An Efficient Formulation for General-Purpose Multibody/Multiphysics Analysis

Author(s):  
Pierangelo Masarati ◽  
Marco Morandini ◽  
Paolo Mantegazza

This paper presents a formulation for the efficient solution of general-purpose multibody/multiphysics problems. The core equations and details on structural dynamics and finite rotations handling are presented. The solution phases are illustrated. Highlights of the implementation are presented, and special features are discussed.

Author(s):  
Alex Ng ◽  
Shiping Chen

Performance testing is one of the vital activities spanning the whole life cycle of software engineering. As a result, there are a considerable number of performance testing products and open source tools available. It has been observed that most of the existing performance testing products and tools are either too expensive and complicated for small projects, or too specific and simple for diverse performance tests. In this chapter, we will present an overview of existing performance test products/tools, provide a summary of some of the contemporary system performance testing frameworks, and capture the key requirements for a general-purpose performance testing framework. Based on our previous works, we propose a system performance testing framework which is suitable for both simple and small, as well as complicated and large-scale performance testing projects. The core of our framework contains an abstraction to facilitate performance testing by separating the application logic from the common performance testing functionality, and a set of general-purpose data model.


Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 193 ◽  
Author(s):  
Sebastian Raschka ◽  
Joshua Patterson ◽  
Corey Nolet

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.


Author(s):  
Dave Vieglais ◽  
Stephen Richard ◽  
Hong Cui ◽  
Neil Davies ◽  
John Deck ◽  
...  

Material samples form an important portion of the data infrastructure for many disciplines. Here, a material sample is a physical object, representative of some physical thing, on which observations can be made. Material samples may be collected for one project initially, but can also be valuable resources for other studies in other disciplines. Collecting and curating material samples can be a costly process. Integrating institutionally managed sample collections, along with those sitting in individual offices or labs, is necessary to faciliate large-scale evidence-based scientific research. Many have recognized the problems and are working to make data related to material samples FAIR: findable, accessible, interoperable, and reusable. The Internet of Samples (i.e., iSamples) is one of these projects. iSamples was funded by the United States National Science Foundation in 2020 with the following aims: enable previously impossible connections between diverse and disparate sample-based observations; support existing research programs and facilities that collect and manage diverse sample types; facilitate new interdisciplinary collaborations; and provide an efficient solution for FAIR samples, avoiding duplicate efforts in different domains (Davies et al. 2021) enable previously impossible connections between diverse and disparate sample-based observations; support existing research programs and facilities that collect and manage diverse sample types; facilitate new interdisciplinary collaborations; and provide an efficient solution for FAIR samples, avoiding duplicate efforts in different domains (Davies et al. 2021) The initial sample collections that will make up the internet of samples include those from the System for Earth Sample Registration (SESAR), Open Context, the Genomic Observatories Meta-Database (GEOME), and Smithsonian Institution Museum of Natural History (NMNH), representing the disciplines of geoscience, archaeology/anthropology, and biology. To achieve these aims, the proposed iSamples infrastructure (Fig. 1) has two key components: iSamples in a Box (iSB) and iSamples Central (iSC). The iSC component will be a permanent Internet service that preserves, indexes, and provides access to sample metadata aggregated from iSBs. It will also ensure that persistent identifiers and sample descriptions assigned and used by individual iSBs are synchronized with the records in iSC and with identifier authorities like International Geo Sample Number (IGSN) or Archival Resource Key (ARK). The iSBs create and maintain identifiers and metadata for their respective collection of samples. While providing access to the samples held locally, an iSB also allows iSC to harvest its metadata records. The metadata modeling strategy adopted by the iSamples project is a metadata profile-based approach, where core metadata fields that are applicable to all samples, form the core metadata schema for iSamples. Each individual participating collectionis free to include additional metadata in their records, which will also be harvested by iSC and are discoverable through the iSC user interface or APIs (Application Programming Interfaces), just like the core. In-depth analysis of metadata profiles used by participating collections, including Darwin Core, has resulted in an iSamples core schema currently being tested and refined through use. See the current version of the iSamples core schema. A number of properties require a controlled vocabulary. Controlled vocabularies used by existing records are kept, while new vocabularies are also being developed to support high-level grouping with consistent semantics across collection types. Examples include vocabularies for Context Category, Material Category, and Specimen Type (Table 1). These vocabularies were also developed in a bottom-up manner, based on the terms used in the existing collections. For each vocabulary, a decision tree graph was created to illustrate relations among the terms, and a card sorting exercise was conducted within the project team to collect feedback. Domain experts are invited to take part in this exercise here, here, and here. These terms will be used as upper-level terms to the existing category terms used in the participating collections and hence create connections among individual participating collections. iSample project members are also active in the TDWG Material Sample Task Group and the global consultation on Digital Extended Specimens. Many members of the iSamples project also lead or participate in a sister research coordination network (RCN), Sampling Nature. The goal of this RCN is to develop and refine metadata standards and controlled vocabularies for the iSamples and other projects focusing on material samples. We cordially invite you to participate in the Sampling Nature RCN and help shape the future standards for material samples. Contact Sarah Ramdeen ([email protected]) to engage with the RCN.


Author(s):  
Volodymyr Kozyrskyi ◽  
Andrii Petrenko ◽  
Mykola Trehub ◽  
Yangibay Charyev

The activity of farm enterprises is directly connected with the efficient use of resources such as electrical energy and water. Taking such conditions as factors of economic expediency and ecological safety into account, it is more reasonable to use wind stations in order to provide consumers with energy and water. The use of conventional wind and electrical stations is an easy and reliable solution. However, annual wind velocity on most settled territories does not exceed 6 m/s or even 4 m/s. It makes the efficient use of wind electrical stations more complicated. One of the solutions can probably be the use of wind and electrical stations on the basis of slow speed non-transmission arc-shaped-stator inductor-type generators with an integrated radial and ring-shaped rotor. Another efficient solution to provide areas with water and electrical energy is to use a combined wind station with a crank-and-rod mechanism and the rod of the driving mechanism of the back-and-forth motion of the core of a magnetic and electrical linear generator and the piston of a plunger pump.


1995 ◽  
Vol 2 (3) ◽  
pp. 193-204 ◽  
Author(s):  
Sang-Ho Lee ◽  
Ted Belytschko

The implementation and application of h-adaptivity in an explicit finite element program for nonlinear structural dynamics is described. Particular emphasis is placed on developing procedures for general purpose structural dynamics programs and efficiently handling adaptivity in shell elements. New projection techniques for error estimation and projecting variables on new meshes after fission or fusion are described. Several problems of severe impact are described.


2019 ◽  
Vol 29 (05) ◽  
pp. 839-870 ◽  
Author(s):  
Taro Kanai ◽  
Kenji Takizawa ◽  
Tayfun E. Tezduyar ◽  
Kenji Komiya ◽  
Masayuki Kaneko ◽  
...  

We present methods for computation of flow-driven string dynamics in a pump and related residence time. The string dynamics computations help us understand how the strings carried by a fluid interact with the pump surfaces, including the blades, and get stuck on or around those surfaces. The residence time computations help us to have a simplified but quick understanding of the string behavior. The core computational method is the Space–Time Variational Multiscale (ST-VMS) method, and the other key methods are the ST Isogeometric Analysis (ST-IGA), ST Slip Interface (ST-SI) method, ST/NURBS Mesh Update Method (STNMUM), a general-purpose NURBS mesh generation method for complex geometries, and a one-way-dependence model for the string dynamics. The ST-IGA with NURBS basis functions in space is used in both fluid mechanics and string structural dynamics. The ST framework provides higher-order accuracy. The VMS feature of the ST-VMS addresses the computational challenges associated with the turbulent nature of the unsteady flow, and the moving-mesh feature of the ST framework enables high-resolution computation near the rotor surface. The ST-SI enables moving-mesh computation of the spinning rotor. The mesh covering the rotor spins with it, and the SI between the spinning mesh and the rest of the mesh accurately connects the two sides of the solution. The ST-IGA enables more accurate representation of the pump geometry and increased accuracy in the flow solution. The IGA discretization also enables increased accuracy in the structural dynamics solution, as well as smoothness in the string shape and fluid dynamics forces computed on the string. The STNMUM enables exact representation of the mesh rotation. The general-purpose NURBS mesh generation method makes it easier to deal with the complex geometry we have here. With the one-way-dependence model, we compute the influence of the flow on the string dynamics, while avoiding the formidable task of computing the influence of the string on the flow, which we expect to be small.


Author(s):  
Aurélien Francillon ◽  
Sam L. Thomas ◽  
Andrei Costin

AbstractThe goal of this chapter is to introduce the reader to the domain of bug discovery in embedded systems which are at the core of the Internet of Things. Embedded software has a number of particularities which makes it slightly different to general purpose software. In particular, embedded devices are more exposed to software attacks but have lower defense levels and are often left unattended. At the same time, analyzing their security is more difficult because they are very “opaque”, while the execution of custom and embedded software is often entangled with the hardware and peripherals. These differences have an impact on our ability to find software bugs in such systems. This chapter discusses how software vulnerabilities can be identified, at different stages of the software life-cycle, for example during development, during integration of the different components, during testing, during the deployment of the device, or in the field by third parties.


Author(s):  
Alex Ng ◽  
Shiping Chen

Performance testing is one of the vital activities spanning the whole life cycle of software engineering. As a result, there are a considerable number of performance testing products and open source tools available. It has been observed that most of the existing performance testing products and tools are either too expensive and complicated for small projects or too specific and simple for diverse performance tests. In this chapter, the authors present an overview of existing performance test products/tools, provide a summary of some of the contemporary system performance testing frameworks, and capture the key requirements for a general-purpose performance testing framework. Based on previous works, the authors propose a system performance testing framework that is suitable for both simple and small as well as complicated and large-scale performance testing projects. The core of the framework contains an abstraction to facilitate performance testing by separating the application logic from the common performance testing functionality and a general-purpose data model.


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