Utilizing Artificial Neural Networks for Entry Vehicle Aerodynamic Characterization

空气动力学 人工神经网络 航空航天工程 表征(材料科学) 大气进入 计算机科学 工程类 材料科学 人工智能 纳米技术
作者
Zachary Ernst,Bradford E. Robertson,Dimitri N. Mavris
出处
期刊:Journal of Spacecraft and Rockets [American Institute of Aeronautics and Astronautics]
卷期号:: 1-11
标识
DOI:10.2514/1.a35737
摘要

Determining the dynamic stability of blunt body entry vehicles is a persistent engineering challenge, particularly in the low supersonic to subsonic flight regime where the behavior of the unsteady wake is a primary contributor. Dynamic stability quantities are determined by fitting measurements of a ballistic range campaign or a computational fluid dynamics (CFD) computational experiment to an assumed functional form in order to regress quasi-static stability coefficients. However, this data reduction process has many implicit assumptions that may not hold. This paper explores novel alternatives to the established methods for modeling blunt body aerodynamics. A six-degree-of-freedom CFD-in-the-loop flight model is used to run “virtual ballistic range tests,” fully capturing the relevant flow physics. Feed-forward and time-delay neural network models are fitted to the time-series trajectory and aerodynamic results, which can then be used to predict aerodynamic forces and moments. These models do not have a prescribed functional form and do not assume linearized aerodynamics. The models are evaluated for goodness-of-fit in their aerodynamic and trajectory prediction. The feed-forward neural network model resulted in a better prediction of the virtual ballistic range tests than a traditional database. The time-delay network had good open-loop performance but suffered from closed-loop instability.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ccc发布了新的文献求助10
刚刚
Muhammad发布了新的文献求助10
刚刚
刚刚
Muhammad发布了新的文献求助10
刚刚
Muhammad发布了新的文献求助10
1秒前
Muhammad发布了新的文献求助10
1秒前
Muhammad发布了新的文献求助10
1秒前
Muhammad发布了新的文献求助10
1秒前
Muhammad发布了新的文献求助10
1秒前
Muhammad发布了新的文献求助10
1秒前
Muhammad发布了新的文献求助10
1秒前
Muhammad发布了新的文献求助10
1秒前
2秒前
3秒前
优秀傲松发布了新的文献求助10
3秒前
3秒前
3秒前
Akim应助科研通管家采纳,获得10
3秒前
邓紫棋发布了新的文献求助10
4秒前
英姑应助科研通管家采纳,获得10
4秒前
深情安青应助科研通管家采纳,获得10
4秒前
东木应助科研通管家采纳,获得20
4秒前
上官若男应助科研通管家采纳,获得10
4秒前
CipherSage应助科研通管家采纳,获得10
4秒前
yar应助科研通管家采纳,获得10
4秒前
传奇3应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
4秒前
4秒前
uppnice发布了新的文献求助10
4秒前
SXYYXS完成签到 ,获得积分10
5秒前
852应助zxy采纳,获得10
5秒前
5秒前
顾矜应助鲸落采纳,获得10
6秒前
CJ发布了新的文献求助10
6秒前
领导范儿应助alan采纳,获得10
7秒前
今后应助wjx采纳,获得10
7秒前
归尘发布了新的文献求助10
8秒前
兴奋千兰完成签到,获得积分10
9秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974943
求助须知:如何正确求助?哪些是违规求助? 3519467
关于积分的说明 11198482
捐赠科研通 3255728
什么是DOI,文献DOI怎么找? 1797904
邀请新用户注册赠送积分活动 877261
科研通“疑难数据库(出版商)”最低求助积分说明 806224