Frequency Response-Based Uncertainty Analysis of Vibration System Utilizing Multiple Response Gaussian Process

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wangbai Pan ◽  
Guoan Tang ◽  
Jiong Tang
Keyword(s):  
Uncertainty Analysis ◽  
Gaussian Process ◽  
Frequency Response ◽  
Model Updating ◽  
Uncertainty Propagation ◽  
Multiple Response ◽  
Vibration System ◽  
Vector Fitting ◽  
Fitting Method ◽  
Small Set

This research concerns the uncertainty analysis and quantification of the vibration system utilizing the frequency response function (FRF) representation with statistical metamodeling. Different from previous statistical metamodels that are built for individual frequency points, in this research we take advantage of the inherent correlation of FRF values at different frequency points and resort to the multiple response Gaussian process (MRGP) approach. To enable the analysis, vector fitting method is adopted to represent an FRF using a reduced set of parameters with high accuracy. Owing to the efficiency and accuracy of the statistical metamodel with a small set of parameters, Bayesian inference can then be incorporated to realize model updating and uncertainty identification as new measurement/evidence is acquired. The MRGP metamodel developed under this new framework can be used effectively for two-way uncertainty propagation analysis, i.e., FRF prediction and uncertainty identification. Case studies are conducted for illustration and verification.

Uncertainty Evaluation on Multi-Hole Aerodynamic Pressure Probes

2020 ◽  
Author(s):  
Andrea Notaristefano ◽  
Paolo Gaetani ◽  
Vincenzo Dossena ◽  
Alberto Fusetti
Keyword(s):  
Uncertainty Analysis ◽  
Evaluation Method ◽  
Experimental Testing ◽  
Uncertainty Propagation ◽  
Calibration Procedure ◽  
Fast Response ◽  
Uncertainty Evaluation ◽  
Sensor Calibration ◽  
Pressure Probe ◽  
Aerodynamic Pressure

Abstract In the frame of a continuous improvement of the performance and accuracy in the experimental testing of turbomachines, the uncertainty analysis on measurements instrumentation and techniques is of paramount importance. For this reason, since the beginning of the experimental activities at the Laboratory of Fluid Machines (LFM) located at Politecnico di Milano (Italy), this issue has been addressed and different methodologies have been applied. This paper proposes a comparison of the results collected applying two methods for the measurement uncertainty quantification to two different aerodynamic pressure probes: sensor calibration, aerodynamic calibration and probe application are considered. The first uncertainty evaluation method is the so called “Uncertainty Propagation” method (UPM); the second is based on the “Monte Carlo” method (MCM). Two miniaturized pressure probes have been selected for this investigation: a pneumatic 5-hole probe and a spherical fast response aerodynamic pressure probe (sFRAPP), the latter applied as a virtual 4-hole probe. Since the sFRAPP is equipped with two miniaturized pressure transducers installed inside the probe head, a specific calibration procedure and a dedicated uncertainty analysis are required.

Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems

2021 ◽  
Vol 21 (4) ◽  
Author(s):  
Seyede Fatemeh Ghoreishi ◽  
Mahdi Imani
Keyword(s):  
Uncertainty Analysis ◽  
Closed Form ◽  
Uncertainty Propagation ◽  
Uncertainty Reduction ◽  
System Level ◽  
Engineering Systems ◽  
Sample Point ◽  
Design Variables ◽  
Multidisciplinary Systems ◽  
Stationary Behavior

Abstract Engineering systems are often composed of many subsystems that interact with each other. These subsystems, referred to as disciplines, contain many types of uncertainty and in many cases are feedback-coupled with each other. In designing these complex systems, one needs to assess the stationary behavior of these systems for the sake of stability and reliability. This requires the system level uncertainty analysis of the multidisciplinary systems, which is often computationally intractable. To overcome this issue, techniques have been developed for capturing the stationary behavior of the coupled multidisciplinary systems through available data of individual disciplines. The accuracy and convergence of the existing techniques depend on a large amount of data from all disciplines, which are not available in many practical problems. Toward this, we have developed an adaptive methodology that adds the minimum possible number of samples from individual disciplines to achieve an accurate and reliable uncertainty propagation in coupled multidisciplinary systems. The proposed method models each discipline function via Gaussian process (GP) regression to derive a closed-form policy. This policy sequentially selects a new sample point that results in the highest uncertainty reduction over the distribution of the coupling design variables. The effectiveness of the proposed method is demonstrated in the uncertainty analysis of an aerostructural system and a coupled numerical example.

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