Convergence Study of Explicit Co-Simulation Approaches with Respect to Subsystem Solver Settings

Coupling different subsystem simulators can be accomplished by a co-simulation [1, 2]. For this purpose, the subsystem solvers are coupled by appropriate input and output variables. In order to analyze the stability of the coupled simulation, not only the coupling technique must be taken into account, but also the subsystem integrators. On the one hand, the stability of the co-simulation is influenced by the extrapolation of the coupling variables and by the macro-step size. On the other hand, the numerical errors arising from the subsystem solvers may directly affect the coupled simulation. The focus of this paper lies on the question, how the subsystem solvers influence the co-simulation. Therefore, numerical studies regarding the numerical stability and the convergence order have been carried out by using a co-simulation test model. We restrict ourselves to explicit co-simulation techniques, based on a zero-stable applied-force coupling approach. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Explicit and Implicit Solver Coupling: Stability Analysis Based on an Eight-Parameter Test Model

Coupling different solvers via co-simulation is frequently applied in the field of multi-physical simulation. The numerical stability and accuracy of the coupled problem strongly depends on the coupling technique used for the co-simulation. In the current work, different co-simulation approaches are analytically investigated based on a linear eight-parameter test model. The test model is formulated in state-space form containing input and output variables so that the results may directly be applied to practical coupling interfaces. For the test model, recurrence formulas are derived for explicit and implicit coupling techniques of Jacobian and Gauss-Seidel type. The numerical stability of the co-simulation approach is examined by an eigenvalue analysis of the corresponding recurrence equation. Three-dimensional stability plots can be analyzed for different coupling methods and for arbitrary order of the extrapolation/interpolation polynomial. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Coupled simulation in vehicle engineering

Modern vehicles are highly complex systems consisting of many subsystems in various physical domains, e.g. electric, electronic, hydraulic and control systems that dynamically interact. For any subsystem, there are tailored simulation tools with specifically developed and adapted numerical solvers. In this context, co-simulation strategies are particularly attractive, where each submodel is solved with appropriate numerical methods. In industrial applications, one is hereby confronted with enormous numerical challenges with respect to efficiency, accuracy and numerical stability – especially in online applications. In this article, we present co-simulation strategies by means of selected application examples from the field of vehicle engineering. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Lagrangian relaxation approach for network inventory control of stochastic revenue management with perishable commodities

Airline seat inventory control is the allocation of seats in the same cabin to different fare classes such that the total revenue is maximized. Seat allocation can be modelled as dynamic stochastic programs, which are computationally intractable in network settings. Deterministic and probabilistic mathematical programming models are therefore used to approximate dynamic stochastic programs. The probabilistic model, which is the focus of this paper, has a nonlinear objective function, which makes the solution of large-scale practical instances with off-the-shelf solvers prohibitively time consuming. In this paper, we propose a Lagrangian relaxation (LR) method for solving the probabilistic model by exploring the fact that LR problems are decomposable. We show that the solutions of the LR problems admit a simple analytical expression which can be resolved directly. Both the booking limit policy and the bid-price policy can be implemented using this method. Numerical simulations demonstrate the effectiveness of the proposed method.     

A Co-Simulation Approach for the 3D Dynamic Simulation of Vehicles Considering Sloshing in Cargo and Fuel Tanks

The sloshing of liquids in cargo and fuel tanks mounted on vehicles can have a significant influence on the vehicle's driving dynamics and stability. To evaluate and optimize the quality of tank designs, we propose a co-simulation approach that consists of a coupled multibody system simulation for the vehicle and a Discrete Element Method and Smoothed Particle Hydrodynamics simulation for the sloshing cargo. This approach is beneficial especially for the simulation of fluid cargos, as Smoothed Particle Hydrodynamics does not require additional models to track and reconstruct free fluid surfaces. By means of dynamic 3D simulations of a double lane change maneuvers we compare the two different cargo models and demonstrate the viability of the co-simulation approach. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Homotopy method for a mean curvature-based denoising model

Variational image denoising models based on regularization of gradients have been extensively studied. The total variation model by Rudin, Osher, and Fatemi (1992) [38] can preserve edges well but for images without edges (jumps), the solution to this model has the undesirable staircasing effect. To overcome this, mean curvature-based energy minimization models offer one approach for restoring both smooth (no edges) and nonsmooth (with edges) images. As such models lead to fourth order (instead of the usual second order) nonlinear partial differential equations, development of fast solvers is a challenging task. Previously stabilized fixed point methods and their associated multigrid methods were developed but the underlying operators must be regularized by a relatively large parameter. In this paper, we first present a fixed point curvature method for solving such equations and then propose a homotopy approach for varying the regularized parameter so that the Newton type method becomes applicable in a predictor-corrector framework. Numerical experiments show that both of our methods are able to maintain all important information in the image, and at the same time to filter out noise.     

Error analysis for co-simulation with force-displacement coupling

Co-simulation is a simulation technique for time dependent coupled problems in engineering that restricts the data exchange between subsystems to discrete communication points in time. In the present paper we follow the block-oriented framework in the recently established industrial interface standard FMI for Model Exchange and Co-Simulation v2.0 and study local and global error of co-simulation algorithms for systems with force-displacement coupling. A rather general convergence result for the co-simulation of coupled systems without algebraic loops shows zero-stability of co-simulation algorithms with force-displacement coupling and proves that order reduction of local errors does not affect the order of global errors. The theoretical investigations are illustrated by numerical tests in the novel FMI-compatible co-simulation environment SNiMoWrapper . (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Finite element modeling of welding processes

This work deals with the simulation of fusion welding by the Finite Element Method. The implemented models include a moving heat source, temperature dependence of thermo-physical properties, elasto-plasticity, non-steady state heat transfer, and mechanical analysis. The thermal problem is assumed to be uncoupled from the mechanical one, so the thermal analysis is performed separately and previously to the mechanical analysis at each time step. The mechanical problem is based on the thermal history. A special treatment is performed on mechanical elements during the liquid/solid and solid/liquid phase changes to account for stress states. The three-dimensional stress state of a butt-welded joint is obtained as an example of an application.     

Experiments in numerical methods for a problem in combustion modeling

In this paper, we have shown that the numerical method of lines can be used effectively to solve time dependent combustion models in one spatial dimension. By the numerical method of lines (NMOL), we mean the reduction of a system of partial differential equations to a system of ordinary differential equations (ODE's), followed by the solution of this ODE system with an appropriate ODE solver. We used finite differences for the spatial discretization and a variant of the GEAR package for the ODE's.We have presented various solution methods of interest for the nonlinear algebraic system in this setting; that is, in the corrector iteration section of the GEAR package applied to combustion models. These methods include Newton/block SOR (SOR denotes successive over-relaxation), block SOR/Newton, Newton/block-diagonal Jacobian, Newton/kinetics-only Jacobian, and Newton/block symmetric SOR. These methods have in common their lack of frequent use in ODE software and their eady applicability to partial differential equations in more than one spatial dimension.Finally, we have given the results of numerical tests, run on the CDC-7600 and Cray-1 computers. By so doing, we indicate the more promising nonlinear system solvers for the NMOL solution of combustion models.     

Stochastic actor‐oriented models for network change

A class of models is proposed for longitudinal network data. These models are along the lines of methodological individualism: actors use heuristics to try to achieve their individual goals, subject to constraints. The current network structure is among these constraints. The models are continuous time Markov chain models that can be implemented as simulation models. They incorporate random change in addition to the purposeful change that follows from the actors’ pursuit of their goals, and include parameters that must be estimated from observed data. Statistical methods are proposed for estimating and testing these models. These methods can also be used for parameter estimation for other simulation models. The statistical procedures are based on the method of moments, and use computer simulation to estimate the theoretical moments. The Robbins‐Monro process is used to deal with the stochastic nature of the estimated theoretical moments. An example is given for Newcomb's fraternity data, using a model that expresses reciprocity and balance.     

Solving optimal control problems for the unsteady Burgers equation in COMSOL Multiphysics

The optimal control of unsteady Burgers equation without constraints and with control constraints are solved using the high-level modelling and simulation package COMSOL Multiphysics. Using the first-order optimality conditions, projection and semi-smooth Newton methods are applied for solving the optimality system. The optimality system is solved numerically using the classical iterative approach by integrating the state equation forward in time and the adjoint equation backward in time using the gradient method and considering the optimality system in the space-time cylinder as an elliptic equation and solving it adaptively. The equivalence of the optimality system to the elliptic partial differential equation (PDE) is shown by transforming the Burgers equation by the Cole-Hopf transformation to a linear diffusion type equation. Numerical results obtained with adaptive and nonadaptive elliptic solvers of COMSOL Multiphysics are presented both for the unconstrained and the control constrained case.     

A structure-conveying modelling language for mathematical and stochastic programming

We present a structure-conveying algebraic modelling language for mathematical programming. The proposed language extends AMPL with object-oriented features that allows the user to construct models from sub-models, and is implemented as a combination of pre- and post-processing phases for AMPL. Unlike traditional modelling languages, the new approach does not scramble the block structure of the problem, and thus it enables the passing of this structure on to the solver. Interior point solvers that exploit block linear algebra and decomposition-based solvers can therefore directly take advantage of the problem’s structure. The language contains features to conveniently model stochastic programming problems, although it is designed with a much broader application spectrum.     

Mixing Monte-Carlo and Partial Differential Equations for Pricing Options

There is a need for very fast option pricers when the financial objects are modeled by complex systems of stochastic differential equations. Here the authors investigate option pricers based on mixed Monte-Carlo partial differential solvers for stochastic volatility models such as Heston’s. It is found that orders of magnitude in speed are gained on full Monte-Carlo algorithms by solving all equations but one by a Monte-Carlo method, and pricing the underlying asset by a partial differential equation with random coefficients, derived by Itô calculus. This strategy is investigated for vanilla options, barrier options and American options with stochastic volatilities and jumps optionally.     

Error estimation approach for controlling the comunication step size for semi-implicit co-simulation methods

In this paper, an approach for controlling the macro-step size in connection with co-simulation methods [1, 2, 4] is suggested. The investigated step-size controller is tailored for semi-implicit co-simulation techniques. Concretely, we consider predictor/corrector co-simulation approaches [3]. By comparing variables from the predictor and the corrector step, an error estimator for the local error can be constructed. Making use of the estimated local error, a step-size controller for the macro step-size can be implemented. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Nyström discretization of parabolic boundary integral equations

A Nyström method for the discretization of thermal layer potentials is proposed and analyzed. The method is based on considering the potentials as generalized Abel integral operators in time, where the kernel is a time dependent surface integral operator. The time discretization is the trapezoidal rule with a corrected weight at the endpoint to compensate for singularities of the integrand. The spatial discretization is a standard quadrature rule for surface integrals of smooth functions. We will discuss stability and convergence results of this discretization scheme for second-kind boundary integral equations of the heat equation. The method is explicit, does not require the computation of influence coefficients, and can be combined easily with recently developed fast heat solvers.     

New Optimization Approach to Multiphase Flow

A new optimization formulation for simulating multiphase flow in porous media is introduced. A locally mass-conservative, mixed finite-element method is employed for the spatial discretization. An unconditionally stable, fully-implicit time discretization is used and leads to a coupled system of nonlinear equations that must be solved at each time step. We reformulate this system as a least squares problem with simple bounds involving only one of the phase saturations. Both a Gauss–Newton method and a quasi-Newton secant method are considered as potential solvers for the optimization problem. Each evaluation of the least squares objective function and gradient requires solving two single-phase self-adjoint, linear, uniformly-elliptic partial differential equations for which very efficient solution techniques have been developed.     

A competitive and cooperative approach to propositional satisfiability

Propositional satisfiability (SAT) has attracted considerable attention recently in both Computer Science and Artificial Intelligence, and a lot of algorithms have been developed for solving SAT. Each SAT solver has strength and weakness, and it is difficult to develop a universal SAT solver which can efficiently solve a wide range of SAT instances. We thus propose parallel execution of SAT solvers each of which individually solves the same SAT instance simultaneously. With this competitive approach, a variety of SAT instances can be solved efficiently in average. We then consider a cooperative method for solving SAT by exchanging lemmas derived by conflict analysis among different SAT solvers. To show the usefulness of our approach, we solve SATLIB benchmark problems, planning benchmark problems as well as the job-shop scheduling problem with good performance. The system has been implemented in Java with both systematic and stochastic solvers.     

On solving the dual for portfolio selection by optimizing Conditional Value at Risk

This note is focused on computational efficiency of the portfolio selection models based on the Conditional Value at Risk (CVaR) risk measure. The CVaR measure represents the mean shortfall at a specified confidence level and its optimization may be expressed with a Linear Programming (LP) model. The corresponding portfolio selection models can be solved with general purpose LP solvers. However, in the case of more advanced simulation models employed for scenario generation one may get several thousands of scenarios. This may lead to the LP model with huge number of variables and constraints thus decreasing the computational efficiency of the model. To overcome this difficulty some alternative solution approaches are explored employing cutting planes or nondifferential optimization techniques among others. Without questioning importance and quality of the introduced methods we demonstrate much better performances of the simplex method when applied to appropriately rebuilt CVaR models taking advantages of the LP duality.     

A block preconditioning technique for the streamfunction‐vorticity formulation of the Navier‐Stokes equations

Iterative methods of Krylov‐subspace type can be very effective solvers for matrix systems resulting from partial differential equations if appropriate preconditioning is employed. We describe and test block preconditioners based on a Schur complement approximation which uses a multigrid method for finite element approximations of the linearized incompressible Navier‐Stokes equations in streamfunction and vorticity formulation. By using a Picard iteration, we use this technology to solve fully nonlinear Navier‐Stokes problems. The solvers which result scale very well with problem parameters. © 2011 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2011