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Lattice Boltzmann and Navier-Stokes Cartesian CFD Approaches for Airframe Noise Predictions

机译:格子噪声Boltzmann和Navier-Stokes笛卡尔CFD方法用于飞机噪声预测

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Lattice Boltzmann (LB) and compressible Navier-Stokes (NS) equations based computational fluid dynamics (CFD) approaches are compared for simulating airframe noise. Both LB and NS CFD approaches are implemented within the Launch Ascent and Vehicle Aerodynamics (LAVA) framework. Both schemes utilize the same underlying Cartesian structured mesh paradigm with provision for local adaptive grid refinement and sub-cycling in time. We choose a prototypical massively separated, wake-dominated flow ideally suited for Cartesian-grid based approaches in this study - The partially-dressed, cavity-closed nose landing gear (PDCC-NLG) noise problem from AIAA's Benchmark problems for Airframe Noise Computations (BANC) series of workshops. The relative accuracy and computational efficiency of the two approaches are systematically compared. Detailed comments are made on the potential held by LB to significantly reduce time-to-solution for a desired level of accuracy within the context of modeling airframe noise from first principles.
机译:比较了基于计算流体动力学(CFD)方法的格子Boltzmann(LB)和可压缩Navier-Stokes(NS)方程来模拟机身噪声。 LB和NS CFD方法都在发射升空和车辆空气动力学(LAVA)框架内实现。两种方案都利用了相同的底层笛卡尔结构化网格范例,并提供了局部自适应网格细化和及时子循环的功能。在本研究中,我们选择一种典型的大规模分离,尾流主导的流量,该流量非常适合基于笛卡尔网格的方法-AIAA的《飞机噪声计算基准问题》中的部分装扮,空腔封闭的前起落架(PDCC-NLG)噪声问题( BANC)系列研讨会。系统比较了两种方法的相对精度和计算效率。在根据第一原理对机身噪声进行建模的背景下,对LB所具有的潜力进行了详细评论,以显着减少解决方案所需的精度水平所需的时间。

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