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Cart3D Simulations for the Second AIAA Sonic Boom Prediction Workshop

机译:第二届AIAA音爆预测研讨会的Cart3D模拟

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摘要

Simulation results are presented for all test cases prescribed in the Second AIAA Sonic Boom Prediction Workshop. An inviscid, embedded-boundary Cartesian-mesh flow solver is used to compute "boom carpets"-pressure signatures at a range of specified distances and off-track angles. To accelerate the process, each boom carpet is automatically decomposed into several independent meshes, computed in parallel. Each mesh uses output-based mesh adaptation to affordably obtain credible results. This paper discusses improved Cartesian meshing strategies for boom prediction, including Mach alignment, azimuthal alignment, high-aspect-ratio cells, and adaptation functional weighting. The resulting nearfield signatures generally exhibit good convergence with mesh refinement. For an independent evaluation of our results, this study introduces a local error estimation procedure that highlights regions of the signatures most sensitive to mesh refinement. Results are also presented for the two atmospheric propagation test cases, which investigate the effects of atmospheric profiles on ground noise. Propagation is handled with an augmented Burgers' equation method (NASA's sBOOM), and ground noise metrics are compared.
机译:在第二届AIAA音爆预测工作坊中规定的所有测试用例中均给出了仿真结果。使用无形的,嵌入边界的笛卡尔网格流动求解器来计算在指定距离和偏离轨迹角度范围内的“动臂毛毯”压力特征。为了加快这一过程,每个动臂地毯都会自动分解为几个独立的网格,并并行计算。每个网格都使用基于输出的网格自适应来负担得起的结果。本文讨论了用于臂杆预测的改进的笛卡尔网格划分策略,包括马赫对齐,方位对齐,高长宽比单元和自适应功能加权。所得的近场签名通常在网格细化方面表现出良好的收敛性。为了对我们的结果进行独立评估,本研究引入了局部误差估计程序,该程序突出显示了对网格细化最敏感的特征区域。还提供了两个大气传播测试案例的结果,这些案例研究了大气剖面对地面噪声的影响。使用增强的Burgers方程方法(NASA的sBOOM)处理传播,并比较地面噪声指标。

著录项

  • 来源
    《Journal of Aircraft》 |2019年第3期|896-911|共16页
  • 作者单位

    Sci & Technol Corp, Computat Aerosci Branch, Moffett Field, CA 94035 USA;

    NASA, Ames Res Ctr, Computat Aerosci Branch, Moffett Field, CA 94035 USA;

    NASA, Ames Res Ctr, Computat Aerosci Branch, Moffett Field, CA 94035 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 04:23:49

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