Silicon MEMS Thermocatalytic Gas Sensor in Miniature Surface Mounted Device Form

A reduced size thermocatalytic gas sensor was developed for the detection of methane over the 20% of the explosive concentration. The sensor chip is formed from two membranes with a 150 µm diameter heated area in their centers and covered with highly dispersed nano-sized catalyst and inert reference, respectively. The power dissipation of the chip is well below 70 mW at the 530 °C maximum operation temperature. The chip is mounted in a novel surface mounted metal-ceramic sensor package in the form-factor of SOT-89. The sensitivity of the device is 10 mV/v%, whereas the response and recovery times without the additional carbon filter over the chip are <500 ms and <2 s, respectively. The tests have shown the reliability of the new design concerning the hotplate stability and massive encapsulation, but the high degradation rate of the catalyst coupled with its modest chemical power limits the use of the sensor only in pulsed mode of operation. The optimized pulsed mode reduces the average power consumption below 2 mW.

Ridon Vehicle: Drive-by-Wire System for Scaled Vehicle Platform and Its Application on Behavior Cloning

For autonomous driving research, using a scaled vehicle platform is a viable alternative compared to a full-scale vehicle. However, using embedded solutions such as small robotic platforms with differential driving or radio-controlled (RC) car-based platforms can be limiting on, for example, sensor package restrictions or computing challenges. Furthermore, for a given controller, specialized expertise and abilities are necessary. To address such problems, this paper proposes a feasible solution, the Ridon vehicle, which is a spacious ride-on automobile with high-driving electric power and a custom-designed drive-by-wire system powered by a full-scale machine-learning-ready computer. The major objective of this paper is to provide a thorough and appropriate method for constructing a cost-effective platform with a drive-by-wire system and sensor packages so that machine-learning-based algorithms can be tested and deployed on a scaled vehicle. The proposed platform employs a modular and hierarchical software architecture, with microcontroller programs handling the low-level motor controls and a graphics processing unit (GPU)-powered laptop computer processing the higher and more sophisticated algorithms. The Ridon vehicle platform is validated by employing it in a deep-learning-based behavioral cloning study. The suggested platform’s affordability and adaptability would benefit broader research and the education community.

Development and Testing of a UAV Laser Scanner and Multispectral Camera System for Eco-Geomorphic Applications

While Uncrewed Aerial Vehicle (UAV) systems and camera sensors are routinely deployed in conjunction with Structure from Motion (SfM) techniques to derive 3D models of fluvial systems, in the presence of vegetation these techniques are subject to large errors. This is because of the high structural complexity of vegetation and inability of processing techniques to identify bare earth points in vegetated areas. Furthermore, for eco-geomorphic applications where characterization of the vegetation is an important aim when collecting fluvial survey data, the issues are compounded, and an alternative survey method is required. Laser Scanning techniques have been shown to be a suitable technique for discretizing both bare earth and vegetation, owing to the high spatial density of collected data and the ability of some systems to deliver dual (e.g., first and last) returns. Herein we detail the development and testing of a UAV mounted LiDAR and Multispectral camera system and processing workflow, with application to a specific river field location and reference to eco-hydraulic research generally. We show that the system and data processing workflow has the ability to detect bare earth, vegetation structure and NDVI type outputs which are superior to SfM outputs alone, and which are shown to be more accurate and repeatable, with a level of detection of under 0.1 m. These characteristics of the developed sensor package and workflows offer great potential for future eco-geomorphic research.

Drone-Based Vibration Monitoring and Assessment of Structures

This paper presents a novel method of procuring and processing data for the assessment of civil structures via vibration monitoring. This includes the development of a custom sensor package designed to minimize the size/weight while being fully self-sufficient (i.e., not relying on external power). The developed package is delivered to the structure utilizing a customized Unmanned Aircraft System (UAS), otherwise known as a drone. The sensor package features an electropermanent magnet for securing it to the civil structure while a second magnet is used to secure the package to the drone during flight. The novel B-Spline Impulse Response Function (BIRF) technique was utilized to extract the Dynamic Signature Response (DSR) from the data collected by the sensor package. Experimental results are presented to validate this method and show the feasibility of deploying the sensor package on structures and collecting data valuable for Structural Health Monitoring (SHM) data processing. The advantages and limitations of the proposed techniques are discussed, and recommendations for further developments are made.

EM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML

In the wake of today's industrial revolution, many advanced technologies and techniques have been developed to address the complex challenges in well integrity evaluation. One of the most prominent innovations is the integration of physics-based data science for robust downhole measurements. This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML), tested and validated on the cross-sections of real metal casings/tubing with defects of various sizes, locations, and spacing. Unlike existing electromagnetism-based inspection tools, where only circumferential average metal thickness is measured, this research investigates the artificial intelligence (AI)-assisted interpretation of a unique arrangement of electromagnetic (EM) sensors. This facilitates the development of a novel solution for through-tubing corrosion imaging that enhances defect detection with pixel-level accuracy. The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver and an artificial neural network (ANN), namely the long short-term memory recurrent neural network (LSTM-RNN). The ANN is trained using the results generated from the FDTD solver, which simulates sensor readings for different scenarios of defects. The integration of the array EM-sensor responses and an ANN enabled generalizable and accurate measurements of metal loss percentage across various experimental defects. It also enabled the precise predictions of the defects’ aperture sizes, numbers, and locations in 360-degree coverage. Results were plotted in customized 2D heat-maps for any desired cross-section of the test casings. Further analysis of different techniques demonstrated that the LSTM-RNN could show higher precision and robustness compared to regular dense NNs, especially in the case of multiple defects. The LSTM-RNN is validated using additional data from simulated and experimental data. The results show reliable predictions even with limited training data. The model accurately predicted defects of larger and smaller sizes that were intentionally excluded from the training data to demonstrate generalizability. This highlights a major advance toward corrosion imaging behind tubing. This novel technique paves the way for the use of similar concepts on other sensors in multiple barriers imaging. Further work includes improvement to the sensor package and ANNs by adding a third dimension to the imaging capabilities to produce 3D images of defects on casings.

Setting a New Standard: PDC Bits Equipped with Compact Vibration Recorders Monitor Entire Run and Reveal Stick-Slip Mitigation System Dysfunction and Downhole Motor Under Performance

Downhole vibration measurements are used real-time and post-run to monitor drilling dynamics. Real-time monitoring tools are applied to facilitate immediate corrective actions but their deployment adds operational constraints and costs. This paper describes a new high-capability vibration recorder embedded in the drill bit as a standard component. The analysis of two case studies in the Middle East shows how memory devices available at a reduced cost and on every run are a valuable option for many appraisal or development wells.Developing a fleet of reliable downhole recording tools typically takes years and involves teams of experts in various fields. The paper describes the strategy followed by a drill bit manufacturer to develop and deploy a compact, high capability and cost-effective vibration recorder to provide continuous readings of accelerations, rotation speed (RPM) and temperature at 100Hz and over 250 hours. Sensors and batteries have been packaged to fit into the drill bit shank or elsewhere in the bottom hole assembly (BHA). The recording starts automatically and thus removes the need for onsite personnel. The paper also presents proprietary data analytics software used to retrieve, process and synchronize the recorded data with other available data (mud logs, Measurement/Logging While Drilling logs) and to present critical drilling events.In the first application, the 8 ½-in. bit drilled a 20,000 ft horizontal drain. More than 250 hr of data were recorded showing intense levels of stick-slip. During the entire run, the drilling team deployed several strategies to mitigate stick-slip, including the use of two surface-based stick-slip mitigation systems. The analysis shows that these systems are sometimes unsuccessful in mitigating stick-slip and are difficult to calibrate. It is demonstrated how the vibration recorder may contribute to fine tuning these mitigation efforts through optimization of their settings. In the second application, the vibration recorder was mounted on a 12 1/4-in. bit used to drill 5,000 ft through cement and formation. The analysis shows the motor was subjected to erratic RPM cycles, leading to frequent stalls and acceleration peaks during the run. It is shown how motor performance then decreased consistently during the last hundreds of feet of the section and how this affected rate of penetration (ROP).Deployment of a vibration recorder over the entire drill bit manufacturer's fleet allows continuous monitoring of critical drilling issues and malfunctions related to a variety of drilling equipment that enables the operator to improve drilling performance. The bit-sensor package makes high frequency data systematically available at a reduced cost for every drilling application.

Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package

Ningbo is a major coastal city in the Yangtze River Delta region, China, with the largest cargo capacity in the world. We conducted a field campaign in Ningbo to measure the impact of the COVID-19 lockdown on air pollutants including NO2, O3 and CO from 21 January to 23 March 2020, using a home-made low-cost sensor package. The average concentrations of NO2, O3 and CO were observed to be 7.2, 37.5 and 648.5 ppb, respectively, during the lockdown. Compared with the previous year, the concentrations of NO2 and CO decreased by 63.1% and 6.9%, while the concentration of O3 increased by 37.9%. The significant reduction of NO2 concentration may be attributed to the reduced emissions of freighters and heavy trucks with lower port cargo throughput, which led to a decrease of NO concentration. The increase of O3 concentration was probably due to the lower titration of O3 by NO. After the lockdown, the concentrations of O3 and NO2 increased by 15.5% and 143.1%, respectively, compared with those during the lockdown. The temporal variations of the concentrations of NO2, O3 and CO measured by the sensor package were coincident with those obtained by the reference apparatus, which proves the sensor package to be suitable for air quality monitoring in field campaigns. This is the first time that a dramatic decrease in NO2 concentration in a coastal city due to a lockdown has been reported.

Assessing Raspberry Shake and Boom Sensors for Recording African Elephant Acoustic Vocalizations

Acoustic sensors are increasingly being used in ecological and conservation research, but the choice of sensor can be fraught with trade-offs. In this work we assess the performance of the Raspberry Shake and Boom (RS&amp;B) sensor package for detecting and monitoring African elephants (Loxodonta africana). This is the first documented test of this particular unit for recording animal behavior; the unit was originally designed for detecting tectonic earthquakes and low frequency (&lt;50 Hz) atmospheric acoustics. During a four day deployment in South Africa we tested five RS&amp;B units for recording acoustic and seismic vocalizations generated by a group of African elephants. Our results highlight a varied degree of success in detecting the signals of interest. The acoustic microphone recorded fundamental frequencies of low-frequency (&lt;50 Hz) harmonic vocalizations that were not clearly recorded by more sensitive instruments, but was not able to record higher frequency harmonics due to the low sampling rate (100 Hz). The geophone was not able to consistently record clear seismic waves generated by vocalizations but was able to record higher harmonics. In addition, seismic signals were detected from footsteps of elephants at &lt;50 m distance. We conclude that the RS&amp;B unit currently shows limited potential as a monitoring tool for African elephants and we propose several future directions and deployment strategies to improve the sensitivity of the sensor package.