Guide for Estimating Carbon Saturation by Temperature Rise upon Immersion

Abstract An analysis by the finite element method and a related computer program is presented for an axisymmetric solid under asymmetric loads. Calculations are carried out on displacements and internal stresses and strains of a radial tire loaded on a road wheel of 600-mm diameter, a road wheel of 1707-mm diameter, and a flat plate. Agreement between calculated and experimental displacements and cord forces is quite satisfactory. The principal shear strain concentrates at the belt edge, and the strain energy increases with decreasing drum diameter. Tire temperature measurements show that the strain energy in the tire is closely related to the internal temperature rise.

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Temperature Rise Times in Pneumatic Tires

Tire Science and Technology ◽

10.2346/1.2167221 ◽

1976 ◽

Vol 4 (3) ◽

pp. 181-189 ◽

Cited By ~ 12

Author(s):

S. K. Clark

Keyword(s):

Heat Transfer ◽

Thermal Properties ◽

Temperature Rise ◽

Thermal Equilibrium ◽

Transfer Coefficients ◽

Pneumatic Tire ◽

Heat Transfer Coefficients ◽

Idealized Model ◽

Pneumatic Tires

Abstract An idealized model is proposed for heating of a pneumatic tire. A solution is obtained for the temperature rise of such a model. Using known thermal properties of rubber and known heat transfer coefficients, the time to reach thermal equilibrium is estimated.

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BRUSHFORM 65

Alloy Digest ◽

10.31399/asm.ad.cu0821 ◽

2013 ◽

Vol 62 (6) ◽

Keyword(s):

Physical Properties ◽

Tensile Properties ◽

Contact Force ◽

Temperature Rise ◽

Power Loss ◽

High Reliability ◽

Superior Performance ◽

Contact Interface ◽

Resistive Heating ◽

Computer Power

Abstract BrushForm 65 is designed for both superior performance and high reliability in appliance, automotive, and computer power applications. Alloy BF-65’s combination of properties limits power loss at the contact interface, controls temperature rise from resistive heating, and provides stable contact force at temperatures to 200 C (390 F). This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fatigue. It also includes information on forming. Filing Code: Cu-821. Producer or source: Materion Brush Performance Alloys.

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Rubber, vulcanized. Determination of temperature rise and resistance to fatigue in flexometer testing

10.3403/30113811 ◽

2007 ◽

Keyword(s):

Temperature Rise

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Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

Recent Patents on Mechanical Engineering ◽

10.2174/2212797612666190530115739 ◽

2019 ◽

Vol 12 (3) ◽

pp. 248-261

Author(s):

Baomin Wang ◽

Xiao Chang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Temperature Rise ◽

High Speed ◽

Ball Bearing ◽

Influence Factors ◽

Ball Bearings ◽

Ann Model ◽

Angular Contact Ball Bearing ◽

Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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