The second report from the Project-Group ESRF-Beamline of the Forschungszentrum Rossendorf covers the period from July 1999 until December 2000. The ROssendorf BeamLine (ROBL) at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France performed quite well during this time. In the beamtime used by the FZR and collaborating institutes 44 scheduled experiments were performed, while in the ESRF scheduled beamtime 12 experiments. Additionally, a distinct amount of beamtime was devoted to in-house research of the FZR and methodical experiments. Since February 2000 ROBL is part of the European Commission's programme "Access to Research Infrastructure" which supports user groups from member and associated states of the EU during experiments at ROBL. In the year 2000 ROBL hosted 6 groups for experiments. The report is organised in three main parts. The first part contains extended contributions on results obtained at ROBL. The second part gives an overview about the scheduled experiments, publications, guests having visited ROBL with support of the EC, and some other information. Finally, the third part collects the experimental reports of the user groups received.

<div>Many real-world application problems can be modeled mathematically as constrained convex optimization problems. The scale of such problems can be extremely large, posing significant challenges to traditional centralized algorithms and calling for efficient and scalable distributed algorithms. However, most of the existing works on distributed optimization have been focusing on block-separable problems with simple, linear constraints, such as the consensus-type constraints. The focus of this dissertation is to propose distributed algorithms to solve (possibly non-separable) large-scale optimization problems with more complicated constraints with parallel updating (aka in Jacobi fashion), instead of sequential updating in the form of Gauss-Seidel iterations. In achieving so, this dissertation extends the predictor corrector proximal multiplier method (PCPM) to address three issues when solving a large-scale constrained convex optimization problem: (i) non-linear coupling constraints; (ii) asynchronous iterative scheme; (iii) non-separable objective function and coupling constraints. </div><div><br></div><div>The idea of the PCPM algorithm is to introduce a predictor variable for the Lagrangian multiplier to avoid the augmented term, hence removing the coupling of block variables while still achieving convergence without restrictive assumptions. Building upon this algorithmic idea, we propose three distributed algorithms: (i) N-block PCPM algorithm for solving N-block convex optimization problems with both linear and nonlinear coupling constraints; (ii) asynchronous N-block PCPM algorithm for solving linearly constrained N-block convex optimization problems; (iii) a distributed algorithm, named PC²PM, for solving large-scale convex quadratically constrained quadratic programs (QCQPs). The global convergence is established for each of the three algorithms. Extensive numerical experiments, using various data sets, are conducted on either a single-node computer or a multi-node computer cluster through message passing interface (MPI). Numerical results demonstrate the efficiency and scalability of the proposed algorithms.</div><div><br></div><div>A real application of the N-block PCPM algorithm to solve electricity capacity expansion models is also studied in this dissertation. A hybrid scenario-node-realization decomposition method, with extended nonanticipativity constraints, is proposed for solving the resulting large-scale optimization problem from a multi-scale, multi-stage stochastic program under various uncertainties with different temporal scales. A technique of orthogonal projection is exploited to simplify the iteration steps, which leads to a simplified N-block PCPM algorithm amenable to massive parallelization during iterations. Such an algorithm exhibits much more scalable numerical performance when compared with the widely used progressive hedging approach (PHA) for solving stochastic programming problems.</div>

In South Africa, the levels of unemployment and poverty are extremely high and these are two of South Africa’s most pressing problems. Over the past 28 years several major programmes have been initiated in South Africa to counter unemployment and poverty. Between 1980 and 1994, the former government spent billions of Rands on large-scale development programmes with the stated objective of using labour-intensive methods during the provision of physical infrastructure, to create employment and alleviate poverty. However, this did not solve the unemployment problem. Since 1994 the African National Congress (ANC) government has implemented large-scale programmes with similar objectives to those before 1994. After an analysis of the theoretical premises and implementation of labour-intensive public works programmes in Africa, the thesis critically evaluated several pre - and post - 1994 large-scale development programmes in South Africa. Major conclusions are that very little sustainable employment was created and there was no long-term programme approach to address poverty alleviation. Furthermore, lessons that could have been learnt from pre – 1994 have not been applied in the post 1994 period. Shortcomings in programme planning and implementation of large-scale development programmes in South Africa still exist. Another major conclusion is that the pre-1994 lessons were not taken into considerations in the post-1994 programme planning and implementation. Based on the research, the author has derived a six phase Programme Management Framework for Development Programmes. This framework embodies a long-term programme management approach to the planning and implementation of large-scale, labour-intensive development programmes.

There is limited information available regarding the strategies surrounding the construction and logistical issues that will be faced in connection with the up-scaling of wind turbine technology. As wind turbines are manufactured at greater sizes, the industry will need to face these issues head-on to avoid many obstacles pertaining to transportation from the manufacturer to the project site, and the installation of massive turbine components at soaring heights.  The purpose of this study is to supply a preliminary base model to evaluate two factors; the maximum heights at which the most popular installation methods in the industry could be used, and if it is feasible to transport and construct these growing components for a neutral project location. Data was collected from open-ended surveys sent out to industry professionals through networking channels and review of prior literature surrounding the issues in question. This data led to the largest reference turbine for this study being chosen at 20 MW.  The model was then developed by analyzing the load sizes and weights of components for five reference turbines ranging from 5 MW to 20 MW. These were cross-examined with crane load charts and the maximum lifting capacity for helicopters to obtain the results.  This research contributes to the body of previously published works and identifies a model that could be useful for wind power developers looking at constructing projects with very large wind turbines.

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