Enabling industrial scale simulation / emulation models

OLE Process Control (OPC) is an industry standard that facilitates the communication between PCs and Programmable Logic Controllers (PLC). This communication allows for the testing of control systems with an emulation model. When models require faster and higher volume communications, limitations within OPC prevent this. In this paper an interface is developed to allow high speed and high volume communications between a PC and PLC enabling the emulation of larger and more complex control systems and their models. By switching control of elements within the model between the model engine and the control system it is possible to use the model to validate the system design, test the real world control systems and visualise real world operation.

Atomic scale simulation of deformation in magnesium single crystals

The deformation behaviour of magnesium single crystals under plane strain conditions has been examined using molecular dynamics modelling. The simulations were based on an existing atomic potential for magnesium taken from the literature. A strain of 10% was applied at rates of 3x10⁹s^-1 and 3x10⁷s^-1. The simulations predicted the formation of mechanical twins that accommodated extension in the c-axis direction of the hexagonal unit cell. However, the predicted twin is not of the same kind found in magnesium, but is that commonly observed in titanium. It is believed that further analysis of the physical properties predicted by this interatomic potential will shed more light on the atomic processes controlling twinning in Magnesium alloys. It also highlights the need for improvements to the interatomic potential such that more accurate deformation behaviour can be attained.

Multi-scale simulation using a hybrid mass-spring system and finite element model

Ply-scale finite element (FE) models are widely used to predict the performance of a composite structure based on material properties of individual plies. When simulating damage, these models neglect microscopic fracture processes which may have a significant effect on how a crack progresses within and between plies of a multidirectional laminate. To overcome this resolution limitation a multi-scale modelling technique is employed to simulate the effect micro-scale damage events have on the macro-scale response of a structure. The current paper discusses the development and validation of a hybrid mass-spring system and finite element modelling technique for multi-scale analysis. The model developed here is limited to elastic deformations; however, it is the first key step towards an efficient multi-scale damage model well suited to simulation of fracture in fibre reinforced composite materials. Various load cases have been simulated using the model developed here which show excellent accuracy compared to analytical and FE results. Future work is discussed, including extension of the model to incorporate damage modelling.

Multi-scale material modelling platform

The data includes material models suitable for modelling and simulation of multi-scale heterogeneous materials, as well as simulation results and experimental observations for verification and validation of simulated results.

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths.

The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Two-stage sequential procedure for nonparametric regression estimation

In nonparametric statistics the functional form of the relationship between the response variable and its associated predictor variables is unspecified but it is assumed to be a smooth function. We develop a procedure for constructing a fixed width confidence interval for the predicted value at a specified point of the independent variable. The optimal sample size for constructing this interval is obtained using a two stage sequential procedure which relies on some asymptotic properties of the Nadaraya--Watson and local linear estimators. Finally, a large scale simulation study demonstrates the applicability of the developed procedure for small and moderate sample sizes. The procedure developed here should find wide applicability since many practical problems which arise in industry involve estimating an unknown function.

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Nonparametric rank tests for non-stationary panels

We develop a set of nonparametric rank tests for non-stationary panels based on multivariate variance ratios which use untruncated kernels. As such, the tests do not require the choice of tuning parameters associated with bandwidth or lag length and also do not require choices with respect to numbers of common factors. The tests allow for unrestricted cross-sectional dependence and dynamic heterogeneity among the units of the panel, provided simply that a joint functional central limit theorem holds for the panel of differenced series. We provide a discussion of the relationships between our setting and the settings for which first- and second generation panel unit root tests are designed. In Monte Carlo simulations we illustrate the small-sample performance of our tests when they are used as panel unit root tests under the more restrictive DGPs for which panel unit root tests are typically designed, and for more general DGPs we also compare the small-sample performance of our nonparametric tests to parametric rank tests. Finally, we provide an empirical illustration by testing for income convergence among countries.

Modeling and simulation for large scale production systems

Computer modeling and simulation provide a foundation upon which industrial processes and systems can be transformed and innovation dramatically accelerated. Computer modeling and simulation is also an indispensable tool of the information age, used extensively in design, analysis, operations, decision-making, optimization, and education and training. Manufacturing, production and design relies upon simulation to develop efficient production systems and factories that produce quality products. Simulation in industry has yet to meet its full potential. The development of models is very time consuming, particularly for geometries of complex engineering systems such as manufacturing plants, automobiles, aircraft and ships. Computer simulation allows scientists and engineers to understand and predict three-dimensional and time-dependent phenomena in science and engineering discipline. This talk will focus on challenges associated with modeling and simulation in the manufacturing sector and through a number of case studies highlight the benefits gained through the use of such technologies.

Influence of task properties and subjectivity on consistency of triage: a simulation study

Aim. This paper reports a study to determine nurses' levels of agreement using a standard 5-point triage scale and to explore the influence of task properties and subjectivity on decision-making consistency.

Background. Triage scales are used to define time-to-treatment in hospital emergency departments. Studies of the inter-rater reliability of these scales using paper-based simulation methods report varying levels of consistency. Understanding how various components of the decision task and individual perceptions of the case influence agreement is critical to the development of strategies to improve consistency of triage.

Method. Simulations were constructed from naturalistic observation, cue types and frequencies were classified. Data collection was conducted in 2002, and the final response rate was 41·3%. Participants were asked to allocate an urgency code for 12 scenarios using the Australasian Triage Scale, and provide estimates of case complexity, levels of certainty and available information. Data were analysed descriptively, agreement between raters was calculated using kappa. The influence of task properties and participants' subjective estimates of case complexity, levels of certainty and available information on agreement were explored using a general linear model.

Findings. Agreement among raters varied from moderate to poor (κ = 0·18–0·64). Participants' subjective estimates of levels of available information were found to influence consistency of triage by statistically significant amounts (F 5·68; ≤0·01).

Conclusions. Strategies employed to optimize consistency of triage should focus on improving the quality of the simulations that are used. In particular, attention should be paid to the development of interactive simulations that will accommodate individual differences in information-seeking behaviour.

Modeling and simulation of collisions of heavy trucks with concrete barriers

On-site collision tests of full-scale concrete barriers are an important method to understand what happens to concrete barriers when vehicles collide with them. However, such tests require both time and money, so modeling and simulation of collisions by computer have been developed as an alternative in this research. First, spring subgrade models were developed to formulate the ground boundary of concrete barriers based on previous experiments. Then, the finite element method models were developed for both heavy trucks and concrete barriers to simulate their dynamic collision performances. Comparison of the results generated from computer simulations and on-site experiments demonstrates that the developed models can be applied to simulate the collision of heavy trucks with concrete barriers, to replicate the movement of the truck at the collision, and to investigate the performance of the concrete barriers. The developed research methodology can be widely used to support the design of new concrete barriers and the safety analysis of existing ones.

Dynamic simulation of collisions of heavy high-speed trucks with concrete barriers

Real vehicle collision experiments on full-scale road safety barriers are important to determine the outcome of a vehicle versus barrier impact accident. However, such experiments require large investment of time and money. Numerical simulation has therefore been imperative as an alternative method for testing concrete barriers. In this research, spring subgrade models were first developed to simulate the ground boundary of concrete barriers. Both heavy trucks and concrete barriers were modeled using finite element methods (FEM) to simulate dynamic collision performances. Comparison of the results generated from computer simulations and on-site full-scale experiments demonstrated that the developed models could be applied to simulate the collision of heavy trucks with concrete barriers to provide the data to design new road safety barriers and analyze existing ones.

Simulation of dynamic recrystallization using random grid cellular automata

Computer simulation is a powerful tool to predict microstructure and its evolution in dynamic and post-dynamic recrystallization. CAFE proposed as an appropriate approach by combining finite element (FE) method and cellular automata (CA) for recrystallization simulation. In the current study, a random grid cellular automaton (CA), as micro-scale model, based on finite element (FE), as macro-scale method, has been used to study initial and evolving microstructural features; including nuclei densities, dislocation densities, grain size and grain boundary movement during dynamic recrystallization in a C-Mn steel. An optimized relation has been established between mechanical variables and evolving microstructure features during recrystallization and grain growth. In this model, the microstructure is defined as cells located within grains and grain boundaries while dislocations are randomly dispersed throughout microstructure. Changes of dislocation density during deformation are described considering hardening, recovery and recrystallization. Recrystallization is assumed to initiate near grain boundaries and nucleation rate was considered constant (site-saturated condition). The model produced a mathematical formulation which captured the initial and evolving microstructural entities and linked their effects to measurable macroscopic variables (e.g. stress).

Possibilities to include in numerical simulation stochastic and discontinuous phenomena occurring in material

Simulation of materials processing has to face new difficulties regarding proper description of various discontinuous and stochastic phenomena occurring in materials. Commonly used rheological models based on differential equations treat material as continuum and are unable to describe properly several important phenomena. That is the reason for ongoing search for alternative models, which can account for non-continuous structure of the materials and for the fact, that various phenomena in the materials occur in different scales from nano to mezo. Accounting for the stochastic character of some phenomena is an additional challenge. One of the solutions may be the coupled Cellular Automata (CA) – Finite Element (FE) multi scale model. A detailed discussion about the advantages given by the developed multi scale CAFE model for strain localization phenomena in contrast to capabilities provided by the conventional FE approaches is a subject of this work. Results obtained from the CAFE model are supported by the experimental observations showing influence of many discontinuities existing in the real material on macroscopic response. An immense capabilities of the CAFE approach in comparison to limitations of the FE method for modeling of real material behavior is are shown this work as well.