James Schrodt

Title: The Innovative Contributions of James Schrodt

Introduction

James Schrodt is a notable inventor based in San Ramon, CA (US). He has made significant contributions to the field of fluid characterization through his innovative patent. His work focuses on enhancing the measurement systems used for determining steam quality in multiphase fluids.

Latest Patents

James Schrodt holds a patent for a system titled "Fluid characterization using acoustics." This invention describes a measurement system and method for determining steam quality, specifically vapor mass fraction measurements of multiphase fluid flowing through pipes. The system utilizes an acoustic sensor device that includes an acoustic transmitter and an acoustic receiver, both designed to be attached to a pipe. These components are exposed to the interior space of the pipe through openings in its wall. The acoustic waves generated by the transmitter and captured by the receiver traverse the multiphase fluid flowing into the pipe. The technique of swept-frequency acoustic interferometry (SFAI) is employed to measure the ultrasonic acoustic properties of the fluid. Additionally, machine-learning techniques, including principal component analysis, support vector machine regression, and support vector machine classification, are utilized to determine steam quality.

Career Highlights

Throughout his career, James has worked with prominent companies such as Triad National Security, LLC and Chevron U.S.A. Inc. His experience in these organizations has contributed to his expertise in fluid characterization and measurement systems.

Collaborations

James has collaborated with talented individuals in his field, including Alessandro Cattaneo and Dipen N Sinha. These collaborations have further enriched his work and innovations.

Conclusion

James Schrodt's contributions to fluid characterization through his innovative patent demonstrate his commitment to advancing measurement technologies. His work not only enhances the understanding of multiphase fluids but also showcases the potential of integrating machine learning with acoustic measurement techniques.