Application of Kalman Filtering with Input Estimation Technique to On-Line Cylindrical Inverse Heat Conduction Problems.

Tuan, Pan-Chio; Fong, Li-Wei; Huang, Wen-Tang

doi:10.1299/jsmeb.40.126

Cited by 17 publications

(6 citation statements)

References 16 publications

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“…It is assumed that the one-dimensional hollow cylinder has a thermally insulated surface at r ¼ b and that the time-varying heat flux, qðtÞ, acts at the inner surface, i.e., r ¼ a. The corresponding system state equations can be expressed in dimensionless form as [22] qT qt…”

Section: S Deng and Y Hwangmentioning

confidence: 99%

Solving the Temperature Distribution Field in Nonlinear Heat Conduction Problems Using the Hopfield Neural Network

Deng¹,

Hwang²

2007

Numerical Heat Transfer, Part B: Fundamentals

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This article employs the continuous-time analog Hopfield neural network (CHNN) to compute the temperature distribution in nonlinear heat conduction problems. The relationship between the CHNN synaptic connection weights and the governing equations of the nonlinear heat conduction problems is established and a corresponding network connectivity circuit design scheme proposed. The CHNN algorithm is used to solve the heat equation for conduction problems with a power-law nonlinearity. The results confirm that the proposed CHNN scheme provides an accurate means of solving the transient temperature distributions of nonlinear heat conduction problems on a real-time basis.

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Section: S Deng and Y Hwangmentioning

confidence: 99%

Solving the Temperature Distribution Field in Nonlinear Heat Conduction Problems Using the Hopfield Neural Network

Deng¹,

Hwang²

2007

Numerical Heat Transfer, Part B: Fundamentals

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“…The boundary and initial conditions are as shown in the figure. The corresponding system state equations can be expressed in dimensionless form as [9] q e T T qt t ¼ q 2 e T T qr r 2 þ 1 r r q e T T qr r a r r b;t t > 0 ð1aÞ e T Tðr r; 0Þ ¼ c0 a r r b ð1bÞ…”

Section: One-dimensional Cylindrical Coordinate Systemmentioning

confidence: 99%

“…(9) and (7b) when all of the neuron weights are known. In practice, this study considers the temperature distributions of heat conduction models with boundary conditions and some of the connective weight strengths are zero.…”

Section: One-dimensional Cylindrical Coordinate Systemmentioning

confidence: 99%

Applying the Hopfield Neural Network to the Solution of the Temperature Distribution Field in Heat Conduction Problems

Deng

Hwang

2006

Numerical Heat Transfer, Part B: Fundamentals

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This article employs the continuous-time analog Hopfield neural network (CHNN) to compute the temperature distribution in one-and two-dimensional transient heat conduction problems. The relationship between the CHNN synaptic connection weights and the governing equations of the problems is established and a corresponding network connectivity circuit design scheme proposed. The CHNN algorithm is initially applied to the solution of conventional problems and is then used to solve more complicated problems involving time-varying heat flux profiles. The results confirm that the CHNN scheme provides an accurate means of solving the transient temperature distributions of heat conduction problems on a real-time basis.

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“…In order to resolve this situation, Tuan in 1996 deduced the on-line input estimation method (On-line, IEM) [10]. Furthermore, he applied it in the unknown heat flux estimation of the inverse heat-conducting problem [12][13][14][15] in cylindrical coordinates, and in estimation of the heat-conducting coefficient [16,17]. In addition Ji and Lee [18] succeeded in 2000 in applying it in the inverse heat flux estimation of the ammunition percussion cap.…”

Section: Introductionmentioning

confidence: 99%

Input Estimation Method in the Use of Electronic Device Temperature Prediction and Heat Flux Inverse Estimation

Chen

Hsu

2007

Numerical Heat Transfer, Part A: Applications

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The input estimation method (IEM) is proposed in this research to estimate the real-time time-variant core heat flux of electronic components. The IEM is an on-line real-time estimation method, which combines the Kalman filter mechanism and recursive least-square estimation (RLSE). On the premise that there exists both system processing error and measurement error, using temperature measurements measured from the exterior of the device package, the core time-variant heat flux of the device can be precisely estimated inversely.

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Application of Kalman Filtering with Input Estimation Technique to On-Line Cylindrical Inverse Heat Conduction Problems.

Cited by 17 publications

References 16 publications

Solving the Temperature Distribution Field in Nonlinear Heat Conduction Problems Using the Hopfield Neural Network

Solving the Temperature Distribution Field in Nonlinear Heat Conduction Problems Using the Hopfield Neural Network

Applying the Hopfield Neural Network to the Solution of the Temperature Distribution Field in Heat Conduction Problems

Input Estimation Method in the Use of Electronic Device Temperature Prediction and Heat Flux Inverse Estimation

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