Identification of thermal conductivity coefficient and volumetric heat capacity of functionally graded materials

Nedin, R.; Нестеров, С. А.; Vatul’yan, A. O.

doi:10.1016/j.ijheatmasstransfer.2016.06.027

Cited by 21 publications

(6 citation statements)

References 32 publications

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“…Then, Chen et al [15] identified the thermal conductivity coefficients of three-dimensional anisotropic media based on the SBM and Levenberg-Marquard (LM) algorithm. Nedin et al [16] simultaneously retrieved the thermal conductivity and volumetric heat capacity of FGMs. Zhou et al [17] retrieved the unknown thermal conductivity for orthotropic FGMs by the differential transformation dual reciprocity boundary element method and LM algorithm; and He and Yang [18] applied the element free Galerkin method [19] and the level-set method to identify the geometric boundary configurations in heat transfer problems.…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity Identification in Functionally Graded Materials via a Machine Learning Strategy Based on Singular Boundary Method

Xu,

Fu,

2022

Mathematics

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A machine learning strategy based on the semi-analytical singular boundary method (SBM) is presented for the thermal conductivity identification of functionally graded materials (FGMs). In this study, only the temperature or heat flux on the surface or interior of FGMs can be measured by the thermal sensors, and the SBM is used to construct the database of the relationship between the thermal conductivity and the temperature distribution of the functionally graded structure. Based on the aforementioned constructed database, the artificial neural network-based machine learning strategy was implemented to identify the thermal conductivity of FGMs. Finally, several benchmark examples are presented to verify the feasibility, robustness, and applicability of the proposed machine learning strategy.

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Section: Introductionmentioning

confidence: 99%

Thermal Conductivity Identification in Functionally Graded Materials via a Machine Learning Strategy Based on Singular Boundary Method

Xu,

Fu,

2022

Mathematics

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“…In the context of inverse analysis of FG materials/structures, there are a few investigations concerned with time-independent FGMs. Such as the identification of constitutive parameters for metal-ceramic FGM (Bocciarelli et al , 2008), the identification of local damage for a FG beam (Lu et al , 2017), the damage identification of cracked FGM plates (Zhu et al , 2020) and the identifications concerned with graded thermal properties (Nedin et al , 2016; Chen et al , 2017; Vatulyan et al , 2019). Unfortunately, there seems nearly no report directly concerned with the inverse problem of VFGMs.…”

Section: Introductionmentioning

confidence: 99%

Numerical modeling of inverse problem of parameter identification for viscoelastic functionally graded materials/structures

Zhang

Yang

2021

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PurposeThis paper attempts to develop an efficient algorithm to solve the inverse problem of identifying constitutive parameters in VFG (viscoelastic functionally graded) materials/structures.Design/methodology/approachAn adaptive recursive algorithm with high fidelity is developed to acquire the derivatives of displacements with respect to constitutive parameters, which are required for the accurate and stable gradient based inverse analysis. A two-step strategy is presented in the process of identification, by which the unknown parameters can be separately identified and the scale and complexity of the inverse VFG problem are reduced. At each step, the process of identification is treated as an optimization problem that is solved by the Levenberg–Marquardt method.FindingsThe solution accuracy of forward problems and derivatives of displacements can be stably achieved with different step sizes, and constitutive parameters of homogenous/regional-inhomogeneous VFG materials/structures can be effectively and accurately identified. By examining the reliability, resolution, impacts of reference information and noisy data, the effectiveness of the proposed approach is numerically verified via three numerical examples.Originality/valueAn adaptive recursive algorithm is developed for derivatives computing with high fidelity, providing a solid platform for the sensitivity analysis and thereby a two-step strategy in conjunction with Levenberg–Marquardt method is presented in the process of identification. Consequently, an effective algorithm is developed to identify constitutive parameters of homogenous/regional-inhomogeneous VFG materials/structures.

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“…To overcome this situation, those parameters can be estimated by solving the corresponding inverse problem. Besides, it is now possible to work out these type of problems using diverse global optimization algorithms (Hào et al, 2017;Wang et al, 2017;Cebo-Rudnicka et al, 2016;Nedin et al, 2016;Chen et al, 2016;Strongin & Sergeyev, 2000;Zhigljavsky & Žilinskas, 2008;Kvasov & Sergeyev, 2014). Previous works related to heat generation proposed estimating heat generation in the case of a rotatory friction welding system (Yang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Equally, they described a sensitivity analysis for the solution of the inverse problem. An inverse problem, oriented to the identification of the heat capacity and thermal conductivity, was solved by Nedin et al ( 2016). They solved it using an iterative algorithm for the Fredholm´s equations of the first kind.…”

Section: Introductionmentioning

confidence: 99%

Internal volumetric heat generation and heat capacity prediction during a material electromagnetic treatment process using hybrid algorithms

2018

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This work considers the estimation of internal volumetric heat generation, as well as the heat capacity of a solid spherical sample, heated by a homogeneous, time-varying electromagnetic field. To that end, the numerical strategy solves the corresponding inverse problem. Three functional forms (linear, sinusoidal, and exponential) for the electromagnetic field were considered. White Gaussian noise was incorporated into the theoretical temperature profile (i.e. the solution of the direct problem) to simulate a more realistic situation. Temperature was pretended to be read through four sensors. The inverse problem was solved through three different kinds of approach: using a traditional optimizer, using modern techniques, and using a mixture of both. In the first case, we used a traditional, deterministic Levenberg-Marquardt (LM) algorithm. In the second one, we considered three stochastic algorithms: Spiral Optimization Algorithm (SOA), Vortex Search (VS), and Weighted Attraction Method (WAM). In the final case, we proposed a hybrid between LM and the metaheuristics algorithms. Results show that LM converges to the expected solutions only if the initial conditions (IC) are within a limited range. Oppositely, metaheuristics converge in a wide range of IC but exhibit low accuracy. The hybrid approaches converge and improve the accuracy obtained with the metaheuristics. The difference between expected and obtained values, as well as the RMS errors, are reported and compared for all three methods.

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Identification of thermal conductivity coefficient and volumetric heat capacity of functionally graded materials

Cited by 21 publications

References 32 publications

Thermal Conductivity Identification in Functionally Graded Materials via a Machine Learning Strategy Based on Singular Boundary Method

Thermal Conductivity Identification in Functionally Graded Materials via a Machine Learning Strategy Based on Singular Boundary Method

Numerical modeling of inverse problem of parameter identification for viscoelastic functionally graded materials/structures

Internal volumetric heat generation and heat capacity prediction during a material electromagnetic treatment process using hybrid algorithms

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