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Dimensional Metrology and Thermo-Fluid Studies on Additively Manufactured Transpiration Cooling Structures.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dimensional Metrology and Thermo-Fluid Studies on Additively Manufactured Transpiration Cooling Structures./
作者:	Min, Zheng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	170 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Heat transfer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28684759
ISBN:	9798544202813

Dimensional Metrology and Thermo-Fluid Studies on Additively Manufactured Transpiration Cooling Structures.
Min, Zheng.

Dimensional Metrology and Thermo-Fluid Studies on Additively Manufactured Transpiration Cooling Structures. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 170 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2021.

This item must not be sold to any third party vendors.

The rapid advancement in metallic additive manufacturing (AM) has provided us with new opportunities and challenges to apply more sophisticated cooling designs to protect high-temperature components in gas turbines. With the benefits of high design freedom and structural complexity, the direct metal laser sintering (DMLS) AM technique can fabricate cooling passages into microscale in a highly compact fashion, making transpiration cooling feasible in turbine airfoil to protect hot surfaces. In this research, an accurate dimensional characterization technique of microscale cooling passages was developed, and the related thermo-fluid performance was studied. The DMLS process produces microchannels with deformations and surface roughness, which significantly impact thermo-fluid performance. The state-of-the-art micro-CT scanners hardly work for intricate AM transpiration cooling structures due mainly to limitations in penetration rate and detection precision on heavy metals. In this research, a high-precision scanning electron microscope (SEM) characterization combined with a multi-level image segmentation method was employed to statistically analyze the geometric dimensions of microchannels made by AM. Based on the characterization results, surface improvement techniques were used to generate expected channel sizes, preparing for the cooling effectiveness studies with various geometric parameters. Most previous experimental studies on transpiration cooling focused only on cooling effectiveness, leaving a significant vacancy in the literature on the heat transfer coefficient (HTC) at the target surfaces. Two classic methods to investigate HTC, the steady-state foil heater method and the transient thermography technique, both fail for transpiration cooling. That is because the foil heater would block numerous coolant outlets, and the transient semi-infinite solid medium theory no longer holds for porous plates. In this study, a micro-lithography technique was employed to precisely coat a patterned surface heater directly on top of the low thermally conductive test plate to determine the HTC distributions. The dimensional variations created by AM fabrication generate inhomogeneity of cooling performance at the target surface. Moreover, the various hole size would cause clogging issues of the smallest channels during operation, which would, in turn, affect the cooling performance as well. A machine learning model was developed in this work to predict cooling effectiveness distributions from these contributing factors.

ISBN: 9798544202813Subjects--Topical Terms:

3391367
Heat transfer.

Dimensional Metrology and Thermo-Fluid Studies on Additively Manufactured Transpiration Cooling Structures.
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520 $a The rapid advancement in metallic additive manufacturing (AM) has provided us with new opportunities and challenges to apply more sophisticated cooling designs to protect high-temperature components in gas turbines. With the benefits of high design freedom and structural complexity, the direct metal laser sintering (DMLS) AM technique can fabricate cooling passages into microscale in a highly compact fashion, making transpiration cooling feasible in turbine airfoil to protect hot surfaces. In this research, an accurate dimensional characterization technique of microscale cooling passages was developed, and the related thermo-fluid performance was studied. The DMLS process produces microchannels with deformations and surface roughness, which significantly impact thermo-fluid performance. The state-of-the-art micro-CT scanners hardly work for intricate AM transpiration cooling structures due mainly to limitations in penetration rate and detection precision on heavy metals. In this research, a high-precision scanning electron microscope (SEM) characterization combined with a multi-level image segmentation method was employed to statistically analyze the geometric dimensions of microchannels made by AM. Based on the characterization results, surface improvement techniques were used to generate expected channel sizes, preparing for the cooling effectiveness studies with various geometric parameters. Most previous experimental studies on transpiration cooling focused only on cooling effectiveness, leaving a significant vacancy in the literature on the heat transfer coefficient (HTC) at the target surfaces. Two classic methods to investigate HTC, the steady-state foil heater method and the transient thermography technique, both fail for transpiration cooling. That is because the foil heater would block numerous coolant outlets, and the transient semi-infinite solid medium theory no longer holds for porous plates. In this study, a micro-lithography technique was employed to precisely coat a patterned surface heater directly on top of the low thermally conductive test plate to determine the HTC distributions. The dimensional variations created by AM fabrication generate inhomogeneity of cooling performance at the target surface. Moreover, the various hole size would cause clogging issues of the smallest channels during operation, which would, in turn, affect the cooling performance as well. A machine learning model was developed in this work to predict cooling effectiveness distributions from these contributing factors.
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