A Historical Analysis of the Administrative Origins of NASA

Hearsey, Christopher M.

doi:10.2139/ssrn.1742163

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…Hearsey assumed the secondary loss to be an exponential relation near the endwall (Hearsey, 1994) and can be expressed as:…”

Section: Appendixmentioning

confidence: 99%

“…The secondary loss models differ among B model, He model and Ho model. The B model uses Banjac’s secondary loss model (Banjac et al , 2015), the He model incorporates Hearsey’s secondary loss model (Hearsey, 1994) and the Ho model selects Howell’s secondary loss model (Howell, 1945). More details for the loss models are listed in Appendix.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…This loss model contains an empirical term which is used to measure the influences of boundary layer thickness on endwall loss. To evaluate the secondary loss, Hearsey (1994) developed a relatively simple correlation which is mainly composed of local profile loss relation and empirical coefficient. These traditional physics-based loss models are constructed based on simplified loss mechanisms and the experience of researchers.…”

Section: Introductionmentioning

confidence: 99%

“…C 1 represents the empirical parameter which is set as 0.002 after calibration. D is diffusion factor and expressed as: where w 1 and w 2 represent the inlet and outlet relative velocities, and w u is the tangential relative velocity. The term Δ ω off-design denotes a correlation for the loss under off-design conditions (Center et al , 1956): where i refers to incidence angle, i ml represents the minimum incidence angle, M 1 is the inlet relative Mach number, and g (·) is an empirical relationship that correlates the experimental data based on high-speed linear cascades: Hearsey’s secondary loss Hearsey assumed the secondary loss to be an exponential relation near the endwall (Hearsey, 1994) and can be expressed as: where ω hub represents the secondary loss in the hub region. ω ml denotes the minimum profile loss, r denotes the radial location, H Loss and n are the empirical coefficients that have been calibrated to be 10 and 6, respectively.…”

mentioning

confidence: 99%

“…Hearsey assumed the secondary loss to be an exponential relation near the endwall (Hearsey, 1994) and can be expressed as: where ω hub represents the secondary loss in the hub region. ω ml denotes the minimum profile loss, r denotes the radial location, H Loss and n are the empirical coefficients that have been calibrated to be 10 and 6, respectively.…”

mentioning

confidence: 99%

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A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

Shi,

Lu,

Song

et al. 2023

HFF

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Purpose In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions. Design/methodology/approach This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss. Findings The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%. Originality/value The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

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“…Hearsey assumed the secondary loss to be an exponential relation near the endwall (Hearsey, 1994) and can be expressed as:…”

Section: Appendixmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

Shi,

Lu,

Song

et al. 2023

HFF

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The Evolution of Outer Space Law: An Economic Analysis of Rule Formation

Hearsey¹

2015

SSRN Journal

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I wish to express my sincere appreciation to my thesis committee for their support, patience, and the freedom to write this thesis. I thank Dr. David Whalen and Dr. James Casler for each serving as my Committee Chair over the last seven years and encouraging me to finish this thesis. I also thank Professor Joseph Vacek for serving on my committee and helping to lay the foundation for this thesis while a graduate student in his (my first) space law course. Without my committee's support and devotion to my education, I would not have completed this body of work. This thesis is the product of over five years of research. In that time, my knowledge and understanding of space law, policy, and history, specifically, and public international law, generally, has grown because I have had the extraordinary benefit of knowing many of the great educators, mentors, friends, and colleagues who have helped me to understand the various fields of study that I incorporate into this thesis. Specifically

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A Historical Analysis of the Administrative Origins of NASA

Cited by 2 publications

References 12 publications

A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

The Evolution of Outer Space Law: An Economic Analysis of Rule Formation

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