使用截断域训练的燃烧器不稳定性降阶建模框架
Reduced Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training
AI总结：
本文提出了一种多保真度框架，用于预测火箭发动机中的燃烧不稳定性，通过在截断域上生成基底来构建降阶模型，从而减少训练成本并提高预测能力。
摘要：
本文建立并验证了一种多保真度框架，用于预测火箭发动机中的燃烧不稳定性。主要思想是为火箭发动机的不同组件适当地选择保真度建模方法，以确保准确且高效的预测。具体而言，所提出的框架整合了基于投影的降阶模型（ROMs），这些ROMs是通过在截断域模拟上生成的基底来开发的。ROM训练是在截断域上进行的，因此不需要在完整的火箭几何上求解全阶模型，从而展示了大幅降低训练成本的潜力。特定几何的训练被边界特征扰动所产生的响应所取代。这种训练方法已被证明可以增强所得到ROMs的预测能力和鲁棒性，包括在训练范围之外的条件。在单元火箭燃烧器的准一维模型上进行了数值测试，并将本文的框架与传统ROM开发方法进行了比较。
英文摘要：
A multi-fidelity framework is established and demonstrated for prediction of combustion instabilities in rocket engines. The major idea is to adapt appropriate fidelity modeling approaches for different components in a rocket engine to ensure accurate and efficient predictions. Specifically, the proposed framework integrates projection-based Reduced Order Models (ROMs) that are developed using bases generated on truncated domain simulations. The ROM training is performed on truncated domains, and thus does not require full order model solutions on the full rocket geometry, thus demonstrating the potential to greatly reduce training cost. Geometry-specific training is replaced by the response generated by perturbing the characteristics at the boundary of the truncated domain. This training method is shown to enhance predictive capabilities and robustness of the resulting ROMs, including at conditions outside the training range. Numerical tests are conducted on a quasi-1D model of a single-element rocket combustor and the present framework is compared to traditional ROM development approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
