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Engineering    2017, Vol. 3 Issue (5) : 596-607     https://doi.org/10.1016/J.ENG.2017.04.006
Research |
用于复合材料结构生产的编织增强材料树脂灌注工艺模拟
Robert S. Pierce1,2,Brian G. Falzon2()
1. Northern Ireland Advanced Composites and Engineering Centre, Belfast BT3 9DZ, UK
2. School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AH, UK
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摘要 商用飞机对减轻机身重量和提高燃料效率的需求日益增强,这一需求促进了复合材料在商用飞机结构中的应用。当飞机的复合材料结构变得更庞大、更复杂时,传统的热压罐生产方法就会变得相当昂贵,这一现象引起了研究者对非热压罐成型技术的关注。在此技术中,树脂被注入强化预浸料层。然而,树脂灌注工艺与操作人员的技术和经验息息相关,特别是在开发复杂部件的生产策略时。作为一种用于预测的计算工具,流程建模旨在解决可靠性问题以及传统反复试验法所导致的浪费。大多数传统建模仍应用于工业,主要针对各向同性多孔强化材料的流体流动模拟。然而,最近的一些研究开始将编织材料的多尺度和多学科的复杂性纳入考虑范畴,其模拟方法可以提供更高的保真度。尤其考虑到具有渗透性和多孔性的强化材料导致的织物变形效应,新的多物理场流程模拟能够通过织物更好地预测树脂的灌注行为。除了综述与流程模拟相关的前人研究和工艺现状,本文还重点论述了最近关于复杂双圆顶组件的多物理场流程模拟与实验灌注的对比验证。通过考虑变形依赖的流动行为,多物理场流程模拟能够预测实际的流动行为,证明其与基础的各向同性渗透模型相比有很大程度的改进。
关键词 复合材料编织增强悬垂灌注数值模拟    
Abstract

Increasing demand for weight reduction and greater fuel efficiency continues to spur the use of composite materials in commercial aircraft structures. Subsequently, as composite aerostructures become larger and more complex, traditional autoclave manufacturing methods are becoming prohibitively expensive. This has prompted renewed interest in out-of-autoclave processing techniques in which resins are introduced into a reinforcing preform. However, the success of these resin infusion methods is highly dependent upon operator skill and experience, particularly in the development of new manufacturing strategies for complex parts. Process modeling, as a predictive computational tool, aims to address the issues of reliability and waste that result from traditional trial-and-error approaches. Basic modeling attempts, many of which are still used in industry, generally focus on simulating fluid flow through an isotropic porous reinforcement material. However, recent efforts are beginning to account for the multiscale and multidisciplinary complexity of woven materials, in simulations that can provide greater fidelity. In particular, new multi-physics process models are able to better predict the infusion behavior through textiles by considering the effect of fabric deformation on permeability and porosity properties within the reinforcing material. In addition to reviewing previous research related to process modeling and the current state of the art, this paper highlights the recent validation of a multi-physics process model against the experimental infusion of a complex double dome component. By accounting for deformation-dependent flow behavior, the multi-physics process model was able to predict realistic flow behavior, demonstrating considerable improvement over basic isotropic permeability models.

Keywords Composite materials      Textile reinforcement      Draping      Infusion      Numerical modeling     
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在线预览日期:    发布日期: 2017-11-08
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Robert S. Pierce
Brian G. Falzon
引用本文:   
Robert S. Pierce,Brian G. Falzon. Simulating Resin Infusion through Textile Reinforcement Materials for the Manufacture of Complex Composite Structures[J]. Engineering, 2017, 3(5): 596-607.
网址:  
http://engineering.org.cn/EN/10.1016/J.ENG.2017.04.006     OR     http://engineering.org.cn/EN/Y2017/V3/I5/596
Fig.1  Composite composition of commercial aircraft by structural weight since 1970.
Fig.2  Hierarchical scales for textile reinforcement materials.
Fig.3  Fabric shearing as a result of deformation and the shear angle definition.
Fig.4  Flow advancement in the CVFE method.
Fig.5  Workings of the multi-physics process model [98]. UDF: user-defined function.
Fig.6  The two-stage experimental process [98]. (a) Forming; (b) infusion.
Fig.7  Deformation-dependent permeability properties defined across the 0°/90° orientation model [98].
Fig.8  Formed double dome samples, with shear measurement locations representative of each quadrant of symmetry [98]. (a) 0°/90° orientation; (b) –45°/45° orientation.
Fig.9  Comparison of experimental and modeled shear measurements across 16 locations in each sample orientation [98]. (a) 0°/90° orientation; (b) –45°/45° orientation.
Fig.10  Flow front comparison at 1255?s for 0°/90° orientation sample [98].
Fig.11  Flow front comparison at 850?s for –45°/45° orientation sample [98].
Fig.12  Experimental and modeled flow front progression through the 0°/90° orientation sample [98].
Fig.13  Experimental and modeled flow front progression through the –45°/45° orientation sample [98].
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