Using the lattice Boltzman method and GPU computing to model flow in organ pipes

2021 ◽  
Vol 150 (4) ◽  
pp. A94-A94
Author(s):  
Connor N. Kaplan ◽  
Jack D. Gabriel ◽  
Adrien David-Sivelle ◽  
Whitney L. Coyle
Keyword(s):  
Gpu Computing ◽  
Model Flow ◽  
Organ Pipes

Karakteristik aliran dua fase pada saluran ekspansi tiba-tiba

2018 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Latif Ngudi Wibawanto ◽  
Budi Santoso ◽  
Wibawa Endra Juwana
Keyword(s):  
Standard Deviation ◽  
Flow Pattern ◽  
Flow Characteristic ◽  
Homogeneous Model ◽  
Flow Equation ◽  
Pressure Recovery ◽  
Sudden Expansion ◽  
Air Velocity ◽  
Model Flow ◽  
Two Phases

This research was conducted to find out the flow characteristic of two phases through the channel with sudden expansion in the form of change of flow pattern and pressure recovery. The test was carried out with variation of superficial velocity of water 0.2-1.3 m / s and superficial air velocity of 0.2-1.9 m / s resulting in pattern of three flow patterns ie bubble, plug, and slug. The expansion channel resulted in some changes to the flow pattern that originally plugs in the upstream channel into bubble in the downstream channel and the slug becomes plug. Pressure recovery experimental results compared with the homogeneous model flow equation and Wadle correlation, both correlations have predictions with standard deviation values of 0.32 and 0.43.

Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

2021 ◽  
pp. 002072092098849
Author(s):  
Soumya Ranjan Nayak ◽  
S Sivakumar ◽  
Akash Kumar Bhoi ◽  
Gyoo-Soo Chae ◽  
Pradeep Kumar Mallick
Keyword(s):  
Credit Card ◽  
Mixed Mode ◽  
Processing Time ◽  
Gpu Computing ◽  
Graphical Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Large Set ◽  
Minimal Processing ◽  
Graphical Processing

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

scholarly journals Picking up the hydrothermal whisper at Ischia Island in the Covid-19 lockdown quiet

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mariarosaria Falanga ◽  
Paola Cusano ◽  
Enza De Lauro ◽  
Simona Petrosino
Keyword(s):  
Pressure Fluctuations ◽  
Hydrothermal Fluids ◽  
Shallow Aquifer ◽  
Two Phase ◽  
Spectral Content ◽  
Ischia Island ◽  
Continuous Noise ◽  
Organ Pipes ◽  
Source Signals ◽  
Phase Fluid

AbstractIn this paper, we analyse the seismic noise at Ischia Island (Italy) with the objective of detecting the hydrothermal source signals taking advantage of the Covid-19 quiescence due to lockdown (strong reduction of anthropogenic noise). We compare the characteristics of the background noise in pre-, during and post-lockdown in terms of spectral content, energy release (RMS) and statistical moments. The continuous noise is decomposed into two independent signals in the 1−2 Hz and 2−4 Hz frequency bands, becoming sharpened around 1 Hz and 3 Hz respectively in lockdown. We propose a conceptual model according to which a dendritic system of fluid-permeated fractures plays as neighbour closed organ pipes, for which the fundamental mode provides the persistent whisper and the first higher mode is activated in concomitance with energy increases. By assuming reasonable values for the sound speed in low vapor–liquid mass fraction for a two-phase fluid and considering temperatures and pressures of the shallow aquifer fed by sea, meteoric and deep hydrothermal fluids, we estimate pipe lengths in the range 200–300 m. In this scheme, Ischia organ-like system can play both continuous whisper and transients, depending on the energy variations sourced by pressure fluctuations in the hydrothermal fluids.

scholarly journals Efficient ResNet Model to Predict Protein-Protein Interactions With GPU Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 127834-127844
Author(s):  
Shengyu Lu ◽  
Qingqi Hong ◽  
Beizhan Wang ◽  
Hongji Wang
Keyword(s):  
Protein Interactions ◽  
Gpu Computing ◽  
Protein Protein Interactions

scholarly journals Probing the Statistical Validity of the Ductile-to-Brittle Transition in Metallic Nanowires Using GPU Computing

2013 ◽  
Vol 9 (12) ◽  
pp. 5558-5566 ◽  
Author(s):  
William R. French ◽  
Amulya K. Pervaje ◽  
Andrew P. Santos ◽  
Christopher R. Iacovella ◽  
Peter T. Cummings
Keyword(s):  
Gpu Computing ◽  
Metallic Nanowires ◽  
Brittle Transition ◽  
Statistical Validity ◽  
Ductile To Brittle Transition

scholarly journals Quantitative study of computing time of direct/iterative solver for MoM by GPU computing

2013 ◽  
Vol 2 (8) ◽  
pp. 359-364 ◽  
Author(s):  
Keisuke Konno ◽  
Hajime Katsuda ◽  
Kei Yokokawa ◽  
Qiang Chen ◽  
Kunio Sawaya ◽  
...  
Keyword(s):  
Quantitative Study ◽  
Gpu Computing ◽  
Computing Time ◽  
Iterative Solver
Sign in / Sign up

Export Citation Format

Share Document