Nicole Poret retrieving decomposition bags to measure mangrove fine root decomposition rates, Shark River Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Left to right: Robert Twilley and Nicole Poret. Washing mangrove fine roots to set up a decomposition experiment, Taylor Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Nicole Poret weighing mangrove fine roots to set up a decomposition experiment, Shark River Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Left to right: Nicole Poret and Carlos Coronado-Molina deploying bags to determine decomposition rates of mangrove fine roots, Taylor Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.

Hierarchical Demand Response for Peak Minimization Using Dantzig-Wolfe Decomposition

Demand response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralized agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus, it is desirable to use a scalable decentralized algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for peak minimization based on Dantzig-Wolfe decomposition (DWD). In addition, a time weighted maximization option is included in the cost function, which improves the quality of service for devices seeking to receive their desired energy sooner rather than later. This paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

Catalytic decomposition of formic acid using supported metal nanoparticles

Upgrade of hydrogen to valuable fuel is a central topic in modern research due to its high availability and low price. For the difficulties in hydrogen storage, different pathways are still under investigation. A promising way is in the liquid-phase chemical hydrogen storage materials, because they can lead to greener transformation processes with the on line development of hydrogen for fuel cells. The aim of my work was the optimization of catalysts for the decomposition of formic acid made by sol immobilisation method (a typical colloidal method). Formic acid was selected because of the following features: it is a versatile renewable reagent for green synthesis studies. The first aim of my research was the synthesis and optimisation of Pd nanoparticles by sol-immobilisation to achieve better catalytic performances and investigate the effect of particle size, oxidation state, role of stabiliser and nature of the support. Palladium was chosen because it is a well-known active metal for the catalytic decomposition of formic acid. Noble metal nanoparticles of palladium were immobilized on carbon charcoal and on titania. In the second part the catalytic performance of the “homemade” catalyst Pd/C to a commercial Pd/C and the effect of different monometallic and bimetallic systems (AuxPdy) in the catalytic formic acid decomposition was investigated. The training period for the production of this work was carried out at the University of Cardiff (Group of Dr. N. Dimitratos).

Study of Co-based hydrotalcite-derived mixed metal oxides partially modified with silver as potential catalysts for N2O decomposition

Co-Al-Ox mixed metal oxides partially modified with Cu or Mg, as well as Ag were successfully prepared, characterized and evaluated as potential catalysts for the N2O decomposition. The materials were characterized by the following techniques: X-Ray Diffraction, Thermogravimetric Analysis (TGA), N2 Physisorption, Hydrogen Temperature-Programmed Reduction (H2-TPR), and X-ray photoelectron spectroscopy (XPS). Ag-modified HT-derived mixed oxides showed enhanced activity compared to the undoped materials, the optimum composition was found for (1 wt.% Ag)CHT-Co3Al. The catalyst characterization studies suggested that the improved catalytic activity of Ag-promoted catalysts were mainly because of the altered redox properties of the materials.

Domain decomposition methods for some aerodynamic noise problems

Abstract not available

A domain decomposition based algorithm for non-linear 2D inverse heat conduction problems

Inverse heat conduction problems (IHCPs) appear in many important scientific and technological fields. Hence analysis, design, implementation and testing of inverse algorithms are also of great scientific and technological interest. The numerical simulation of 2-D and –D inverse (or even direct) problems involves a considerable amount of computation. Therefore, the investigation and exploitation of parallel properties of such algorithms are equally becoming very important. Domain decomposition (DD) methods are widely used to solve large scale engineering problems and to exploit their inherent ability for the solution of such problems.

Time domain decomposition for European options in financial modelling

Finance is one of the fastest growing areas in modern applied mathematics with real world applications. The interest of this branch of applied mathematics is best described by an example involving shares. Shareholders of a company receive dividends which come from the profit made by the company. The proceeds of the company, once it is taken over or wound up, will also be distributed to shareholders. Therefore shares have a value that reflects the views of investors about the likely dividend payments and capital growth of the company. Obviously such value will be quantified by the share price on stock exchanges. Therefore financial modelling serves to understand the correlations between asset and movements of buy/sell in order to reduce risk. Such activities depend on financial analysis tools being available to the trader with which he can make rapid and systematic evaluation of buy/sell contracts. There are other financial activities and it is not an intention of this paper to discuss all of these activities. The main concern of this paper is to propose a parallel algorithm for the numerical solution of an European option. This paper is organised as follows. First, a brief introduction is given of a simple mathematical model for European options and possible numerical schemes of solving such mathematical model. Second, Laplace transform is applied to the mathematical model which leads to a set of parametric equations where solutions of different parametric equations may be found concurrently. Numerical inverse Laplace transform is done by means of an inversion algorithm developed by Stehfast. The scalability of the algorithm in a distributed environment is demonstrated. Third, a performance analysis of the present algorithm is compared with a spatial domain decomposition developed particularly for time-dependent heat equation. Finally, a number of issues are discussed and future work suggested.