Abstract

The proposed project addresses several major challenges encountered in the assimilation of measurement data in aerodynamic testing.
Optimization plays a central role in the design of current and future transportation systems such as trains, airplanes or automobiles. The objective is to develop designs with reduced energy consumption, smaller environmental footprint and increased customer comfort. For this, numerical simulations and experimental tests are being performed, both with their own set of constraints such as turnover times and cost. The Institute of Fluid Dynamics (IFD) operates a wind tunnel for such testing, and the research is focused on the development of novel, efficient measurement techniques to enhance the science data return from such cost-intensive facilities.
The proposed project will focus on two central problems limiting the productivity of experimental aerodynamic test campaigns. Strategies using machine learning will be developed to dynamically assimilate acquired data into a global description of the flow field being measured. Predictive analysis of the data will be employed to direct the measurements process towards regions of significant information as the global knowledge evolves. The measurement time will be reduced relying on adaptive guidance based on real-time data interpretation. The software design will explicitly include the option of a human operator in the loop.
The collaboration between SDSC and IFD offers an attractive way to merge complementary competencies. The tasks of aerodynamic flow field reconstruction, uncertainty quantification and probe guidance will be broken down into distinct activities / work packages such as (1) development of learning algorithms for sparse flow data assimilation using physics-based constraint models, (2) real-time implementation of the software in a suitable computing infrastructure and (3) testing and evaluation of the complete hardware/software system in situ in the wind tunnel facility at IFD.