scholarly journals Identification of radiant source in an enclosure by reduced model

2021 ◽  
Vol 2116 (1) ◽  
pp. 012112
Author(s):  
Benjamin Gaume ◽  
Yassine Rouizi ◽  
Frédéric Joly ◽  
Olivier Quéméner
Keyword(s):  
Thermal Field ◽  
Trust Region ◽  
Original Method ◽  
Reduced Model ◽  
Average Error ◽  
Thermal Source ◽  
Reduced Order ◽  
Model Method ◽  
Modal Model ◽  
Higher Rank

Abstract We propose an original method to recover from a few measurement points the integrity of the temperature field of a furnace heated by a radiant thermal source. The radiant thermal source is first identified via a low order reduced model based on based on AROMM (Amalgam Reduced Order Modal Model) method which preserves the integrity of the geometry. The minimization is performed via a trust-region reflective least squares algorithm implemented in MATLAB “lsqcurvefit” function. From that identified heat flux, the integrity of the thermal field is then recovered by direct simulation thanks to a reduced model of higher rank to have a better precision. The treated application is a complex titanium piece heated by two radiant panels placed in a furnace. With four measuring points, the temperature of the whole thermal scene is retrieved at all times with an average error around 1 K on the studied object.

Simulation of Fluid Flow Using Reduced-Order Modeling by POD Approach Applied to a Fixed Tube Bundle System

2010 ◽  
Author(s):  
Marie Pomarede ◽  
Erwan Liberge ◽  
Aziz Hamdouni ◽  
Elisabeth Longatte ◽  
Jean-Franc¸ois Sigrist
Keyword(s):  
Dynamic Analysis ◽  
Steam Generator ◽  
Tube Bundle ◽  
Fluid Structure Interaction ◽  
Reduced Model ◽  
Steam Generator Tube ◽  
Fluid Structure ◽  
Reduced Order ◽  
Structure Interaction ◽  
Tube Bundles

Tube bundles in steam boilers of nuclear power plants and nuclear on-board stokehold are known to be exposed to high levels of vibrations. This coupled fluid-structure problem is very complex to numerically set up, because of its three-dimensional characteristics and because of the large number of degrees of freedom involved. A complete numerical resolution of such a problem is currently not viable, all the more so as a precise understanding of this system behaviour needs a large amount of data, obtained by very expensive calculations. We propose here to apply the now classical reduced order method called Proper Orthogonal Decomposition to a case of 2D flow around a tube bundle. Such a case is simpler than a complete steam generator tube bundle; however, it allows observing the POD projection behaviour in order to project its application on a more realistic case. The choice of POD leads to reduced calculation times and could eventually allow parametrical investigations thanks to a low data quantity. But, it implies several challenges inherent to the fluid-structure characteristic of the problem. Previous works on the dynamic analysis of steam generator tube bundles already provided interesting results in the case of quiescent fluid [J.F. Sigrist, D. Broc; Dynamic Analysis of a Steam Generator Tube Bundle with Fluid-Structure Interaction; Pressure Vessel and Piping, July 27–31, 2008, Chicago]. Within the framework of the present study, the implementation of POD in academic cases (one-dimensional equations, 2D-single tube configuration) is presented. Then, firsts POD modes for a 2D tube bundle configuration is considered; the corresponding reduced model obtained thanks to a Galerkin projection on POD modes is finally presented. The fixed case is first studied; future work will concern the fluid-structure interaction problem. Present study recalls the efficiency of the reduced model to reproduce similar problems from a unique data set for various configurations as well as the efficiency of the reduction for simple cases. Results on the velocity flow-field obtained thanks to the reduced-order model computation are encouraging for future works of fluid-structure interaction and 3D cases.

scholarly journals A posture measurement approach for an articulated manipulator by RGB-D cameras

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983813
Author(s):  
Haobin Shi ◽  
Meng Xu ◽  
Kao-Shing Hwang ◽  
Chia-Hung Hung
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Consensus Method ◽  
Average Error ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Sample Standard Deviation ◽  
Cylindrical Model ◽  
Cloud Data ◽  
Posture Measurement

The objective of this article aims at the safety problems where robots and operators are highly coupled in a working space. A method to model an articulated robot manipulator by cylindrical geometries based on partial cloud points is proposed in this article. Firstly, images with point cloud data containing the posture of a robot with five resolution links are captured by a pair of RGB-D cameras. Secondly, the process of point cloud clustering and Gaussian noise filtering is applied to the images to separate the point cloud data of three links from the combined images. Thirdly, an ideal cylindrical model fits the processed point cloud data are segmented by the random sample consensus method such that three joint angles corresponding to three major links are computed. The original method for calculating the normal vector of point cloud data is the cylindrical model segmentation method, but the accuracy of posture measurement is low when the point cloud data is incomplete. To solve this problem, a principal axis compensation method is proposed, which is not affected by number of point cloud cluster data. The original method and the proposed method are used to estimate the three joint angular of the manipulator system in experiments. Experimental results show that the average error is reduced by 27.97%, and the sample standard deviation of the error is reduced by 54.21% compared with the original method for posture measurement. The proposed method is 0.971 piece/s slower than the original method in terms of the image processing velocity. But the proposed method is still feasible, and the purpose of posture measurement is achieved.

scholarly journals Modeling a Rotating Circle Thermal Field with a Thermal Source on the Edge

2015 ◽  
Vol 129 ◽  
pp. 317-320
Author(s):  
A.V. Herreinstein ◽  
E.A. Herreinstein ◽  
N. Mashrabov
Keyword(s):  
Thermal Field ◽  
Thermal Source

scholarly journals Reduced Order Model for Nonlinear Multi-Field-Coupled Spinning Shaft During Transient Operation Based on Coupled Modes Extracted by POD

2021 ◽  
Author(s):  
Mohammad A. Bani-Khaled ◽  
Ioannis Georgiou
Keyword(s):  
Natural Frequencies ◽  
Body Motion ◽  
Reduced Model ◽  
Order Model ◽  
Coupled Modes ◽  
Reduced Order ◽  
Operational Conditions ◽  
Proper Orthogonal ◽  
Deformation Modes ◽  
Spinning Shaft

Abstract Processing the numerical solution for the nonlinear spinning shaft using the Time- (Proper Orthogonal Decomposition) transform identifies the coupling between the rigid body motion and deformation as well as the coupling between the deformation modes. Laying on the fact that the POD characterizes the motion into set of optimum coupled modes, it is convenient to relay on them to derive nonlinear reduced order models. In this work, the discrete dynamics of nonlinear spinning shaft are processed using the POD method to produce optimum modes that are used to furnish bases to derive nonlinear coupled reduced model. The derived reduced model is tested at several operational conditions and compared to the full model characteristics. The reduced model produces back the dynamics; captures the natural frequencies and whirling.

scholarly journals Exploring a new method based on generalized eigenvalues for the trust-region subproblem

2019 ◽  
Author(s):  
Jean Carlos Aparecido Medeiros ◽  
Sandra Augusta Santos
Keyword(s):  
Trust Region ◽  
Poor Performance ◽  
Original Method ◽  
New Method ◽  
Trust Region Subproblem ◽  
Generalized Eigenvalues ◽  
Geometric Characteristics

In 2017, a method for solving the trust-region subproblem using generalized eigenvalues was rediscovered and improved. In 1989, when the original method was proposed, it presented a poor performance, which was caused by the low quality of eigensolvers available at that time. In this work we explore some geometric characteristics of this method.

scholarly journals Some aspects of modelling and simulation of spot welding

2018 ◽  
Vol 178 ◽  
pp. 03006
Author(s):  
Viorel Cohal
Keyword(s):  
Welded Joints ◽  
Spot Welding ◽  
Thermal Field ◽  
Base Material ◽  
Heat Losses ◽  
Thermal Source ◽  
Thermal Processes ◽  
Element Analysis ◽  
Material Density ◽  
Composition And Structure

Mathematical modelling and finite element analysis of thermal processes, much more complex in welding different metals in terms of chemical composition and structure, have allowed investigation and deepening of heat transfer phenomena and the establishment of a new technological spot welding variant for these joints. The distribution of temperatures in welded joints is influenced by the linear energy of the thermal source, the thermal properties of the base material (heat specific heat conductivity, material density and thermal diffusivity) and heat losses to the environment. Thermal field viewing, longitudinal and transverse variations of temperature in heterogeneous welded joints, as well as temperature values recorded at different nodes (points) located in the welding area and adjacent areas, lead to conclusions that will result in specific spot welding technologies.

Reduced Order Model for Efficient Engineering Analysis

2020 ◽  
Author(s):  
Allan X. Zhong ◽  
Haoyue Zhang
Keyword(s):  
Finite Element ◽  
Design Of Experiments ◽  
Reduced Order Model ◽  
Reduced Model ◽  
Complex Structures ◽  
Engineering Analysis ◽  
Order Model ◽  
Element Analysis ◽  
Reduced Order ◽  
Numerical Design

Abstract Engineering analysis of complex structures or mechanical systems typically involves contact with multiple components, large deformation, and material nonlinearity, which requires the application of nonlinear finite element methods. Despite the advancement of commercial software for finite element analysis (FEA), nonlinear FEA of a multi-component mechanical assembly will take hours to days, and even weeks to complete. It is highly desired to develop a reduced-order model for a family of complex structures that can reduce an original problems’ complexity and degree of freedom but has a reasonably small discrepancy with the full model and significantly reduces the computation time. The typical approach to construct a reduced model includes 1) the response surface method via numerical design of experiments and, 2) the simplified physics approach. In this paper, it is proposed to develop a reduced model through the combination of simplified physics, dimensional analysis [1], and numerical design of experiments. The approach is applied to the construction of a reduced model for the analysis of a downhole plug [2]. The developed reduced model is verified by full-scale FEA models and validated through physical tests. The reduced model is implemented in a spreadsheet and takes only seconds to complete a calculation in contrast to hours using a full FEA model, enabling engineers’ quick evaluation of the corresponding designs.

Reduced Order Thermal Modeling of Data Centers via Distributed Sensor Data

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Emad Samadiani ◽  
Yogendra Joshi ◽  
Hendrik Hamann ◽  
Madhusudan K. Iyengar ◽  
Steven Kamalsy ◽  
...  
Keyword(s):  
Temperature Field ◽  
Data Center ◽  
Data Centers ◽  
Sensor Data ◽  
Temperature Fields ◽  
Average Error ◽  
Local Error ◽  
Flow Rates ◽  
Reduced Order ◽  
Distributed Sensor

In this paper, an effective and computationally efficient proper orthogonal decomposition (POD) based reduced order modeling approach is presented, which utilizes selected sets of observed thermal sensor data inside the data centers to help predict the data center temperature field as a function of the air flow rates of computer room air conditioning (CRAC) units. The approach is demonstrated through application to an operational data center of 102.2 m2 (1100 square feet) with a hot and cold aisle arrangement of racks cooled by one CRAC unit. While the thermal data throughout the facility can be collected in about 30 min using a 3D temperature mapping tool, the POD method is able to generate temperature field throughout the data center in less than 2 s on a high end desktop personal computer (PC). Comparing the obtained POD temperature fields with the experimentally measured data for two different values of CRAC flow rates shows that the method can predict the temperature field with the average error of 0.68 °C or 3.2%. The maximum local error is around 8 °C, but the total number of points where the local error is larger than 1 °C, is only ∼6% of the total domain points.

Reduced Order Thermal Modeling of Data Centers via Distributed Sensor Data

2009 ◽  
Author(s):  
Emad Samadiani ◽  
Yogendra Joshi ◽  
Hendrik Hamann ◽  
Madhusudan K. Iyengar ◽  
Steven Kamalsy ◽  
...  
Keyword(s):  
Temperature Field ◽  
Data Center ◽  
Data Centers ◽  
Sensor Data ◽  
Temperature Fields ◽  
Average Error ◽  
Flow Rates ◽  
Reduced Order ◽  
Distributed Sensor ◽  
Mapping Tool

In this paper, an effective and computationally efficient Proper Orthogonal Decomposition (POD) based reduced order modeling approach is presented, which utilizes selected sets of observed thermal sensor data inside the data centers to help predict the data center temperature field as a function of the air flow rates of Computer Room Air Conditioning (CRAC) units. The approach is demonstrated through application to an operational data center of 102.2 m2 (1,100 square feet) with a hot and cold aisle arrangement of racks cooled by one CRAC unit. While the thermal data throughout the facility can be collected in about 30 minutes using a 3D temperature mapping tool, the POD method is able to generate temperature field throughout the data center in less than 2 seconds on a high end desktop PC. Comparing the obtained POD temperature fields with the experimentally measured data for two different values of CRAC flow rates shows that the method can predict the temperature field with the average error of 0.68 °C or 3.2%.

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