include,
at a minimum:
- Emphasis on physics-based, predictive modeling. In
particular, transition, turbulence, separation, chemically
reacting flows, radiation, heat transfer, and constitutive
models must reflect the underlying physics more closely
than ever before. - Management of errors and uncertainties resulting from
all possible sources: (a) physical modeling errors and
uncertainties addressed in item #1, (b) numerical errors
arising from mesh and discretization inadequacies, and
(c) aleatory uncertainties derived from natural variability,
as well as epistemic uncertainties due to lack of
knowledge in the parameters of a particular fluid flow
problem. -
A much higher degree of automation in all steps of the
analysis process is needed including geometry creation,
mesh generation and adaptation, the creation of large databases
of simulation results, the extraction and understanding
of the vast amounts of information generated,
and the ability to computationally steer the process. Inherent
to all these improvements is the requirement that
every step of the solution chain executes high levels of
reliability/robustness to minimize user intervention.
至少包括：1。强调基于物理的预测建模。特别是，过渡、湍流、分离、化学反应流、辐射、传热和本构模型必须比以往任何时候都更密切地反映基础物理。
2对所有可能来源引起的误差和不确定性的管理：（a）第1项所述的物理建模误差和不确定性；（b）由于网格和离散化不足而产生的数值误差；（c）由自然变异性引起的偶然不确定性，以及由于缺乏对特定流体流动问题的参数。
三。分析过程的所有步骤都需要更高程度的自动化，包括几何创建、网格生成和调整、创建大型仿真结果数据库、提取和理解生成的大量信息，以及计算指导过程的能力。所有这些改进的内在要求是，解决方案链的每一步都执行高级别的可靠性/健壮性，以尽量减少用户干预。
image1 - Ability to effectively utilize massively parallel, heterogeneous, and fault-tolerant HPC architectures that will be available in the 2030 time frame. For complex physical models with nonlocal interactions, the challenges of mapping the underlying algorithms onto computers with multiple memory hierarchies, latencies, and bandwidths must be overcome.
- Flexibility to tackle capability- and capacity-computing tasks in both industrial and research environments so that both very large ensembles of reasonably-sized solutions (such as those required to populate full-flight envelopes, operating maps, or for parameter studies and design optimization) and small numbers of very-largescale solutions (such as those needed for experiments of discovery and understanding of flow physics) can be readily accomplished.
-
Seamless integration with multidisciplinary analyses that will be the norm in 2030 without sacrificing accuracy or numerical stability of the resulting coupled simulation, and without requiring a large amount of effort such that only a handful of coupled simulations are possible.
4能够有效利用将在2030年提供的大规模并行、异构和容错HPC架构。对于具有非本地交互的复杂物理模型，必须克服将底层算法映射到具有多个内存层次、延迟和带宽的计算机上的挑战。
5灵活地处理工业和研究环境中的能力和容量计算任务，以便既有非常大的合理规模的解决方案（例如填充完整飞行包线、运行图或参数研究和设计优化所需的解决方案）和少量非常大规模的解决方案（如因为这些实验所需要的发现和理解流动物理）可以很容易地完成。
6与多学科分析的无缝集成将成为2030年的标准，而不会牺牲耦合模拟的精度或数值稳定性，也不需要大量的工作，以致只有少数耦合模拟是可能的。
grand challenge
