NSF Transport Phenomena cluster includes 1) Combustion and Fire Systems program; 2) Fluid Dynamics program; 3) Particulate and Multiphase Processes program; and 4) Thermal Transport Processes program

Fluid Dynamics program  PD 21-1443   https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505698 
Major areas of interest and activity in the program include:

• Turbulence and transition: High Reynolds number experiments; large eddy simulation; direct numerical simulation; transition to turbulence; 3-D boundary layers; separated flows; multi-phase turbulent flows; flow control and drag reduction. A new area of emphasis is high speed boundary layer transition and turbulence; the focus would be for flows at Mach numbers greater than 5 to understand cross-mode interactions leading to boundary layer transition and the ensuing developing and fully developed turbulent boundary layer flows. Combined experiments and simulations are encouraged.
• Bio-fluid physics: Bio-inspired flows; biological flows with emphasis on flow physics.
• Non-Newtonian fluid mechanics: Viscoelastic flows; solutions of macro-molecules.
• Microfluidics and nanofluidics: Micro-and nano-scale flow physics. 
• Wind and ocean energy harvesting: Focused on fundamental fluid dynamics associated with renewal energy.
• Fluid-structure interactions: NSF interests are in general FSI applications across the low- to high-Reynolds number range. In addition an NSF-AFOSR (Air Force Office of Scientific Research) joint funding area is the theory, modeling and/or experiments for hypersonic applications. Proposals will be jointly reviewed by NSF and AFOSR using the NSF merit review process. Actual funding format and agency split for an award (depending on availability of funds) will be determined after the proposal selection process. The AFOSR program that participates in this initiative is the Aerothermodynamics program (program officer: Dr. Sarah Popkin).
• Canonical configurations: Experimental research is encouraged to develop spatiotemporally resolved databases for canonical configurations to either confirm historical results or to provide data in an unexplored parameter region. Fidelity and completeness for theoretical/computational validation are key attributes of the proposed experimental data.
• Artificial intelligence (AI)/machine learning: Innovative AI ideas related to the use of machine learning and other AI approaches in fluid dynamics research to model and control the flows are encouraged. Verifying new models with canonical configurations, when appropriate, is encouraged for the Computational and Data-Enabled Science & Engineering (CDS&E) program.
• Instrumentation and Flow Diagnostics: Instrument development for time-space resolved measurements; shear stress sensors; novel flow imaging; and velocimetry.