A compact broadband ion beam focusing device based on laser-driven megagauss thermoelectric magnetic fields

Ultra-intense lasers can nowadays routinely accelerate kiloampere ion beams. These unique sources of particle beams could impact many societal (e.g., proton-therapy or fuel recycling) and fundamental (e.g., neutron probing) domains. However, this requires overcoming the beam angular divergence at the source. This has been attempted, either with large-scale conventional setups or with compact plasma techniques that however have the restriction of short (<1 mm) focusing distances or a chromatic behavior. Here, we show that exploiting laser-triggered, long-lasting (>50 ps), thermoelectric multi-megagauss surface magnetic (B)-fields, compact capturing, and focusing of a diverging laser-driven multi-MeV ion beam can be achieved over a wide range of ion energies in the limit of a 5° acceptance angle.

Improving fuel economy application of the organic Rankine Cycle on a hybrid bus

Globally vehicle operators are experiencing rising fuel costs and increased
running expenses as governments around the world attempt to decrease carbon dioxide emissions and fossil fuel consumption, due to global warming and the drive to reduce dependency on fossil fuels. Recent advances in hybrid vehicle design have made great strides towards more efficient operation, with regenerative braking being widely used to capture otherwise lost energy. In this paper a hybrid series bus is developed a step further, by installing another method of energy capture on the vehicle. In this case, it is in the form of the Organic Rankine Cycle (ORC). The waste heat expelled to the exhaust and coolant streams is recovered and converted to electrical energy which is then stored in the hybrid vehicles batteries. The electrical energy can then be used for the auxiliary power circuit or to assist in vehicle propulsion, thus reducing the load on the engine, thereby improving the overall fuel economy of the vehicle and reducing carbon dioxide emissions.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Development of a PtSn bimetallic catalyst for direct fuel cells using bio-butanol fuel

Pt and PtSn catalysts were studied for n-butanol electro-oxidation at various temperatures. PtSn showed a higher activity towards butanol electro-oxidation compared to Pt in acidic media. The onset potential for n-butanol oxidation on PtSn is ~520 mV lower than that found on Pt, and significantly lower activation energy was found for PtSn compared with that for Pt.

Increasing Fruit and Vegetable Intake Has No Dose-Response Effect on Conventional Cardiovascular Risk Factors in Overweight Adults at High Risk of Developing Cardiovascular Disease

BACKGROUND: Improving diet and lifestyle is important for prevention of cardiovascular disease (CVD). Observational evidence suggests that increasing fruit and vegetable (FV) consumption may lower CVD risk, largely through modulation of established risk factors, but intervention data are required to fully elucidate the mechanisms by which FVs exert benefits on vascular health.

OBJECTIVE: The aim of this study was to examine the dose-response effect of FV intake on cardiovascular risk factors in adults at high CVD risk.

METHODS: This was a randomized controlled parallel group study involving overweight adults (BMI: >27 and ≤35 kg/m(2)) with a habitually low FV intake (≤160 g/d) and a high total risk of developing CVD (estimated ≥20% over 10 y). After a 4-wk run-in period where FV intake was limited to <2 portions/d (<160 g/d), 92 eligible participants were randomly assigned to 1 of 3 groups: to consume either 2, 4, or 7 portions (equivalent to 160 g, 320 g, or 560 g, respectively) of FVs daily for 12 consecutive weeks. Fasting venous blood samples were collected at baseline (week 4) and post-intervention (week 16) for analysis of lipid fractions and high-sensitivity C-reactive protein (hsCRP) concentrations. Compliance with the FV intervention was determined with use of self-reported FV intake and biomarkers of micronutrient status. Ambulatory blood pressure and body composition were also measured pre- and post-intervention.

RESULTS: A total of 89 participants completed the study and body composition remained stable throughout the intervention period. Despite good compliance with the intervention, no significant difference was found between the FV groups for change in measures of ambulatory blood pressure, plasma lipids, or hsCRP concentrations.

CONCLUSIONS: There was no evidence of a dose-response effect of FV intake on conventional CVD risk factors measured in overweight adults at high CVD risk. This trial was registered at clinicaltrials.gov as NCT00874341.

A hybrid harmony search with arithmetic crossover operation for economic dispatch

Economic dispatch (ED) problems often exhibit non-linear, non-convex characteristics due to the valve point effects. Further, various constraints and factors, such as prohibited operation zones, ramp rate limits and security constraints imposed by the generating units, and power loss in transmission make it even more challenging to obtain the global optimum using conventional mathematical methods. Meta-heuristic approaches are capable of solving non-linear, non-continuous and non-convex problems effectively as they impose no requirements on the optimization problems. However, most methods reported so far mainly focus on a specific type of ED problems, such as static or dynamic ED problems. This paper proposes a hybrid harmony search with arithmetic crossover operation, namely ACHS, for solving five different types of ED problems, including static ED with valve point effects, ED with prohibited operating zones, ED considering multiple fuel cells, combined heat and power ED, and dynamic ED. In this proposed ACHS, the global best information and arithmetic crossover are used to update the newly generated solution and speed up the convergence, which contributes to the algorithm exploitation capability. To balance the exploitation and exploration capabilities, the opposition based learning (OBL) strategy is employed to enhance the diversity of solutions. Further, four commonly used crossover operators are also investigated, and the arithmetic crossover shows its efficiency than the others when they are incorporated into HS. To make a comprehensive study on its scalability, ACHS is first tested on a group of benchmark functions with a 100 dimensions and compared with several state-of-the-art methods. Then it is used to solve seven different ED cases and compared with the results reported in literatures. All the results confirm the superiority of the ACHS for different optimization problems.

A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells.

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.

Investigation into the effect of molybdenum-site substitution on the performance of Sr2Fe1.5Mo0.5O6-δ for intermediate temperature solid oxide fuel cells

In this paper, niobium doping is evaluated as a means of enhancing the electrochemical performance of a Sr₂Fe_1.5Mo_0.5O_6-δ (SFM) perovskite structure cathode material for intermediate temperature solid oxide fuel cells (IT-SOFCs) applications. As the radius of Nb approximates that of Mo and exhibits +4/+5 mixed valences, its substitution is expected to improve material performance. A series of Sr₂Fe_1.5Mo_0.5-xNb_xO_6-δ (x = 0.05, 0.10, 0.15, 0.20) cathode materials are prepared and the phase structure, chemical compatibility, microstructure, electrical conductivity, polarization resistance and power generation are systematically characterized. Among the series of samples, Sr₂Fe_1.5Mo_0.4Nb_0.10O_6-δ (SFMNb_0.10) exhibits the highest conductivity value of 30 S cm^-1 at 550°C, and the lowest area specific resistance of 0.068 Ω cm² at 800°C. Furthermore, an anode-supported single cell incorporating a SFMNb_0.10 cathode presents a maximum power density of 1102 mW cm^-2 at 800°C. Furthermore no obvious performance degradation is observed over 15 h at 750°C with wet H₂(3% H₂O) as fuel and ambient air as the oxidant. These results demonstrate that SFMNb shows great promise as a novel cathode material for IT-SOFCs.