Physics Based Multi-Fidelity Data Fusion for Efficient Characterization of Mode Shape Variation Under Uncertainties

2020 ◽  
Author(s):  
K. Zhou ◽  
J. Tang
Keyword(s):  
Data Fusion ◽  
Gaussian Process ◽  
Mode Shape ◽  
Response Strategy ◽  
High Fidelity ◽  
Shape Variation ◽  
Meta Model ◽  
Shape Prediction ◽  
Intrinsic Correlation ◽  
And Control

Abstract Efficient prediction of mode shape variation under uncertainties is important for design and control. While Monte Carlo simulation (MCS) is straightforward, it is computationally expensive and not feasible for complex structures with high dimensionalities. To address this issue, in this study we develop a multi-fidelity data fusion approach with an enhanced Gaussian process (GP) architecture to evaluate mode shape variation. Since the process to acquire high-fidelity data from full-scale physical model usually is costly, we involve an order-reduced model to rapidly generate a relatively large amount of low-fidelity data. Combining these with a small amount of high-fidelity data altogether, we can establish a Gaussian process meta-model and use it for efficient model shape prediction. This enhanced meta-model allows one to capture the intrinsic correlation of model shape amplitudes at different locations by incorporating a multi-response strategy. Comprehensive case studies are performed for methodology validation.

scholarly journals Uncertainty Quantification of Mode Shape Variation Utilizing Multi-Level Multi-Response Gaussian Process

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
K. Zhou ◽  
J. Tang
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Mode Shape ◽  
Complex Structure ◽  
Response Strategy ◽  
Direct Monte Carlo Simulation ◽  
Shape Variation ◽  
Data Set ◽  
Shape Information ◽  
Multi Level

Abstract Mode shape information plays the essential role in deciding the spatial pattern of vibratory response of a structure. The uncertainty quantification of mode shape, i.e., predicting mode shape variation when the structure is subjected to uncertainty, can provide guidance for robust design and control. Nevertheless, computational efficiency is a challenging issue. Direct Monte Carlo simulation is unlikely to be feasible especially for a complex structure with a large number of degrees-of-freedom. In this research, we develop a new probabilistic framework built upon the Gaussian process meta-modeling architecture to analyze mode shape variation. To expedite the generation of input data set for meta-model establishment, a multi-level strategy is adopted which can blend a large amount of low-fidelity data acquired from order-reduced analysis with a small amount of high-fidelity data produced by high-dimensional full finite element analysis. To take advantage of the intrinsic relation of spatial distribution of mode shape, a multi-response strategy is incorporated to predict mode shape variation at different locations simultaneously. These yield a multi-level, multi-response Gaussian process that can efficiently and accurately quantify the effect of structural uncertainty to mode shape variation. Comprehensive case studies are carried out for demonstration and validation.

Data fusion and simulation-based planning and control in cyber physical system for digital assembly of aeroplane

2015 ◽  
Vol 06 (03) ◽  
pp. 1550027 ◽  
Author(s):  
Hui Li ◽  
Linxuan Zhang ◽  
Tianyuan Xiao ◽  
Jietao Dong
Keyword(s):  
Data Fusion ◽  
Hybrid Control ◽  
Sliding Mode ◽  
Estimation Algorithm ◽  
Asynchronous Sampling ◽  
Planning And Control ◽  
Adaptive Sliding Mode Controller ◽  
Simulation Based ◽  
And Control ◽  
Distributed Fusion

This paper introduces a CPS application for intelligent aeroplane assembly. At first, the CPS structure is presented, which acquires the characteristics of general CPS and enables "simulation-based planning and control" to achieve high level intelligent assembly. Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling, respectively. The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation, and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration, it can also directly link to data, and the error is smaller. This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control. A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness. At the same time, an outer collaborative trajectory planning is also designed. All the experiment results show the feasibility of above proposed methods.

scholarly journals A Distributed Algorithm for Cooperative 3D Exploration under Communication Constraints

2013 ◽  
Vol 4 (4) ◽  
Author(s):  
Ravi Kulan Rathnam ◽  
Andreas Birk
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
High Fidelity ◽  
Optimization Strategy ◽  
Complete Control ◽  
Exploration Process ◽  
Communication Range ◽  
Navigation And Control ◽  
3D Exploration ◽  
And Control

AbstractAn algorithm for distributed exploration in 3D is presented which always keeps the robots within communication range of each other. The method is based on a greedy optimization strategy that uses a heuristic utility function. This makes it computationally very efficient but it can also lead to local minimums; but related deadlocks can be easily detected during the exploration process and there is an efficient strategy to recover from them. The exploration algorithm is integrated into a complete control infrastructure for Autonomous Underwater Vehicles (AUV) containing sensors, mapping, navigation, and control of actuators. The algorithm is tested in a high fidelity simulator which takes into account the dynamics of the robot, and simulates the required sensors. The effect of the communication range and the number of robots on the algorithm is investigated.

In-Process Data Fusion for Process Monitoring and Control of Metal Additive Manufacturing

2021 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Simin Li ◽  
Jennifer Li ◽  
Yande Ndiaye ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Data Fusion ◽  
Real Time ◽  
Process Data ◽  
Multi Scale ◽  
And Control ◽  
Fusion Framework ◽  
Scale Data ◽  
Metal Additive

Abstract To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

Wireless Sensor Network based Data Fusion and Control Model for an Oil Production Platform

2010 ◽  
pp. 113-137
Author(s):  
Mitun Bhattacharyya ◽  
Ashok Kumar ◽  
Magdy Bayoumi
Keyword(s):  
Control System ◽  
Data Fusion ◽  
Sensor Network ◽  
Hierarchical Control ◽  
Oil Production ◽  
Fusion Model ◽  
Production Platform ◽  
Network Manager ◽  
Association Data ◽  
And Control

In this chapter the authors propose methodologies for improving the efficiency of a control system in an industrial environment, specifically an oil production platform. They propose a data fusion model that consists of four steps – preprocessing, classification and association, data association and correlation association, and composite decision. The first two steps are executed at the sensor network level and the last two steps are done at the network manager or controller level. Their second proposal is a distributed hierarchical control system and network management system. Here the central idea is that the network manager and controller coordinate in order to make delays in feedback loops as well as for increasing the lifetime of the sensor network. The authors finally conclude the control system proposal by giving a controlling model using sensor networks to control the flow of hydrocarbons in an oil production platform.

Model-Based Simulation to Examine Command and Control Issues with Remotely Operated Vehicles

2008 ◽  
pp. 199-218 ◽  
Author(s):  
Sasanka Prabhala ◽  
Subhashini Ganapathy ◽  
S. Narayanan ◽  
Jennie J. Gallimore ◽  
Raymond R. Hill
Keyword(s):  
Cognitive Model ◽  
Command And Control ◽  
Simulation Models ◽  
High Fidelity ◽  
Remotely Operated Vehicles ◽  
Design Concepts ◽  
Model Based ◽  
Critical Issues ◽  
Commercial Applications ◽  
And Control

With increased interest in the overall employment of pilotless vehicles functioning in the ground, air, and marine domains for both defense and commercial applications, the need for high-fidelity simulation models for testing and validating the operational concepts associated with these systems is very high. This chapter presents a model-based approach that we adopted for investigating the critical issues in the command and control of remotely operated vehicles (ROVs) through an interactive model-based architecture. The domain of ROVs is highly dynamic and complex in nature. Hence, a proper understanding of the simulation tools, underlying system algorithms, and user needs is critical to realize advanced simulation system concepts. Our resulting simulation architecture integrates proven design concepts such as the model-view-controller paradigm, distributed computing, Web-based simulations, cognitive model-based high-fidelity interfaces and object-based modeling methods.

Design of a Passively Balanced Spatial Linkage Haptic Interface

2004 ◽  
Vol 126 (6) ◽  
pp. 984-991 ◽  
Author(s):  
R. Steger ◽  
K. Lin ◽  
B. D. Adelstein ◽  
H. Kazerooni
Keyword(s):  
Mass Production ◽  
Haptic Interface ◽  
High Fidelity ◽  
Force Interaction ◽  
Design And Implementation ◽  
Power Amplification ◽  
Control Electronics ◽  
Three Degree Of Freedom ◽  
And Control ◽  
Actuation Power

This paper describes the design and implementation of a compact high fidelity desktop haptic interface that provides three-degree-of-freedom point-force interaction through a handheld pen-like stylus. The complete haptic device combines a spatial linkage, actuation, power amplification, and control electronics in a standalone package with a footprint similar to that of a notebook computer 33cm×25cm×10cm. The spatial linkage is composed of one planar and two spherical subloops. Two versions of the spatial linkage were designed: a lightweight polycarbonate plastic version suitable for inexpensive mass production, and an aluminum and stainless steel linkage that offers greater reliability and higher stiffness. Both linkages were designed to be statically balanced over their full workspace.

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