过度磨耗钢轨的打磨廓形设计方法

林凤涛; 邓卓鑫; 庞华飞; 王松涛; 杨建; 丁军君; 陈道云

doi:10.19818/j.cnki.1671-1637.2022.02.008

过度磨耗钢轨的打磨廓形设计方法

doi: 10.19818/j.cnki.1671-1637.2022.02.008

林凤涛^1,,
邓卓鑫^{1, 2},
庞华飞^{1, 3},
王松涛¹,
杨建⁴,
丁军君⁵,
陈道云¹

1.
华东交通大学载运工具与装备教育部重点实验室，江西南昌 330013
2.
广州铁路职业技术学院，广东广州 510430
3.
广东城际铁路运营有限公司，广东广州 510310
4.
安徽辉瑞轨道智能设备有限公司，安徽合肥 230001
5.
西南交通大学机械工程学院，四川成都 610031

基金项目:

国家自然科学基金项目 51865009

国家自然科学基金项目 52065021

江西省自然科学基金项目 20202BABL214028

江西省自然科学基金项目 20212BBE53024

江西省主要学科与技术领军人才培养计划项目 20213BCJL22040

江西省研究生创新专项资金项目 YC2021-S423

详细信息

作者简介:
林凤涛(1977-)，男，内蒙古赤峰人，华东交通大学教授，工学博士，从事轮轨关系研究

中图分类号: U211.5
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出版历程
- 收稿日期: 2021-12-07
- 刊出日期: 2022-04-25

Design method of grinding profile of over worn rail

1.
Key Laboratory of Ministry of Education for Conveyance and Equipment, East China Jiaotong University, Nanchang 330013, Jiangxi, China
2.
Guangzhou Railway Polytechnic, Guangzhou 510430, Guangdong, China
3.
Guangdong Intercity Railway Operation Co., Ltd., Guangzhou 510310, Guangdong, China
4.
Anhui Huirui Rail Intelligent Equipment Co., Ltd., Hefei 230001, Anhui, China
5.
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Funds:

National Natural Science Foundation of China 51865009

National Natural Science Foundation of China 52065021

Natural Science Foundation of Jiangxi Province 20202BABL214028

Natural Science Foundation of Jiangxi Province 20212BBE53024

Major Disciplines and Technology Leading Talents Training Program of Jiangxi Province 20213BCJL22040

Special Fund Project for Postgraduate Innovation of Jiangxi Province YC2021-S423

More Information

Author Bio:
LIN Feng-tao(1977-), male, professor, PhD, 46473697@qq.com

摘要

摘要: 针对过度磨耗钢轨的打磨，提出一种以圆弧切点为关键参数的钢轨廓形设计方法；以轮轨接触位置为优化区域，以钢轨磨耗和打磨材料去除量作为优化目标函数，以廓形边界范围、凹凸性、脱轨系数和轮轨横向力为约束条件，建立磨耗钢轨打磨设计廓形多目标函数；集成多元模拟退火寻优算法进行求解；为了得到能代表重载线路曲线区段的钢轨廓形，作为优化的输入数据，采用最小二乘距离算法、算术平均算法、加权平均算法和散点重构算法得出4种钢轨代表廓形；使用Pearson相关系数、Kendall秩相关系数和Spearman秩相关系数计算出4种算法的钢轨代表廓形与实测廓形接触点概率分布曲线的相关性，取相关性最高的代表廓形为等效重载线路曲线区段的实际廓形；对某重载线路过度磨耗钢轨的经济性打磨廓形以及采用圆弧型廓形设计方法的优化廓形进行分析。分析结果表明：优化廓形与现场打磨廓形相较，截面廓形磨削量减少69.56 mm²，下降64.98%，脱轨系数小幅增大，轮轨横向力基本不变，轮对横移变化较小，曲线通过性能相近，80万次通过量下的磨耗面积增加2.19 mm²，钢轨的磨耗速率略微增大，整体仍延长了钢轨寿命。
- 轨道车辆动力学 /
- 钢轨廓形设计 /
- 圆弧参数 /
- 模拟退火 /
- 代表廓形 /
- 轮轨接触几何
Abstract: A rail profile design method with the arc tangency point as the key parameter was proposed for the grinding of over worn rail. Specifically, taking the wheel-rail contact region as the optimization area and the rail wear and the removed amount of grinding material as the optimization objective function, taking the profile boundary, concavity and convexity, derailment coefficient and wheel-rail lateral force as the constraint conditions, the multi-objective function of designed grinding profile of worn rail was established. The multiple simulated annealing optimization algorithm was integrated for solutions. To obtain the rail profile representing the curve of a heavy haul line, which was adopted as the optimized input data, the representative profiles of four kinds of rails were obtained by using the least square distance algorithm, arithmetic average algorithm, weighted average algorithm and scatter reconstruction algorithm. The correlations between the rail representative profiles of the four algorithms and the measured profile contact point probability distribution curve were calculated by using the Pearson correlation coefficient, Kendall rank correlation coefficient and Spearman rank correlation coefficient, and the representative profile with the highest correlation was taken as the actual profile of the curve section of the equivalent heavy haul line. The economical grinding profile of over worn rail in a heavy haul line and the optimized profile using the arc profile design method were analyzed. Analysis results show that compared with the on-site grinding profile of rail, the optimized rail profile has a reduced grinding and cutting amount for its sectional profile by 69.56 mm², a decrease of 64.98%, a slightly increased derailment coefficient, the same lateral wheel-rail force, small lateral wheelset displacement change, and similar curve passing performance. Although the wear area under 800 000 passes increases by 2.19 mm², and the wear rate of rail slightly rises, the overall service life of rail is still prolonged. 3 tabs, 17 figs, 30 refs.
- railway vehicle dynamics /
- rail profile design /
- arc parameter /
- simulated annealing /
- representative profile /
- wheel-rail contact geometry

HTML全文

图 1 某重载线路曲线区段实测廓形

Figure 1. Measured profile of curve section of a heavy haul railway

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图 2 曲线区段钢轨廓形采集点位

Figure 2. Acquisition points of rail profile in curve section

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图 3 曲线段上股钢轨实测廓形

Figure 3. Rail profiles measured in external curve section

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图 4 各算法所得代表廓形

Figure 4. Representative profiles obtained by each algorithm

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图 5 轮轨接触点在钢轨上的概率分布曲线

Figure 5. Probability distribution curves of wheel-rail contact points on rail

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图 6 圆弧型曲线优化区域

Figure 6. Optimization zone of arc curve

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图 7 钢轨廓形上下边界约束范围

Figure 7. Constraint ranges of upper and lower boundaries of rail profile

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图 8 优化流程中圆弧型廓形曲线

Figure 8. Circular arc profile curves in optimization process

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图 9 全局最优解与局部最优解廓形曲线

Figure 9. Profile curves of global optimal solution and local optimal solution

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图 10 优化前后钢轨廓形曲线

Figure 10. Rail profile curves before and after optimization

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图 11 轮轨接触点分布

Figure 11. Distribution of wheel-rail contact points

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图 12 等效锥度曲线

Figure 12. Equivalent conicity curves

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图 13 优化前后脱轨系数变化曲线

Figure 13. Variation curves of derailment coefficient before and after optimization

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图 14 优化前后轮轨横向力变化曲线

Figure 14. Variation curves of wheel-rail lateral force before and after optimization

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图 15 优化前后轮对横移变化曲线

Figure 15. Variation curves of lateral displacement of wheelset before and after optimization

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图 16 磨耗后R_grind及累计磨耗

Figure 16. R_grind after wear and cumulative wear

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图 17 磨耗后R_opt及累计磨耗

Figure 17. R_opt after wear and cumulative wear

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表 1 曲线区段代表廓形接触点分布相关系数

Table 1. Correlations coefficient of distribution of representative profile contact points in curve section

算法	Pearson 相关系数	Kendall 秩相关系数	Spearman 秩相关系数	相关系数平均值
算术平均算法	0.877	0.726	0.836	0.813
最小二乘距离算法	0.806	0.507	0.591	0.635
加权平均算法	0.619	0.525	0.632	0.592
散点重构算法	0.622	0.736	0.837	0.732

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表 2 钢轨廓形构成元素

Table 2. Elements of rail profiles

钢轨廓形	构成元素	车轮接触部位	廓形轨顶半径/mm	轨头/ mm
CN60	5段圆弧，2段直线	R300、R80、R13	300	70.78
60N	7段圆弧，2段直线	R200、R60、R16	200	70.52
CN75	7段圆弧，2段直线	R200、R50、R16	200	71.96
UIC54	5段圆弧，2段直线	R300、R80、R13	300	69.92
UIC60	5段圆弧，2段直线	R300、R80、R13	300	72.02
美标68	5段圆弧，2段直线	R254、R31.75、R14.29	254	70.40

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表 3 局部最优解与全局最优解目标函数f(T)及约束限值

Table 3. Objective function f(T) and constraint limit value of local optimal solution and global optimal solution

迭代次数	预测磨耗/mm²	金属磨削量/mm²	f(T)归一值	上下界	凹凸性	脱轨系数	轮轨横向力/kN
50	13.35	115.27	0.846	符合	符合	0.257	40.6
100	12.76	164.47	0.787	符合	符合	0.183	27.0
150	13.16	109.79	0.524	符合	符合	0.181	27.6
200	12.53	78.93	0.342	符合	符合	0.157	26.0
300	12.65	37.49	0.286	符合	符合	0.141	23.4

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