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02:46Quantifying Heat
Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the atoms and...
01:19Mechanism of heat transfer
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01:14Mechanisms of Heat Transfer I
Just as interesting as the effects of heat transfer on a system are the methods by which the heat transfer occur. Whenever there is a temperature difference, heat transfer occurs. It may occur rapidly, such as through a cooking pan, or slowly, such as through the walls of a picnic ice box. So many processes involve heat transfer that it is hard to imagine a situation where no heat transfer occurs. Yet, every heat transfer takes place by only three methods: conduction, convection, and radiation.
01:20Mechanisms of Heat Transfer II
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01:14Mechanisms of Heat Transfer
Heat transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
Conduction, accounting for approximately 3% of body heat loss at rest, is the process of exchanging heat between molecules of two materials in direct contact. This can result in both heat loss and gain. For instance, when the body is submerged in water, which conducts heat 20 times more effectively than air, it can either lose or gain significant heat.
Conduction, accounting for approximately 3% of body heat loss at rest, is the process of exchanging heat between molecules of two materials in direct contact. This can result in both heat loss and gain. For instance, when the body is submerged in water, which conducts heat 20 times more effectively than air, it can either lose or gain significant heat.
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Hybrid AI models for predicting heat distribution in complex tissue structures with bioheat transfer simulation.
Bhawani Sankar Panigrahi1, Srigitha S Nath2, Pankaj Agarwal3
1Department of Computer Science & Engineering, GITAM School of Technology, GITAM University, Vishakhapatnam, India.
Journal of Thermal Biology
|May 1, 2025
Summary
This study introduces a new deep learning model for precise thermal behavior prediction in tissues. It enhances thermal therapy and tissue engineering by enabling faster, accurate temperature control.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Medical Physics
Background:
- Accurate thermal behavior prediction in biological tissues is vital for medical treatments like hyperthermia and thermal ablation.
- Existing models often lack the precision and speed required for real-time applications in tissue engineering and thermal therapies.
- Understanding thermal effects is crucial for optimizing treatment outcomes and patient safety.
Purpose of the Study:
- To develop and validate a novel deep learning-enhanced bioheat transfer model for precise thermal effect prediction in engineered tissue constructs.
- To integrate a Fractional Legendre wavelet approach for enhanced predictive accuracy and computational efficiency.
- To assess the model's performance across various tissue types and thermal load conditions.
Main Methods:
- A multi-phase bioheat transfer model incorporating blood perfusion, thermal conductivity, and metabolic heat generation was developed.
- A deep learning framework was integrated with a Fractional Legendre wavelet approach for enhanced predictive capabilities.
- Experimental validation was performed on a 5 cm³ tissue construct with temperature monitoring under a controlled heat source.
Main Results:
- The model accurately predicted temperature gradients, ranging from 37°C to 48°C in experimental validation.
- Achieved a mean absolute error of 2.5°C, with prediction errors below 0.4°C across different tissue types and power inputs (10W-30W).
- Demonstrated a 15% increase in prediction speed compared to conventional methods, enabling real-time capabilities.
Conclusions:
- The deep learning-enhanced bioheat transfer model offers a significant advancement in predicting thermal behavior in biological tissues.
- The model's accuracy, speed, and versatility make it highly suitable for real-time thermal therapy planning, tumor ablation, and tissue engineering.
- This approach holds promise for improving the precision and efficacy of various medical treatments involving thermal manipulation.