scholarly journals Examining Photolysis Rates with a Prototype Online Photolysis Module in CMAQ

2007 ◽  
Vol 46 (8) ◽  
pp. 1252-1256 ◽  
Author(s):  
Francis S. Binkowski ◽  
Saravanan Arunachalam ◽  
Zachariah Adelman ◽  
Joseph P. Pinto
Keyword(s):  
Nitrogen Dioxide ◽  
Cross Sections ◽  
Lookup Table ◽  
Processing Unit ◽  
Computationally Efficient ◽  
Time Step ◽  
Central Processing ◽  
Online Module ◽  
Table Method ◽  
Photolysis Rates

Abstract A prototype online photolysis module has been developed for the Community Multiscale Air Quality (CMAQ) modeling system. The module calculates actinic fluxes and photolysis rates (j values) at every vertical level in each of seven wavelength intervals from 291 to 850 nm, as well as the total surface irradiance and aerosol optical depth within each interval. The module incorporates updated opacity at each time step, based on changes in local ozone, nitrogen dioxide, and particle concentrations. The module is computationally efficient and requires less than 5% more central processing unit time than using the existing CMAQ “lookup” table method for calculating j values. The main focus of the work presented here is to describe the new online module as well as to highlight the differences between the effective cross sections from the lookup-table method currently being used and the updated effective cross sections from the new online approach. Comparisons of the vertical profiles for the photolysis rates for nitrogen dioxide (NO2) and ozone (O3) from the new online module with those using the effective cross sections from a standard CMAQ simulation show increases in the rates of both NO2 and O3 photolysis.

Approximate method for the analysis of components undergoing ratchetting and failure

1998 ◽  
Vol 33 (1) ◽  
pp. 55-65 ◽  
Author(s):  
J Lin ◽  
F P E Dunne ◽  
D R Hayhurst
Keyword(s):  
Approximate Method ◽  
Mechanical Loading ◽  
Thermal Loading ◽  
Cyclic Plasticity ◽  
Processing Unit ◽  
Computationally Efficient ◽  
Element Analysis ◽  
Cyclic Thermal Loading ◽  
Central Processing ◽  
Practical Design

An approximate method has been presented for the design analysis of engineering components subjected to combined cyclic thermal and mechanical loading. The method is based on the discretization of components using multibar modelling which enables the effects of stress redistribution to be included as creep and cyclic plasticity damage evolves. Cycle jumping methods have also been presented which extend previous methods to handle problems in which incremental plastic straining (ratchetting) occurs. Cycle jumping leads to considerable reductions in computer CPU (central processing unit) resources, and this has been shown for a range of loading conditions. The cycle jumping technique has been utilized to analyse the ratchetting behaviour of a multibar structure selected to model geometrical and thermomechanical effects typically encountered in practical design situations. The method has been used to predict the behaviour of a component when subjected to cyclic thermal loading, and the results compared with those obtained from detailed finite element analysis. The method is also used to analyse the same component when subjected to constant mechanical loading, in addition to cyclic thermal loading leading to ratchetting. The important features of the two analyses are then compared. In this way, the multibar modelling is shown to enable the computationally efficient analysis of engineering components.

Experiments on multi-agent capture of a stochastically moving object using modified projective path planning

Robotica ◽  
2012 ◽  
Vol 31 (2) ◽  
pp. 267-284 ◽  
Author(s):  
Vijaysingh Shinde ◽  
Ashish Dutta ◽  
Anupam Saxena
Keyword(s):  
Path Planning ◽  
Past Research ◽  
Moving Object ◽  
Processing Unit ◽  
Time Step ◽  
Object A ◽  
Central Processing ◽  
Form Closure ◽  
Minimum Number ◽  
Planning Algorithm

SUMMARYMost of the past research in swarm robotics has considered object capture and transport using a specified and very large number of agents. The objects therein were either stationary or moving deterministically (i.e., along a known path). In most previous efforts, the obstacles were also considered stationary. Here we present a modified projective path planning algorithm and illustrate via laboratory experiments that an object exhibiting stochastic (unplanned) but low-speed motion can be restrained by a limited number of agents guided in real-time across randomly moving obstacles. Relaxation of certain restrictions in the grasping objective allows for the determination of a minimum number and placement of agents around the perimeter of any generically shaped prismatic object. A closed loop experiment is designed using a single overhead camera that provides the visual feedback and helps determine the instantaneous positions of all entities in the workspace. Control signals are sent to the robots via wireless modules by a central processing unit to navigate and guide them to their respective new positions in the subsequent time-step. Agents continue to receive signals until they restrict the moving object in form closure.

scholarly journals GPU ACCELERATION OF DOPPLER BROADENING FOR NEUTRON TRANSPORT CALCULATIONS1

2021 ◽  
Vol 247 ◽  
pp. 04017
Author(s):  
Paul E. Burke ◽  
Kyle E. Remley ◽  
David P. Griesheimer
Keyword(s):  
Monte Carlo ◽  
Cross Section ◽  
Cross Sections ◽  
Radiation Transport ◽  
Neutron Transport ◽  
Processing Unit ◽  
Complex Data ◽  
Doppler Broadening ◽  
Central Processing ◽  
Monte Carlo Transport

In radiation transport calculations, the effects of material temperature on neutron/nucleus interactions must be taken into account through Doppler broadening adjustments to the microscopic cross section data. Historically, Monte Carlo transport simulations have accounted for this temperature dependence by interpolating among precalculated Doppler broadened cross sections at a variety of temperatures. More recently, there has been much interest in on-the-fly Doppler broadening methods, where reference data is broadened on-demand during particle transport to any temperature. Unfortunately, Doppler broadening operations are expensive on traditional central processing unit (CPU) architectures, making on-the-fly Doppler broadening unaffordable without approximations or complex data preprocessing. This work considers the use of graphics processing unit (GPU)s, which excel at parallel data processing, for on-the-fly Doppler broadening in continuous-energy Monte Carlo simulations. Two methods are considered for the broadening operations – a GPU implementation of the standard SIGMA1 algorithm and a novel vectorized algorithm that leverages the convolution properties of the broadening operation in an attempt to expose additional parallelism. Numerical results demonstrate that similar cross section lookup throughput is obtained for on-the-fly broadening on a GPU as cross section lookup throughput with precomputed data on a CPU, implying that offloading Doppler broadening operations to a GPU may enable on-the-fly temperature treatment of cross sections without a noticeable reduction in cross section processing performance in Monte Carlo transport codes.

scholarly journals Analysis of the performance of a hybrid CPU/GPU 1D2D coupled model for real flood cases

2020 ◽  
Vol 22 (5) ◽  
pp. 1198-1216
Author(s):  
Isabel Echeverribar ◽  
Mario Morales-Hernández ◽  
Pilar Brufau ◽  
Pilar García-Navarro
Keyword(s):  
High Speed ◽  
High Performance ◽  
Data Transfer ◽  
Coupled Model ◽  
Main Channel ◽  
Processing Unit ◽  
Step Size ◽  
Time Step ◽  
Time Step Size ◽  
Central Processing

Abstract Coupled 1D2D models emerged as an efficient solution for a two-dimensional (2D) representation of the floodplain combined with a fast one-dimensional (1D) schematization of the main channel. At the same time, high-performance computing (HPC) has appeared as an efficient tool for model acceleration. In this work, a previously validated 1D2D Central Processing Unit (CPU) model is combined with an HPC technique for fast and accurate flood simulation. Due to the speed of 1D schemes, a hybrid CPU/GPU model that runs the 1D main channel on CPU and accelerates the 2D floodplain with a Graphics Processing Unit (GPU) is presented. Since the data transfer between sub-domains and devices (CPU/GPU) may be the main potential drawback of this architecture, the test cases are selected to carry out a careful time analysis. The results reveal the speed-up dependency on the 2D mesh, the event to be solved and the 1D discretization of the main channel. Additionally, special attention must be paid to the time step size computation shared between sub-models. In spite of the use of a hybrid CPU/GPU implementation, high speed-ups are accomplished in some cases.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

CENTRAL PROCESSING UNIT (CPU)

2020 ◽  
Author(s):  
Ika Milia wahyunu Siregar
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing

Perkembangan IT di dunia sangat pesat, mulai dari perkembangan sofware hingga hardware. Teknologi sekarang telah mendominasi sebagian besar di permukaan bumi ini. Karena semakin cepatnya perkembangan Teknologi, kita sebagai pengguna bisa ketinggalan informasi mengenai teknologi baru apabila kita tidak up to date dalam pengetahuan teknologi ini. Hal itu dapat membuat kita mudah tergiur dan tertipu dengan berbagai iklan teknologi tanpa memikirkan sisi negatifnya. Sebagai pengguna dari komputer, kita sebaiknya tahu seputar mengenai komponen-komponen komputer. Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware komputer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Central Processing System/ Central Processing Unit (CPU) adalah salah satu jenis perangkat keras yang berfungsi sebagai tempat untuk pengolahan data atau juga dapat dikatakan sebagai otak dari segala aktivitas pengolahan seperti penghitungan, pengurutan, pencarian, penulisan, pembacaan dan sebagainya.

Perangkat lunak komputer

2020 ◽  
Author(s):  
Intan khadijah simatupang
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware computer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Software komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program/prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan instruksi (data) yang diberikan. Pada prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras pemprosesan (processing/ central processing unit) – perangkat keras keluaran (output/output device system), perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (Storage device system/external memory).

PERANGKAT KERAS ( HARDWARE ) PADA KOMPUTER

2020 ◽  
Author(s):  
Siti Kumala Dewi
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device ◽  
System P ◽  
System 2

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Berdasarkan fungsinya, perangkat keras terbagi menjadi :1.Sistem Perangkat Keras Masukan (Input Device System )2.Sistem Pemrosesan ( Central Processing System/ Central Processing Unit(CPU)3.Sistem Perangkat Keras Keluaran ( Output Device System )4.Sistem Perangkat Keras Tambahan (Peripheral/Accessories Device System)

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