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摘要:
升力体式混合飞艇是全球远距离大载重运输的重要选择，随着全球贸易的发展，逐渐成为国内外的研究热点。作为航空宇航技术、新能源技术和高性能材料技术相结合的新概念飞行器，混合飞艇设计过程需对多个学科进行综合考虑和优化。为了将多学科设计优化（MDO）方法引入到混合飞艇的总体设计中，将其分解为能源子系统、气动和推进子系统以及结构和重量子系统。在子系统模型构建的基础上，提出具有自适应能力的基于响应面的并行子空间优化（CSSO-RS）算法，将重量平衡和能量平衡作为实现远距离载重运输的约束条件，并提出爬升、日间巡航、滑翔和夜间巡航的多阶段任务剖面，以充分利用太阳能电池、燃料电池和锂电池的优势，实现混合飞艇的最优化设计。优化结果表明：具有自适应能力的优化算法在精确度和计算效率上均有明显的优势，同时重量分配的结果也为混合飞艇结构轻量化设计和能源系统设计提出了更高的要求。
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关键词:
- 混合飞艇 /
- 混合能源 /
- 多学科设计优化(MDO) /
- 基于响应面的并行子空间优化(CSSO-RS) /
- 近似模型
Abstract:Lift-type hybrid airship is an important choice of long-distance and large-load transportation. With the development of global trade, it has gradually become a research hotspot at home and abroad. As a new concept aircraft combining aeronautical science and technology, new energy technology and high-performance material technology, multiple disciplines should be considered and optimized in the design process of hybrid airship comprehensively. To introduce the Multidisciplinary Design Optimization (MDO) method into the conceptual design of hybrid airship, it is decomposed into energy subsystem, aerodynamic and propulsion subsystem, and structure and weight subsystem. On the basis of building subsystem model, a Concurrent Subsystem Optimization algorithm based on Response Surface (CSSO-RS) with the self-adaptive ability is put forward. The weight balance and energy balance are set as the constraints to achieve long-distance transportation. Meanwhile, a multi-stage task profile with climb, day cruise, gliding and night cruise is proposed to make full use of solar energy battery, fuel cell and lithium batteries and realize the optimal design of hybrid airship. The optimization results show that the adaptive optimization algorithm has obvious advantages in accuracy and computational efficiency, and the weight distribution results also put forward higher requirements for lightweight design and energy system design of hybrid airships.
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图 2 混合飞艇载重运输任务剖面图
Figure 2. Mission profile of hybrid airship for loading transportation
图 5 任意斜面接收太阳光照示意
Figure 5. Position of the sun relative to an arbitrarily oriented plane
图 7 混合飞艇等效传统旋成体飞艇示意
Figure 7. Equivalent of hybrid airship and conventional rotated airship
图 10 设计优化迭代收敛过程对比
Figure 10. Comparison of design optimization iterativeconvergence process
图 11 迭代收敛过程重量平衡对于近似模型的选择
Figure 11. Selection of approximate model for weight balance in iterative convergence process
图 12 迭代收敛过程能量平衡对于近似模型的选择
Figure 12. Selection of approximate model for energy balance in iterative convergence process
图 15 混合飞艇能源子系统重量分配
Figure 15. Weight distribution among energy subsystem of hybrid airship
表 1 混合飞艇优化初始参数
Table 1. Initial parameters of hybrid airship optimization
初始参数 数值 巡航速度v/(m·s-1) 20 囊体材料面密度ρenv/(kg·m-2) 0.2 太阳能电池面密度ρsa/(kg·m-2) 0.3 载荷重量mpayload/kg 1 000 燃料电池能量密度ρESS/(Wh·kg-1) 1 000 电机和螺旋桨功率质量比SPprop/(W·kg-1) 440 推进系统效率ηprop 0.72 燃料电池放电效率ηESS 0.55 
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表 2 优化结果对比
Table 2. Comparison of optimization results
参数 数值 传统CSSO-RS算法 自适应CSSO-RS算法 等效飞艇长半轴a/m 51.23 48.75 太阳能电池起点位置坐标xs/m 22.35 18.11 混合飞艇体积V/m3 129 797.74 128 009.89 混合飞艇总质量mtotal/kg 22 551.23 21 698.86 燃料电池质量mESS/kg 4 948.79 4 019.28 太阳能电池质量msa/kg 1 487.81 1 554.63 锂电池质量mLi/kg 2 537.72 3 019.44 能量供给Qsup/kWh 4 968.84 4 642.65 等效飞艇短半轴b/m 19.5 19.5 太阳能电池结束位置坐标xe/m 96.32 94.68 太阳能电池面积Asa/m2 3 796.47 3 986.23 能源子系统重量menergy/kg 8 974.32 8 592.95 结构子系统重量mstructure/kg 10 379.59 10 076.42 推进子系统重量mthrust/kg 2 197.32 2 029.49 载荷子系统重量mpayload/kg 1 000 1 000 能量需求Qreq/kWh 4 968.83 4 642.64
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