Design and Evaluation of an Optically Accessible Canonical Inlet Model for CFD-Validation Experiments

Computational Fluid Dynamics (CFD) validation experiments require careful documentation of the boundary conditions. Many validation experiments contain complex geometries or facilities that are not accesible for meaurement using calipers. This paper describes a method for providing accurate optical measurement of physical geometries using an Nd:YAG laser, a CCD camera, and two 1-dimensional traverses. The facility used is a cylinder array test section. The laser sheet is aligned perpendicular to the cylinder span and the camera is aligned parallel to the cylinders. After images were acquired, a code was written to analyze the images based on the Gaussian-like intensity profiles of the illuminated cylinder edges and walls in the facility. The peaks were extracted and a least squares fit applied to the peaks resulting in the center coordinates and diameter of the cross-sections. These were then reconstructed into a surface which can be imported into a CFD simulation package. This study shows that the cylinder diameters and x- and y-coordinates can be determined accurately. The effects of various surface finishes are also compared. Three finishes were used: transparent, painted flat black, and a rhodamine painted surface with an appropriate band pass filter. The intensity profiles were compared using a single laser power setting.

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CFD Validation of Mixing in Pilot-Scale Ladle

The 8th International SteelSim Conference ◽

10.33313/503/046 ◽

2019 ◽

Author(s):

E. Harbers ◽

D. van der Plas ◽

A. Richardson ◽

K. Subramanian

Keyword(s):

Pilot Scale ◽

Cfd Validation

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CFD Validation of the NASA-CRM using Cflow

AEROSPACE TECHNOLOGY JAPAN THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES ◽

10.2322/astj.jsass-d-17-00037 ◽

2018 ◽

Vol 17 (0) ◽

pp. 163-172

Author(s):

Hidemasa YASUDA ◽

Taku NAGATA ◽

Yosuke UENO ◽

Akio OCHI

Keyword(s):

Cfd Validation

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Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

Scientific Reports ◽

10.1038/s41598-021-88538-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

A. Wong ◽

Z. Q. Lin ◽

L. Wang ◽

A. G. Chung ◽

B. Shen ◽

...

Keyword(s):

Neural Networks ◽

Monte Carlo ◽

Lung Disease ◽

Disease Severity ◽

Deep Neural Networks ◽

Cross Validation ◽

X Rays ◽

Computer Aided ◽

Monte Carlo Cross Validation ◽

Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

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Interclass Interference Suppression in Multi-Class Problems

Applied Sciences ◽

10.3390/app11010450 ◽

2021 ◽

Vol 11 (1) ◽

pp. 450

Author(s):

Jinfu Liu ◽

Mingliang Bai ◽

Na Jiang ◽

Ran Cheng ◽

Xianling Li ◽

...

Keyword(s):

Classification Accuracy ◽

Cross Validation ◽

Selection Process ◽

Interference Suppression ◽

Generalization Ability ◽

Suppression Effect ◽

Binary Classifiers ◽

The One ◽

Fold Cross Validation ◽

Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

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Genotypic Variation in Nutrient Uptake Requirements of Rice Using the QUEFTS Model

Agronomy ◽

10.3390/agronomy11010026 ◽

2020 ◽

Vol 11 (1) ◽

pp. 26

Author(s):

Tao Sun ◽

Xin Yang ◽

Sheng Tang ◽

Kefeng Han ◽

Ping He ◽

...

Keyword(s):

Nutrient Uptake ◽

Field Experiments ◽

Hybrid Rice ◽

Genotypic Variation ◽

Tropical Soils ◽

Study Data ◽

Japonica Rice ◽

Rice Varieties ◽

Indica Hybrid Rice ◽

Validation Experiments

Nutrient requirements for single-season rice using the quantitative evaluation of the fertility of tropical soils (QUEFTS) model in China have been estimated in a previous study, which involved all the rice varieties; however, it is unclear whether a similar result can be obtained for different rice varieties. In this study, data were collected from field experiments conducted from 2016 to 2019 in Zhejiang Province, China. The dataset was separated into two parts: japonica/indica hybrid rice and japonica rice. To produce 1000 kg of grain, 13.5 kg N, 3.6 kg P, and 20.4 kg K were required in the above-ground plant dry matter for japonica/indica hybrid rice, and the corresponding internal efficiencies (IEs) were 74.0 kg grain per kg N, 279.1 kg grain per kg P, and 49.1 kg grain per kg K. For japonica rice, 17.6 kg N, 4.1 kg P, and 23.0 kg K were required to produce 1000 kg of grain, and the corresponding IEs were 56.8 kg grain per kg N, 244.6 kg grain per kg P, and 43.5 kg grain per kg K. Field validation experiments indicated that the QUEFTS model could be used to estimate nutrient uptake of different rice varieties. We suggest that variety should be taken into consideration when estimating nutrient uptake for rice using the QUEFTS model, which would improve this model.

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