AIAA JournalPub Date : 2024-07-02DOI: 10.2514/1.j063413
F. Di Fiore, P. C. Berri, L. Mainini
{"title":"Diagnosing Incipient Faults for a Faster Adoption of Sustainable Aerospace Technologies","authors":"F. Di Fiore, P. C. Berri, L. Mainini","doi":"10.2514/1.j063413","DOIUrl":"https://doi.org/10.2514/1.j063413","url":null,"abstract":"AIAA Journal, Ahead of Print. <br/>","PeriodicalId":7722,"journal":{"name":"AIAA Journal","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AIAA JournalPub Date : 2024-06-27DOI: 10.2514/1.j063712
Tom Fridlender, N. Benard, J. P. Bonnet, E. Moreau
{"title":"Mixing Enhancement Downstream of an Active Square-Mesh Grid Using Plasma Actuation","authors":"Tom Fridlender, N. Benard, J. P. Bonnet, E. Moreau","doi":"10.2514/1.j063712","DOIUrl":"https://doi.org/10.2514/1.j063712","url":null,"abstract":"AIAA Journal, Ahead of Print. <br/>","PeriodicalId":7722,"journal":{"name":"AIAA Journal","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AIAA JournalPub Date : 2024-06-25DOI: 10.2514/1.j064075
Shahrzad Daghighi, Giovanni Zucco, Paul M. Weaver
{"title":"Efficient Design Methods for Variable-Angle-Tow Composite Toroidal Pressure Vessels","authors":"Shahrzad Daghighi, Giovanni Zucco, Paul M. Weaver","doi":"10.2514/1.j064075","DOIUrl":"https://doi.org/10.2514/1.j064075","url":null,"abstract":"AIAA Journal, Ahead of Print. <br/>","PeriodicalId":7722,"journal":{"name":"AIAA Journal","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AIAA JournalPub Date : 2024-06-10DOI: 10.2514/1.j063322
Shubham Shubham, Richard D. Sandberg, A. Kushari
{"title":"More General Wall Pressure Spectra Models: Combining Feature Engineering with Evolutionary Algorithms","authors":"Shubham Shubham, Richard D. Sandberg, A. Kushari","doi":"10.2514/1.j063322","DOIUrl":"https://doi.org/10.2514/1.j063322","url":null,"abstract":"This paper presents an improved mathematical expression for semi-empirical wall pressure spectra modeling based on gene expression programming (GEP). The main focus of this work is to obtain a model that applies to a wide range of cases in terms of parameters and the source of data. The dataset comprises flat plate and airfoil cases with adverse and favorable pressure gradients at various Reynolds numbers. First, a characterization of the dataset is performed to understand the low-dimensional distribution of parameters. Then, a feature importance study is conducted to choose the most suitable model input variables from the exhaustive list of nondimensional parameters. The GEP algorithm is modified to ensure that trained models adhere to the basic structure of previously published semi-empirical models. Following training on the diverse database, the new model is compared against existing, best-performing empirical models to quantify the performance improvements. The models are tested on cases with completely different configurations and parameter ranges, unseen during training, and maintain their superior performance. Finally, a comparison is made between models developed with GEP and neural networks in terms of their efficacy, complexity, and interpretability.","PeriodicalId":7722,"journal":{"name":"AIAA Journal","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AIAA JournalPub Date : 2024-06-10DOI: 10.2514/1.j063964
Dominic F. Gallegos, Ethan Schlussel, Greg Young
{"title":"Characterization of a Cavity Flameholding Solid-Fuel Ramjet in an Optical Combustor","authors":"Dominic F. Gallegos, Ethan Schlussel, Greg Young","doi":"10.2514/1.j063964","DOIUrl":"https://doi.org/10.2514/1.j063964","url":null,"abstract":"Three fuel grain geometries were investigated in a two-dimensional, optically accessible solid fuel ramjet combustor to improve the understanding of the internal ballistics as it pertains to flameholding and overall performance. Two-cavity flameholding-configured fuel grains demonstrated increased flameholding and similar performance to the baseline flat fuel grain. Optical diagnostics such as high-speed CH* chemiluminescence and high-speed three-color camera pyrometry were utilized to measure and analyze the emissive species to draw conclusions about the reacting flowfield. Additionally, optical access afforded the ability to determine spatially resolved regression rates within the flameholding region of each fuel grain. The measured fuel grain regression rates demonstrated similar performance between the three configurations; however, the spatial distribution of the regression rates resulting from the high local heat flux due to the cavity corner is postulated to be the driving factor in increasing the flameholding capability of the cavity fuel grains.","PeriodicalId":7722,"journal":{"name":"AIAA Journal","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AIAA JournalPub Date : 2024-06-08DOI: 10.2514/1.j063967
Zhaoyi Xu, Gangtie Zheng
{"title":"Gradient-Based Optimization Method for Experimental Modal Parameter Estimation with Finite Element Model","authors":"Zhaoyi Xu, Gangtie Zheng","doi":"10.2514/1.j063967","DOIUrl":"https://doi.org/10.2514/1.j063967","url":null,"abstract":"This paper presents a novel gradient-based optimization algorithm for improving the accuracy of experimentally estimated modal parameters with the assistance of finite element models. Initially, we recast the discrete vibration response equation into a matrix form and formulate the parameter estimation problem in modal analysis as an optimization problem. Then the problem is solved with a gradient-based iterative algorithm, which explicitly exhibits the closed form of gradients used in optimization. Initial values for this iteration are parameters derived from finite element models, since every important engineering structure should be analyzed with a finite element model before it is constructed. Subsequently, the performance of this algorithm is validated by both pure numerical experiments, which simulate the physical world, and experiments using real measurement data gathered by sensors in the real physical world. The algorithm’s performance is further enhanced by incorporating gradient clipping and an adaptive iteration threshold. As a comparison, a discussion on classical least-squares time-domain method for the problem is provided. For practical applications, the Shi–Tomasi corner detection and Lucas–Kanade optical flow methods are deployed to detect corner points from videos taken during the vibration of a structure and track the motion of these points in the videos.","PeriodicalId":7722,"journal":{"name":"AIAA Journal","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}