Physical properties of a new Deep Eutectic Solvent based on lithium bis[(trifluoromethyl)sulfonyl]imide and N-methylacetamide as superionic suitable electrolyte for lithium ion batteries and electric double layer capacitors

Herein we present a study on the physical/chemical properties of a new Deep Eutectic Solvent (DES) based on N-methylacetamide (MAc) and lithium bis[(trifluoromethyl)sulfonyl]imide (LiTFSI). Due to its interesting properties, such as wide liquid-phase range from -60°C to 280°C, low vapor pressure, and high ionic conductivity up to 28.4mScm at 150°C and at x=1/4, this solution can be practically used as electrolyte for electrochemical storage systems such as electric double-layer capacitors (EDLCs) and/or lithium ion batteries (LiBs). Firstly, relationships between its transport properties (conductivity and viscosity) as a function of composition and temperature were discussed through Arrhenius' Law and Vogel-Tamman-Fulcher (VTF) equations, as well as by using the Walden classification. From this investigation, it appears that this complex electrolyte possesses a number of excellent transport properties, like a superionic character for example. Based on which, we then evaluated its electrochemical performances as electrolyte for EDLCs and LiBs applications by using activated carbon (AC) and lithium iron phosphate (LiFePO) electrodes, respectively. These results demonstrate that this electrolyte has a good compatibility with both electrodes (AC and LiFePO) in each testing cell driven also by excellent electrochemical properties in specific capacitance, rate and cycling performances, indicating that the LiTFSI/MAc DES can be a promising electrolyte for EDLCs and LiBs applications especially for those requiring high safety and stability. © 2013 Elsevier Ltd.

A Theoretical and Experimental Study of Resonance in a High Performance Engine Intake System: Part 2

The unsteady gas dynamic phenomena in a racecar airbox have been examined, and resonant tuning effects have been considered. A coupled 1D/3D analysis, using the engine simulation package Virtual 4-Stroke and the CFD package FLUENT, was used to model the engine and airbox. The models were experimentally validated. An airbox was designed with a natural frequency in the region of 75 Hz. A coupled 1D/3D analysis of the airbox and a Yamaha R6 4 cylinder engine predicted resonance at the single-cylinder induction frequency; 75 Hz at an engine speed of 9000 rpm.

Improving the Fuel Economy of a Tuned 600cc FSAE Engine

To maintain its relevance, motorsport cannot be exempt from
the trend of increasing fuel economy. This bears obvious
competitive benefits as well, either in decreasing the
frequency of pit stops or the mass of fuel carried. Given the
increased points weighting of fuel economy for the Formula
Student (FS) competition, a complete analysis was performed
on the Queen's Formula Racing 600cc motorcycle engine in
preparation for the 2011 competition.
The criteria for such high performance fuel economy differ to
a degree from most mass transportation counterparts and were
divided into three distinct regimes; full load, part load and no
load conditions.
Full load positions naturally demand maximum torque for
performance but that does not imply that fuel savings cannot
be made whilst preserving this. The point at which maximum
torque is produced with minimum air -fuel ratio, Leanest
mixture for Best Torque (LBT), was therefore sought and
mapped for full load.
At part load, torque is less of a concern, and maintaining a
sustainable engine temperature and transient response become
more important. With decreasing AFR, engine temperatures
can rise dramatically so temperatures were measured close to
the exhaust port for a wide range of air-fuel ratios.
Competition track data was analysed to highlight key part load
operating regions and these were mapped according to
measured safe temperature limits. Torque response to a step
throttle change was also measured to ensure suitable engine
transient performance was maintained.
At no load conditions, with low engine speed only idle
conditions need to be satisfied. In the situation where the
engine is still at high speed without load, the engine is being
motored and no fuel is required. An overrun fuel cut was
employed to reflect this giving significant fuel savings. The
effect on torque and engine pickup was measured.
Modifications were also made to the fuel injector location to
improve fuel mixing and evaporation at this lower air flow
condition.
These mapping regimes were implemented and tested using
fully transient lap simulations using competition track data
and a four quadrant AC engine dynamometer. The experiment
indicated a reduction in fuel consumption for 22 laps of the FS
track from 5.08litres to 3.67litres, around 27% in total. The
actual fuel used at the 2011 competition was 3.6 litres while
placing 8th in the endurance event, further validating the
benefits of these mapping regimes.

The Effects of the Catalytic Converter on Two-Stroke Engine Performance, SAE Technical Paper 972741

The two-stroke engine, by its nature is very dependent on the unsteady gas dynamics within an exhaust system. This is demonstrated by the tuning effects on two-stroke engines, which have been well documented. In consideration of current emissions legislation, a two-stroke engine can be fitted with a catalytic converter for the outboard, utility or automotive markets. The catalytic substrate represents a major obstruction to the flow of exhaust gas, which hinders the progression of the main exhausted pulse, and in turn effects the scavenging of the cylinder and ultimately the performance of the engine.

The Impact of Vehicle-to-Grid on Electric Power Systems: A Review

Environmental concerns and fossil fuel shortage put pressure on both power and transportation systems. Electric vehicles (EVs) are thought to be a good solution to these problems. With EV adoption, energy flow is two way: from grid to vehicle and from vehicle to grid, which is known as vehicle-to-grid (V2G) today. This paper considers electric power systems and provides a review of the impact of V2G on power system stability. The concept and basics of V2G technology are introduced at first, followed by a description of EV application in the world. Several technical issues are detailed in V2G modeling and capacity forecasting, steady-state analysis and stability analysis. Research trends of such topics are declared at last.

Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.

Partial purification of lymphokine activities by iso-electric focusing

A continuous cell-line has been shown to be adaptable to preparation of supernatants containing lymphokine activities. Separation and partial purification of migration inhibiting, colony stimulating and interferon activities from such preparations has been achieved by the use of density gradient iso-electric focusing.

Switching ferroelectric domain configurations using both electric and magnetic fields in Pb(Zr,Ti)O3–Pb(Fe,Ta)O3 single-crystal lamellae

Thin single-crystal lamellae cut from Pb(Zr,Ti)O3–Pb(Fe,Ta)O3 ceramic samples have been integrated into simple coplanar capacitor devices. The influence of applied electric and magnetic fields on ferroelectric domain configurations has been mapped, using piezoresponse force microscopy. The extent to which magnetic fields alter the ferroelectric domains was found to be strongly history dependent: after switching had been induced by applying electric fields, the susceptibility of the domains to change under a magnetic field (the effective magnetoelectric coupling parameter) was large. Such large, magnetic field-induced changes resulted in a remanent domain state very similar to the remanent state induced by an electric field. Subsequent magnetic field reversal induced more modest ferroelectric switching.

The Engine of Thought Is a Hybrid: Roles of Associative and Structured Knowledge in Reasoning

Across a range of domains in psychology different theories assume different mental representations of knowledge. For example, in the literature on category-based inductive reasoning, certain theories (e.g., Rogers & McClelland, 2004; Sloutsky & Fisher, 2008) assume that the knowledge upon which inductive inferences are based is associative, whereas others (e.g., Heit & Rubinstein, 1994; Kemp & Tenenbaum, 2009; Osherson, Smith, Wilkie, López, & Shafir, 1990) assume that knowledge is structured. In this article we investigate whether associative and structured knowledge underlie inductive reasoning to different degrees under different processing conditions. We develop a measure of knowledge about the degree of association between categories and show that it dissociates from measures of structured knowledge. In Experiment 1 participants rated the strength of inductive arguments whose categories were either taxonomically or causally related. A measure of associative strength predicted reasoning when people had to respond fast, whereas causal and taxonomic knowledge explained inference strength when people responded slowly. In Experiment 2, we also manipulated whether the causal link between the categories was predictive or diagnostic. Participants preferred predictive to diagnostic arguments except when they responded under cognitive load. In Experiment 3, using an open-ended induction paradigm, people generated and evaluated their own conclusion categories. Inductive strength was predicted by associative strength under heavy cognitive load, whereas an index of structured knowledge was more predictive of inductive strength under minimal cognitive load. Together these results suggest that associative and structured models of reasoning apply best under different processing conditions and that the application of structured knowledge in reasoning is often effortful.

Multi-Fidelity Multidisciplinary Whole Engine Thermo-Mechanical Design Optimization

Traditionally, the optimization of a turbomachinery engine casing for tip clearance has involved either twodimensional transient thermomechanical simulations or three-dimensional mechanical simulations. This paper illustrates that three-dimensional transient whole-engine thermomechanical simulations can be used within tip clearance optimizations and that the efficiency of such optimizations can be improved when a multifidelity surrogate modeling approach is employed. These simulations are employed in conjunction with a rotor suboptimization using surrogate models of rotor-dynamics performance, stress, mass and transient displacements, and an engine parameterization.

Enhanced AIMD based decentralized residential charging of EVs

Moving from combustion engine to electric vehicle (EV)-based transport is recognized as having a major role to play in reducing pollution, combating climate change and improving energy security. However, the introduction of EVs poses major challenges for power system operation. With increasing penetration of EVs, uncontrolled coincident charging may overload the grid and substantially increase peak power requirements. Developing smart grid technologies and appropriate charging strategies to support the role out of EVs is therefore a high priority. In this paper, we investigate the effectiveness of distributed additive increase and multiplicative decrease (AIMD) charging algorithms, as proposed by Stu¨dli et al. in 2012, at mitigating the impact of domestic charging of EVs on low-voltage distribution networks. In particular, a number of enhancements to the basic AIMD implementation are introduced to enable local power system infrastructure and voltage level constraints to be taken into account and to reduce peak power requirements. The enhanced AIMD EV charging strategies are evaluated using power system simulations for a typical low-voltage residential feeder network in Ireland. Results show that by using the proposed AIMD-based smart charging algorithms, 50% EV penetration can be accommodated, compared with only 10% with uncontrolled charging, without exceeding network infrastructure constraints.

On the scaling behavior of electric conductivity in [C(4)mim][NTf2]

In this work we examine, for the first time, the molar conductivity behavior of the deeply supercooled room temperature ionic liquid [C₄mim][NTf₂] in the temperature, pressure and volume thermodynamic space in terms of density scaling (TV^γ)⁻¹ combined with the equation of state (EOS). The exponent γ_σ determined from the Avramov model analysis is compared with the coefficient obtained from the viscosity studies carried out at moderate temperatures. Therefore, the experimental results presented herein provide the answer to the long-standing question regarding the validity of thermodynamic scaling of ionic liquids over a wide temperature range, i.e. from the normal liquid state to the glass transition point. Finally, we investigate the relationship between the dynamic and thermodynamic properties of [C₄mim][NTf₂] represented by scaling exponent γ and Grüneisen constant γ_G, respectively.

Development of a Bridge Weigh-in-Motion Sensor: Performance comparison using fibre optic and electric resistance strain sensor systems

This paper addresses the problems of effective in situ measurement of the real-time strain for bridge weigh in motion in reinforced concrete bridge structures through the use of optical fiber sensor systems. By undertaking a series of tests, coupled with dynamic loading, the performance of fiber Bragg grating-based sensor systems with various amplification techniques were investigated. In recent years, structural health monitoring (SHM) systems have been developed to monitor bridge deterioration, to assess load levels and hence extend bridge life and safety. Conventional SHM systems, based on measuring strain, can be used to improve knowledge of the bridge's capacity to resist loads but generally give no information on the causes of any increase in stresses. Therefore, it is necessary to find accurate sensors capable of capturing peak strains under dynamic load and suitable methods for attaching these strain sensors to existing and new bridge structures. Additionally, it is important to ensure accurate strain transfer between concrete and steel, adhesives layer, and strain sensor. The results show the benefits in the use of optical fiber networks under these circumstances and their ability to deliver data when conventional sensors cannot capture accurate strains and/or peak strains.