History of Bio-Nano Fluid Flow

Ghassemi, Majid; Shahidian, Azadeh

doi:10.1016/b978-0-12-803779-9.00001-7

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…The temperature-dependent physical properties of tissue can be represented as linear functions of temperature using the following form (Ghassemi and Shahidian, 2017):…”

Section: Developing Computational Methodsmentioning

confidence: 99%

Developing computational methods of heat flow using bioheat equation enhancing skin thermal modeling efficiency

Ostadhossein,

Hoseinzadeh

2023

HFF

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Purpose The main objective of this paper is to investigate the response of human skin to an intense temperature drop at the surface. In addition, this paper aims to evaluate the efficiency of finite difference and finite volume methods in solving the highly nonlinear form of Pennes’ bioheat equation. Design/methodology/approach One-dimensional linear and nonlinear forms of Pennes’ bioheat equation with uniform grids were used to study the behavior of human skin. The specific heat capacity, thermal conductivity and blood perfusion rate were assumed to be linear functions of temperature. The nonlinear form of the bioheat equation was solved using the Newton linearization method for the finite difference method and the Picard linearization method for the finite volume method. The algorithms were validated by comparing the results from both methods. Findings The study demonstrated the capacity of both finite difference and finite volume methods to solve the one-dimensional and highly nonlinear form of the bioheat equation. The investigation of human skin’s thermal behavior indicated that thermal conductivity and blood perfusion rate are the most effective properties in mitigating a surface temperature drop, while specific heat capacity has a lesser impact and can be considered constant. Originality/value This paper modeled the transient heat distribution within human skin in a one-dimensional manner, using temperate-dependent physical properties. The nonlinear equation was solved with two numerical methods to ensure the validity of the results, despite the complexity of the formulation. The findings of this study can help in understanding the behavior of human skin under extreme temperature conditions, which can be beneficial in various fields, including medical and engineering.

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“…The temperature-dependent physical properties of tissue can be represented as linear functions of temperature using the following form (Ghassemi and Shahidian, 2017):…”

Section: Developing Computational Methodsmentioning

confidence: 99%

Developing computational methods of heat flow using bioheat equation enhancing skin thermal modeling efficiency

Ostadhossein,

Hoseinzadeh

2023

HFF

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“…The Pennes bioheat transfer equation is standard theory for predicting the temperature field in living tissues, which is expressed as formula (7.8). 213,229 rC…”

Section: Numerical Simulation Of Magnetic Induction Hyperthermiamentioning

confidence: 99%

“…The Pennes bioheat transfer equation is standard theory for predicting the temperature field in living tissues, which is expressed as formula (7.8). 213,229 where T is the temperature (°C), ρ is the tissue density (kg m −3 ), C is the specific heat capacity of tissue (J kg °C) −1 ), k is the thermal conductivity of tissue (W m °C −1 ), Q m is the metabolic heat generation (W m −3 ), Q p is the blood perfusion effect (W m −3 ), and Q other is the source terms such as hyperthermia (W m −3 ).…”

Section: Self-regulated Temperature Characteristic Of Magnetic Nanopa...mentioning

confidence: 99%

The Curie temperature: a key playmaker in self-regulated temperature hyperthermia

Niraula,

Wu,

et al. 2024

J. Mater. Chem. B

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“…For forward mathematical modeling, Pennes’ bio-heat equation was used. Equation (1) presents the relationship between temperature, thermal conductivity, heat capacity, volumetric metabolic heat generation rate and influence of the blood perfusion on the temperature distribution based on the energy conservation principle: 14 where T (°C) is temperature of the tissue, which is equal to 37 °C, as the temperature of the core body; k is the thermal conductivity of tissue ( W/m·K ) equals to 0.16 W/m-K ; ρ is the density ( kg/m 3 ), which is equal to the density of the silicone, used in the artificial breasts for the experiments; q m is the metabolic heat generation per unit volume ( W/m 3 ) which depends on the size of the tumor. Next part of the equation describes the influence of the blood perfusion w b on the temperature distribution in the breast, which is in the case of study was equal to zero, as the conducted experiments were based on the single layer model without considering the blood perfusion rate.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Inverse thermal modeling and experimental validation for breast tumor detection by using highly personalized surface thermal patterns and geometry of the breast

Mukhmetov

Mashekova

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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Infrared (IR) Thermography is currently a supplementary technique for breast cancer diagnosis. There have been studies using IR thermography and numerical modeling in an attempt to detect tumor inside the breast. Most of these studies focused on either the “forward modeling” problem or only used idealized or population-averaged patients’ data, whereas identification of the tumor inside the breast based on the thermal pattern is an “inverse modeling” problem dependent on personalized information of the patient. Inverse modeling is based on the idea that the surface thermal pattern of the breast can be used to determine the tumor features based on physical and physiological principles. The current study aims to develop a well-validated inverse thermal modeling framework that could be used to determine the depth and size of tumor inside a breast based on personalized patients’ breast data, such as thermogram and 3D geometry using efficient design optimization techniques and Finite Element Modeling (FEM) to support the process. The numerical modeling was validated by the experiments, conducted using artificial breasts. Results show that although DIRECT Optimization method can be employed to find the depth and size of the tumor with good accuracy, the technique can be very time consuming. On the other hand, Response Surface Optimization method is also able to find the depth and size of the tumor with less accuracy but faster when compared with DIRECT Optimization. The last method tested, Nelder-Mead method, failed to detect the tumor. The study concludes that Response Surface Optimization method should be used first, and after the range of parameters are found, the DIRECT optimization method can be applied for more accurate results. However the GA method was found to be the only viable and efficient design optimization method for reverse modeling when blood perfusion was adopted in the breast model and many parameters were searched for with patient specific data input for breast tumor diagnosis.

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History of Bio-Nano Fluid Flow

Cited by 7 publications

References 4 publications

Developing computational methods of heat flow using bioheat equation enhancing skin thermal modeling efficiency

Developing computational methods of heat flow using bioheat equation enhancing skin thermal modeling efficiency

The Curie temperature: a key playmaker in self-regulated temperature hyperthermia

Inverse thermal modeling and experimental validation for breast tumor detection by using highly personalized surface thermal patterns and geometry of the breast

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