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Machine learning optimizes nozzle performance with reduced computation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning approach to optimize fluidic injection parameters for nozzle performance. This method utilizes a pretrained neural network to replace computationally expensive CFD simulations, significantly reducing optimization time. The system employs a prior-based prediction strategy for accuracy and uses back-propagation for efficient gradient calculation. In a test case, this approach improved the average nozzle thrust coefficient of a specific nozzle type by 1.14% across seven operating conditions. AI

RANK_REASON This is a research paper detailing a novel machine learning method for a specific engineering problem. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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Machine learning optimizes nozzle performance with reduced computation

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This is a research paper detailing a novel machine learning method for a specific engineering problem. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yunjia Yang, Jiazhe Li, Yufei Zhang, Haixin Chen ·

    Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance

    arXiv:2409.12707v2 Announce Type: replace-cross Abstract: Fluidic injection offers a promising solution to improve the performance of the overexpanded single expansion ramp nozzles (SERNs) during vehicle acceleration. However, determining the injection parameters that yield the b…

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