Issues using COTS simulation software packages for the interoperation of models

This paper intends to examine the interoperation of simulation models from the viewpoint of a simulation engineer who uses standard tools and methods to create these models. The paper will look at the models in the context of COTS (commercially available off-the shelf) simulation packages with a view to applying distributed simulation (DS) theory to the subject. By studying current methods employed which enable COTS simulation packages to interoperate, this paper will discuss the tools currently used and examine their appropriateness. The paper will also suggest how an example COTS simulation package could be modified to provide the necessary functions and interoperability required to allow full distributed simulation

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

Hybrid Simulation Safety: Limbos and Zero Crossings

Physical systems can be naturally modeled by combining continuous and discrete models. Such hybrid models may simplify the modeling task of complex system, as well as increase simulation performance. Moreover, modern simulation engines can often efficiently generate simulation traces, but how do we know that the simulation results are correct? If we detect an error, is the error in the model or in the simulation itself? This paper discusses the problem of simulation safety, with the focus on hybrid modeling and simulation. In particular, two key aspects are studied: safe zero-crossing detection and deterministic hybrid event handling. The problems and solutions are discussed and partially implemented in Modelica and Ptolemy II

The Development of the Use of Expert Testimony

The steadily increasing performance of modern computer systems is having a large influence on simulation technologies. It enables increasingly detailed simulations of larger and more comprehensive simulation models. Increasingly large amounts of numerical data are produced by these simulations. This thesis presents several contributions in the field of mechanical system simulation and visualisation. The work described in the thesis is of practical relevance and results have been tested and implemented in tools that are used daily in the industry i.e., the BEAST (BEAring Simulation Tool) tool box. BEAST is a multibody system (MBS) simulation software with special focus on detailed contact calculations. Our work is primarily focusing on these types of systems. focusing on these types of systems. Research in the field of simulation modelling typically focuses on one or several specific topics around the modelling and simulation work process. The work presented here is novel in the sense that it provides a complete analysis and tool chain for the whole work process for simulation modelling and analysis of multibody systems with detailed contact models. The focus is on detecting and dealing with possible problems and bottlenecks in the work process, with respect to multibody systems with detailed contact models. The following primary research questions have been formulated: How to utilise object-oriented techniques for modelling of multibody systems with special reference tocontact modelling? How to integrate visualisation with the modelling and simulation process of multibody systems withdetailed contacts. How to reuse and combine existing simulation models to simulate large mechanical systems consistingof several sub-systems by means of co-simulation modelling? Unique in this work is the focus on detailed contact models. Most modelling approaches for multibody systems focus on modelling of bodies and boundary conditions of such bodies, e.g., springs, dampers, and possibly simple contacts. Here an object oriented modelling approach for multibody simulation and modelling is presented that, in comparison to common approaches, puts emphasis on integrated contact modelling and visualisation. The visualisation techniques are commonly used to verify the system model visually and to analyse simulation results. Data visualisation covers a broad spectrum within research and development. The focus is often on detailed solutions covering a fraction of the whole visualisation process. The novel visualisation aspect of the work presented here is that it presents techniques covering the entire visualisation process integrated with modeling and simulation. This includes a novel data structure for efficient storage and visualisation of multidimensional transient surface related data from detailed contact calculations. Different mechanical system simulation models typically focus on different parts (sub-systems) of a system. To fully understand a complete mechanical system it is often necessary to investigate several or all parts simultaneously. One solution for a more complete system analysis is to couple different simulation models into one coherent simulation. Part of this work is concerned with such co-simulation modelling. Co-simulation modelling typically focuses on data handling, connection modelling, and numerical stability. This work puts all emphasis on ease of use, i.e., making mechanical system co-simulation modelling applicable for a larger group of people. A novel meta-model based approach for mechanical system co-simulation modelling is presented. The meta-modelling process has been defined and tools and techniques been created to fully support the complete process. A component integrator and modelling environment are presented that support automated interface detection, interface alignment with automated three-dimensional coordinate translations, and three dimensional visual co-simulation modelling. The integrated simulator is based on a general framework for mechanical system co-simulations that guarantees numerical stability

On the Simulation of Polynomial NARMAX Models

In this paper, we show that the common approach for simulation non-linear stochastic models, commonly used in system identification, via setting the noise contributions to zero results in a biased response. We also demonstrate that to achieve unbiased simulation of finite order NARMAX models, in general, we require infinite order simulation models. The main contributions of the paper are two-fold. Firstly, an alternate representation of polynomial NARMAX models, based on Hermite polynomials, is proposed. The proposed representation provides a convenient way to translate a polynomial NARMAX model to a corresponding simulation model by simply setting certain terms to zero. This translation is exact when the simulation model can be written as an NFIR model. Secondly, a parameterized approximation method is proposed to curtail infinite order simulation models to a finite order. The proposed approximation can be viewed as a trade-off between the conventional approach of setting noise contributions to zero and the approach of incorporating the bias introduced by higher-order moments of the noise distribution. Simulation studies are provided to illustrate the utility of the proposed representation and approximation method.Comment: Accepted in IEEE CDC 201

Construction of dynamic stochastic simulation models using knowledge-based techniques

Over the past three decades, computer-based simulation models have proven themselves to be cost-effective alternatives to the more structured deterministic methods of systems analysis. During this time, many techniques, tools and languages for constructing computer-based simulation models have been developed. More recently, advances in knowledge-based system technology have led many researchers to note the similarities between knowledge-based programming and simulation technologies and to investigate the potential application of knowledge-based programming techniques to simulation modeling. The integration of conventional simulation techniques with knowledge-based programming techniques is discussed to provide a development environment for constructing knowledge-based simulation models. A comparison of the techniques used in the construction of dynamic stochastic simulation models and those used in the construction of knowledge-based systems provides the requirements for the environment. This leads to the design and implementation of a knowledge-based simulation development environment. These techniques were used in the construction of several knowledge-based simulation models including the Advanced Launch System Model (ALSYM)

A novel indicator for kinematic hardening effect quantification in deep drawing simulation

Deep drawing simulation techniques reduce tool design costs and improve tool performance and reliability. In terms of strain hardening, mixed models capturing the kinematic effect are sometimes more accurate than isotropic models. Indeed, nonlinearity in strain paths can lead to inconsistent simulation results. However, the use of such models requires a greater number of tests including strain path changes. Therefore, the use of such mixed models shall be required only if the simulation includes non-linear strain paths and the material exhibits a pronounced Bauschinger effect. New tools to help engineers choose between models could ease the spread of more advanced models in simulation of deep drawing processes when needed. With this in mind, an indicator predicting the influence of kinematic effects could help to select an adequate model. In this study, a new indicator is introduced with the idea of characterising strain path non-linearity in order to assess kinematic hardening influence. The indicator is computed using the forming history taken from a purely isotropic simulation – which is easier to set up and parametrise. The ability of the indicator to predict inconsistencies within the isotropic simulation is investigated using U-channel simulations

Verification and validation of simulation models

Simulation Models;econometrics

Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Background: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. Results: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. Conclusions: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined