scholarly journals Development of acoustic sensitivity analysis based on experimental data and its applications.

1990 ◽  
Vol 56 (1) ◽  
pp. 98-102
Author(s):  
Nobuyuki OKUBO ◽  
Katsumi MASUDA
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Acoustic Sensitivity

scholarly journals Simulative investigation of ring creep on a planetary bearing of a wind turbine gearbox

2021 ◽  
Author(s):  
Jonas Gnauert ◽  
Felix Schlüter ◽  
Georg Jacobs ◽  
Dennis Bosse ◽  
Stefan Witter
Keyword(s):  
Experimental Data ◽  
Finite Element Method ◽  
Sensitivity Analysis ◽  
Planetary Gear ◽  
The Finite Element Method ◽  
Relative Movement ◽  
Wind Turbine Gearbox ◽  
Interference Fit ◽  
Planetary Gears ◽  
Module Coefficient

AbstractWind turbines (WT) must be further optimized concerning availability and reliability. One of the major reasons of WT downtime is the failure of gearbox bearings. Some of these failures occur, due to the ring creep phenomenon, which is mostly detected in the planetary bearings. The ring creep phenomenon describes a relative movement of the outer ring to the planetary gear. In order to improve the understanding of ring creep, the finite element method (FEM) is used to simulate ring creep in planetary gears. First, a sensitivity analysis is carried out on a small bearing size (NU205), to characterize relevant influence parameters for ring creep—considered parameters are teeth module, coefficient of friction, interference fit and normal tooth forces. Secondly, a full-scale planetary bearing (SL185030) of a 1MW WT is simulated and verified with experimental data.

MATHEMATICAL MODELING OF IMMUNE-INFLAMMATORY REACTION IN ACUTE PNEUMONIA

1995 ◽  
Vol 03 (02) ◽  
pp. 429-439 ◽  
Author(s):  
S. G. RUDNEV ◽  
A. A. ROMANYUKHA
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Mathematical Model ◽  
Immune Response ◽  
Sensitivity Analysis ◽  
Inflammatory Reaction ◽  
Humoral Immune ◽  
Disease Characteristics ◽  
Acute Pneumonia ◽  
Lung Resistance

Using ordinary differential equations, we propose a mathematical model describing an “averaged” dynamics of variables involved in which some parameters are shown to be important characteristics of lung resistance. The model consists of modified D.A. Lauffenburger’s mathematical model for inflammatory reaction in lungs, and the model of humoral immune response (G. I. Marchuk). Coefficients are identified against clinical and experimental data. We attempt to elucidate some disease characteristics in terms of sensitivity analysis of model solutions with respect to parameters variations.

Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model

2021 ◽  
Author(s):  
Sebastien Sequeira ◽  
Kevin Bennion ◽  
J. Emily Cousineau ◽  
Sreekant Narumanchi ◽  
Gilberto Moreno ◽  
...  
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Permanent Magnet ◽  
Power Density ◽  
Heat Removal ◽  
Maximum Difference ◽  
Modeling Framework ◽  
Element Analysis ◽  
Lumped Parameter ◽  
Input Parameters

Abstract One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet synchronous motor within the commercially available Motor-CAD® modeling environment. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot-winding and the end-winding - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot-winding and end-winding. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance.

scholarly journals Assessment of CFD Solvers and Turbulent Models for Water Free Jets in Spillways

Fluids ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 104
Author(s):  
António Muralha ◽  
José F. Melo ◽  
Helena M. Ramos
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Velocity Field ◽  
Numerical Simulations ◽  
Free Water ◽  
Concentration Profiles ◽  
Air Concentration ◽  
Free Jets ◽  
Free Jet ◽  
Turbulent Models

The capability of two different OpenFOAM® solvers, namely interFoam and twoPhaseEulerFoam, in reproducing the behavior of a free water jet was investigated. Numerical simulations were performed in order to obtain the velocity and air concentration profiles along the jet. The turbulence intensity was also analyzed. The obtained results were compared with published experimental data and, in general, similar velocity and air concentration profiles were found. InterFoam solver is able to reproduce the velocity field of the free jet but has limitations in the simulation of the air concentration. TwoPhaseEulerFoam performs better in reproducing the air concentration along the jet, the results being in agreement with the experimental data, although the computational runs are less stable and more time consuming. The sensitivity analysis of the inlet turbulent intensity showed that it has no influence in the characteristics of the jet core. With this research it is possible to conclude that: interFoam with k-Epsilon (k-ε) turbulence model is the best choice if the goal of the numerical simulations is the simulation of the velocity field of the jet. Meanwhile, twoPhaseEulerFoam with mixturek-Epsilon (mk-ε) shall be considered if the objective is the simulation of the velocity field and the air concentration.

Calculating Individual and Total Muscular Translational Stiffness: A Knee Example

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
Joshua G. A. Cashaback ◽  
Michael R. Pierrynowski ◽  
Jim R. Potvin
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Knee Joint ◽  
Knee Flexion ◽  
Pennation Angle ◽  
Biomechanical Models ◽  
The Individual ◽  
Full Activation ◽  
Translational Stiffness ◽  
Anterior Posterior

Research suggests that the knee joint may be dependent on an individual muscle's translational stiffness (KT) of the surrounding musculature to prevent or compensate for ligament tearing. Our primary goal was to develop an equation that calculates KT. We successfully derived such an equation that requires as input: a muscle's coordinates, force, and stiffness acting along its line of action. This equation can also be used to estimate the total joint muscular KT, in three orthogonal axes (AP: anterior-posterior; SI: superior-inferior; ML: medial-lateral), by summating individual muscle KT contributions for each axis. We then compared the estimates of our equation, using a commonly used knee model as input, to experimental data. Our total muscular KT predictions (44.0 N/mm), along the anterior/posterior axis (AP), matched the experimental data (52.2 N/mm) and was well within the expected variability (22.6 N/mm). We then estimated the total and individual muscular KT in two postures (0 deg and 90 deg of knee flexion), with muscles mathematically set to full activation. For both postures, total muscular KT was greatest along the SI-axis. The extensors provided the greatest KT for each posture and axis. Finally, we performed a sensitivity analysis to explore the influence of each input on the equation. It was found that pennation angle had the largest effect on SI KT, while muscle line of action coordinates largely influenced AP and ML muscular KT. This equation can be easily embedded within biomechanical models to calculate the individual and total muscular KT for any joint.

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