OPTIMAL SENSOR PLACEMENT FOR THE ESTIMATION OF TURBULENCE MODEL PARAMETERS IN CFD

2015 ◽  
Vol 5 (6) ◽  
pp. 545-568 ◽  
Author(s):  
Dimitrios I. Papadimitriou ◽  
Costas Papadimitriou
Keyword(s):  
Turbulence Model ◽  
Sensor Placement ◽  
Model Parameters ◽  
Optimal Sensor Placement

Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder

2017 ◽  
Vol 823 ◽  
pp. 230-277 ◽  
Author(s):  
Vincent Mons ◽  
Jean-Camille Chassaing ◽  
Pierre Sagaut
Keyword(s):  
Data Assimilation ◽  
Sensor Placement ◽  
Computational Cost ◽  
Unsteady Flows ◽  
Variational Data Assimilation ◽  
Model Parameters ◽  
Oscillating Cylinder ◽  
Optimal Sensor Placement ◽  
Initial Flow ◽  
Placement Procedure

An optimal sensor placement procedure is proposed within the framework of variational data assimilation (DA) for unsteady flows, with the aim of maximizing the efficiency of the DA procedure. It is dedicated to the a priori design of a sensor network, and relies on a first-order adjoint approach. The proposed methodology first consists in identifying, via optimal control, the locations in the flow that have the greatest sensitivity with respect to a change in the initial condition, boundary conditions or model parameters. In a second step, sensors are placed at these locations for DA purposes. The use of this optimal sensor placement procedure does not require extra development in the case where a variational DA suite is available. The proposed methodology is applied to the reconstruction of unsteady bidimensional flows past a rotationally oscillating cylinder. More precisely, the possibilities of reconstructing the rotational speed of the cylinder and the initial flow, which here encompasses upstream conditions, from various types of observations are investigated via variational DA. Then, the observation optimization procedure is employed to identify optimal locations for placing velocity sensors downstream of the cylinder. Both reduction in the computational cost and improvement in the quality of the reconstructed flow are achieved through optimal sensor placement, encouraging the application of the proposed methodology to more complex and realistic flows.

Bayesian Optimal Sensor Placement for Modal Identification of Civil Infrastructures

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Costas Argyris ◽  
Costas Papadimitriou ◽  
Panagiotis Panetsos
Keyword(s):  
Posterior Distribution ◽  
Sensor Placement ◽  
Modal Identification ◽  
Model Parameters ◽  
Optimal Sensor Placement ◽  
Civil Infrastructures ◽  
Spatially Correlated ◽  
Placement Algorithm ◽  
Insightful Analysis ◽  
Stochastic Optimization Algorithm

A Bayesian optimal experimental design (OED) method is proposed in this work for estimating the best locations of sensors in structures so that the measured data are most informative for estimating reliably the structural modes. The information contained in the data is measured by the Kullback-Leibler (K-L) divergence between the prior and posterior distribution of the model parameters taken in modal identification to be the modal coordinates. The optimal sensor placement that maximizes the expected K-L divergence is shown also to minimize the information entropy of the posterior distribution. Unidentifiability issues observed in existing formulations when the number of sensors is less than the number of identified modes, are resolved using a non-uniform prior in the Bayesian OED. An insightful analysis is presented that demonstrates the effect of the variances of Bayesian priors on the optimal design. For dense mesh finite element models, sensor clustering phenomena are avoided by integrating in the methodology spatially correlated prediction error models. A heuristic forward sequential sensor placement algorithm and a stochastic optimization algorithm are used to solve the optimization problem in the continuous physical domain of variation of the sensor locations. The theoretical developments and algorithms are applied for the optimal sensor placement design along the deck of a 537 m concrete bridge.

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

scholarly journals A Data Driven Method for Optimal Sensor Placement in Multi-Zone Buildings

2021 ◽  
pp. 110956
Author(s):  
Gowri Suryanarayana ◽  
Javier Arroyo ◽  
Lieve Helsen ◽  
Jesus Lago
Keyword(s):  
Sensor Placement ◽  
Data Driven ◽  
Optimal Sensor Placement

scholarly journals Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3400
Author(s):  
Tulay Ercan ◽  
Costas Papadimitriou
Keyword(s):  
Utility Function ◽  
Measurement Errors ◽  
Structural Model ◽  
Information Gain ◽  
Sensor Placement ◽  
Optimal Sensor Placement ◽  
Modal Expansion ◽  
Expected Information ◽  
Virtual Sensing ◽  
Multidimensional Integral

A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

CORDIC-Based Azimuth Calculation and Obstacle Tracing via Optimal Sensor Placement on a Mobile Robot

2016 ◽  
Vol 21 (5) ◽  
pp. 2317-2329 ◽  
Author(s):  
Pranjal Vyas ◽  
Leena Vachhani ◽  
K. Sridharan ◽  
Vikramkumar Pudi
Keyword(s):  
Mobile Robot ◽  
Sensor Placement ◽  
Optimal Sensor Placement
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