Modelling of building performance under the UK climate change projections and the prediction of future heating and cooling design loads in building spaces

New climate change projections for the UK were published by the United Kingdom Climate Impacts Programme in 2009. They form the 5th and most comprehensive set of predictions of climate change developed for the UK to date. As one of main products of UK Climate Projections 2009 (UKCP09),the Weather Generator, can generate a set of daily and hourly future weather variables at different time periods (2020s to 2080s) and carbon emission scenarios (low, medium, high) on a 5 km grid scale. In a radical departure from previous methods, the 2009 Projections are statistical- probabilistic in nature. A tool has been developed in Matlab to generate future Test Reference Year (TRY) and Design Reference Years (DRY) weather files from these Projections and the results were verified against results from alternative tools produced by Manchester University and Exeter University as well as with CIBSE's Future Weather Years (FWYs) which are based on earlier (4th generation) climate change scenarios and are currently used by practitioners. The Northumbria tool is computationally efficient and can extract a single Test Reference Year and 2 Design Reference Years from 3000 years of raw data in less than 6 minutes on a typical modern PC. It uses an established ISO method for generating Test Reference Year data and an alternative method of constructing future Design Reference data is proposed

Water Budget Analysis of Coachella Valley Aquifer

The Coachella Valley is a desert region located in Southern California with high temperatures and low variable rainfall. It is also an oasis due to its aquifer, which is in both the Whitewater River and Salton Sea catchment regions. The water from this aquifer is used for a variety of applications by the Coachella Valley community, but it is a finite water resource and the natural recharge in this desert region is low. If the aquifer is depleted it can collapse, which would lead to infrastructure damage and affect the local economy. This study used GIS to create models and tools to assist the Coachella Valley Economic Partnership in analyzing aspects of the water budget, and what the effect of land cover change will have. These models and tools were used for estimating infiltration, change an area of interest’s land cover classifications, and generated water depth surfaces. These outputs made it possible to create maps showing these results, and the results were visualized in 2-D and 3-D to find possible trends

Data Simulation and Trend Removal Optimization Applied to Electrochemical Noise

A well-known technique, electrochemical noise analysis (ENA), measures the potential fluctuations produced by kinetic variations along the electrochemical corrosion process. This practice requires the application of diverse signal processing methods. Therefore, in order to propose and evaluate new methodologies, it is absolutely necessary to simulate signals by computer data generation using different algorithms. In the first approach, data were simulated by superimposing Gaussian noise to nontrivial trend lines. Then, several methods were assessed by using this set of computer-simulated data. These results indicate that a new methodology based on medians of moving intervals and cubic splines interpolation show the best performance. Nevertheless, relative errors are acceptable for the trend but not for noise. In the second approach, we used artificial intelligence for trend removal, combining an interval signal processing with backpropagation neural networks. Finally, a non-Gaussian noise function that simulates non-stationary pits was proposed and all detrending methods were re-evaluated, resulting that when increasing difference between trend and noise, the accuracy of the artificial neural networks (ANNs) was reduced. In addition, when polynomial fitting, moving average removal (MAR) and moving median removal (MMR) were evaluated, MMR yielded best results, though it is not a definitive solution

Integrated modeling and analysis methodologies for architecture-level vehicle design.

In order to satisfy customer expectations, a ground vehicle must be designed to meet a broad range of performance requirements. A satisfactory vehicle design process implements a set of requirements reflecting necessary, but perhaps not sufficient conditions for assuring success in a highly competitive market. An optimal architecture-level vehicle design configuration is one of the most important of these requirements. A basic layout that is efficient and flexible permits significant reductions in the time needed to complete the product development cycle, with commensurate reductions in cost. Unfortunately, architecture-level design is the most abstract phase of the design process. The high-level concepts that characterize these designs do not lend themselves to traditional analyses normally used to characterize, assess, and optimize designs later in the development cycle. This research addresses the need for architecture-level design abstractions that can be used to support ground vehicle development. The work begins with a rigorous description of hierarchical function-based abstractions representing not the physical configuration of the elements of a vehicle, but their function within the design space. The hierarchical nature of the abstractions lends itself to object orientation - convenient for software implementation purposes - as well as description of components, assemblies, feature groupings based on non-structural interactions, and eventually, full vehicles. Unlike the traditional early-design abstractions, the completeness of our function-based hierarchical abstractions, including their interactions, allows their use as a starting point for the derivation of analysis models. The scope of the research in this dissertation includes development of meshing algorithms for abstract structural models, a rigid-body analysis engine, and a fatigue analysis module. It is expected that the results obtained in this study will move systematic design and analysis to the earliest phases of the vehicle development process, leading to more highly optimized architectures, and eventually, better ground vehicles. This work shows that architecture level abstractions in many cases are better suited for life cycle support than geometric CAD models. Finally, substituting modeling, simulation, and optimization for intuition and guesswork will do much to mitigate the risk inherent in large projects by minimizing the possibility of incorporating irrevocably compromised architecture elements into a vehicle design that no amount of detail-level reengineering can undo

Statistical methods for NHS incident reporting data

The National Reporting and Learning System (NRLS) is the English and Welsh NHS’ national repository of incident reports from healthcare. It aims to capture details of incident reports, at national level, and facilitate clinical review and learning to improve patient safety. These incident reports range from minor ‘near-misses’ to critical incidents that may lead to severe harm or death. NRLS data are currently reported as crude counts and proportions, but their major use is clinical review of the free-text descriptions of incidents. There are few well-developed quantitative analysis approaches for NRLS, and this thesis investigates these methods. A literature review revealed a wealth of clinical detail, but also systematic constraints of NRLS’ structure, including non-mandatory reporting, missing data and misclassification. Summary statistics for reports from 2010/11 – 2016/17 supported this and suggest NRLS was not suitable for statistical modelling in isolation. Modelling methods were advanced by creating a hybrid dataset using other sources of hospital casemix data from Hospital Episode Statistics (HES). A theoretical model was established, based on ‘exposure’ variables (using casemix proxies), and ‘culture’ as a random-effect. The initial modelling approach examined Poisson regression, mixture and multilevel models. Overdispersion was significant, generated mainly by clustering and aggregation in the hybrid dataset, but models were chosen to reflect these structures. Further modelling approaches were examined, using Generalized Additive Models to smooth predictor variables, regression tree-based models including Random Forests, and Artificial Neural Networks. Models were also extended to examine a subset of death and severe harm incidents, exploring how sparse counts affect models. Text mining techniques were examined for analysis of incident descriptions and showed how term frequency might be used. Terms were used to generate latent topics models used, in-turn, to predict the harm level of incidents. Model outputs were used to create a ‘Standardised Incident Reporting Ratio’ (SIRR) and cast this in the mould of current regulatory frameworks, using process control techniques such as funnel plots and cusum charts. A prototype online reporting tool was developed to allow NHS organisations to examine their SIRRs, provide supporting analyses, and link data points back to individual incident reports

Assessing the impact of computer use on landscape architecture professional practice: efficiency, effectiveness, and design creativity

Landscape architects claim that computers are efficient and effective presentation tools. However, to date, no one has evaluated the impact of computer use on the nature and quality of design in a practice setting. To further explore this issue, a trial was conducted with landscape architecture students in which they worked in conventional, mixed and digital media. Results indicated that although computer use was efficient in some tasks, the nature of the design process did not yield itself effectively yet to complete computerization. In addition, to assess the impact of computer use more broadly on office practice today, a survey was conducted of over 100 Chapter Executive Members of the American Society of Landscape Architects in the United States of America.Survey results indicated that computer use has permeated all areas of landscape architecture practice, and that it has genuinely improved drawing quality and capability. However, it has not significantly impacted the artistic or creative aspects. Few respondents believed the computer can improve these facets of the profession or that traditional practice methods will be totally replaced by the computer.The results suggest that academic and professional sectors of landscape architecture must help educate existing professionals to fully grasp the benefits of current and emerging computer technologies and to prepare the future professionals for an increasingly digital practice

Design of a Supersonic Wind Tunnel

The goal of this project was to design a supersonic wind tunnel (SWT) for use in the laboratory. This SWT will be the indraft type, with the necessary pressure ratio provided by an existing vacuum chamber. The design constraints included interfacing with existing flanges on the vacuum chamber, optical access for the test section of the tunnel, and maintaining costs within the allocated budget. Mechanical design of the tunnel was completed using solid modeling software and the supersonic nozzle was designed using the method of characteristics. This report details the process of determining critical dimensions (throat area and expansion ratio), estimating the attainable test duration, and design of a supersonic nozzle to prevent shocks in the test section

Modeling drivers’ naturalistic driving behavior on rural two-lane curves

This dissertation examined drivers’ naturalistic driving behavior on rural two-lane curves using the Strategic Highway Research Program 2 Naturalistic Driving Study data. It is a state-of-the-art naturalistic driving study that collected more than 3,000 drivers’ daily driving behavior over two years in the U.S. The major data sources were vehicle network, lane tracking system, front and rear radar, driver demographics, driver surveys, vehicle characteristics, and video cameras. This dissertation has three objectives: 1) examine the contributing factors to crashes and near-crashes on rural two-lane curves; 2) understand drivers’ normal driving behavior on rural two lane curves; 3) evaluate how drivers continuously interact with curve geometries using functional data analysis. The first study analyzed the crashes and near-crashes on rural two-lane curves using logistic regression model. The model was used to predict the binary event outcomes using a number of explanatory variables, including driver behavior variables, curve characteristics, and traffic environments. The odds ratio of getting involved in safety critical events was calculated for each contributing factor. Furthermore, the second study focused on the analysis of drivers’ normal curve negotiation behavior on rural two-lane curves. Significant relationships were found between curve radius, lateral acceleration, and vehicle speeds. A linear mixed model was used to predict mean speeds based on curve geometry and driver factors. The third analysis applied functional data analysis method to analyze the time series speed data on four example curves. Functional data analysis was found to be a useful method to analyze the time series observations and understand driver’s behavior from naturalistic driving study. Overall, this dissertation is one of the first studies to investigate drivers’ curve negotiation behavior using naturalistic driving study data, and greatly enhanced our understanding about the role of driver behavior in curve negotiation process. This dissertation had many important implications for curve geometry design, policy making, and advanced vehicle safety system. This dissertation also discussed the opportunities and challenges of analyzing the Strategic Highway Research Program 2 Naturalistic Driving Study data, and the implications for future research