Solution of Parameter Optimization and Control Problems in Thermal Systems by means of a Local Adaptive Filter

Matsevity, Yu.; Moultanovsky, Alexander V.

doi:10.1007/978-1-4613-8936-1_34

Cited by 4 publications

(6 citation statements)

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“…A nonlinear system whose state-variable model is Equations (1) and (2); nominal system model is Equations (3) and (4) and discretized perturbation state-variable model is Equations (7) and (8). The EKF is developed in predictor-corrector format [16].…”

Section: Discussionmentioning

confidence: 99%

“…Huang [6] adopted an algorithm based on the conjugate gradient method to estimate the external forces in inverse nonlinear force vibration problems. Additionally, a variety of methods based on KF approaches for inverse engineering, such as the iterative filter (IF) method and local adaptive filter (LAF) method by Matsevity and Moultanovsky [7,8], also the adaptive iterative filter method (AIFM) by Moultanovsky [9] in the inverse heat conduction problem (IHCP) are well-established.…”

Section: Introductionmentioning

confidence: 99%

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Input estimation for nonlinear systems

Lin

2010

Inverse Problems in Science and Engineering

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A novel input estimation inverse methodology of determining the time-varying exciting forces, named as the input, in a nonlinear system is presented. These forces are estimated from the measured dynamic response data of a nonlinear system using the approach. The algorithm includes the extended Kalman filter (KF) with a recursive estimator. The extended KF generates the residual innovation sequences. The estimator uses a least-squares algorithm, which employs the residual innovation sequences to evaluate the magnitudes and, therefore, the onset time histories of the exciting forces. Numerical simulations of a nonlinear system, an air-damped isolator model, demonstrate the accuracy of the proposed approach.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Input estimation for nonlinear systems

Lin

2010

Inverse Problems in Science and Engineering

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“…The resulting expression is then added to the right-hand side of Equation (6) to obtain the following extended expression for the functional J [g(x, y)]:…”

Section: The Adjoint Problemmentioning

confidence: 99%

“…This particular issue has been analysed by Matsevity and Moultanovsky [6] and Matsevity et al [25]. The peculiar difficulties of this kind of SIHCP are mainly related to the high number of degrees of freedom of the problem (i.e., to the number of unknowns) and therefore to the computational cost of the solution algorithm.…”

Section: Introductionmentioning

confidence: 98%

“…The desired information is then suitably restored from either the steady or the unsteady local energy balance in the wall by solving the corresponding steady or unsteady inverse heat conduction problem (also referred to, respectively, as SIHCP and UIHCP) with the temperature distribution as input data [1][2][3]. More precisely, because the unknown quantity is a boundary surface function, this problem is often referred to as a boundary inverse heat transfer problem [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Comparative application of CGM and Wiener filtering techniques for the estimation of heat flux distribution

Bozzoli

Rainieri

2011

Inverse Problems in Science and Engineering

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The conjugate gradient method (CGM), formulated with the adjoint problem, is adopted in this study as the functional minimization tool for the solution of the 2-D steady-state linear inverse heat conduction problem with the aim of estimating the heat flux density distribution on a given surface using the temperature distribution as input data. The robustness of the solution strategy, based on an iterative regularization scheme, is compared with another consolidated estimation methodology adopted in the literature to handle the same problem: the Wiener filtering technique. The comparison is performed by adopting a noisy test signal aimed at simulating infrared temperature maps. Using a comparative approach, the problem's particular difficulties related to the high number of unknowns and to the deficiency of the estimation techniques at the domain's geometrical boundaries are discussed. NomenclatureSignal amplitude, A ( C) Spatial domain, D Error, Equation (33), E Heat flux density, g (W m À2 ) Performance function, J ( C 2 ) J/M, J* ( C 2 ) Total number of sensors, M Direction of the descent, q (W m À2 ) Heat rate generation per unit volume, q g (W m À3 ) Thickness, s (m) Temperature, T ( C) Spatial coordinates, x, y, X, Y (m) Search step size, Dirac delta function,

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Mobile HVAC System Evaporator Optimization and Cooling Capacity Estimation by Means of Inverse Problem Solution

Moultanovsky¹

2002

Inverse Problems in Engineering

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Solution of Parameter Optimization and Control Problems in Thermal Systems by means of a Local Adaptive Filter

Cited by 4 publications

References 1 publication

Input estimation for nonlinear systems

Input estimation for nonlinear systems

Comparative application of CGM and Wiener filtering techniques for the estimation of heat flux distribution

Mobile HVAC System Evaporator Optimization and Cooling Capacity Estimation by Means of Inverse Problem Solution

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