Hot deformation and recrystallization of a metastable beta titanium ingot

Hot deformation behavior and microstructure evolution of a coarse grain metastable beta titanium alloy (Ti-5Al-5Mo-5V-3Cr) was investigated using uniaxial compression testing followed by a subsequent beta annealing treatment. Compression testing was carried out at 720 °C and strain rates between 0.001-10 s-1 on samples with beta annealed condition and aged microstructure containing high volume fraction of relatively large alpha precipitates. The peak load of the aged samples are higher than the non-aged specimens but they show rather similar steady state flow stress. The subsequent beta annealing treatment on the compressed aged samples leads to breaking down the ingot microstructure and formation of a fully recrystallized beta phase with massive grain refinement (order of millimeter to ∼100 μm). However, after annealing such grain refinement is not seen for the non-aged samples except at high strain rates that showed partial and local recrystallization.

Distributed generation capacity planning for distribution networks to minimize energy loss: an unbalanced multi-phase optimal power flow based approach

This chapter presents an unbalanced multi-phase optimal power flow (UMOPF) based planning approach to determine the optimum capacities of multiple distributed generation units in a distribution network. An adaptive weight particle swarm optimization algorithm is used to find the global optimum solution. To increase the efficiency of the proposed scheme, a co-simulation platform is developed. Since the proposed method is mainly based on the cost optimization, variations in loads and uncertainties within DG units are also taken into account to perform the analysis. An IEEE 123 node distribution system is used as a test distribution network which is unbalanced and multi-phase in nature, for the validation of the proposed scheme. The superiority of the proposed method is investigated through the comparisons of the results obtained that of a Genetic Algorithm based OPF method. This analysis also shows that the DG capacity planning considering annual load and generation uncertainties outperform the traditional well practised peak-load planning.

Determining the size of PHEV charging stations powered by commercial grid-integrated PV systems considering reactive power support

Due to low electricity rates at nighttime, home charging for electric vehicles (EVs) is conventionally favored. However, the recent tendency in support of daytime workplace charging that absorbs energy produced by solar photovoltaic (PV) panels appears to be the most promising solution to facilitating higher PV and EV penetration in the power grid. This paper studies optimal sizing of workplace charging stations considering probabilistic reactive power support for plug-in hybrid electric vehicles (PHEVs), which are powered by PV units in medium voltage (MV) commercial networks. In this study, analytical expressions are first presented to estimate the size of charging stations integrated with PV units with an objective of minimizing energy losses. These stations are capable of providing reactive power support to the main grid in addition to charging PHEVs while considering the probability of PV generation. The study is further extended to investigate the impact of time-varying voltage-dependent charging load models on PV penetration. The simulation results obtained on an 18-bus test distribution system show that various charging load models can produce dissimilar levels of PHEV and PV penetration. Particularly, the maximum energy loss and peak load reductions are achieved at 70.17% and 42.95% respectively for the mixed charging load model, where the system accommodates respective PHEV and PV penetration levels of 9.51% and 50%. The results of probabilistic voltage distributions are also thoroughly reported in the paper.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.

A smart load appliance application using a single compressor fridge-freezer to support grid operations

This paper describes a fridge-freezer smart load model, which responds to external signals from the wholesale electricity market to support grid operations while switching the fridge-freezer on and off to maintain optimum operations for the owner. The key parameters of the model are the appliance dimensions, thermal mass, the fridge and freezer thermal time constants and the compressor power consumption. The model demonstrates that control strategies help to minimise load at times when the grid is under stress from high demand, and shift some load to a lower wholesale price or when there is excess renewable power. Three control strategies are proposed, based on peak shaving and valley filling, price signals and wind availability.

An aggregated fridge-freezer peak shaving and valley filling control strategy for enhanced grid operations

The need for fast response demand side participation (DSP) has never been greater due to increased wind power penetration. White domestic goods suppliers are currently developing a `smart' chip for a range of domestic appliances (e.g. refrigeration units, tumble dryers and storage heaters) to support the home as a DSP unit in future power systems. This paper presents an aggregated population-based model of a single compressor fridge-freezer. Two scenarios (i.e. energy efficiency class and size) for valley filling and peak shaving are examined to quantify and value DSP savings in 2020. The analysis shows potential peak reductions of 40 MW to 55 MW are achievable in the Single wholesale Electricity Market of Ireland (i.e. the test system), and valley demand increases of up to 30 MW. The study also shows the importance of the control strategy start time and the staggering of the devices to obtain the desired filling or shaving effect.

The peak of the load; the waiting months on the hilltop from the entrance of the Stars and Stripes to the second victory of the Marne.

Mode of access: Internet.

Load-sharing in heavy vehicle suspensions : new metrics for old

Dynamic load sharing can be defined as a measure of the ability of a heavy vehicle multi-axle group to equalise load across its wheels under typical travel conditions; i.e. in the dynamic sense at typical travel speeds and operating conditions of that vehicle. Various attempts have been made to quantify the ability of heavy vehicles to equalise the load across their wheels during travel. One of these was the concept of the load sharing coefficient (LSC). Other metrics such as the dynamic load coefficient (DLC), peak dynamic wheel force (PDWF) and dynamic impact force (DIF) have been used to compare one heavy vehicle suspension with another for potential road damage. This paper compares these metrics and determines a relationship between DLC and LSC with sensitivity analysis of this relationship. The shortcomings of the presently-available metrics are discussed with a new metric proposed - the dynamic load equalisation (DLE) measure.

Demand side response to mitigate electrical peak demand in Eastern and Southern Australia

The aim of this work is to develop a Demand-Side-Response (DSR) model, which assists electricity end-users to be engaged in mitigating peak demands on the electricity network in Eastern and Southern Australia. The proposed innovative model will comprise a technical set-up of a programmable internet relay, a router, solid state switches in addition to the suitable software to control electricity demand at user's premises. The software on appropriate multimedia tool (CD Rom) will be curtailing/shifting electric loads to the most appropriate time of the day following the implemented economic model, which is designed to be maximizing financial benefits to electricity consumers. Additionally the model is targeting a national electrical load be spread-out evenly throughout the year in order to satisfy best economic performance for electricity generation, transmission and distribution. The model is applicable in region managed by the Australian Energy Management Operator (AEMO) covering states of Eastern-, Southern-Australia and Tasmania.

Transit line passenger transmission and productiveness under high load conditions

Deterministic transit capacity analysis applies to planning, design and operational management of urban transit systems. The Transit Capacity and Quality of Service Manual (1) and Vuchic (2, 3) enable transit performance to be quantified and assessed using transit capacity and productive capacity. This paper further defines important productive performance measures of an individual transit service and transit line. Transit work (p-km) captures the transit task performed over distance. Passenger transmission (p-km/h) captures the passenger task delivered by service at speed. Transit productiveness (p-km/h) captures transit work performed over time. These measures are useful to operators in understanding their services’ or systems’ capabilities and passenger quality of service. This paper accounts for variability in utilized demand by passengers along a line and high passenger load conditions where passenger pass-up delay occurs. A hypothetical case study of an individual bus service’s operation demonstrates the usefulness of passenger transmission in comparing existing and growth scenarios. A hypothetical case study of a bus line’s operation during a peak hour window demonstrates the theory’s usefulness in examining the contribution of individual services to line productive performance. Scenarios may be assessed using this theory to benchmark or compare lines and segments, conditions, or consider improvements.

High load transit line passenger transmission and productiveness efficiencies

Performance of urban transit systems may be quantified and assessed using transit capacity and productive capacity in planning, design and operational management activities. Bunker (4) defines important productive performance measures of an individual transit service and transit line, which are extended in this paper to quantify efficiency and operating fashion of transit services and lines. Comparison of a hypothetical bus line’s operation during a morning peak hour and daytime hour demonstrates the usefulness of productiveness efficiency and passenger transmission efficiency, passenger churn and average proportion line length traveled to the operator in understanding their services’ and lines’ productive performance, operating characteristics, and quality of service. Productiveness efficiency can flag potential pass-up activity under high load conditions, as well as ineffective resource deployment. Proportion line length traveled can directly measure operating fashion. These measures can be used to compare between lines/routes and, within a given line, various operating scenarios and time horizons to target improvements. The next research stage is investigating within-line variation using smart card passenger data and field observation of pass-ups. Insights will be used to further develop practical guidance to operators.

High load transit line passenger transmission and productiveness efficiencies

Performance of urban transit systems may be quantified and assessed using transit capacity and productive capacity in planning, design and operational management activities. Bunker (4) defines important productive performance measures of an individual transit service and transit line, which are extended in this paper to quantify efficiency and operating fashion of transit services and lines. Comparison of a hypothetical bus line’s operation during a morning peak hour and daytime hour demonstrates the usefulness of productiveness efficiency and passenger transmission efficiency, passenger churn and average proportion line length traveled to the operator in understanding their services’ and lines’ productive performance, operating characteristics, and quality of service. Productiveness efficiency can flag potential pass-up activity under high load conditions, as well as ineffective resource deployment. Proportion line length traveled can directly measure operating fashion. These measures can be used to compare between lines/routes and, within a given line, various operating scenarios and time horizons to target improvements. The next research stage is investigating within-line variation using smart card passenger data and field observation of pass-ups. Insights will be used to further develop practical guidance to operators.

Smart grid-demand side response model to mitigate prices and peak impact on the electrical system

The aims of this project is to develop demand side response model which assists electricity consumers who are exposed to the market price through aggregator to manage the air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimise the energy cost caused by the air-conditioning load considering the electricity market price and network overload. The model is tested with selected characteristics of the room, Queensland electricity market data from Australian Energy Market Operator and data from the Bureau of Statistics on temperatures in Brisbane, during weekdays on hot days from 2011 - 2012.