Thermal Sensor Allocation for Effective and Efficient Heat Transfer Measurements in Transportation Systems

Area of Science:

• Thermal Management
• Computational Engineering
• Energy Systems

Background:

• Power systems rely on components like generators and battery storage that generate heat.
• Active cooling is necessary but increases power demand, impacting system efficiency and autonomy.
• Efficient thermal management is crucial for performance and longevity of transportation and energy distribution networks.

Purpose of the Study:

• To develop a methodology for efficient heat flux load estimation from internal heat sources.
• To optimize cooling requirements by accurately computing heat flux with minimal sensors.
• To provide an affordable and efficient method for controlling thermal loads in power systems.

Main Methods:

• Utilizing local thermal measurements with a Kriging interpolator for accurate heat flux computation.
• Employing global optimization to strategically allocate a minimal number of temperature sensors.
• Reconstructing surface temperature distribution and feeding it into a heat conduction solver.
• Performing Conjugate URANS simulations to validate the method on an aluminum casing.

Main Results:

• The Kriging-based approach accurately computes heat flux, minimizing sensor requirements.
• Optimized sensor placement reduces reconstruction error for surface temperature.
• The methodology enables efficient identification of coolant needs for optimized resource utilization.
• Simulations demonstrated the effectiveness of the proposed thermal load control method.

Conclusions:

• This method offers an affordable and efficient way to monitor and control thermal loads in power systems.
• Accurate heat flux estimation is key to optimizing active cooling strategies.
• The approach enhances the performance and endurance of critical energy and transportation components.