Coupled Electromagnetic and Heat Transfer ODE Model for Microwave Heating With Temperature-Dependent Permittivity

Zhong, Jiaqi; Liang, Shan; Yuan, Yupeng; Xiong, Qingyu

doi:10.1109/tmtt.2016.2584613

Cited by 37 publications

(20 citation statements)

References 44 publications

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“…These numerical models can well represent the change of physical fields, but it is not convenient to extract state characteristics or design a controller. For this reason, an effective method, which can derive a state space representation from the homogeneous PDE, is proposed in [13] for the microwave heating temperature model, which is employed in this paper. It considers the homogeneous Neuman boundary conditions,…”

Section: Model Description and Multi-rate Discretizationmentioning

confidence: 99%

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Adaptive Multi-Point Temperature Control for Microwave Heating Process via Multi-Rate Sampling

Liang

Liu

Song

et al. 2019

SICE Journal of Control, Measurement, and System Integration

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Microwave heating has been gradually extended to industrial material process from domestic microwave ovens because of its substantial advantages such as high-efficiency, pollution free, and selective heating. Unfortunately, the drawback of the temperature non-uniformity, which may cause thermal runaway, becomes an obstruction for the development of microwave energy. Besides, a common problem associated with microwave heating systems is that the speed of microwave power transmission is faster than the temperature detection period. Thus, to ensure the global temperature uniformity and to enhance the system adaptivity for deviation of the temperature detecting position in the microwave heating system with input constraints, a multi-rate simple adaptive multi-point temperature control strategy based on almost strictly positive real conditions is proposed, where the use of multi-rate sampling and lifting technique is to solve the case that the system has less inputs than outputs. Finally, simulation results demonstrate the effectiveness of the proposed control strategy.

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Section: Model Description and Multi-rate Discretizationmentioning

confidence: 99%

“…The boundary conditions adopted here are different from our previous study [12]. The corresponding state-space representation for the plant is described by G c [13]:…”

Section: Model Description and Multi-rate Discretizationmentioning

confidence: 99%

Adaptive Multi-Point Temperature Control for Microwave Heating Process via Multi-Rate Sampling

Liang

Liu

Song

et al. 2019

SICE Journal of Control, Measurement, and System Integration

Self Cite

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“…The heat convection between human body and water bolus depends on the thermal properties and thickness of the latter [12]. The temperature distribution in the tissues has been computed with FEM [20], boundary element method (BEM) [21], Monte Carlo method [22] and time-space decoupled form [23].…”

Section: Introductionmentioning

confidence: 99%

Electroquasistatic Model of Capacitive Hyperthermia Affected by Heat Convection

Chen¹,

Kiang²

2019

PIER C

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An electroquasistatic (EQS) model of capacitive hyperthermia for treating lung tumors is proposed, based on which the finite element method is applied to compute the electrical potential in a human thorax model. The temperature distribution in the thorax model, which is surrounded by a bolus maintained at a constant temperature, is computed by numerically solving a bio-heat equation, which includes metabolic heat generated in the tissues, heat convection mechanism in tissues and bolus, as well as the heat delivered by the microwave field computed with the EQS model and finite element method. Temperature-dependent blood perfusion rates of blood and muscle, respectively, are adopted to account for the physiological reaction of tissues to temperature variation. By simulations, it is observed that adjusting the dielectric properties of adipose tissue via injection, the time evolution of temperature distribution can be controlled to some extent, providing more flexibility to customize a hyperthermia treatment plan for specific patient.

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“…To improve the safety of microwave heating technology in industrial applications, an accurate model is required for the purpose of temperature prediction and control. From the underlying physics, microwave heating process (MHP) can be modeled by several partial differential equations (PDEs) [3]. These PDEs describe the characteristics of thermodynamics and model the conversion of microwave energy, which are highly complex.…”

Section: Introductionmentioning

confidence: 99%

A Multiple Local Model Learning for Nonlinear and Time-Varying Microwave Heating Process

Liu

Liang

Chen

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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This paper proposes a multiple local model learning approach for nonlinear and nonstationary microwave heating process (MHP). The proposed local learning framework performs model adaption at two levels: (1) adaptation of the local linear model set, which adaptively partitions the process's data into multiple process states, each fitted with a local linear model; (2) online adaptation of model prediction, which selects a subset of candidate local linear models and linearly combines them to produce the model prediction. Adaptive process state partition and fitting a new local linear model to the newly emerging process state is based on statistical hypothesis testing, and the optimal combining coefficients of the selected subset linear models are obtained by minimizing the mean square error with the constraint that the sum of these coefficients is unity. A case study involving a real-world industrial MHP is used to demonstrate the superior performance of the proposed multiple local model learning approach, in terms of online modeling accuracy and computational efficiency.

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Coupled Electromagnetic and Heat Transfer ODE Model for Microwave Heating With Temperature-Dependent Permittivity

Cited by 37 publications

References 44 publications

Adaptive Multi-Point Temperature Control for Microwave Heating Process via Multi-Rate Sampling

Adaptive Multi-Point Temperature Control for Microwave Heating Process via Multi-Rate Sampling

Electroquasistatic Model of Capacitive Hyperthermia Affected by Heat Convection

A Multiple Local Model Learning for Nonlinear and Time-Varying Microwave Heating Process

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