Littérature scientifique sur le sujet « Models and simulation »

This thesis presents a two step automatic transformation of Field Artillery Mission Space Conceptual Models (ACMs) into High Level Architecture (HLA) Federation Architecture Models (FAMs) into executable distributed simulation code. The approach followed in the course of this thesis adheres to the Model-Driven Engineering (MDE) philosophy. Both ACMs and FAMs are formally defined conforming to their metamodels, ACMM and FAMM, respectively. ACMM is comprised of a behavioral component, based on Live Sequence Charts (LSCs), and a data component based on UML class diagrams. Using ACMM, the Adjustment Followed by Fire For Effect (AdjFFE) mission, which serves as the source model for the model transformation case study, is constructed. The ACM to FAM transformation, which is defined over metamodel-level graph patterns, is carried out with the Graph Rewriting and Transformation (GReAT) tool. Code generation from a FAM is accomplished by employing a model interpreter that produces Java/AspectJ code. The resulting code can then be executed on an HLA Run-Time Infrastructure (RTI). Bringing a fully fledged transformation approach to conceptual modeling is a distinguishing feature of this thesis. This thesis also aims to bring the chart notations to the attention of the mission space modeling community regarding the description of military tasks, particularly their communication aspect. With the experience gained, a set of guidelines for a domainindependent transformer from any metamodel-based conceptual model to FAM is offered.

This work is a theoretical study of fermionic models. We focus on problems where highly controllable quantum simulators of these models have an important role, and we utilise both the analogue and the digital paradigm of quantum simulation. In the part on analogue quantum simulation, we focus on the proposed 'spin-asymmetric' Josephson effect where Cooper-paired spins display frequency synchronized Josephson oscillations with spin-dependent amplitudes. We consider different scenarios where the phenomenon could manifest in ultracold atomic Fermi gases. We study a Fermi gas Josephson junction in the recently realized Josephson plasma oscillation regime with an additional spin-dependent potential and show that the asymmetry in the resulting spin-dependent plasma oscillation amplitudes is on the order of a couple of per cent. We also demonstrate numerically that spin-asymmetric Josephson-like currents occur in a one-dimensional spin-dependent optical superlattice, with amplitude asymmetries up to 39%. Finally, we show that at zero temperature the tunable critical current in ferromagnetic Josephson junctions can be explained by the spin-asymmetric Josephson effect. In the part where digital quantum simulation is used, we propose a hybrid quantum-classical approach to studying strongly correlated fermion models. In this approach, a digital quantum simulator works in conjunction with a classical feedback loop to solve the infinite-dimensional Hubbard model directly in the thermodynamic limit. The scheme implements the well-established dynamical mean-field theory (DMFT) method, such that the digital quantum simulator solves the classically hard DMFT impurity problem and self-consistency is taken care of in a classical computer. We first present a few-qubit proof-of-principle setup for equilibrium systems that implements the simplified 'two-site' DMFT. This few-qubit setup is used for a qualitative description of the Mott transition in the half-filled infinite-dimensional Hubbard model. We then describe a scalable setup for simulating non-equilibrium many-body quantum dynamics by proposing the implementation of the non-equilibrium extension of DMFT with the hybrid device.

In today's world, with ever increasing competition, modelling and simulation proves to be a very helpful tool. Many methodologies exist to help build a simulation model from scratch. In terms of adaptability, most current attempts focus on either the operational side, ie the automated integration of data into a model, or the creation of new software. However, very few attempts are being made to improve the adaptability of shelved models built in existing simulation software. As a result, there is a certain reluctance, in some areas, to use simulation to its full potential. Based on these facts, it is obvious that anything, which makes reuse of simulation models easier, can help improve the use and spread of simulation as a valuable tool to maintain a company's competitiveness. In order to find such a solution, the following issues are looked at in this thesis: The changes to a simulation model that constitute the biggest problem, ways to minimise those changes, and possibilities to simplify the implementation of those changes. Those factors are evaluated, first by investigating current practices of building adaptable simulation models via a literature review, then the most difficult changes to implement in a simulation model, and the most frequent types of simulation software, are identified by means of interviews and questionnaire surveys. Next, parameters describing the adaptability of a simulation model are defined. In a further step, two of the most widely used simulation packages are benchmarked against a variety of tasks, reflecting the changes most frequent to models. The benchmarking study also serves to define and test certain elements regarding their suitability for adaptable models. Based on all those steps, model building guidelines for the creation of adaptable simulation models are developed and then validated by means of interviews and a framed field experiment. The interviews and questionnaire reveal that deleting is the easiest task and modifying the most complicated, while handling devices are the most difficult element to modify. The results also show that simulators (eg Arena) are the most widespread type of simulation software. The benchmarking showed that Arena is overall more adaptable than Simul8, and confirms the findings from the user survey. Also, it shows that sequencing is very helpful for modifying models, while the use of sub-models decrease the adaptability. Finally, the validation proves that the model building guidelines substantially increase the adaptability of models.

Computer simulation models are widely and frequently used to model real systems to predict output responses under specified input conditions. Choosing optimal simulation parameters leads to improved operation of the model but it is still a challenge as to how to go about optimally selecting these parameter values. The aim of this thesis was to see if a method could be found to optimise a simulation model provided by a client. This thesis provides a review of the literature of various simulation optimisation techniques that exist. Five of these simulation optimisation techniques - Simulated Annealing, Genetic Algorithms, Nested Partitions, Ordinal Optimisation and the Nelson-Matejcik Method - were selected and applied to a test case stochastic simulation model to gain an understanding into the techniques for their use in optimising the test model. These techniques were then used and applied to optimise a real life simulation model provided by a client. A technique combining the Ordinal Optimisation and Simulated Annealing optimisation methods provided the best results. This technique was provided to the client as a strategy to implement into their simulation model.

This paper describes the use of corporate decision and strategy simulations as a decision-support instrument under varying market conditions in the tourism industry. It goes on to illustrate this use of simulations with an experiment which investigates how successful different market segmentation approaches are in destination management. The experiment assumes a competitive environment and various cycle-length conditions with regard to budget and strategic planning. Computer simulations prove to be a useful management tool, allowing customized experiments which provide insight into the functioning of the market and therefore represent an interesting tool for managerial decision support. The main drawback is the initial setup of a customized computer simulation, which is time-consuming and involves defining parameters with great care in order to represent the actual market environment and to avoid excessive complexity in testing cause-effect-relationships. (author's abstract)<br>Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Computational fluid dynamics (CFD) is a computer-based analysis of the dynamics of fluid flow, and it is widely used in chemical and process engineering applications. However, computation usually becomes a herculean task when calibration of the CFD models with experimental data or sensitivity analysis of the output relative to the inputs is required. This is due to the simulation process being highly computationally intensive, often requiring a large number of simulation runs, with a single simulation run taking hours or days to be completed. Hence, in this research project, the kriging meta-modelling method was coupled with expected improvement (EI) global optimisation approach to address the CFD model calibration challenge. In addition, a kriging meta-model based sensitivity analysis technique was implemented to study the model parameter input-output relationship. A novel EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution, where existing normal distribution-based EI measures are not applicable. This novel EI measure suggested the values of CFD model parameters to simulate with, hence minimising SSE and improving the match between simulation and experiments. To test the proposed methodology, a non-CFD numerical simulation case of the semi-batch reactor was considered as a case study which confirmed a saving in computational time, and an improvement of the simulation model with the actual plant data. The usefulness of the developed method has been subsequently demonstrated through a CFD case study of a single-phase flow in both a straight type and convergent-divergent type annular jet pump, where both a single turbulent model parameter, C_μ and two turbulent model parameters, C_μ and C_2ε where considered for calibration. Sensitivity analysis was subsequently based on C_μ as the input parameter. In calibration using both single and two model parameters, a significant improvement in the agreement with experimental data was obtained. The novel method gave a significant reduction in simulation computational time as compared to traditional CFD. A new correlation was proposed relating C_μ to the flow ratio, which could serve as a guide for future simulations. The meta-model based calibration aids exploration of different parameter combinations which would have been computationally challenging using CFD. In addition, computational time was significantly reduced with kriging-assisted sensitivity analysis studies which explored effect of different C_μ values on the output, the pressure coefficient. The numerical simulation case of the semi-batch reactor was also used as a basis of comparison between the previous EI measure and the newly proposed EI measure, which overall revealed that the latter gave a significant improvement at fewer number of simulation runs as compared to the former. The research studies carried out has hence been able to propose and successfully demonstrate the use of a novel methodology for faster calibration and sensitivity analysis studies of computational fluid dynamics simulations. This is essential in the design, analysis and optimisation of chemical and process engineering systems.

Harm from medical errors costs tens of billions of dollars and causes tens of thousands of deaths each year in the United States alone. This is not so surprising considering the lack of opportunity for medical students and consultants alike to practice rare events, to be systematically exposed to common scenarios, or to be objectively assessed. Similarly, there are limited opportunities to test new medical devices or procedures without putting patients at harm. Simulation has provided the airline industry, in particular, with such opportunities and is a contributing factor to the safety of air travel. Simulation in medicine has the same potential but there are few, if any, concrete standards to adhere to. My objectives were to provide a structure for such standards to be set, to develop methods for evaluating the modelled physiology of simulators, and to further the mathematical modelling needed for autonomous, realistic, and extendable simulators. To these ends, I have analysed the key components of simulation and reviewed existing simulators and the modelling which underpins their responses to interventions. A framework for standards was developed with a focus on the physiological modelling of anaesthetic simulators. Methods for evaluating the repeatability and concordance of simulators were explored using simple interventions. I created an extendable database of accurate, complete physiological and interventional time series data from anaesthetic cases. Some of these cases were used to confirm the repeatability and concordance results, and then used to develop more advanced methods for evaluating fidelity. Finally, I used a novel modelling approach to create an integrated model of the human cardio respiratory system encompassing cellular through to systemic physiological processes which produced promising results. It is my hope that the work in this thesis may pave the way for more realistic simulators and a more standardised approach to simulation so that medical errors are reduced.