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Designers commonly use computer-based modeling and simulation methods to predict artifact behavior. Such predictions are central to engineering decision making. As such, determining how well they correspond to actual artifact behavior is a problem of critical importance. A significant aspect of this problem is determining whether the model used to generate the behavioral predictionsi.e., the behavioral modelreflects the relevant physical phenomena. The process of doing this is referred to as behavioral model validation. Prior works take an integrated approach to validation in which model creators and model users interact throughout the modeling and simulation process. Although effective for many problems, this type of approach is not appropriate for model reuse scenarios. Model validation requires knowledge about the model and its use. In model reuse scenarios, model creators and model users operate in independent processes with limited inter-process communication. The core challenge to behavioral model validation in this setting is that, in general, neither model creators nor model users possess the requisite knowledge to perform behavioral model validation. Presented in this thesis is a conceptual framework for validating reusable behavioral models in model reuse scenarios. This framework solves the problem of creator-user separation by defining specific validation responsibilities for each and an interface by which they communicate. This interface consists of a formal description of the models limitations and the domain over which these limitations are known to be true. The framework is illustrated through basic engineering examples.

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Formula One race cars have to pass rigorous safety tests before they are allowed on track. This type of testing has been in place for years but the requirements for testing are continually increasing in order to reduce the amount of risk to the drivers’ safety during a race. The number of structures that need to be made and tested can quickly make this process an expensive one. Additionally, it is necessary to pass the mandated tests within a reasonable amount of time so as not to have an impact on the development on the rest of the car. There is a desire to reduce the number of structures needed for testing through finite element analysis (FEA), and as such, to reduce the time needed to pass the safety tests. FEA of laminated composites can be complex and is a balance between accuracy and the time it takes to find a solution. The current project looks into increasing understanding of the requirements for material characterisation, experimental impact testing, and explicit simulation of a carbon fibre fabric pre-impregnated with epoxy resin. Mercedes-Benz Grand Prix (MGP) Formula One Team has provided a pre-preg material for evaluation. Material experiments were performed per the American Society for Materials and Testing (ASTM) in order to find the tensile modulus, tensile strength, Poisson’s ratio, compressive strength, shear modulus, and shear strength of the material. Nine tubes were manufactured at MGP and tested in the drop tower at the Cranfield Impact Centre (CIC) ... [cont.].

Model driven development is gaining momentum, and thus, larger and more complex systems are being represented and developed with the help of modeling. Complex systems often suffer from a number of problems such as difficulties in keeping the model understandable, long compilation times and high coupling. With modeling comes the possibility to validate the models against constraints, which makes it possible to handle problems that traditional static analysis tools can't solve. This thesis is a study on to what degree the usage of automatic model validation can be a useful tool in addressing some of the problems that appear in the development of complex systems. This is done by compiling a list of validation constraints based on existing problems, implementing and applying fixes for these and measuring how a number of different aspects of the model is affected. After applying the fixes and measuring the impact on the models ,it could be seen that validation of dependencies can have a signicant impact on the models by reducing build times of the generated code. Other types of validation constraints require further study to decide what impact they might have on model quality.

The 3D structures of proteins may provide important clues to their functions and roles in complex biological pathways. Traditional methods such as X-ray crystallography and NMR are not feasible for all proteins, while theoretical models are typically not validated by experimental data. This project investigates the use of chemical crosslinkers as an experimental means of validating these models. Five target proteins were successfully purified from yeast whole cell extract: Transketolase (TKL1), inorganic pyrophosphatase (IPP1), amidotransferase/cyclase HIS7, phosphoglycerate kinase (PGK1) and enolase (ENO1). These TAP-tagged target proteins from yeast <em>Saccharomyces cerevisiae</em> allowed the protein to be isolated in two affinity purification steps. Subsequent structural analysis used the homobifunctional chemical crosslinker BS³ to join pairs of lysine residues on the surface of the purified protein via a flexible spacer arm. Mass spectrometry (MS) analysis of the crosslinked protein generated a set of mass values for crosslinked and non-crosslinked peptides, which was used to identify surface lysine residues in close proximity. The Automatic Spectrum Assignment Program was used to assign sequence information to the crosslinked peptides. This data provided inter-residue distance constraints that can be used to validate or refute theoretical protein structure models generated by structure prediction software such as SWISS-MODEL and RAPTOR. This approach was able to validate the structure models for four of the target proteins, TKL1, IPP1, HIS7 and ENO1. It also successfully selected the correct models for TKL1 and IPP1 from a protein model library and provided weak support for the HIS7, PGK1 and ENO1 models.

The 3D structures of proteins may provide important clues to their functions and roles in complex biological pathways. Traditional methods such as X-ray crystallography and NMR are not feasible for all proteins, while theoretical models are typically not validated by experimental data. This project investigates the use of chemical crosslinkers as an experimental means of validating these models. Five target proteins were successfully purified from yeast whole cell extract: Transketolase (TKL1), inorganic pyrophosphatase (IPP1), amidotransferase/cyclase HIS7, phosphoglycerate kinase (PGK1) and enolase (ENO1). These TAP-tagged target proteins from yeast <em>Saccharomyces cerevisiae</em> allowed the protein to be isolated in two affinity purification steps. Subsequent structural analysis used the homobifunctional chemical crosslinker BS³ to join pairs of lysine residues on the surface of the purified protein via a flexible spacer arm. Mass spectrometry (MS) analysis of the crosslinked protein generated a set of mass values for crosslinked and non-crosslinked peptides, which was used to identify surface lysine residues in close proximity. The Automatic Spectrum Assignment Program was used to assign sequence information to the crosslinked peptides. This data provided inter-residue distance constraints that can be used to validate or refute theoretical protein structure models generated by structure prediction software such as SWISS-MODEL and RAPTOR. This approach was able to validate the structure models for four of the target proteins, TKL1, IPP1, HIS7 and ENO1. It also successfully selected the correct models for TKL1 and IPP1 from a protein model library and provided weak support for the HIS7, PGK1 and ENO1 models.

Today, the threat to humanity survival, economic crisis, financial crisis, global warming, ecological extinction, greenhouse effect ... etc., are gradually grow by both detail and dynamic complexity process. Most current humanity facing problems is because human can¡¦t handle the gradually growing complexity system problems on our environment. The main purpose of this research is to explore the causal feedback diagram model translation into stock flow diagram model, and to discover key transfer principle from current system dynamics and fundamental components. This will improve dynamic system accuracy and validity. According to model transformation design, this research is to provide a model based architecture, on simulating actual causal feedback diagram module with Maria 2 Plus provided function. Maria is the first Chinese language interface for the system dynamics simulation software. Through Software development tools transfer model and natural language operation interface to user easy use on causal feedback diagram and stock flow diagram model rapid creation. This will also decrease the learning cycle and will increase model creation speed and validity.

Mathematical model-building is one kind of mathematical thinking, which uses mathematical signs and methods to build a mathematical tool which can describe and solve practical problem. However, System Dynamic is to solve complicated non-linear dynamic problem. It uses the model-building software of diagram interface to make dynamic simulation. Behind the dynamic simulation is the calculation of a set of mathematical equation. The purpose of this research is , by the transition between Mathematics Model and System Dynamic Model, to discuss the mathematical principle of System Dynamic ,and to avoid distorting model and making the model validation harder due to the model-builder's misusing function. Using the conclusion of the transition will help validating the model and raising the reliability and the efficiency of the model. From the conclusion of this research, we find out that System Dynamic is non-linear dynamic mathematical model. Both are exchangeable. From the transition, we also find that the graph function used in the process of System Dynamic model-building is a piecewise-linear- approximate function. The numerical value setting of the graph function can influence the system action. After the transition from System Dynamic model to mathematical model, we can use some other mathematical software to draw the phase-plane-plot or phase-portrait-plot. It will analyze clearly the system action in any situation, and validate the correction of model construction.

Usage of simulation models is an essential part in many modern engineering disci- plines. Computer models of complex physical systems can be used to expedite the design of control systems and reduce the number of physical tests. Model valida- tion tries to answer the question if the model is a good enough representation of the physical system. This thesis describes techniques to visualize multi-dimensional validation results and the search for an automated validation process. The work is focused on a simulation model of the Primary Environmental Control System of Gripen E, but can be applied on validation results from other simulation models. The result from the thesis can be divided into three major components, static validation, dynamic validation and model coverage. To present the results from the static validation different multi-dimensional visualization techniques are in- vestigated and evaluated. The visualizations are compared to each other and to properly depict the static validation status of the model, a combination of visual- izations are required. Two methods for validation of the dynamic performance of the model are examined. The first method uses the singular values of an error model estimated from the residual. We show that the singular values of the error model relay important information about the model’s quality but interpreting the result is a considerable challenge. The second method aims to automate a visual inspection procedure where interesting quantities are automatically computed. Coverage is a descriptor of how much of the applicable operating conditions that has been validated. Two coverage metrics, volumetric coverage and nearest neigh- bour coverage, are examined and the strengths and weaknesses of these metrics are presented. The nearest neighbour coverage metric is further developed to account for validation performance, resulting in a total static validation quantity.