Measurement of thermal diffusivity of insulating material using an artificial neural network

Chudzik, S.

doi:10.1088/0957-0233/23/6/065602

Cited by 11 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks have been used to solve the inverse problem for depth profiling of heat source distribution, 26 optical penetration via photothermal radiometry, 27 to relate sea color from satellite imagery to chlorophyll concentrations, 28 and to determine the thermal diffusivity of a slab of insulation material. 29 By making use of non-linear functions such as hyperbolic tangent sigmoids, neural network approaches are particularly efficient in dealing with non-linear aspects of the inverse problem. 30,31 This is an improvement over a simple, standard least squares fit by a sigmoidal function of the spectral feature 32 required to create the non-linear temperature relationship.…”

Section: Introductionmentioning

confidence: 99%

CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

Munro

Liu

Glorieux

et al. 2016

Journal of Applied Physics

View full text Add to dashboard Cite

Articles you may be interested in Fluorescence resonance energy transfer measured by spatial photon migration in CdSe-ZnS quantum dots colloidal systems as a function of concentration Appl. Phys. Lett. 105, 203108 (2014) As a system of interest gets small, due to the influence of the sensor mass and heat leaks through the sensor contacts, thermal characterization by means of contact temperature measurements becomes cumbersome. Non-contact temperature measurement offers a suitable alternative, provided a reliable relationship between the temperature and the detected signal is available. In this work, exploiting the temperature dependence of their fluorescence spectrum, the use of quantum dots as thermomarkers on the surface of a fiber of interest is demonstrated. The performance is assessed of a series of neural networks that use different spectral shape characteristics as inputs (peak-based-peak intensity, peak wavelength; shape-based-integrated intensity, their ratio, full-width half maximum, peak normalized intensity at certain wavelengths, and summation of intensity over several spectral bands) and that yield at their output the fiber temperature in the optically probed area on a spider silk fiber. Starting from neural networks trained on fluorescence spectra acquired in steady state temperature conditions, numerical simulations are performed to assess the quality of the reconstruction of dynamical temperature changes that are photothermally induced by illuminating the fiber with periodically intensitymodulated light. Comparison of the five neural networks investigated to multiple types of curve fits showed that using neural networks trained on a combination of the spectral characteristics improves the accuracy over use of a single independent input, with the greatest accuracy observed for inputs that included both intensity-based measurements (peak intensity) and shape-based measurements (normalized intensity at multiple wavelengths), with an ultimate accuracy of 0.29 K via numerical simulation based on experimental observations. The implications are that quantum dots can be used as a more stable and accurate fluorescence thermometer for solid materials and that use of neural networks for temperature reconstruction improves the accuracy of the measurement. Published by AIP Publishing.

show abstract

Section: Introductionmentioning

confidence: 99%

CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

Munro

Liu

Glorieux

et al. 2016

Journal of Applied Physics

View full text Add to dashboard Cite

show abstract

“…It showed better precision when compared with free-volume and conventional back-propagation models. More recently, machine learning and neural networks models have also been applied to the estimation of the thermal diffusivity of hydrocarbons [23], aromatic compounds insulating material [24], and diffusivity of solutes in supercritical carbon dioxide [25].…”

Section: Introductionmentioning

confidence: 99%

Predictive Models for the Binary Diffusion Coefficient at Infinite Dilution in Polar and Nonpolar Fluids

2021

View full text Add to dashboard Cite

Experimental diffusivities are scarcely available, though their knowledge is essential to model rate-controlled processes. In this work various machine learning models to estimate diffusivities in polar and nonpolar solvents (except water and supercritical CO2) were developed. Such models were trained on a database of 90 polar systems (1431 points) and 154 nonpolar systems (1129 points) with data on 20 properties. Five machine learning algorithms were evaluated: multilinear regression, k-nearest neighbors, decision tree, and two ensemble methods (random forest and gradient boosted). For both polar and nonpolar data, the best results were found using the gradient boosted algorithm. The model for polar systems contains 6 variables/parameters (temperature, solvent viscosity, solute molar mass, solute critical pressure, solvent molar mass, and solvent Lennard-Jones energy constant) and showed an average deviation (AARD) of 5.07%. The nonpolar model requires five variables/parameters (the same of polar systems except the Lennard-Jones constant) and presents AARD = 5.86%. These results were compared with four classic models, including the 2-parameter correlation of Magalhães et al. (AARD = 5.19/6.19% for polar/nonpolar) and the predictive Wilke-Chang equation (AARD = 40.92/29.19%). Nonetheless Magalhães et al. requires two parameters per system that must be previously fitted to data. The developed models are coded and provided as command line program.

show abstract

“…ANNs, which constitute an important element of the current solution method, have been previously considered an interesting alternative method to solve general IHTPs alongside genetic algorithms (GA), particle swarm optimization (PSO), and proper orthogonal decomposition (POD) [18]. Their successful application has been demonstrated in the case of inverse and optimization problems involving conduction [19][20][21][22][23][24][25][26][27], radiation [28][29][30][31], and convection [32][33][34], but also in power engineering [35][36][37]. In particular, Krejsa et al assessed their potential and discussed various strategies regarding their application for the inverse problem of heat conduction [19].…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Pietrak

Muszyński

Marek

et al. 2021

View full text Add to dashboard Cite

The presented results are for the numerical verification of a method devised to identify an unknown spatio-temporal distribution of heat flux that occurs at the surface of a thin aluminum plate, as a result of pulsed laser beam excitation. The presented identification of boundary heat flux function is a part of the newly proposed laser beam profiling method and utilizes artificial neural networks trained on temperature distributions generated with the ANSYS Fluent solver. The paper focuses on the selection of the most effective neural network hyperparameters and compares the results of neural network identification with the Levenberg-Marquardt method used earlier and discussed in previous articles. For the levels of noise measured in physical experiments (0.25-0.5 K), the accuracy of the current parameter estimation method is between 5 and 10%. Design changes that may increase its accuracy are thoroughly discussed.

show abstract

Measurement of thermal diffusivity of insulating material using an artificial neural network

Cited by 11 publications

References 18 publications

CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

Predictive Models for the Binary Diffusion Coefficient at Infinite Dilution in Polar and Nonpolar Fluids

Bulletin of the Polish Academy of Sciences: Technical Sciences

Contact Info

Product

Resources

About