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English(EN) Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance

机器学习优化喷嘴性能，计算量减少

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种机器学习方法来优化喷嘴性能的流体喷射参数。该方法利用预训练的神经网络取代计算成本高昂的CFD模拟，显著减少了优化时间。该系统采用基于先验的预测策略以提高准确性，并使用反向传播进行有效的梯度计算。在一个测试案例中，该方法在七种运行条件下将特定类型喷嘴的平均喷嘴推力系数提高了1.14%。 AI

排序理由 这是一篇研究论文，详细介绍了一种针对特定工程问题的创新机器学习方法。[lever_c_research降级：ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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机器学习优化喷嘴性能，计算量减少

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这是一篇研究论文，详细介绍了一种针对特定工程问题的创新机器学习方法。[lever_c_research降级：ic=1 ai=0.7]
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报道来源 [1]

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

    基于机器学习的流体喷射参数多点优化以提高喷嘴性能

    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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