scholarly journals Input data requirements for special processors in the computation system containing the VENTURE neutronics code. [DVENTR (VENTURE input), DCRSPR (cross section processor input), DUTLIN (module control input), DCMACR (CITATION macroscopic data), and DENMAN (nuclide concentrations), in FORTRAN for IBM 360]

1976 ◽  
Author(s):  
D.R. Vondy ◽  
T.B. Fowler ◽  
G.W. Cunningham
Keyword(s):  
Cross Section ◽  
Input Data ◽  
Control Input ◽  
Data Requirements

A Parallel Implementation of 3D Computed Tomography Algorithm

2015 ◽  
Vol 770 ◽  
pp. 491-494
Author(s):  
Andrey E. Kovtanyuk
Keyword(s):  
Computed Tomography ◽  
3D Reconstruction ◽  
Density Distribution ◽  
Cross Section ◽  
Cross Sections ◽  
Input Data ◽  
Parallel Implementation ◽  
Back Projection ◽  
Single Cross Section ◽  
Single Cross

A computed tomography problem as a 3D reconstruction of density distribution is considered. The input data are obtained as a result of irradiations. The solution of the computed tomography problem is presented as a set of cross-section images. The reconstruction in a single cross-section is performed by algorithm of convolution and back projection. The parallelization is fulfilled over a set of cross-sections by use of the MPI technology.

scholarly journals Uncertainty and Sensitivity Analysis of Void Reactivity Feedback for 3D BWR Assembly Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Andrius Slavickas ◽  
Raimondas Pabarčius ◽  
Aurimas Tonkūnas ◽  
Eugenijus Ušpuras
Keyword(s):  
Sensitivity Analysis ◽  
Cross Section ◽  
Input Data ◽  
Statistical Approach ◽  
Assembly Model ◽  
Uncertainty And Sensitivity Analysis ◽  
Geometrical Dimensions ◽  
Basic Input ◽  
Propagation Of Uncertainties ◽  
Selection Of

Uncertainty and sensitivity analysis of void reactivity feedback for 3D BWR fuel assembly model is presented in this paper. Uncertainties in basic input data, such as the selection of different cross section library, manufacturing uncertainties in material compositions, and geometrical dimensions, as well as operating data are considered. An extensive modelling of different input data realizations associated with their uncertainties was performed during sensitivity analysis. The propagation of uncertainties was analyzed using the statistical approach. The results revealed that important information on the code predictions can be obtained by analyzing and comparing the codes estimations and their associated uncertainties.

The prediction of water yield from a 5 ha experimental catchment, Krawarree, NSW

Soil Research ◽  
1980 ◽  
Vol 18 (2) ◽  
pp. 149 ◽  
Author(s):  
AR Aston ◽  
FX Dunin
Keyword(s):  
Computer Model ◽  
Input Data ◽  
Water Yield ◽  
Monthly Variation ◽  
Modes Of Operation ◽  
Hydrologic Processes ◽  
Recorded Data ◽  
Data Requirements

A computer model called WATSIM was developed and used to predict the water yield of a 5 ha experimental catchment. The model is deterministic in describing the major hydrologic processes, has realistic input data requirements, and can treat the catchment as a series of cascaded hydrologic areas or as a single lumped entity. In both the cascaded and lumped modes of operation, the predicted monthly and annual yields agreed well with recorded data over a six year period, and accounted for approximately 98% of the monthly variation in yield.

scholarly journals Multicriteria Fuzzy Sets Application in Economic Clustering Problems

2017 ◽  
Vol 17 (3) ◽  
pp. 29-46
Author(s):  
Irina Radeva
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Input Data ◽  
Multi Criteria Decision Making ◽  
Promethee Ii ◽  
Specific Input ◽  
Clustering Problems ◽  
Data Requirements

Abstract This paper presents an approach for small and medium-sized enterprises selection in economic clusters, where the problem of integration is defined as “ill structured under condition of uncertainty”. The proposed solution demonstrates applying several fuzzy multi-criteria decision making algorithms along with discussion over specific input data requirements. The results are compared with classical multi-criteria decision-making algorithm PROMETHEE II.

scholarly journals Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

2020 ◽  
Vol 12 (3) ◽  
pp. 372
Author(s):  
Meredith G. L. Brown ◽  
Sergii Skakun ◽  
Tao He ◽  
Shunlin Liang
Keyword(s):  
Machine Learning ◽  
Photosynthetically Active Radiation ◽  
Input Data ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
Learning Methods ◽  
Active Radiation ◽  
Machine Learning Methods ◽  
Robust Model ◽  
Data Requirements

Satellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an R 2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W / m 2 , and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W / m 2 , respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.

scholarly journals Development and Application of a SWMM-Based Simulation Model for Municipal Scale Hydrologic Assessments

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1644
Author(s):  
Tyler Dell ◽  
Mostafa Razzaghmanesh ◽  
Sybil Sharvelle ◽  
Mazdak Arabi
Keyword(s):  
Input Data ◽  
Processing Time ◽  
Simulation Analysis ◽  
Control Measure ◽  
Storm Events ◽  
Storm Water Management Model ◽  
Continuous Simulation ◽  
Stormwater Control ◽  
Data Requirements ◽  
Hydrologic Performance

There is growing interest for the installation of green stormwater infrastructure (GSI) to improve stormwater control, increase infiltration of stormwater, and improve receiving water body quality. Planning level tools are needed to inform municipal scale decisions on the type and extent of GSI to apply. Here, a modified methodology is developed for the EPA Storm Water Management Model (SWMM) to create SWMM for Low Impact Technology Evaluation (SWWM-LITE) that enables municipal scale assessment of stormwater control measure (SCM) performance with minimal input data requirements and low processing time. Hydrologic outputs of SWMM-LITE are compared to those for SWMM and the National Stormwater Calculator (SWC) to assess the performance of SWMM-LITE. Three scenarios including the baseline without SCMs and the installation of varying SCMs were investigated. Across the three scenarios, SWMM-LITE estimates of annual average hydrologic performance (runoff, infiltration, and evaporation) were within +/−0.1% of estimates from a rigorously developed SWMM model in the City of Fort Collins, CO, for an evaluation of 30 years of continuous simulation. Analysis conducted for 2 year (y), 10 y, and 100 y storm events showed less than +/−2.5% difference between SWMM and SWMM-LITE hydrologic outputs. SWC provided reasonable estimates of hydrologic parameters for the case study area, but was designed for site level analyses of performance of SCMs rather than on the municipal scale. A sensitivity analysis revealed that the most sensitive parameters were primarily consistent for the SWMM-LITE and the complete SWMM. SWMM-LITE has low input data requirements and processing time and can be applied for assessing the hydrologic performance of SCMs to inform planning level decisions.

scholarly journals Meteorological input data requirements to predict cross-pollination of GMO Maize with Lagrangian approaches

2006 ◽  
Vol 5 (3) ◽  
pp. 151-168 ◽  
Author(s):  
Kai Lipsius ◽  
Ralf Wilhelm ◽  
Otto Richter ◽  
Klaus Jürgen Schmalstieg ◽  
Joachim Schiemann
Keyword(s):  
Input Data ◽  
Cross Pollination ◽  
Meteorological Input ◽  
Data Requirements
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