Quantification of shear stress field using tuft visualization and deep learning
AssigneeCalifornia Institute of Technology
InventorsMorteza Gharib, Emile K. Oshima, Pablo Hermoso Moreno
Life of this patent
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Abstract
A system and method for estimating the shear wall stress of an aerodynamic surface using a tuft visualization technique combined with a physics-informed neural network. The tuft visualization technique is a simplified method of generating velocity profile data of an aerodynamic model that can subsequently be used to generate a shear wall stress profile of the model. Systems and methods described herein also provide for additional input data using an augmented tuft and taps inputs for the physics-informed neural network to generate the shear wall stress profile.
Claims17
A method for determining shear wall stress comprising:
- providing a model of an aerodynamic surface;
- determining a first set of input variables from spatial coordinates of the model;
- disposing the model within a testing system;
- determining a second set of input variables from spatial coordinates of the testing system;
- connecting a plurality of tufts to the model in a manner such that the tufts can flow freely in a direction of an applied flow;
- applying a generally horizontal dynamic flow force to the model thereby creating movement of the plurality of tufts;
- using a camera visualization system to generate input data from the movement of the tufts for use as a third set of input variables;
- applying the first, second, and third sets of input variables to a physics informed neural network to generate a set of output flow variables;
- utilizing the output flow variables to determine velocity profile of the model;
- and
- utilizing the velocity profile as well as a set of pressure outputs of the model to estimate the shear walls stress of the model.
A system for estimating the shear wall stress of an aerodynamic surface comprising:
- a testing apparatus wherein the testing apparatus is configured to generate a dynamic flow force;
- a model disposed within the testing apparatus in such a manner that the model is exposed to the dynamic flow force;
- a plurality of tufts disposed on an outer surface of the model such that the plurality of tufts move in response to exposure to the dynamic flow force;
- at least one camera visualization system configured to capture image data of the movement of the tufts when exposed to the dynamic flow force;
- and
- estimating the shear wall stress of the model using at least the captured image data in a physics-informed neural network.
Dependencies are inferred from the claim text.