More bang for your buck: Super-adiabatic quantum engines

The practical untenability of the quasi-static assumption makes any realistic engine intrinsically irreversible and its operating time finite, thus implying friction effects at short cycle times. An important technological goal is thus the design of maximally efficient engines working at the maximum possible power. We show that, by utilising shortcuts to adiabaticity in a quantum engine cycle, one can engineer a thermodynamic cycle working at finite power and zero friction. Our findings are illustrated using a harmonic oscillator undergoing a quantum Otto cycle.

Biomass-derived Oxymethylene Ethers as Diesel Additives: A Thermodynamic Analysis

Conversion of biomass for production of liquid fuels can help in reducing the greenhouse gas (GHG) emissions which are predominantly generated by combustion of fossil fuels. Adding oxymethylene ethers (OMEs) in conventional diesel fuel has the potential to reduce soot formation during the combustion in a diesel engine. OMEs are downstream products of syngas, which can be generated by the gasification of biomass. In this research, a thermodynamic analysis has been conducted through development of data intensive process models of all the unit operations involved in production of OMEs from biomass. Based on the developed model, the key process parameters affecting the OMEs production including equivalence ratio, H2/CO ratio, and extra water flow rate were identified. This was followed by development of an optimal process design for high OMEs production. It was found that for a fluidized bed gasifier with heat capacity of 28 MW, the conditions for highest OMEs production are at an air amount of 317 tonne/day, at H2/CO ratio of 2.1, and without extra water injection. At this level, the total OMEs production is 55 tonne/day (13 tonne/day OME3 and 9 tonne/day OME4). This model would further be used in a techno-economic assessment study of the whole biomass conversion chain to determine the most attractive pathways.

A Parametric Study Of A Turbogenerator On A Diesel-Electric Hybrid Bus

The fuel consumption of automotive vehicles has become a prime consideration to manufacturers and operators as fuel prices continue to rise steadily, and legislation governing toxic emissions becomes ever more strict. This is particularly true for bus operators as government fuel subsidies are cut or removed.

In an effort to reduce the fuel consumption of a diesel-electric hybrid bus, an exhaust recovery turbogenerator has been selected from a wide ranging literature review as the most appropriate method of recovering some of the wasted heat in the exhaust line. This paper examines the effect on fuel consumption of a turbogenerator applied to a 2.4-litre diesel engine.

A validated one-dimensional engine model created using Ricardo WAVE was used as a baseline, and was modified in subsequent models to include a turbogenerator downstream, and in series with, the turbocharger turbine. A fuel consumption map of the modified engine was produced, and an in-house simulation tool was then used to examine the fuel economy benefit delivered by the turbogenerator on a bus operating on various drive-cycles.

A parametric study is presented which examined the performance of turbogenerators of various size and power output. The operating strategy of the turbogenerator was also discussed with a view to maximising turbine efficiency at each operating point.

The performance of the existing turbocharger on the hybrid bus was also investigated; both the compressor and turbine were optimised and the subsequent benefits to the fuel consumption of the vehicle were shown.

The final configuration is then presented and the overall improvement in fuel economy of the hybrid bus was determined over various drive-cycles.

Optimal deployment of geographically distributed workflow engines on the cloud

When orchestrating Web service workflows, the geographical placement of the orchestration engine (s) can greatly affect workflow performance. Data may have to be transferred across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper, we present a framework that, given a DAG-based workflow specification, computes the optimal Amazon EC2 cloud regions to deploy the orchestration engines and execute a workflow. The framework incorporates a constraint model that solves the workflow deployment problem, which is generated using an automated constraint modelling system. The feasibility of the framework is evaluated by executing different sample workflows representative of scientific workloads. The experimental results indicate that the framework reduces the workflow execution time and provides a speed up of 1.3x-2.5x over centralised approaches.