空气动力学学报
空氣動力學學報
공기동역학학보
ACTA AERODYNAMICA SINICA
2015年
4期
515-522
,共8页
缝翼凹槽填充%多段翼型%人工神经网络%气动噪声%混合方法
縫翼凹槽填充%多段翼型%人工神經網絡%氣動譟聲%混閤方法
봉익요조전충%다단익형%인공신경망락%기동조성%혼합방법
slat cove filler%multi-element%artificial neural network%aerodynamic noise%hy-brid method
缝翼凹槽填充技术作为一种缝翼降噪方法，有可能会造成气动性能的损失，如最大升力系数和失速迎角的减小。基于这种情况，针对某多段翼型建立了缝翼凹槽填充构型的数据库，挑选出参考构型，利用置信度推理确定了优化方向，生成了20个优化构型；采用 back propagation（BP）人工神经网络快速预测各优化构型的气动性能，选择其中气动性能最好的构型作为设计构型进行校核计算，求解定常 Navier-Stokes 方程评估其气动性能与基准构型作对比，应用 CFD 和声类比相结合的混合方法评估其气动噪声性能并与基准构型作对比。结果表明：在保持多段翼型气动性能的同时，对于给定观测点，所设计的缝翼凹槽填充构型使得气动噪声明显降低。
縫翼凹槽填充技術作為一種縫翼降譟方法，有可能會造成氣動性能的損失，如最大升力繫數和失速迎角的減小。基于這種情況，針對某多段翼型建立瞭縫翼凹槽填充構型的數據庫，挑選齣參攷構型，利用置信度推理確定瞭優化方嚮，生成瞭20箇優化構型；採用 back propagation（BP）人工神經網絡快速預測各優化構型的氣動性能，選擇其中氣動性能最好的構型作為設計構型進行校覈計算，求解定常 Navier-Stokes 方程評估其氣動性能與基準構型作對比，應用 CFD 和聲類比相結閤的混閤方法評估其氣動譟聲性能併與基準構型作對比。結果錶明：在保持多段翼型氣動性能的同時，對于給定觀測點，所設計的縫翼凹槽填充構型使得氣動譟聲明顯降低。
봉익요조전충기술작위일충봉익강조방법，유가능회조성기동성능적손실，여최대승력계수화실속영각적감소。기우저충정황，침대모다단익형건립료봉익요조전충구형적수거고，도선출삼고구형，이용치신도추리학정료우화방향，생성료20개우화구형；채용 back propagation（BP）인공신경망락쾌속예측각우화구형적기동성능，선택기중기동성능최호적구형작위설계구형진행교핵계산，구해정상 Navier-Stokes 방정평고기기동성능여기준구형작대비，응용 CFD 화성류비상결합적혼합방법평고기기동조성성능병여기준구형작대비。결과표명：재보지다단익형기동성능적동시，대우급정관측점，소설계적봉익요조전충구형사득기동조성명현강저。
Airframe noise becomes one of the dominant noise sources during landing and tak-ing off phases of a civil aircraft because that jet engine noise is decreased obviously in recent years.High lift device noise is the main source of airframe noise,decreasing high lift device noise makes a significant contribution to the overall noise reduction for a civil aircraft.Slat cove filler (SCF),as a type of slat noise reduction method,may decrease the aerodynamic performances, such as the maximum lift coefficient and the stall angle.To this situation,a database of SCF is built for a multi-element airfoil,and one of the SCFs in the database is selected as the reference configuration,20 optimized configurations are generated through confidence coefficient reason-ing.Aerodynamic performance of each optimized configuration is predicted by a back propagation (BP)artificial neural network,and the one with the best aerodynamic performance is selected as the design configuration for verifying computation,steady Navier-Stokes simulations are executed to compare aerodynamic performances of the design configuration with that of the baseline config-uration.A hybrid method,combining CFD with acoustic analogy,is applied to compare the acoustic performances of the design configuration with that of the baseline configuration.The re-sults indicate that the noises are reduced significantly at the given observation points by adding the designed SCF,while the aerodynamic performances have been maintained.
