Pipeline

Résumé

Un pipeline est un ouvrage destiné à transporter, sous pression et sur de grandes distances, des matières fluides (liquides, gaz, mélanges polyphasiques). C'est un système industriel constitué de tubes et d'accessoires (robinets, vannes, compresseurs…), souvent organisés en réseau. Les produits véhiculés par pipeline sont généralement : le pétrole et autres hydrocarbures liquides, le gaz naturel et autres gaz combustibles, ainsi que certains produits chimiques. Le transit de produits alimentaires (bière, lait, grains…) dans de longues conduites ou , ainsi que l'évacuation gravitaire des effluents (eaux usées, eaux pluviales, tout-à-l'égout…), ou encore l'amenée d'eau dans un ouvrage hydraulique (conduite forcée), peuvent entrer dans une acception élargie de ce terme. Nature des fluides transportés Selon les fluides transportés, les pipelines portent des noms spécifiques et sont assujettis à une règlementation qui leur est propre, tout comme le sont leurs techniques de c

Source officielle

https://fr.wikipedia.org/wiki/Pipeline

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (68)

Publications associées

Chargement

Personnes associées (30)

Personnes associées

Chargement

Unités associées (13)

Unités associées

Chargement

Concepts associés (65)

Concepts associés

Chargement

Cours associés (15)

The students assess and compare all renewable energy resources, their real potentials, limitations and best applications (energy services). Solar thermal, solar electric, wood, bioliquids, biogas, hydropower incl. tidal and wave power, wind, geothermal incl. heat pumps and buildings.

Ce cours traite des divers domaines techniques intervenant dans la conception et la réalisation d'un bâtiment, soit : physique du bâtiment, structures, matériaux, construction et installations techniques; ceci dans une approche globale et transversale.

Présenter aux étudiants: 1 - les notions de base de l'accidentologie industrielle par le biais du traitement de cas concrets (processus chimiques, stockages pétroliers, gazoduc,...) 2 - la mise en oeuvre des méthodologies usuelles d'analyse des dangers et d'évaluation des risques.

Afficher plus

Cours associés

Chargement

Séances de cours associées (18)

Afficher plus

Séances de cours associées

Chargement

Automation of the diagnosis process in the railway system : Detection of defects in concrete sleepers using vision-based machine learning models

Linah Charif

inspectors that walk over the track and check the defects on the rail surface, fasteners and sleepers. In the case of concrete sleepers, rail inspectors classify defects according to their size and occurrence over 20 sleepers. The manual inspection is error prone, it is time consuming and it can be sometimes risky. In an eort to overcome these issues, SBB has developed a project called AISI (Articial Intelligence Streckeninspection) in order to automatize the rail inspection by collecting and analysing images with diagnosis trains equipped with cameras. The objective of this thesis is integrated within the AISI Project, since its goal is the conception of a deep learning model to detect and classify concrete sleepers defects using computer vision and more specically, object detection. The defects studied in this thesis are cracks and akings (or spallings). In the literature, many researchers have tackled the subject of defect detection in concrete, especially in civil infrastructures. The most common methods a combination of image-processing and Machine Learning (image pre-processing and a classier) whereas the latest methods use deep learning (CNN, FCN and RPN, YOLO). The proposed methodology uses a Fast-RCNN model developed with Tensor ow Object Detection API. The evaluation of the model is given on a total of 996 defects and its precision and recall are given. The model shows average results as the number of false positives is high. It has been noted that the labels provided needed to be improved. The proposed approach has a great potential of development since a large amount of training data can be added thanks to the produced relabelling process. Also, the designed pipeline establishes a base that can be adjustable for various algorithms and congurations.

2021

Chargement

Dynamic modeling and simulation of a pipeline used for transportation of a fluid near the saturation

Zeli Zhang

A new district energy network has been developed by using CO2 as a heat transfer fluid. Only two pipes are required by the network, thus allowing the recovery of waste heat from the cooling users to be valorized by the heating users. Understanding and predicting the dynamics as well as identifying control methods and strategies is one of the key issues for the development of such a CO2 network that would operate in a safe, reliable and energy efficient manner. To assess the technical feasibility and address the safety problems, a physical-based model was developed to describe the fluid dynamics of CO2 pipelines used for transportation of a fluid near the saturation. Dynamic simulations were carried out to investigate pressure and flow fluctuation during transient flow and under changes of external perturbations. It shows that the system was able to response all these perturbations although the corresponding pressure wave propagates along the pipe.

2013

Chargement

, ,

Live imaging of organoid growth remains a challenge: it requires long-term imaging of several samples simultaneously and dedicated analysis pipelines. Here the authors report an experimental and image processing framework to turn long-term light-sheet imaging of intestinal organoids into digital organoids. Organoids provide an accessible in vitro system to mimic the dynamics of tissue regeneration and development. However, long-term live-imaging of organoids remains challenging. Here we present an experimental and image-processing framework capable of turning long-term light-sheet imaging of intestinal organoids into digital organoids. The framework combines specific imaging optimization combined with data processing via deep learning techniques to segment single organoids, their lumen, cells and nuclei in 3D over long periods of time. By linking lineage trees with corresponding 3D segmentation meshes for each organoid, the extracted information is visualized using a web-based "Digital Organoid Viewer" tool allowing combined understanding of the multivariate and multiscale data. We also show backtracking of cells of interest, providing detailed information about their history within entire organoid contexts. Furthermore, we show cytokinesis failure of regenerative cells and that these cells never reside in the intestinal crypt, hinting at a tissue scale control on cellular fidelity.

NATURE PORTFOLIO2022

Chargement