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Abstract The objective of this work is to evaluate the surge characteristics of an axial compressor operating under surge conditions, by using a hybrid BDF/harmonic balance method. During a surge event, a large amount of backflow occurs in the compressor. Consequently, the airflow oscillates along the axial direction at a low frequency, which can considerably damage the compressor. In this paper, a numerical investigation is performed to examine the effects of the system volume and average mass flow on the surge characteristics of a transonic high speed single stage axial compressor (stage 35) designed and evaluated at the NASA Glenn Center. The results show that the oscillation frequency of the airflow in the compressor generally ranges from 14.32–26.04 Hz during surge conditions, and both the system volume and average mass flow considerably influence the surge characteristics. For a constant average mass flow, as the system volume decreases, the oscillation frequency of the airflow in the compressor increases slightly, while the oscillation amplitude of the exit static pressure decreases significantly, and the maximum static pressure at the outlet remains nearly constant. In the case of a constant system volume, with the decrease in the average mass flow, the oscillation frequency of the airflow in the compressor gradually decreases, and the oscillation amplitude of the exit pressure increases significantly. With a further decrease in the average mass flow, the oscillation frequency stabilizes at a certain point and does not change thenceforth. Furthermore, the surge frequency obtained using the numerical method shows a same tendency with the Helmholtz frequency, but there is a considerable difference between them.

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.

AbstractBioenergy projects can lead to direct and indirect land use change (LUC), which can substantially affect greenhouse gas balances with both beneficial and adverse outcomes for bioenergy's contribution to climate change mitigation. The causes behind LUC are multiple, complex, interlinked, and change over time. This makes quantification uncertain and sensitive to many factors that can develop in different directions—including land use productivity, trade patterns, prices and elasticities, and use of by‐products associated with biofuels production. Quantifications reported so far vary substantially and do not support the ranking of bioenergy options with regard to LUC and associated emissions. There are however several options for mitigating these emissions, which can be implemented despite the uncertainties. Long‐rotation forest management is associated with carbon emissions and sequestration that are not in temporal balance with each other and this leads to mitigation trade‐offs between biomass extraction for energy use and the alternative to leave the biomass in the forest. Bioenergy's contribution to climate change mitigation needs to reflect a balance between near‐term targets and the long‐term objective to hold the increase in global temperature below 2°C (Copenhagen Accord). Although emissions from LUC can be significant in some circumstances, the reality of such emissions is not sufficient reason to exclude bioenergy from the list of worthwhile technologies for climate change mitigation. Policy measures to minimize the negative impacts of LUC should be based on a holistic perspective recognizing the multiple drivers and effects of LUC.This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

We present the architecture and design of the IANOS scheduling framework. The goal of the new Grid scheduling system is to provide a general job submission framework allowing optimal positioning and scheduling of HPCN applications. The scheduling algorithms used to calculate best-suited resources are based on an objective cost function that exploits information on the parameterization of applications and resources. This standard-based, interoperable scheduling framework comprises four general web services and three modules. The middleware is complemented with one client and one admin console. The implementation is based on proposed Grid and Web services standards (WSRF, WS-Agreement, JSDL, and GLUE). It is agnostic to a specific Grid middleware. The beta version of IANOS has been tested and integrated with UNICORE. The validation of IANOS is in progress by running different types of HPCN applications on a large-scale Grid testbed.

This deliverable includes the methodology in EnergyPROSPECTS for an in-depth study of energy citizenship. It features the criteria used for selecting the cases for indepth study, the list of cases selected for in-depth study as well as key research foci and empirical research questions.

Facile sol–gel synthesis of Mo:BiVO4 thin films with optimized morphology results in reduced surface recombination and enhanced hole transfer efficiency.

A worldwide increase in tree decline and mortality has been linked to climate change and, where these represent foundation species, this can have important implications for ecosystem functions. This study tests a combined approach of phylogeographic analysis and species distribution modelling to provide a climate change context for an observed decline in crown health and an increase in mortality in Eucalyptus wandoo, an endemic tree of south-western Australia.Phylogeographic analyses were undertaken using restriction fragment length polymorphism analysis of chloroplast DNA in 26 populations across the species distribution. Parsimony analysis of haplotype relationships was conducted, a haplotype network was prepared, and haplotype and nucleotide diversity were calculated. Species distribution modelling was undertaken using Maxent models based on extant species occurrences and projected to climate models of the last glacial maximum (LGM).A structured pattern of diversity was identified, with the presence of two groups that followed a climatic gradient from mesic to semi-arid regions. Most populations were represented by a single haplotype, but many haplotypes were shared among populations, with some having widespread distributions. A putative refugial area with high haplotype diversity was identified at the centre of the species distribution. Species distribution modelling showed high climatic suitability at the LGM and high climatic stability in the central region where higher genetic diversity was found, and low suitability elsewhere, consistent with a pattern of range contraction.Combination of phylogeography and paleo-distribution modelling can provide an evolutionary context for climate-driven tree decline, as both can be used to cross-validate evidence for refugia and contraction under harsh climatic conditions. This approach identified a central refugial area in the test species E. wandoo, with more recent expansion into peripheral areas from where it had contracted at the LGM. This signature of contraction from lower rainfall areas is consistent with current observations of decline on the semi-arid margin of the range, and indicates low capacity to tolerate forecast climatic change. Identification of a paleo-historical context for current tree decline enables conservation interventions to focus on maintaining genetic diversity, which provides the evolutionary potential for adaptation to climate change.