Contact performance analysis of pressure controller's sealing interface in deep in-situ pressure-preserved coring system

The sealing performance of contact interfaces plays the most important role in the design and operation of the in-situ pressure-preserved coring system.To meet the demand of ultra-high pressure-retained coring for oil and gas exploration in deep reservoirs,a quantitative analysis of the contact mechanical behavior of the pressure controller was performed.Based on the micro-contact theory of rough surfaces,a three-dimensional numerical model of the rough contact interface between the valve cover and the valve seat was constructed,and the micro-contact behavior of the metal contact surfaces was comprehensively studied.The results show that the actual contact area of the valve interface increases with the increase of surface roughness before the critical contact point,but decreases after that.Compared with the real contact model with double rough surfaces,although the simplified hard-contact model with a single rough surface can reflect the micro-contact behavior of the rough surface to a certain extent,it cannot truly reveal the microchannel morphology between the sealing interfaces under pressure.Therefore,the realistic double-rough-surface model should be recommended to evaluate the sealing performance of coring tools,particularly for high pressure conditions.The material properties of valves have a significant effect on the contact characteristics of rough surfaces,which suggested that the actual contact area decreases with the increase of the elastic modulus of the contact material under the same loading conditions.The knowledge of this work could help to enhance the seal design of pressure controllers for in-situ pressure-preserved coring.

Connecting the Dots in Self-Supervised Learning:A Brief Survey for Beginners

The artificial intelligence(AI)community has recently made tremendous progress in developing self-supervised learning(SSL)algorithms that can learn high-quality data representations from massive amounts of unlabeled data.These methods brought great results even to the fields outside of AI.Due to the joint efforts of researchers in various areas,new SSL methods come out daily.However,such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses.This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work.We see these researches as the"dots"of SSL and connect them through how they evolve.Hopefully,by viewing the connections of these dots,readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing,computer vision,graph learning,audio processing,and protein learning.