Identification of time-variable coefficients of heat transfer by solving a nonlinear inverse problem of heat conduction

Matsevityi, Yu. M.; Malyarenko, Vitaliy; Multanovskii, A. V.

doi:10.1007/bf00860365

Cited by 5 publications

(2 citation statements)

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“…), and have also been applied successfully in IHCP in order to get a stable estimation. In this frame, the well-known Kalman filtering technique [15] has been used to resolve linear and non-linear IHCP [16,17]. The use of an artificial neural network (ANN) has also been considered in the IHCP [18].…”

Section: Introductionmentioning

confidence: 99%

A sequential algorithm of inverse heat conduction problems using singular value decomposition

Cabeza

García

Rodríguez

2005

International Journal of Thermal Sciences

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Section: Introductionmentioning

confidence: 99%

A sequential algorithm of inverse heat conduction problems using singular value decomposition

Cabeza

García

Rodríguez

2005

International Journal of Thermal Sciences

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“…The method explained in [15], with all its positiv~ aspects (invariance to different problems of heat conduction, high accuracy of the obtained results, etc. ), has nevertheless, like other attempts to use the optimum filter for solving problems of heat conduction, a substantial shortcoming consisting in the fact that the solution of problems with a large number of nodes requires a very large memory capacity and high speed of the computing means, which the present digital computers do not have.…”

Section: \I/apimentioning

confidence: 99%

Hybrid modeling of direct and inverse problems of heat conduction

Matsevityi¹

1980

Journal of Engineering Physics

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Statistical identification of local heat-transfer parameters

Matsevityi¹,

Multanovskii²

1983

Journal of Engineering Physics

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This article examines an external inverse heat,conduction problem on determining thermal parameters which are variable over time and along a boundary.Since mathematical models are generally inadequate for fully describing thermal processes, identification of the parameters of a thermal system (parametric identification) usually entails simultaneous solution of a problem of structural identification (refinement of the mathematical model itself).Naturally, the structural identification is the more complicated task. Thus, to reduce its importance in the overall problem, when constructing the model it is necessary to account as fully as possible for all internal relationships, thermophysical characteristics and external effects.In particular, in solving the internal inverse heatconduction problem, it is best to determine not the mean values of the characteristics but values dependent onthe space and time coordinates.The latter instance more accurately reflects processes actually occurring in objects with distributed parameters. Such processes are usually described by an imprecise structural model [i] which, in the most general formulation, is nonlinear and requires linearization --since it is necessary to construct transfer matrices to describe a nonsteady thermal process in the form of a dynamic recursion system. When methods of statistical identification are used, it is expedient to also employ statistical linearization, entailing the best probability approximation of nonlinear relations by relations linearized on the basis of normalization of the laws of the distribution of random processes [2]. A simple and adequate course of action here is to replace the nonlinear Px, ~) U(~),where fo is a vectorial statistical characteristic of nonlinearity, i.e., a function of the probability moments of the variables X.. The norm of the vector fo --Am X is sufficiently l small.The components of the matrices A and B and the vector fo can be found from the criterion of the minimum of the mean square error [3]. In the formulation being considered, the vector of state will include the desired local heat-transfer parameters, which vary along the boundary.It should be noted that, in identifying local parameters (such as heat-~rans-fer coefficients variable over the contour of the investigated region), we deal with individual closed contour loops which are joined together into a single multiple-connected automatic control system.The number of loops, meanwhile, is determined by the number of sections of the surface being investigated within which the heat-transfer parameters are taken as constant. In other words, since determining the local boundary conditions requires making allowance for the correlations between all of the control loops, it is best to include characteristic temperatures of each of the investigated local regions in the measurement vector.Thus, the object function, which has to be constructed, should include all of the sought parameters and the results of temperature measurements.Since the problem being examined is ...

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Identification of time-variable coefficients of heat transfer by solving a nonlinear inverse problem of heat conduction

Cited by 5 publications

References 2 publications

A sequential algorithm of inverse heat conduction problems using singular value decomposition

A sequential algorithm of inverse heat conduction problems using singular value decomposition

Hybrid modeling of direct and inverse problems of heat conduction

Statistical identification of local heat-transfer parameters

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