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.
在零點幾秒可以決定一個金牌，在奧運會場地自行車第四位的分別。
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年雅典奧運會上，英國自行車隊發現了一個獨特的方式幫助拯救這些珍貴的第二個分數：車隊委托計算（CFD）的在英國謝菲爾德大學的研究從體育工程研究集團（塞格），以流體動力學提高其設備的整體空氣動力學。一種掃描技術，Geomagic Studio中的逆向工程軟件，從良好的CFD方案，EnSight三維可視化軟件結合產生的結果，幫助球隊獲得4枚獎牌。
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.
最后一分鐘的CFD分析
2004年6月，前幾個星期的夏季奧運會，單車管理機構制定的規則的變化說明，只有在一個頭盔通過正式認可的實驗室安全測試可在奧運田徑比賽使用。英國自行車隊有4個頭盔設計適合的規格，與每一個不同的空氣動力學造型。
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.
為了幫助確定哪些是競爭最好的頭盔，車隊轉向塞格的快速CFD分析。英國自行車隊曾與塞格個月前，以優化的把手和車輪在為奧運會準備的球隊自行車/前叉設計的空氣動力學。
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.
塞格的博士約恩哈特排除作為建立所需的CFD分析的CAD數字模型：有沒有足夠的時間從頭到模型，以及CAD不是很適合創造的精確建模和計算流體力學分析所需的有機形狀該頭盔和運動員。赫德決定，最好的解決辦法是捕捉到的運動員和三維掃描儀'ModelMaker X70鋼的非接觸式三維激光法魯黃金手臂上安裝，然后頭盔幾何合并掃描并創建一個在Geomagic Studio中的數據的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工程師在比計算流體力學分析所需的各種公差的工作，”赫德說。 “即使我們曾經為頭盔的CAD文件，我們將不得不花費大量的時間清理模型，以使其水密。逆向工程的頭盔及堆焊在Geomagic Studio中他們保證了非常詳細的，在較短的時間水密模式。“
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.
掃描是相對簡單的頭盔。每個頭盔花了大約25分鐘，掃描根據設計的復雜性。在法魯手臂到處移動的對象，捕捉點云數據和深入的信息。
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.
塞格計劃掃描捕獲他們從不同的賽車運動員的位置數據，一個姿勢，一個地方的空氣動力學的車手都有他低著頭，測試更充分的頭盔形狀的影響。
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.
由于時間緊絀，不過，赫德沒有獲得一個騎單車，他不得不為人類掃描幾何同事。掃描的主題是兩小時以上的課程，讓休息期間掃描會議。完成掃描被分成區段，隨后相繼密切 - 上臂，下臂，手 - 幫助消除突然移動過程中的問題。
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.
煉油復雜的掃描數據
點云數據，從四個不同的頭盔收集到的掃描到Geomagic Studio中，逆向工程軟件用于生成精確的計算流體力學分析模型，并定制生產設備進口的適合個人的身體部位。
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自動對齊掃描數據和采用的多邊形網格。該模型是清洗，以消除漏洞和缺陷的數據。然后是創造了補丁的多邊形，概述了NURBS曲面的立場。
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.
從人類主體掃描數據的處理方式大致相同，除了額外的工作，都必須進行，以減少噪音和調整資料，因為從哈特的同事微妙的運動，他被掃描。
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，以及編輯和過濾工具，以完善人的模型。然后，他使用的軟件的多邊形幾何重建功能，以填補如體毛和眉毛在不丟失數據捕獲由于激光散射。

“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的編輯工具，有能力處理大型，復雜的數據集使它成為該工程的偉大的比賽，”赫德說。 “我們使用的工具，以改進在耳朵周圍掃描數據和嚴密的差距，使我們能夠保持這樣的幾何現實挑戰人類掃描近600萬點的原始數據的高度。”
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.

多邊形和NURBS補丁應用到人體模型和Geomagic Studio中輸出文件的一個步驟。

“此項STEP文件格式提供了一個強大的幾何文件，該文件不是太大，”赫德說。 “我們可以結束與一個大數目的NURBS模型了補丁程序，以捕捉細節我們需要的。作者：CFD研究精度高而在該組裝模型幾何精度的依賴。“

可視化的結果證明
該文件包含了每個步驟頭盔的設計和幾何是人類進入良好的開局軟件，它被用于計算流體力學分析和流域網狀進口大約為每個模型生成的。該網范圍從2至7萬個單元格，具體取決于是參照幾何。只要有可能，棱鏡細胞表面幾何產生了捕捉詳細邊界層流動特征。

Fluent軟件被用于計算流體力學分析，其中納入邊界和塞格已經從以前的英國自行車項目獲得身體狀況的數據。

計算流體力學的結果導入到中歐倡議的EnSight軟件，該軟件制作的非常詳細的流程可視化，展示了頭盔的空氣動力特性。塞格選擇集中在電梯中的阻力和模擬部隊用等值面顯示喚醒結構和粒子流線可視化旋轉，循環流動路徑。

隨著結構的基礎上，在可視化和循環流動，塞格能夠迅速確定如何不同的幾何模型組件 - 如頭盔和騎自行車 - 相互作用和相互影響。他們還特別指出，在高阻力大的力量，導致醒來。

哈特彩色內不同方面的EnSight模型，應用性能，如自行車，頭盔和織物和皮膚磨砂表面反射面。燈光效果應用到模型幫助完成逼真的外觀，根據哈特，使結果更為可信。

“流可視化和圖像是在理解的方式呈現一物理為基礎的模擬結果的單車團的重要，”赫德說。 “能夠清楚地顯示出客戶所發生的事情，就必須要了解的結果。”

赫德和他的同事們使用的EnSight圖像和動畫，以加強從良好的硬數據模擬輸出，幫助工程師了解流動物理，創造了升力和阻力的力量。根據研究結果，塞格能推薦一個最佳頭盔式，減少空氣阻力和升力。

他說：“從Geomagic Studio和現實的彩色效果圖和曲面幾何模型的質量，我們在EnSight應用使我們能夠納入可視化的現實主義很多，”赫德說。

優化設計的自行車和頭盔建議塞格作出貢獻的選手們在奧運會自行車項目金牌。塞格正在再次通過英國體育2008年北京夏季奧運會的隊伍。