No description available.

Afin d'optimiser la mise aux milles au laminage à froid, la thèse consiste à développer un modèle prédictif de variation de largeur à la fois précis et rapide pour des utilisations en temps réel. Des efforts ont commencé en 1960s en développant des formules empiriques. Par la suite, la Méthode des Bornes Supérieures (MBS) est devenue la plus connue. [Oh 1975] utilisant le champ de vitesse 3D "simple" prédit bien la variation de largeur au laminage en conditions d'un tandem finisseur. [Komori 2002] a proposé une combinaison des champs fondamentaux et obtenu une structure informatique peu dépendante aux champs de vitesse. Néanmoins, seuls deux champs fondamentaux ont été introduits qui forment un sous-ensemble de la famille 3D "simple". [Serek 2008] a étudié des champs de vitesse quadratique qui inclue la famille "simple" et donne des meilleurs résultats avec un temps de calcul plus long. Le premier résultat de la thèse est un modèle 2D (MBS) avec des champs de vitesse oscillante. Ce modèle aboutit à une vitesse optimale qui oscille spatialement le long de l'emprise. Les résultats (puissance, vitesse...) sont plus proches des ceux de Lam3-Tec3 que la MBS 2D "simple". Pour une modélisation 3D, nous avons choisi la MBS avec la vitesse 3D "simple" et obtenu un très bon accord avec les expériences réalisées sur des produits étroits à Arcelor Mittal [64]. En outre, un nouveau modèle MBS est développé pour une bande bombée et des cylindres droits. Les résultats montrent que la variation de largeur diminue avec la bombée de la bande et correspondent bien à ceux de Lam3-Tec3. Cependant, la MBS admet un comportement rigide-plastique tandis qu'au laminage des bandes larges les déformations élastique et thermique ont des impacts importants sur la déformation plastique. Les modèles existant prenant en compte ces phénomènes [23,64] sont couteux en temps de calcul. Ainsi, l'idée est de décomposer la variation de la largeur de plastique en trois termes : les variations de largeur totales, élastique et thermique à travers la zone de plastique déterminés par trois nouveaux modèles simplifiés. Les deux premiers permettent d'estimer les variations de largeur élastique et plastique avant et après l'emprise. Ils donnent aussi les conditions aux limites au modèle d'emprise qui est en effet la MBS avec le champ de vitesse 3D "simple" permettant d'estimer la variation de la largeur totale. En outre, avec les puissances de déformation et de dissipation plastique de frottement données par le même modèle, la variation de largeur thermique est également obtenue. Le modèle de variation de largeur est donc appelée UBM-Slab combiné, très rapide (0,05 s) et prédit avec précision la largeur de variation par rapport à Lam3-Tec3 (<6%) / In order to save material yields in cold rolling process, the thesis aims at developing a predictive width variation model accurate and fast enough to be used online. Many efforts began in the 1960s in developing empirical formula. Afterward, the Upper Bound Method (UBM ) became more common. [Oh 1975]'s model with 3D "simple" velocity field estimates well the width variation for finishing mill rolling conditions. [Komori 2002] proposed a combination of fundamental ones to obtain a computer program depending minimally on the assumed velocity fields. However, only two fundamental fields were introduced and formed a subset of the "simple" family. [Serek 2008] studied a quadratic velocity family that includes the "simple" one and leads to better results with a higher computing time. Focusing on UBM , the first result of the thesis is a 2D model with an oscillating velocity field family. The model results to an optimum velocity that oscillates spatially throughout the roll-bite. The optimum power and the velocity field are closer to Lam3-Tec3 results than the "simple" one. For 3D modelling, we chose the 3D "simple" UBM and carried a comparison to the experiments performed at Arcelor Mittal using narrow strips [64]. A very good agreement is obtained. Further, a new UBM model is developed for a crowned strip with cylindrical work-rolls. It shows that the width variation decreases as a function of the strip crown and the results match well those of Lam3-Tec3 . However, the UBM considers only a rigid-plastic behaviour while in large strip rolling, the elastic and thermal deformations have important impacts on the plastic one. There exist some models considering these phenomena [23,64] but they are all time-consuming. Thus, the idea is to decompose the plastic width variation into three terms : total, elastic and thermal width variations through the plastic zone that are determined by three new models. The simplified roll-bite entry & exit models allow estimating the elastic and plastic width variations before and after the roll-bite. They give equally the longitudinal stresses defining the boundary conditions for the roll-bite model which is indeed the 3D "simple" UBM approximating the total width variation term. Moreover, with the plastic deformation and friction dissipation powers given by the same model, the thermal width variation term is also obtained. The width variation model, called UBM-Slab combined is very fast (0.05s) and predicts accurately the width variation in comparison with Lam3-Tec3 (<6%)

Querying and efficiently analyzing complex data is required to gain valuable business insights, to support machine learning applications, and to make up-to-date information available. Therefore, this thesis investigates opportunities and challenges of selecting the most efficient execution strategy for analytical queries. These challenges include hard-to-capture data characteristics such as skew and correlation, the support of arbitrary data types, and the optimization time overhead of complex queries. Existing approaches often rely on optimistic assumptions about the data distribution, which can result in significant response time delays when these assumptions are not met. On the contrary, we focus on robust query optimization, emphasizing consistent query performance and applicability. Our presentation follows the general select-project-join query pattern, representing the fundamental stages of analytical query processing. To support arbitrary data types and complex filter expressions in the select stage, a novel sampling-based selectivity estimator is developed. Our approach exploits information from filter subexpressions and estimates correlations that are not captured by existing sampling-based methods. We demonstrate improved estimation accuracy and query execution time. Further, to minimize the runtime overhead of sampling, we propose new techniques that exploit access patterns and auxiliary database objects such as indices. For the join stage, we introduce a robust optimization approach by developing an upper-bound join enumeration strategy that connects accurate filter selectivity estimates –e.g., using our sampling-based approach– to join ordering. We demonstrate that join orders based on our upper-bound join ordering strategy achieve more consistent performance and faster workload execution on state-of-the-art database systems. However, besides identifying good logical join orders, it is crucial to determine appropriate physical join operators before query plan execution. To understand the importance of fine-grained physical operator selections, we exhaustively execute fixed join orders with all possible operator combinations. This analysis reveals that none of the investigated query optimizers fully reaches the potential of optimal operator decisions. Based on these insights and to achieve fine-grained operator selections for the previously determined join orders, the thesis presents a lightweight learning-based physical execution plan refinement component called. We show that this refinement component consistently outperforms existing approaches for physical operator selection while enabling a novel two-stage optimizer design. We conclude the thesis by providing a framework for the two-stage optimizer design that allows users to modify, replicate, and further analyze the concepts discussed throughout this thesis.:1 INTRODUCTION 1.1 Analytical Query Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2 Select-Project-Join Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.3 Basics of SPJ Query Optimization . . . . . . . . . . . . . . . . . . . . . . . 14 1.3.1 Plan Enumeration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3.2 Cost Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.3.3 Cardinality Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4 Robust SPJ Query Optimization . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4.1 Tail Latency Root Cause Analysis . . . . . . . . . . . . . . . . . . . 17 1.4.2 Tenets of Robust Query Optimization . . . . . . . . . . . . . . . . . 19 1.5 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.6 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2 SELECT (-PROJECT) STAGE 2.1 Sampling for Selectivity Estimation . . . . . . . . . . . . . . . . . . . . . . 24 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.2.1 Combined Selectivity Estimation (CSE) . . . . . . . . . . . . . . . . 29 2.2.2 Kernel Density Estimator . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.3 Beta Estimator for 0-Tuple-Situations . . . . . . . . . . . . . . . . . . . . . 33 2.3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.3.2 Beta Distribution in Non-0-TS . . . . . . . . . . . . . . . . . . . . . . 35 2.3.3 Parameter Estimation in 0-TS . . . . . . . . . . . . . . . . . . . . . . 37 2.3.4 Selectivity Estimation and Predicate Ordering . . . . . . . . . . . 39 2.3.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.4 Customized Sampling Techniques . . . . . . . . . . . . . . . . . . . . . . 53 2.4.1 Focused Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.4.2 Conditional Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.4.3 Zone Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3 JOIN STAGE: LOGICAL ENUMERATION 3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.1.1 Point Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.1.2 Join Cardinality Upper Bound . . . . . . . . . . . . . . . . . . . . . 64 3.2 Upper Bound Join Enumeration with Synopsis (UES) . . . . . . . . . . . . 66 3.2.1 U-Block: Simple Upper Bound for Joins . . . . . . . . . . . . . . . . 67 3.2.2 E-Block: Customized Enumeration Scheme . . . . . . . . . . . . . 68 3.2.3 UES Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.3.1 General Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.3.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4 JOIN STAGE: PHYSICAL OPERATOR SELECTION 4.1 Operator Selection vs Join Ordering . . . . . . . . . . . . . . . . . . . . . 77 4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.2.1 Adaptive Query Processing . . . . . . . . . . . . . . . . . . . . . . 80 4.2.2 Bandit Optimizer (Bao) . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.3 TONIC: Learned Physical Join Operator Selection . . . . . . . . . . . . . 82 4.3.1 Query Execution Plan Synopsis (QEP-S) . . . . . . . . . . . . . . . 83 4.3.2 QEP-S Life-Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.3.3 QEP-S Design Considerations . . . . . . . . . . . . . . . . . . . . . . 87 4.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.4.1 Performance Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.4.2 Rate of Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.4.3 Data Shift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.4.4 TONIC - Runtime Traits . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 TWO-STAGE OPTIMIZER FRAMEWORK 5.1 Upper-Bound-Driven Join Ordering Component . . . . . . . . . . . . . 101 5.2 Physical Operator Selection Component . . . . . . . . . . . . . . . . . . 103 5.3 Example Query Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 103 6 CONCLUSION 107 BIBLIOGRAPHY 109 LIST OF FIGURES 117 LIST OF TABLES 121 A APPENDIX A.1 Basics of Query Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 A.2 Why Q? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 A.3 0-TS Proof of Unbiased Estimate . . . . . . . . . . . . . . . . . . . . . . . . 125 A.4 UES Upper Bound Property . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 A.5 TONIC – Selectivity-Aware Branching . . . . . . . . . . . . . . . . . . . . . 128 A.6 TONIC – Sequences of Query Execution . . . . . . . . . . . . . . . . . . . 129

Querying and efficiently analyzing complex data is required to gain valuable business insights, to support machine learning applications, and to make up-to-date information available. Therefore, this thesis investigates opportunities and challenges of selecting the most efficient execution strategy for analytical queries. These challenges include hard-to-capture data characteristics such as skew and correlation, the support of arbitrary data types, and the optimization time overhead of complex queries. Existing approaches often rely on optimistic assumptions about the data distribution, which can result in significant response time delays when these assumptions are not met. On the contrary, we focus on robust query optimization, emphasizing consistent query performance and applicability. Our presentation follows the general select-project-join query pattern, representing the fundamental stages of analytical query processing. To support arbitrary data types and complex filter expressions in the select stage, a novel sampling-based selectivity estimator is developed. Our approach exploits information from filter subexpressions and estimates correlations that are not captured by existing sampling-based methods. We demonstrate improved estimation accuracy and query execution time. Further, to minimize the runtime overhead of sampling, we propose new techniques that exploit access patterns and auxiliary database objects such as indices. For the join stage, we introduce a robust optimization approach by developing an upper-bound join enumeration strategy that connects accurate filter selectivity estimates –e.g., using our sampling-based approach– to join ordering. We demonstrate that join orders based on our upper-bound join ordering strategy achieve more consistent performance and faster workload execution on state-of-the-art database systems. However, besides identifying good logical join orders, it is crucial to determine appropriate physical join operators before query plan execution. To understand the importance of fine-grained physical operator selections, we exhaustively execute fixed join orders with all possible operator combinations. This analysis reveals that none of the investigated query optimizers fully reaches the potential of optimal operator decisions. Based on these insights and to achieve fine-grained operator selections for the previously determined join orders, the thesis presents a lightweight learning-based physical execution plan refinement component called. We show that this refinement component consistently outperforms existing approaches for physical operator selection while enabling a novel two-stage optimizer design. We conclude the thesis by providing a framework for the two-stage optimizer design that allows users to modify, replicate, and further analyze the concepts discussed throughout this thesis.:1 INTRODUCTION 1.1 Analytical Query Processing . . . . . . . . . . . . . . . . . . . 12 1.2 Select-Project-Join Queries . . . . . . . . . . . . . . . . . . . 13 1.3 Basics of SPJ Query Optimization . . . . . . . . . . . . . . . . . 14 1.3.1 Plan Enumeration . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3.2 Cost Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.3.3 Cardinality Estimation . . . . . . . . . . . . . . . . . . . . . 15 1.4 Robust SPJ Query Optimization . . . . . . . . . . . . . . . . . . 16 1.4.1 Tail Latency Root Cause Analysis . . . . . . . . . . . . . . . . 17 1.4.2 Tenets of Robust Query Optimization . . . . . . . . . . . . . . 19 1.5 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.6 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2 SELECT (-PROJECT) STAGE 2.1 Sampling for Selectivity Estimation . . . . . . . . . . . . . . . 24 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.2.1 Combined Selectivity Estimation (CSE) . . . . . . . . . . . . . 29 2.2.2 Kernel Density Estimator . . . . . . . . . . . . . . . . . . . . 31 2.2.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . 32 2.3 Beta Estimator for 0-Tuple-Situations . . . . . . . . . . . . . . 33 2.3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.3.2 Beta Distribution in Non-0-TS . . . . . . . . . . . . . . . . . 35 2.3.3 Parameter Estimation in 0-TS . . . . . . . . . . . . . . . . . . 37 2.3.4 Selectivity Estimation and Predicate Ordering . . . . . . . . . 39 2.3.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.4 Customized Sampling Techniques . . . . . . . . . . . . . . . . . . 53 2.4.1 Focused Sampling . . . . . . . . . . . . . . . . . . . . . . . . 54 2.4.2 Conditional Sampling . . . . . . . . . . . . . . . . . . . . . . 56 2.4.3 Zone Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3 JOIN STAGE: LOGICAL ENUMERATION 3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.1.1 Point Estimates . . . . . . . . . . . . . . . . . . . . . . . . 63 3.1.2 Join Cardinality Upper Bound . . . . . . . . . . . . . . . . . . 64 3.2 Upper Bound Join Enumeration with Synopsis (UES) . . . . . . . . . 66 3.2.1 U-Block: Simple Upper Bound for Joins . . . . . . . . . . . . . 67 3.2.2 E-Block: Customized Enumeration Scheme . . . . . . . . . . . . . 68 3.2.3 UES Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.3.1 General Performance . . . . . . . . . . . . . . . . . . . . . . 72 3.3.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4 JOIN STAGE: PHYSICAL OPERATOR SELECTION 4.1 Operator Selection vs Join Ordering . . . . . . . . . . . . . . . 77 4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.2.1 Adaptive Query Processing . . . . . . . . . . . . . . . . . . . 80 4.2.2 Bandit Optimizer (Bao) . . . . . . . . . . . . . . . . . . . . . 81 4.3 TONIC: Learned Physical Join Operator Selection . . . . . . . . . 82 4.3.1 Query Execution Plan Synopsis (QEP-S) . . . . . . . . . . . . . 83 4.3.2 QEP-S Life-Cycle . . . . . . . . . . . . . . . . . . . . . . . . 84 4.3.3 QEP-S Design Considerations . . . . . . . . . . . . . . . . . . 87 4.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.4.1 Performance Factors . . . . . . . . . . . . . . . . . . . . . . 90 4.4.2 Rate of Improvement . . . . . . . . . . . . . . . . . . . . . . 92 4.4.3 Data Shift . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.4.4 TONIC - Runtime Traits . . . . . . . . . . . . . . . . . . . . . 97 4.4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 TWO-STAGE OPTIMIZER FRAMEWORK 5.1 Upper-Bound-Driven Join Ordering Component . . . . . . . . . . . . 101 5.2 Physical Operator Selection Component . . . . . . . . . . . . . . 103 5.3 Example Query Optimization . . . . . . . . . . . . . . . . . . . . 103 6 CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 A APPENDIX A.1 Basics of Query Execution . . . . . . . . . . . . . . . . . . . . 123 A.2 Why Q? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 A.3 0-TS Proof of Unbiased Estimate . . . . . . . . . . . . . . . . . 125 A.4 UES Upper Bound Property . . . . . . . . . . . . . . . . . . . . . 127 A.5 TONIC – Selectivity-Aware Branching . . . . . . . . . . . . . . . 128 A.6 TONIC – Sequences of Query Execution . . . . . . . . . . . . . . . 129

Power system state estimation (PSSE) is an energy management system function responsible for the computation of the most likely values of state variables viz., bus voltage magnitudes and angles. The state estimation is obtained within a network at a given instant by solving a system of mostly non-linear equations whose parameters are the redundant measurements, both static such as transformer/line parameters and dynamic such as, status of circuit breakers/isolators, transformer tap positions, active/reactive power flows, generator active/reactive power outputs etc. PSSE involves solving an over determined set of nonlinear equations by minimizing a weighted norm of the measurement residuals. Typically, the L1 and L2 norms are employed. The use of L2 norm leads to state estimation based on the weighted least squares (WLS) criterion. This method is known to exhibit efficient filtering capability when the errors are Gaussian but fails in the case of presence of bad data. The method of hypothesis testing identification can be incorporated into the WLS estimator to detect and identify bad data. Nevertheless, it is prone to failure when the measurement is a leverage point. On the other hand state estimation based on the weighted least absolute value (WLAV) criterion using L1 norm, has superior bad data suppression capability. But it also fails in rejecting bad data measurements associated with leverage points. Leverage points are highly influential measurements that attract the state estimator solution towards them. Consequently, much research effort has focused recently, on producing a LAV estimator that remains robust in the presence of bad leverage measurements. This problem has been addressed in the thesis work. Two methods, which aims development of robust estimator that are insensitive to bad leverage points, have been proposed viz., (i) The objective function used here is obtained by linearizing L2 norm of the error function. In addition to the constraints corresponding to measurement set, constraints corresponding to bounds of state variables are also involved. Linear programming (LP) optimization is carried out using upper bound optimization technique. (ii) A hybrid optimization algorithm which is combination of”upper bound optimization technique” and ”an improved algorithm for discrete l1 linear approximation”, to restrict the state variables not to leave the basis during optimization process. Linear programming optimization, with bounds of state variables as additional constraints is carried out using the proposed hybrid optimization algorithm. The proposed state estimator algorithms are tested on 24-bus EHV equivalent of southern power network, 36-bus EHV equivalent of western grid, 205-bus interconnected grid system of southern region and IEEE-39 bus New England system. Performances of the proposed two methods are compared with the WLAV estimator in the presence of bad data associated with leverage points. Also, the effect of bad leverage measurements on the interacting bad data, which are non-leverage, has been compared. Results show that proposed state estimator algorithms rejects bad data associated with leverage points efficiently.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho apresenta uma estimativa a priori para o limite superior da distribuição de temperatura considerando um problema em regime permanente em um corpo com uma condutividade térmica dependente da temperatura. A discussão é realizada supondo que as condições de contorno são lineares (lei de Newton do resfriamento) e que a condutividade térmica é constante por partes (quando considerada como uma função da temperatura). Estas estimativas consistem em uma ferramenta poderosa que pode prescindir da necessidade de uma simulação numérica cara de um problema de transferência de calor não linear, sempre que for suficiente conhecer o valor mais alto de temperatura. Nestes casos, a metodologia proposta neste trabalho é mais eficaz do que as aproximações usuais que assumem tanto a condutividade térmica quanto as fontes de calor como constantes. / This article presents an a priori upper bound estimate for the steady-state temperature distribution in a body with a temperature-dependent thermal conductivity. The discussion is carried out assuming linear boundary conditions (Newton law of cooling) and a piecewise constant thermal conductivity (when regarded as a function of the temperature). These estimates consist of a powerful tool that may circumvent an expensive numerical simulation of a nonlinear heat transfer problem, whenever it suffices to know the highest temperature value. In these cases the methodology proposed in this work is more effective than the usual approximations that assume thermal conductivities and heat sources as constants.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho apresenta uma estimativa a priori para o limite superior da distribuição de temperatura considerando um problema em regime permanente em um corpo com uma condutividade térmica dependente da temperatura. A discussão é realizada supondo que as condições de contorno são lineares (lei de Newton do resfriamento) e que a condutividade térmica é constante por partes (quando considerada como uma função da temperatura). Estas estimativas consistem em uma ferramenta poderosa que pode prescindir da necessidade de uma simulação numérica cara de um problema de transferência de calor não linear, sempre que for suficiente conhecer o valor mais alto de temperatura. Nestes casos, a metodologia proposta neste trabalho é mais eficaz do que as aproximações usuais que assumem tanto a condutividade térmica quanto as fontes de calor como constantes. / This article presents an a priori upper bound estimate for the steady-state temperature distribution in a body with a temperature-dependent thermal conductivity. The discussion is carried out assuming linear boundary conditions (Newton law of cooling) and a piecewise constant thermal conductivity (when regarded as a function of the temperature). These estimates consist of a powerful tool that may circumvent an expensive numerical simulation of a nonlinear heat transfer problem, whenever it suffices to know the highest temperature value. In these cases the methodology proposed in this work is more effective than the usual approximations that assume thermal conductivities and heat sources as constants.

No description available.

In dieser Arbeit werden die zu einem m-sektoriellen Operator assoziierten Halbgruppen betrachtet, die die Lösungen des Anfangswertproblems der zugehörigen Evolutionsgleichung beschreiben. Es wird eine 1987 von Davies veröffentlichte Methode zur Abschätzung dieser Halbgruppen verallgemeinert. Einen Schwerpunkt bilden die zu Dirichlet-Formen assoziierten Markov-Halbgruppen. Für diese werden die Resultate spezialisiert und der Zusammenhang zur intrinsischen Metrik dargelegt. Die Arbeit schließt mit verschiedenen Beispielen, die zeigen, wie mit diesen Verallgemeinerungen von Davies Methode neue Anwendungsgebiete erschlossen werden können.:Einleitung Funktionalanalytische Grundlagen Spezielle Halbgruppeneigenschaften Symmetrische Dirichlet-Formen Obere Schranken für die Halbgruppe Anwendungen Ausblick Komplexe Maße Anhang / This thesis is about m-sectorial operators and their associated semigroups describing the solutions of the initial value problem of the corresponding evolution equation. We generalize a method published by Davies 1987 to estimate these semigroups. A focus is set on Markov semigroups associated with Dirchlet forms. The results are applied to them and the connection to the intrinsic metric is presented. The thesis ends with different examples showing how this generalization of Davies method can be applied into new fields of application.:Einleitung Funktionalanalytische Grundlagen Spezielle Halbgruppeneigenschaften Symmetrische Dirichlet-Formen Obere Schranken für die Halbgruppe Anwendungen Ausblick Komplexe Maße Anhang

This research aims to investigate the behaviour of glass fibre reinforced polymer bars (GFRP) reinforced continuous concrete deep beams. For this purpose, experimental, analytical and numerical studies were conducted. Nine continuous concrete deep beams reinforced with GFRP bars and one specimen reinforced with steel bars were experimentally tested to failure. The investigated parameters included shear span-to-overall depth ratio (𝑎/ℎ), size effect and web reinforcement ratio. Two 𝑎/ℎ ratios of 1.0 and 1.7 and three section heights of 300 mm, 600 mm and 800 mm as well as two web reinforcement ratios of 0% and 0.4% were used. The longitudinal reinforcement, compressive strength and beam width were kept constant at 1.2%, ≈55 MPa and 175 mm, respectively. The web reinforcement ratio achieved the minimum requirements of the CSA S806-12. The experimental results highlighted that the web reinforcement ratio improved the load capacities by about 10% and 18% for specimens having 𝑎/ℎ ratios of 1.0 and 1.7, respectively. For specimens with web reinforcement, the increase of 𝑎/ℎ ratio from 1.0 to 1.7 led to reductions in the load carrying capacity by about 33% and 29% for beams with overall depths of 300 mm and 600 mm, respectively. Additionally, a considerable reduction occurred in the shear strength due to the increase of the section depth from 300 mm to 600 mm. The experimental results confirmed the impacts of web reinforcement and size effect that were not considered by the strut-and-tie method (STM) of the only code provision, the Canadian S806-12, that addressed such elements. In this study, the STM was illustrated and simplified to be adopted for GFRP RC continuous deep beams, and then, the experimental results obtained from this study were employed to assess the performance of the effectiveness factors suggested by the STMs of the American (ACI 318-2014), European (EC2-04) and Canadian (S806-12) codes as well as those factors recommended by the previous studies to predict the load capacities. It was found that these methods were unable to reflect the influences of member size and/or web reinforcement reasonably, the impact of which has been confirmed by the current experimental investigation. Therefore, a new effectiveness factor was recommended to be used with the STM. Additionally, an upper bound analysis was developed to predict the load capacities of the tested specimens considering a reduced bond strength of GFRP bars after assessing the old version recommended for steel RC continuous deep beams. A good agreement between the predicted results and the measured ones was obtained with the mean and coefficient of variation values for experimental/calculated results of 1.02 and 5.9%, respectively, for the STM and 1.03 and 8.6%, respectively, for the upper-bound analysis. A 2D finite element analysis using ABAQUS/Explicit approach was carried out to introduce a model able to estimate the response of GFRP RC continuous deep beams. Based on the experimental results extracted from the pullout tests, the interface between the longitudinal reinforcement and concrete surface was modelled using a cohesive element (COH2D4) tool available in ABAQUS. Furthermore, a perfect bond between the longitudinal reinforcement and surrounding concrete was also modelled to evaluate the validity of this assumption introduced by many previous FE studies. To achieve a reasonable agreement with the test results, a sensitivity analysis was implemented to select the proper mesh size and concrete model variables. The suitability and capability of the developed FE model were demonstrated by comparing its predictions with the test results of beams tested experimentally. Model validation showed a reasonable agreement with the experiments in terms of the failure mode, total failure load and the load-deflection responses. The perfect bond model has overestimated the predicted results in terms of stiffness behaviour and failure load, while the cohesive element model was more suitable to reflect the behaviour of those specimens. The validated FE model was then employed to implement a parametric study for the key parameters that govern the behaviour of beams tested and to achieve an in depth understanding of such elements. The parametric study showed that the higher the 𝑎/ℎ ratio the more pronounced the effect of web and the longitudinal reinforcements and the lower the effect of concrete compressive strength; and vice versa when 𝑎/ℎ ratio reduces.