The primary objective of this thesis is to assess the different statistical methods used forcalibrating the Samgods model, which is a transportation planning tool employed in Sweden toforecast the demand for freight transport. By focusing on the specific context of national logisticsmodels, this research aims to enhance the accuracy and reliability of the Samgods model throughproposed improvements.In addition to evaluating the calibration techniques for the Samgods model, this thesisexplores the broader application of statistical estimation methods in national logistics models.It examines their potential benefits and limitations in order to shed light on their significance.The findings of this research highlight the crucial role of statistical estimations in improvingthe accuracy of national logistics models, thus enabling better-informed decision-making intransport planning and logistics management.By estimating the cost sensitivity parameter in the Swedish national logistics model, Samgods,this thesis contributes to a deeper understanding of the role of statistical estimations in optimizingsuch models. It underscores the importance of reliable and accurate data analysis in transportationplanning and logistics management. Ultimately, the aim is to provide valuable insights into howstatistical estimations can enhance the effectiveness of national logistics models.

No / In this work, we discuss two new multicarrier modulating kernels that can be adopted for multicarrier signaling. These multicarrier transforms are the fractional Forurier transform (FrFT) and discrete wavelet transforms (DWT). At first, we relate the transforms in terms of mathematical relationships, and then using numerical and simulation comparisons we show their performances in terms of bit error ratio (BER) for Multiple Input Multiple Output (MIMO) applications. Numerical results using BPSK and QPSK support that both can be applied for multicarrier signaling, however, it can be resource effective to drive the DWT as the baseband multicarrier kernel at the expense of the FrFT

The core idea of Ramsey theory is that complete disorder is impossible. Given a large structure, no matter how complex it is, we can always find a smaller substructure that has some sort of order. One view of this problem is in edge-colorings of complete graphs. For any graphs G, H1, ..., Hk, we write G → (H1, ..., Hk), or G → (H)k when H1 = ··· = Hk = H, if every k-edge-coloring of G contains a monochromatic Hi in color i for some i ∈ {1,...,k}. The Ramsey number rk(H1, ..., Hk) is the minimum integer n such that Kn → (H1, ..., Hk), where Kn is the complete graph on n vertices. Computing rk(H1, ..., Hk) is a notoriously difficult problem in combinatorics. A weakening of this problem is to restrict ourselves to Gallai colorings, that is, edge-colorings with no rainbow triangles. From this we define the Gallai-Ramsey number grk(K3,G) as the minimum integer n such that either Kn contains a rainbow triangle, or Kn → (G)k . In this thesis, we determine the Gallai-Ramsey numbers for C7 with multiple colors. We believe the method we developed can be applied to find grk(K3, C2n+1) for any integer n ≥ 2, where C2n+1 denotes a cycle on 2n + 1 vertices.

Project management has become a key component for improving organizational performance and is applied in many business areas and industries. Resource-constrained stochastic project networks are quite common. Managing such projects to maximize resource utilization and reduce project duration simultaneously is difficult. Resource loading, assignment rules, and priorities significantly affect project performance, especially in shared-resource, multi-project environments. This thesis provides an approach for using discrete-event simulation to analyze the behavior and performance of project networks that use resource pools. A method to translate project networks into simulation models is developed. The translator is used to convert and evaluate a benchmark test set of resource constrained stochastic project networks. The effect of factors like project network complexity, resource availability and allocation strategies on project performance is analyzed using a completely randomized design with factorial arrangement of the aforementioned factors. The conversion process and results from the analysis are discussed.

The discrete-element method for predicting skin friction for turbulent flow over rough surfaces considers the drag on the surface to be the sum of the skin friction on the flat part of the surface and the drag on the individual roughness elements that protrude into the boundary layer. The discrete-element method considers heat transfer from a rough surface to be the sum of convection through the fluid on the flat part of the surface and the convection from each of the roughness elements. The discrete-element method has been widely used and validated for roughness composed of sparse, ordered, and deterministic elements. Modifications made to the discrete-element roughness method to extend the validation to real surface roughness are detailed. These modifications include accounting for the deviation of the roughness element cross sections from circular configurations, determining the location of the computational "surface" that differs from the physical surface, and accounting for temperature changes along the height of the roughness elements. Two randomly-rough surfaces found on high-hour gas-turbine blades were characterized using a Taylor-Hobson Form Talysurf Series 2 profilometer. A method for using the three-dimensional profilometer output to determine the geometry input required in the discrete-element method for randomly-rough surfaces is presented. Two randomly-rough surfaces, two elliptical-analog surfaces, and two cone surfaces were generated for wind-tunnel testing using a three-dimensional printer. The analog surfaces were created by replacing each random roughness element from the original randomly-rough surface with an elliptical roughness element with the equivalent planorm area and eccentricity. The cone surfaces were generated by placing conical roughness elements on a flat plate to create surfaces with equivalent values of centerline-averaged height or root-mean-square (RMS) height as the randomly-rough surfaces. The results of the wind tunnel skin friction coefficient and Stanton number measurements and the discrete-element method predictions for each of the six surfaces are presented and discussed. For the randomly-rough surfaces studied, the discrete-element method predictions are within 7% of the experimentally measured skin friction coefficients. The discrete-element predictions are within 16% of the experimentally measured Stanton numbers for the randomly-rough surfaces.

This study seeks to reduce the degree of uncertainty that often arises in computational fluid dynamics simulations about the computed accuracy of functional outputs. An error estimation methodology based on discrete adjoint sensitivity analysis is developed to provide a quantitative measure of the error in computed outputs. The developed procedure relates the local residual errors to the global error in output function via adjoint variables as weight functions. The three major steps in the error estimation methodology are: (1) development of adjoint sensitivity analysis capabilities; (2) development of an efficient error estimation procedure; (3) implementation of an output-based grid adaptive scheme. Each of these steps are investigated. For the first step, parallel discrete adjoint capabilities are developed for the variable Mach version of the U2NCLE flow solver. To compare and validate the implementation of adjoint solver, this study also develops direct sensitivity capabilities. A modification is proposed to the commonly used unstructured flux-limiters, specifically, those of Barth-Jespersen and Venkatakrishnan, to make them piecewise continuous and suitable for sensitivity analysis. A distributed-memory message-passing model is employed for the parallelization of sensitivity analysis solver and the consistency of linearization is demonstrated in sequential and parallel environments. In the second step, to compute the error estimates, the flow and adjoint solutions are prolongated from a coarse-mesh to a fine-mesh using the meshless Moving Least Squares (MLS) approximation. These error estimates are used as a correction to obtain highlyurate functional outputs and as adaptive indicators in an iterative grid adaptive scheme to enhance the accuracy of the chosen output to a prescribed tolerance. For the third step, an output-based adaptive strategy that takes into account the error in both the primal (flow) and dual (adjoint) solutions is implemented. A second adaptive strategy based on physics-based feature detection is implemented to compare and demonstrate the robustness and effectiveness of the output-based adaptive approach. As part of the study, a general-element unstructured mesh adaptor employing h-refinement is developed using Python and C++. Error estimation and grid adaptation results are presented for inviscid, laminar and turbulent flows.

Even though simulation technology provides great benefits to industry, it is largely underutilized. One of the biggest barriers to utilizing simulation is the lack of interoperability between simulation models. This is especially true when simulation models that need to interact with each other span an enterprise or supply chain. These models are likely to be distributed and developed in disparate simulation application software. In order to analyze the dynamic behavior of the systems they represent, the models must interoperate. However, currently this interoperability is nearly impossible. The interaction of models also refers to the understanding of them among stakeholders in the different stages of models¡Š lifecycles. The lack of interoperability also makes it difficult to share the knowledge within disparate models. This research first investigates this problem by identifying, defining, and analyzing the types of simulation model interactions. It then identifies and defines possible approaches to allow models to interact. Finally, a framework that adopts the strength of Structured Modeling (SM) and the Object-Oriented (OO) concept is proposed for representing discrete event simulation models. The framework captures the most common simulation elements and will serve as an intermediate language between disparate simulation models. Because of the structured nature of the framework, the resulting model representation is concise and easily understandable. Tools are developed to implement the framework. A Common User Interface (CUI) with software specified controllers is developed for using the proposed framework with various commercial simulation software packages. The CUI is also used to edit simulation models in a neutral environment. A graphical modeling tool is also developed to facilitate conceptual modeling. The resulting graphic can be translated into the common model representation automatically. This not only increases the understanding of models for all stakeholders, but also shifts model interactions to the ¡§formulating¡š stage, which can prevent problems later in the model¡Šs lifecycle. Illustration of the proposed framework and the tools will be given, as well as future work needs.

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