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广恢。強(qiáng)調(diào)基于物理的預(yù)測(cè)建模。特別是巷懈，過渡嫩实、湍流掠拳、分離、化學(xué)反應(yīng)流、輻射利虫、傳熱和本構(gòu)模型必須比以往任何時(shí)候都更密切地反映基礎(chǔ)物理肯腕。
2對(duì)所有可能來源引起的誤差和不確定性的管理：（a）第1項(xiàng)所述的物理建模誤差和不確定性献宫；（b）由于網(wǎng)格和離散化不足而產(chǎn)生的數(shù)值誤差；（c）由自然變異性引起的偶然不確定性实撒，以及由于缺乏對(duì)特定流體流動(dòng)問題的參數(shù)遵蚜。
三。分析過程的所有步驟都需要更高程度的自動(dòng)化奈惑，包括幾何創(chuàng)建吭净、網(wǎng)格生成和調(diào)整、創(chuàng)建大型仿真結(jié)果數(shù)據(jù)庫肴甸、提取和理解生成的大量信息寂殉，以及計(jì)算指導(dǎo)過程的能力。所有這些改進(jìn)的內(nèi)在要求是原在，解決方案鏈的每一步都執(zhí)行高級(jí)別的可靠性/健壯性友扰，以盡量減少用戶干預(yù)彤叉。
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能夠有效利用將在2030年提供的大規(guī)模并行、異構(gòu)和容錯(cuò)HPC架構(gòu)村怪。對(duì)于具有非本地交互的復(fù)雜物理模型秽浇，必須克服將底層算法映射到具有多個(gè)內(nèi)存層次、延遲和帶寬的計(jì)算機(jī)上的挑戰(zhàn)甚负。
5靈活地處理工業(yè)和研究環(huán)境中的能力和容量計(jì)算任務(wù)柬焕，以便既有非常大的合理規(guī)模的解決方案（例如填充完整飛行包線、運(yùn)行圖或參數(shù)研究和設(shè)計(jì)優(yōu)化所需的解決方案）和少量非常大規(guī)模的解決方案（如因?yàn)檫@些實(shí)驗(yàn)所需要的發(fā)現(xiàn)和理解流動(dòng)物理）可以很容易地完成梭域。
6與多學(xué)科分析的無縫集成將成為2030年的標(biāo)準(zhǔn)斑举，而不會(huì)犧牲耦合模擬的精度或數(shù)值穩(wěn)定性，也不需要大量的工作病涨，以致只有少數(shù)耦合模擬是可能的富玷。
grand challenge
