The behavior of transient period of nonterminating simulations: an experimental analysis

Cataloged from PDF version of article.The design and control of many industrial and service systems require the analysts to account for uncertainty. Computer simulation is a frequently used technique for analyzing uncertain (or stochastic) systems. One disadvantage of simulation modeling is that simulation results are only estimates of model performance measures. Therefore, to obtain better estimates, the outputs of a simulation run should undergo a careful statistical analysis. Simulation studies can be classified as terminating and nonterminating according to the output analysis techniques used. One of the major problems in the output analysis of nonterminating simulations is the problem of initial transient. This problem arises due to initializing simulation runs in an unrepresentative state of the steady-state conditions. Many techniques have been proposed in the literature to deal with the problem of initial transient. However, existing studies try to improve the efficiency and effectiveness of currently proposed techniques. No research has been encountered that analyzes the behavior of the transient period. In this thesis, we investigate the factors affecting the length of the transient period for nonterminating manufacturing simulations, particularly for serial production lines and job-shop production systems. Factors such as variability of processing times, system size, existence of bottleneck, reliability of system, system load level, and buffer capacity are investigated.Sandıkçı, BurhaneddinM.S

A data warehouse environment for storing and analyzing simulation output data

Discrete event simulation modelling has been extensively used in modelling complex systems. Although it offers great conceptual-modelling flexibility, it is both computationally expensive and data intensive. There are several examples of simulation models that generate millions of observations to achieve satisfactory point and confidence interval estimations for the model variables. In these cases, it is exceptionally cumbersome to conduct the required output and sensitivity analysis in a spreadsheet or statistical package. In this paper, we highlight the advantages of employing data warehousing techniques for storing and analyzing simulation output data. The proposed data warehouse environment is capable of providing the means for automating the necessary algorithms and procedures for estimating different parameters of the simulation. These include initial transient in steady-state simulations and point and confidence interval estimations. Previously developed models for evaluating patient flow through hospital epartments are used to demonstrate the problem and the proposed solutions

Adaptive Multi-Rate Wavelet Method for Circuit Simulation

In this paper a new adaptive algorithm for multi-rate circuit simulation encountered in the design of RF circuits based on spline wavelets is presented. The ordinary circuit differential equations are first rewritten by a system of (multi-rate) partial differential equations (MPDEs) in order to decouple the different time scales. Second, a semi-discretization by Rothe's method of the MPDEs results in a system of differential algebraic equations DAEs with periodic boundary conditions. These boundary value problems are solved by a Galerkin discretization using spline functions. An adaptive spline grid is generated, using spline wavelets for non-uniform grids. Moreover the instantaneous frequency is chosen adaptively to guarantee a smooth envelope resulting in large time steps and therefore high run time efficiency. Numerical tests on circuits exhibiting multi-rate behavior including mixers and PLL conclude the paper

Robust quantum parameter estimation: coherent magnetometry with feedback

We describe the formalism for optimally estimating and controlling both the state of a spin ensemble and a scalar magnetic field with information obtained from a continuous quantum limited measurement of the spin precession due to the field. The full quantum parameter estimation model is reduced to a simplified equivalent representation to which classical estimation and control theory is applied. We consider both the tracking of static and fluctuating fields in the transient and steady state regimes. By using feedback control, the field estimation can be made robust to uncertainty about the total spin number

Feedback control of unstable steady states of flow past a flat plate using reduced-order estimators

We present an estimator-based control design procedure for flow control, using reduced-order models of the governing equations, linearized about a possibly unstable steady state. The reduced models are obtained using an approximate balanced truncation method that retains the most controllable and observable modes of the system. The original method is valid only for stable linear systems, and we present an extension to unstable linear systems. The dynamics on the unstable subspace are represented by projecting the original equations onto the global unstable eigenmodes, assumed to be small in number. A snapshot-based algorithm is developed, using approximate balanced truncation, for obtaining a reduced-order model of the dynamics on the stable subspace. The proposed algorithm is used to study feedback control of 2-D flow over a flat plate at a low Reynolds number and at large angles of attack, where the natural flow is vortex shedding, though there also exists an unstable steady state. For control design, we derive reduced-order models valid in the neighborhood of this unstable steady state. The actuation is modeled as a localized body force near the leading edge of the flat plate, and the sensors are two velocity measurements in the near-wake of the plate. A reduced-order Kalman filter is developed based on these models and is shown to accurately reconstruct the flow field from the sensor measurements, and the resulting estimator-based control is shown to stabilize the unstable steady state. For small perturbations of the steady state, the model accurately predicts the response of the full simulation. Furthermore, the resulting controller is even able to suppress the stable periodic vortex shedding, where the nonlinear effects are strong, thus implying a large domain of attraction of the stabilized steady state.Comment: 36 pages, 17 figure