The subject of my master’s thesis is creating a computational model of a vertical turning centre frame. The frame is influenced by the heat generated in a motion system. The computational model was created to evaluate a temperature distribution in machine frame structure and thermal deformations caused by temperature gradients.

Master’s thesis deals with issues of controlling the amount of fuel injected by the control unit. Model of four-cylinder engine and computational model of control unit were constructed in the simulation software Lotus Engine Simulation. Then was made model checking of the control unit by simulation two transients (acceleration and shifting). In conclusion was made application a created model on the New European Driving Cycle (NEDC) and appropriately evaluated and processed the results of this simulation.

This Diploma thesis describes design of the computational model of Uehara power cycle, with ammonia-water mixture used as working fluid. First part is dedicated to issue of determination working mixture thermodynamic properties, which are essential for computational model design. The second part of this thesis describes the methodology of computing power cycle by system matrix solving method. For purposes of methodology testing, model of Kalina power cycle was also created. Computational models of Uehara and Kalina cycles are designed in Excel and are an integral part of this thesis. Text part also includes a description of their user interface, calculation algorithm and detailed description of the design methodology.

The aim of this diploma thesis is to create a thermodynamic computational model of a single cylinder IC engine for the Formula SAE car. The single cylinder SI engine KTM 500 EXC is considered as a powertrain unit. The intake manifold of the serial enduro motorcycle is modified according to the Formula SAE 2014 rules. Analysis of the one dimensional flow is performed by using Lotus engine simulation software.

This diploma thesis is engaged by analyses of steel structure. Structure was modelled in two variants, in software ANSYS Mechanical. Both models were at first counted by linear analyses, then they were counted by non-linear analyses. At the end models were counted by modal analyses. Aim of the study was evaluated and decide, which one of variant is structurally and economically most competently. Selected model was assessed on ultimate and serviceability limit state.

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The humerus of a racing horse Thoroughbred is highly susceptible to stress fractures at a characteristic location as a result of cyclic loading. The propensity of a Thoroughbred to exhibit humeral fracture has made equines useful models in the epidemiology of stress fractures. In this study, a racing Thoroughbred humerus was simulated during training using a 3D finite element model in conjunction with a bone remodeling algorithm. Nine muscle forces and two contact forces were applied to the 3-dimensional finite element model, which contains four separate load cases representing fore-stance, mid-stance, aft-stance, and standing. Four different training programs were incorporated into the model, which represent Baseline Layup and Long Layup training programs along with two newly implemented programs for racing, which have an absence of a layup period, last a period of 24 weeks, and a race once every four weeks. Muscle and contact forces were rescaled for all load cases to simulate dirt, turf, and synthetic track surfaces. Bone porosity, damage, and BMU activation frequency were examined at the stress fracture site and compared with a control location called the caudal diaphysis. It was found that race programs exhibited similar remodeling patterns between each other. Damage at the stress fracture site and caudal diaphysis was reduced during all training programs for the turf and synthetic track surfaces with respect to the dirt track surface. Key findings also included changes in bone remodeling at the stress fracture site and caudal diaphysis as a result of turf and synthetic track surfaces. This model can serve as a framework for further studies in human or equine athletes who are susceptible to stress fractures.

In the last decades, computational models have become useful tools for studying biological neural networks. These models are typically constrained by either behavioural data from neuropsychological studies or by biological data from neuroscience. One model of the latter kind is the Bayesian Confidence Propagating Neural Network (BCPNN) - an attractor network with a Bayesian learning rule which has been proposed as a model for various types of memory. In this thesis, I have further studied the potential of the BCPNN in short-term sequential memory. More specifically, I have investigated if the network can be used to qualitatively replicate behaviours of immediate verbal serial recall, and thereby offer insight into the network-level mechanisms which give rise to these behaviours. The simulations showed that the model was able to reproduce various benchmark effects such as the word length and irrelevant speech effects. It could also simulate the bow shaped positional accuracy curve as well as some backward recall if the to-be recalled sequence was short enough. Finally, the model showed some ability to handle sequences with repeated patterns. However, the current model architecture was not sufficient for simulating the effects of rhythm such as temporally grouping the inputs or stressing a specific element in the sequence. Overall, even though the model is not complete, it showed promising results as a tool for investigating biological memory and it could explain various benchmark behaviours in immediate serial recall through neuroscientifically inspired learning rules and architecture. / Under de senaste årtionden har datorsimulationer blivit ett allt mer populärt verktyg för att undersöka biologiska neurala nätverk. Dessa modeller är vanligtvis inspirerade av antingen beteendedata från neuropsykologiska studier eller av biologisk data från neurovetenskapen. En modell av den senare typen är ett Bayesian Confidence Propagating Neural Network (BCPNN) - ett autoassociativt nätverk med en Bayesiansk inlärningsregel, vilket tidigare har använts för att modellera flera typer av minne. I det här examensarbetet har jag vidare undersökt om nätverket kan användas som en modell för sekventiellt korttidsminne genom att undersöka dess förmåga att replikera beteenden inom verbalt sekventiellt korttidsminne. Experimenten visade att modellen kunde simulera ett flertal viktiga nyckeleffekter såsom the word length effect och the irrelevant speech effect. Däröver kunde modellen även simulera den bågformade kurvan som beskriver andelen lyckade repetitioner som en funktion av position, och den kunde dessutom repetera korta sekvenser baklänges. Modellen visade också på viss förmåga att hantera sekvenser där ett element återkom senare i sekvensen. Den nuvarande modellen var däremot inte tillräcklig för att simulera effekterna som tillkommer av rytm, såsom temporär gruppering eller en betoning på specifika element i sekvensen. I sin helhet ser modellen däremot lovande ut, även om den inte är fullständig i sin nuvarande form, då den kunde simulera ett flertal viktiga nyckeleffekter och förklara dessa med hjälp av neurovetenskapligt inspirerade inlärningsregler.

<p>This thesis presents a computational model of the surgical stitching tasks and a path planning algorithm for robotic assisted stitching. The overall goal of the research is to enable surgical robots to perform automatic suturing. Suturing comprises several distinct steps, one of them is the stitching. During stitching, reaching the desired exit point is difficult because it must be accomplished without direct visual feedback. Moreover, the stitching is a time consuming procedure repeated multiple times during suturing. Therefore, it would be desirable to enhance the surgical robots with the ability of performing automatic suturing. The focus of this work is on the automation of the stitching task. The thesis presents a model based path planning algorithm for the autonomous stitching. The method uses a nonlinear model for the curved needle - soft tissue interaction. The tissue is modeled as a deformable object using continuum mechanics tools. This thesis uses a mesh free deformable tissue model namely, Reproducing Kernel Particle Method (RKPM). RKPM was chosen as it has been proven to accurately handle large deformation and requires no re-meshing algorithms. This method has the potential to be more realistic in modeling various material characteristics by using appropriate strain energy functions. The stitching task is simulated using a constrained deformable model; the deformable tissue is constrained by the interaction with the curved needle. The stitching model was used for needle trajectory path planning during stitching. This new path planning algorithm for the robotic stitching was developed, implemented, and evaluated. Several simulations and experiments were conducted. The first group of simulations comprised random insertions from different insertion points without planning to assess the modeling method and the trajectory of the needle inside the tissue. Then the parameters of the simulations were set according to the measured experimental parameters. The proposed path planning method was tested using a surgical ETHICON needle of type SH 1=2 Circle with the radius of 8:88mm attached to a robotic manipulator. The needle was held by a grasper which is attached to the robotic arm. The experimental results illustrate that the path planned curved needle insertions are fifty percent more accurate than the unplanned ones. The results also show that this open loop approach is sensitive to model parameters.</p> / Master of Applied Science (MASc)