Experimental study of velocity-scalar filtered joint density function for LES of turbulent combustion

2005 ◽  
Vol 30 (1) ◽  
pp. 567-574 ◽  
Author(s):  
Danhong Wang ◽  
Chenning Tong
Keyword(s):  
Experimental Study ◽  
Density Function ◽  
Turbulent Combustion ◽  
Joint Density ◽  
Joint Density Function

Experimental study of velocity filtered joint density function for large eddy simulation

2004 ◽  
Vol 16 (10) ◽  
pp. 3599-3613 ◽  
Author(s):  
Danhong Wang ◽  
Chenning Tong ◽  
Stephen B. Pope
Keyword(s):  
Experimental Study ◽  
Large Eddy Simulation ◽  
Density Function ◽  
Joint Density ◽  
Joint Density Function ◽  
Eddy Simulation ◽  
Large Eddy

Investigation of scalar–scalar-gradient filtered joint density function for large eddy simulation of turbulent combustion

2021 ◽  
Vol 33 (3) ◽  
pp. 035121
Author(s):  
Mengyuan Yuan ◽  
Hengbin Zhang ◽  
Chenning Tong
Keyword(s):  
Large Eddy Simulation ◽  
Density Function ◽  
Turbulent Combustion ◽  
Joint Density ◽  
Joint Density Function ◽  
Eddy Simulation ◽  
Large Eddy

scholarly journals Comparison of models for missing pedigree in single-step genomic prediction

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yutaka Masuda ◽  
Shogo Tsuruta ◽  
Matias Bermann ◽  
Heather L Bradford ◽  
Ignacy Misztal
Keyword(s):  
Density Function ◽  
Single Step ◽  
Joint Density ◽  
Pedigree Information ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Breeding Values ◽  
Joint Density Function ◽  
Information Updates ◽  
Genetic Trends

Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas–Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g′). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g′)=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.

Evaluating policy feedback rules using the joint density function of a stochastic model

2006 ◽  
Vol 93 (1) ◽  
pp. 1-5 ◽  
Author(s):  
R. Barrell ◽  
S.G. Hall ◽  
I. Hurst
Keyword(s):  
Stochastic Model ◽  
Density Function ◽  
Joint Density ◽  
Policy Feedback ◽  
Joint Density Function

A joint density function approach to logging problems

1974 ◽  
Vol 50 (6) ◽  
pp. 238-239 ◽  
Author(s):  
C. H. Meng
Keyword(s):  
Density Function ◽  
Joint Density ◽  
Function Approach ◽  
Joint Density Function

Two-point joint-density function of the intensity for a laser-generated speckle field after propagation through the turbulent atmosphere

1990 ◽  
Vol 7 (6) ◽  
pp. 1008 ◽  
Author(s):  
V. S. Rao Gudimetla ◽  
J. Fred Holmes ◽  
Richard A. Elliott
Keyword(s):  
Density Function ◽  
Turbulent Atmosphere ◽  
Joint Density ◽  
Speckle Field ◽  
Joint Density Function

The joint density function of three characteristics on jump-diffusion risk process

2003 ◽  
Vol 32 (3) ◽  
pp. 445-455 ◽  
Author(s):  
Chunsheng Zhang ◽  
Guojing Wang
Keyword(s):  
Density Function ◽  
Jump Diffusion ◽  
Risk Process ◽  
Joint Density ◽  
Joint Density Function
Sign in / Sign up

Export Citation Format

Share Document