Abstract:
Advances in high performance computing capabilities have enabled higher resolution simulations that significantly reduce the numerical errors associated with the application of CFD to aerospace design. Increased computing capacity enables expanded use of scale resolving turbulent simulation methods as well as higher fidelity models of combustion and transition to turbulence. Currently, the large number of simulations required for conceptual design, detailed design, and certification preclude the use of wall resolved large eddy simulation (LES) for the bulk of CFD applications related to product development. Despite improvements enabled by increased computational power, errors associated with physical modeling of transition, turbulence and combustion processes remain an obstacle to rapidly accelerating the use of CFD to reduce design cycle duration and development testing requirements. The lack of accurate, robust, and automated methods for prediction of transition remains a challenge, particularly for vehicles designed for use with significant extents of laminar flow. Models employed to account for the effects of turbulence including RANS, hybrid RANS-LES and wall modeled LES each have unique accuracy limitations relative to wall resolved LES and test derived data. In addition, they each have very different computational requirements. A particularly challenging problem is quantification of physical model uncertainty for vehicles without extensive existing test data. To realize the CFD 2030 Vision, significant advances in both accuracy and computational efficiency of physical modeling approaches are required. This panel will discuss the critical physical modeling issues that need to be addressed and potential solutions to enable the virtual model-based engineering design paradigm.
Panelists:
Abstract:
The CFD Vision 2030 Study, contracted by NASA, was published in 2014 and has become an aspirational objective for focusing CFD development on aerospace applications. The Study identified six main technical domains for aligning research: high performance computing (HPC), physical modeling, algorithms, geometry and grid generation, knowledge extraction, and multi-disciplinary analysis and optimization (MDAO). Within each domain, sub-disciplines (aka elements) illustrate the maturation of technology during the time period from 2015 to 2030. Each of the elements have their own timeline to identify key technology milestones and technology demonstrations. This Roadmap provides a compact and informative summary of future objectives and milestones, but some details are necessarily omitted from the graphic due to space constraints. These maturing technologies target the use of CFD for aerospace applications of increasing complexity which are represented in the Study by a series of grand challenge problems. At the 2019 AIAA Aviation forum, five years after the Study was published, a special session was held with invited lectures reviewing the progress and future in the six domains. Recognizing the ongoing utility of the Vision toward improving CFD technology, AIAA formed the CFD 2030 Integration Committee (IC). One of the charters of the IC is to maintain the Roadmap by assessing progress and highlighting the ongoing needs of aerospace CFD. Shortly after the 2019 Aviation Forum special session, the IC formed a Roadmap subcommittee to provide a detailed technical review of the Roadmap and identify progress with (and challenges to) following the outlined trajectory. The Roadmap subcommittee released a report in June of 2021 with their findings to provide further guidance to the research community. While this report cannot be exhaustive and there are likely some elements omitted or inadvertently neglected, it represents inputs from a diverse group of experts in the field. This paper describes the approach taken, summarizes the findings, and provides an update to the Vision 2030 Roadmap reflecting the present understanding.
Authors:
The CFD 2030 Integration Committee held a special session of invited talks for “Defining Grand Challenge Problems for the CFD 2030 Vision.” The CFD 2030 Vision report laid out a bold vision for future computational capabilities and their potential impact on aerospace engineering and design. In the report, a series of four Grand Challenge (GC) problems were suggested in order to demonstrate the potential impact that advanced computational capabilities will have on aerospace engineering. In this session, more detailed formulations of a small number of GC problems will be proposed by experts in the field. Additionally, the potential of these GC problems in advancing the state-of-the-art as well as for serving as a benchmark of technological progress will be discussed.
The three technical paper authors from the previous session were joined by the following three invited experts to serve on the panel.