Further Large-Scale Implementation of Advanced Pipeline Technologies

2008 ◽  
Author(s):  
Joe Zhou ◽  
David Taylor ◽  
David Hodgkinson
Keyword(s):  
Technology Implementation ◽  
Ultrasonic Testing ◽  
High Performance ◽  
Large Scale ◽  
Phased Array ◽  
Tandem Welding ◽  
Implementation Program ◽  
Wide Range ◽  
High Performance Composite ◽  
Welding System

TransCanada PipeLines Limited (TransCanada) has continued its leading effort in developing and implementing pipeline technologies. With a well structured and large-scale technology implementation program and collaboration of many partners over a period of three years, TransCanada has successfully implemented a number of technologies in a 38 km long NPS 42 pipeline construction project. The technology implementation program included installation of 7.3 km Grade 690 (X100) pipe supplied by two manufacturers, deployment of tandem welding system, field trial of a phased array automated ultrasonic testing (AUT) system, the application of high performance composite coating (HPCC) and Alternative Integrity Validation (AIV) process that led to first ever construction hydrostatic test waiver from National Energy Board. The paper provides an overview of the technology implementation program and the experience gained in applying a wide range of advanced pipeline technologies.

scholarly journals Process Design Kit and Design Automation for Flexible Hybrid Electronics

2019 ◽  
Vol 16 (3) ◽  
pp. 117-123
Author(s):  
Tsung-Ching Huang ◽  
Ting Lei ◽  
Leilai Shao ◽  
Sridhar Sivapurapu ◽  
Madhavan Swaminathan ◽  
...  
Keyword(s):  
Process Design ◽  
Design Automation ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Thin Film Transistor ◽  
Solution Process ◽  
Oxide Semiconductor ◽  
Wearable Electronics ◽  
Wide Range

Abstract High-performance low-cost flexible hybrid electronics (FHE) are desirable for applications such as internet of things and wearable electronics. Carbon nanotube (CNT) thin-film transistor (TFT) is a promising candidate for high-performance FHE because of its high carrier mobility, superior mechanical flexibility, and material compatibility with low-cost printing and solution processes. Flexible sensors and peripheral CNT-TFT circuits, such as decoders, drivers, and sense amplifiers, can be printed and hybrid-integrated with thinned (<50 μm) silicon chips on soft, thin, and flexible substrates for a wide range of applications, from flexible displays to wearable medical devices. Here, we report (1) a process design kit (PDK) to enable FHE design automation for large-scale FHE circuits and (2) solution process-proven intellectual property blocks for TFT circuits design, including Pseudo-Complementary Metal-Oxide-Semiconductor (Pseudo-CMOS) flexible digital logic and analog amplifiers. The FHE-PDK is fully compatible with popular silicon design tools for design and simulation of hybrid-integrated flexible circuits.

scholarly journals Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Vinícius da Fonseca Vieira ◽  
Carolina Ribeiro Xavier ◽  
Nelson Francisco Favilla Ebecken ◽  
Alexandre Gonçalves Evsukoff
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
High Performance ◽  
Large Scale ◽  
Fine Tuning ◽  
Detection Algorithms ◽  
Research Areas ◽  
Structure Detection ◽  
Wide Range ◽  
Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

scholarly journals High-Sensitivity Flexible Pressure Sensor-Based 3D CNTs Sponge for Human–Computer Interaction

Polymers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 3465
Author(s):  
Jianli Cui ◽  
Xueli Nan ◽  
Guirong Shao ◽  
Huixia Sun
Keyword(s):  
Pressure Sensor ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Pressure Sensors ◽  
Chemical Vapor ◽  
High Sensitivity ◽  
Wearable Electronics ◽  
Wide Range ◽  
Flexible Pressure Sensors

Researchers are showing an increasing interest in high-performance flexible pressure sensors owing to their potential uses in wearable electronics, bionic skin, and human–machine interactions, etc. However, the vast majority of these flexible pressure sensors require extensive nano-architectural design, which both complicates their manufacturing and is time-consuming. Thus, a low-cost technology which can be applied on a large scale is highly desirable for the manufacture of flexible pressure-sensitive materials that have a high sensitivity over a wide range of pressures. This work is based on the use of a three-dimensional elastic porous carbon nanotubes (CNTs) sponge as the conductive layer to fabricate a novel flexible piezoresistive sensor. The synthesis of a CNTs sponge was achieved by chemical vapor deposition, the basic underlying principle governing the sensing behavior of the CNTs sponge-based pressure sensor and was illustrated by employing in situ scanning electron microscopy. The CNTs sponge-based sensor has a quick response time of ~105 ms, a high sensitivity extending across a broad pressure range (less than 10 kPa for 809 kPa−1) and possesses an outstanding permanence over 4,000 cycles. Furthermore, a 16-pixel wireless sensor system was designed and a series of applications have been demonstrated. Its potential applications in the visualizing pressure distribution and an example of human–machine communication were also demonstrated.

scholarly journals Micromechanical Process Integration and Material Optimization for High Performance Silicon-Germanium Bolometers

2012 ◽  
Vol 1437 ◽  
Author(s):  
Gunnar B. Malm ◽  
Mohammadreza Kolahdouz ◽  
Fredrik Forsberg ◽  
Niclas Roxhed ◽  
Frank Niklaus
Keyword(s):  
Integrated Circuits ◽  
Silicon Germanium ◽  
High Performance ◽  
Large Scale ◽  
Process Integration ◽  
Low Frequency ◽  
Single Layer ◽  
Sige Layer ◽  
Frequency Noise ◽  
Wide Range

ABSTRACTSemiconductor-based thermistors are very attractive sensor materials for uncooled thermal infrared (IR) bolometers. Very large scale heterogeneous integration of MEMS is an emerging technology that allows the integration of epitaxially grown, high-performance IR bolometer thermistor materials with pre-processed CMOS-based integrated circuits for the sensor read-out. Thermistor materials based on alternating silicon (Si) and silicon-germanium (SiGe) epitaxial layers have been demonstrated and their performance is continuously increasing. Compared to a single layer of silicon or SiGe, the temperature coefficient of resistance (TCR) can be strongly enhanced to about 3 %/K, by using thin alternating layers. In this paper we report on the optimization of alternating Si/SiGe layers by advanced physically based simulations, including quantum mechanical corrections. Our simulation framework provides reliable predictions for a wide range of SiGe layer compositions, including concentration gradients. Finally, our SiGe thermistor layers have been evaluated in terms of low-frequency noise performance, in order to optimize the bolometer detectivity.

scholarly journals From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

2013 ◽  
Vol 21 (1-2) ◽  
pp. 1-16 ◽  
Author(s):  
Marek Blazewicz ◽  
Ian Hinder ◽  
David M. Koppelman ◽  
Steven R. Brandt ◽  
Milosz Ciznicki ◽  
...  
Keyword(s):  
Code Generation ◽  
High Performance ◽  
Large Scale ◽  
Einstein Equations ◽  
Efficient Manner ◽  
Code Transformations ◽  
Wide Range ◽  
Problem Description ◽  
Physics Model ◽  
High Level

Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, theChemoraframework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.

Design of wide-band dual–polarized aperture array antennas

2012 ◽  
Vol 4 (3) ◽  
pp. 373-378 ◽  
Author(s):  
Yongwei Zhang ◽  
Anthony K. Brown
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Wide Band ◽  
Array Antennas ◽  
Wide Range ◽  
Square Kilometer Array ◽  
Aperture Array ◽  
Dual Polarized ◽  
Rf Performance ◽  
The Cost

This paper describes the design of high-performance compact aperture array antennas for radio astronomy and other applications. Three recent antenna developments for square kilometer array design study (SKADS) have been investigated and the performances are compared. In addition to the radio frequency (RF) performance, an essential requirement for the square kilometer array application is the cost per square area. Based on initial large–scale finite array studies, prototypes with different geometries have been fabricated and measured. Guidelines are derived for large–scale wide–band dual-polarized array designs in applications where low cross-polarization and a wide range of scan angles are required.

A Distributed Computing Tool for Generating Neural Simulation Databases

2006 ◽  
Vol 18 (12) ◽  
pp. 2923-2927 ◽  
Author(s):  
Robert J. Calin-Jageman ◽  
Paul S. Katz
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Model Neuron ◽  
Scale Model ◽  
Large Set ◽  
Experimental Conditions ◽  
Entry Level ◽  
Neural Simulation ◽  
Wide Range ◽  
Parameter Values

After developing a model neuron or network, it is important to systematically explore its behavior across a wide range of parameter values or experimental conditions, or both. However, compiling a very large set of simulation runs is challenging because it typically requires both access to and expertise with high-performance computing facilities. To lower the barrier for large-scale model analysis, we have developed NeuronPM, a client/server application that creates a “screen-saver” cluster for running simulations in NEURON (Hines & Carnevale, 1997). NeuronPM provides a user-friendly way to use existing computing resources to catalog the performance of a neural simulation across a wide range of parameter values and experimental conditions. The NeuronPM client is a Windows-based screen saver, and the NeuronPM server can be hosted on any Apache/PHP/MySQL server. During idle time, the client retrieves model files and work assignments from the server, invokes NEURON to run the simulation, and returns results to the server. Administrative panels make it simple to upload model files, define the parameters and conditions to vary, and then monitor client status and work progress. NeuronPM is open-source freeware and is available for download at http://neuronpm.homeip.net . It is a useful entry-level tool for systematically analyzing complex neuron and network simulations.

scholarly journals MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data

2018 ◽  
Author(s):  
LM Simon ◽  
S Karg ◽  
AJ Westermann ◽  
M Engel ◽  
AHA Elbehery ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Human Disease ◽  
High Performance ◽  
Large Scale ◽  
Expression Patterns ◽  
Rna Seq ◽  
Wide Range ◽  
Eukaryotic Gene ◽  
Public Repositories ◽  
Performance Computing

AbstractBackgroundWith the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts.FindingsWe developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from 6 independent controlled infection experiments of cell line models and comparison with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from >17,000 samples from >400 studies relevant to human disease using state-of-the-art high performance computing systems. The resulting data of this large-scale re-analysis are made available in the presented MetaMap resource.ConclusionsOur results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation towards the role of the microbiome in human disease.

Data-streaming workflow for seismic source location with PyCOMPSs parallel computational framework

2021 ◽  
Author(s):  
Natalia Poiata ◽  
Javier Conejero ◽  
Rosa M. Badia ◽  
Jean-Pierre Vilotte
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Programming Model ◽  
Automatic Detection ◽  
Seismic Source ◽  
Seismic Sources ◽  
Data Streaming ◽  
Waveform Data ◽  
Wide Range ◽  
Seismic Networks

<p>Modern digital seismic networks record a wealth of high-quality continuous waveforms that contain a variety of signals associated to a wide range of seismic sources (e.g., earthquakes, volcanic, tectonic tremors, environmental sources) that probe transient energy release processes. Efficient and automatic detection, location and characterization of these different seismic sources is critical to understand slowly-driven evolution of active tectonic and volcanic systems toward catastrophic events. Developing a common analysis framework for systematic exploration of the increasing wealth of seismic observation streams is important for improving seismic monitoring systems and extracting large and accurately resolved seismic source catalogues.</p><p>To this end, we present a scalable parallelization with PyCOMPSs (Tejedor et al., 2017) of the python-based BackTrackBB data-streaming workflow (Poiata et al., 2016; 2018) for automatic detection and location of seismic sources from continuous waveform streams recorded by large seismic networks. This allows achieving an efficient distribution and orchestration of BackTrackBB code on different architectures. PyCOMPSs is a task-based programming model for python applications that relies in a powerful runtime able to extract dynamically the parallelism among tasks and executing them in distributed environments (e.g. HPC Clusters, Cloud infrastructures, etc.) transparently to the users.</p><p>We will provide details of the PyCOMPSs-based BackTrackBB workflow implementation. Results of scalability tests and memory usage analysis will be also discussed. Tests have been performed, in the context of the European Centre Of Excellence (CoE) ChEESE for Exascale computing in solid earth sciences, on the MareNostrum4 High-Performance computer of the Barcelona Supercomputing Centre, using large-scale datasets of synthetic and real-case seismological continuous waveform data sets.</p>

scholarly journals Scalability of high-performance PDE solvers

2020 ◽  
Vol 34 (5) ◽  
pp. 562-586 ◽  
Author(s):  
Paul Fischer ◽  
Misun Min ◽  
Thilina Rathnayake ◽  
Som Dutta ◽  
Tzanio Kolev ◽  
...  
Keyword(s):  
Finite Elements ◽  
High Performance ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Fixed Number ◽  
Peak Performance ◽  
Trade Offs ◽  
Wide Range ◽  
P Type ◽  
Pde Solvers

Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.

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