The lack of large reliable data sets in fluid dynamics for deep learning

Fluid flow method using regression forest method by Ladicky et. al (Source)

Deep learning has gained prominence in varied sectors. Fluid dynamics simulations and experiments have also been explored to improve predictions over the past few years.

Ladicky et. al has explored a novel idea combining machine learning with fluid dynamics simulation. Here they made use of physics-based simulation as a regression problem, estimating the acceleration of every particle in every frame. The created a feature vector, directly modeling individual forces and constraints from the Navier-Stokes equations, predicting reliably the positions and velocities of particles in large time step on an unseen test video. A large training set of simulations obtained using traditional solvers were used for training using regression forest to obtain an approximate behavior of particles. Simulations like these reduce the need for computational resources for high-resolution real-time simulation.

“Given the inherent difficulties of long-term predictions, our vision for CFD in 2030 is grounded on a desired set of capabilities that must be present for a radical improvement in CFD predictions of critical flow phenomena associated with the key aerospace product/application categories, including commercial and military aircraft, engine propulsion, rotorcraft, space exploration systems, launch vehicle programs, air-breathing space-access configurations, and spacecraft entry, descent, and landing (EDL). “- NASA’s CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences (NASA/CR–2014-218178

Modern day simulations in fluid dynamics have reached its pinnacle with traditional turbulence modelling. In NASA’s CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences (NASA/CR–2014-218178) report the most critical area in CFD simulation capability that will remain a pacing item by 2030 will be the ability to adequately predict viscous turbulent flows with possible boundary layer transition and flow separation present.

But modeling knowledge and predictive capabilities can be built only on the basis of good data under varied conditions. The single thing that the fluid dynamics community suffers in the lack of reliable data under different conditions and for different applications. Training, algorithms, modeling techniques and machine learning approaches are secondary to develop data-driven turbulence modeling capabilities. This necessitates a global repository or database of reliable data both experimental and computational, where researchers from around the globe can pool in data and can benefit researchers focused on data-driven fluid dynamic simulations. Such a repository would open up better capabilities of predicting fluid dynamics and understanding flow in varied areas of interest unlike anything seen in history.