The Full Potential of Continuous System Simulation Modelling

Abstract

Continuous System Simulation (CSS) is a powerful way to study the behaviour of differential-algebraic equation models. Differential-algebraic equation modelling goes back to Newton and works well for models of e.g. physical systems where stochasticity plays almost no role, and where only a single attribute of an object is studied over time. However, when results from a deterministic CSS model are compared with results from a Discrete Event Simulation (DES) model, they are often inconsistent. The reasons behind this CSS-DES inconsistency are nowadays well understood. In this paper, we demonstrate that a CSS model can contain both continuous state variables (compartments) that change continuously over time and discrete state variables (also compartments) that model discrete entities and change by integer amounts. In both cases, the time-slicing method is used to advance time. Furthermore, stochasticity can and should play the same role in CSS as it does in DES. This paper first explains how a well-defined conceptual model can be stepwise transformed in a consistent way into a CSS model. These transformation steps provide insights into how to construct a consistent CSS model. In short, this is about preserving uncertainties, attributes and dynamic properties. An additional benefit of this approach is that continuous and discrete sub-models can interact within the same model, without having to combine different types of simulation languages, types of time handling and incongruent concepts. To facilitate the use and understanding of stochastic CSS, this paper is also intended to serve as a guide to performing consistent CSS modelling and simulation. Furthermore, an open source tool for collecting and analyzing the outputs from a stochastic CSS model and analyzing and presenting the results in statistical form is also developed and made available to the reader.

Técnicas hidricamente eficientes e modelagem: estratégias para a sustentabilidade e intensificação da agricultura frente às mudanças do clima em ambientes suscetíveis à desertificação

“…Neste caso supõe-se que o estado do sistema não se altera ao longo do intervalo entre a ocorrência dos dois eventos consecutivos, isto é, não se altera de forma sucessiva (Bogado et al, 2011). Já no modelo de simulação contínuo, o tempo é adotado como se fosse realmente contínuo, muito embora, sejam utilizados em pequenos intervalos, em virtude do método empregado e do próprio computador, sendo muito adotado no estudo dos sistemas que envolvem equações algébricas diferenciais (Gustafsson et al, 2017). Por outro lado, naqueles modelos considerados combinados (contínuos/discretos), as variáveis independentes podem se alterar tanto de forma discreta quanto contínua, e o comportamento do sistema é simulado pelo exame das variáveis em pequenos incrementos de tempo e os atributos das entidades na ocorrência dos eventos (Kettenis, 1992).…”

Measuring the Realtime Capability of Parallel-Discrete-Event-Simulations

“…In contrast to continuous simulation, DESs do not assume a uniform flow of time. Whereas a continuous simulation is in a permanent state of flux, state changes in discrete event models happen only at specific points in time [13]. These time points are bound to certain events, as shown in Figure 1.…”