Techn. Universiẗat, Diss., 2003--Berlin.

University, Diss., 2005--Saarbrücken.

No description available.

The analysis of fracture toughness test data from standard specimens is often based upon the assumptions of planar crack fronts and homogenous material properties. However, these assumptions do not hold true for all test geometries or real components. The overall objective of this EngD was therefore to develop the methodologies used in fracture assessment of steel components, by incorporating a reduction in the conservatisms inherent in the assessment procedures. These conservatisms are associated with applying a ‘lower bound’ treatment to steel components, which in reality contain significant variability in effective fracture toughness, due to either material considerations (macroscopic or microstructural), or geometrical considerations including the effect of crack tip constraint.The first method developed allows a comparison of a variation of fracture toughness values throughout a component, to a variation of the localised effective crack driving force. The main feature of this method takes advantage of the nature of the ductile-to-brittle transition regime of fracture toughness, where there is significant scatter. This leads to a probabilistic prediction of the location of fracture initiation, and a less conservative estimate of failure load, used to derive enhanced fracture toughness for the component. The second method calculates less conservative fracture toughness values for steels where there is significant heterogeneity in the dataset. The effects of measurement uncertainty on derived fracture toughness values can be monitored to improve probabilistic estimates of the heterogeneous fracture toughness values. These methods have been developed into predictive software tools, validated against data from the literature.Finite element analysis of various configurations of compact tension and bend specimen, under different constraint conditions, was used to identify fracture mechanics parameters and constraint factors that will be of use in deriving accurate fracture toughness relationships from future testing programmes. The viability of low constraint specimens for accurately characterising increases in fracture toughness has been assessed. These recommendations enhance the relationships and advice suggested in the testing standards and literature. Loss of constraint in thin components can be quantified by a triaxiality parameter, which can be used to predict an increase in fracture toughness through use of a damage model, in this case developed based on a ductility exhaustion approach. This model can be used to predict initiation of ductile fracture in configurations with low constraint, leading to less conservative fracture toughness values, enhancing the guidance in the various defect tolerance assessment procedures.

Constraint programming (CP) is a technology in which a combinatorial problem is modeled declaratively as a conjunction of constraints, each of which captures some of the combinatorial substructure of the problem. Constraints are more than a modeling convenience: every constraint is partially implemented by an inference algorithm, called a propagator, that rules out some but not necessarily all infeasible candidate values of one or more unknowns in the scope of the constraint. Interleaving propagation with systematic search leads to a powerful and complete solution method, combining a high degree of re-usability with natural, high-level modeling. A propagator can be characterized as a sound approximation of a constraint on an abstraction of sets of candidate values; propagators that share an abstraction are similar in the strength of the inference they perform when identifying infeasible candidate values. In this thesis, we consider abstractions of sets of candidate values that may be described by an elegant mathematical formalism, the Galois connection. We develop a theoretical framework from the correspondence between Galois connections and propagators, unifying two disparate views of the abstraction-propagation connection, namely the oft-overlooked distinction between representational and computational over-approximations. Our framework yields compact definitions of propagator strength, even in complicated cases (i.e., involving several types, or unknowns with internal structure); it also yields a method for the principled derivation of propagators from constraint definitions. We apply this framework to the extension of an existing CP solver to constraints over strings, that is, words of finite length. We define, via a Galois connection, an over-approximation for bounded-length strings, and demonstrate two different methods for implementing this overapproximation in a CP solver. First we use the Galois connection to derive a bounded-length string representation as an aggregation of existing scalar types; propagators for this representation are obtained by manual derivation, or automated synthesis, or a combination. Then we implement a string variable type, motivating design choices with knowledge gained from the construction of the over-approximation. The resulting CP solver extension not only substantially eases modeling for combinatorial string problems, but also leads to substantial efficiency improvements over prior CP methods.

One of the challenges for the constraint satisfaction community has been to develop an automated approach to solving Constraint Satisfaction Problems (CSPs) rather than creating specific algorithms for specific problems. Much of this work has concentrated on the development and improvement of general purpose backtracking techniques. However, the success of relatively simple local search techniques on larger satisfiability problems [Selman et a!. 1992] and CSPs such as the n-queens [Minton et al. 1992] has caused interest in applying local search to constraint satisfaction. In this thesis we look at the usefulness of constraint weighting as a local search technique for constraint satisfaction. The work is based on the clause weighting ideas of Selman and Kautz [1993] and Moths [1993] and applies, evaluates and extends these ideas from the satisfiability domain to the more general domain of CSPs. Specifically, the contributions of the thesis are: 1. The introduction of a local search taxonomy. We examine the various better known local search techniques and recognise four basic strategies: restart, randomness, memory and weighting. 2. The extension of the CSP modelling framework. In order to represent and efficiently solve more realistic problems we extend the C SP modelling framework to include array-based domains and array-based domain use constraints. 3. The empirical evaluation of constraint weighting. We compare the performance of three constraint weighting strategies on a range of CSP and satisflability problems and with several other local search techniques. We find that no one technique dominates in all problem domains. 4. The characterisation of constraint weighting performance. Based on our empirical study we identiIS' the weighting behaviours and problem features that favour constrtt weighting. We conclude weighting does better on structured problems where the algorithm can recognise a harder sub-group of constraints. 5. The extension of constraint weighting. We introduce an efficient arc weighting algorithm that additionally weights connections between constraints that are simultaneously violated at a local minimum. This algorithm is empirically shown to outperform standard constraint weighting on a range of CSPs and within a general constraint solving system. Also we look at combining constraint weighting with other local search heuristics and find that these hybrid techniques can do well on problems where the parent algorithms are evenly matched. 6. The application of constraint weighting to over constrained domains. Our empirical work suggests constraint weighting does well for problems with distinctions between constraint groups. This led us to investigate solving real-world over constrained problems with hard and soft constraint groups and to introduce two dynamic constraint weighting heuristics that maintain a distinction between hard and soft constraint groups while still adding weights to violated constraints in a local minimum. In an empirical study, the dynamic schemes are shown to outperform other fixed weighting and non-weighting systems on a range of real world problems. In addition, the performance of weighting is shown to degrade less severely when soft constraints are added to the system, suggesting constraint weighting is especially applicable to realistic, hard and soft constraint problems

Many real world problems are naturally distributed, whether they are spatially, cognitively, or otherwise. Distributed problems naturally lend themselves to solutions using multi-agent paradigms. Distributed Constraint Satisfaction Problems (DisCSPs) are a class of such distributed problems. In DisCSPs, variables and constraints are distributed between agents. Most distributed algorithms, although exponential in the worst-case, can have a good performance in the average case. The main purpose of this research is to statistically assess difference between the empirical performances of major state of the art DisCSP algorithms including Multi-Sectioned Constraint Network (MSCN) based algorithms, that have never been empirically compared against other DisCSP algorithms. In this thesis, we select a set of state of the art DisCSP algorithms and compare them on randomly generated instances of binary DisCSPs with a wide range of characteristics. Distributed algorithms ADOPT, DSA, DPOP, and MSCN based algorithms were selected based on a set of high level criteria. We explore how these algorithms relatively compare with each other on a range of DisCSPs with different parameters. Their performances are evaluated according to computation time (in the form of non-concurrent computational steps or NCCCs) and communication load (in the form of number of messages as well as volume of messages). Statistical parametric tests are used to aid interpretation of the performance results. In addition, this thesis discusses privacy issues associated with these DisCSP algorithms.

Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial (optimisation) problems. Constraint solving a given problem proceeds in two phases: modelling and solving. Effective modelling has an huge impact on the performance of the solving process. This thesis presents a framework in which the users are not required to make modelling decisions, concrete CP models are automatically generated from a high level problem specification. In this framework, modelling decisions are encoded as generic rewrite rules applicable to many different problems. First, modelling decisions are divided into two broad categories. This categorisation guides the automation of each kind of modelling decision and also leads us to the architecture of the automated modelling tool. Second, a domain-specific declarative rewrite rule language is introduced. Thanks to the rule language, automated modelling transformations and the core system are decoupled. The rule language greatly increases the extensibility and maintainability of the rewrite rules database. The database of rules represents the modelling knowledge acquired after analysis of expert models. This database must be easily extensible to best benefit from the active research on constraint modelling. Third, the automated modelling system Conjure is implemented as a realisation of these ideas; having an implementation enables empirical testing of the quality of generated models. The ease with which rewrite rules can be encoded to produce good models is shown. Furthermore, thanks to the generality of the system, one needs to add a very small number of rules to encode many transformations. Finally, the work is evaluated by comparing the generated models to expert models found in the literature for a wide variety of benchmark problems. This evaluation confirms the hypothesis that expert models can be automatically generated starting from high level problem specifications. An method of automatically identifying good models is also presented. In summary, this thesis presents a framework to enable the automatic generation of efficient constraint models from problem specifications. It provides a pleasant environment for both problem owners and modelling experts. Problem owners are presented with a fully automated constraint solution process, once they have a precise description of their problem. Modelling experts can now encode their precious modelling expertise as rewrite rules instead of merely modelling a single problem; resulting in reusable constraint modelling knowledge.

We propose a prototype model of market dynamics in which all functional relationships are linear. We take into account three borders, defined by linear functions, which are intrinsic to the economic reasoning: non-negativity of prices; downward rigidity of capacity (depreciation) and a capacity constraint for the production decision. Given the linear specification, the borders are the only source for the emerging of cyclical and more complex dynamics. In particular, we discuss centre bifurcations, border collision bifurcations and degenerate flip bifurcations - dynamic phenomena the occurrence of which are intimately related to the existence of borders. / Series: Department of Economics Working Paper Series

We explore the effects of Brexit on trade patterns and on the spatial distribution of industry between the United Kingdom and the European Union and within the EU. Our study adopts a new economic geography (NEG) perspective developing a linear model with three regions, the UK and two separated regions composing the EU. The 3-region framework and linear demands allow for different trade patterns. Two possible ante-Brexit situations are possible, depending on the interplay between local market size, local competition and trade costs: industrial agglomeration or dispersion. Considering a soft and a hard Brexit scenario, the ante-Brexit situation is altered substantially, depending on which scenario prevails. UK firms could move to the larger EU market, even in the peripheral region, reacting to the higher trade barriers, relocation representing a substitute for trade. Alternatively, some EU firms could move in the more isolated UK market finding shelter from the competition inside the EU. We also consider the post-Brexit scenario of deeper EU integration, leading to a weakening of trade links between the EU and the UK. Our analysis also reveals a highly complex bifurcation sequence leading to many instances of multistability, intricate basins of attraction and cyclical and chaotic dynamics. / Series: Department of Economics Working Paper Series