Estimation of the apparent thermal diffusivity coefficient using an inverse technique

Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.1080/17415970802285293

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…Among the deterministic methods, the LevenbergeMarquardt method has been used successfully in several areas (Kanevce, Kanevce, & Dulikravich, 2005;Mejias, Orlande, & Özisik, 2003;Mendonça, Filho, & Silva, 2005). Among the stochastic methods, the Differential Evolution method has been applied less frequently to inverse problems, but has been used by Kanevce et al (2005); Mariani and Coelho (2009a); Mariani and Coelho (2009b); Mariani, Lima, and Coelho (2008); da Silva, da Silva, and Mariani (2009).…”

Section: Methods To Estimate the Diffusion Coefficientmentioning

confidence: 99%

Mass transfer during the osmotic dehydration of West Indian cherry

Silva

Mariani

et al. 2012

LWT - Food Science and Technology

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a b s t r a c tThe main purpose of this work was to study water loss, solids gain, and weight and moisture reduction during the osmotic dehydration process of the West Indian cherry (Malpighia punicifolia). The diffusion coefficient of West Indian cherry was estimated by the inverse method using average moisture contents. Osmotic dehydration was examined for 12 h in a 65 Brix solution at temperature of 27 C, without agitation, using a fruit:solution mass ratio of 1:4, 1:10, and 1:15. The kinetics and internal changes occurring during the osmotic dehydration of West Indian cherry are reported. The product's drying kinetics was simulated using the diffusion model, and two optimization methods, LevenbergeMarquardt and Differential Evolution algorithm, were used to predict the diffusion coefficient. The results indicated that the two optimization methods performed similarly in estimating the diffusion coefficient adequately. The average calculated diffusion coefficient was 1.663 Â 10 À10 m 2 s À1 , which is consistent with values reported in the literature.

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Section: Methods To Estimate the Diffusion Coefficientmentioning

confidence: 99%

Mass transfer during the osmotic dehydration of West Indian cherry

Silva

Mariani

et al. 2012

LWT - Food Science and Technology

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“…The parameter estimates were reported along with its root mean square error (RMSE) and 95% confidence interval. The RMSE for the estimate was calculated based on Equation (14). The 95% confidence interval of parameter were calculated using MATLAB ® built-in function nlparci (parameter, residual, sensitivity coefficient).…”

Section: Scaled Sensitivity Coefficient and Sequential Estimationmentioning

confidence: 99%

“…Due to the lack of rapid methods, estimation of thermal properties is usually performed from experiments in a constant temperature environment [1]. The parameter estimation technique has been widely used in estimating the thermal properties of various food products [3,[5][6][7][8][9][10][11][12][13][14][15]. It has also been used to estimate the fluid-to-particle heat transfer coefficient during aseptic processing of particulate foods [16] and heat flux during baking [17].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Thermal Properties Estimation Using Sensitivity Coefficients for Rapid Heating Process

Muniandy

Benyathiar

Mishra

et al. 2021

Foods

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Thermal conductivity determination of food at temperatures >100 °C still remains a challenge. The objective of this study was to determine the temperature-dependent thermal conductivity of food using rapid heating (TPCell). The experiments were designed based on scaled sensitivity coefficient (SSC), and the estimated thermal conductivity of potato puree was compared between the constant temperature heating at 121.10 °C (R12B10T1) and the rapid heating (R22B10T1). Temperature-dependent thermal conductivity models along with a constant conductivity were used for estimation. R22B10T1 experiment using the k model provided reliable measurements as compared to R12B10T1 with thermal conductivity values from 0.463 ± 0.011 W m−1 K−1 to 0.450 ± 0.016 W m−1 K−1 for 25–140 °C and root mean squares error (RMSE) of 1.441. In the R12B10T1 experiment, the analysis showed the correlation of residuals, which made the estimation less reliable. The thermal conductivity values were in the range of 0.444 ± 0.012 W m−1 K−1 to 0.510 ± 0.034 W m−1 K−1 for 20–120 °C estimated using the k model. Temperature-dependent models (linear and k models) provided a better estimate than the single parameter thermal conductivity determination with low RMSE for both types of experiments. SSC can provide insight in designing dynamic experiments for the determination of thermal conductivity coefficient.

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“…BFGS has, over time, demonstrated to be the most able among all quasi-Newton methods [34]. Also, this optimization technique has become widely used in general engineering problems, as well as in thermal property estimation [35][36][37]. The BFGS technique is an iterative and indirect search method.…”

Section: Thermal Properties Simultaneous Estimationmentioning

confidence: 99%

Contact resistance analysis applied to simultaneous estimation of thermal properties of metals

Ramos

Carollo

Silva

2020

Meas. Sci. Technol.

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This work aims to present a method for simultaneously estimating the thermal conductivity, k, and specific heat, c p , in samples of stainless steel 304 and cemented carbide by accounting the imperfect thermal contact at the heater-sample interface. A transient one-dimensional heat conduction model with constant thermal properties is used. The metallic sample is placed in the middle of a polyimide heater and a thermal insulator. A constant heat flux is applied on the specimen upside surface and a thermal insulation condition is maintained on the opposing surface, where a type T thermocouple measures the temperature response. Contact resistance is determined and accounted for as a reducing factor on heat flux. Instead of considering a lumped model, a microscopic approach of the contacting regions is used to describe both the surface roughness and the fluid gap. With the intention of guaranteeing simultaneous and reliable estimates for both thermal properties, sensitivity analysis is used to establish heat flux intensity, experiment duration, time step for data acquisition, and other characteristics of the experiments. To simultaneously achieve both thermal properties, the optimization method BFGS (Broyden-Fletcher-Goldfarb-Shanno) is employed to minimize a least-squares objective function comparing experientially measured and numerically calculated temperatures. The numerical solution for the transient conduction problem is determined with COMSOL, which solves the transient heat diffusion problem by discretizing it applying the finite element method (FEM). The linear system of equations is solved using the PARDISO solver and backward differentiation formula (BDF) method. Heat flux is estimated employing the sequential function specification method (SFSM) as a means of verifying the robustness of parameter estimation and experimental procedure. Moreover, uncertainty analysis is performed to confirm the quality of the results obtained. Finally, the uncertainty analysis outcomes and thermal properties estimated are compared with literature.

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Estimation of the apparent thermal diffusivity coefficient using an inverse technique

Cited by 15 publications

References 28 publications

Mass transfer during the osmotic dehydration of West Indian cherry

Mass transfer during the osmotic dehydration of West Indian cherry

Dynamic Thermal Properties Estimation Using Sensitivity Coefficients for Rapid Heating Process

Contact resistance analysis applied to simultaneous estimation of thermal properties of metals

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