Scenario-based implications of liquid hydrogen storage tank insulation quality for a short-range aircraft concept

Burschyk, Tim; Silberhorn, Daniel; Wehrspohn, Jennifer; Kühlen, Markus; Zill, Thomas

doi:10.2514/6.2023-3522

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…It is equipped with two hydrogen tanks in the unpressurized part of the fuselage behind the rear pressure bulkhead. For more details on the aircraft configuration, the reader is referred to Burschyk et al [25].…”

Section: Aircraft Configurationmentioning

confidence: 99%

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

Hesse,

Allebrodt,

Teschner

et al. 2024

Preprint

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The climate targets set out in the "European Green Deal" call for the consideration and implementation of climate-friendly propulsion concepts and sustainable fuels in future aircraft configurations. This puts the use of very efficient propeller engines into the focus of aircraft design. However, these pose major challenges, especially to cabin acoustics, due to high, tonal sound pressure levels on the fuselage. The design of noise control measures requires the competence to predict the resulting interior noise as early as possible in the design process of the vehicle. This requires a high level of geometric and structural detail regarding the fuselage structure and cabin components. With increasing frequency range, the necessary structural details also increase, which have to be resolved in the simulation models due to the associated decreasing structural wavelength. Especially in the context of aircraft pre-design, there is usually not enough information available for a detailed vibro-acoustic modeling of the fuselage and cabin components, which allows a meaningful prediction of the vibrations and thus the cabin noise. Therefore, the knowledge-based tool FUGA (Fuselage Geometry Assembler) is developed for the targeted enrichment of preliminary design data with knowledge for detailed numerical analyses. This paper describes the knowledge-based geometry and model generation in FUGA, which can consider the necessary (increasing) level of detail for the vibro-acoustic prediction already in the preliminary design. For this purpose, aircraft data sets in the preliminary design data format CPACS (Common Parametric Aircraft Configuration Schema) form the modeling basis. Originating from the aircraft preliminary design, these initially describe the outer shell of the vehicle and are extended by detailed structural information that defines the geometric boundary conditions for component placement in cabin design. For the cabin components, the open-source geometry kernel OCCT (Open Cascade Technology) is used to provide geometries at the level of detail required for subsequent analyses. The geometry models are then discretized in open source (e.g. Gmsh) or commercial meshers and further used for numerical analysis. Finally, the prediction of cabin noise is demonstrated as a Proof of Concept using the example of a short-haul propeller-driven aircraft and the sensitivity of resulting simulation models to the fuselage skin thickness is investigated.

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Section: Aircraft Configurationmentioning

confidence: 99%

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

Hesse,

Allebrodt,

Teschner

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

Hesse,

Allebrodt,

Teschner

et al. 2024

CEAS Aeronaut J

View full text Add to dashboard Cite

The climate targets set out in the “European Green Deal” call for the consideration and implementation of climate-friendly propulsion concepts and sustainable fuels in future aircraft configurations. This puts the use of very efficient propeller engines into the focus of aircraft design. However, these pose major challenges, especially to cabin acoustics, due to high, tonal sound pressure levels on the fuselage. The design of noise control measures requires the competence to predict the resulting interior noise as early as possible in the design process of the vehicle. This requires a high level of geometric and structural detail regarding the fuselage structure and cabin components. With increasing frequency range, the necessary structural details also increase, which have to be resolved in the simulation models due to the associated decreasing structural wavelength. Especially in the context of aircraft pre-design, there is usually not enough information available for a detailed vibro-acoustic modeling of the fuselage and cabin components, which allows a meaningful prediction of the vibrations and thus the cabin noise. Therefore, the knowledge-based tool Fuselage Geometry Assembler (FUGA) is developed for the targeted enrichment of preliminary design data with knowledge for detailed numerical analyses. This paper describes the knowledge-based geometry and model generation in FUGA, which can consider the necessary (increasing) level of detail for the vibro-acoustic prediction already in the preliminary design. For this purpose, aircraft data sets in the preliminary design data format Common Parametric Aircraft Configuration Schema (CPACS) form the modeling basis. Originating from the aircraft preliminary design, these initially describe the outer shell of the vehicle and are extended by detailed structural information that defines the geometric boundary conditions for component placement in cabin design. For the cabin components, the open-source geometry kernel Open Cascade Technology (OCCT) is used to provide geometries at the level of detail required for subsequent analyses. The geometry models are then discretized in open source (e.g. Gmsh) or commercial meshers and further used for numerical analysis. Finally, the prediction of cabin noise is demonstrated as a Proof of Concept using the example of a short-haul propeller-driven aircraft and the feasibility of the proposed method is indicated by investigating the sensitivity of resulting simulation models to the fuselage skin thickness.

show abstract

Scenario-based implications of liquid hydrogen storage tank insulation quality for a short-range aircraft concept

Cited by 2 publications

References 23 publications

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

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