Engineering topics
Publications and source records attributed to Bukka, Sandeep Reddy.
Distributed fiber optic sensors allow the measurement of structural parameters such as static/dynamic strain, temperature, pressure, and vibrations at thousands of locations along a single fiber cable. Deep neural network (DNN) algorithms were developed for rapid data processing speed and vibration event classification.
Reduced order model for simulation of Guided wave propagation is presented here. The utilization of reduced order models ensures efficient data generation for a variety of parameters where it takes huge computational effort to simulate, crucial for timely monitoring and decision-making. Autoencoder based reduced order models are proposed here, which are trained on simulated data from open-source finite element framework, Firedrake.
Reduced order model for simulation of Guided wave propagation is presented here. The utilization of reduced order models ensures efficient data generation for a variety of parameters where it takes huge computational effort to simulate, crucial for timely monitoring and decision-making. Autoencoder based reduced order models are proposed here, which are trained on simulated data from open-source finite element framework, Firedrake.
This research aims to propose a simple experiment for third party damage classification problem by generating a dataset of third-party damage events on a laboratory scale utilizing single mode-multi mode-single mode (SMS) fiber acoustic sensor. The sound samples representative of various third-party activities, such as vehicle movements, excavation, and digging, were sourced from open-source databases. These samples were then played through a speaker in proximity to an SMS sensor, and the resultant fiber acoustic vibration data were recorded for each event. This process yielded a collection of 200 samples across 13 distinct third-party events. Convolutional Neural Networks (CNNs) were employed to classify these samples into their respective categories, and an accuracy exceeding 97% was obtained from our results.
This research aims to mitigate the challenges of field tests for classification of third-party damages by generating a dataset of third-party damage events on a laboratory scale utilizing single mode-multi mode-single mode (SMS) fiber acoustic sensor. The sound samples representative of various third-party activities, such as vehicle movements, excavation, and digging, were sourced from open-source databases. These samples were then played through a speaker in proximity to an SMS sensor, and the resultant fiber acoustic vibration data were recorded for each event. This process yielded a collection of 200 samples across 13 distinct third-party events. Convolutional Neural Networks (CNNs) were employed to classify these samples into their respective categories, and an accuracy exceeding 97% was obtained from our results.
In this work, the previously demonstrated capability of the optical fiber sensor (OFS) for successful monitoring of humidity has been extended to monitor the humidity, CH4, and CO2 with different gas composition based on the strain produced along the single mode fiber (SMF) sensor. This is enabled by absorption of H2O/gases on to the commercially available polyacrylate coated jacketed portion of the fiber resulting a change in strain. Under equilibrium, a differential microstrain was observed along the jacketed portion of the SMF with N2, CH4, and CO2 at different humidity conditions, while the unjacketed portion of the fiber was used only for sensing pressure/temperature induced strain. In case of N2 at 800 psig pressure, the observed microstrain was approximately 80, 65, 50 and 35 µε at 100, 75.0, 46.8, and 23.4 RH% respectively. Comparatively, a microstrain of approximately 95 µε was observed with 100% RH CH4 which demonstrates that SMF produces a measurable CH4 response alongside water. Similarly, the observed microstrain with CO2 was approximately 105, 90, 85, 80, and 70 µε at 100, 75.0, 46.8, 23.4, and 0 RH% respectively. Linear regression and principal component analysis of these dataset provided deconvolution of the impact of strain from H2O, CH4, and CO2 enabling good cross sensitivity. Additionally, modified OFS comprising of Fe coated fiber section was employed to monitor corrosion, using Fe as corrosion proxy, under harsh corrosive environment.
In this work, the previously demonstrated capability of the optical fiber sensor (OFS) for successful monitoring of humidity has been extended to monitor the humidity, CH4, and CO2 with different gas composition based on the strain produced along the single mode fiber (SMF) sensor. This is enabled by absorption of H2O/gases on to the commercially available polyacrylate coated jacketed portion of the fiber resulting a change in strain. Under equilibrium, a differential microstrain was observed along the jacketed portion of the SMF with N2, CH4, and CO2 at different humidity conditions, while the unjacketed portion of the fiber was used only for sensing pressure/temperature induced strain. In case of N2 at 800 psig pressure, the observed microstrain was ~80, ~65, ~50 and ~35 µε at 100, 75.0, 46.8, and 23.4 RH% respectively. Comparatively, a microstrain of ~95 µε was observed with 100% RH CH4 which demonstrates that SMF produces a measurable CH4 response alongside water. Similarly, the observed microstrain with CO2 was ~105, ~90, ~85, ~ 80, and ~70 µε at 100, 75.0, 46.8, 23.4, and 0 RH% respectively. Linear regression and principal component analysis of these dataset provided deconvolution of the impact of strain from H2O, CH4, and CO2 enabling good cross sensitivity. Additionally, modified OFS comprising of Fe coated fiber section was employed to monitor corrosion based on the increase in backscattered intensity amplitude of the light being passed once corrosion occurs.