Chemical equilibrium analysis and CFD simulation of coal combustion and NOx formation

A full two-fluid model of reacting gas-particle flows with an algebraic unified second-order moment (AUSM) turbulence-chemistry model is used to simulate Beijing coal combustion and NOx formation. The sub-models are the k-epsilon-kp two-phase turbulence model, the EBU-Arrhenius volatile and CO combustion model, the six-flux radiation model, coal devolatilization model and char combustion model. The blocking effect on NOx formation is discussed. In addition, the chemical equilibrium analysis is used to predict NOx concentration at different temperature. Results of CID simulation and chemical equilibrium analysis show that, optimizing air dynamic parameters can delay the NOx formation and decrease NOx emission, but it is effective only in a restricted range. In order to decrease NOx emission near to zero, the re-burning or other chemical methods must be used.

Model based control for closed loop testing of 1-D engine simulation models

1-D engine simulation models are widely used for the analysis and verification of air-path design concepts and prediction of the resulting engine transient response. The latter often requires closed loop control over the model to ensure operation within physical limits and tracking of reference signals. For this purpose, a particular implementation of Model Predictive Control (MPC) based on a corresponding Mean Value Engine Model (MVEM) is reported here. The MVEM is linearised on-line at each operating point to allow for the formulation of quadratic programming (QP) problems, which are solved as the part of the proposed MPC algorithm. The MPC output is used to control a 1-D engine model. The closed loop performance of such a system is benchmarked against the solution of a related optimal control problem (OCP). As an example this study is focused on the transient response of a light-duty car Diesel engine. For the cases examined the proposed controller implementation gives a more systematic procedure than other ad-hoc approaches that require considerable tuning effort. © 2012 IFAC.

High-order detached eddy simulation, zonal LES and URANS of cavity and labyrinth seal flows

Hybrid numerical large eddy simulation (NLES), detached eddy simulation (DES) and URANS methods are assessed on a cavity and a labyrinth seal geometry. A high sixth-order discretization scheme is used and is validated using the test case of a two-dimensional vortex. The hybrid approach adopts a new blending function. For the URANS simulations, the flow within the cavity remains steady, and the results show significant variation between models. Surprisingly, low levels of resolved turbulence are observed in the cavity for the DES simulation, and the cavity shear layer remains two dimensional. The hybrid RANS-NLES approach does not suffer from this trait.For the labyrinth seal, both the URANS and DES approaches give low levels of resolved turbulence. The zonal Hamilton-Jacobi approach on the other had given significantly more resolved content. Both DES and hybrid RANS-NLES give good agreement with the experimentally measured velocity profiles. Again, there is significant variation between the URANS models, and swirl velocities are overpredicted. © 2013 John Wiley & Sons, Ltd.

Analysis and Simulation of S-shaped Waveguide in Silicon-on-insulator

The simulation and analysis of S-shaped waveguide bend are presented.Bend radius larger than 30 mm assures less than 0.5 dB radiation loss for a 4-μm-wide silicon-on-insulator waveguide bend with 2-μm etch depth.Intersection angle greater than 20° provides negligible crosstalk (＜-30 dB) and very low insertion loss.Any reduction in bend radius and intersection angle is at the cost of the degradation of characteristics of bent waveguide and intersecting waveguide, respectively.

Bump-Surface Multicompartment Micelles from a Linear ABC Triblock Copolymer: A Combination Study by Experiment and Computer Simulation

Novel bump-surface multicompartment micelles formed by a linear amphiphilic ABC triblock copolymer via self-assembly in selective solvent were successfully observed both in simulation and experiment. The results revealed that the block A forms the most inner core, and the blocks B and C form the inner and outer layers, respectively, and the bumps were formed by block A and more likely to be born on curving surfaces. Moreover, the micelle shape could be controlled by changing the solvent selectivity of the blocks A and B. Spherical, cylindrical, and discoidal micelles with bumpy surfaces were obtained both in experiment and simulation.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

An individual-based model of the early life history of mackerel (Scomber scombrus) in the eastern North Atlantic, simulating transport, growth and mortality

The main purpose of this paper is to provide the core description of the modelling exercise within the Shelf Edge Advection Mortality And Recruitment (SEAMAR) programme. An individual-based model (IBM) was developed for the prediction of year-to-year survival of the early life-history stages of mackerel (Scomber scombrus) in the eastern North Atlantic. The IBM is one of two components of the model system. The first component is a circulation model to provide physical input data for the IBM. The circulation model is a geographical variant of the HAMburg Shelf Ocean Model (HAMSOM). The second component is the IBM, which is an i-space configuration model in which large numbers of individuals are followed as discrete entities to simulate the transport, growth and mortality of mackerel eggs, larvae and post-larvae. Larval and post-larval growth is modelled as a function of length, temperature and food distribution; mortality is modelled as a function of length and absolute growth rate. Each particle is considered as a super-individual representing 10 super(6) eggs at the outset of the simulation, and then declining according to the mortality function. Simulations were carried out for the years 1998-2000. Results showed concentrations of particles at Porcupine Bank and the adjacent Irish shelf, along the Celtic Sea shelf-edge, and in the southern Bay of Biscay. High survival was observed only at Porcupine and the adjacent shelf areas, and, more patchily, around the coastal margin of Biscay. The low survival along the shelf-edge of the Celtic Sea was due to the consistently low estimates of food availability in that area.

Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids.

Abstract: Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R-2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (>= 2 double bonds per chain) was also well predicted with R-2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R-2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R-2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.

Phase Behaviour, Interactions, and Structural Studies of (Amines+Ionic Liquids) Binary Mixtures

We present a study on the phase equilibrium behaviour of binary mixtures containing two 1-alkyl-3-methylimidazolium bis{(trifluoromethyl)sulfonyl}imide-based ionic liquids, [Cnmim] [NTf2] (n=2 and 4), mixed with diethylamine or triethylamine as a function of temperature and composition using different experimental techniques. Based on this work, two systems showing an LCST and one system with a possible hourglass shape are measured. Their phase behaviours are then correlated and predicted by using Flory–Huggins equations and the UNIQUAC method implemented in Aspen. The potential of the COSMO-RS methodology to predict the phase equilibria was also tested for the binary systems studied. However, this methodology is unable to predict the trends obtained experimentally, limiting its use for systems involving amines in ionic liquids. The liquid-state structure of the binary mixture ([C2mim] [NTf2]+diethylamine) is also investigated by molecular dynamics simulation and neutron diffraction. Finally, the absorption of gaseous ethane by the ([C2mim][NTf2]+diethylamine) binary mixture is determined and compared with that observed in the pure solvents.

Stress correlation between instrumentation and simulation analysis of the die for high pressure die casting

A strain gauge instrumentation trial on a high pressure die casting ‘HPDC’ die was compared to a corresponding simulation model using Magmasoft® casting simulation software at two strain gauge rosette locations. The strains were measured during the casting cycle, from which the von Mises stress was determined and then compared to the simulation model. The von Mises stress from the simulation model correlated well with the findings from the instrumentation trial, showing a difference of 5.5%, ~ 10 MPa for one strain gauge rosette located in an area of low stress gradient. The second rosette was in a region of steep stress gradient, which resulted in a difference of up to 40%, ~40 MPa between the simulation and instrumentation results. Factors such as additional loading from die closure force or metal injection pressure which are not modelled by Magmasoft® were seen to have very little influence on the stress in the die, less than 7%.

Modelling and Instrumentation of Stretch Blow Moulding

The injection stretch blow moulding process is used to manufacture PET containers used in the soft drinks and carbonated soft drinks industry. The process consists of a test tube like specimen known as a preform which is heated, stretch and blown into a mould to form the container. This research is focused on developing a validated simulation of the process thus enabling manufacturers to design their products in a virtual environment without the need to waste time, material and energy. The simulation has been developed using the commercial FEA package Abaqus and has been validated using state of the art data acquisition system consisting of measurements for preform temperature (inner and outer wall) using a device known as THERMOscan (Figure 1), stretch rod force and velocity, internal pressure and air temperature inside the preform using an instrumented stretch rod and the?exact?timing of when the preform touches the mould wall using contact sensors.? In addition, validation studies have also been performed by blowing a perform without a mould and using high sped imaging technology in cooperation with an advanced digital image correlation system (VIC 3D) to provided new quantitative information on the behaviour of PET during blowing.? The approach has resulted in a realistic simulation in terms of accurate input parameters, preform shape evolution and prediction of final properties.

Evidence and Spatial Planning for Sustainable Development: A Model for Spatially Allocating Material Flows

The aspiration the spatial planning should act as the main coordinating function for the transition to a sustainable society is grounded on the assumption that it is capable of incorporating both a strong evidence base of environmental accounting for policy, coupled with opportunities for open, deliberative decision-making. While there are a number of increasingly sophisticated methods (such as material flow analysis and ecological footprinting) that can be used to longitudinally determine the impact of policy, there are fewer that can provide a robust spatial assessment of sustainability policy. In this paper, we introduce the Spatial Allocation of Material Flow Analysis (SAMFA) model, which uses the concept of socio-economic metabolism to extrapolate the impact of local consumption patterns that may occur as a result of the local spatial planning process at multiple spatial levels. The initial application the SAMFA model is based on County Kildare in the Republic of Ireland, through spatial temporal simulation and visualisation of construction material flows and associated energy use in the housing sector. Thus, while we focus on an Ireland case study, the model is applicable to spatial planning and sustainability research more generally. Through the development and evaluation of alternative scenarios, the model appears to be successful in its prediction of the cumulative resource and energy impacts arising from consumption and development patterns. This leads to some important insights in relation to the differential spatial distribution of disaggregated allocation of material balance and energy use, for example that rural areas have greater resource accumulation (and are therefore in a sense “less sustainable”) than urban areas, confirming that rural housing in Ireland is both more material and energy intensive. This therefore has the potential to identify hotspots of higher material and energy use, which can be addressed through targeted planning initiatives or focussed community engagement. Furthermore, due to the ability of the model to allow manipulation of different policy criteria (increased density, urban conservation etc), it can also act as an effective basis for multi-stakeholder engagement.

Flow due to multiple jets downstream of a barrage: Experiments, 3-D CFD and depth-averaged modelling

The flow through and downstream of a row of seven open draft tubes in a barrage has been investigated through laboratory experiments in a wide flume, a three-dimensional (3D) computational fluid dynamics simulation, and a two-dimensional depth-averaged computation. Agreement between the experiments and the 3D modeling is shown to be good, including the prediction of an asymmetric Coandă effect. One aim is to determine the distance downstream at which depth-averaged modeling provides a reasonable prediction; this is shown to be approximately 20 tube diameters downstream of the barrage. Upstream of this, the depth-averaged modeling inaccurately predicts water level, bed shear, and the 3D flow field. The 3D model shows that bed shear stress can be markedly magnified near the barrage, particularly where the jets become attached.

Sustainable non-automotive vehicles: The simulation challenges

Simulation is a well-established and effective approach to the development of fuel-efficient and low-emissions vehicles in both on-highway and off-highway applications.

The simulation of on-highway automotive vehicles is widely reported in literature, whereas research relating to non-automotive and off-highway vehicles is relatively sparse. This review paper focuses on the challenges of simulating such vehicles and discusses the differences in the approach to drive cycle testing and experimental validation of vehicle simulations. In particular, an inner-city diesel-electric hybrid bus and an ICE (Internal Combustion Engine) powered forklift truck will be used as case studies.

Computer prediction of fuel consumption and emissions of automotive vehicles on standardised drive cycles is well-established and commercial software packages such as AVL CRUISE have been specifically developed for this purpose. The vehicles considered in this review paper present new challenges from both the simulation and drive-cycle testing perspectives. For example, in the case of the forklift truck, the drive cycles involve reversing elements, variable mass, lifting operations, and do not specify a precise velocity-time profile. In particular, the difficulties associated with the prediction of productivity, i.e. the maximum rate of completing a series of defined operations, are discussed. In the case of the hybrid bus, the standardised drive cycles are unrepresentative of real-life use and alternative approaches are required in the development of efficient and low-emission vehicles.

Two simulation approaches are reviewed: the adaptation of a standard automotive vehicle simulation package, and the development of bespoke models using packages such as MATLAB/Simulink.

Wind energy resource modelling in Portugal and its future large-scale alteration due to anthropogenic induced climate changes

The high dependence of Portugal from foreign energy sources (mainly fossil fuels), together with the international commitments assumed by Portugal and the national strategy in terms of energy policy, as well as resources sustainability and climate change issues, inevitably force Portugal to invest in its energetic self-sufficiency. The 20/20/20 Strategy defined by the European Union defines that in 2020 60% of the total electricity consumption must come from renewable energy sources. Wind energy is currently a major source of electricity generation in Portugal, producing about 23% of the national total electricity consumption in 2013. The National Energy Strategy 2020 (ENE2020), which aims to ensure the national compliance of the European Strategy 20/20/20, states that about half of this 60% target will be provided by wind energy. This work aims to implement and optimise a numerical weather prediction model in the simulation and modelling of the wind energy resource in Portugal, both in offshore and onshore areas. The numerical model optimisation consisted in the determination of which initial and boundary conditions and planetary boundary layer physical parameterizations options provide wind power flux (or energy density), wind speed and direction simulations closest to in situ measured wind data. Specifically for offshore areas, it is also intended to evaluate if the numerical model, once optimised, is able to produce power flux, wind speed and direction simulations more consistent with in situ measured data than wind measurements collected by satellites. This work also aims to study and analyse possible impacts that anthropogenic climate changes may have on the future wind energetic resource in Europe. The results show that the ECMWF reanalysis ERA-Interim are those that, among all the forcing databases currently available to drive numerical weather prediction models, allow wind power flux, wind speed and direction simulations more consistent with in situ wind measurements. It was also found that the Pleim-Xiu and ACM2 planetary boundary layer parameterizations are the ones that showed the best performance in terms of wind power flux, wind speed and direction simulations. This model optimisation allowed a significant reduction of the wind power flux, wind speed and direction simulations errors and, specifically for offshore areas, wind power flux, wind speed and direction simulations more consistent with in situ wind measurements than data obtained from satellites, which is a very valuable and interesting achievement. This work also revealed that future anthropogenic climate changes can negatively impact future European wind energy resource, due to tendencies towards a reduction in future wind speeds especially by the end of the current century and under stronger radiative forcing conditions.