Have you ever been given a mesh file but had no way to look at it or evaluate some of its quality metrics? The Pointwise Viewer provides precisely that functionality and is available for free. How about starting the new year with a free, handy tool? Read on for the details.
Start the new year off right with a healthy dose of the CFD Vision 2030 at AIAA SciTech where there will be a special session of invited papers followed by a panel session, both on the topic of grand challenge problems for evaluating CFD’s progress toward the 2030 vision.
The CFD 2030 Vision Study laid out a bold vision for future computational capabilities and their potential impact on aerospace engineering and design. In the Study, a series of 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 several 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.
Update: In the original version of this article, I neglected to note the date and time of these sessions.
Both the special session and the panel will be held on 14 Jan 2021.
The special session (CFD2030-01) begins at 1:00 pm EST.
The panel discussion (CFD2030-02) begins at 2:30 pm EST.
A Grand Challenge for the Advancement of Numerical Prediction of High Lift Aerodynamics
This paper presents an ambitious Computational Fluid Dynamics (CFD) grand challenge problem, including a proposed set of increasingly complex and connected challenge problems, to advance the technological state-of-the-art in the numerical prediction of commercial airplane low-speed, high-lift aerodynamic characteristics and performance. It describes the critical need for a vastly improved computational capability for high-lift airplane design, system development, and product certification, highlights current technology gaps and shortcomings, and details key research and development focus areas where significant progress is required. A key goal of this effort is to energize the aerospace CFD/Aerodynamics communities by coordinating and collaborating across multiple levels of government, industry, academia, and other technology providers to accelerate the use of efficient and robust computational tools to ultimately create products with increased aerodynamic performance that are environmentally cleaner, more fuel efficient, and ensure safe flight while reducing non-recurring product development cost and risk.
Vision 2030 Aircraft Propulsion Grand Challenge Problem: Full-engine CFD Simulations with High Geometric Fidelity and Physics Accuracy
In 2014 NASA published the outcome of the 2030 CFD (Computational Fluid Dynamics) Vision study: “CFD Vision 2030: A path to Revolutionary Computational Aerosciences” . The study provided a comprehensive review of the state of the art of CFD in 2014 for aerospace applications including, but not limited to, numerical algorithms, physics models, MDAO (Multidisciplinary Design Analysis and Optimization) and HPC (High Performance Computing) hardware. The study also proposed four conceptual ideas of Grand Challenge problems that would build on and benefit from advances outlined in the roadmap. The proposed challenges were meant to foster more detailed descriptions of grand challenge problems for specific disciplines. One of the proposed challenges was in the gas turbine propulsion area, focusing on transient full engine simulations. The current paper addresses detailed technical aspects of that challenge, and proposes a plan to approach it in a gradual manner, which includes high fidelity modeling of components, component coupling, and targeted experimental campaigns relying on common research models.
CFD 2030 Grand Challenge: CFD in the Loop Monte Carlo Simulation for Space Vehicle Design
Space vehicle design and certification differs widely from aircraft design relying more on probabilistic approaches than deterministic. Monte Carlo simulation plays an important role in the probabilistic design of space vehicles to ensure robust and reliable operation. Today, Monte Carlo flight simulation requires 1000’s of trajectory simulations that use databases to provide aerosciences models. These databases can be extremely expensive and time consuming to develop. Replacing these databases with unsteady computational fluid dynamics directly in the simulation loop has potential to significantly reduce the time required to analyze space vehicle concepts, improve simulation accuracy, and reduce the cost of space vehicle development. The CFD Vision 2030 Study outlined gaps and roadblocks to meeting the vision described in the study. The geometric, physical, and computational challenges associated with CFD-in-the-loop Monte Carlo simulation for space vehicle design are substantial and serve as an excellent grand challenge to advance the CFD 2030 vision.
You may have heard us (and others) promote something called Tutorial Tuesday. Exactly what it is and how can it benefit you? Let’s just say there’s lots of good information out there shared in bite-sized pieces. Read on for the details.
This week’s This Week in CFD is 2020’s last hurrah and while we tried to include everything, we ran out of time. LOTS of event news as though people are anxious to begin meeting in-person. (Let’s face it – online conferences are awful. Folks are doing the best they can under the circumstances but it ain’t the same.) Lots of new software news as well, as though everyone was trying to clear the decks before year’s end. And the image of the week – well, it demonstrates that the real world can be as abstract as any painting. Shown here is a wind turbine flowfield from Tecplot 360 2020 R2.
Pointwise and ISimQ developed an efficient adaptation procedure for effective control of discretization error on real-world cases that adapts to underlying geometry while still efficiently resolving the mesh with high aspect ratios in boundary layers. Shown here are results computed using ANSYS CFX on an adapted Pointwise mesh showing shear stress on the hub.