Investigation of Flow Characteristics in Lean Direct Injection Combustors

Dewanji, D.; Rao, Arvind Gangoli; Pourquié, Mathieu; Buijitenen, J.P Van

doi:10.2514/1.57028

Cited by 5 publications

(9 citation statements)

References 14 publications

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“…LPP technology will have low levels of emissions if the design and development are reasonable, but due to premixed and prevaporized combustion, the combustor is prone to auto ignition, flashback and combustion instability, and especially the severe combustion instability restricts the development of LPP technology. In the LDI concept, fuel and air are directly injected into the combustor, so that the auto-ignition and flashback would be avoided due to the non-premixed combustion, and the combustion instability is also not as serious as in LPP, furthermore, the LDI approach is not inferior to LPP in its ability to reduce emissions [5].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Discharge Coefficients for Single Element Lean Direct Injection Modules

Zhu

Suo

et al. 2018

Energies

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Lean direct injection (LDI) combustion has a high potential as a low pollution combustion method for gas turbines. The present research aims to further investigate the discharge coefficient of an LDI module, axial swirler and convergent outlet under non-reaction and reaction conditions by theoretical, numerical and experimental methods. The functional relationship between the discharge coefficient of the LDI module, axial swirler and convergent outlet was established, and the effect of swirl angle (30 • , 32 • , 34 • , 36 • , 38 • , 40 • ) and vane number (11,12,13,14,15,16) on discharge coefficient was studied, and finally the differences in effective flow area of LDI combustor under different inlet conditions were analyzed. The results indicate that the flow separation on the suction side increases as the swirl angle increases, which leads to a decrease of the discharge coefficient of the axial swirler, however the discharge coefficient of the convergent outlet remains unchanged first and then decreases. As the vane number increases, the flow separation on the suction side decreases and the flow friction loss increases, so that the discharge coefficient of the axial swirler and convergent outlet will first increase with the increase of vane number and then decrease with further increases. The effective flow area of combustor changes as the conditions change, but it is approximately equal under high power conditions and normal temperature and pressure conditions.

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Section: Introductionmentioning

confidence: 99%

Investigation of Discharge Coefficients for Single Element Lean Direct Injection Modules

Zhu

Suo

et al. 2018

Energies

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“…Results from the spray velocity measurements provide comparison to gas phase flow and show that smaller droplets match the gas phase flow field closer than that of larger droplets. This paper is highly cited in computational studies as an experimental reference in computational studies [ (Dewanji, 2012), (Patel and Menon, 2007), (Knudsen and Pitsch, 2010), and (Iannetti, et al 2008)].…”

Section: Figure 21 Swirl-venturi Lean Direct Injectionmentioning

confidence: 99%

“…The LDI has demonstrated low emissions relative to current commercial aircraft technologies [ (Marek, et al 2005), (Tacina, et al 2012)]. The LDI offers a simple design, shorter combustor length and larger operating range than the LPP (Dewanji, 2012). Source: GE: (Chang, et al 2013), NASA: (Hicks, et al 2002), Goeke, et al 2014 Research from NASA, sponsored university programs, and aero-engine subsystem and component suppliers of fuel injectors shows the direction of industry towards multipoint arrays in the next generation combustor.…”

Section: Introductionmentioning

confidence: 99%

Atomization and mixing performance of swirl-venturi lean direct injection

Burkhalter¹

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To my parents, Bill and Cindy Burkhalter, for all of their support and love. To Helen Pope for all of your encouragement and patience. I am very thankful for the technical mentorship I have received over the past few years. Thanks to Dr. Hui Hu at Iowa State for my introduction to laser diagnostics. Many thanks to my current advisor, Dr. Albert Ratnerwho has championed my path from the beginning; with application to graduate school, didactic training, and continued support through all my research efforts. While training at NASA Glenn Research Center I was able to have an excellent mentor in Dr. Yolanda Hicks, who gave me detailed guidance and also autonomy to make mistakes and learn by action. To Dr. Sarah Tedder, thank you for your advice and support. To both Dr. Clarence Chang and Dr. Kathy Tacina, I appreciate the many discussions of practice. I would like to thank Bill Jennings, Steve Struckman and Austin Krebill for help fabricating parts in the machine shop. I would like to thank Jianan Zhang and Daniel Lin for their assistance with data acquisition. Thank you to

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“…Para as análises URANS e SAS, foi utilizada uma malha não-estruturada, contendo aproximadamente 3,8 milhões de elementos, conforme podemos observar na fig. 77: A escolha desta malha teve, como base, os resultados numéricos obtidos em Dewanji et al (2012).…”

Section: Discretização Espacialunclassified

“…Para a condição de entrada, segundo a ilustração na fig. 78, foi atribuída uma vazão mássica de 0,49 kg/min de ar (CAI; JENG; TACINA, 2005) e uma intensidade de nível de turbulência de 10%, conforme Dewanji et al (2012).…”

Section: Condições De Contorno Entradaunclassified

Análise computacional de casos característicos de câmaras de combustão empregando simulação de escalas adaptativas

Bovolato¹

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O projeto de pesquisa propôs avaliar a metodologia de Simulação de Escalas Adaptativas (SAS) para descrever escoamentos turbulentos e não-reativos utilizando estudos de casos característicos, amplamente documentados, os quais possuem comportamentos do escoamento distintos presentes em diferentes regiões de uma câmara de combustão. O primeiro estudo de caso foi a análise do escoamento sobre um degrau, em que foi avaliada a capacidade do modelo Simulação de Escalas Adaptativas, frente aos modelos de Navier-Stokes com Média de Reynolds (RANS) e Simulação de Grandes Escalas (LES) e aos dados experimentais, em prever a distribuição de pressão, ponto de recolamento e de perfis de velocidade ao longo do domínio após a separação. Pode-se notar que o modelo SAS apresentou resultados praticamente idênticos aos resultados obtidos pelo modelo RANS com relação à distribuição de pressão e a posição ponto de recolamento. Porém, os perfis de velocidade apresentaram algumas discrepâncias com relação aos perfis de velocidade dos modelos RANS e LES e dos resultados experimentais. Um segundo estudo de caso foi a análise do escoamento através de um turbilhonador, em que a capacidade do modelo SAS foi avaliada, comparando seus resultados com os resultados do modelo de Navier-Stokes Não-Estacionárias com Média de Reynolds (URANS) e com os dados experimentais, em prever perfis de velocidade em regiões de recirculação presentes neste estudo de caso. Pode-se observar que ambos os modelos conseguiram prever as principais estruturas de recirculação do escoamento, porém, os perfis de velocidade apresentaram significativas discrepâncias com relação aos dados experimentais. Em seguida, foram feitas comparações entre os modelos SAS e URANS com relação à previsão da precessão central de vórtice e de estruturas de vórtices, das quais foi observado que o modelo SAS apresenta uma maior capacidade para prever estas estruturas em relação ao modelo URANS. Palavras-chave: Simulação de escalas adaptativas. Simulação de grandes escalas. Navierstokes com média de Reynolds. Escoamento sobre degrau. Escoamento turbilhonado.

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Investigation of Flow Characteristics in Lean Direct Injection Combustors

Cited by 5 publications

References 14 publications

Investigation of Discharge Coefficients for Single Element Lean Direct Injection Modules

Investigation of Discharge Coefficients for Single Element Lean Direct Injection Modules

Atomization and mixing performance of swirl-venturi lean direct injection

Análise computacional de casos característicos de câmaras de combustão empregando simulação de escalas adaptativas

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