CFD analysis from scanned models help

cowrie

by Erin Hatfield
作者：Erin Hatfield
Tenths of a second can mean the difference between a gold medal and fourth place in Olympic track cycling.
在零点几秒可以决定一个金牌，在奥运会场地自行车第四位的分别。
Before the 2004 Olympics in Athens, the British Cycling Team found a unique way to help save those precious fractions of a second: The team commissioned computational fluid dynamics (CFD) studies from the Sports Engineering Research Group (SERG) at the University of Sheffield to improve the overall aerodynamics of their equipment. A combination of 3D scanning technology, Geomagic Studio reverse-engineering software, CFD programs from Fluent, and EnSight visualization software produced results that helped the team earn four medals.
之前在2004年雅典奥运会上，英国自行车队发现了一个独特的方式帮助拯救这些珍贵的第二个分数：车队委托计算（CFD）的在英国谢菲尔德大学的研究从体育工程研究集团（塞格），以流体动力学提高其设备的整体空气动力学。一种扫描技术，Geomagic Studio中的逆向工程软件，从良好的CFD方案，EnSight三维可视化软件结合产生的结果，帮助球队获得4枚奖牌。
Last-minute CFD analysis
In June 2004, just weeks before the Summer Olympics, the cycling governing body enacted a rule change stating that only helmets passing a formal safety test in an accredited laboratory could be used in Olympic track competition. The British Cycling Team had four helmet designs that fit the specifications, each with different aerodynamic stylings.
最后一分钟的CFD分析
2004年6月，前几个星期的夏季奥运会，单车管理机构制定的规则的变化说明，只有在一个头盔通过正式认可的实验室安全测试可在奥运田径比赛使用。英国自行车队有4个头盔设计适合的规格，与每一个不同的空气动力学造型。
To help determine which helmet was best for competition, the team turned to SERG for a quick CFD analysis. The British Cycling Team had worked with SERG months before to optimize the aerodynamics of the handlebar and wheel/fork designs on the team bikes in preparation for the Olympics.
为了帮助确定哪些是竞争最好的头盔，车队转向塞格的快速CFD分析。英国自行车队曾与塞格个月前，以优化的把手和车轮在为奥运会准备的球队自行车/前叉设计的空气动力学。
SERG’s Dr. John Hart ruled out CAD as an option for creating the digital models needed for CFD analysis: There was not enough time to model from scratch, and CAD is not well-suited to create the organic shapes required for accurate modeling and CFD analysis of the helmets and athletes. Hart decided that the best solution was to capture the geometry of the athletes and helmets with 3D Scanners’ ModelMaker X70 non-contact 3D laser fitted on a Faro Gold arm, then merge the scans and create a NURBS model of the data in Geomagic Studio.
塞格的博士约恩哈特排除作为建立所需的CFD分析的CAD数字模型：有没有足够的时间从头到模型，以及CAD不是很适合创造的精确建模和计算流体力学分析所需的有机形状该头盔和运动员。赫德决定，最好的解决办法是捕捉到的运动员和三维扫描仪'ModelMaker X70钢的非接触式三维激光法鲁黄金手臂上安装，然后头盔几何合并扫描并创建一个在Geomagic Studio中的数据的NURBS模型。
“CAD engineers work at different tolerances than those required for CFD analysis,” Hart says. “Even if we had the CAD files for the helmets, we would have had to spend a great deal of time cleaning up the model to make it watertight. Reverse engineering the helmets and surfacing them in Geomagic Studio guaranteed a highly detailed, watertight model in less time.”
“CAD工程师在比计算流体力学分析所需的各种公差的工作，”赫德说。 “即使我们曾经为头盔的CAD文件，我们将不得不花费大量的时间清理模型，以使其水密。逆向工程的头盔及堆焊在Geomagic Studio中他们保证了非常详细的，在较短的时间水密模式。“
Scanning the helmets was relatively straightforward. Each helmet took approximately 25 minutes to scan depending on the complexity of the design. The Faro arm moved around the object, capturing point-cloud data and depth information.
扫描是相对简单的头盔。每个头盔花了大约25分钟，扫描根据设计的复杂性。在法鲁手臂到处移动的对象，捕捉点云数据和深入的信息。
SERG planned to capture data from the athletes by scanning them in different racing positions; one aerodynamic posture and one where the cyclist has his or her head down to test more fully the effect of the helmet shapes.
塞格计划扫描捕获他们从不同的赛车运动员的位置数据，一个姿势，一个地方的空气动力学的车手都有他低着头，测试更充分的头盔形状的影响。
Because of the time crunch, however, Hart did not have access to a cyclist; he had to scan a colleague for the human geometry. The subject was scanned over the course of two hours, allowing for rest breaks during the scan session. Completed scans were broken into sections that followed closely in succession – upper arm, lower arm, hand – to help eliminate issues from sudden movement during the process.
由于时间紧绌，不过，赫德没有获得一个骑单车，他不得不为人类扫描几何同事。扫描的主题是两小时以上的课程，让休息期间扫描会议。完成扫描被分成区段，随后相继密切 - 上臂，下臂，手 - 帮助消除突然移动过程中的问题。
Refining complex scan data
Point-cloud data collected from the scans of the four different helmets was imported into Geomagic Studio, reverse-engineering software used to generate models for accurate CFD analysis, and to custom manufacture devices fit to an individual’s body parts.
炼油复杂的扫描数据
点云数据，从四个不同的头盔收集到的扫描到Geomagic Studio中，逆向工程软件用于生成精确的计算流体力学分析模型，并定制生产设备进口的适合个人的身体部位。
Geomagic Studio automatically aligned the scan data and a polygon mesh was applied. The model was cleaned to remove holes and defects in the data. Then patches were created over the polygons, outlining the positions of the NURBS surfaces.
Geomagic Studio自动对齐扫描数据和采用的多边形网格。该模型是清洗，以消除漏洞和缺陷的数据。然后是创造了补丁的多边形，概述了NURBS曲面的立场。
Scan data from the human subject was handled in much the same way, except additional work had to be done to reduce noise and align the data due to subtle movement from Hart’s colleague as he was being scanned.
从人类主体扫描数据的处理方式大致相同，除了额外的工作，都必须进行，以减少噪音和调整资料，因为从哈特的同事微妙的运动，他被扫描。
Hart used Geomagic Studio’s noise-reduction feature, as well as editing and filter tools, to refine the human model. He then used the software’s polygon geometric reconstruction functions to fill in missing data such as body hair and eyebrows that weren’t captured due to laser scatter.
哈特利用Geomagic Studio的降噪功能，以及编辑和过滤工具，以完善人的模型。然后，他使用的软件的多边形几何重建功能，以填补如体毛和眉毛在不丢失数据捕获由于激光散射。

“Geomagic Studio’s editing tools and ability to handle large, complex data sets made it a great match for this project,” says Hart. “We used the tools to refine scan data around the ears and in tight gaps, which enabled us to maintain a high degree of geometric realism on such a challenging human scan with nearly six million raw data points.”
“Geomagic Studio的编辑工具，有能力处理大型，复杂的数据集使它成为该工程的伟大的比赛，”赫德说。 “我们使用的工具，以改进在耳朵周围扫描数据和严密的差距，使我们能够保持这样的几何现实挑战人类扫描近600万点的原始数据的高度。”
Polygons and NURBS patches were applied to the human model and output by Geomagic Studio as a STEP file.

“The STEP file format provides a robust geometric file that’s not too large,” Hart says. “We can end up with a model with a large number of NURBS patches in order to capture the detail we need. The accuracy of the CFD study was highly dependent upon the geometrical accuracy of the assembled model.”

Visualization that proves results
The STEP file containing each helmet design and the human geometry was imported into Fluent’s Gambit software, where it was meshed for CFD analysis and a flow domain was generated around each model. The meshes ranged from two to seven million cells, depending on the geometry that was modeled. Wherever possible, prism cells were generated over the surface geometry to capture boundary-layer flow features in detail.

Fluent software was used for CFD analysis, which incorporated data on boundary and physical conditions that SERG had acquired from a previous British Cycling project.

CFD results were imported into CEI’s EnSight software, which produced highly detailed flow visualizations showing the aerodynamic properties of the helmets. SERG chose to concentrate on the drag and lift forces in the simulations, using isosurfaces to show wake structures and particle streamlines to visualize swirling, recirculating flow paths.

Based on the wake structures and recirculating flows in the visualizations, SERG was able to quickly identify how different geometric components of the models – such as the helmet and cyclist – interacted and influenced each other. They were also able to pinpoint large wakes that resulted in high drag forces.

Hart colored different aspects of the model within EnSight, and applied properties such as reflective surfaces for the bike and helmet and matte surfaces for fabric and skin. Lighting effects applied to the model helped complete the life-like look, according to Hart, making the results more believable.

“The flow visualizations and images were vital in presenting the physics-based simulation results in an understandable manner to the cycling team,” Hart says. “Being able to clearly show a client what is happening is essential to their understanding of the results.”

Hart and his colleagues used the EnSight images and animations to reinforce the hard data output from the Fluent simulations and help the engineers understand the flow physics that created the lift and drag forces. Based on the results, SERG was able to recommend an optimal helmet style that reduced aerodynamic drag and lift.

“The quality of the model geometry from Geomagic Studio and realistic color renderings and surfaces we applied in EnSight enabled us to incorporate a great deal of realism in the visualization,” says Hart.

The optimized bike design and helmet recommendations SERG made contributed to the team’s medal haul in the Olympic cycling events. SERG is working with the team again through UK Sport for the 2008 Beijing Summer Olympics.

多边形和NURBS补丁应用到人体模型和Geomagic Studio中输出文件的一个步骤。

“此项STEP文件格式提供了一个强大的几何文件，该文件不是太大，”赫德说。 “我们可以结束与一个大数目的NURBS模型了补丁程序，以捕捉细节我们需要的。作者：CFD研究精度高而在该组装模型几何精度的依赖。“

可视化的结果证明
该文件包含了每个步骤头盔的设计和几何是人类进入良好的开局软件，它被用于计算流体力学分析和流域网状进口大约为每个模型生成的。该网范围从2至7万个单元格，具体取决于是参照几何。只要有可能，棱镜细胞表面几何产生了捕捉详细边界层流动特征。

Fluent软件被用于计算流体力学分析，其中纳入边界和塞格已经从以前的英国自行车项目获得身体状况的数据。

计算流体力学的结果导入到中欧倡议的EnSight软件，该软件制作的非常详细的流程可视化，展示了头盔的空气动力特性。塞格选择集中在电梯中的阻力和模拟部队用等值面显示唤醒结构和粒子流线可视化旋转，循环流动路径。

随着结构的基础上，在可视化和循环流动，塞格能够迅速确定如何不同的几何模型组件 - 如头盔和骑自行车 - 相互作用和相互影响。他们还特别指出，在高阻力大的力量，导致醒来。

哈特彩色内不同方面的EnSight模型，应用性能，如自行车，头盔和织物和皮肤磨砂表面反射面。灯光效果应用到模型帮助完成逼真的外观，根据哈特，使结果更为可信。

“流可视化和图像是在理解的方式呈现一物理为基础的模拟结果的单车团的重要，”赫德说。 “能够清楚地显示出客户所发生的事情，就必须要了解的结果。”

赫德和他的同事们使用的EnSight图像和动画，以加强从良好的硬数据模拟输出，帮助工程师了解流动物理，创造了升力和阻力的力量。根据研究结果，塞格能推荐一个最佳头盔式，减少空气阻力和升力。

他说：“从Geomagic Studio和现实的彩色效果图和曲面几何模型的质量，我们在EnSight应用使我们能够纳入可视化的现实主义很多，”赫德说。

优化设计的自行车和头盔建议塞格作出贡献的选手们在奥运会自行车项目金牌。塞格正在再次通过英国体育2008年北京夏季奥运会的队伍。