Experimental and numerical evaluation of the wind load on the 516 Arouca pedestrian suspension bridge

The present work analyses the wind load effects on the 516 Arouca bridge, the world's longest pedestrian suspension bridge in 2020. Computational fluid dynamics (CFD) was used to model a range of wind angles of attack between −8° and +8°. The simulations were performed by solving the steady-state Reynolds averaged Navier-Stokes (RANS) equations with the k-ω shear stress transport (SST) model. The fluid domain size was analysed by comparing the fluid flow behaviour for three different downstream sizes. It was shown that the downstream flow is not greatly affected by the bridge body due to the high opening surfaces of the bridge. Therefore, the most appropriate domain size considering the computation time was selected. The simulations were carried out for different bridge configurations to determine the influence of the upper guard of the tray deck and the suspended cables on the generated loads. The numerical results were validated by performing different wind tunnel tests using a reduced scale prototype. The predicted aerodynamic characteristics showed good agreement with the experimental results.FITEC – Fundo de Inovaçao, Tecnologia e Economia Circular CIT/2018/23Ministerio de Ciencia, Innovación y Universidades PID 2019-109622RBFEDER Andalucía US-12649

H2-Optimal Sensor Location

Optimal sensor placement is an important problem with many applications; placing thermostats in rooms, installing pressure sensors in chemical columns or attaching vibration detection devices to structures are just a few of the examples. Frequently, this placement problem is encountered while noise is present. The H_2-optimal control is a strategy designed for systems that have exogenous disturbing inputs. Therefore, one approach for the optimal sensor location problem is to combine it with the H_2-optimal control. In this work the H_2-optimal control is explained and combined with the sensor placement problem to create the H_2-optimal sensor location problem. The problem is examined for the one-dimensional beam equation and the two-dimensional diffusion equation in an L-shaped region. The optimal sensor location is calculated numerically for both models and multiple scenarios are considered where the location of the disturbance and the actuator are varied. The effect of different model parameters such as the weight of the state and the disturbance are investigated. The results show that the optimal sensor location tends to be close to the disturbance location

Bus-driven floorplanning.

Law Hoi Ying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 101-106).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- VLSI Design Cycle --- p.2Chapter 1.2 --- Physical Design Cycle --- p.6Chapter 1.3 --- Floorplanning --- p.10Chapter 1.3.1 --- Floorplanning Objectives --- p.11Chapter 1.3.2 --- Common Approaches --- p.12Chapter 1.3.3 --- Interconnect-Driven Floorplanning --- p.14Chapter 1.4 --- Motivations and Contributions --- p.15Chapter 1.5 --- Organization of the Thesis --- p.17Chapter 2 --- Literature Review on 2D Floorplan Representations --- p.18Chapter 2.1 --- Types of Floorplans --- p.18Chapter 2.2 --- Floorplan Representations --- p.20Chapter 2.2.1 --- Slicing Floorplan --- p.21Chapter 2.2.2 --- Non-slicing Floorplan --- p.22Chapter 2.2.3 --- Mosaic Floorplan --- p.30Chapter 2.3 --- Summary --- p.35Chapter 3 --- Literature Review on 3D Floorplan Representations --- p.37Chapter 3.1 --- Introduction --- p.37Chapter 3.2 --- Problem Formulation --- p.38Chapter 3.3 --- Previous Work --- p.38Chapter 3.4 --- Summary --- p.42Chapter 4 --- Literature Review on Bus-Driven Floorplanning --- p.44Chapter 4.1 --- Problem Formulation --- p.44Chapter 4.2 --- Previous Work --- p.45Chapter 4.2.1 --- Abutment Constraint --- p.45Chapter 4.2.2 --- Alignment Constraint --- p.49Chapter 4.2.3 --- Bus-Driven Floorplanning --- p.52Chapter 4.3 --- Summary --- p.53Chapter 5 --- Multi-Bend Bus-Driven Floorplanning --- p.55Chapter 5.1 --- Introduction --- p.55Chapter 5.2 --- Problem Formulation --- p.56Chapter 5.3 --- Methodology --- p.57Chapter 5.3.1 --- Shape Validation --- p.58Chapter 5.3.2 --- Bus Ordering --- p.65Chapter 5.3.3 --- Floorplan Realization --- p.72Chapter 5.3.4 --- Simulated Annealing --- p.73Chapter 5.3.5 --- Soft Block Adjustment --- p.75Chapter 5.4 --- Experimental Results --- p.75Chapter 5.5 --- Summary --- p.77Chapter 6 --- Bus-Driven Floorplanning for 3D Chips --- p.80Chapter 6.1 --- Introduction --- p.80Chapter 6.2 --- Problem Formulation --- p.81Chapter 6.3 --- The Representation --- p.82Chapter 6.3.1 --- Overview --- p.82Chapter 6.3.2 --- Review of TCG --- p.83Chapter 6.3.3 --- Layered Transitive Closure Graph (LTCG) --- p.84Chapter 6.3.4 --- Aligning Blocks --- p.85Chapter 6.3.5 --- Solution Perturbation --- p.87Chapter 6.4 --- Simulated Annealing --- p.92Chapter 6.5 --- Soft Block Adjustment --- p.92Chapter 6.6 --- Experimental Results --- p.93Chapter 6.7 --- Summary --- p.94Chapter 6.8 --- Acknowledgement --- p.95Chapter 7 --- Conclusion --- p.99Bibliography --- p.10

A cultural heritage framework for preserving Qatari vernacular domestic architecture

Architecture and urbanism in the Arabian Gulf region, and specifically in the State of Qatar, offer many scenes to observe the loss of urban identity and cultural heritage in the various components of the built environment, including residential architecture. Many people attribute this to rapid development in globalization and the adoption of Western standardization in planning and design practice. Conversely, in the field of architectural sociology, scholars argue that socio-cultural factors such as privacy, gender segregation, and hospitality are the important variables for determining the spatial form of Islamic residential architecture. This research study aims to investigate the degree to which the shaping of the spatial form in a sample of Qatari vernacular courtyard houses embeds socio-cultural factors based on morphological analysis of human behavior and activities in domestic space. The study utilizes space syntax analysis to explore the spatial connectivity of four Qatari vernacular courtyard houses related to domestic functions as a realization of inhabitants' system of activities and a manifestation of culture as a way of life. The study's findings shed light on the spatial formation of Qatari vernacular courtyard houses as a realization of socio-cultural imperatives, thus reflecting the essence of societal formation in the domestic architecture of old Qatari settlements. The insights from this research study can help to contribute to a cultural heritage-framework for the preservation of distinctive Qatari Vernacular Residential Architecture based on the analytical criteria of housing spatial form, socio-cultural factors, and the interrelation between both.Open access publication of this paper was supported by Qatar University (Grant ID: QUCG-CENG-20/21-1)

Adaptive algorithms for partial differential equations with parametric uncertainty

In this thesis, we focus on the design of efficient adaptive algorithms for the numerical approximation of solutions to elliptic partial differential equations (PDEs) with parametric inputs. Numerical discretisations are obtained using the stochastic Galerkin Finite Element Method (SGFEM) which generates approximations of the solution in tensor product spaces of finite element spaces and finite-dimensional spaces of multivariate polynomials in the random parameters. Firstly, we propose an adaptive SGFEM algorithm which employs reliable and efficient hierarchical a posteriori energy error estimates of the solution to parametric PDEs. The main novelty of the algorithm is that a balance between spatial and parametric approximations is ensured by choosing the enhancement associated with dominant error reduction estimates. Next, we introduce a two-level a posteriori estimate of the energy error in SGFEM approximations. We prove that this error estimate is reliable and efficient. Then we provide a rigorous convergence analysis of the adaptive algorithm driven by two-level error estimates. Four different marking strategies for refinement of stochastic Galerkin approximations are proposed and, in particular, for two of them, we prove that the sequence of energy errors computed by associated algorithms converges linearly. Finally, we use duality techniques for the goal-oriented error estimation in approximating linear quantities of interest derived from solutions to parametric PDEs. Adaptive enhancements in the proposed algorithm are guided by an innovative strategy that combines the error reduction estimates computed for spatial and parametric components of corresponding primal and dual solutions. The performance of all adaptive algorithms and the effectiveness of the error estimation strategies are illustrated by numerical experiments. The software used for all experiments in this work is available online

Design of a microfabricated device for Ligase Detection Reaction (LDR)

The Ligase Detection Reaction (LDR) is a mutation detection technique used to identify point mutations in deoxyribonucleic acid (DNA). Developed by Francis Barany and associates at Cornell University it is used to find specific low abundant point mutations that may lead to colorectal cancer in the early stages of disease development. The research objective was to design and manufacture a microscale Ligase Detection Reaction (LDR) device in polycarbonate. The LDR module will be incorporated with other microdevices such as: Continuous Flow Polymerase Chain Reaction (CFRCR) and Capillary Electrophoresis (CE) in modular lab-on-a-chip technology. In making the microdevice, the duration of original reaction had to be scaled down from the current 2½ hours for 20 cycles for the macroscale reaction. It was found that an excess of primers in relation to PCR product was needed for efficient ligation. By changing the concentrations, volumes and time for the process the current time is down to 40 minutes for 20 cycles with indications that further time reductions are possible on the microscale. There are two mixing stages involved in the reaction. Micromixers were simulated in Fluent (v5.4, Lebanon, NH) and several test geometries selected for fabrication. Passive diffusion mixing was used based on obtaining high aspect ratios, 7 to 20. The mixers were made by SU-8 lithography, LIGA, laser ablation, and micromilling to characterize each fabrication method. It was found that LIGA was best for making the micromixers, but was the longest process. The micromixers are fabricated and tested using chemi-luminescence technique. For a successful reaction, temperatures of 0°C, 95°C and 65°C were needed. A stationary chamber was used for thermal cycling in which the sample sits while the temperature is cycled. Finite element analysis showed uniform temperatures in the rectangular 1.5μl chambers and that air slits can effectively separate the thermal cycle zone from the 0°C cooling zone and also isolate the mixing region. A test device was laid out and micromilled with the temperature zones maintained and fluid flow controlled. A commercial thin film heater and a thermoelectric module were used with PID controls to obtain the required process temperatures. Heating from 65°C to 95°C took 10 seconds, while cooling from 95°C to 65°C also took 10 seconds. The residence times at the required temperatures can be adapted to changes in the LDR

Process optimization under uncertainty

The ability of a production plant to be flexible by adjusting the operating conditions to changing demands, prices of the products and the raw materials is crucial to maintain a profitable operation. In this respect, the application of mathematical optimization techniques is unanimously recognized to be successful to improve the decision-making process. Typical examples are production planning, scheduling, real-time optimization and advanced process control. The more information are available to the optimization approach, the more "optimal" are the resulting decisions: the "optimal" production strategy cannot reduce the inventory costs if no supply-chain model is integrated into the production planning optimization. This thesis lies in the context of Enterprise-wide optimization with the goal of integrating decision layers and functions while accounting for uncertain information. A stochastic programming approach is adopted to integrate production scheduling with energy management and production planning with predictive maintenance. The approaches are analysed from a formulation perspective and from a computational point of view, which is necessary to deal with one of the challenges of the presented methods consisting in the size of the resulting optimization problems. To reduce the electricity cost that is generated by the uncertain peaks of the dayahead price, a two-stage risk-averse optimization is proposed to simultaneously define the optimal bidding curves for the day-ahead market and the optimal production schedule. The large-scale MILP problem is solved with a scenario-based decomposition technique, the progressive hedging algorithm. Heuristic procedures are applied to speed up the solution phase and to avoid the oscillatory behaviour due to the integer variables. Since large electricity consumers rely on Time-Of-Use power contracts to handle the volatility of the day-ahead price, the two-stage formulation is expanded into a multi-stage optimization to optimally purchase electricity from different sources and to generate electric power with a power plant. The unpractical size of the resulting problem is handled by approximating the multi-stage tree with a series of two-stage scenario-trees within a rolling horizon procedure. A mixed time grid handles the multi-scale nature of the problem by making short-term decisions with a detailed model and catching their effect on the long-term future with an aggregated model. While the electricity prices introduce exogenous uncertain information into the optimization problem, the predictive maintenance optimization carries endogenous uncertain sources into the production planning problem. Endogenous uncertainties, contrary to the exogenous ones, are uncertain information that can be modified (in the probability or in the timing of the realization) by the decision maker. The prognosis technique of the Cox model is embedded into a multi-stage stochastic program to consider an uncertain Remaining Useful Life of the equipment when the optimal operating conditions of the plant are defined. Two modelling approaches (based on superstructure-scenario trees and on conditional non-anticipativity constraints) are proposed to formulate the optimization problem with endogenous uncertainties. Two Benders-like decomposition techniques and several branching priority schemes are applied to handle the high complexity of the resulting optimization problems

Article Segmentation in Digitised Newspapers

Digitisation projects preserve and make available vast quantities of historical text. Among these, newspapers are an invaluable resource for the study of human culture and history. Article segmentation identifies each region in a digitised newspaper page that contains an article. Digital humanities, information retrieval (IR), and natural language processing (NLP) applications over digitised archives improve access to text and allow automatic information extraction. The lack of article segmentation impedes these applications. We contribute a thorough review of the existing approaches to article segmentation. Our analysis reveals divergent interpretations of the task, and inconsistent and often ambiguously defined evaluation metrics, making comparisons between systems challenging. We solve these issues by contributing a detailed task definition that examines the nuances and intricacies of article segmentation that are not immediately apparent. We provide practical guidelines on handling borderline cases and devise a new evaluation framework that allows insightful comparison of existing and future approaches. Our review also reveals that the lack of large datasets hinders meaningful evaluation and limits machine learning approaches. We solve these problems by contributing a distant supervision method for generating large datasets for article segmentation. We manually annotate a portion of our dataset and show that our method produces article segmentations over characters nearly as well as costly human annotators. We reimplement the seminal textual approach to article segmentation (Aiello and Pegoretti, 2006) and show that it does not generalise well when evaluated on a large dataset. We contribute a framework for textual article segmentation that divides the task into two distinct phases: block representation and clustering. We propose several techniques for block representation and contribute a novel highly-compressed semantic representation called similarity embeddings. We evaluate and compare different clustering techniques, and innovatively apply label propagation (Zhu and Ghahramani, 2002) to spread headline labels to similar blocks. Our similarity embeddings and label propagation approach substantially outperforms Aiello and Pegoretti but still falls short of human performance. Exploring visual approaches to article segmentation, we reimplement and analyse the state-of-the-art Bansal et al. (2014) approach. We contribute an innovative 2D Markov model approach that captures reading order dependencies and reduces the structured labelling problem to a Markov chain that we decode with Viterbi (1967). Our approach substantially outperforms Bansal et al., achieves accuracy as good as human annotators, and establishes a new state of the art in article segmentation. Our task definition, evaluation framework, and distant supervision dataset will encourage progress in the task of article segmentation. Our state-of-the-art textual and visual approaches will allow sophisticated IR and NLP applications over digitised newspaper archives, supporting research in the digital humanities