Characterizing Water and Water-Related Energy Use in Multi-Unit Residential Structures with High Resolution Smart Metering Data

As urban populations continue to grow and expand, localized demands on water supplies continue to increase as well. These water supplies, which have been historically stable, are also threatened by an increasingly erratic climate. Together, these two factors have significantly increased the likelihood of long-term drought conditions in the American West. In response, water suppliers are investigating new ways to record water use in urban areas to better understand how water is used. One of these methods is smart meters; advanced devices that can record and transmit water use information directly to the water supplier. However, these devices can produce extremely large amounts of data, which can often be difficult to manage. This research investigated methods for data collection and management to advance the feasibility of larger smart meter networks. The techniques we developed are described, as well as how these techniques were used to estimate water and water-related energy use in several student dormitories on Utah State University’s campus. We also detail how water and water-related energy use were estimated. These results offer insight into how water and water-related energy are used in buildings like these, which may be of interest to water suppliers looking for ways to increase their understanding of water use beyond just the number of gallons used

Integrated Urban Metabolism Analysis Tool (IUMAT)

A number of tools are available today for simulating different aspects of urban activity, but these efforts are fragmented and do not effectively reflect the interrelationships between very diverse groups of urban sectors and resource flows. There is a critical need for robust and reliable urban metabolism analysis tools that integrate socio-economic elements of urbanization and physicality of the built environment into evaluating sustainability in cities. This dissertation outlines the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) that dynamically measures the environmental impacts of land cover, transportation, and consumption of energy, water and materials employing a holistic framework. It includes examination of the existing scholarship on urban metabolism as well as description of the calculative framework for IUMAT. The scope of work is establishment of the Residential Energy Model that would serve as a template for the larger Energy, Water and Materials (EWM) Model. The EWM model takes a bottom-up approach to generate spatial resource demand profiles based on building and neighborhood characteristics. Finally, Residential Energy Consumption Survey (RECS) 2009 data is used to explain how the proposed framework makes use of actual data to find determinants of resources’ demand and unravel correlations between environmental consequences and myriad of urban variables. Quantile regression is explored as a robust method for large-scale energy modeling that is a prototype for resource use projection within other urban sectors

Volume 1 – Symposium: Tuesday, March 8

Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Components:Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Component

Simulator test to study hot-flow problems related to a gas cooled reactor

An advance study of materials, fuel injection, and hot flow problems related to the gas core nuclear rocket is reported. The first task was to test a previously constructed induction heated plasma GCNR simulator above 300 kW. A number of tests are reported operating in the range of 300 kW at 10,000 cps. A second simulator was designed but not constructed for cold-hot visualization studies using louvered walls. A third task was a paper investigation of practical uranium feed systems, including a detailed discussion of related problems. The last assignment resulted in two designs for plasma nozzle test devices that could be operated at 200 atm on hydrogen

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber-secure resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber-secure resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Modelling of flows in vertical pipes and its application to multiphase flow metering at high gas content and to the prediction of well performance

Imperial Users onl

2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor