5 research outputs found

    Quasi real-time model for security of water distribution network

    No full text
    Le but de cette thĂšse est de modĂ©liser la propagation d’un contaminant au sein d’un rĂ©seau d’eau potable muni de capteurs temps rĂ©el. Elle comporte les trois axes de dĂ©veloppement suivant: la rĂ©solution des Ă©quations de transport, celle du problĂšme d’identification des sources de contamination et le placement des capteurs.Le transport d’un produit chimique est modĂ©lisĂ© dans un rĂ©seau d’eau potable par l’équation de transport rĂ©action 1-D avec l’hypothĂšse de mĂ©lange parfait aux noeuds. Il est proposĂ© d’amĂ©liorer sa prĂ©diction par l’ajout d’un modĂšle de mĂ©lange imparfait aux jonctions double T et d’un modĂšle de dispersion prenant en compte un profil de vitesse 3-D et la diffusion radiale. Le premier modĂšle est crĂ©Ă© Ă  l’aide d’un plan d’expĂ©riences avec triangulation de Delaunay, de simulations CFD, et de la mĂ©thode d’interpolation krigeage. Le second utilise les Ă©quations adjointes du problĂšme de transport avec l’ajout de particules Ă©voluant Ă  l’aide d’une marche alĂ©atoire, cette derniĂšre modĂ©lisant la diffusion radiale dans la surface droite du tuyau.Le problĂšme d’identification des sources consiste, Ă  l’aide de rĂ©ponses positives ou nĂ©gatives Ă  la contamination des noeuds capteurs, Ă  trouver l’origine, le temps d’injection et la durĂ©e de la contamination. La rĂ©solution de ce problĂšme inverse est faite par la rĂ©solution des Ă©quations de transport adjointes par formulation backtracking. La mĂ©thode donne la liste des sources potentielles ainsi que le classement de celles-ci selon leur probabilitĂ© d’ĂȘtre la vraie source de contamination. Elle s’exprime en fonction de combien, en pourcentage, cette source potentielle peut expliquer les rĂ©ponses positives aux capteurs.Le placement des capteurs est optimisĂ© pour l’identification des sources. L’objectif est la maximisation du potentiel de dĂ©tection de la vĂ©ritable source de contamination. Deux rĂ©solutions sont testĂ©es. La premiĂšre utilise un algorithme glouton combinĂ© Ă  une mĂ©thode de Monte Carlo.La seconde utilise une mĂ©thode de recherche locale sur graphe.Finalement les mĂ©thodes sont appliquĂ©es Ă  un cas test rĂ©el avec dans l’ordre : le placement des capteurs, l’identification de la source de contamination et l’estimation de sa propagation.The aim of this thesis is to model the propagation of a contaminant inside a water distribution network equipped with real time sensors. There are three research directions: the solving of the transport equations, the source identification and the sensor placement. Classical model for transport of a chemical product in a water distribution network isusing 1D-advection-reaction equations with the hypothesis of perfect mixing at junctions. It isproposed to improve the predictions by adding a model of imperfect mixing at double T-junctions and by considering dispersion effect in pipes which takes into account a 3-D velocity profile. The first enhancement is created with the help of a design of experiment based on the Delaunay triangulation, CFD simulations and the interpolation method Kriging. The second one uses the adjoint formulation of the transport equations applied with an algorithm of particle backtracking and a random walk, which models the radial diffusion in the cross-section of a pipe.The source identification problem consists in finding the contamination origin, itsinjection time and its duration from positive and negative responses given by the sensors. The solution to this inverse problem is computed by solving the adjoint transport equations with a backtracking formulation. The method gives a list of potential sources and the ranking of thosemore likely to be the real sources of contamination. It is function of how much, in percentage, they can explain the positive responses of the sensors.The sensor placement is chosen in order to maximize the ranking of the real source of contamination among the potential sources. Two solutions are proposed. The first one uses agreedy algorithm combined with a Monte Carlo method. The second one uses a local search method on graphs. Finally the methods are applied to a real test case in the following order: the sensor placement, the source identification and the estimation of the contamination propagation

    ModĂšle quasi-temps rĂ©el pour la sĂ©curitĂ© des rĂ©seaux d’alimentation en eau potable

    No full text
    The aim of this thesis is to model the propagation of a contaminant inside a water distribution network equipped with real time sensors. There are three research directions: the solving of the transport equations, the source identification and the sensor placement. Classical model for transport of a chemical product in a water distribution network isusing 1D-advection-reaction equations with the hypothesis of perfect mixing at junctions. It isproposed to improve the predictions by adding a model of imperfect mixing at double T-junctions and by considering dispersion effect in pipes which takes into account a 3-D velocity profile. The first enhancement is created with the help of a design of experiment based on the Delaunay triangulation, CFD simulations and the interpolation method Kriging. The second one uses the adjoint formulation of the transport equations applied with an algorithm of particle backtracking and a random walk, which models the radial diffusion in the cross-section of a pipe.The source identification problem consists in finding the contamination origin, itsinjection time and its duration from positive and negative responses given by the sensors. The solution to this inverse problem is computed by solving the adjoint transport equations with a backtracking formulation. The method gives a list of potential sources and the ranking of thosemore likely to be the real sources of contamination. It is function of how much, in percentage, they can explain the positive responses of the sensors.The sensor placement is chosen in order to maximize the ranking of the real source of contamination among the potential sources. Two solutions are proposed. The first one uses agreedy algorithm combined with a Monte Carlo method. The second one uses a local search method on graphs. Finally the methods are applied to a real test case in the following order: the sensor placement, the source identification and the estimation of the contamination propagation.Le but de cette thĂšse est de modĂ©liser la propagation d’un contaminant au sein d’un rĂ©seau d’eau potable muni de capteurs temps rĂ©el. Elle comporte les trois axes de dĂ©veloppement suivant: la rĂ©solution des Ă©quations de transport, celle du problĂšme d’identification des sources de contamination et le placement des capteurs.Le transport d’un produit chimique est modĂ©lisĂ© dans un rĂ©seau d’eau potable par l’équation de transport rĂ©action 1-D avec l’hypothĂšse de mĂ©lange parfait aux noeuds. Il est proposĂ© d’amĂ©liorer sa prĂ©diction par l’ajout d’un modĂšle de mĂ©lange imparfait aux jonctions double T et d’un modĂšle de dispersion prenant en compte un profil de vitesse 3-D et la diffusion radiale. Le premier modĂšle est crĂ©Ă© Ă  l’aide d’un plan d’expĂ©riences avec triangulation de Delaunay, de simulations CFD, et de la mĂ©thode d’interpolation krigeage. Le second utilise les Ă©quations adjointes du problĂšme de transport avec l’ajout de particules Ă©voluant Ă  l’aide d’une marche alĂ©atoire, cette derniĂšre modĂ©lisant la diffusion radiale dans la surface droite du tuyau.Le problĂšme d’identification des sources consiste, Ă  l’aide de rĂ©ponses positives ou nĂ©gatives Ă  la contamination des noeuds capteurs, Ă  trouver l’origine, le temps d’injection et la durĂ©e de la contamination. La rĂ©solution de ce problĂšme inverse est faite par la rĂ©solution des Ă©quations de transport adjointes par formulation backtracking. La mĂ©thode donne la liste des sources potentielles ainsi que le classement de celles-ci selon leur probabilitĂ© d’ĂȘtre la vraie source de contamination. Elle s’exprime en fonction de combien, en pourcentage, cette source potentielle peut expliquer les rĂ©ponses positives aux capteurs.Le placement des capteurs est optimisĂ© pour l’identification des sources. L’objectif est la maximisation du potentiel de dĂ©tection de la vĂ©ritable source de contamination. Deux rĂ©solutions sont testĂ©es. La premiĂšre utilise un algorithme glouton combinĂ© Ă  une mĂ©thode de Monte Carlo.La seconde utilise une mĂ©thode de recherche locale sur graphe.Finalement les mĂ©thodes sont appliquĂ©es Ă  un cas test rĂ©el avec dans l’ordre : le placement des capteurs, l’identification de la source de contamination et l’estimation de sa propagation

    Quasi real-time model for security of water distribution network

    No full text
    Le but de cette thĂšse est de modĂ©liser la propagation d’un contaminant au sein d’un rĂ©seau d’eau potable muni de capteurs temps rĂ©el. Elle comporte les trois axes de dĂ©veloppement suivant: la rĂ©solution des Ă©quations de transport, celle du problĂšme d’identification des sources de contamination et le placement des capteurs.Le transport d’un produit chimique est modĂ©lisĂ© dans un rĂ©seau d’eau potable par l’équation de transport rĂ©action 1-D avec l’hypothĂšse de mĂ©lange parfait aux noeuds. Il est proposĂ© d’amĂ©liorer sa prĂ©diction par l’ajout d’un modĂšle de mĂ©lange imparfait aux jonctions double T et d’un modĂšle de dispersion prenant en compte un profil de vitesse 3-D et la diffusion radiale. Le premier modĂšle est crĂ©Ă© Ă  l’aide d’un plan d’expĂ©riences avec triangulation de Delaunay, de simulations CFD, et de la mĂ©thode d’interpolation krigeage. Le second utilise les Ă©quations adjointes du problĂšme de transport avec l’ajout de particules Ă©voluant Ă  l’aide d’une marche alĂ©atoire, cette derniĂšre modĂ©lisant la diffusion radiale dans la surface droite du tuyau.Le problĂšme d’identification des sources consiste, Ă  l’aide de rĂ©ponses positives ou nĂ©gatives Ă  la contamination des noeuds capteurs, Ă  trouver l’origine, le temps d’injection et la durĂ©e de la contamination. La rĂ©solution de ce problĂšme inverse est faite par la rĂ©solution des Ă©quations de transport adjointes par formulation backtracking. La mĂ©thode donne la liste des sources potentielles ainsi que le classement de celles-ci selon leur probabilitĂ© d’ĂȘtre la vraie source de contamination. Elle s’exprime en fonction de combien, en pourcentage, cette source potentielle peut expliquer les rĂ©ponses positives aux capteurs.Le placement des capteurs est optimisĂ© pour l’identification des sources. L’objectif est la maximisation du potentiel de dĂ©tection de la vĂ©ritable source de contamination. Deux rĂ©solutions sont testĂ©es. La premiĂšre utilise un algorithme glouton combinĂ© Ă  une mĂ©thode de Monte Carlo.La seconde utilise une mĂ©thode de recherche locale sur graphe.Finalement les mĂ©thodes sont appliquĂ©es Ă  un cas test rĂ©el avec dans l’ordre : le placement des capteurs, l’identification de la source de contamination et l’estimation de sa propagation.The aim of this thesis is to model the propagation of a contaminant inside a water distribution network equipped with real time sensors. There are three research directions: the solving of the transport equations, the source identification and the sensor placement. Classical model for transport of a chemical product in a water distribution network isusing 1D-advection-reaction equations with the hypothesis of perfect mixing at junctions. It isproposed to improve the predictions by adding a model of imperfect mixing at double T-junctions and by considering dispersion effect in pipes which takes into account a 3-D velocity profile. The first enhancement is created with the help of a design of experiment based on the Delaunay triangulation, CFD simulations and the interpolation method Kriging. The second one uses the adjoint formulation of the transport equations applied with an algorithm of particle backtracking and a random walk, which models the radial diffusion in the cross-section of a pipe.The source identification problem consists in finding the contamination origin, itsinjection time and its duration from positive and negative responses given by the sensors. The solution to this inverse problem is computed by solving the adjoint transport equations with a backtracking formulation. The method gives a list of potential sources and the ranking of thosemore likely to be the real sources of contamination. It is function of how much, in percentage, they can explain the positive responses of the sensors.The sensor placement is chosen in order to maximize the ranking of the real source of contamination among the potential sources. Two solutions are proposed. The first one uses agreedy algorithm combined with a Monte Carlo method. The second one uses a local search method on graphs. Finally the methods are applied to a real test case in the following order: the sensor placement, the source identification and the estimation of the contamination propagation

    SMaRT-OnlineWDN deliverable 4.1 : SpĂ©cification des phĂ©nomĂšnes qui doivent ĂȘtre modĂ©lisĂ©s

    No full text
    The main objective of the SMaRT-OnlineWDN project is the development of an online security management toolkit for water distribution networks that is based on sensor measurements of water quality as well as water quantity. Pseudo-real time modelling of water quantity and water quality variables are the cornerstone of the project. Existing transport model tools are not adapted for online modelling and ignore some important phenomena that may be dominant when looking at the network in greater detail with an observation time of several minutes. The aim of this deliverable is to define which phenomena should be considered in the online models. Firstly, existing models are described and the way water distribution networks are represented by graphs. Then, important phenomena that are missing are presented. The results are based on a bibliography study and the experience of the partners. Finally, a summary of conclusions is given. In summary, it is important to consider: 1)Inertia terms to make slow transient predictions of the hydraulic state. The velocity output will be slow-varying; 2)The hydrodynamic dispersion and possibly the molecular diffusion to improve the transport along a pipe and at junctions; 3)The imperfect mixing at Tee and Cross junctions depending on velocity inlets; 4)The diameter reduction and the wall roughness; It is proposed to calibrate these parameters on a regular basis (annual); 5)One chemical substance. It was also decided not to develop the model for 6)Pathogens; 7)Behaviour of multi-species; 8)Sedimentation. These are outside the scope of the project

    SMaRT-OnlineWDN deliverable 4.1 : SpĂ©cification des phĂ©nomĂšnes qui doivent ĂȘtre modĂ©lisĂ©s

    No full text
    The main objective of the SMaRT-OnlineWDN project is the development of an online security management toolkit for water distribution networks that is based on sensor measurements of water quality as well as water quantity. Pseudo-real time modelling of water quantity and water quality variables are the cornerstone of the project. Existing transport model tools are not adapted for online modelling and ignore some important phenomena that may be dominant when looking at the network in greater detail with an observation time of several minutes. The aim of this deliverable is to define which phenomena should be considered in the online models. Firstly, existing models are described and the way water distribution networks are represented by graphs. Then, important phenomena that are missing are presented. The results are based on a bibliography study and the experience of the partners. Finally, a summary of conclusions is given. In summary, it is important to consider: 1)Inertia terms to make slow transient predictions of the hydraulic state. The velocity output will be slow-varying; 2)The hydrodynamic dispersion and possibly the molecular diffusion to improve the transport along a pipe and at junctions; 3)The imperfect mixing at Tee and Cross junctions depending on velocity inlets; 4)The diameter reduction and the wall roughness; It is proposed to calibrate these parameters on a regular basis (annual); 5)One chemical substance. It was also decided not to develop the model for 6)Pathogens; 7)Behaviour of multi-species; 8)Sedimentation. These are outside the scope of the project
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