AIAA Propulsion and Energy 2019 Forum 2019
DOI: 10.2514/6.2019-4363
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The Effect of Surface Roughness on LCH4 Boiling Heat Transfer Performance of Conventionally and Additively Manufactured Rocket Engine Regenerative Cooling Channels

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Cited by 5 publications
(3 citation statements)
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“…Cryogenic flow boiling experiments are performed using a high heat flux test facility (HHFTF), shown in Figure 2, capable of operating up to 4 MPa pressure and 755 K temperature using liquid nitrogen (LN 2 ) and liquid methane (LCH 4 ) as the working fluids [26][27][28]. The HHFTF is a thermal concentrator system based on conduction that asymmetrically transfers heat to a horizontal cooling channel resembling regenerative cooling during combustion.…”
Section: High Heat Flux Test Facility (Hhftf)mentioning
confidence: 99%
“…Cryogenic flow boiling experiments are performed using a high heat flux test facility (HHFTF), shown in Figure 2, capable of operating up to 4 MPa pressure and 755 K temperature using liquid nitrogen (LN 2 ) and liquid methane (LCH 4 ) as the working fluids [26][27][28]. The HHFTF is a thermal concentrator system based on conduction that asymmetrically transfers heat to a horizontal cooling channel resembling regenerative cooling during combustion.…”
Section: High Heat Flux Test Facility (Hhftf)mentioning
confidence: 99%
“…The improvement of the understanding of the phenomena by means of dedicated sub-scale representative breadboards is recommended [15]. From the point of view of the numerical procedure, researchers still continue the work on behaviour characterization in near-critical conditions, deterioration mode conditions, and comparisons with engineering tools, as shown by [16][17][18][19][20]. The additive manufacturing process has become one of the technologies to be massively adopted in the near future.…”
Section: Introductionmentioning
confidence: 99%
“…The additive manufacturing process has become one of the technologies to be massively adopted in the near future. However, certain problems, which must addressed, are associated with the surface finishing of the cooling channels, since the cooling system walls can be affected by very high values of roughness and will require special treatments in the finishing of their surfaces [19]. The application of new methods, such as neural networks or machine learning approaches, to support design tools and the identification of the optimal configurations, are discussed in [21,22].…”
Section: Introductionmentioning
confidence: 99%