Boudaoud, Nassim (19..-....).
Overview
| Works: | 4 works in 7 publications in 1 language and 7 library holdings |
|---|---|
| Roles: | Thesis advisor, Author |
| Classifications: | TL214.P6, 620 |
Publication Timeline
.
Most widely held works by
Nassim Boudaoud
Méthode d'aide à la modélisation en calcul de structures pilotée par le contexte de calcul en conception by
Yassine Benhafid(
Book
)
2 editions published in 2006 in French and held by 2 WorldCat member libraries worldwide
The use of simulation in engineering design comes to help to analyze behaviours of physical systems before even as they really exist. This characterizes simulation while having disadvantages relating to the problems involved in simplifications of the simulated real systems. In the diversity of the simplification models in simulation, the approached models are this thesis relate to the physical model (which generate the modelling error) and the simulation model (which gives the discretization error). While there are not structured methods for the modelling error control, the goal of this thesis is to develop an assistance method to the simplification choices for the modelling error control. The developed method in this thesis is based on the development of knowledge in modelling for-the reference analysis cases, which will be exploited to help to modelling another similar analysis cases
2 editions published in 2006 in French and held by 2 WorldCat member libraries worldwide
The use of simulation in engineering design comes to help to analyze behaviours of physical systems before even as they really exist. This characterizes simulation while having disadvantages relating to the problems involved in simplifications of the simulated real systems. In the diversity of the simplification models in simulation, the approached models are this thesis relate to the physical model (which generate the modelling error) and the simulation model (which gives the discretization error). While there are not structured methods for the modelling error control, the goal of this thesis is to develop an assistance method to the simplification choices for the modelling error control. The developed method in this thesis is based on the development of knowledge in modelling for-the reference analysis cases, which will be exploited to help to modelling another similar analysis cases
Conception d'un système de diagnostic adaptatif en ligne pour la surveillance des systèmes évolutifs by
Nassim Boudaoud(
Book
)
2 editions published in 1997 in French and held by 2 WorldCat member libraries worldwide
La surveillance des procédés (industriels, environnementaux, etc.) est devenue un enjeu économique majeur dont le diagnostic est une composante importante. Le diagnostic d'un procédé consiste à déterminer à chaque instant l'état sous lequel il évolue. La conception d'un système de diagnostic suppose une connaissance des modes de fonctionnement du procédé. Cette connaissance est rarement exhaustive. Des procédures d'adaptation permettent d'enrichir cette connaissance au fur et à mesure que de nouveaux modes de fonctionnement apparaissent. L'étude a porté sur la recherche de solutions au problème d'adaptation en ligne d'un système de diagnostic par reconnaissance des formes dans le cadre de systèmes évolutifs. La solution proposée consiste à associer au système de diagnostic une boucle de supervision. Le système de diagnostic ainsi développé est capable de détecter des dérivés à partir d'un mode de fonctionnement connu, de détecter la stabilisation dans un mode connu ou inconnu et d'anticiper l'évolution de l'état de fonctionnement du système. Le diagnostic est généré sous forme symbolique. L'originalité de ce travail réside dans le fait que nous avons fait collaborer une approche par reconnaissance des formes et une approche par modèles pour élaborer un système diagnostic auto-adaptatif en ligne. Les performances de ce système ont été évaluées sur deux applications : - la surveillance du réseau téléphonique francais. - la surveillance de la qualité de l'eau. Les résultats ont montré la capacité du système à détecter des dérivés à partir d'un état de fonctionnement normal et à anticiper le degré de pollution de la rivière
2 editions published in 1997 in French and held by 2 WorldCat member libraries worldwide
La surveillance des procédés (industriels, environnementaux, etc.) est devenue un enjeu économique majeur dont le diagnostic est une composante importante. Le diagnostic d'un procédé consiste à déterminer à chaque instant l'état sous lequel il évolue. La conception d'un système de diagnostic suppose une connaissance des modes de fonctionnement du procédé. Cette connaissance est rarement exhaustive. Des procédures d'adaptation permettent d'enrichir cette connaissance au fur et à mesure que de nouveaux modes de fonctionnement apparaissent. L'étude a porté sur la recherche de solutions au problème d'adaptation en ligne d'un système de diagnostic par reconnaissance des formes dans le cadre de systèmes évolutifs. La solution proposée consiste à associer au système de diagnostic une boucle de supervision. Le système de diagnostic ainsi développé est capable de détecter des dérivés à partir d'un mode de fonctionnement connu, de détecter la stabilisation dans un mode connu ou inconnu et d'anticiper l'évolution de l'état de fonctionnement du système. Le diagnostic est généré sous forme symbolique. L'originalité de ce travail réside dans le fait que nous avons fait collaborer une approche par reconnaissance des formes et une approche par modèles pour élaborer un système diagnostic auto-adaptatif en ligne. Les performances de ce système ont été évaluées sur deux applications : - la surveillance du réseau téléphonique francais. - la surveillance de la qualité de l'eau. Les résultats ont montré la capacité du système à détecter des dérivés à partir d'un état de fonctionnement normal et à anticiper le degré de pollution de la rivière
Méthodes de recalage de modèles et application aux émissions des moteurs diesel by
Sébastien Castric(
Book
)
2 editions published in 2007 in French and held by 2 WorldCat member libraries worldwide
Since some decades, European vehicles are subjected to normative laws about pollutant emissions. To face these constraints, car manufacturers have used more and more complex technologies especially for diesel engine cars. This situation has led to a complexification of engine tuning since the numbers of setting parameters has increased too. The present research work was made for the car manufacturer Renault SAS. It aims at proposing methods that allow readjusting models and applications over pollutant models of diesel engine. Renault decided to use techniques of design of experiments, modelling and optimization to solve the problem of diesel engine tuning for emissions. Even if this approach gave good results, it has some drawbacks. The tuning process is composed of loops. Each loop involves hardware changes in the engine. In this case, the model representing the engine's behaviour, which is a LOLIMOT model, is not valid anymore. Considering that it is not possible to completely rebuild a model, a question appears: “How is it possible to readjust the model after an hardware change by doing as few tests as possible?” This PhD proposes some ways to solve this problem. The first one consists in using the bayesian theory. By using the initial model as an a priori, we created an algorithm permitting to readjust LOLIMOT models. In addition, we proposed a method derived from the first one, and, which aims at using the tuner knowledge about the engine as a prior knowledge. We tested our methods by simulation and owing to tests made on a 2L diesel engine, which was subjected to different hardware changes. In a second time, we considered that even if the Bayesian theory is able to take into account some knowledge, it does not take into account the hardware change characteristics. Thus, we decided to create a new model integrating physical parameters, like, for example, the number of holes in the injectors. We developed a model of diesel combustion. It simulates the evolution of thermodynamic variables inside the combustion chamber even for the multi injection case. Next, we adapted models of pollutants using these variables as inputs. We tested the whole model on prediction of cylinder pressure and pollutants over 2L diesel engine tests
2 editions published in 2007 in French and held by 2 WorldCat member libraries worldwide
Since some decades, European vehicles are subjected to normative laws about pollutant emissions. To face these constraints, car manufacturers have used more and more complex technologies especially for diesel engine cars. This situation has led to a complexification of engine tuning since the numbers of setting parameters has increased too. The present research work was made for the car manufacturer Renault SAS. It aims at proposing methods that allow readjusting models and applications over pollutant models of diesel engine. Renault decided to use techniques of design of experiments, modelling and optimization to solve the problem of diesel engine tuning for emissions. Even if this approach gave good results, it has some drawbacks. The tuning process is composed of loops. Each loop involves hardware changes in the engine. In this case, the model representing the engine's behaviour, which is a LOLIMOT model, is not valid anymore. Considering that it is not possible to completely rebuild a model, a question appears: “How is it possible to readjust the model after an hardware change by doing as few tests as possible?” This PhD proposes some ways to solve this problem. The first one consists in using the bayesian theory. By using the initial model as an a priori, we created an algorithm permitting to readjust LOLIMOT models. In addition, we proposed a method derived from the first one, and, which aims at using the tuner knowledge about the engine as a prior knowledge. We tested our methods by simulation and owing to tests made on a 2L diesel engine, which was subjected to different hardware changes. In a second time, we considered that even if the Bayesian theory is able to take into account some knowledge, it does not take into account the hardware change characteristics. Thus, we decided to create a new model integrating physical parameters, like, for example, the number of holes in the injectors. We developed a model of diesel combustion. It simulates the evolution of thermodynamic variables inside the combustion chamber even for the multi injection case. Next, we adapted models of pollutants using these variables as inputs. We tested the whole model on prediction of cylinder pressure and pollutants over 2L diesel engine tests
Système à base de connaissances pour le processus de plan d'expériences numériques by
Gaëtan Blondet(
)
1 edition published in 2017 in French and held by 1 WorldCat member library worldwide
In order to improve industrial competitiveness, product design relies more and more on numerical tools, such as numerical simulation, to develop better and cheaper products faster. Numerical Design of Experiments (NDOE) are more and more used to include variabilities during simulation processes, to design more robust, reliable and optimized product earlier in the product development process. Nevertheless, a NDOE process may be too expensive to be applied to a complex product, because of the high computational cost of the model and the high number of required experiments. Several methods exist to decrease this computational cost, but they required expert knowledge to be efficiently applied. In addition to that, NDoE process produces a large amount of data which must be managed. The aim of this research is to propose a solution to define, as fast as possible, an efficient NDoE process, which produce as much useful information as possible with a minimal number of simulations, for complex products. The objective is to shorten both process definition and execution steps. A knowledge-based system is proposed, based on a specific ontology and a bayesian network, to capitalise, share and reuse knowledge and data to predict the best NDoE process definition regarding to a new product. This system is validated on a product from automotive industry
1 edition published in 2017 in French and held by 1 WorldCat member library worldwide
In order to improve industrial competitiveness, product design relies more and more on numerical tools, such as numerical simulation, to develop better and cheaper products faster. Numerical Design of Experiments (NDOE) are more and more used to include variabilities during simulation processes, to design more robust, reliable and optimized product earlier in the product development process. Nevertheless, a NDOE process may be too expensive to be applied to a complex product, because of the high computational cost of the model and the high number of required experiments. Several methods exist to decrease this computational cost, but they required expert knowledge to be efficiently applied. In addition to that, NDoE process produces a large amount of data which must be managed. The aim of this research is to propose a solution to define, as fast as possible, an efficient NDoE process, which produce as much useful information as possible with a minimal number of simulations, for complex products. The objective is to shorten both process definition and execution steps. A knowledge-based system is proposed, based on a specific ontology and a bayesian network, to capitalise, share and reuse knowledge and data to predict the best NDoE process definition regarding to a new product. This system is validated on a product from automotive industry
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Related Identities
- Université de Technologie de Compiègne Degree grantor
- École doctorale 71, Sciences pour l'ingénieur (Compiègne) Other
- Castric, Sébastien (1980-....). Author
- Cherfi-Boulanger, Zohra (19..-....). Thesis advisor
- Benhafid, Yassine Author
- Masson, Marie-Hélène (19..-....). Thesis advisor
- Troussier, Nadège (19..-....). Thesis advisor
- Le Duigou, Julien (1982-....). Thesis advisor
- Blondet, Gaëtan (19..-....). Author
Languages