<p> This thesis addresses itself to one of the most general theoretical problems associated with the art of engineering design. Viewed in its entirety the proposed approach integrates the relation between the design and production engineers through the theory of nonlinear optimization. The conventional optimization problem is extended to include the optimal allocation of the upper and lower limits of the random variables of an engineering system. The approach is illustrated by an example using a sequence of increasingly generalized formulations, while the general mathematical theory is also provided. The method appears to offer a practical technique provided a satisfactory cost function can be defined.</p> <p> The thesis presents an analytical approach to full acceptability design conditions as well as less than full acceptability or scrap design conditions. An important distinction between the design and the manufacturing scrap has been introduced and illustrated through examples.</p> <p> The space regionalization technique is utilized to estimate the system design scrap. Optimization strategies are introduced to the mathematically defined upper and lower limits of the regionalization region. This region is then discretized into a number of cells depending upon the probabilistic characteristic of the system random variables.</p> <p> The analytical approach exhibited does not rely explicitly on evaluation of partial derivatives of either the system cost objective or any of its constraints at any point. Moreover, the technique could be applied to engineering systems with either convex or nonconvex feasible regions. It could also be exercised irrespective of the shape of the probabilistic distributions that describe the random variables variation.</p> <p> Industrially oriented design examples are furnished to justify the applicability of the theory in different engineering disciplines.</p> / Thesis / Doctor of Philosophy (PhD)

<p> The separation of distilled water and coconut fatty acid (CNFA) was investigated in a 4 in. x 16 in. vertical gravity decanter to determine the most significant design variables. The design variables studied were tangential versus perpendicular feed flow, height/diameter ratio, the total feed flow rate, the temperature and the intertace position with respect to feed inlet. The criteria used were the calculated immiscible concentration of water in CNFA overflow (Cw) and that of CNFA in water underflow (Co), The test conditions were arranged according to a Central Composite Design based on Box Method of Experimentation. Two levels of inlet geometry and five levels of each of the other four variables were studied. The range of variables were 1. Inlet geometry Tangential and Perpendicular Feed Flow 2. Height/Diameter Ratio 3/1 to 4/1 3· Total Feed Flow rate 160 cc/min. to 320 cc/min. 4. Temperature 40° to 80°c. 5· Interrace Position 2" below feed inlet to 2" above feed inlet The Overall residence time in the decanter ranged from 7.5 to 12.5 minutes. The drop size distribution of the incoming dispersion was measured by taking photomicrographs for all the runs. </p> <p> It was found that the tangential feed flow was slightly better but not significantly better for the separation of two immiscible liquids. Statistical analysis revealed that 95% confidence level height/diameter ratio, flow rate and temperature, respectively, are the most important design variables for the separation of CNFA from water underflow. The dependence of Cw on height/diameter ratio of Co on the interface position was interpreted to mean that the theoretical, plug flow residence time within each phase is the most important design variable. Interaction between variables and second order constants were not found significant at 95% confidence level. Sedimentation rather than coalescence appeared to be the controlling factor in the present study. </p> / Thesis / Master of Engineering (MEngr)

2011 IEEE Congress on Evolutionary Computation (CEC). June 5-8, 2011, Ritz-Carlton, New Orleans, LA, USA

This licentiate thesis focuses on surface patterns, spatiality, and pattern relations in textile design, and aims to explore surface patterns as spatial definers and what they mean in the context of surface patterns. A secondary focus relates to applying conceptual spatial determinations as alternative design variables in design processes, and exploring how these could be used to define and analyse pattern relations. Through a series of exploratory design experiments that used printed and projected surface patterns in a three-dimensional setting, which were documented using photographs and film, the notion of pattern relations, wherein scale was used as a design variable, was explored. The outcome of the experiments showed the expressional possibilities that surface patterns may provide in a defined space, and how these are connected to pattern relations. In order to encourage an accompanying discussion regarding alternative methods of analysing surface patterns, the construction of a theoretical model was initiated. Workshops with design students were used as another practical method in this work. The results showed that there is great potential in using conceptual spatial determinations to define pattern relations by viewing surface patterns as spatial definers, rather than taking a traditional perspective on their functions. Another outcome is the theoretical model, which proposes a specific approach to pattern relations. This research demonstrates how conceptual spatial determinations can benefit the textile design process, as well as design teaching, which could in turn provide the field with new expressions that may lead to a change in or fruitful addition to the practice.

Yes

Η παρούσα διπλωματική εργασία εκπονήθηκε στο τμήμα Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Υπολογιστών του Πανεπιστημίου Πατρών. Πραγματεύεται τη βελτιστοποίηση των σχεδιαστικών παραμέτρων της τριφασικής ασύγχρονης μηχανής με στόχο την επίτευξη μεγιστοποίησης της ροπής εκκίνησης και ελαχιστοποίησης του ρεύματος εκκίνησης. Μελετήθηκε η σχέση που υπάρχει ανάμεσα στα γεωμετρικά χαρακτηριστικά (διαστάσεις) και στις επιδόσεις και τη γενικότερη λειτουργία της μηχανής. Η σχεδίαση και η παραμετροποίηση του τριφασικού επαγωγικού κινητήρα βραχυκυκλωμένου κλωβού που μελετήθηκε, έγινε μέσω του προγράμματος «Opera» της Vector Fields, το οποίο χρησιμοποιεί τη μέθοδο των πεπερασμένων στοιχείων. Όσον αφορά τη διαδικασία της εύρεσης του βέλτιστου μοντέλου, αυτή πραγματοποιήθηκε με τη βοήθεια του εργαλείου Optimizer που παρέχεται από το πρόγραμμα «Opera». Στη συνέχεια μελετήθηκε και αξιολογήθηκε το αρχικό και το βέλτιστο μοντέλο ώστε να αποκτηθεί σαφής εικόνα για τη λειτουργία τους καθώς επίσης και για τις διαφορές που παρουσιάζουν. Στο πρώτο κεφάλαιο παρουσιάζεται η βελτιστοποίηση σαν έννοια αρχικά και στη συνέχεια περιγράφονται τα βήματα που ακολουθούνται για το σωστό ορισμό ενός προβλήματος βελτιστοποίησης. Γίνεται μια ταξινόμηση των προβλημάτων σύμφωνα με την πολυπλοκότητα, το μέγεθος και την ταχύτητα εύρεσης της λύσης. Επιπλέον, αναφέρονται και αναλύονται οι κυριότερες μέθοδοι βελτιστοποίησης και όσες χρησιμοποιούνται περισσότερο με βάση τη βιβλιογραφία. Στο δεύτερο κεφάλαιο παρουσιάζονται οι βασικές αρχές κατασκευής και λειτουργίας της τριφασικής ασύγχρονης μηχανής. Μέσα από αυτή την ανάλυση προκύπτουν συμπεράσματα για τα πιθανά πεδία λειτουργίας της Α.Μ. στα οποία μπορεί να υπεισέλεθει ζήτημα βελτιστοποίησης. Αναφέρονται και σχολιάζονται για κάθε πεδίο τα μεγέθη και τα φυσικά φαινόμενα που συμμετέχουν. Επίσης, μελετώνται οι σχεδιαστικές παράμετροι του στάτη και του δρομέα που καθορίζουν τη χαρακτηριστική ροπής-στροφών και την απόδοση της μηχανής και οι οποίες στη συνέχεια θα χρησιμοποιηθούν στο δικό μας πρόβλημα. Το τρίτο καφάλαιο, περιγράφει τη διαδικασία παραμετροποίησης του μοντέλου του προς μελέτη τριφασικού ασύγχρονου κινητήρα βραχυκυκλωμένου κλωβού στο πρόγραμμα Opera. Εξηγείται η πορεία σχεδίασης για κάθε «κομμάτι» της μηχανής ξεχωριστά ξεκινώντας από το στάτη, συνεχίζοντας με το δρομέα και τέλος με την εισαγωγή του διακένου. Υπάρχουν επίσης αναφορές σχετικές με την ανάλυση και μετ-επεξεργασία του μοντέλου. Ακόμη, δίνεται ένα τμήμα του αρχείου στο οποίο υπάρχει το παραμετροποιημένο μοντέλο σαν μια πρώτη εικόνα για το πώς αυτό δημιουργήθηκε. Η παραμετροποίηση έγινε κατά το μεγαλύτερο μέρος της στο περιβάλλον εργασίας του Pre and Post Processor του Opera-2D και κατά ένα μικρότερο μέρος γράφτηκε κώδικας. Στο τέταρτο κεφάλαιο καθορίζεται και διατυπώνεται πλέον το πρόβλημα που θα μας απασχολήσει στην παρούσα εργασία. Το κεφάλαιο αυτό αποτελεί έναν πλήρη «οδηγό χρήσης» του εργαλείου Optimizer που χρησιμοποιήθηκε για την εύρεση του βέλτιστου μοντέλου στην περίπτωση που αυτό είναι σχεδιασμένο στις δύο διαστάσεις με τη βοήθεια του προγράμματος Opera-2D. Αναφέρονται με κάθε λεπτομέρεια τα βήματα που ακολουθούνται ώστε να μπορέσει ένα οποιοδήποτε μοντέλο να δοθεί για βελτιστοποίηση στο συγκεκριμένο πρόγραμμα όπως επίσης και τα βήματα για τον ορισμό των αντικειμενικών συναρτήσεων, των σχεδιαστικών παραμέτρων και των περιορισμών στον Optimizer. Υπάρχουν επισημάνσεις και παρατηρήσεις στα σημεία που αντιμετωπίστηκαν οι περισσότερες δυσκολίες και καθυστερήσεις κατά τη διάρκεια εκπόνησης της εργασίας. Τέλος, στο πέμπτο κεφάλαιο παρουσιάζονται τα αποτελέσμα της βελτιστοποίησης, αναλύεται το αρχικό και το βέλτιστο μοντέλο, γίνεται αντιπαραβολή των αποτελεσμάτων, πραγματοποιείται σύγκριση των δύο μοντέλων και τελικά διατυπώνονται τα συμπεράσματα που προκύπτουν. / -

Pfeffermann, Krieger e Rinott (1998) apresentaram uma metodologia para modelar processos de amostragem que pode ser utilizada para avaliar se este processo de amostragem é informativo. Neste cenário, as probabilidades de seleção da amostra são aproximadas por uma função polinomial dependendo das variáveis resposta e concomitantes. Nesta abordagem, nossa principal proposta é investigar a aplicação do teste de significância FBST (Full Bayesian Significance Test), apresentado por Pereira e Stern (1999), como uma ferramenta para testar a ignorabilidade amostral, isto é, para avaliar uma relação de significância entre as probabilidades de seleção da amostra e a variável resposta. A performance desta modelagem estatística é testada com alguns experimentos computacionais. / Pfeffermann, Krieger and Rinott (1998) introduced a framework for modeling sampling processes that can be used to assess if a sampling process is informative. In this setting, sample selection probabilities are approximated by a polynomial function depending on outcome and auxiliary variables. Within this framework, our main purpose is to investigate the application of the Full Bayesian Significance Test (FBST), introduced by Pereira and Stern (1999), as a tool for testing sampling ignorability, that is, to detect a significant relation between the sample selection probabilities and the outcome variable. The performance of this statistical modelling framework is tested with some simulation experiments.

The primary objective of this study is to develop new computational methods for robust design optimization (RDO) and reliability-based design optimization (RBDO) of high-dimensional, complex engineering systems. Four major research directions, all anchored in polynomial dimensional decomposition (PDD), have been defined to meet the objective. They involve: (1) development of new sensitivity analysis methods for RDO and RBDO; (2) development of novel optimization methods for solving RDO problems; (3) development of novel optimization methods for solving RBDO problems; and (4) development of a novel scheme and formulation to solve stochastic design optimization problems with both distributional and structural design parameters. The major achievements are as follows. Firstly, three new computational methods were developed for calculating design sensitivities of statistical moments and reliability of high-dimensional complex systems subject to random inputs. The first method represents a novel integration of PDD of a multivariate stochastic response function and score functions, leading to analytical expressions of design sensitivities of the first two moments. The second and third methods, relevant to probability distribution or reliability analysis, exploit two distinct combinations built on PDD: the PDD-SPA method, entailing the saddlepoint approximation (SPA) and score functions; and the PDD-MCS method, utilizing the embedded Monte Carlo simulation (MCS) of the PDD approximation and score functions. For all three methods developed, both the statistical moments or failure probabilities and their design sensitivities are both determined concurrently from a single stochastic analysis or simulation. Secondly, four new methods were developed for RDO of complex engineering systems. The methods involve PDD of a high-dimensional stochastic response for statistical moment analysis, a novel integration of PDD and score functions for calculating the second-moment sensitivities with respect to the design variables, and standard gradient-based optimization algorithms. The methods, depending on how statistical moment and sensitivity analyses are dovetailed with an optimization algorithm, encompass direct, single-step, sequential, and multi-point single-step design processes. Thirdly, two new methods were developed for RBDO of complex engineering systems. The methods involve an adaptive-sparse polynomial dimensional decomposition (AS-PDD) of a high-dimensional stochastic response for reliability analysis, a novel integration of AS-PDD and score functions for calculating the sensitivities of the failure probability with respect to design variables, and standard gradient-based optimization algorithms, resulting in a multi-point, single-step design process. The two methods, depending on how the failure probability and its design sensitivities are evaluated, exploit two distinct combinations built on AS-PDD: the AS-PDD-SPA method, entailing SPA and score functions; and the AS-PDD-MCS method, utilizing the embedded MCS of the AS-PDD approximation and score functions. In addition, a new method, named as the augmented PDD method, was developed for RDO and RBDO subject to mixed design variables, comprising both distributional and structural design variables. The method comprises a new augmented PDD of a high-dimensional stochastic response for statistical moment and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms, leading to a multi-point, single-step design process. The innovative formulations of statistical moment and reliability analysis, design sensitivity analysis, and optimization algorithms have achieved not only highly accurate but also computationally efficient design solutions. Therefore, these new methods are capable of performing industrial-scale design optimization with numerous design variables.

Pfeffermann, Krieger e Rinott (1998) apresentaram uma metodologia para modelar processos de amostragem que pode ser utilizada para avaliar se este processo de amostragem é informativo. Neste cenário, as probabilidades de seleção da amostra são aproximadas por uma função polinomial dependendo das variáveis resposta e concomitantes. Nesta abordagem, nossa principal proposta é investigar a aplicação do teste de significância FBST (Full Bayesian Significance Test), apresentado por Pereira e Stern (1999), como uma ferramenta para testar a ignorabilidade amostral, isto é, para avaliar uma relação de significância entre as probabilidades de seleção da amostra e a variável resposta. A performance desta modelagem estatística é testada com alguns experimentos computacionais. / Pfeffermann, Krieger and Rinott (1998) introduced a framework for modeling sampling processes that can be used to assess if a sampling process is informative. In this setting, sample selection probabilities are approximated by a polynomial function depending on outcome and auxiliary variables. Within this framework, our main purpose is to investigate the application of the Full Bayesian Significance Test (FBST), introduced by Pereira and Stern (1999), as a tool for testing sampling ignorability, that is, to detect a significant relation between the sample selection probabilities and the outcome variable. The performance of this statistical modelling framework is tested with some simulation experiments.

The application of optimization algorithms in the design of many economical and industrial problems currently represents a significant assignment. The development of high-powered computers allows an application of difficult mathematical techniques and physical phenomena to simulate real problems with sufficient accuracy. The optimization techniques used in engineering designs are mostly represented by modified mathematical programming methods with extension of their usability. The aim of the presented thesis "Using Optimization´s Algorithms by Designing of Structures" is to analyze the applicability of optimization procedures which are available in the widely used computing system ANSYS in civil and mechanical engineering practice. The numerical analyses were performed within the frame of multi-extreme, one to three dimensional optimization problems, multi-dimensional problems expressed by minimizing the weight of a truss beam and efficient design of air gap location in wooden studs from the point of view of thermal features of the structure. The analyzed optimization processes are in plurality verified with accurate manual computing and graphical solutions and the accent is put on optimization methods´ possibilities to improve robustness, efficiency and accuracy of the optimization algorithms in civil engineering problems´ designs. The optimization methods represent a suitable approach to improve the efficient design of a wide range of civil and mechanical engineering structures and elements. By combination of their advantages and FEM/FEA method it is possible to achieve very good results, although robustness of the solutions is not guaranteed. The robustness and accuracy of the procedure could be increased by competent exploration of design space and suitable selections of optimization methods´ features.