Multidisciplinary Design Optimization of a Low-Noise and Efficient Next-Generation Aero-Engine Fan

Jaron, Robert; Moreau, Antoine; Guérin, Sébastien; Enghardt, Lars; Lengyel, Timea; Otten, Tom; Nicke, Eberhard

doi:10.1115/1.4051935

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…With the advent of optimization algorithms and the increase in computational power, multi-objective optimizations started to be performed on fans [45][46][47] and on general aviation aircraft propellers [48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

Analysis of MAV Rotors Optimized for Low Noise and Aerodynamic Efficiency with Operational Constraints

Li Volsi,

Brogna,

Gojon

et al. 2024

Fluids

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The rapid growth of drone use in urban areas has prompted authorities to review airspace regulations, forcing drone manufacturers to anticipate and reduce the noise emissions during the design stage. Additionally, micro air vehicles (MAVs) are designed to be aerodynamically efficient, allowing them to fly farther, longer and safer. In this study, a steady aerodynamic code and an acoustic propagator based on the non-linear vortex lattice method (NVLM) and Farassat’s formulation-1A of the Ffowcs Williams and Hawkings (FW-H) acoustic analogy, respectively, are coupled with pymoo, a python-based optimization framework. This tool is used to perform a multi-objective (noise and aerodynamic efficiency) optimization of a 20 cm diameter two-bladed rotor under hovering conditions. From the set of optimized results, (i.e., the Pareto front), three different rotors are 3D-printed using a stereolithography (SLA) technique and tested in an anechoic room. Here, an array of far-field microphones captures the acoustic radiation and directivity of the rotor, while a balance measures the aerodynamic performance. Both the aerodynamic and aeroacoustic performance of the three different rotors, in line with what has been predicted by the numerical codes, are compared and guidelines for the design of aerodynamically and aeroacoustically efficient MAV rotors are extracted.

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Section: Introductionmentioning

confidence: 99%

Analysis of MAV Rotors Optimized for Low Noise and Aerodynamic Efficiency with Operational Constraints

Li Volsi,

Brogna,

Gojon

et al. 2024

Fluids

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Performance prediction and design optimization of a transonic rotor based on deep transfer learning

Deng,

Zhang,

Xia

et al. 2024

Physics of Fluids

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Deep transfer learning is frequently employed to address the challenges arising from limited or hard-to-obtain training data in the target domain, but its application in axial compressors has been scarcely explored thus far. In this paper, a multi-objective optimization framework of a transonic rotor is established using deep transfer learning. This framework first pre-trains deep neural networks based on the peak efficiency condition of 100% design speed and then fine-tunes the networks to predict the performance of off-design conditions based on the small training dataset. Finally, the design optimization of the transonic rotor is carried out through non-dominated sorting genetic algorithm II. Compared to neural networks that are trained directly, transfer learning models can achieve higher prediction accuracy, particularly in scenarios with small training datasets. This is because the pre-trained weights can offer a better initial state for transfer learning models. Moreover, transfer learning models can use fewer samples to obtain an approximate Pareto front, making the optimized rotor increase the isentropic efficiency at both peak efficiency and high loading conditions. The efficiency improvement of the optimized rotor is attributed to the reduction of the loss associated with the tip leakage flow by adjusting the tip loading distribution. Overall, this study fully demonstrates the effectiveness of transfer learning in predicting compressor performance, which provides a promising approach to solving high-cost compressor design problems.

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Optimization of Three-Dimensional Blade and Variable Stators for Efficiency and Stability Enhancement of Multistage Axial Flow Compressor at Variable Speeds

Zhang,

Ju,

et al. 2023

Journal of Turbomachinery

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High efficiency and wide stability at variable speeds are equally important to design and operation of multi-stage axial flow compressor. However, published research works on three-dimensional design optimization of compressors are so far mainly limited to a single blade row or stage at design speed due to the curse-of-dimensionality. Moreover, optimization of variable inlet guide vanes (IGV)/stators during off-design operations is carried out by using rapid but low-fidelity prediction tool and is generally independent of design optimization of blade geometry. To tackle these issues, a three-dimensional holistic design optimization method is developed in which both three-dimensional blade geometry and variable IGV/stators are optimized simultaneously for efficiency and stability at design and off-design conditions. Metamodel-interpreted data mining method, metamodel-based design optimization method and adaptive infilling strategy are used to enhance the capability of metamodeling and optimization. The developed method is applied to a modern highly-loaded 3.5-stage transonic axial flow compressor at both design and part-design speeds. The results show that stall margin is extended from 0 to 11.17% at 70% design speed while peak efficiency is slightly improved at design speed. The flow mechanisms responsible for efficiency enhancement at design speed are associated with the reduced total pressure loss in stators as well as inter- and intra- stage loading re-distribution. The stability enhancement at 70% design speed is achieved by loading the front blade rows while unloading the limiting rear blade row.

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Multidisciplinary Design Optimization of a Low-Noise and Efficient Next-Generation Aero-Engine Fan

Cited by 4 publications

References 31 publications

Analysis of MAV Rotors Optimized for Low Noise and Aerodynamic Efficiency with Operational Constraints

Analysis of MAV Rotors Optimized for Low Noise and Aerodynamic Efficiency with Operational Constraints

Performance prediction and design optimization of a transonic rotor based on deep transfer learning

Optimization of Three-Dimensional Blade and Variable Stators for Efficiency and Stability Enhancement of Multistage Axial Flow Compressor at Variable Speeds

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