Modelling the effectiveness of oil lubrication in reducing both friction and wear in a fretting contact

Lubrication is often employed in fretting contacts to reduce wear and stresses associated with high friction. Owing to the very small displacements associated with fretting, penetration of lubricating oils into the contact may not be effective. The efficacy of the penetration of the lubricant into the contact is very difficult to observe experimentally, and accordingly, this paper presents a numerical simulation of a lubricated fretting contact using a Coupled–Eulerian–Lagrangian (CEL) finite element method. Meso-scale CEL finite element models are developed to simulate the cylinder–on–flat arrangement used experimentally at the University of Nottingham in which the roughness of contact surfaces is characterized as fractal geometry by the Weierstrass-Mandelbrot (W-M) function. The fluid–solid and solid–solid contact in the lubricated fretting contact are simulated, and from these, wear and friction coefficients are determined. The effects of contact geometry on lubricated fretting contacts and lubricant on fretting wear are modelled and compared with experimental observations. Results indicate that oil lubrication reduces fretting wear and friction effectively in the less–conforming contacts but has little effect in the more–conforming contacts

Runoff regulation and nitrogen and phosphorus removal performance of a bioretention substrate with HDTMA-modified zeolite

As a commonly used material in bioretention substrates, natural zeolite (NZ) provides decent adsorption capacity for cation pollutants and heavy metals, but limited ability to remove anion pollutants. Hexadecyltrimethylammonium bromide (HDTMA)-modified zeolite (MZ) was used as the bioretention substrate material. The performance of the media including runoff reduction, nitrate nitrogen (NO3−-N) removal, ammonium nitrogen (NH4+-N) removal, and total phosphorus (TP) removal was assessed by the column experiment. The effects of different levels of modification, ratio of zeolite in the substrate, and rainfall intensity on media performance were investigated. The results indicate that HDTMA-modified zeolite significantly improves the NO3−-N (up to 38.2 times of NZ) and TP (up to17.5 times of NZ) removal rate of media and slightly increases the NH4+-N (up to 1.5 times of NZ) purification performance of the substrate. Compared with the media with NZ, decline on both runoff volume reduction (maximum decline up to 32.9%) and flow rate reduction (maximum decline up to 29.9%) of the media with MZ were observed. Based on multiple regression analysis, quantitative relationship models between influencing factors and response variables were established (R2 > 0.793), the level of the effect of influencing factors on response variables was investigated, and the interactions between influencing factors were explored. The main effect analysis found that the degree of modification affects NO3−-N and TP removal rate of the substrate the most, and when the amount of HDTMA molecules loaded on the zeolite surface exceeds 0.09meq/g, the modification can no longer improve NO3−-N removal efficiency

Supervised Off-Policy Ranking

Off-policy evaluation (OPE) leverages data generated by other policies to evaluate a target policy. Previous OPE methods mainly focus on precisely estimating the true performance of a policy. We observe that in many applications, (1) the end goal of OPE is to compare two or multiple candidate policies and choose a good one, which is actually a much simpler task than evaluating their true performance; and (2) there are usually multiple policies that have been deployed in real-world systems and thus whose true performance is known through serving real users. Inspired by the two observations, in this work, we define a new problem, supervised off-policy ranking (SOPR), which aims to rank a set of new/target policies based on supervised learning by leveraging off-policy data and policies with known performance. We further propose a method for supervised off-policy ranking that learns a policy scoring model by correctly ranking training policies with known performance rather than estimating their precise performance. Our method leverages logged states and policies to learn a Transformer based model that maps offline interaction data including logged states and the actions taken by a target policy on these states to a score. Experiments on different games, datasets, training policy sets, and test policy sets show that our method outperforms strong baseline OPE methods in terms of both rank correlation and performance gap between the truly best and the best of the ranked top three policies. Furthermore, our method is more stable than baseline methods

Utilisation d'un Moteur SMT pour générer des Automates Symboliques - Version étendue

Open pNets are used to model the behaviour of open systems, both synchronousor asynchronous, expressed in various calculi or languages. They are endowed with a symbolicoperational semantics in terms of so-called “Open Automata”. This allows us to check properties ofsuch systems in a compositional manner. We implement an algorithm computing these semantics,building predicates expressing the synchronization conditions between the events of the pNet subsystems.Checking such predicates requires symbolic reasoning over first order logics, but alsoover application-specific data. We use the Z3 SMT engine to check satisfiability of the predicates,and prune the open automaton of its unsatisfiable transitions. As an industrial oriented use-case,we use so-called "architectures" for BIP systems, that have been used in the framework of anESA project and to specify the control software of a nanosatellite at the EPFL Space EngineeringCenter. We use pNets to encode a BIP architecture extended with explicit data, and compute itsopen automaton semantics. This automaton may be used to prove behavioural properties; we give2 examples, a safety and a liveness property.Les pNets ouverts sont utilisés pour modéliser le comportement des systèmes ouverts,synchrones ou asynchrones, exprimée dans divers calculs ou langages de programmation. Ils sontdotés d’une sémantique opérationnelle symbolique en termes d’«Automata Ouverts». Cela nouspermet de vérifier les propriétés de ces systèmes d’une manière compositionnelle. Nous avonsimplémenté un algorithme calculant ces sémantiques, en construisant des prédicats exprimant lesconditions de synchronisation entre les actions des composants du pNet. La vérification de telsprédicats nécessite un raisonnement symbolique sur les logiques de premier ordre, mais égalementsur des données spécifiques à l’application. Nous utilisons le moteur SMT Z3 pour vérifier lasatisfiabilité des prédicats, et ne conserver dans l’automate ouvert que les transitions satisfiables.Nous illustrons notre approche par un exemple d’inspiration industrielle. Pour cela nouspartons d’«architectures» de systèmes BIP, qui ont été utilisés dans le cadre d’un projet del’Agence Spatiale Européenne pour spécifier le logiciel de contrôle d’un nanosatellite au Centred’ingénierie spatiale de l’EPFL. Nous utilisons les pNets pour encoder une architecture BIPétendu avec des données explicites, et calculer sa sémantique en termes d’automates ouverts.Cet automate peut être utilisé pour prouver des propriétés comportementales; nous donnons 2exemples, une propriete de sureté et une de vivacité

Using SMT engine to generate Symbolic Automata

International audienceOpen pNets are used to model the behaviour of open systems , both synchronous or asynchronous, expressed in various calculi or languages. They are endowed with a symbolic operational semantics in terms of so-called "Open Automata". This allows us to check properties of such systems in a compositional manner. We implement an algorithm computing these semantics, building predicates expressing the synchronization conditions between the events of the pNet subsystems. Checking such predicates requires symbolic reasoning over first order logics, but also over application-specific data. We use the Z3 SMT engine to check satisfiability of the predicates, and prune the open automaton of its unsatisfiable transitions. As an industrial oriented use-case, we use so-called "architectures" for BIP systems, that have been used in the framework of an ESA project and to specify the control software of a nanosatellite at the EPFL Space Engineering Center. We use pNets to encode a BIP architecture extended with explicit data, and compute its open automaton semantics. This automaton may be used to prove be-havioural properties; we give 2 examples, a safety and a liveness property

Real-time Short Video Recommendation on Mobile Devices

Short video applications have attracted billions of users in recent years, fulfilling their various needs with diverse content. Users usually watch short videos on many topics on mobile devices in a short period of time, and give explicit or implicit feedback very quickly to the short videos they watch. The recommender system needs to perceive users' preferences in real-time in order to satisfy their changing interests. Traditionally, recommender systems deployed at server side return a ranked list of videos for each request from client. Thus it cannot adjust the recommendation results according to the user's real-time feedback before the next request. Due to client-server transmitting latency, it is also unable to make immediate use of users' real-time feedback. However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate. In this paper, we propose to deploy a short video recommendation framework on mobile devices to solve these problems. Specifically, we design and deploy a tiny on-device ranking model to enable real-time re-ranking of server-side recommendation results. We improve its prediction accuracy by exploiting users' real-time feedback of watched videos and client-specific real-time features. With more accurate predictions, we further consider interactions among candidate videos, and propose a context-aware re-ranking method based on adaptive beam search. The framework has been deployed on Kuaishou, a billion-user scale short video application, and improved effective view, like and follow by 1.28%, 8.22% and 13.6% respectively.Comment: Accepted by CIKM 2022, 10 page

Application and design of a new quick-opening seal device connected by D-shape shearing bolts in hypersonic wind tunnel

Paper presented at the 5th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 1-4 July, 2007.Application and design of a new quick-opening seal device connected by D-shape shearing bolts in hypersonic wind tunnel is introduced in this paper. This device is compact in structure, reliable in sealing, easy in assembly and disassembly, appropriate for end closure of pressure vessels or joints of pipes. Mechanical models are established for all major components, and strength calculation formulas are obtained which can be used for the design of the structure.cs201

Vibration-based gearbox fault diagnosis using deep neural networks

Vibration-based analysis is the most commonly used technique to monitor the condition of gearboxes. Accurate classification of these vibration signals collected from gearbox is helpful for the gearbox fault diagnosis. In recent years, deep neural networks are becoming a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. In this paper, a study of deep neural networks for fault diagnosis in gearbox is presented. Four classic deep neural networks (Auto-encoders, Restricted Boltzmann Machines, Deep Boltzmann Machines and Deep Belief Networks) are employed as the classifier to classify and identify the fault conditions of gearbox. To sufficiently validate the deep neural networks diagnosis system is highly effective and reliable, herein three types of data sets based on the health condition of two rotating mechanical systems are prepared and tested. Each signal obtained includes the information of several basic gear or bearing faults. Totally 62 data sets are used to test and train the proposed gearbox diagnosis systems. Corresponding to each vibration signal, 256 features from both time and frequency domain are selected as input parameters for deep neural networks. The accuracy achieved indicates that the presented deep neural networks are highly reliable and effective in fault diagnosis of gearbox