Application of Non-Linear Turbulence Models on the Numerical Simulation of Cavitating Flows

2012 ◽  
Author(s):  
Ying Chen ◽  
Xin Chen ◽  
Jiayi Cao ◽  
Chuanjing Lu
Keyword(s):  
Linear Models ◽  
Nonlinear Models ◽  
Density Ratio ◽  
Turbulence Models ◽  
Computer Code ◽  
Cavitating Flows ◽  
Non Linear ◽  
Cavity Profile ◽  
Large Density Ratio ◽  
Disk Cavitator

Quadratic and cubic non-linear eddy-viscosity turbulence Models (NLEVM) with low Reynolds number correction were employed to provide better treatment about the anisotropic turbulence stresses in cavitating flows, in which large density ratio and swirling structures consist. These models were carried out through a self-developed computer code, and were validated by the case of the cavity over disk cavitator. It was proven that, the nonlinear models could effectively eliminate the non-physical numerical oscillation of the cavity profile which was usually caused by linear models. It had also been experimentally proved that the computed cavity shapes and pressure inside the cavity were accurately captured by these nonlinear models. One of such nonlinear models was further applied on the simulation of the cavitating flows around submerged vehicles. The numerical results were compared with experiment data to investigate the influence of the vehicle’s head and the after-body on the characteristics of the cavity. Ultimately, the cavitating flows around an especially designed complex underwater vehicle were predicted using the cubic k–ε turbulence model. The corresponding cavitation behaviors were studied to provide a beneficial experience for the research in future.

Comparison of Different Physical Models for the Simulation of Cavitating Flows Around a Hydrofoil

2005 ◽  
Author(s):  
Maria Grazia De Giorgi ◽  
Antonio Ficarella ◽  
Domenico Laforgia
Keyword(s):  
Density Ratio ◽  
Turbulence Models ◽  
General Purpose ◽  
Physical Models ◽  
Cavitating Flows ◽  
Two Phase ◽  
Liquid Phases ◽  
The Standard Model ◽  
Phase Medium ◽  
Large Density Ratio

The simulation of cavitating flow is a difficult task, due to the large density ratio between liquid and vapor phases. Investigation of currently known cavitation and turbulence models and their mutual interaction can be a significant asset to improving their performance. Several previous works present results on cavitating flows obtained by various physical models, that mainly differ by the treatment of the mass transfer calculation between vapor and liquid phases. In this study different cavitation models have been implemented in a commercial, general-purpose CFD code (Fluent), and numerical results were compared to experimental ones. Different turbulence models have been also compared, in particularly, to improve numerical simulations by taking into account the influence of the compressibility of two-phase medium on turbulence, a modified k-ε RNG model and compared with the standard model. The simulations were carried out on 2D hydrofoil with an Eppler E817 cross section. The inception cavitation number, the cavity length and the pressure coefficients have been compared with the experimental data.

Selection of an Appropriate Turbulence Model to Evaluate Performances of Novel Fuel Geometries for the “IRIS” Reactor

2003 ◽  
Author(s):  
Emilio Baglietto ◽  
Hisashi Ninokata
Keyword(s):  
Linear Models ◽  
Flow Direction ◽  
Turbulence Models ◽  
Shear Stresses ◽  
Plane Normal ◽  
Rod Bundle ◽  
Commercial Code ◽  
Non Linear ◽  
Turbulence Closures ◽  
Tight Lattice

A comparative study of different turbulence models is presented to select the most appropriate one for the evaluation of thermo-hydraulic performances of innovative core designs. The standard k-ε and different, linear and non linear, low Reynolds k-ε models are applied to fully developed flow in a triangular lattice rod bundle. Shear stresses and velocity distributions are evaluated using the commercial code Star-CD. The relative performance of the models is assessed indicating different predictions between linear and non linear turbulence closures. The results show that the capability of non linear models to account for anisotropic effects leads to better performances in modeling turbulent flow in tight lattice rod bundles. This capability is clearly shown by the existence of a secondary flow field in the plane normal to the flow direction.

scholarly journals Linear and Non-Linear Modelling of Nigerian Crude Oil Prices

2021 ◽  
pp. 52-63
Author(s):  
Wiri Leneenadogo ◽  
Sibeate Pius U
Keyword(s):  
Crude Oil ◽  
Linear Model ◽  
Regime Switching ◽  
Linear Models ◽  
Nonlinear Models ◽  
Oil Prices ◽  
Information Criterion ◽  
Ar Model ◽  
Crude Oil Prices ◽  
Non Linear

To model Nigeria crude oil prices, this analysis compared univariate linear models to univariate nonlinear models. The data for this analysis was gathered from the Central Bank of Nigeria (CBN) Monthly Statistical Bulletin. The upward and downward movement in the series revealed by the time plot suggests that the series exhibit a regime-switching pattern: the cycle of expansion and contraction. At lag one, the Augmented Dickey-Fuller test was used to test for stationarity. For univariate linear ARIMA (p, d, q)) and univariate non-linear MS-AR, seven models were estimated for the linear model and two for the non-linear model. The best model was chosen based on the criterion of least information criterion,  AIC (2.006612), SC (2.156581), and the maximum log-likelihood of   (-150.5480) for the crude oil prices were used to pick MS-AR (1) for the series. In analysing crude oil prices data, the MS-AR model proposed by Hamilton outperforms the linear autoregressive models proposed by Box- Jenkins. The model was used to predict the series' values over a one-year cycle (12 months).

scholarly journals Evaluation of Non-linear Models to Predict Potential Milk Yield of Beef Cows According to Parity Order Under Grazing

2021 ◽  
Vol 8 ◽  
Author(s):  
Matheus Fellipe de Lana Ferreira ◽  
Luciana Navajas Rennó ◽  
Isabela Iria Rodrigues ◽  
Sebastião de Campos Valadares Filho ◽  
Luiz Fernando Costa e Silva ◽  
...  
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Linear Models ◽  
Nonlinear Models ◽  
Information Criterion ◽  
Beef Cows ◽  
Total Solids ◽  
Lactation Curve ◽  
Non Linear ◽  
Milking Machine

This study aimed to evaluate the effect of parity order on milk yield (MY) and composition over time of grazing beef cows and to evaluate non-linear models to describe the lactation curve. Thirty-six pregnant Nellore cows (12 nulliparous, 2 years; 12 primiparous, 3 years; and 12 multiparous, 4–6 years) were included in the study. With calving day assigned as day 0, milking was performed using a milking machine to estimate MY on days 7, 14, 21, 42, 63, 91, 119, 154, and 203. Dummy variable analyses were applied to estimate its effects on MY, composition (kg and percentage), afternoon/morning, and afternoon/total proportions. Since multiparous cows had higher MY than nulliparous and primiparous cows, two different groups were used for lactation curve analysis: Mult (multiparous) and Null/Prim (nulliparous and primiparous). The MY estimated by the last edition of BR-Corte (Nutrient Requirements of Zebu and Crossbred Cattle) equation was compared with the observed values from this study. Five nonlinear models proposed by Wood (WD), Jenkins & Ferrell (JF), Wilmink (WK), Henriques (HR) and Cobby & Le Du (CL) were evaluated. Models were validated using an independent dataset of multiparous and primiparous cows. The estimates for parameters a, b, and c of the CL equation were compared between groups, and the BR-Corte equation used the model identity methodology. Nulliparous and primiparous cows displayed similar MY (P > 0.05); however, multiparous cows had an average MY that is 0.70 kg/day greater than that of nulliparous and primiparous cows (P < 0.05). Milk protein and total solids were higher for multiparous cows (P < 0.05). Effect of days in milking was found for milk fat, protein, and total solids (P < 0.05). The yield of all milk components was higher for multiparous cows than for nulliparous and primiparous cows. The afternoon/morning and afternoon/total proportions of milk production were not affected by parities and days in milking (P > 0.05), with an average of 0.76 and 0.42, respectively. The BR-Corte equation did not correctly estimate the MY (P < 0.05). The equations of WD, WK, and CL had the best estimate of MY for both Mult and Null/Prim datasets. The equations had a very similar Akaike's information criterion with correction and mean square error of prediction.

STRESS-STRAIN STATE PREDICTION SURROUNDING UNDERGROUND STRUCTURE, CONSTRUCTED IN NONLINEAR DEFORMED MEDIUM-SOFT SOILS

2020 ◽  
Vol 2 (1) ◽  
pp. 215-228
Author(s):  
A.G. PROTOSENYA ◽  
◽  
G.A. IOVLEV ◽  
Keyword(s):  
Strain State ◽  
Linear Models ◽  
Nonlinear Models ◽  
Underground Structure ◽  
Stress Strain ◽  
Soft Soils ◽  
Stress Strain State ◽  
Perfectly Plastic ◽  
Non Linear ◽  
Elastic Perfectly Plastic

Article proposes an approach for constructing a computational model for calculating the stress-strain state around tunnel, in medium soft soils. Set of deformations and strengths properties of which a given by elastic, elastic perfectly-plastic, and non-linear models. It founded, that with the input parameters used in model for elastic perfectly-plastic, and nonlinear models around tunnel formed yield surfaces. Analysis of the distinctions between elastic perfectly-plastic, and non-linear models was made. Was showed, that maximum deviatoric stress q is over-estimated in Mohr-Coulomb model. For hardening soil model was determined boundary of the plastic deformations.

scholarly journals Forecasting Stock Prices With Linear And Nonlinear Settings: A Comparison

2013 ◽  
Vol 17 (4) ◽  
pp. 257
Author(s):  
Dimitri Tsoukalas
Keyword(s):  
Stock Prices ◽  
Linear Models ◽  
Financial Time Series ◽  
Nonlinear Models ◽  
Future Research ◽  
Regression Splines ◽  
Forecasting Models ◽  
Non Linear ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

This paper is devoted to the application and comparison of linear (VAR) and nonlinear Multiple Adaptive Regression Splines (MARS) forecasting models, in estimating, evaluating, and selecting among linear and non-linear forecasting models for economic and financial time series. We argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. Nonlinear models reduce nonlinearity and Gaussianity in the residuals of the linear models. Linear models, however, demonstrate better forecasts than nonlinear. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Linear and non-linear models for national health expenditures in the USA

2018 ◽  
Vol 102 (554) ◽  
pp. 193-197
Author(s):  
Allan J. Kroopnick
Keyword(s):  
United States ◽  
Health Care ◽  
National Health ◽  
Linear Models ◽  
Nonlinear Models ◽  
Elementary Theory ◽  
Health Expenditures ◽  
The United States ◽  
Non Linear ◽  
The Usa

In this brief Article, using the elementary theory of differential equations as well as some basic economic theory, we will develop several estimates for national health expenditures for the United States: one using a linear model and three using non-linear models. We will derive the nonlinear models first and then compare them to the linear one in order to see if they differ significantly. While these estimates are for the United States, the methods used here, because they are robust, could be used for any country. Statistical information may be obtained from the World Bank databases which store health statistics by country [1].What we will do here is estimate the total health costs as a percentage of gross domestic product (GDP) if no further copayments are required. In other words, we are seeking to estimate the total cost of health care as a percentage of GDP when all health care costs are covered by insurance and government subsidy. Several models will be discussed here since such estimates may be made using a variety of assumptions. There is no ‘best’ model, although such a decision is possible when comparing the estimates to actual data.

scholarly journals A Caveat on the Savage-Dickey Density Ratio: The Case of Computing Bayes Factors for Regression Parameters

2018 ◽  
Author(s):  
Daniel W. Heck
Keyword(s):  
Regression Models ◽  
Bayes Factor ◽  
Linear Models ◽  
Nonlinear Models ◽  
Density Ratio ◽  
Equality Constraint ◽  
Process Models ◽  
Linear Regression Models ◽  
Simple Method ◽  
Regression Parameters

The Savage-Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage-Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys-Zellner-Siow (JZS) prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage-Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, nonlinear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage-Dickey density ratio.

scholarly journals The Performance Of Linear Versus Non-Linear Models In Forecasting Returns On The Johannesburg Stock Exchange

2013 ◽  
Vol 12 (8) ◽  
pp. 985 ◽  
Author(s):  
Michael Van Gysen ◽  
Chun-Sung Huang ◽  
Ryan Kruger
Keyword(s):  
Regression Models ◽  
Linear Models ◽  
Predictive Accuracy ◽  
Stock Exchange ◽  
Nonlinear Models ◽  
Recovery Period ◽  
Markov Switching ◽  
Forecast Performance ◽  
Johannesburg Stock Exchange ◽  
Non Linear

In this paper we provide a comprehensive comparison of the predictive accuracy of linear and non-linear models when forecasting financial returns, using a number of macroeconomic variables, on the Johannesburg Stock Exchange. We implement a range of linear specifications, Markov switching ARMA and Dynamic Regression models, and univariate models in which the conditional heteroskedasticity is captured by GARCH or EGARCH innovations. Our results indicate that Markov switching models provide the most significant in-sample fit. However, results for the stable portion of the out-of-sample period and the recent recovery period are mixed with both EGARCH-based linear models and 2-state Dynamic Regression models outperforming the alternatives. Over the market crisis period we find that the forecast performance of the nonlinear models is worse than that of the linear models, which suggests that the benefit of the nonlinear treatment of conditional volatility diminishes over this period.

G Optimal Design in Non linear Models to Increase Silicon Oxide Purity Levels and Electrical Conductivity

2018 ◽  
pp. 150-155
Author(s):  
Muklas Rivai
Keyword(s):  
Electrical Conductivity ◽  
Optimal Design ◽  
Linear Models ◽  
Silicon Oxide ◽  
Information Matrix ◽  
Relevant Information ◽  
Non Linear

Optimal design is a design which required in determining the points of variable factors that would be attempted to optimize the relevant information so that fulfilled the desired criteria. The optimal fulfillment criteria based on the information matrix of the selected model.

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