大载重吊落式缓冲气囊设计及多目标优化

翟政博; 杨臻; 王端奇; 刘斌; 李茂林; 刘万川

doi:10.11883/bzycj-2025-0148

大载重吊落式缓冲气囊设计及多目标优化

doi: 10.11883/bzycj-2025-0148

翟政博^1,,
杨臻^1, ,,
王端奇²,
刘斌³,
李茂林⁴,
刘万川⁴

1.
中北大学，山西太原 030051
2.
杭州智元研究院有限公司，浙江杭州 310013
3.
中国北方车辆研究所，北京 100072
4.
建设工业集团（云南）股份有限公司，重庆 404100

基金项目: 瞬态冲击技术重点实验室基金项目(JCKY2023209C003)；

详细信息

作者简介:
翟政博（2000－　），男，硕士研究生，sf20230102@st.nuc.edu.cn

通讯作者:
杨　臻（1965－　），男，博士，教授，yzhen@nuc.edu.cn

中图分类号: TH122
计量
- 文章访问数: 685
- HTML全文浏览量: 150
- PDF下载量: 44
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-21
- 修回日期: 2025-10-10
- 网络出版日期: 2025-10-10

Design and multi-objective optimization of heavy-load lifting buffer airbag

1.
North University of China, Taiyuan 030051, China
2.
Zhiyuan Research Institute, Hangzhou 310013, China
3.
China North Vehicle Research Institute, Beijing 100072, China
4.
Jianshe Industry Group (Yunnan) Co., Ltd., Chongqing 404100, China

摘要

摘要: 针对大载重无人平台吊落式着陆的冲击防护问题，设计了一种实现安全着陆的缓冲气囊系统。基于理论模型初步确定气囊几何参数和排气孔面积，利用控制体积法（control volume method, CVM）建立有限元仿真模型，模拟气囊缓冲过程。通过单参数分析，揭示了排气孔尺寸、临界排气压力、气囊底面积及高度对缓冲性能的影响规律，发现上述参数在降低最大过载与提升比吸能之间存在冲突。为解决此问题，采用最优拉丁超立方设计采样，结合神经网络构建目标函数代理模型，并集成NSGA-II遗传算法进行多目标优化。优化结果表明，当排气孔面积为3952 mm²、临界排气压力158 kPa、气囊底面积1.08 m²时，最大过载从16.8g降至14.5g，比吸能保持在1529 J/kg。试验显示最大过载试验值为15.2g，与仿真结果误差仅为4.8%，证实了优化方案的有效性。
- 大载重无人平台 /
- 缓冲气囊 /
- 冲击动力学 /
- 多目标优化
Abstract: To address the challenges of rapid deployment for heavy-duty unmanned platforms and impact protection during drop-landing, a buffer airbag system was designed to ensure the safe landing of the unmanned platform. First, theoretical modeling was used to preliminarily determine the geometric parameters of the buffer airbag and the size of the vent holes. A finite element simulation model that included the buffer airbag, the unmanned platform and the ground was established using the control volume method (CVM) to simulate the cushioning process of the airbag. The impact dynamics response during the landing attenuation process was analyzed, and the specific energy absorption (SEA) of the buffer airbag system was introduced to evaluate its cushioning performance. The analysis results indicate that during the landing buffer process, the unmanned platform reached a maximum overload value of 16.8g at 0.06s, and the platform velocity reached a post-rebound maximum of 0.79 m/s at 0.1 s. At 0.18 s, the platform shifted laterally due to the center of gravity, and one side of the platform first contacted the ground. This led to fluctuations in overload value and velocity, but the overall landing process remained stable. The calculated SEA of the buffer airbag system was 1527 J/kg. Using the control variable method, the influence of key parameters on the buffer performance of the airbag was analyzed, revealing the effects of vent hole size, critical exhaust pressure, airbag base area and airbag height on the performance of the buffer. The study found that the influence of airbag height on the two key indicators (maximum overload and SEA) was consistent, presenting no optimization conflict. However, after the height exceeded 0.37 m, a diminishing returns effect appeared. When the other three variables were changed individually, they caused a conflict between the goals of minimizing maximum overload and maximizing SEA, making it impossible to improve both simultaneously through independent optimization. To resolve this issue, an optimal latin hypercube design was used for sampling, and a three-layer neural network was employed to construct proxy models for the objective functions. The root mean square error (RMSE) and coefficient of determination (R²) of these two types of proxy models were calculated to evaluate their regression performance. The NSGA-II genetic algorithm was integrated for multi-objective optimization. The results showed that for the proxy models predicting maximum overload and SEA, the RMSEs were 0.4895 and 0.7262, and the R² values were 0.9833 and 0.9364, respectively. All R² values were greater than the industry benchmark of 0.9, indicating high reliability of the proxy models. When the vent hole area was 3952 mm², the critical exhaust pressure, the airbag base area were 158 kPa and 1.08 m², respectively. Meanwhile, the maximum overload was reduced from 16.8g to 14.5g, and the SEA remained stable at 1529 J/kg. Based on the characteristics of the airbag's buffer process, a gantry crane-based simulation test method for unmanned platform airdrop and lifting was designed. A piezoelectric IEPE acceleration sensor was used to measure the overload value of the unmanned platform. Experimental verification showed that the measured maximum overload value was 15.2g, with a mere 4.8% error compared to the simulation result, confirming the correctness of the optimization scheme. This research provides efficient and low-cost design technical support for the soft landing systems of unmanned platforms, enhancing the safety and efficiency of operational deployment.
- Heavy load unmanned platform /
- buffer airbag /
- impact dynamics /
- multi-objective optimization

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图 1 缓冲过程示意图

Figure 1. Schematic diagram of buffering process

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图 2 气囊、无人平台模型

Figure 2. Airbag、unmanned platform model

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图 3 缓冲气囊着陆过程应力云图

Figure 3. Stress nephogram during buffer airbag landing process

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图 4 无人平台的动态响应

Figure 4. Dynamic response of unmanned platform

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图 5 缓冲气囊气体状态

Figure 5. State of airbag

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图 6 气囊缓冲性能敏感度分析

Figure 6. Sensitivity analysis of airbag cushioning performance

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图 7 最优拉丁超立方体设计

Figure 7. Optimal latin hypercube design

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图 8 代理模型线性回归图

Figure 8. Proxy model linear regression diagram

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图 9 缓冲气囊Pareto前沿图

Figure 9. Buffer airbag Pareto front line diagram

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图 10 优化前后过载值比较

Figure 10. Comparison of overload values before and after optimization

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图 11 现场测试布置

Figure 11. Field test layout

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图 12 气囊缓冲试验过程

Figure 12. Airbag cushioning test process

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图 13 着陆过载动态特性验证

Figure 13. Verification of dynamic characteristics of landing overload

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表 1 缓冲气囊模型参数

Table 1. Buffer airbag model parameters

定容比热/(J·kg⁻¹·K⁻¹)	定压比热/(J·kg⁻¹·K⁻¹)	温度/K	排气孔面积/m²	大气压力/kPa	环境空气密度/(kg·m⁻³)	重力转换常数	临界排气压力/kPa
717	1004	293	3.6×10⁻³	101	1.2	1	173

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表 2 缓冲气囊材料参数

Table 2. Buffer airbag material parameters

密度/(kg·m⁻³)	弹性模量/MPa	泊松比	剪切模量/MPa
876.03	380	0.2	40

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表 3 不同参数组合案例

Table 3. Cases of different parameter combinations

工况	A_a/mm²	p_cr/kPa	A/m²	h₀/m	a_max/g	e/(J$ \cdot $kg⁻¹)
1	3600	173	0.95	0.35	16.8	1527
2	3750	173	0.95	0.35	16.0	1492
3	3900	173	0.95	0.35	15.4	1453
4	4050	173	0.95	0.35	14.7	1420
5	4200	173	0.95	0.35	14.2	1381
6	3900	183	0.95	0.35	16.1	1498
7	3900	193	0.95	0.35	16.9	1540
8	3900	203	0.95	0.35	17.6	1589
9	3900	203	1.00	0.35	16.2	1498
10	3900	203	1.05	0.35	15.0	1434
11	3900	203	1.15	0.35	13.4	1356
12	3900	173	0.95	0.32	15.8	1427
13	3900	173	0.95	0.37	14.9	1505
14	3900	173	0.95	0.39	14.7	1522

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表 4 优化结果

Table 4. optimization results

参数	排气孔尺寸/mm²	排气临界压力/kPa	气囊底面积/m²	最大过载值/g	比吸能/(J·kg⁻¹)
优化前	3600	173	0.95	16.8	1527
优化后	3952	158	1.08	14.5	1529

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