Development Of A Micro Water Grid (MWG) Pilot Platform For Green Buildings

The objectives of this Micro Water Grid (MWG) pilot platform project are to i) address the need for reliable municipal water supplies, ii) identify and strengthen vulnerable water system elements, and iii) design an optimal micro water grid pilot platform for green buildings. This paper describes the overall context of the MWG and considers appropriate analytical methods for water demand, hydraulic analysis and decision models for optimal MWG pilot platforms. This is an on-going research project and various MWG design scenarios, along with numerical results, will be presented as the research progresses

Half-Skyrmions, Tensor Forces and Symmetry Energy in Cold Dense Matter

In a previous article, the 4D half-skyrmion (or 5D dyonic salt) structure of dense baryonic matter described in crystalline configuration in the large $N_c$ limit was shown to impact nontrivially on how anti-kaons behave in compressed nuclear matter with a possible implication on an "ice-9" phenomenon of deeply bound kaonic matter and condensed kaons in compact stars. We extend the analysis to make a further prediction on the scaling properties of hadrons that have a surprising effect on the nuclear tensor forces, the symmetry energy and hence on the phase structure at high density. We treat this problem relying on certain topological structure of chiral solitons. Combined with what can be deduced from hidden local symmetry for hadrons in dense medium and the "soft" dilatonic degree of freedom associated with the trace anomaly of QCD, we uncover a novel structure of chiral symmetry in the "supersoft" symmetry energy that can influence the structure of neutron stars.Comment: 8 pages, 4 figures; contents unchanged but expanded for a journa

Thermoeconomic Analysis of Organic Rankine Cycle Using Zeotropic Mixtures

The selection of the working fluid is an important part of design and optimization of ORC system as it effects the systems efficiency, design of ORC components, stability, safety and environmental impact. Present study aims to investigate the performance of ORC system using pure working fluids and zeotropic mixtures for low temperature geothermal heat source on the basis of thermodynamic and economic parameters of ORC system. Evaporator, expander, condenser and feed pump models are developed in MATLAB. The control volume approach is adopted for evaporator and condenser model with appropriate database of heat transfer and pressure drop correlations. For comparison, pure working fluids are taken as the base case. The ORC system with pure working fluid and zeotropic mixture under same heat and sink source conditions are optimized using multi objective genetic algorithm for maximum exergy efficiency and minimum specific investment cost. The exergy efficiency of ORC system with zeotropic mixture is improved by 14.33% compared to pure working fluid. The exergy destruction in evaporator and condenser is reduced by 24~30%. The fraction of more volatile component in zeotropic mixture effected the thermal and economic performance of ORC system, for current study the mass fraction of 40% of R245fa corresponds to optimum exergy efficiency and specific investment cost. For same condensing pressure and expander power, area of evaporator for pure working fluids and zeotropic mixture is also calculated. The required heat transfer area for zeotropic mixture is approximately 13% less than required for pure working fluid

Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%. Document type: Articl