NASA SBIR 2019-II Solicitation

Proposal Summary

 19-2- Z11.01-2784
 NDE Sensors, Modeling, and Analysis
 Generative Adversarial Networks for Real-Time Realistic Physics Simulations
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Lynntech, Inc.
2501 Earl Rudder Freeway South
College Station, TX 77845
(979) 764-2200

PRINCIPAL INVESTIGATOR (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Christian Bruccoleri PhD
2501 Earl Rudder Freeway South
College Station, TX 77845 - 6023
(979) 764-2200

BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Darla Hisaw
2501 Earl Rudder Freeway South
College Station, TX 77845 - 6023
(979) 764-2219

Estimated Technology Readiness Level (TRL) :
Begin: 3
End: 5
Technical Abstract (Limit 2000 characters, approximately 200 words)

NASA is seeking to develop real-time realistic nondestructive evaluation (NDE) and structural health monitoring (SHM) physics-based simulations and automated data reduction/analysis methods for large datasets. We propose the combination of a neural network approach with a traditional finite element simulation to generate realistic thermal-based NDE methods for precise determination of structural defects such as cracks, delaminations, and ageing. The proposed approach will allow simulating the structural behavior of complex structures and different types of materials, including any metal alloy and composites. Although the method will be first developed to simulate thermal-based measurements such as thermography, flash thermography, and vibrothermography, the framework could be expanded to other domains including, ultrasonic, microwave, Terahertz, and X-ray. The proposed method has the potential to reduce simulation time by 2 orders of magnitude and an increase the compression rate by 2 orders of magnitude also. Due to the machine learning approach of the method, the accuracy and reliability will increase overtime as the number of validated experimental data increases.

Potential NASA Applications (Limit 1500 characters, approximately 150 words)

The method will improve the quantitative data interpretation and understanding of large amounts of NDE/SHM data that will lead to safer, more robust, and more enduring structures operating in space. Performance prediction and defect characterization will also be greatly improved, leading to more efficient and timely maintenance operations and scheduling, which will also reduce costs. Application of this technology is envisioned in the very near future, such as inspection of fuel tanks, reusable rockets, and reentry vehicles.

Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words)

The Real-time realistic simulations capability of this technology allows integration within existing software as a plugin in popular computational packages such as COMSOL. The method could also be implemented in existing commercially available NDE setups (flash thermography and vibrothermography) to provide robust extraction of defect features in virtually any type of experimental setup.

Duration: 24

Form Generated on 05/04/2020 06:24:08