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You searched for subject:(adjoint probability method). Showing records 1 – 2 of 2 total matches.

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University of Colorado

1. Jin, Qi. Contaminant Source Identification in Building Hvac Systems Using Adjoint Probability Method.

Degree: MS, Civil, Environmental & Architectural Engineering, 2015, University of Colorado

Although high efficiency filter is one critical component in the Air Handler Unit (AHU), HVAC system is potential contaminant emission source. Released contaminants can be transported through HVAC system and impacts the indoor air quality (IAQ). Effective control and improvement measures are required to remove the contaminant source located in HVAC systems in order to eliminate its influence on the IAQ. Accurate and fast identification of contaminant sources in HVAC systems makes it. This thesis studies the application of adjoint backward probability model in identification of contaminant source in Building HVAC system. The adjoint backward probability model was mostly applied to identify contaminant source information in groundwater and inside building. According to the similar properties between water and air, and same contaminant transport fate in water and air, the adjoint probability model is applied to study the contaminant source identification in HVAC systems. Sensors are used to detect contaminant concentration change in certain sampling locations of HVAC ductwork. Using sensor detection information, we can trace back and find the source information. In this research CONTAM is used to provide a steady state airflow field. A simple building model with three zones and detailed duct work is built. This model is applied into later research in identification of contaminant source in HVAC system. Four cases are analyzed in the research to study the application of adjoint backward probability method. The first case is identifying an instantaneous contaminant source location with known source release time and source release mass. The second case is identifying the location of a dynamic contaminant source with known release time and known release mass. The third case is identifying source release time and release location simultaneously for a decaying contaminant source with known source release mass. The fourth case is identifying the location of a dynamic contaminant source in a two-floor building with known release time and known release mass. The conclusions come to that a sensor network with two sensors reading historical concentrations can identify source information accurately. Further, in future research, contaminant source information will be recovered without knowing any source information in advance. Advisors/Committee Members: John Zhai, Michael Brandemuehl, Moncef Krarti.

Subjects/Keywords: adjoint probability method; CONTAM; contaminant source identification; HVAC systems; indoor air quality; Civil Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jin, Q. (2015). Contaminant Source Identification in Building Hvac Systems Using Adjoint Probability Method. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/cven_gradetds/185

Chicago Manual of Style (16th Edition):

Jin, Qi. “Contaminant Source Identification in Building Hvac Systems Using Adjoint Probability Method.” 2015. Masters Thesis, University of Colorado. Accessed January 29, 2020. https://scholar.colorado.edu/cven_gradetds/185.

MLA Handbook (7th Edition):

Jin, Qi. “Contaminant Source Identification in Building Hvac Systems Using Adjoint Probability Method.” 2015. Web. 29 Jan 2020.

Vancouver:

Jin Q. Contaminant Source Identification in Building Hvac Systems Using Adjoint Probability Method. [Internet] [Masters thesis]. University of Colorado; 2015. [cited 2020 Jan 29]. Available from: https://scholar.colorado.edu/cven_gradetds/185.

Council of Science Editors:

Jin Q. Contaminant Source Identification in Building Hvac Systems Using Adjoint Probability Method. [Masters Thesis]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/cven_gradetds/185


Université de Grenoble

2. Sabouri, Pouya. Application of perturbation theory methods to nuclear data uncertainty propagation using the collision probability method : Application de la théorie des perturbations à la propagation des incertitudes des données nucléaires par la méthode des probabilités de première collision.

Degree: Docteur es, Mécanique des fluides, procédés, énergétique, 2013, Université de Grenoble

Dans cette thèse, nous présentons une étude rigoureuse des barres d'erreurs et des sensibilités de paramètres neutroniques (tels le keff) aux données nucléaires de base utilisées pour les calculer. Notre étude commence au niveau fondamental, i.e. les fichiers de données ENDF et leurs incertitudes, fournies sous la forme de matrices de variance/covariance, et leur traitement. Lorsqu'un calcul méthodique et consistant des sensibilités est consenti, nous montrons qu'une approche déterministe utilisant des formalismes bien connus est suffisante pour propager les incertitudes des bases de données avec un niveau de précision équivalent à celui des meilleurs outils disponibles sur le marché, comme les codes Monte-Carlo de référence. En appliquant notre méthodologie à trois exercices proposés par l'OCDE, dans le cadre des Benchmarks UACSA, nous donnons des informations, que nous espérons utiles, sur les processus physiques et les hypothèses sous-jacents aux formalismes déterministes utilisés dans cette étude.

This dissertation presents a comprehensive study of sensitivity/uncertainty analysis for reactor performance parameters (e.g. the k-effective) to the base nuclear data from which they are computed. The analysis starts at the fundamental step, the Evaluated Nuclear Data File and the uncertainties inherently associated with the data they contain, available in the form of variance/covariance matrices. We show that when a methodical and consistent computation of sensitivity is performed, conventional deterministic formalisms can be sufficient to propagate nuclear data uncertainties with the level of accuracy obtained by the most advanced tools, such as state-of-the-art Monte Carlo codes. By applying our developed methodology to three exercises proposed by the OECD (UACSA Benchmarks), we provide insights of the underlying physical phenomena associated with the used formalisms.

Advisors/Committee Members: Kodeli, Ivan Alexander (thesis director).

Subjects/Keywords: Données nucléaires; Propagation des incertitudes/erreurs; Analyse des sensibilités; Méthode des probabilités de première collision; Théorie des perturbations; Flux adjoint et adjoints généralisés; Nuclear data; Uncertainty propagation; Sensitivity analysis; Collision probability method; Perturbation theory; Adjoint flux and generalized adjoints; 620

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sabouri, P. (2013). Application of perturbation theory methods to nuclear data uncertainty propagation using the collision probability method : Application de la théorie des perturbations à la propagation des incertitudes des données nucléaires par la méthode des probabilités de première collision. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2013GRENI071

Chicago Manual of Style (16th Edition):

Sabouri, Pouya. “Application of perturbation theory methods to nuclear data uncertainty propagation using the collision probability method : Application de la théorie des perturbations à la propagation des incertitudes des données nucléaires par la méthode des probabilités de première collision.” 2013. Doctoral Dissertation, Université de Grenoble. Accessed January 29, 2020. http://www.theses.fr/2013GRENI071.

MLA Handbook (7th Edition):

Sabouri, Pouya. “Application of perturbation theory methods to nuclear data uncertainty propagation using the collision probability method : Application de la théorie des perturbations à la propagation des incertitudes des données nucléaires par la méthode des probabilités de première collision.” 2013. Web. 29 Jan 2020.

Vancouver:

Sabouri P. Application of perturbation theory methods to nuclear data uncertainty propagation using the collision probability method : Application de la théorie des perturbations à la propagation des incertitudes des données nucléaires par la méthode des probabilités de première collision. [Internet] [Doctoral dissertation]. Université de Grenoble; 2013. [cited 2020 Jan 29]. Available from: http://www.theses.fr/2013GRENI071.

Council of Science Editors:

Sabouri P. Application of perturbation theory methods to nuclear data uncertainty propagation using the collision probability method : Application de la théorie des perturbations à la propagation des incertitudes des données nucléaires par la méthode des probabilités de première collision. [Doctoral Dissertation]. Université de Grenoble; 2013. Available from: http://www.theses.fr/2013GRENI071

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