Proper orthogonal decomposition-based data mining of aerodynamic shape for design optimization

Duan Yan-Hui; Wu Wen-Hua; Fan Zhao-Lin; Luo Jia-Qi

doi:10.7498/aps.66.220203

Abstract

Global optimization methods are becoming more and more important in aerodynamic shape optimization. A large number of proceeding data will be generated during design optimization, from which the implicit but valuable design knowledge can be extracted. The design knowledge can then be used to help the designers to acquire the effects of geometric variations on the aerodynamic performance changes. In this paper, we strive to extract the implicit design knowledge from proceeding data by a data mining method based on proper orthogonal decomposition (POD), by which the design knowledge more enriched and more visualized than those obtained from other data mining methods can be obtained. Proceeding data for data mining are ingathered from aerodynamic shape optimization of a transonic compressor rotor blade, NASA Rotor 37. The design optimization attempts to maximize the adiabatic efficiency of Rotor 37 under the operation condition near peak efficiency with the constrains of mass flow rate and total pressure ratio. The parallel synchronous particle swarm optimization method is employed to search for the optimization in the design space. The particles with improved adiabatic efficiency, while within the optimization constrain tolerances are picked up from the design optimization, which are then used for data mining. The geometric coordinates of the aerodynamic shape with respect to the ingathered particles are regarded as the snapshots. Then the POD modes of the aerodynamic shape can be obtained by singular value decomposition on the snapshots. The results show that the universal rules of geometry variations for the optimization maximizing the adiabatic efficiency of Rotor 37 can be directly visualized by the design knowledge extracted from the proceeding data by POD-based data mining technique. Furthermore, the optimization results are also verified by the design knowledge extracted by data mining.

Keywords:

PACS:

02.60.Pn	(Numerical optimization)
47.85.Gj	(Aerodynamics)
47.11.-j	(Computational methods in fluid dynamics)
29.85.Ca	(Data acquisition and sorting)

Authors and contacts

1.
China Aerodynamic Research and Development Center, Computational Aerodynamics Research Institute, Mianyang 621000, China;
2.
College of Engineering, Peking University, Beijing 100871, China

Corresponding author: Duan Yan-Hui, duanyanhui@foxmail.com;jiaqil@pku.edu.cn ; Luo Jia-Qi, duanyanhui@foxmail.com;jiaqil@pku.edu.cn

Funds: Projects supported by The National Nature Science Foundation of China (Grant Nos. 51676003, 51206003).

References

[1]	Jeong S, Shimoyama K 2011 Proc. Inst. Mech. Eng. Part G: J. Aerosp. Eng. 225 469
[2]	Jeong S, Chiba K, Obayashi S 2005 J. Aeros. Comp. Inf. Com. 2 452
[3]	Chiba K, Obayashi S 2008 J. Spacecraft Rockets 45 975
[4]	Oyama A, Nonomura T, Fujii K 2010 J. Aircraft 47 1756
[5]	Oyama A, Verburg P, Nonomura T, Harry W M, Fujii K Holmes P, Lumley J L, Berkooz G 1997 Q. J. Roy. Meteor. Soc. 123 2500
[6]	Holmes P, Lumley J L, Berkooz G 1997 Q. J. Roy. Meteor. Soc. 123 2500
[7]	Guo Z D, Song L M, Li J, Li G J, Feng Z P (in Chinese) [郭振东, 宋立明, 李军, 李国君, 丰镇平 2015 推进技术 36 207]
[8]	Wang W, Mo R, Zhang Y 2013 Comput. Eng. Appl. 49 11 (in Chinese) [汪伟, 莫蓉, 张岩 2013 计算机工程与应用 49 11]
[9]	Sirvoich L, Kirby M 1987 Quart. Appl. Math. 45 561
[10]	Duan Y H, Cai J S, Li Y Z 2012 AIAA J. 50 968
[11]	LeGresley P, Alonso J Toal D J J, Bressloff N W, Keane A J, Holden C M E 2010 AIAA J. 48 916
[12]	Toal D J J, Bressloff N W, Keane A J, Holden C M E 2010 AIAA J. 48 916
[13]	Ghoman S, Wang Z, Chen P, Kapania K Luo J, Duan Y, Tang X, Liu F Luo J Q, Duan Y H, Xia Z H 2016 Acta Phys. Sin. 65 124702 (in Chinese) [罗佳奇, 段焰辉, 夏振华 2016 物理学报 65 124702]
[14]	Luo J, Duan Y, Tang X, Liu F 2015 ASME Paper 2015 42876
[15]	Luo J Q, Duan Y H, Xia Z H 2016 Acta Phys. Sin. 65 124702 (in Chinese) [罗佳奇, 段焰辉, 夏振华 2016 物理学报 65 124702]
[16]	Duan Y H, Wu W H, Fan Z L, Chen T Venter G, Sobieszczanski-Sobieski J 2003 AIAA J. 41 1583
[17]	Venter G, Sobieszczanski-Sobieski J 2003 AIAA J. 41 1583
[18]	Hicks R M, Henne P A 1987 J. Aircraft 15 407
[19]	Spalart P R A, Allmaras S 1992 AIAA Paper 1992 0439
[20]	Reid L, Moore R D 1978 NASA TP 1978 1337
[21]	Denton J D 1998 J. Therm. Sci. 6 1

Cited By

期刊类型引用(11)

1.	郭振东，王杰，陈云，蒋首民，宋立明，李军. 融合多源数据挖掘信息的非轴对称端壁/叶身联合成型设计空间知识挖掘. 西安交通大学学报. 2024(01): 30-41 . 百度学术
2.	余婧，蒋安林，刘亮，吴晓军，桂业伟，刘深深. 基于PCA降维的气动外形参数化方法. 航空学报. 2024(10): 67-86 . 百度学术
3.	茅晓晨，焦英辰，陈璇，李民，高丽敏. 基于POD和多层感知器的跨声速压气机叶型精细优化设计方法. 海军航空大学学报. 2024(06): 715-725 . 百度学术
4.	徐信芯，郑江溢，张陈，张辉，顾海荣，张大庆. 热风加热沥青路面的湍流共轭传热与温度场预测. 东南大学学报(自然科学版). 2023(04): 637-646 . 百度学术
5.	吴涌钏，孙刚，陶俊. 基于深度置信网络与多目标粒子群算法的通用飞机机翼优化设计. 空气动力学学报. 2023(12): 16-27 . 百度学术
6.	童哲铭，辛佳格，童水光. 基于动态模态分解的叶道涡非定常解耦与重构. 中国机械工程. 2022(07): 769-776 . 百度学术
7.	姜金俊，周宣赤，陈连忠，崔宁，蒋岩. 风洞试验方案智能优化设计方法研究. 实验流体力学. 2022(03): 11-19 . 百度学术
8.	杨海强，黄俊，黎茂锋，刘志勤. 一种基于SVD的改进LTS气动数据异常检测方法. 电光与控制. 2021(07): 78-82 . 百度学术
9.	朱强华，杨恺，梁钰，高效伟. 基于特征正交分解的一类瞬态非线性热传导问题的新型快速分析方法. 力学学报. 2020(01): 124-138 . 百度学术
10.	张天姣，钱炜祺，周宇，何磊，邵元培. 人工智能与空气动力学结合的初步思考. 航空工程进展. 2019(01): 1-11 . 百度学术
11.	梁钰，郑保敬，高效伟，吴泽艳，王峰. 基于POD模型降阶法的非线性瞬态热传导分析. 中国科学:物理学力学天文学. 2018(12): 36-45 . 百度学术

其他类型引用(7)

[1]	Jeong S, Shimoyama K 2011 Proc. Inst. Mech. Eng. Part G: J. Aerosp. Eng. 225 469
[2]	Jeong S, Chiba K, Obayashi S 2005 J. Aeros. Comp. Inf. Com. 2 452
[3]	Chiba K, Obayashi S 2008 J. Spacecraft Rockets 45 975
[4]	Oyama A, Nonomura T, Fujii K 2010 J. Aircraft 47 1756
[5]	Oyama A, Verburg P, Nonomura T, Harry W M, Fujii K Holmes P, Lumley J L, Berkooz G 1997 Q. J. Roy. Meteor. Soc. 123 2500
[6]	Holmes P, Lumley J L, Berkooz G 1997 Q. J. Roy. Meteor. Soc. 123 2500
[7]	Guo Z D, Song L M, Li J, Li G J, Feng Z P (in Chinese) [郭振东, 宋立明, 李军, 李国君, 丰镇平 2015 推进技术 36 207]
[8]	Wang W, Mo R, Zhang Y 2013 Comput. Eng. Appl. 49 11 (in Chinese) [汪伟, 莫蓉, 张岩 2013 计算机工程与应用 49 11]
[9]	Sirvoich L, Kirby M 1987 Quart. Appl. Math. 45 561
[10]	Duan Y H, Cai J S, Li Y Z 2012 AIAA J. 50 968
[11]	LeGresley P, Alonso J Toal D J J, Bressloff N W, Keane A J, Holden C M E 2010 AIAA J. 48 916
[12]	Toal D J J, Bressloff N W, Keane A J, Holden C M E 2010 AIAA J. 48 916
[13]	Ghoman S, Wang Z, Chen P, Kapania K Luo J, Duan Y, Tang X, Liu F Luo J Q, Duan Y H, Xia Z H 2016 Acta Phys. Sin. 65 124702 (in Chinese) [罗佳奇, 段焰辉, 夏振华 2016 物理学报 65 124702]
[14]	Luo J, Duan Y, Tang X, Liu F 2015 ASME Paper 2015 42876
[15]	Luo J Q, Duan Y H, Xia Z H 2016 Acta Phys. Sin. 65 124702 (in Chinese) [罗佳奇, 段焰辉, 夏振华 2016 物理学报 65 124702]
[16]	Duan Y H, Wu W H, Fan Z L, Chen T Venter G, Sobieszczanski-Sobieski J 2003 AIAA J. 41 1583
[17]	Venter G, Sobieszczanski-Sobieski J 2003 AIAA J. 41 1583
[18]	Hicks R M, Henne P A 1987 J. Aircraft 15 407
[19]	Spalart P R A, Allmaras S 1992 AIAA Paper 1992 0439
[20]	Reid L, Moore R D 1978 NASA TP 1978 1337
[21]	Denton J D 1998 J. Therm. Sci. 6 1

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期刊类型引用(11)

1.	郭振东，王杰，陈云，蒋首民，宋立明，李军. 融合多源数据挖掘信息的非轴对称端壁/叶身联合成型设计空间知识挖掘. 西安交通大学学报. 2024(01): 30-41 . 百度学术
2.	余婧，蒋安林，刘亮，吴晓军，桂业伟，刘深深. 基于PCA降维的气动外形参数化方法. 航空学报. 2024(10): 67-86 . 百度学术
3.	茅晓晨，焦英辰，陈璇，李民，高丽敏. 基于POD和多层感知器的跨声速压气机叶型精细优化设计方法. 海军航空大学学报. 2024(06): 715-725 . 百度学术
4.	徐信芯，郑江溢，张陈，张辉，顾海荣，张大庆. 热风加热沥青路面的湍流共轭传热与温度场预测. 东南大学学报(自然科学版). 2023(04): 637-646 . 百度学术
5.	吴涌钏，孙刚，陶俊. 基于深度置信网络与多目标粒子群算法的通用飞机机翼优化设计. 空气动力学学报. 2023(12): 16-27 . 百度学术
6.	童哲铭，辛佳格，童水光. 基于动态模态分解的叶道涡非定常解耦与重构. 中国机械工程. 2022(07): 769-776 . 百度学术
7.	姜金俊，周宣赤，陈连忠，崔宁，蒋岩. 风洞试验方案智能优化设计方法研究. 实验流体力学. 2022(03): 11-19 . 百度学术
8.	杨海强，黄俊，黎茂锋，刘志勤. 一种基于SVD的改进LTS气动数据异常检测方法. 电光与控制. 2021(07): 78-82 . 百度学术
9.	朱强华，杨恺，梁钰，高效伟. 基于特征正交分解的一类瞬态非线性热传导问题的新型快速分析方法. 力学学报. 2020(01): 124-138 . 百度学术
10.	张天姣，钱炜祺，周宇，何磊，邵元培. 人工智能与空气动力学结合的初步思考. 航空工程进展. 2019(01): 1-11 . 百度学术
11.	梁钰，郑保敬，高效伟，吴泽艳，王峰. 基于POD模型降阶法的非线性瞬态热传导分析. 中国科学:物理学力学天文学. 2018(12): 36-45 . 百度学术

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