Identification and Self Tuning Control for Active Magnetic Bearing Systems: Levitation of Unknown Rotors

The present paper deals with the problem of levitating rotors with unknown characteristics by means of active magnetic bearings whose properties are known. This problem is of interest in a technical setting to shorten the development time of AMB systems, in particular for controller design. Theoretical interest arises from the fact that several issues in the area of identification and self tuning control are addressed for an unstable system. Our aim is to identify the flexible rotor including gyroscopic effects and to automatically design a robustly stabilizing controller for this system that can be used for running the system under regular operating conditions. To this end, a rigid body model of the rotor is identified based on measured step responses from the plant. Then, the bearings are adjusted to have very low stiffness, and a controller with steep roll‐off is designed in order to avoid excitation of the unknown flexible modes of the system. Once the rotor is floating, the identification algorithm from [1] is applied to obtain information on the flexible modes of the system. Based on this extended model, a robust controller allowing for slow rotation of the rotor is designed. With the rotor rotating at a moderate speed, the frequency response functions are measured, and based on these measurements, the gyroscopic matrixof the system is identified, completing the system model and allowing for design of the desired controller for normal operation. The present contribution focusses on identification of the rigid body model of the flexible rotor.     

The Use of Closed Queues in the Deployment of Coal-Face Machinery

Expensive coal-face machinery is deployed to collieries from centralized pools. This paper describes a study of the deployment cycle of such equipment to determine what scope for improvement existed. A queueing model was used to establish the main factors affecting the system and this model enabled broad operating principles to be established. Further analysis was done using a simulation model, the salient features of which are described. The paper goes on to describe how the simulation model was used to lead to the conclusion that the complexity of the deployment cycle could be reduced to the consideration of two simple parameters.     

DYNAMICS OF SPATIAL EXPLOITATION: A METAPOPULATION APPROACH

ABSTRACT. In this paper we present a bioeconomic model of a harvesting industry operating over a heterogeneous environment comprised of discrete biological populations interconnected by dispersal processes. The model generalizes the Gordon [1954]/Smith [1968] model of open-access rent dissipation by accounting for intertemporal and spatial “Ricardian” patterns of exploitation. This model yields a simple, but insightful, framework from which one can investigate factors that contribute to the evolution of resource exploitation patterns over space and time. For example, we find that exploitation patterns are driven by biological and fleet dispersal and biological and economic heterogeneity. We conclude that one cannot really understand the biological processes operating in an exploited system without knowing as much about the harvesting system as about the biological system.     

Queueing Theory in the Solution of a Transport Evaluation Problem

The problem of minimizing the overall cost of operating the internal transport system of a steelworks is examined. The paper describes the practical problem encountered by an OR team and the approach that was adopted in the subsequent study. This approach led to the development of a simple queueing model of the rail transport system, which accounted for 90 per cent of the weight of material moved within the steelworks complex. The model was used to evaluate alternative methods of operating and controlling the rail transport system. The findings of the study were implemented and some of the operational details are outlined in the paper. Savings of at least 10 per cent in operating costs were achieved by the study.Hence this paper presents a detailed case study of a comprehensive approach to a transport planning and evaluation problem.     

CFD modeling of hydrodynamic characteristics of a gas–liquid two-phase stirred tank

A three-dimensional CFD model was developed in this work to simulate hydrodynamic characteristics of a gas–liquid two-phase stirred tank with two six-bladed turbines and four baffles, coupling of the Multiple Size Group model to determine bubble size distribution. Important hydrodynamic parameters of the multi-phase system such as volume-averaged overall and time-averaged local gas holdups and axial liquid velocities along time and transversal courses were simulated and analyzed in detail, under varied operating conditions (inlet air flow rate and impeller rotation speed). Model predictions of local transient gas holdup and liquid velocity distributions on vertical and horizontal sections of the tank were also carried out. The overall flow patterns were discussed in detail to assess the mixing. Bubble size distributions were further predicted to reveal the unique properties of gas phase. Experimental measurements of overall gas holdups and local axial liquid velocities were used to validate the developed model.     

Fractional non-linear modelling of ultracapacitors

In this paper, it is demonstrated that an ultracapacitor exhibits a non-linear behaviour in relation to the operating voltage. A set of fractional order linear systems resulting from a frequency analysis of the ultracapacitor at various operating points is first obtained. Then, a non-linear model is deduced from the linear systems set, so that its Taylor linearization around the considered operating points (for the frequency analysis), produces the linear system set. The resulting non-linear model is validated on a Hybrid Electric Vehicle (HEV) application.     

NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage

In this paper, we propose a testing-coverage software reliability model that considers not only the imperfect debugging (ID) but also the uncertainty of operating environments based on a non-homogeneous Poisson process (NHPP). Software is usually tested in a given control environment, but it may be used in different operating environments by different users, which are unknown to the developers. Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures, but most of the underlying common assumptions of these models are that the operating environment is the same as the developing environment. But in fact, due to the unpredictability of the uncertainty in the operating environments for the software, environments may considerably influence the reliability and software's performance in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the theory reliability, and also from all its similar applications in other fields. In this paper, a new model is proposed with the consideration of the fault detection rate based on the testing coverage and examined to cover ID subject to the uncertainty of operating environments. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real software failure data based on seven criteria. Improved normalized criteria distance (NCD) method is also used to rank and select the best model in the context of a set of goodness-of-fit criteria taken all together. All results demonstrate that the new model can give a significant improved goodness-of-fit and predictive performance. Finally, the optimal software release time based on cost and reliability requirement and its sensitivity analysis are discussed.     

Cost optimal allocation of rail passenger lines

We consider the problem of cost optimal railway line allocation for passenger trains for the Dutch railway system. At present, the allocation of passenger lines by Dutch Railways is based on maximizing the number of direct travelers. This paper develops an alternative approach that takes operating costs into account. A mathematical programming model is developed which minimizes the operating costs subject to service constraints and capacity requirements. The model optimizes on lines, line types, routes, frequencies and train lengths. First, the line allocation model is formulated as an integer nonlinear programming model. This model is transformed into an integer linear programming model with binary decision variables. An algorithm is presented which solves the problem to optimality. The algorithm is based upon constraint satisfaction and a Branch and Bound procedure. The algorithm is applied to a subnetwork of the Dutch railway system for which it shows a substantial cost reduction. Further application and extension seem promising.     

Inventory Control with a Randomly Available Discount Purchase Price

A mathematical model is developed for a basic single-commodity storage system in which the stock controller is faced with a randomly fluctuating purchase price. High- and low-price periods are defined and a purchasing policy is specified which depends upon both the number of units in storage and the price period in which the system is operating. For the case of random demand the limiting stochastic behaviour of the system is obtained and the cost of operating the system is derived.     

Numerical simulation of internal flow fields of swirl coaxial injector in a hot environment

Based on the fractional volume of fluid (VOF), a pure Eulerian model for defining and capturing the gas/liquid interface is developed in this paper. This model can describe gas/liquid interface in high refinement, which is better than the original VOF methodology. To validate the proposed model and the algorithm, the computational code is employed to predict the flow performance in a cylindrical swirl injector under cold-flow condition, and the predicted results agree well with experimental measurements. Furthermore, the proposed model is used to simulate gas-liquid reacting flows inside a gas/liquid coaxial swirl injector operating in a hot environment. The turbulent combustion process is simulated with the k−ε−f−g model. The numerical simulation is carried out under actual operating condition of the coaxial injector. The injector performances, such as liquid film thickness, liquid film injection velocity, spray angle, pressure drop, are obtained based on the detailed information of the internal flow field. The predicted results also show that droplets are shed from the liquid film in the recess cup of the coaxial injector because of the large velocity gradient between the gas and liquid streams, and a burning area, which is characterized by high temperature, is present inside the injector.     

The application of a vehicle routing model to a waste-collection problem: two case studies

In this paper we suggest a unique model for estimating the operating cost of each of three waste-collection systems. Under the traditional system, which is widely used, waste is typically collected in plastic bags and a three-man crew is needed on each vehicle. The other two systems require a one-man crew for vehicle collecting street containers. The side-loader system with fixed body automatically empties street containers into the vehicle body and empties the load at the disposal site. The side-loader system with demountable body allows the separation of the waste collection phase from transport to the disposal site, since the vehicle body can be demounted. We also present two case studies and show how the estimation of operating costs is a critical issue in decisions regarding the type of system to be used for waste collection.     

Identification of distributed-parameter systems from sparse measurements

In this paper, a method for the identification of distributed-parameter systems is proposed, based on finite-difference discretization on a grid in space and time. The method is suitable for the case when the partial differential equation describing the system is not known. The sensor locations are given and fixed, but not all grid points contain sensors. Per grid point, a model is constructed by means of lumped-parameter system identification, using measurements at neighboring grid points as inputs. As the resulting model might become overly complex due to the involvement of neighboring measurements along with their time lags, the Lasso method is used to select the most relevant measurements and so to simplify the model. Two examples are reported to illustrate the effectiveness of the method, a simulated two-dimensional heat conduction process and the construction of a greenhouse climate model from real measurements.     

Forward mixing model in a rotating disc contactor for kerosene–acetic acid–water system

The application of an extraction column model which takes into account the influence of the forward mixing model is brought forward by generation of values of the mass transfer and axial dispersion coefficients required by the model. Values of these coefficients were generated from solute concentration profile measurements in a rotary disc contactor (RDC). The use of the Handlos–Baron drop mass transfer model was justified. The resulting continuous phase transfer coefficients were found to be dependent largely on disc speed. The accuracy of the forward mixing model for kerosene–acetic acid–water system was adequately presented and explained by the axial dispersion coefficient.     

Stability estimates for some parameters of a reaction-diffusion equation coupled with an ODE

In this work, we are interested by the monodomain equation which describes the evolution of the cardiac electrical potential and which corresponds to a coupled system involving a reaction–diffusion equation and an ordinary differential equation. We show Lipschitz stability inequalities for the identification of some parameters of the model from measurements on the cardiac potential and the ionic variable.     

Stochastic Simulation and Monte Carlo methods applied to the assessment of hydro-thermal generating system operation

Summary Simulation can be defined as a numerical technique for conducting experiments on a digital computer, which involves certain types of mathematical and logical models that describe the behaviour of a system over extended periods of real time. Simulation is, in a wide sense, a technique for performing sampling experiments on a model of the system. Stochastic simulation implies experimenting with the model over time including sampling stochastic variates from probability distributions. This paper describes the main concepts of the application of Stochastic Simulation and Monte Carlo methods to the analysis of the operation of electric energy systems, in particular to hydro-thermal generating systems. These techniques can take into account virtually all contingencies inherent in the operation of the system. Also, the operating policies that have an important effect on the performance of these systems can be realistically represented.     

Multivariate Gaussian criteria in SMAA

We consider stochastic multicriteria decision-making problems with multiple decision makers. In such problems, the uncertainty or inaccuracy of the criteria measurements and the partial or missing preference information can be represented through probability distributions. In many real-life problems the uncertainties of criteria measurements may be dependent. However, it is often difficult to quantify these dependencies. Also, most of the existing methods are unable to handle such dependency information.In this paper, we develop a method for handling dependent uncertainties in stochastic multicriteria group decision-making problems. We measure the criteria, their uncertainties and dependencies using a stochastic simulation model. The model is based on decision variables and stochastic parameters with given distributions. Based on the simulation results, we determine for the criteria measurements a joint probability distribution that quantifies the uncertainties and their dependencies. We then use the SMAA-2 stochastic multicriteria acceptability analysis method for comparing the alternatives based on the criteria distributions. We demonstrate the use of the method in the context of a strategic decision support model for a retailer operating in the liberated European electricity market.     

Supporting strategic conversations: the significance of a quantitative model building process

This paper reports on the use of a quantitative system dynamics simulation model building process that followed on from a ‘soft OR’ strategy making intervention with a top management team. The work was carried out for the Chief Constable and top team of a UK police force. The output of the ‘soft OR’ strategy making process provided the reason for, and so focus of, the simulation model. The focus of the strategic conversation was on exploring the tension between the supply of resources (in this case police officers) and the demands placed on those officers. The project involved a series of strategic conversations with members of the top team that were stimulated through the process of building a system dynamics model. The process was facilitated by two modellers—one focussing on managing the strategic enquiry, while the other considered the implications of the conversation for the construction of the system dynamics model. Through the process of gradually building the system dynamics model, a number of issues and lessons were raised for the development and delivery of effective strategy making and the strategic management of public organisations. In addition there were implications for operational research model building processes, especially when operating at senior levels and within a strategic context.     

A numerical model of the reacting multiphase flow in a pulp digester

This paper summarises the development, implementation and typical results from a new full-scale three-dimensional numerical model of the multiphase chemically reacting flow in a continuous digester. The model is based on continuity and momentum equations for wood chips and free liquid. A new sub-model describing the tangential stresses for the solid phase has been developed and incorporated into the digester model. The solution procedure that takes into account the high inter-phase friction terms and the high values of the solid pressure has been introduced. In order to illustrate the model’s performance, simulations have been carried on for the industrial digester. The results are in quantitative agreement with available field measurements and in qualitative agreement with operating personnel observations. The model utilises curvilinear body fitted coordinates and can be used to simulate the operation of various pulp digesters or any other chemical reactors working in a similar regime.     

A procedure to design the mainline system in natural gas networks

With the start up of West–East Natural Gas Transmission Project, the construction of natural gas-pipeline will enter on a new era in China. The development tendency will be towards large-diameter, high-pressure and long-distance for natural gas-pipelines. Correspondingly, the life cycle cost of natural gas-pipeline networks is increasing gradually. The mainline system is a vital part of natural gas network systems. The investment required for the mainline system is enormous, usually accounting for 80% of the total investment for this system. In general, the investment required for a gas-pipeline depends on its operating parameters. Therefore, based on the characteristics of gas networks, optimization for investment becomes indispensable to gas networks design. A comprehensive and optimal mathematic model of a gas networks system is established in this paper which considers all the factors influencing the total investment of a gas networks system (e.g. pipe diameter, thickness, pressure, length, compression ratio, etc). From the standpoint of the characteristics of a model comprising both continuous and discrete variables, a new methodology, rank-optimization, is presented. On the basis of this model, a simple and visual high-pressure networks optimization program has been compiled. Furthermore, the developed optimization program has been applied to a practical project and the effects of operating parameters on the total investment have been analyzed. The simulation model in this paper is shown to be an effective method to solve optimization on mainline system in high-pressure gas networks.     

A two-echelon allocation model and the value of information under correlated forecasts and demands

In this article we explore the effects of incorporating forecasts explicitly in a two-echelon allocation model which consists of a central depot and several retailers. In particular, we investigate the possible benefits on the system costs and inventory levels. The depot does not hold any inventory and the demand is observed only at the retailers. Under a general correlated demand-forecast structure we obtain the approximate system-wide order-up-to level and the expected system cost. In order to assess the value of information provided by keeping track of the forecasts, we construct a comparable inventory system operating under a standard demand model. We demonstrate that the standard demand model results in higher order-up-to levels and higher system costs.