Snapshot-Based Methods and Algorithms

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Control-Oriented Modeling for Managed Pressure Drilling Automation Using Model Order Reduction

Automation of Managed Pressure Drilling (MPD) enables fast and accurate pressure control in drilling operations. The performance that can be achieved by automated MPD is determined by, firstly, the controller design and, secondly, the hydraulics model that is used as a basis for controller design. On the one hand, such hydraulics model should be able to accurately capture essential flow dynamics, e.g., wave propagation effects, for which typically complex models are needed. On the other hand, a suitable model should be simple enough to allow for extensive simulation studies supporting well scenario analysis and high-performance controller design. In this paper, we develop a model order reduction approach for the derivation of such a control-oriented model for {single-phase flow} MPD {operations}. In particular, a nonlinear model order reduction procedure is presented that preserves key system properties such as stability and provides guaranteed (accuracy) bounds on the reduction error. To demonstrate the quality of the derived control-oriented model, {comparisons with field data and} both open-loop and closed-loop simulation-based case studies are presented

Proper initial solution to start periodic steady-state-based methods

We present a numerical technique that automatically identifies a suitable initial solution to start periodic steady-state methods for simulating non-autonomous circuits at transistor-level. The method avoids the guessing of the initial solution, which may result in divergence of the steady-state method used. For high-Q oscillating circuits, acceleration methods are used to compute the periodic solution. For strongly nonlinear circuits, such as delay-locked loops and switching-mode power supplies, time-domain methods are preferred, e.g., Shooting-Newton. Usually, a number of pre-integration periods is guessed to provide an initial solution for the acceleration method. However, the method may diverge, then the guessing has to be repeated with no clue on the next one. Instead, the technique described here identifies a proper initial solution that makes the method converges, works in the time-domain and makes use of information stored during the integration process, thus is non-invasive for commercial circuit simulators and can be implemented with little effort. Besides, it works in parallel with the integration process, thus computations are cheap to perform. We show experimental results from applying our technique and then start shooting-Newton on five circuits, among which three are industrial and two are strongly nonlinear, that confirm the validity of our mathematical analyses

An Improved CSPM Approach for Accurate Second-Derivative Approximations with SPH

We compare several approximations for second derivatives with Smoothed Particle Hydrodynamics (SPH). A first-order consistent approximation, derived from the zeroth-order consistent Corrective Smoothed Particle Method (CSPM), is proposed. The accuracy of the new method (ICSPM) is similar to that of the Finite Particle Method (FPM) and Modified Smoothed Particle Hydrodynamics (MSPH), but it is computationally less expensive. We demonstrate the accuracy of our method by studying heat conduction in a slab with discontinuous conductivity coefficients. We use both uniformly and pseudo-randomly distributed particles

Structure-Preserving Spatial Discretization of a Two-Fluid Model

We present a structure-preserving spatial discretization method for infinite-dimensional non-linear port-Hamiltonian representations of a commonly used one-dimensional two-phase flow model: the Two-Fluid Model. We introduce the port-Hamiltonian representation of this two-phase flow model and then invoke a mixed-finite-element method to perform a structure-preserving spatial discretization. Consequently, we obtain a finite-dimensional realization of a recently proposed novel Stokes-Dirac structure for this model. The properties of the resulting finite-dimensional realization are assessed and the conditions under which it is known to respect the properties of a finite-dimensional Dirac structure are discussed. Moreover, we derive the complete finite-dimensional interconnected port-Hamiltonian model by invoking the notion of power-preserving interconnection