Fabrication of a state of the art mesh lock polymer for water based solid free drilling fluid

Polymers are used widely in various kinds of drilling fluid to maintain the proper rheological properties. However, most of them are not available for high-temperature or salt solutions due to poor temperature and salt resistance. To ameliorate the temperature and salt resistance of polymer used in the solid-free water-based drilling fluid, a novel polymer with a kind of "Mesh-Lock" reinforced network cross structure, named PLY-F [main monomer acrylic acid (AA), acrylamide (AM), functional monomers 2-acrylamide-2-methylpropanesulfonic acid (AMPS) N-vinylpyrrolidone (NVP) and C16DMAAC] were prepared through free radical polymerization of an aqueous solution of organic cross-linking agent pentaerythritol triallyl ether (PTE) as a cross-linking system, Potassium persulfate (KPS) and sodium bisulfite as the initiator for the first time. The surface morphology, crosslinking architecture and temperature and salt resistance of the PLY-F were fully characterized with several means including SEM, FT-IR, 13CNMR, dynamic rheology, and long-term thermal stability. The SEM observation indicated that the PLY-F exhibits a regular “Mesh-Lock” reinforced network cross structure. FT-IR, 13CNMR analysis indicated that the characteristic functional groups of each monomer such as AM, AA, AMPS and NVP were all together in the polymer. The results show that the apparent viscosity retention rate of the PLY-F in the potassium formate solution (with a density of 1.3 g/cm3) was more than 80% after heat rolling for 72 h at 200 °C and the plastic viscosity retention rate reached 90.3%. Moreover, the salt resistance of the polymer can reach the density of 1.4 g/cm3 (potassium formate solution) under 200 °C and the temperature resistance can reach 220 °C under the density of 1.3 g/cm3 (potassium formate solution). Besides, the PLY-F still has good rheological properties in other saturated solutions (NaCl, HCOONa) under 210 °C.

Distribution adaptation deep transfer learning method for cross-structure health monitoring using guided waves

Deep learning algorithm can effectively obtain damage information using labeled samples, and has become a promising feature extraction tool for ultrasonic guided wave detection. But it is difficult to apply the monitoring expertise of structure A to structure B in most cases due to the differences in the dispersion and receiving modes of different waveguides. For multi-structure monitoring at the system level, how to transfer a trained structural health monitoring model to another different structure remains a major challenge. In this article, a cross-structure ultrasonic guided wave structural health monitoring method based on distribution adaptation deep transfer learning is proposed to solve the feature generalization problem in different monitoring structures. First, the joint distribution adaptation method is employed to adapt both the marginal distribution and conditional distribution of the guided wave signals from different structures. Second, convolutional long short-term memory network is constructed to learn the mapping relationship from adapted training samples in source domain. Batch normalization layer is implemented to balance the input tensors of each sample to the same distribution. Finally, the multi-sensor damage indexes are utilized to visually present the damage by probability imaging. The experimental results show that proposed method can utilize the single-sensor monitoring data in one structure to implement the multi-sensor damage monitoring in another structure and achieve the damage imaging visualization. The imaging performance is significantly superior to the existing principal component analysis, transfer component analysis, and other state-of-art comparison methods.

Construction and Automation of a Microcontrolled Solar Tracker

A solar tracker can be defined as an electromechanical system capable of following the apparent path of the Sun, in order to orient an array of solar panels and/or collectors directly to the solar rays, maximizing the collected energy. Accordingly, the present work describes the process of building and automating a micro-controlled solar tracker. Two mobile structures were built, one equipped with high-precision step motors and four luminosity sensors separated in quadrants by a cross structure, and the other equipped with DC motors and the 275 Wp solar panel, allowing the design and evaluation of the behavior of each structure separately. The control and automation system is centralized in an Arduino MEGA2560 microcontroller, which runs the tracking and positioning algorithms. The built prototype allows us to carry out studies of solar tracking strategies based on sensor and control systems applied to DC motors.