AIAA held their first Generation STEM event and Intelligent Light is proud to have supported this special program at its launch. Generation STEM is an education outreach program anchored by a Science, Technology, Engineering, and Math fair. Nearly 350 students from middle schools (6th through 8th grade) in the Los Angeles area came to the event and spent a few hours exploring and observing real-world phenomena, participating in design projects, exploring engineering, astronomy and chemistry. The experiences and discussions provided a glimpse of what is happening in science and engineering today and offered an invitation to a future in technical disciplines.
Observation to experiment to simulation and visualization, Intelligent Light explored fluid mechanics with the students using a wind tunnel, ping pong balls, and CFD simulation with FieldView and XDBview. Other favorite activities included driving a Mars rover prototype, making lava lamps, a paper airplane design challenge, and a planetarium program.
AIAA’s Generation STEM event is unique in that it is an outreach from the aerospace industry directly to the students. This personal connection created a great deal of enthusiasm among the students and gave many a glimpse into a future in aerospace that they can believe in. The explorations, experiments and projects were immediately relevant to them.
Students were engaged by a large complex of hands-on explorations, mini-design challenges, demonstrations and special speakers lead by Dr. Sandy Magnus, a NASA astronaut and now AIAA Executive Director. Additional speakers included young engineering professionals and students who described the motivations, educational paths and skills they had developed that were important to their success. (AIAA photos)
Intelligent Light, a longtime corporate partner of AIAA, engaged students in large numbers through a station co-presented by NASA that introduced some basics of observing and understanding fluid mechanics via Bernoulli’s Principle. Students were directly involved in hands-on fluid flow experiments, wind tunnel demonstrations that allowed airflow to be seen and explored and were introduced to the use of CFD simulation with powerful visualizations that illuminated fluid flows in ways the students had never before been able to observe. We had energetic discussions about the skills of observation, importance of understanding, need for analytical methods, engineering intuition and places to observe the effects of fluid flows in the day to day world around us. Students learned that their real-life observations and understanding would power their ability to discern “correct” presentations of behaviors and create ideas for advancing discovery and design.
Scalable Knowledge Capture is Essential to Avoid CFD Bottlenecks
Image produced by Intelligent Light via XDB’s from an Air Force Research Laboratory (AFRL) sponsored Phase II SBIR, Contract FA8650-14-C-2439.
NASA’s CFD Vision 2030 Study details the many challenges that remain to routinely obtain accurate physics-based predictions of complex turbulent flows, including how to streamline and automate analysis to gain knowledge. Evolving HPC architectures will produce huge amounts of data, and future CFD technologies must be built to both realize the promise and avoid the pitfalls of this uncertain landscape. At Aviation 2015 this summer, Intelligent Light’s Dr. Earl Duque participated in an expert panel that discussed visions for post-processing and knowledge capture to meet the NASA 2030 CFD goals. Dr. Duque will be the lead author on the summary paper targeted for SciTech 2016.
Reduced Order Modeling Identified in the Study as an Enabling Technology
Reduced Order Modeling (ROM) can both compress and summarize, in a physics-oriented way, large unsteady CFD results and experimental data. Dr. Duque’s Applied Research Group at Intelligent Light has been successfully collaborating with BYU in an Air Force Research Laboratory-funded research effort to apply ROMs and Self-Organizing Maps (SOMs) to turbomachinery CFD. This is one example of how a partnership of government, industry and university researchers is working to make NASA’s 2030 CFD vision a reality.
New Surface Flows seeding option, improved readers, growing pathlines and many more improvements
Surface Flows obtained with the new “From File” seeding option
The FieldView Development Team has been working hard on great new capabilities for FieldView 16, coming this fall. We’re halfway through our yearly release cycle: the perfect time to step back and package all the new capabilities and improvements already available so you can benefit from them now.
Our recently adopted Agile development method allows us to deliver this patch release quickly so we can spend more time working on FieldView 16.Only some of the files in your FieldView 15 installation will be updated in the process. Our next full installation package will come with FieldView 16, which will be a new major release of the type our users are more familiar with.
Highlights of this patch release are:
More control over the location of Surface Flow lines with the new “From File” seeding option.
4x faster read times for the AcuSolve Direct Reader.
Parallel post-processing and support for FLOW-3D v11 with the FLOW-3D Direct Reader.
Animate the full path followed by your particles over time, even in moving grids, with the new “Growing” display type for particles.
Save time on particle animation by reading a single time step and animating only your particles with the new “Read as Steady State” option for single-file transient results.
Increased maximum parallel partitions count.
Changing grid counts in PLOT3D Function files during transient sweep.
A more verbose mode for debugging corrupted arbitrary polyhedra from your solver export.
For this project, we used FieldView XDB workflows to enable the investigation of “mysteries involved in the recovery process of the wake momentum deficit, downstream of utility-scale wind turbines in the atmosphere.” The “High-resolution surface data extracts provide new insight into the complex recovery process of the wake momentum deficit governed by turbulence transport phenomena. “