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

Generalized-ensemble simulations and cluster algorithms

The importance-sampling Monte Carlo algorithm appears to be the universally optimal solution to the problem of sampling the state space of statistical mechanical systems according to the relative importance of configurations for the partition function or thermal averages of interest. While this is true in terms of its simplicity and universal applicability, the resulting approach suffers from the presence of temporal correlations of successive samples naturally implied by the Markov chain underlying the importance-sampling simulation. In many situations, these autocorrelations are moderate and can be easily accounted for by an appropriately adapted analysis of simulation data. They turn out to be a major hurdle, however, in the vicinity of phase transitions or for systems with complex free-energy landscapes. The critical slowing down close to continuous transitions is most efficiently reduced by the application of cluster algorithms, where they are available. For first-order transitions and disordered systems, on the other hand, macroscopic energy barriers need to be overcome to prevent dynamic ergodicity breaking. In this situation, generalized-ensemble techniques such as the multicanonical simulation method can effect impressive speedups, allowing to sample the full free-energy landscape. The Potts model features continuous as well as first-order phase transitions and is thus a prototypic example for studying phase transitions and new algorithmic approaches. I discuss the possibilities of bringing together cluster and generalized-ensemble methods to combine the benefits of both techniques. The resulting algorithm allows for the efficient estimation of the random-cluster partition function encoding the information of all Potts models, even with a non-integer number of states, for all temperatures in a single simulation run per system size.Comment: 15 pages, 6 figures, proceedings of the 2009 Workshop of the Center of Simulational Physics, Athens, G

Accelerated Test Methods

Neural network systems were evaluated for use in predicting wear of mechanical systems. Three different neural network software simulation packages were utilized in order to create models of tribological wear tests. Representative simple, medium, and high complexity simulation packages were selected. Pin-on-disk, rub shoe, and four-ball tribological test data was used for training, testing, and verification of the neural network models. Results showed mixed success. The neural networks were able to predict results with some accuracy if the number of input variables was low or the amount of training data was high. Increased neural network complexity resulted in more accurate results, however there was a point of diminishing return. Medium complexity models were the best trade off between accuracy and computing time requirements. A NASA Technical Memorandum and a Society of Tribologists and Lubrication Engineers paper are being published which detail the work

Using Organic Modeling Techniques to Create Scientific Models for Flow Analysis in Biomechanical Research: Exploring 3-D software typically used in creative media for model creation.

Biomedical Engineering research utilizes digital three-dimensional models of human anatomical systems that are used as components in specific types of simulation. The data collected from the simulations provide quantifiable information that has a physical basis. The use of the digital models allows engineers the freedom of experimentation that may not be possible in the real world and allows them to quickly change the parameters. Although these models can provide reliable information, the models represent a mechanical ideal and therefore do not accurately represent organic matter. The consequence of using an ideal model to represent tissue may give flawed data, since the mechanical simulation does not identically represent living tissue. The purpose of this thesis was to develop the methodology and actual creation of a three-dimensional model that was a physically accurate representation of organic lung acinar tissue. The objective is to have the model input into analytical software that will calculate the flow dynamics of the organic tissue represented by the model. Specifically, the model represents alveolar ducts in lung tissue. The model starts from the transitional bronchioles through the alveolar ducts and ends at the terminal alveolar sacs. Creating this model was challenging due to the microscopic size and inherent density of the tissue, making it difficult to determine structure

Passive system integration for office buildings in hot climates

Passive ventilation and cooling systems can offer energy savings when combined into a mechanical ventilation and cooling strategy for office buildings. At early design stages, it is difficult to predict actual energy savings as current design and calculation tools are limited and do not allow assessment for energy reductions when attempting to use typical passive options such as solar chimneys, rain screen facades, ventilated double facades, passive downdraught evaporative cooling and earth ducts. The only passive systems that are directly incumbent to dynamic thermal modelling software are natural ventilation and external solar shading. Currently, impacts of passive systems on annual building energy performance is unclear and lacks clarity. In hot climates, this is even more problematic as buildings need to endure higher external temperatures and solar irradiation. Understanding minimal energy performance reductions for each passive system can aid with design decisions regarding building ventilation and cooling strategies. The aim of this study is to investigate how existing passive ventilation and cooling system design and operational strategies can be improved to reduce mechanical ventilation and cooling energy consumption for commercial buildings in hot climates. Theoretical commercial building models are created using dynamic thermal simulation software to determine minimum mechanical ventilation and cooling energy values, which are verified against published bench marks, known as base case models. These base case models are simulated using weather data from four different hot climates (Egypt, Portugal, Kenya and Abu Dhabi). Impacts of passive system energy performance are afforded by using either dynamic thermal simulation or fundamental steady state analysis identifying approximate passive ventilation and cooling potentials for reducing mechanical energy. These percentage reductions are created based upon passive system parameters and weather data, using appropriate methodology. From these findings new simplified design guidelines, integration strategies and performance design tools are created including a new passive system energy assessment tool (PSEAT) using Microsoft Excel platform to ensure that a wider audience can be achieved in industry. The design guidance and integration strategies are developed and simplified to enable architects, building services engineers and alike, to apply with speed and accuracy influencing the design process and improve confidence in desired passive solution

An evolutionary complex systems decision-support tool for the management of operations

Purpose - The purpose of this is to add both to the development of complex systems thinking in the subject area of operations and production management and to the limited number of applications of computational models and simulations from the science of complex systems. The latter potentially offer helpful decision-support tools for operations and production managers. Design/methodology/approach - A mechanical engineering firm was used as a case study where a combined qualitative and quantitative methodological approach was employed to extract the required data from four senior managers. Company performance measures as well as firm technologies, practices and policies, and their relation and interaction with one another, were elicited. The data were subjected to an evolutionary complex systems (ECS) model resulting in a series of simulations. Findings - The findings highlighted the effects of the diversity in management decision making on the firm's evolutionary trajectory. The CEO appeared to have the most balanced view of the firm, closely followed by the marketing and research and development managers. The manufacturing manager's responses led to the most extreme evolutionary trajectory where the integrity of the entire firm came into question particularly when considering how employees were utilised. Research limitations/implications - By drawing directly from the opinions and views of managers, rather than from logical "if-then" rules and averaged mathematical representations of agents that characterise agent-based and other self-organisational models, this work builds on previous applications by capturing a micro-level description of diversity that has been problematical both in theory and application. Practical implications - This approach can be used as a decision-support tool for operations and other managers providing a forum with which to explore: the strengths, weaknesses and consequences of different decision-making capacities within the firm; the introduction of new manufacturing technologies, practices and policies; and the different evolutionary trajectories that a firm can take. Originality/value - With the inclusion of "micro-diversity", ECS modelling moves beyond the self-organisational models that populate the literature but has not as yet produced a great many practical simulation results. This work is a step in that direction

Nonlinear Systems

Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems

A randomized integral error criterion for parametric identification of dynamic models of mechanical systems

This paper proposes a new approach to the identification of reduced order models for complex mechanical vibration systems. Parametric identification is commonly conducted by the regression of time-series data, but when this includes significant unmodelled modes, the error process has a high variance and autocorrelation. In such cases, optimization using least-squares methods can lead to excessive parameter bias. The proposed method takes advantage of the inherent boundedness of mechanical vibrations to design a new regression set with dramatically reduced error variance. The principle is first demonstrated using a simple two-mass simulation model, and from this a practicable approach is derived. Extensive investigation of the new randomized integral error criterion method is then conducted using the example of identification of a quarter-car suspension system. Simulation results are contrasted with those from comparable direct least-squares identifications. Several forms of the identification equations and error sources are used, and in all cases the new method has clear advantages, both in accuracy and consistency of the resulting identification model

Failure Predictions in Repairable Multi-Component Systems.

The subject of this research is the prediction of failures in repairable multi-component systems from statistical models that utilize the historical failure data for the systems. Failures occurring in repairable systems are examples of a series of discrete events which occur randomly in a continuum. Such stochastic point processes are analyzed using the statistics of event series. The Crow nonhomogenous Poisson process, NHPP, model is recognized by the reliability community as being one of the best models for repairable systems. The objective of this research is to show that the Crow NHPP model, with its overall failure predictions for a repairable system, can be utilized as a guide for testing the accuracy of a Monte Carlo simulation that utilizes the individual component Weibull distribution parameters to predict system failures. Failure data, from multiple versions of six different mechanical systems, are modelled by Crow\u27s NHPP model. A program is presented that performs an iteration of Crow\u27s equations to obtain the NHPP parameters that are then utilized to develop a failure intensity function for each respective system. Failure predictions are then determined from the mean value function of the NHPP model. The individual component failure data for each system are fitted to Weibull distributions and the resulting distribution function parameters are utilized in the respective Monte Carlo simulations. In each of the six cases a Monte Carlo simulation, based on the Weibull distributions of the major component failure modes, is used to predict the number of failures expected for each system. The Monte Carlo simulation predictions are shown to closely match the Crow nonhomogenous Poisson process predictions for the respective systems. In addition, the Monte Carlo simulations give failure prediction results that can be traced to individual components. The Crow model predicts when the overall system will be down, and then the simulation predicts the number of failures from each of the included components. The simulation can identify a finite number of parts that contribute to the overall system downtime. This information can be used to design an optimum preventive maintenance program or guide research into more reliable components