Uncertainty quantification in store separation analysis using Kestrel, Design of Experiments and surrogate modelling

2022 ◽  
Author(s):  
Haimin Huang ◽  
Matteo Giacobello
Keyword(s):  
Design Of Experiments ◽  
Uncertainty Quantification ◽  
Surrogate Modelling ◽  
Store Separation

Uncertainty quantification of squeal instability via surrogate modelling

2015 ◽  
Vol 60-61 ◽  
pp. 887-908 ◽  
Author(s):  
Amir Nobari ◽  
Huajiang Ouyang ◽  
Paul Bannister
Keyword(s):  
Uncertainty Quantification ◽  
Surrogate Modelling

scholarly journals Mô hình hóa kết cấu bằng phương pháp mặt đáp ứng-một nghiên cứu áp dụng cho công trình ngầm

2019 ◽  
Vol 70 (4) ◽  
pp. 236-244
Author(s):  
Chính Bùi Đức
Keyword(s):  
Design Of Experiments ◽  
Uncertainty Quantification ◽  
Response Surface ◽  
Response Surface Method ◽  
Surface Method

Bài báo này giới thiệu những kết quả nghiên cứu về mô hình hóa hệ thống/kết cấu bằng phương pháp mặt đáp ứng (Response Surface Method - RSM). Bài báo đã phân tích các kỹ thuật trong thiết kế thí nghiệm (Design of Experiments - DoE), các vấn đề liên quan đến mô hình hóa kết cấu sử dụng RSM, từ đó đề xuất trình tự mô hình hóa kết cấu bằng RSM. Dựa trên các phần mềm, Design of Expert và TUNA, đã tiến hành mô hình hóa một công trình ngầm (CTN) với các yếu tố đầu vào thay đổi. Những kết quả nhận được cho thấy RSM là một công cụ có hiệu quả trong mô hình hóa kết cấu. Các mặt đáp ứng tìm được bằng RSM rất thuận lợi trong nghiên cứu lượng hóa độ không chắc chắn (Uncertainty Quantification - UQ) của hệ thống/kết cấu.

On the Potential of a Multi-Fidelity G-POD Based Approach for Optimization and Uncertainty Quantification

2014 ◽  
Author(s):  
David J. J. Toal
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Uncertainty Quantification ◽  
Surrogate Models ◽  
Orthogonal Decomposition ◽  
High Fidelity ◽  
Computational Domain ◽  
Surrogate Modelling ◽  
Proper Orthogonal ◽  
Fidelity Model ◽  
Entire Computational Domain

Traditional multi-fidelity surrogate models require that the output of the low fidelity model be reasonably well correlated with the high fidelity model and will only predict scalar responses. The following paper explores the potential of a novel multi-fidelity surrogate modelling scheme employing Gappy Proper Orthogonal Decomposition (G-POD) which is demonstrated to accurately predict the response of the entire computational domain thus improving optimization and uncertainty quantification performance over both traditional single and multi-fidelity surrogate modelling schemes.

scholarly journals Design of experiments: a statistical tool for PIV uncertainty quantification

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Sagar Adatrao ◽  
Andrea Sciacchitano ◽  
Simone van der Velden ◽  
Mark-Jan van der Meulen ◽  
Marc Cruellas Bordes
Keyword(s):  
Design Of Experiments ◽  
Uncertainty Quantification ◽  
Interrogation Window ◽  
Window Size ◽  
Measured Quantity ◽  
Test Case ◽  
Statistical Tool ◽  
Total Uncertainty ◽  
Systematic Uncertainties ◽  
The Individual

A statistical tool called Design of Experiments (DOE) is introduced for uncertainty quantification in particle image velocimetry (PIV). DOE allows to quantify the total uncertainty as well as the systematic uncertainties arising from various experimental factors. The approach is based on measuring a quantity (e.g. time-averaged velocity from PIV) several times by varying the levels of the experimental factors which are known to affect the value of the measured quantity. In this way, using Analysis of Variances (ANOVA), the total variance in the measured quantity can be computed and hence the total uncertainty. Moreover, the analysis provides the individual variances for each of the experimental factors leading to the estimation of the systematic uncertainties from each factor and their contribution to the total uncertainty. The methodology is assessed for an experimental test case of the flow at the outlet of a ducted Boundary Layer Ingesting (BLI) propulsor to quantify the total uncertainty in time-averaged velocity from stereoscopic PIV measurements as well as the constituent systematic uncertainties due to the experimental factors, namely, camera aperture, inter-frame time separation, interrogation window size and stereoscopic camera angle.

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