Generic Simulation Framework Research Articles

GPSF: a generic and object-oriented framework for crop simulation

In this paper we present the result of an effort to reengineer an existing plant growth and development simulation program and model (TOMGRO) originally written in FORTRAN. The program was completely redesigned in terms of the OO paradigm and implemented in the Smalltalk OO programming system. Much thought and effort was put into the design and implementation of a conceptual framework that the authors believe has potential as a generic toolbox and specification for the construction of a whole family of plant growth and development simulation models. A relatively detailed description of the OO framework and of the OO implementation of TOMGRO is given. The objective is to provide neophyte readers with a practical example of an OO implementation of a non-trivial model and to interest readers who are more familiar with software engineering issues in general through the presentation and discussion of the various software constructs.

Finite element simulation of the rolling and extrusion of multi-phase materials Application to the rolling of prepared sugar cane

This paper descrbes a general framework for the finite element simulation of the rolling and extrusion of multi-phase materials. Emphasis is placed on the following aspects: The characterization of the coupling between the liquid and solid phases of the material; The modelling of the (highly nonlinear) behaviour of the solid skeleton; The adaptive mesh refinement strategy, required in view of the magnitude of the strains and complex deformations involved in the processes; The use of an efficient contact algorithm and; The inverse identification of material parameters for the solid phase by means of ‘numerical experiments’. Practical application of the developed framework is made to the numerical simulation of the process by which juice is extracted from prepared sugar can by rolling.

Hybrid-neural modeling for particulate solid drying processes

In this work, a general framework for modelling and simulation of particulate solid drying processes is presented, based on fundamental conservation laws associated with a neural network, for model uncertain parameter estimation. The modeling approach, which leads to a hybrid-neural model, is applied in order to describe the dynamic behavior of two important drying systems: a direct flow rotary dryer and a batch fluidized bed dryer. Both models are built using simple mass and energy balances, where heat and mass transfer parameters are estimated with neural networks. Model behavior was evaluated by comparing experimental and simulation data. It is concluded that the hybrid-neural modeling approach is better for adaptation and prediction than its black box type counterpart.

VLSI logic and fault simulation on general-purpose parallel computers

The authors define a general framework for the parallel simulation of digital systems and develop and evaluate tools for logic and fault simulation that have a good cost-performance ratio. They first review previous work and identify central issues. Then a high-level process model of parallel simulation is presented to clarify essential design choices. Algorithms for parallel logic and fault simulation of synchronous gate-level designs are introduced. The algorithms are based on a partitioning approach that reduces the number of necessary synchronizations between processors. A simple performance model characterizes the dependence on some crucial parameters. Experimental results for some large benchmarks are given, using prototype implementations for both message-passing and shared-memory machines. >

A general framework for concurrent simulation on neural network models

The analysis of complex neural network models via analytical techniques is often quite difficult due to the large numbers of components involved and the nonlinearities associated with these components. The authors present a framework for simulating neural networks as discrete event nonlinear dynamical systems. This includes neural network models whose components are described by continuous-time differential equations or by discrete-time difference equations. Specifically, the authors consider the design and construction of a concurrent object-oriented discrete event simulation environment for neural networks. The use of an object-oriented language provides the data abstraction facilities necessary to support modification and extension of the simulation system at a high level of abstraction. Furthermore, the ability to specify concurrent processing supports execution on parallel architectures. The use of this system is demonstrated by simulating a specific neural network model on a general-purpose parallel computer.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Coupling algorithms for mixed-level circuit and device simulation

A general framework for mixed-level circuit and device simulation is described. This framework was used in the development of the simulation program CODECS (coupled device and circuit simulator). Various algorithms to couple the device and circuit simulators for DC and transient analyses have been implemented in CODECS. These algorithms are evaluated based on their convergence properties and run-time performance. This study provides guidelines for choosing a particular coupling algorithm. A modified two-level Newton algorithm is used for DC analysis, whereas a full block-LU decomposition algorithm is used for transient analysis. This combination of algorithms provides reasonable convergence and run-time performance. A simple latency scheme provides a 50% speedup. Coupling for small-signal AC and pole-zero analyses are described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Stochastic Petri net representation of discrete event simulations

In the context of discrete event simulation, the marking of a stochastic Petri net (SPN) corresponds to the state of the underlying stochastic process of the simulation and the firing of a transition corresponds to the occurrence of an event. A study is made of the modeling power of SPNs with timed and immediate transitions, showing that such Petri nets provide a general framework for simulation. The principle result is that for any (finite or) countable state GSMP (generalized semi-Markov process) there exists an SPN having a marking process that mimics the GSMP in the sense that the two processes (and their underlying general state-space Markov chains) have the same finite dimensional distributions. >

Stochastic petri nets with timed and immediate transitions

Stochastic Petri nets with timed and immediate transitions permit representation of concurrency, synchronization, and communication and provide a general framework for discrete event simulation. Formal definition of the marking process of a stochastic Petri net is in terms of a general state space Markov chain that describes the net at successive marking change epochs. We obtain a limit theorem for irreducible marking processes with finite timed marking set. In addition, we provide conditions on the building blocks of a stochastic Petri net under which the marking process is a regenerative process in continuous time with finite cycle length moments. These results establish the regenerative method for simulation analysis in the stochastic Petri net setting

System dynamics of a metastatic process from an implanted tumor

A lumped-parameter, deterministic model of the hematogenous metastatic process from a solid tumor is developed to provide a general theoretical framework for analysis and simulation. The model consists of a series of population compartments: tumor cells in the tumor mass, tumor vessels, tumor cells penetrating the vessels, tumor cells arrested in the target organ, and metastatic foci in the target organ. Model simulations are compared with experimental results from a group of mice implanted with a fibrosarcoma which leads to pulmonary metastases. The model is found to provide good agreement with the general process dynamics. In addition, the experimental consequences of tumor trauma and tumor amputation are compared with model simulations of these perturbations to validate the model behaviour. Finally, the effects of inhibited tumor vessel growth, retarded vessel penetration by tumor cells, and pulmonary vessel damage are simulated, which suggest future experiments.