of centrifugal and Coriolis forces. Here, we will derive a wall function for rotating passages, through means of machine-learning. The algorithm is directly implemented in the NS equations solver. Cross-validation results show that the machine-learnt wall treatment is able to effectively correct the turbulent kinetic energy field near the solid walls, without impairing the accuracy of the Reynolds-averaged numerical simulations (RANS) turbulence model in any way.
This paper proposes a new video surveillance system designed for Deep Learning. The proposed system uses three steps to transfer RTSP streams to pictures for Deep Learning. First it decapsulates the streams, then deco...
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ISBN:
(纸本)9781728138688
This paper proposes a new video surveillance system designed for Deep Learning. The proposed system uses three steps to transfer RTSP streams to pictures for Deep Learning. First it decapsulates the streams, then decodes and converts color space & extracts frames. The proposed system has two ways to decode RTSP streams, hardware decoding and software decoding. By checking the processor's version of CPU firstly, system chooses a better way to decode. The proposed system has GPU and CPU. CPU is used to process RTSP streams, extract frames and do human-machine interaction. GPU is used for computing and analyzing the algorithms of Deep Learning. So the complex computing does not run on the CPU. The proposed system runs on Linux system and has python interface, so it can easily connect with the models of Deep Learning. By running on multiple machines, the result shows that the proposed system can process up to 16 channels of stream. After 7*24 hours of testing on several machines, this system can run continuously without downtime and the delay time is less than 7 seconds.
In recent years, open-source applications have replaced proprietary software in many fields. Especially open-source software tools based on Linux operating system have wide range of utilization. In terms of CNC soluti...
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ISBN:
(纸本)9788001064740
In recent years, open-source applications have replaced proprietary software in many fields. Especially open-source software tools based on Linux operating system have wide range of utilization. In terms of CNC solutions, an open-source system LinuxCNC can be used. However, the LinuxCNC control software and the graphical user interface (GUI) could be developed only on top of Hardware Abstraction Layer. Nevertheless, the LinuxCNC community provided python interface, which allows for controlling CNC machine using python programming language, therefore whole control software can be developed in python. The paper focuses on a development of a multi-process control software mainly for in-house developed loading devices operated at our institute. The software tool is based on the LinuxCNC python interface and Qt framework, which gives the software an ability to be modular and effectively adapted for various devices.
Many important partial differential equation problems in homogeneous media, such as those of acoustic or electromagnetic wave propagation, can be represented in the form of integral equations on the boundary of the do...
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Many important partial differential equation problems in homogeneous media, such as those of acoustic or electromagnetic wave propagation, can be represented in the form of integral equations on the boundary of the domain of interest. In order to solve such problems, the boundary element method (BEM) can be applied. The advantage compared to domain-discretisation-based methods such as finite element methods is that only a discretisation of the boundary is necessary, which significantly reduces the number of unknowns. Yet, BEM formulations are much more difficult to implement than finite element methods. In this article, we present BEM++, a novel open-source library for the solution of boundary integral equations for Laplace, Helmholtz and Maxwell problems in three space dimensions. BEM++ is a C++ library with python bindings for all important features, making it possible to integrate the library into other C++ projects or to use it directly via python scripts. The internal structure and design decisions for BEM++ are discussed. Several examples are presented to demonstrate the performance of the library for larger problems.
A python tool for manipulating netCDF files in a parallel infrastructure is proposed. The parallel interface, PyPnetCDF, manages netCDF properties in a similar way to the serial version from Scientificpython, but hidi...
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A python tool for manipulating netCDF files in a parallel infrastructure is proposed. The parallel interface, PyPnetCDF, manages netCDF properties in a similar way to the serial version from Scientificpython, but hiding parallelism to the user. Implementations details and capabilities of the developed interfaces are given. Numerical experiments that show the friendly use of the interfaces and their behaviour compared with the native routines, are presented. (C) 2009 Civil-Comp Ltd. and Elsevier Ltd. All rights reserved.
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