Optimal design of mesostructured materials under uncertainty

The main objective of the topology optimization is to fulfill the objective function with the minimum amount of material. This reduces the overall cost of the structure and at the same time reduces the assembly, manufacturing and maintenance costs because of the reduced number of parts in the final structure. The concept of reliability analysis can be incorporated into the deterministic topology optimization method; this incorporated scheme is referred to as Reliability-based Topology Optimization (RBTO). In RBTO, the statistical nature of constraints and design problems are defined in the objective function and probabilistic constraint. The probabilistic constraint can specify the required reliability level of the system. In practical applications, however, finding global optimum in the presence of uncertainty is a difficult and computationally intensive task, since for every possible design a full stochastic analysis has to be performed for estimating various statistical parameters. Efficient methodologies are therefore required for the solution of the stochastic part and the optimization part of the design process. This research will explore a reliability-based synthesis method which estimates all the statistical parameters and finds the optimum while being less computationally intensive. The efficiency of the proposed method is achieved with the combination of topology optimization and stochastic approximation which utilizes a sampling technique such as Latin Hypercube Sampling (LHS) and surrogate modeling techniques such as Local Regression and Classification using Artificial Neural Networks (ANN). Local regression is comparatively less computationally intensive and produces good results in case of low probability of failures whereas Classification is particularly useful in cases where the reliability of failure has to be estimated with disjoint failure domains. Because classification using ANN is comparatively more computationally demanding than Local regression, classification is only used when local regression fails to give the desired level of goodness of fit. Nevertheless, classification is an indispensible tool in estimating the probability of failure when the failure domain is discontinuous. Representative examples will be demonstrated where the method is used to design customized meso-scale truss structures and a macro-scale hydrogen storage tank. The final deliverable from this research will be a less computationally intensive and robust RBTO procedure that can be used for design of truss structures with variable design parameters and force and boundary conditions.M.S.Committee Chair: Choi, Seung-Kyum; Committee Member: Muhanna, Rafi; Committee Member: Rosen, Davi

Reliability-based design with system reliability and design improvement

This thesis focuses on developing a methodology for accurately estimating series system probability of failure. Existing methods for series system based design optimization are not that accurate because they assign reliability to each failure mode; as a result complete system reliability goes down. According to method proposed in this work, the user will assign required system reliability at the start and then optimizer will apportion reliability to every failure mode in order to meet required system reliability level. Detlevson second order upper bounds are used to estimate system probability of failure. Several examples have been shown to verify the results obtained --Abstract, page iii

Undergraduate Student Catalog 2012-2013

The contents of this document highlight the central pillars of Qatar University’s mission, namely the provision of high-quality education and the pursuit of an active role in the development of Qatari society. The courses described here have been designed, reviewed and assessed to meet the highest educational standards, with a strong focus on the knowledge- and skill-bases needed for a graduate to be competitive in today’s labor market or in higher education pursuits

The 25th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

Papers in the following categories are presented: recent developments in rubidium, cesium, and hydrogen-based frequency standards, and in cryogenic and trapped-ion technology; international and transnational applications of precise time and time interval (PTTI) technology with emphasis on satellite laser tracking networks, GLONASS timing, intercomparison of national time scales and international telecommunication; applications of PTTI technology to the telecommunications, power distribution, platform positioning, and geophysical survey industries; application of PTTI technology to evolving military communications and navigation systems; and dissemination of precise time and frequency by means of GPS, GLONASS, MILSTAR, LORAN, and synchronous communications satellites

Fault Prognostics Using Logical Analysis of Data and Non-Parametric Reliability Estimation Methods

RÉSUMÉ : Estimer la durée de vie utile restante (RUL) d’un système qui fonctionne suivant différentes conditions de fonctionnement représente un grand défi pour les chercheurs en maintenance conditionnelle (CBM). En effet, il est difficile de comprendre la relation entre les variables qui représentent ces conditions de fonctionnement et la RUL dans beaucoup de cas en pratique à cause du degré élevé de corrélation entre ces variables et leur dépendance dans le temps. Il est également difficile, voire impossible, pour des experts d’acquérir et accumuler un savoir à propos de systèmes complexes, où l'échec de l'ensemble du système est vu comme le résultat de l'interaction et de la concurrence entre plusieurs modes de défaillance. Cette thèse présente des méthodologies pour le pronostic en CBM basé sur l'apprentissage automatique, et une approche de découverte de connaissances appelée Logical Analysis of Data (LAD). Les méthodologies proposées se composent de plusieurs implémentations de la LAD combinées avec des méthodes non paramétriques d'estimation de fiabilité. L'objectif de ces méthodologies est de prédire la RUL du système surveillé tout en tenant compte de l'analyse des modes de défaillance uniques ou multiples. Deux d’entre elles considèrent un mode de défaillance unique et une autre considère de multiples modes de défaillance. Les deux méthodologies pour le pronostic avec mode unique diffèrent dans la manière de manipuler les données. Les méthodologies de pronostique dans cette recherche doctorale ont été testées et validées sur la base d'un ensemble de tests bien connus. Dans ces tests, les méthodologies ont été comparées à des techniques de pronostic connues; le modèle à risques proportionnels de Cox (PHM), les réseaux de neurones artificiels (ANNs) et les machines à vecteurs de support (SVMs). Deux ensembles de données ont été utilisés pour illustrer la performance des trois méthodologies: l'ensemble de données du turboréacteur à double flux (turbofan) qui est disponible au sein de la base de données pour le développement d'algorithmes de pronostic de la NASA, et un autre ensemble de données obtenu d’une véritable application dans l'industrie. Les résultats de ces comparaisons indiquent que chacune des méthodologies proposées permet de prédire avec précision la RUL du système considéré. Cette recherche doctorale conclut que l’approche utilisant la LAD possède d’importants mérites et avantages qui pourraient être bénéfiques au domaine du pronostic en CBM. Elle est capable de gérer les données en CBM qui sont corrélées et variantes dans le temps. Son autre avantage et qu’elle génère un savoir interprétable qui est bénéfique au personnel de maintenance.----------ABSTRACT : Estimating the remaining useful life (RUL) for a system working under different operating conditions represents a big challenge to the researchers in the condition-based maintenance (CBM) domain. The reason is that the relationship between the covariates that represent those operating conditions and the RUL is not fully understood in many practical cases, due to the high degree of correlation between such covariates, and their dependence on time. It is also difficult or even impossible for the experts to acquire and accumulate the knowledge from a complex system, where the failure of the system is regarded as the result of interaction and competition between several failure modes. This thesis presents systematic CBM prognostic methodologies based on a pattern-based machine learning and knowledge discovery approach called Logical Analysis of Data (LAD). The proposed methodologies comprise different implementations of the LAD approach combined with non-parametric reliability estimation methods. The objective of these methodologies is to predict the RUL of the monitored system while considering the analysis of single or multiple failure modes. Three different methodologies are presented; two deal with single failure mode and one deals with multiple failure modes. The two methodologies for single mode prognostics differ in the way of representing the data. The prognostic methodologies in this doctoral research have been tested and validated based on a set of widely known tests. In these tests, the methodologies were compared to well-known prognostic techniques; the proportional hazards model (PHM), artificial neural networks (ANNs) and support vector machines (SVMs). Two datasets were used to illustrate the performance of the three methodologies: the turbofan engine dataset that is available at NASA prognostic data repository, and another dataset collected from a real application in the industry. The results of these comparisons indicate that each of the proposed methodologies provides an accurate prediction for the RUL of the monitored system. This doctoral research concludes that the LAD approach has attractive merits and advantages that add benefits to the field of prognostics. It is capable of dealing with the CBM data that are correlated and time-varying. Another advantage is its generation of an interpretable knowledge that is beneficial to the maintenance personnel

A study of an Extended Lunar Orbital Rendezovous /ELOR/ mission. Volume 1 - Technical analysis Final report

Feasibility of Extended Lunar Orbital Rendezvous /ELOR/ for use in lunar application program

Implementing building information modeling in Tanzania

The key objective of this thesis was to appraise the Building Information Modelling (BIM) in Tanzania. The thesis has shown that in Tanzania Building Information Modeling (BIM) can be used to improve Bills of Quantities (BOQ) productivity in the public construction project delivery. The thesis key contribution is the proposed fundamentals in developing a country specific BIM model. Equally, the study has given a more specific definition of Building Information Modeling (BIM) and shown the existing relationship of BIM and Bills of Quantities in improving performance construction projects delivery in Tanzania. This study, designed the hypothesis approach to describe the association between Building Information Modeling (BIM) and Bills of Quantities (BOQ), which facilitated the development of an economical and technologically simple BIM model parameters for Tanzania. Although the relationship found was insignificant, the developed 5DBIMBOQ model is expected to be more productive in the Total Cost Management (TCM) and hence improve information integration in the construction project deliveries in Tanzania. Further areas of study should include the empirical testing of hypothesis in order to further measure the behaviour of other key parameters like collaboration in order to develop the Tanzanian BIM Model