The inverse estimation of the thermal behavior and the viscosity of fluid between two horizontal concentric cylinders with rotating inner cylinder

Hsu, Pao-Tung

doi:10.1016/j.applthermaleng.2007.09.002

Cited by 14 publications

(4 citation statements)

References 20 publications

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“…Inverse analysis is however computationally expensive and mathematically more complicated compared to its forward counterpart due to ill posed and iterative nature of the problem [14][15][16] and many a times multiple combinations of parameters are obtained that satisfy the same given reference output. This is one of the unique features of inverse analysis due to which it has become very popular and successfully applied in many engineering problems in the last decade [17][18][19][20][21][22][23][24][25][26][27][28]. Various methods such as conjugate gradient method, steepest descent method, linear least-squares error method, golden section technique, genetic algorithm and DE are used in the inverse analysis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of operating parameters of a reheat regenerative power cycle using simplex search and differential evolution based inverse methods

Gogoi

Pandey

Das

2015

Energy Conversion and Management

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Section: Introductionmentioning

confidence: 99%

Estimation of operating parameters of a reheat regenerative power cycle using simplex search and differential evolution based inverse methods

Gogoi

Pandey

Das

2015

Energy Conversion and Management

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“…Traditionally, trial and error, along with sophisticated and expensive approaches, have been applied to obtain such critical knowledge about the system, when they have been feasible in practice. More rigorous mathematical approaches have also been developed and applied to solve inverse heat conduction problems, such as the regularization approach of Tikhonov [ 1 ], linear least square methods [ 2 ], and genetic algorithm-based solutions [ 3 ]. Depending on the settings of the heat conduction problem, such as the type of boundary conditions and the number of unknown parameters, the commonly existing approaches might not fully or accurately identify the inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Material Data Identification in an Induction Hardening Test Rig with Physics-Informed Neural Networks

2023

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Physics-Informed neural networks (PINNs) have demonstrated remarkable performance in solving partial differential equations (PDEs) by incorporating the governing PDEs into the network’s loss function during optimization. PINNs have been successfully applied to diverse inverse and forward problems. This study investigates the feasibility of using PINNs for material data identification in an induction hardening test rig. By utilizing temperature sensor data and imposing the heat equation with initial and boundary conditions, thermo-physical material properties, such as specific heat, thermal conductivity, and the heat convection coefficient, were estimated. To validate the effectiveness of the PINNs in material data estimation, benchmark data generated by a finite element model (FEM) of an air-cooled cylindrical sample were used. The accurate identification of the material data using only a limited number of virtual temperature sensor data points was demonstrated. The influence of the sensor positions and measurement noise on the uncertainty of the estimated parameters was examined. The study confirms the robustness and accuracy of this approach in the presence of measurement noise, albeit with lower efficiency, thereby requiring more time to converge. Lastly, the applicability of the presented approach to real measurement data obtained from an air-cooled cylindrical sample heated in an induction heating test rig was discussed. This research contributes to the accurate offline estimation of material data and has implications for optimizing induction heat treatments.

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“…The author carried out a correlation of the heat transfer rate for a large range of rotational Reynolds number (2400 \ Re \ 45,000). Using an inverse approach, Hsu 18 estimated the viscosity of fluid and the thermal behavior in cylindrical annulus. Shu et al 19 studied the effect of eccentricity on the flow pattern.…”

Section: Introductionmentioning

confidence: 99%

Improved Energy Efficiency of Mixed Convection Heating Process in Eccentric Annulus

Abdelmlek

Fayçal

2021

Advances in Mechanical Engineering

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This paper deals with a numerical study of mixed convection heat transfer in horizontal eccentric annulus. The inner cylinder is supposed hot and rotating, however the outer one is kept cold and motionless. The numerical problem was solved using COMSOL Multiphysics® which is based on finite element method. The resolution of the partial differential equations was conducted through an implicit scheme with the use of the damped Newton’s method. The present numerical analysis concerns the effect of eccentricity, rotation speed and Rayleigh number on the flow patterns, heat transfer rate, and energy efficiency of the process. It was found that the heat transfer rate increases with the increase of Rayleigh number. In addition, the heat transfer rate drops with the increase of rotation speed. Finally, we have demonstrated that maximum energy efficiency is achieved not only with higher Rayleigh number but also it is maximum with small eccentricity.

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The inverse estimation of the thermal behavior and the viscosity of fluid between two horizontal concentric cylinders with rotating inner cylinder

Cited by 14 publications

References 20 publications

Estimation of operating parameters of a reheat regenerative power cycle using simplex search and differential evolution based inverse methods

Estimation of operating parameters of a reheat regenerative power cycle using simplex search and differential evolution based inverse methods

Material Data Identification in an Induction Hardening Test Rig with Physics-Informed Neural Networks

Improved Energy Efficiency of Mixed Convection Heating Process in Eccentric Annulus

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