Sensitivity analysis of influence factors on multi-zone indoor airflow CFD simulation

2020 ◽  
pp. 143298
Author(s):  
Jack C.P. Cheng ◽  
Helen H.L. Kwok ◽  
Alison T.Y. Li ◽  
Jimmy C.K. Tong ◽  
Alexis K.H. Lau
Keyword(s):  
Sensitivity Analysis ◽  
Cfd Simulation ◽  
Influence Factors ◽  
Indoor Airflow

Kriging-Based Reliability Analysis and Variance-Based Sensitivity Analysis for an Idler Shaft

2015 ◽  
Author(s):  
Weimin Cui ◽  
Wei Guo ◽  
Zhongchao Sun ◽  
Tianxiang Yu
Keyword(s):  
Sensitivity Analysis ◽  
Influence Factors ◽  
Random Variables ◽  
Kriging Model ◽  
Element Analysis ◽  
Performance Function ◽  
The Finite Element Analysis ◽  
Inner Diameter ◽  
Sensitivity Method ◽  
The Relationship

In order to analyze the reason of failure and improve the reliability of the idler shaft, this paper studies the reliability and sensitivity for the idler shaft based on Kriging model and Variance Methods respectively. The finite element analysis (FEA) of idler shaft is studied in ABAQUS firstly. Then, combining the performance function and various random variables, the Kriging model of idler shaft is established and verified. Based on Kriging model which has been established, the relationship between random variables and the response value is studied, and the function reliability is calculated which explains why the failure of the idler shaft occurred frequently in service. Finally, the variance-based sensitivity method is used for sensitivity analysis of influence factors, the result shows that the reliability of idler shaft is sensitive to the inner diameter of body A and inner diameter of body B, which could contribute for the analysis and further improvement of idler shaft.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

scholarly journals High-Precision Dynamics Characteristic Modeling Method Research considering the Influence Factors of Hydropneumatic Suspension

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Wenguang Wu ◽  
Hongliang Tang ◽  
Sha Zhang ◽  
Lin Hu ◽  
Fanhao Zhang
Keyword(s):  
Cfd Simulation ◽  
Ride Comfort ◽  
Fluid Dynamic ◽  
Influence Factors ◽  
Kriging Model ◽  
Modeling Method ◽  
Effect Of Temperature ◽  
Oil Viscosity ◽  
Nonlinear Characteristics ◽  
And Control

In recent years, hydropneumatic suspension (HPS) has come into widespread use for improving the ride comfort and handling of mining dump trucks and off-road vehicles. Therefore, it is critical to improve the mathematical modeling accuracy to enhance the design and control efficiency and accuracy of HPS. This paper aims to propose a model for improving the modeling precision by considering the effect of different factors on HPS characteristics. A computational fluid dynamic (CFD) model of a HPS was developed, and the volume of fluid (VOF) method was used for the transient calculations in order to simulate the fluid dynamic characteristics and determine the damping and stiffness forces of HPS. The effect of temperature, oil viscosity, nitrogen dissolution rate, and suspension vibration speed on the nonlinear characteristics of HPS was investigated. A limited number of simulation sample points were designed based on the variation ranges of the above factors using the design of experiment (DOE) method. The corresponding damping and stiffness force of each sample point were calculated by CFD simulation. The obtained simulation data were utilized for the fitting of a Kriging model. The results demonstrated that the Kriging model can provide high accuracy, with a prediction error lower than 5%. The proposed modeling method of the HPS nonlinear characteristics is highly efficient, accurate, and faster than traditional methods.

Ethanol Mechanism Reduction Based on DRGEPSA Method at Various Operating Ranges

2020 ◽  
Author(s):  
Shrabanti Roy ◽  
Omid Askari
Keyword(s):  
Sensitivity Analysis ◽  
Error Propagation ◽  
Cfd Simulation ◽  
Reduction Process ◽  
Chemical Kinetic ◽  
Operating Conditions ◽  
Computational Time ◽  
Wide Range ◽  
Relation Graph ◽  
Future Work

Abstract Reducing the size of a detail chemical kinetic is necessary in the prospect of numerical computation. In this work a skeleton reduction is done on a detail mechanism of ethanol. The detailed ethanol mechanism used here is developed through reaction mechanism generator (RMG). The generated mechanism is validated at wide range of engine relevant operating conditions using laminar burning speed (LBS), ignition delay time (IDT) and species mole fraction calculation at different reactor conditions. This detail mechanism consists of 67 species and 1031 reactions. Though the mechanism is in a very good agreement at various operating ranges with experimental data, it is costly to use a detail mechanism for 3D computational fluid dynamics (CFD) analysis. To make the mechanism applicable for CFD simulation further reduction of species and reactions is essential. In this work a skeleton mechanism is generated using directed relation graph technique with error propagation and sensitivity analysis (DRGEPSA). The DRGEPSA method, works based on error calculation at user defined condition. This technique is a combination of two methods, directed relation graph with error propagation (DRGEP) and directed relation graph with sensitivity analysis (DRGASA). To ensure the wide range of applicability of the skeleton mechanism, IDT is calculated at temperature, pressure, and equivalence ratio ranges from 700–2000 K, 1–40 atm and 0.6–1.4 respectively. A 10% error estimation is considered during the process. Initially DRGEP is applied on the detail mechanism to eliminate unimportant species. Further, sensitivity analysis helps to identify and reduce more unimportant species from the mechanism. Reactions related to the deleted species are automatically removed from the mechanism in each step. The final skeleton mechanism has 42 species and 464 reactions. This skeleton mechanism is validated and compared with different IDT data for the conditions not used in reduction technique. Results of LBS and different species concentration from reactor conditions is considered for validation. The skeleton mechanism can reduce computational time by 35% for LBS and 25% for IDT calculation. For future work, this skeleton mechanism will be considered in optimum reduction process.

S1110205 Fundamental Study on CFD Simulation of Indoor Airflow Field for Mobile Robot Gas Source Localization

2015 ◽  
Vol 2015 (0) ◽  
pp. _S1110205--_S1110205-
Author(s):  
Naoko GOTO ◽  
Takumi SETO ◽  
Tomoaki TAKEZAWA ◽  
Haruka MATSUKURA ◽  
Hiroshi ISHIDA
Keyword(s):  
Mobile Robot ◽  
Source Localization ◽  
Cfd Simulation ◽  
Fundamental Study ◽  
Indoor Airflow ◽  
Gas Source ◽  
Gas Source Localization ◽  
Airflow Field

Sensitivity Analysis on the Influence Factors of Mid-Span Deflection for Continuous Rigid Frame Bridge

2014 ◽  
Vol 1065-1069 ◽  
pp. 908-911
Author(s):  
Tian Hua Jiang ◽  
Li Ai ◽  
Lei Chen ◽  
Hong Xu
Keyword(s):  
Sensitivity Analysis ◽  
Influence Factors ◽  
Continuous Rigid Frame Bridge ◽  
Structural Rigidity ◽  
Rigid Frame ◽  
Vehicle Loads ◽  
The Common ◽  
Rigid Frame Bridge

The common problem of continuous rigid frame bridge is excessive mid-span deflection. The three factors of excessive deflection including vehicle loads, structural rigidity and prestressed loss are discussed on the case of a three spans continuous rigid frame bridge. Midas Civil is applied to analyze the sensitivity of mid-span deflection for these factors. The results indicate that the deflection is increasing with the increase of vehicle loads or the decrease of structural rigidity or the increase of prestressed loss. Furthermore, mid-span deflection is the most sensitive to the change of prestressed loss.

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