Title:
An Analytical Model for Oscillating Heat Pipe Performance and Experimental Testing of a Novel Helix-Shaped Design
An Analytical Model for Oscillating Heat Pipe Performance and Experimental Testing of a Novel Helix-Shaped Design
Author(s)
Pawlick, Maxwell
Advisor(s)
Peterson, G.P.
Kumar, Satish
Kumar, Satish
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Abstract
The research presented focuses on the development and assessment of a novel mechanistic model for oscillating heat pipes (OHPs), also known as pulsating heat pipes (PHPs) and the development of a novel helix-shaped OHP design inspired by insights gained from the model developed. OHPs are passive heat transfer devices with potential applications in fields such as electronics cooling, heat recovery systems, and hypersonic vehicles. Despite their potential, their adoption in industry has been slow due to the lack of reliable design tools. The complex physics governing OHP performance and the need for accurate modeling techniques have hindered the development of such tools.
An OHP consists of a sealed capillary channel filled with alternating liquid slugs and vapor bubbles. When a temperature difference is present, evaporation and condensation cause fluid motion, leading to passive convective heat transfer. Traditional OHP modeling approaches, ranging from experimental correlations to complex 3D computational models, have had limited success in providing rapid and reliable performance predictions without experimental data.
This research aims to develop a mechanistic model capable of predicting the performance of a basic closed-loop OHP design without experimental input. The model is intended to predict temperature profiles and performance trends, allowing designers to narrow down potential OHP designs for further analysis. Insights gained from the model were used to design a novel helix-shaped OHP, which was designed to leverage buoyancy-driven circulation flow for improved performance.
The research establishes that analytical modeling methods can significantly enhance the understanding and prediction of OHP performance. The contributions of this study include a comprehensive evaluation of OHP literature, identifying various operating modes that influence performance, developing an analytical framework for understanding some of these modes, and using this framework to develope and test a novel OHP design. This operating mode framework classifies OHP operation based on liquid distribution, fluid motion type, and flow regime, providing a basis for comparing different OHP designs.
The analytical model successfully predicted the temperature drop across multiple OHP datasets, although it has limitations in certain operating modes and complex geometries. To address these limitations, the research suggests augmenting the model with machine learning techniques, particularly for phenomena that are difficult to model analytically, such as oscillation amplitude in designs with flooded condensers.
Experimental validation of the novel helix-shaped OHP demonstrated that the design generally improved the effective thermal conductivity and maximum heat transport capacity relative to a control design. Further studies on helix-shaped OHPs with different sizes and working fluids are recommended to extend the advantages of this design. Additionally, the insights gained from the model provide further opportunity for other novel designs that improve performance.
This work represents significant progress in understanding and modeling OHP operation. The analytical model developed and the insights into OHP mechanisms provide a foundation for designing and optimizing OHPs for various applications. Further research that utilizes machine learning techniques to predict the most complex mechanisms in OHP operation is encouraged to increase the reliability and accuracy of the model. This work paves the way for broader and more effective use of OHPs in fields such as energy recovery and thermal management, which will contribute to a shift in how OHP technology is viewed and utilized in both academia and industry.
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Date Issued
2024-07-27
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Text
Resource Subtype
Dissertation