# US 2021/0278330 A1 · Quantification of Shear Stress Field Using Tuft Visualization and Deep Learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0278330 A1 - Document ID: US20210278330A1 - Status: Published application - Kind: A1 - Application number: 17/195,228 - Filed: 2021-03-08 - Published: 2021-09-09 - Earliest claimed: 2020-03-06 - Assignees: California Institute of Technology - Applicants: California Institute of Technology - Inventors: Morteza Gharib; Emile K. Oshima; Pablo Hermoso Moreno - Web page: https://patentgrep.com/patent/US20210278330A1 ## CPC - G01N 3/12: Pressure testing - G01M 9/08: Aerodynamic models - G01N 3/068: {with optical indicating or recording means} - G06F 30/15: Vehicle, aircraft or watercraft design - G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model - G06F 30/28: using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD] - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/0499: Feedforward networks - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - B64C 3/14: Aerofoil profile - G01N 2203/0246: Special simulation of "in situ" conditions, scale models or dummies - G01N 2203/0641: using optical, X-ray, ultraviolet, infrared or similar detectors - G06F 2113/28: Fuselage, exterior or interior - G06F 2119/14: Force analysis or force optimisation, e.g. static or dynamic forces - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - B64C: AEROPLANES; HELICOPTERS ## 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. ## Claims ### Claim 1 A method for determining shear wall stress comprising: providing a model of an aerodynamic surface; determining the spatial coordinates of the model for use as a first set of input variables; disposing the model within a testing system; determining the spatial coordinates of the testing system for use as a second set of input variables; 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. ### Claim 2 (depends on 1) The method of claim 1, further comprising: performing automatic differentiation on the set of output flow variables to generate a set of residual data wherein the set of residual data is used to generate a loss function and wherein the loss function is used to augment the sets of input variables to improve the output flow variables. ### Claim 3 (depends on 1) The method of claim 1, further comprising: disposing a plurality of pressure taps on the model wherein each of the plurality of pressure taps can generate pressure data with respect to the dynamic flow force and wherein the pressure data is used as a fourth set of input variables; and wherein the fourth set of input variable is utilized to generate the output flow variables. ### Claim 4 (depends on 1) The method of claim 1, wherein the model is an airfoil. ### Claim 5 (depends on 1) The method of claim 1, wherein the model is a scaled model of an aircraft. ### Claim 6 (depends on 1) The method of claim 1, wherein the model is disposed at an angle with respect to the horizontal direction of the dynamic flow force. ### Claim 7 (depends on 1) The method of claim 1, further comprising a plurality of pressure taps disposed within the testing system, wherein the pressure taps are used to generate and provide pressure data with respect to the dynamic flow force within the testing system. ### Claim 8 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 can be moved by the 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 wherein the image data can be used in a physics-informed neural network to estimate the shear wall stress of the model. ### Claim 9 (depends on 8) The system of claim 8, wherein the plurality of tufts and image data can be used to generate a first set of input variables that can be used to determine the wall shear stress of the model. ### Claim 10 (depends on 9) The system of claim 9, wherein the testing apparatus comprises a plurality of spatial coordinates that can be used as a second set of input variables, wherein the first and second set of input variables can be applied to the physics-informed neural network to estimate the wall shear stress of the model. ### Claim 11 (depends on 9) The system of claim 9 further comprising a plurality of pressure taps disposed on the model wherein each of the plurality of pressure taps can generate pressure data with respect to the dynamic flow force and wherein the pressure data is used as a third set of input variables. ### Claim 12 (depends on 8) The system of claim 8, wherein the model is an airfoil. ### Claim 13 (depends on 8) The system of claim 8, wherein the model is a scaled version of an aircraft. ### Claim 14 (depends on 8) The system of claim 8, wherein the model is positioned at an angle with respect to the horizontal flow of the dynamic air flow. ### Claim 15 (depends on 8) The system of claim 8, wherein the apparatus is a wind tunnel. ### Claim 16 (depends on 14) The system of claim 14 wherein the angle is at least 2 degrees off of horizontal. ### Claim 17 (depends on 14) The system of claim 14, wherein the angle does not exceed 12 degrees off of horizontal.