Influence of rice straw forming factors on ring die wear and improved wear prediction model during briquetting

2022 ◽  
Vol 214 ◽  
pp. 122-137
Author(s):  
Shuren Chen ◽  
Hantao Ding ◽  
Zhong Tang ◽  
Shuaihua Hao ◽  
Yunfei Zhao
Keyword(s):  
Prediction Model ◽  
Rice Straw ◽  
Wear Prediction ◽  
Die Wear

scholarly journals Numerical Simulation of Solid Particle Erosion in Aluminum Alloy Spool Valve

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Jin-gang Liu ◽  
Gao-sheng Wang ◽  
Tian-heng Peng ◽  
Sheng-qiang Jiang
Keyword(s):  
Particle Size ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Particle Shape ◽  
Two Phase ◽  
Wear Prediction ◽  
Valve Body ◽  
Fluent Software ◽  
Improved Design ◽  
Spool Valve

Aluminum alloy spool valve body material is prone to severe wear on the wall under the condition of oil contamination. Aiming at this problem, combined with the theory of liquid-solid two-phase flow and erosion wear, the wear prediction model of aluminum alloy sliding valve wall is established based on computational fluid dynamics, and the effects of turbulence and wall materials, particle size distribution, and particle shape on particle motion are discussed. The calculation of the wear prediction model is done with Fluent software. This study predicts the wear of the wall under actual working conditions and calculates the influence of particle size, particle shape, and pressure difference on the wall wear of the aluminum alloy sliding valve. The research results have certain significance for the maintenance and upkeep of aluminum alloy sliding valve wall, improved design, and life prediction.

scholarly journals Experimental Analysis and Wear Prediction Model for Unfilled Polymer–Polymer Sliding Contacts

2019 ◽  
Vol 62 (2) ◽  
pp. 176-188 ◽  
Author(s):  
Vikram Ramesh ◽  
Julien van Kuilenburg ◽  
Wessel W. Wits
Keyword(s):  
Prediction Model ◽  
Experimental Analysis ◽  
Sliding Contacts ◽  
Wear Prediction

The Derivation and Validation of TBM Disc Cutter Wear Prediction Model

2018 ◽  
Vol 36 (6) ◽  
pp. 3391-3398 ◽  
Author(s):  
Yandong Yang ◽  
Kairong Hong ◽  
Zhenchuan Sun ◽  
Kui Chen ◽  
Fengyuan Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Disc Cutter ◽  
Wear Prediction ◽  
Cutter Wear ◽  
Disc Cutter Wear

Experimental Study and Prediction Model of Casing Wear in Oil and Gas Wells

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Zhanghua Lian ◽  
Qiang Zhang ◽  
Tiejun Lin ◽  
Kuncheng Li ◽  
Yonghui Liu
Keyword(s):  
Prediction Model ◽  
Contact Force ◽  
Oil And Gas ◽  
Economic Losses ◽  
Wear Depth ◽  
Gas Wells ◽  
Wear Prediction ◽  
Oil And Gas Wells ◽  
Casing Wear ◽  
Force Calculation

With the development of drilling technology and reinforced exploration and exploitation of unconventional reservoirs, there has been a great increase of complex wells. Meanwhile, however, consequent casing wear is and will continue to be a serious problem that causes enormous economic losses and many safety issues. The purpose of this paper is to find out the mechanism of casing wear and establish casing wear prediction model. Casing wear experiment was carried out to study the effect of contact force, rotation speed, and casing grade on wear depth. Meanwhile, wear coefficients under different working conditions were obtained through the normalizing of data. With the extensive research of downhole drag and torque calculation method, a contact force calculation model was established. Through the combination of crescent-shaped model and wear-efficiency model, the past complicated casing wear prediction models and confusing empirical formulae were greatly simplified. Therefore, the wear volume and depth of the casing string can be accurately predicted. Finally, a prediction software was developed to predict downhole casing wear of oil and gas wells. Comparison with the field data confirmed that the established model and software had enough accuracy to help predict and analyze casing wear at field.

A dynamic tooth wear prediction model for reflecting “two-sides” coupling relation between tooth wear accumulation and load sharing behavior in compound planetary gear set

2020 ◽  
Vol 234 (9) ◽  
pp. 1746-1763
Author(s):  
Haibo Zhang ◽  
Xiaofang Shen
Keyword(s):  
Prediction Model ◽  
Planetary Gear ◽  
Load Sharing ◽  
Tooth Wear ◽  
Transmission Error ◽  
Eccentric Load ◽  
Wear Prediction ◽  
Two Sides ◽  
Sharing Behavior ◽  
Initial Transmission

In planetary gear set, load sharing behavior determines the average amplitude of meshing force, so determines the tooth wear. Meanwhile, tooth wear accumulation increases the backlash, so affects the load sharing behavior. As a result, a “two-sides” coupling relation appears between load sharing behavior and tooth wear. In order to reveal this coupling relation, a static tooth wear prediction model for compound planetary gear set is employed to describe the wear distribution along tooth profile and backlash generated by tooth wear accumulation. Then, a “translational-rotational” lumped-parameter dynamic model of compound planetary gear set is built to calculate the eccentric load coefficient for each meshing pair, under the excitation from the backlash generated by tooth wear accumulation, transmission error, and other excitation. Based on the aforementioned two models, a dynamic tooth wear prediction model is put forward to reflect “two-sides” coupling relation between tooth wear accumulation and load sharing behavior in compound planetary gear set. Lastly, in order to improve the reliability, a 3D-diformable model is built to study the influence of tooth wear on load sharing. The results indicated that, with the system running and tooth wear increasing, under the condition of no-initial transmission error, tooth wear accumulation decreases the load sharing at meshing engagement and disengagement, and under the condition of initial transmission error, tooth wear accumulation improves load sharing behavior slightly.

A study on die wear prediction for automobile panels stamping based on dynamic model

2018 ◽  
Vol 97 (5-8) ◽  
pp. 1823-1833
Author(s):  
Xiaoyong Qiao ◽  
Aiguo Cheng ◽  
Xin Nie ◽  
Minqing Ning
Keyword(s):  
Dynamic Model ◽  
Wear Prediction ◽  
Die Wear
Sign in / Sign up

Export Citation Format

Share Document