To be an effective tool in studying cardiovascular disease and designing new treatments, computational fluid dynamics (CFD) needs to move from the realm of “high performance” to “high throughput” computing by improving efficiency while retaining accuracy. Solution adaptive mesh refinement (AMR) has the potential to decrease simulation time and benefits would be greater if the mesh could dynamically adapt throughout a heartbeat. This study used cut‐cell Cartesian grids with AMR at each time step. The refinement criteria was based on subgrid scale (SGS). The potential efficiency improvements from AMR were investigated with the FDA nozzle benchmark case at Re 500, 3500 and 6500. Using AMR with SGS = 1% mean throat velocity simulations could be performed in under 24 h on 16 CPUs with high accuracy compared to both FDA particle image velocimetry experiments and refined uniform grid CFD. This was an efficiency improvement of over an order of magnitude compared to uniform grids and other AMR SGS values. Using AMR with SGS = 5 or 0.2% mean throat velocity did not result in large efficiency improvements. Lastly, simulations of pulsatile flow suggest that performance improvements for typical cardiovascular flows may be even greater than were found simulating the statistically stationary FDA nozzle experiments.

中文翻译：

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切割单元笛卡尔网格的解决方案自适应优化可以提高FDA喷嘴计算流体动力学效率

为了成为研究心血管疾病和设计新疗法的有效工具，计算流体力学（CFD）需要通过在保持准确性的同时提高效率来从“高性能”领域转移到“高通量”领域。解决方案自适应网格细化（AMR）有可能减少仿真时间，并且如果网格可以在整个心跳中动态适应，则好处将更大。这项研究在每个时间步使用带有AMR的切割单元笛卡尔网格。细化标准基于子网格规模（SGS）。在FDA喷嘴基准案例Re 500、3500和6500上研究了AMR可能带来的效率提高。与FDA颗粒图像测速实验和精确的均匀网格CFD相比，使用SGS = 1％的AMR可以在24小时内在16个CPU上以高精度进行平均喉咙速度模拟。与统一网格和其他AMR SGS值相比，这是效率提高了一个数量级。使用SGS = 5或平均嗓音速度为0.2％的AMR不会导致效率的大幅提高。最后，对脉动血流的模拟表明，典型心血管血流的性能改善可能甚至比模拟统计上稳定的FDA喷嘴实验所发现的要大。平均喉咙速度2％并没有导致效率的大幅提高。最后，对脉动血流的模拟表明，典型心血管血流的性能改善可能甚至比模拟统计上稳定的FDA喷嘴实验所发现的要大。平均喉咙速度2％并没有导致效率的大幅提高。最后，对脉动血流的模拟表明，典型心血管血流的性能改善可能甚至比模拟统计上稳定的FDA喷嘴实验所发现的要大。