scholarly journals Factor Effects in Numerical Simulations

2020 ◽  
Vol 77 (7) ◽  
pp. 2439-2451
Author(s):  
Judah L. Cleveland ◽  
Jeffrey A. Smith ◽  
James P. Collins
Keyword(s):  
Design Of Experiments ◽  
Numerical Simulations ◽  
Computational Cost ◽  
Regression Coefficients ◽  
Atmospheric Sciences ◽  
Multiple Input ◽  
Water Simulation ◽  
Main Effects ◽  
Factor Separation ◽  
Experimental Runs

AbstractNumerical simulations allow users to adjust factor settings in experimental runs to understand how changes in those factors affect the output. However, it is not straightforward to analyze these outputs when multiple input factors are changed, especially simultaneously. For the atmospheric sciences, Stein and Alpert introduced a method they termed “factor separation” in order to separate the “pure contribution” of a factor from “pure interactions” of combinations of factors. Although factor separation appears to be used exclusively within the atmospheric sciences, other communities achieve a similar result by computing “main effects” via design of experiments methods. While both methods yield different estimates for the factor effects or contributions, we show that factor separation effects are identical to “simple effects” in the design of experiments literature. We demonstrate how both factor separation effects and design of experiments main effects correspond to multiple linear regression coefficients with different coding methods; thus, effect estimates produced by each method are equivalent through a variable transformation. We illustrate the application of both methods using a shallow-water simulation. This connection between factor separation and the design of experiments discipline extends factor separation to more applications by making available design of experiments methods for decreasing the computational cost and calculating effects for factors with more than two settings, both of which are limitations of factor separation.

scholarly journals Waves and vortices in the inverse cascade regime of stratified turbulence with or without rotation

2016 ◽  
Vol 806 ◽  
pp. 165-204 ◽  
Author(s):  
Corentin Herbert ◽  
Raffaele Marino ◽  
Duane Rosenberg ◽  
Annick Pouquet
Keyword(s):  
Reynolds Number ◽  
Numerical Simulations ◽  
Direct Numerical Simulations ◽  
Vertical Shear ◽  
Energy Spectra ◽  
Stratified Turbulence ◽  
Viscous Effects ◽  
Inverse Cascade ◽  
Main Effects ◽  
The Waves

We study the partition of energy between waves and vortices in stratified turbulence, with or without rotation, for a variety of parameters, focusing on the behaviour of the waves and vortices in the inverse cascade of energy towards the large scales. To this end, we use direct numerical simulations in a cubic box at a Reynolds number $Re\approx 1000$, with the ratio between the Brunt–Väisälä frequency $N$ and the inertial frequency $f$ varying from $1/4$ to 20, together with a purely stratified run. The Froude number, measuring the strength of the stratification, varies within the range $0.02\leqslant Fr\leqslant 0.32$. We find that the inverse cascade is dominated by the slow quasi-geostrophic modes. Their energy spectra and fluxes exhibit characteristics of an inverse cascade, even though their energy is not conserved. Surprisingly, the slow vortices still dominate when the ratio $N/f$ increases, also in the stratified case, although less and less so. However, when $N/f$ increases, the inverse cascade of the slow modes becomes weaker and weaker, and it vanishes in the purely stratified case. We discuss how the disappearance of the inverse cascade of energy with increasing $N/f$ can be interpreted in terms of the waves and vortices, and identify the main effects that can explain this transition based on both inviscid invariants arguments and viscous effects due to vertical shear.

scholarly journals Optimization of machining parameters during Drilling by Taguchi based Design of Experiments and Validation by Neural Network

2018 ◽  
Vol 15 (2) ◽  
pp. 294-301
Author(s):  
Reddy Sreenivasulu ◽  
Chalamalasetti SrinivasaRao
Keyword(s):  
Neural Network ◽  
Design Of Experiments ◽  
Computational Cost ◽  
Machining Parameters ◽  
Burr Size ◽  
Taguchi Design Of Experiments ◽  
The Neural Network ◽  
Input Parameters ◽  
Hole Making ◽  
The Cost

Drilling is a hole making process on machine components at the time of assembly work, which are identify everywhere. In precise applications, quality and accuracy play a wide role. Nowadays’ industries suffer due to the cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a serious topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. The effect of alteration of drill geometry on thrust force and burr size of drilled hole was investigated by the Taguchi design of experiments and found an optimum combination of the most significant input parameters from ANOVA to get optimum reduction in terms of burr size by design expert software. Drill thrust influences more on burr size. The clearance angle of the drill bit causes variation in thrust. The burr height is observed in this study.  These output results are compared with the neural network software @easy NN plus. Finally, it is concluded that by increasing the number of nodes the computational cost increases and the error in nueral network decreases. Good agreement was shown between the predictive model results and the experimental responses.  

scholarly journals rpe v5: An emulator for reduced floating-point precision in large numerical simulations

2016 ◽  
Author(s):  
Andrew Dawson ◽  
Peter Düben
Keyword(s):  
Numerical Simulations ◽  
Climate Models ◽  
Numerical Models ◽  
Computational Cost ◽  
Floating Point ◽  
Climate Modelling ◽  
Acceptable Quality ◽  
Weather And Climate ◽  
Code Changes ◽  
Numerical Precision

Abstract. This paper describes the rpe library which has the capability to emulate the use of arbitrary reduced floating-point precision within large numerical models written in Fortran. The rpe software allows model developers to test how reduced floating-point precision affects the result of their simulations without having to make extensive code changes or port the model onto specialised hardware. The software can be used to identify parts of a program that are problematic for numerical precision and to guide changes to the program to allow a stronger reduction in precision. The development of rpe was motivated by the strong demand for more computing power. If numerical precision can be reduced for an application under consideration while still achieving results of acceptable quality, computational cost can be reduced, since a reduction in numerical precision may allow an increase in performance or a reduction in power consumption. For simulations with weather and climate models, savings due to a reduction in precision could be reinvested to allow model simulations at higher spatial resolution or complexity, or to increase the number of ensemble members to improve predictions. rpe was developed with particular focus on the community of weather and climate modelling, but the software could be used with numerical simulations from other domains.

scholarly journals On Dynamic Parallelization of Multilevel Monte Carlo Algorithm

2020 ◽  
Vol 20 (6) ◽  
pp. 116-125
Author(s):  
Nikolay Shegunov ◽  
Oleg Iliev
Keyword(s):  
Monte Carlo ◽  
Numerical Simulations ◽  
Random Fields ◽  
Stochastic Partial Differential Equations ◽  
Computational Cost ◽  
Monte Carlo Algorithm ◽  
Multilevel Monte Carlo ◽  
Deterministic Problem ◽  
Speed Up ◽  
Main Components

AbstractMultiLevel Monte Carlo (MLMC) attracts great interest for numerical simulations of Stochastic Partial Differential Equations (SPDEs), due to its superiority over the standard Monte Carlo (MC) approach. MLMC combines in a proper manner many cheap fast simulations with few slow and expensive ones, the variance is reduced, and a significant speed up is achieved. Simulations with MC/MLMC consist of three main components: generating random fields, solving deterministic problem and reduction of the variance. Each part is subject to a different degree of parallelism. Compared to the classical MC, MLMC introduces “levels” on which the sampling is done. These levels have different computational cost, thus, efficiently utilizing the parallel resources becomes a non-trivial problem. The main focus of this paper is the parallelization of the MLMC Algorithm.

GENOTYPE-ENVIRONMENT INTERACTION OF YIELD IN CEREAL CROPS IN NORTHWESTERN CANADA

1981 ◽  
Vol 61 (2) ◽  
pp. 255-263 ◽  
Author(s):  
R. M. De PAUW ◽  
D. G. FARIS ◽  
C. J. WILLIAMS
Keyword(s):  
Triticum Aestivum L ◽  
Regression Coefficients ◽  
Cereal Crops ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Hordeum Vulgare L ◽  
Genotype Environment Interaction ◽  
Early Maturing ◽  
Frost Free Period ◽  
Main Effects

Three cultivars of each crop, wheat (Triticum aestivum L.), oats (Avena sativa L.), and barley (Hordeum vulgare L.), were grown for 4 yr at five locations north of the 55th parallel in northwestern Canada. There were highly significant differences among all main effects and interactions. Galt barley produced the highest seed yield followed by Centennial barley, Random oats and Harmon oats. Victory oats, Olli barley, Neepawa wheat and Pitic 62 wheat yielded similarly to each other while Thatcher wheat was significantly lower yielding. Mean environment yields ranged from 2080 to 5610 kg/ha. The genotype-environment (GE) interaction of species and cultivars was sufficiently complicated that it could not be characterized by one or two statistics (e.g., stability variances or regression coefficients). However, variability in frost-free period among years and locations contributed to the GE interaction because, for example, some cultivars yielded well (e.g., Pitic 62) only in those year-location environments with a relatively long frost-free period while other early maturing cultivars (e.g., Olli) performed well even in a short frost-free period environment.

scholarly journals A geometric diffuse-interface method for droplet spreading

2020 ◽  
Vol 476 (2233) ◽  
pp. 20190222
Author(s):  
Darryl D. Holm ◽  
Lennon Ó Náraigh ◽  
Cesare Tronci
Keyword(s):  
Numerical Simulations ◽  
Large Scale ◽  
Computational Cost ◽  
Computational Method ◽  
Diffuse Interface ◽  
Precursor Film ◽  
Droplet Spreading ◽  
Wetting Fluids ◽  
Thin Film Equation ◽  
Wetting Fluid

This paper exploits the theory of geometric gradient flows to introduce an alternative regularization of the thin-film equation valid in the case of large-scale droplet spreading—the geometric diffuse-interface method. The method possesses some advantages when compared with the existing models of droplet spreading, namely the slip model, the precursor-film method and the diffuse-interface model. These advantages are discussed and a case is made for using the geometric diffuse-interface method for the purpose of numerical simulations. The mathematical solutions of the geometric diffuse interface method are explored via such numerical simulations for the simple and well-studied case of large-scale droplet spreading for a perfectly wetting fluid—we demonstrate that the new method reproduces Tanner’s Law of droplet spreading via a simple and robust computational method, at a low computational cost. We discuss potential avenues for extending the method beyond the simple case of perfectly wetting fluids.

Revisiting V-3 Canon for the Application of Single Stage Gas Gun

2017 ◽  
Author(s):  
Arun Tom Mathew ◽  
Tirumala Rao Koka ◽  
Murali Krishnan Payangapadan
Keyword(s):  
Design Of Experiments ◽  
Numerical Simulations ◽  
Response Surface ◽  
Three Dimensional ◽  
Optimal Location ◽  
Single Stage ◽  
One Dimensional ◽  
Alternative Design ◽  
Gas Gun ◽  
Gas Guns

Single stage gas guns are typically used for accelerating the projectiles in bird and hail impact tests of aerospace components and engines. In this paper an alternative design for single stage gas gun is studied, which is derived from V3 canon. Three dimensional numerical simulations is carried out for the optimal secondary connection angle with the main barrel. A one dimensional code is developed for the V3 canon based design. Design of experiments conducted to find the response surface for the optimal location of the secondary connection, volume and pressure of the secondary tank.

Intelligent Optimal Design of a Beam During the Crash

2000 ◽  
Author(s):  
Joseph Zarka ◽  
Pirouz Navidi
Keyword(s):  
United Kingdom ◽  
Optimal Design ◽  
Design Of Experiments ◽  
Numerical Simulations ◽  
Complex Problem ◽  
Complex Cells

Abstract We consider the optimal design of a beam. To improve the safety of a car during the crash, it is needed to dissipate the maximum of energy within a limited displacement but with a limited acceleration at the level of the driver/passengers. The beam may have different complex cells linked with continuous or spot solders. In a special office, they have to design a beam. In Germany, in United Kingdom, and also inside all the automotive industries, dedicated centers to test such beams were created. Other centers have developped numerical simulations. Usually, for each family of beams, a Design of Experiments is performed. It is necessary to find its optimal design. Until now, this was not possible, as the CRASH is a very complex problem and the tests or numerical simulations are too expensive to allow the succssive iterations.

A Numerical Study on Correlation Between the Bow Design Parameters and Added Resistance Using the Design of Experiments

2016 ◽  
Author(s):  
Gwan Hoon Kim ◽  
Hyun Joon Shin ◽  
Jeonghwa Seo ◽  
Shin Hyung Rhee
Keyword(s):  
Design Of Experiments ◽  
Numerical Simulations ◽  
Numerical Study ◽  
Design Parameters ◽  
Added Resistance ◽  
Hull Form ◽  
Entrance Angle ◽  
Wave Condition ◽  
Added Resistance In Waves ◽  
Bulbous Bow

In this study, numerical computation was carried out for evaluating the effects of the design parameter variations on the added resistance of Aframax tanker in head seas. The design of experiments (DOE) was used to efficiently conduct the numerical simulations with the hull form variations and save computational resources. A computational fluid dynamics (CFD) code based on the continuity and Reynolds averaged Navier-Stokes (RANS) equation was used for the numerical simulation. The simulation was performed in a short wave condition where the wave length was half of the ship length, which is expected to be most frequent in the vessel operation. Five design parameters of fore-body hull form were selected for the variations: design waterline length (DWL), bulbous bow height (BBH), bulbous bow volume (BBV), bow flare angle (BFA) and bow entrance angle (BEA). Each parameter had two levels in the variations, thus total 32 cases were designed initially. The results of the numerical simulations were analyzed statistically to determine the main effects and correlations in the five design parameters variations. Among them, the most significant parameter that influences on the added resistance in waves was DWL, followed by BBV and BEA. The other parameters had little effects on the added resistance in waves. By the computations, it was revealed that Extending DWL and decreasing BEA promoted the reflection of waves more toward the side than forward. In addition, there existed two-way interactions for the following two-factor combinations: DWL-BFA, DWL-BEA, DWL-BBV, BBH-BBV.

scholarly journals A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 273
Author(s):  
Ioannis E. Livieris ◽  
Spiros D. Dafnis ◽  
George K. Papadopoulos ◽  
Dionissios P. Kalivas
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Computational Cost ◽  
Economic Benefits ◽  
Mixed Data ◽  
Cotton Production ◽  
Proposed Model ◽  
Multiple Input ◽  
Fully Connected

Cotton constitutes a significant commercial crop and a widely traded commodity around the world. The accurate prediction of its yield quantity could lead to high economic benefits for farmers as well as for the rural national economy. In this research, we propose a multiple-input neural network model for the prediction of cotton’s production. The proposed model utilizes as inputs three different kinds of data (soil data, cultivation management data, and yield management data) which are treated and handled independently. The significant advantages of the selected architecture are that it is able to efficiently exploit mixed data, which usually requires being processed separately, reduces overfitting, and provides more flexibility and adaptivity for low computational cost compared to a classical fully-connected neural network. An empirical study was performed utilizing data from three consecutive years from cotton farms in Central Greece (Thessaly) in which the prediction performance of the proposed model was evaluated against that of traditional neural network-based and state-of-the-art models. The numerical experiments revealed the superiority of the proposed approach.

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