Die vorliegende Dissertation befasst sich mit Entstehungsmechanismen kardialer Arrhythmien auf der Grundlage pathologisch veränderten Myokards. Es wurde eine systematische Analyse der elektrophysiologischen Veränderungen, die als Folge von Gap-Junction- Lateralisierung und zellulärer Hypertrophie auftreten, durchgeführt. Die Analyse beruht auf einem mathematischen Computermodell, das zur Simulation der Aktionspotentialausbreitung innerhalb einer Einzelzellschicht humaner ventrikulärer Kardiomyozyten entwickelt wurde. Ausgehend von bestehenden Einzelzellmodellen wurde ein räumlich und zeitlich hoch aufgelöstes Multizellmodell generiert und in der Programmiersprache Object Pascal implementiert. Nach Validierung des Modells wurde es zur gezielten, an experimentellen Daten orientierten Manipulation geometrischer Eigenschaften der Zellen (Länge, Durchmesser) und des Zellverbandes (Anordnung der Zellen untereinander) sowie der Gap-Junction-Verteilung genutzt. Die Analyse der elektrophysiologischen Effekte im Vergleich zur Kontrolle fand sowohl unter Normalbedingungen als auch unter Bedingungen, die pathologischen Veränderungen entsprechen (Entkopplung der Gap-Junctions, verringerte Aktivität des schnellen Natriumkanals, erhöhte Inhomogenität), statt. Es zeigte sich, dass ein größerer Zelldurchmesser bzw. erhöhte laterale Gap-Junction-Leitfähigkeit (Simulation von kardialer Hypertrophie bzw. Connexin- Lateralisierung) die Entstehungswahrscheinlichkeit eines unidirektionalen Leitungsblocks erhöhte. Die Erregungsausbreitungsgeschwindigkeit in hypertrophierten Zellen war zudem weniger stabil als in normalen Zellen. Beide Effekte gehören zu den Hauptursachen der Entstehung und Aufrechterhaltung ventrikulärer Arrhythmien. Die Ergebnisse der Arbeit erklären somit Ursachen des erhöhten Arrhythmierisikos in pathologisch veränderten und hypertrophierten Herzen und liefern eine theoretische Grundlage für zukünftige Studien.

Construction sites are among the most common areas to experience soil erosion and sediment transport due to the mandatory foundation tasks such as excavation and land grubbing. Thus, temporary sediment barriers are installed along the perimeter to prevent sediment transport from the site. Erosion and sediment transport control measures may include, but not limited to, physical and chemical processes such as the use of a silt fence and polyacrylamide product. Runoff from construction sites and other impervious surfaces are routinely discharged into ponds for treatment before being released into a receiving water body. Stormwater harvesting from a pond for irrigation of adjacent lands is promoted as one approach to reducing pond discharge while supplementing valuable potable water used for irrigation. The reduction of pond discharge reduces the mass of pollutants in the discharge. In the dissertation, presented is the investigation of the effectiveness of temporary sediment barriers and then, development of a modeling approach to a stormwater harvesting pond to provide a comprehensive stormwater management pollution reduction assessment tool. The first part of the research presents the investigation of the performance efficiencies of silt fence fabrics in turbidity and sediment concentration removal, and the determination of flowthrough-rate on simulated construction sites in real time. Two silt fence fabrics, (1) woven and the other (2) nonwoven were subjected to material index property tests and a series of field-scale tests with different rainfall intensities and events for different embankment slopes on a tilting test-bed. Collected influent and effluent samples were analyzed for sediment concentration and turbidity, and the flow-through-rate for each fabric was evaluated. Test results revealed that the woven and nonwoven silt fence achieved 11 and 56 percent average turbidity reduction iv efficiency, respectively. Each fabric also achieved 20 and 56 percent average sediment concentration removal efficiency, respectively. Fabric flow-through-rates were functions of the rainfall intensity and embankment slope. The nonwoven fabric exhibited higher flow-throughrates than the woven fabric in both field-scale and laboratory tests. In the second part of the study, a Stormwater Harvesting and Assessment for Reduction of Pollution (SHARP) model was developed to predict operation of wet pond used for stormwater harvesting. The model integrates the interaction of surface water and groundwater in a catchment area. The SHARP model was calibrated and validated with actual pond water elevation data from a stormwater pond at Miramar Lakes, Miramar, Florida. Model evaluation showed adequate prediction of pond water elevation with root mean square error between 0.07 and 0.12 m; mean absolute error was between 0.018 and 0.07 m; and relative index of agreement was between 0.74 and 0.98 for both calibration and validation periods. The SHARP model is capable of assessing harvesting safe-yield and discharge from a pond, including the prediction of the percentage of runoff into a harvesting pond that is not discharged. The combination of silt fence and/or polyacrylamide PAM before stormwater harvesting pond in a treatment train for the reduction of pollutants from construction sites has the potential of significantly exceeding a performance standard of 85 percent reduction typically required by local authorities. In fact, the stringent requirement of equaling pre- and post-development pollutant loading is highly achievable by the treatment train approach. The significant contribution from the integration of the SHARP model to the treatment train is that real-time assessment of pollutant loading reduction by volume can be planned and controlled to achieve target performance standards.

Space weather is the field within the space sciences that studies how the Earths magnetosphere is influenced by the Sun. The Sun is constantly emitting dangerous radiation and plasma which in some cases can affect or damage the systems on Earth. Scientists have an interest in studying this interaction and therefore visualizations of space weather data are useful. OpenSpace is an interactive software that visualizes the entire known universe with real-time data. OpenSpace supports a range of different visualization methods and techniques, for this work, the relevant visualization tools are field lines and cut planes. GAMERA is a simulation model that simulates a wide range of situations where plasma is subjected to the influence of magnetic fields, the simulations are based on curvilinear grids. This project focuses on implementing data from GAMERA into OpenSpace. OpenSpace already supports a variety of different simulation models, although none that uses curvilinear grids for the data. The curvilinear grid can adapt to the specific shape and geometry of the data, allowing for more accurate data representation. The project aims to create a pipeline for reading data files from simulation runs and visualize it as field lines and cut planes. The files used in this project contain data suitable for volumes and field lines. The method was to first develop a reader to extract and manage desired data from HDF5 files in which the simulation data is stored. The data used to visualize field lines is rendered with an already existing component in OpenSpace. Secondly, a slice operation was developed to extract cut planes from the files containing data for volume visualization, these are then visualized with the help of a component for rendering cut planes which was developed during this work. The work led to a pipeline that reads and manages simulation data from GAMERA and the data is successfully visualized. However, there is room for improvement in color rendering, robustness and level of user interaction during runtime. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

В результате условий эксплуатации коммунальной техники снижается надёжность гидравлических систем, повышаются затраты на техническое обслуживание и резко снижается производительность машины в целом. Для выявления недостатков гидропривода щеточного узла коммунальной машины и разработки мероприятий по их устранению, необходима имитационная модель с возможностью изменения кинематических и динамических параметров. В ходе данной работы были рассчитаны параметры гидропривода с помощью программы Mathcad, создана твердотельная имитационная модель щеточного узла МК2000 с подключенным гидроприводом используя пакета программ MATLAB Simulink Multibody/Fluids. Наличие имитационной модели позволяет в дальнейшем анализировать изменения в динамике механизма на этапе проектирования; верно подобрать ответственные узлы; уменьшить затраты времени на сложные расчеты; повысить качество построения механизма с исключением человеческих ошибок. / The operating conditions of municipal equipment reduce the reliability of hydraulic systems, increase maintenance costs, and drastically reduce the overall performance of the machine. A simulation model with the possibility of changing kinematic and dynamic parameters is needed to identify the disadvantages of the hydraulic drive of the municipal machine brush unit and to develop measures for their elimination. In the course of this work parameters of hydraulic drive were calculated using Mathcad program, a solid-state simulation model of the sweep gear MK2000 with connected hydraulic drive was created using MATLAB software package Simulink Multibody/Fluids. The simulation model allows further analysis of changes in mechanism dynamics at the design stage; to correctly select responsible units; to reduce time spent on complex calculations; to increase quality of mechanism construction with exclusion of human errors.

В данной работе были рассмотрены различные испытательные машины, изучен принцип и особенности работы разрывной машины для статических испытаний металлов Р-10. Проведены расчеты геометрических параметров гидроцилиндров (основного рабочего и захватов), прочностной и расчет на устойчивость. Также, исходя из перечисленных расчетов подобран рабочий насос и среда – масло. Смоделированы детали модели в пакете программ SolidWorks, а в пакете MATLAB Simscape составлена сборка разрывной машины и добавлена система с гидроприводом. В результате исследования была получена имитационная модель статической разрывной машины для металлов, при помощи которой было проведено моделирование испытания на растяжение. Для этого был выбран образец стали известной марки с заранее определенными геометрическими параметрами. После чего к образцу были приложены различные усилия, и сняты показания удлинения образца. На основе этого был получен график. Затем с помощью данных усилие/перемещение и начальных геометрических параметров были рассчитаны пределы пропорциональности, текучести, прочности на основании которых и был определен материал образца. Данный пример демонстрирует правильность разработки подобных моделей в системе MATLAB, которые могут позволить провести детальные исследования процессов работы механизмов машины во время испытаний, в том числе оценить характеристики гидравлической системы разрывной испытательной машины. / In this dissertation, various testing machines were considered, the principle and features of the operation of a tensile testing machine for static testing of metals R-10 were studied. Calculations of the geometrical parameters of hydraulic cylinders (main working and grippers), strength and stability calculations were carried out. Also, based on the above calculations, a working pump and a medium - oil were selected. The details of the model were modeled in SolidWorks, and the tensile testing machine assembly was assembled in MATLAB Simscape and the hydraulic actuator system was added. As a result of the study, a simulation model of a static tensile testing machine for metals was obtained, with the help of which a simulation of a tensile test was carried out. For this, a sample of well-known brand steel with predetermined geometric parameters was selected. After that, various forces were applied to the sample, and indications of the elongation of the sample were taken. Based on this, a graph was obtained. Then, using the force / displacement data and the initial geometric parameters, the limits of proportionality, yield, strength were calculated, on the basis of which the sample material was determined. This example demonstrates the correctness of developing such models in MATLAB. They can make it possible to conduct detailed studies of the operation of the machine mechanisms during testing, including the evaluation of the characteristics of the hydraulic system of the tensile testing machine.

Die strukturmodellbasierte Korrektur wird genutzt, um thermisch bedingte Fehler von Werkzeugmaschinen mithilfe von physikalischen Modellen zu reduzieren. Diese Modelle bilden das thermo-elastische Verhalten der Werkzeugmaschine ab, einschließlich ihrer Strukturvariabilität. Als Modelleingangsdaten werden maschineninterne technologische Daten, wie die Achspositionen, -geschwindigkeiten, Motorströme sowie die Umgebungstemperatur verwendet. In der Maschinensteuerung erfolgt eine volumetrische Korrektur der berechneten thermisch bedingten Fehler. In dieser Arbeit werden zunächst die Grundfunktionen der strukturmodellbasierten Korrektur von der thermo-elastischen Wirkungskette abgeleitet. Es werden die drei wesentlichen Echtzeitbereiche für die Module der Korrektur sowie die Schnittstellen zwischen diesen Echtzeitbereichen definiert. Die modularisierte Korrektur wird am Beispiel eines Hexapods demonstriert. Die für die technologischen Daten erforderlichen Abtastzeiten werden aus den Bewegungsgrenzwerten der Achsen, der Diskretisierung der Randbedingungen im thermischen Modell und dem thermischen Zeitverhalten der Maschine hergeleitet. Des Weiteren wird die geeignete Verdichtung der Eingangsgrößen auf die Lastschrittweite des Simulationsmodells unter Verwendung von positionsabhängigen Lastprofilen beschrieben. Das Vorgehen wird an einem Knotenpunktmodell einer kugelgewindegetriebenen Achse demonstriert. Die Verdichtung der Eingangsgrößen führt in diesem Beispiel zu einer starken Reduktion der benötigten Rechenzeit bei einem lediglich geringen Genauigkeitsverlust. Das Starttemperaturfeld für das Korrekturmodell hat einen wesentlichen Einfluss auf die Korrekturgenauigkeit. Deswegen wird ein Vorgehen für die Bestimmung des Starttemperaturfelds unter verschiedenen Randbedingungen und unter Berücksichtigung des thermischen Zeitverhaltens der Maschine entwickelt. Das Vorgehen wird am Beispiel einer kartesischen 3-Achs-Maschine demonstriert. Für kurze Unterbrechungen und Fortsetzung der Simulation mit dem letzten bekannten Temperaturfeld liegt der Restfehler im Bereich der Referenzsimulation ohne Unterbrechung. Eine geraffte Simulation zur Bestimmung des Starttemperaturfelds führt ebenfalls zu einem Restfehler in derselben Größenordnung wie die Referenzsimulation ohne Unterbrechung. Durch das Strukturmodell wird ein räumliches Fehlergitter mit thermisch bedingten Fehlern im Arbeitsraum der Maschine berechnet. Dieses Fehlergitter ist die Eingangsgröße für drei untersuchte Implementationsvarianten der volumetrischen Korrektur. Die Auswirkungen der Varianten und der Anzahl der Gitterpunkte auf die Korrekturgenauigkeit wird mit einer Monte-Carlo-Simulation untersucht. Das Vorgehen wird ebenfalls am Beispiel der 3-Achs-Maschine demonstriert. Es zeigt sich, dass die Wahl der Implementationsvariante für die volumetrische Korrektur nur einen geringen Einfluss auf die Korrekturgenauigkeit hat. Mit zunehmender Gitterpunktanzahl fällt der Restfehler asymptotisch ab.:1. Einleitung 2. Stand der Technik 2.1 Maßnahmen zur Reduktion thermisch bedingter Fehler 2.2 Modellbasierte Korrekturansätze 2.2.1 Korrelative Korrektur 2.2.2 Eigenschaftsmodellbasierte Korrektur 2.2.3 Strukturmodellbasierte Korrektur 2.3 Strukturmodelle für die Korrektur 2.3.1 Knotenpunktmodelle 2.3.2 Entfeinerte FE-Modelle 2.3.3 FE-Modelle mit reduzierter Modellordnung 2.4 Volumetrische Korrektur anWerkzeugmaschinen 2.4.1 Fehlerparameter der Maschinenachsen 2.4.2 Kinematisches Fehlermodell auf Basis von homogenen Transformationsmatrizen 2.5 Einflüsse auf die Korrekturqualität der strukturmodellbasierten Korrektur 2.6 Hemmnisse für den Einsatz der strukturmodellbasierten Korrektur 2.6.1 Modellierungsaufwand 2.6.2 Echtzeitfähigkeit 2.6.3 Versuchsaufwand 2.6.4 Steuerungsintegration 2.6.5 Startzustand des thermischen Modells 3. Zielsetzung und Vorgehensweise 4. Demonstratormaschinen 4.1 Parallelkinematik Hexapod 4.2 Versuchsträger MAX 5. Module der strukturmodellbasierten Korrektur 5.1 Grundfunktionen der strukturmodellbasierten Korrektur 5.2 Echtzeitbereiche 5.3 Anforderungen an steuerungsnahe Module 5.4 Implementation der Korrektur am Beispiel eines Hexapods 5.4.1 Gewählte Implementationsvariante 5.4.2 Lastdatenerfassung 5.4.3 Strukturmodell 5.4.4 Parameterabgleich 5.4.5 Fehler im Arbeitsraum 5.4.6 Korrektur auf Achsebene 5.4.7 Versuchsaufbau und -durchführung 5.4.8 Ergebnisse der Validierung 5.5 Zusammenfassung 6. Eingangsdatenverarbeitung 6.1 Abtasttakt der Lastdaten 6.1.1 Abtasttakt des Stroms 6.1.2 Abtasttakt der Geschwindigkeit 6.1.3 Abtasttakt der Position 6.2 Lastdatenverdichtung 6.2.1 Positionsabhängiges Lastprofil 6.2.2 Einfluss der Lastdatenverdichtung auf die Genauigkeit 6.3 Eingangsdatenverarbeitung am Beispiel eines Kugelgewindetriebs 6.4 Zusammenfassung 7. Startzustand der strukturmodellbasierten Korrektur 7.1 Charakterisierung des thermischen Zeitverhaltens 7.2 Fortsetzen der Simulation mit letztem Temperaturfeld 7.3 Zeitlich geraffte Simulation 7.3.1 Abschätzung der Lasten im Stillstand 7.3.2 Abschätzung der fortgesetzten Belastung in der Serienfertigung 7.3.3 Abschätzung Umgebungstemperatur anhand von typischem Tagesverlauf 7.4 Abklingen des Fehlers nach Unterbrechung 7.5 Bewertung des ermittelten Starttemperaturfeldes 7.6 Abschätzung des Temperaturfelds anhand von Messwerten 7.6.1 Variante 1: Mittlere Temperatur der Komponenten 7.6.2 Variante 2: Ähnliches bekanntes Temperaturfeld 7.6.3 Variante 3: Temperaturfeld interpoliert zwischen Messpunkten 7.6.4 Bewertung der Temperaturfeldschätzung 7.7 Genauigkeit der Temperaturfeldschätzung für ein einfaches Beispielmodell 7.7.1 Modell und Zeitverhalten 7.7.2 Lastregime und Referenzsimulation 7.7.3 Fortsetzen der Simulation mit letztem Temperaturfeld 7.7.4 Geraffte Simulation 7.7.5 Abklingen des Fehlers nach Unterbrechung 7.7.6 Abschätzung des Temperaturfelds anhand von Messwerten 7.8 Wiederanlauf am Beispiel des Versuchsträgers MAX 7.8.1 Reduziertes thermisches FE-Modell 7.8.2 Zeitverhalten 7.8.3 Wiederanlauf nach einer kurzen Unterbrechung 7.8.4 Geraffte Simulation mit bekannten Lastdaten 7.8.5 Geraffte Simulation mit geschätzten Lastdaten 7.8.6 Abklingen des Fehlers nach Unterbrechung 7.8.7 Abschätzung des Starttemperaturfeldes anhand von Messwerten 7.9 Zusammenfassung 8. Volumetrische thermo-elastische Korrektur 8.1 Varianten für kombinierte geometrische und thermo-elastische Korrektur 8.1.1 Variante 1: Aufschaltung auf Fehlerparameter 8.1.2 Variante 2: Aufschaltung auf aktuellen Fehler am TCP 8.1.3 Variante 3: Aufschaltung auf Achssollwerte 8.2 Typische Fehlerparameter von Werkzeugmaschinen 8.3 Bewertung anhand des Fehlers am TCP 8.4 Untersuchung der Varianten am Beispiel des Versuchsträgers MAX 8.5 Zusammenfassung 9. Zusammenfassung und Ausblick 9.1 Zusammenfassung 9.2 Ausblick A Anhang A.1 Positionen der im Versuchsträger verbauten Sensoren A.2 Beispiel für kinematisches Modell einer Maschine A.3 Beispiel für typische generierte Fehler am TCP A.4 Ermitteln der Achskorrekturwerte mittels Rücktransformation A.5 Visualisierung Lastprofile A.6 Veröffentlichungen A.7 Vorträge Literaturverzeichnis / Structure model based correction is used to reduce thermally induced errors in machine tools utilizing physical models. These models simulate the thermo-elastic behavior of the machine tool, including its structural variability. Machine-internal technological data, such as axis positions, velocities, motor currents, and ambient temperature, are used as model input data. In the machine control, a volumetric correction of the calculated thermally induced errors is performed. In this thesis, the basic functions of the structure model based correction are derived from the thermo-elastic functional chain. Three main real-time domains for the modules of the correction as well as the interfaces between these real-time domains are defined. The modularized correction is demonstrated using a hexapod as an example. The sampling times required for the load data are derived from the motion limits of the axes, the discretization of the boundary conditions in the thermal model, and the thermal time behavior of the machine. Furthermore, the appropriate compression of the input variables to the load step size of the simulation model using position-dependent load profiles is described. The procedure is demonstrated on a model with lumped parameters for a ball screw driven axis. In this example, the compression of the input variables leads to a strong reduction of the required computation time with only a small loss of accuracy. The start temperature field for the correction model has a significant influence on the correction accuracy. Therefore, a procedure for the determination of the start temperature field under different boundary conditions and under consideration of the thermal time behavior of the machine is developed. The procedure is demonstrated using the example of a Cartesian 3-axis machine. For short interruptions and continuation of the simulation with the last known temperature field, the residual error is in the range of the reference simulation without interruption. A streamlined simulation to determine the starting temperature field also leads to a residual error of the same order of magnitude as the reference simulation without interruption. The structural model is used to calculate a spatial error grid with thermally induced errors in the working space of the machine. This error grid is the input variable for three investigated implementation variants of the volumetric correction. The effects of the variants and the number of grid points on the correction accuracy are investigated with a Monte Carlo simulation. The procedure is also demonstrated using the 3-axis machine as an example. It is shown that the choice of the implementation variant for the volumetric correction has only a minor influence on the correction accuracy. With an increasing number of grid points, the residual error decreases asymptotically.:1. Einleitung 2. Stand der Technik 2.1 Maßnahmen zur Reduktion thermisch bedingter Fehler 2.2 Modellbasierte Korrekturansätze 2.2.1 Korrelative Korrektur 2.2.2 Eigenschaftsmodellbasierte Korrektur 2.2.3 Strukturmodellbasierte Korrektur 2.3 Strukturmodelle für die Korrektur 2.3.1 Knotenpunktmodelle 2.3.2 Entfeinerte FE-Modelle 2.3.3 FE-Modelle mit reduzierter Modellordnung 2.4 Volumetrische Korrektur anWerkzeugmaschinen 2.4.1 Fehlerparameter der Maschinenachsen 2.4.2 Kinematisches Fehlermodell auf Basis von homogenen Transformationsmatrizen 2.5 Einflüsse auf die Korrekturqualität der strukturmodellbasierten Korrektur 2.6 Hemmnisse für den Einsatz der strukturmodellbasierten Korrektur 2.6.1 Modellierungsaufwand 2.6.2 Echtzeitfähigkeit 2.6.3 Versuchsaufwand 2.6.4 Steuerungsintegration 2.6.5 Startzustand des thermischen Modells 3. Zielsetzung und Vorgehensweise 4. Demonstratormaschinen 4.1 Parallelkinematik Hexapod 4.2 Versuchsträger MAX 5. Module der strukturmodellbasierten Korrektur 5.1 Grundfunktionen der strukturmodellbasierten Korrektur 5.2 Echtzeitbereiche 5.3 Anforderungen an steuerungsnahe Module 5.4 Implementation der Korrektur am Beispiel eines Hexapods 5.4.1 Gewählte Implementationsvariante 5.4.2 Lastdatenerfassung 5.4.3 Strukturmodell 5.4.4 Parameterabgleich 5.4.5 Fehler im Arbeitsraum 5.4.6 Korrektur auf Achsebene 5.4.7 Versuchsaufbau und -durchführung 5.4.8 Ergebnisse der Validierung 5.5 Zusammenfassung 6. Eingangsdatenverarbeitung 6.1 Abtasttakt der Lastdaten 6.1.1 Abtasttakt des Stroms 6.1.2 Abtasttakt der Geschwindigkeit 6.1.3 Abtasttakt der Position 6.2 Lastdatenverdichtung 6.2.1 Positionsabhängiges Lastprofil 6.2.2 Einfluss der Lastdatenverdichtung auf die Genauigkeit 6.3 Eingangsdatenverarbeitung am Beispiel eines Kugelgewindetriebs 6.4 Zusammenfassung 7. Startzustand der strukturmodellbasierten Korrektur 7.1 Charakterisierung des thermischen Zeitverhaltens 7.2 Fortsetzen der Simulation mit letztem Temperaturfeld 7.3 Zeitlich geraffte Simulation 7.3.1 Abschätzung der Lasten im Stillstand 7.3.2 Abschätzung der fortgesetzten Belastung in der Serienfertigung 7.3.3 Abschätzung Umgebungstemperatur anhand von typischem Tagesverlauf 7.4 Abklingen des Fehlers nach Unterbrechung 7.5 Bewertung des ermittelten Starttemperaturfeldes 7.6 Abschätzung des Temperaturfelds anhand von Messwerten 7.6.1 Variante 1: Mittlere Temperatur der Komponenten 7.6.2 Variante 2: Ähnliches bekanntes Temperaturfeld 7.6.3 Variante 3: Temperaturfeld interpoliert zwischen Messpunkten 7.6.4 Bewertung der Temperaturfeldschätzung 7.7 Genauigkeit der Temperaturfeldschätzung für ein einfaches Beispielmodell 7.7.1 Modell und Zeitverhalten 7.7.2 Lastregime und Referenzsimulation 7.7.3 Fortsetzen der Simulation mit letztem Temperaturfeld 7.7.4 Geraffte Simulation 7.7.5 Abklingen des Fehlers nach Unterbrechung 7.7.6 Abschätzung des Temperaturfelds anhand von Messwerten 7.8 Wiederanlauf am Beispiel des Versuchsträgers MAX 7.8.1 Reduziertes thermisches FE-Modell 7.8.2 Zeitverhalten 7.8.3 Wiederanlauf nach einer kurzen Unterbrechung 7.8.4 Geraffte Simulation mit bekannten Lastdaten 7.8.5 Geraffte Simulation mit geschätzten Lastdaten 7.8.6 Abklingen des Fehlers nach Unterbrechung 7.8.7 Abschätzung des Starttemperaturfeldes anhand von Messwerten 7.9 Zusammenfassung 8. Volumetrische thermo-elastische Korrektur 8.1 Varianten für kombinierte geometrische und thermo-elastische Korrektur 8.1.1 Variante 1: Aufschaltung auf Fehlerparameter 8.1.2 Variante 2: Aufschaltung auf aktuellen Fehler am TCP 8.1.3 Variante 3: Aufschaltung auf Achssollwerte 8.2 Typische Fehlerparameter von Werkzeugmaschinen 8.3 Bewertung anhand des Fehlers am TCP 8.4 Untersuchung der Varianten am Beispiel des Versuchsträgers MAX 8.5 Zusammenfassung 9. Zusammenfassung und Ausblick 9.1 Zusammenfassung 9.2 Ausblick A Anhang A.1 Positionen der im Versuchsträger verbauten Sensoren A.2 Beispiel für kinematisches Modell einer Maschine A.3 Beispiel für typische generierte Fehler am TCP A.4 Ermitteln der Achskorrekturwerte mittels Rücktransformation A.5 Visualisierung Lastprofile A.6 Veröffentlichungen A.7 Vorträge Literaturverzeichnis

The report presents a study of the operations at Rollmek AB. Rollmek is a company that manufactures various components for Panthera wheelchairs including backrests, chassis and leg rests. The thesis aims to develop various improvement proposals for the case company's painting department with regard to increasing production capacity. To achieve the goal, a simulation model has been constructed in the simulation program FACTS Analyzer, which reflects the department in question. The simulation model is based on three different variants with five different colours. For the construction of simulation models, data collected via various qualitative and quantitative research methods, including time studies, observations, and interviews. Improvement proposals that were developed were implemented in the simulation model and experimented based on the project's requirements. The study's results have led to five specific suggestions for improvement. Through the use of simulation models, data has been generated on the number of units produced and units produced/hour for the three product variations with associated color variations. With the help of these improvement suggestions, the company could implement them in production to increase its capacity to meet future demand.

Desenvolveu-se uma metodologia para integração de dois modelos matemáticos, um de quantidade de água, em rede de fluxo, denominado ACQUANET com outro de qualidade de água, de uma dimensão, aplicado a represas, denominado CE-QUAL-R1. Para tanto, foi elaborada uma INTERFACE em linguagem de programação possibilitando que as vazões resultantes, simuladas pelo ACQUANET, servissem como dados de entrada ao CE-QUAL-R1 para simular a distribuição vertical das variáveis de qualidade de água em uma represa. Essa metodologia foi aplicada à Represa Jaguari-Jacareí no Sistema Cantareira em São Paulo, Brasil, como alternativa de gerenciamento quali-quantitativo, além de possibilitar o uso de retirada de água em diferentes profundidades, através da operação de tomadas d’água seletivas existentes. / A methodology was developed for the integration of two mathematical models, one of water quantity in network named ACQUANET with other of water quality, in one dimension, applied in revervoirs, named CE-QUAL-R1. In order to achieve this goal, an INTERFACE was developed to link the CE-QUAL-R1 with ACQUANET outflow results. It should be highlighted that ACQUANET has been used for beginning values of CE-QUAL-R1 and to simulate the vertical distribution of water quality variables in a reservoir. This methodology was applied to Jaguari-Jacarei Reservoir, of Cantareira System in Sao Paulo, Brazil, as a management quality and quantity tool of the system and it showed the use possibility of withdrawal of outflowing waters from different depths, through existing selective withdrawals ports operation.

The fate of the carbon and nitrogen in dairy farm effluent (DFE) applied onto soil was investigated through laboratory experiments and field lysimeter studies. They resulted in the development and testing of a complex carbon (C) and nitrogen (N) simulation model (CaNS-Eff) of the soil-plant-microbial system. To minimise the risk of contamination of surface waters, regulatory authorities in New Zealand promote irrigation onto land as the preferred treatment method for DFE. The allowable annual loading rates for DFE, as defined in statutory regional plans are based on annual N balance calculations, comparing N inputs to outputs from the farming system. Little information is available, however, to assess the effects that these loading rates have on the receiving environment. It is this need, to understand the fate of land-applied DFE and develop a tool to describe the process, that is addressed in this research. The microbially mediated net N mineralisation from DFE takes a central role in the turnover of DFE, as the total N in DFE is dominated by organic N. In a laboratory experiment, where DFE was applied at the standard farm loading rate of 68 kg N ha⁻¹, the net C mineralisation from the DFE was finished 13 days after application and represented 30% of the applied C, with no net N mineralisation being measured by Day 113. The soluble fraction of DFE appeared to have a microbial availability similar to that of glucose. The low and gradually changing respiration rate measured from DFE indicated a semi-continuous substrate supply to the microbial biomass, reflecting the complex nature and broad range of C compounds in DFE. The repeated application of DFE will gradually enhance the mineralisable fraction of the total soil organic N and in the long term increase net N mineralisation. To address the lack of data on the fate of faecal-N in DFE, a ¹⁵N-labelled faecal component of DFE was applied under two different water treatments onto intact soil cores with pasture growing on them. At the end of 255 days, approximately 2% of the applied faecal ¹⁵N had been leached, 11 % was in plant material, 11 % was still as effluent on the surface, and 40% remained in the soil (39% as organic N). Unmeasured gaseous losses and physical losses from the soil surface of the cores supposedly account for the remaining ¹⁵N (approximately 36%). Separate analysis of the total and ammonium nitrogen contents and ¹⁵N enrichments of the DFE and filtered sub-samples (0.5 mm, 0.2µm) showed that the faecal-N fraction was not labelled homogeneously. Due to this heterogeneity, which was exacerbated by the filtration of DFE on the soil surface, it was difficult to calculate the turnover of the total faecal-N fraction based on ¹⁵N results. By making a simplifying assumption about the enrichment of the ¹⁵N in the DFE that infiltrated the soil, the contribution from DFE-N to all plant available N fractions including soil inorganic N was estimated to have been approximately 11 % of the applied DFE-N. An initial two-year study investigating the feasibility of manipulating soil water conditions through controlled drainage to enhance denitrification from irrigated DFE was extended a further two years for this thesis project. The resulting four-year data set provided the opportunity to evaluate the sustainability of DFE application onto land, an extended data set against which to test the adequacy of CaNS-Eff, and to identify the key processes in the fate of DFE irrigated onto soil under field conditions. In the final year of DFE irrigation, 1554 kg N ha⁻¹ of DFE-N was applied onto the lysimeters, with the main removal mechanism being pasture uptake (700 kg N ha⁻¹ yr⁻¹ removed). An average of 193 kg N ha⁻¹ yr⁻¹ was leached, with 80% of this being organic N. The nitrate leaching decreased with increasing soil moisture conditions through controlled drainage. At the high DFE loading rate used, the total soil C and N, pH and the microbial biomass increased at different rates over the four years. The long-term sustainability of the application of DFE can only be maintained when the supply of inorganic N is matched by the demand of the pasture. The complex simulation model (CaNS-Eff) of the soil-plant-microbial system was developed to describe the transport and transformations of C and N components in effluents applied onto the soil. The model addresses the shortcomings in existing models and simulates the transport, adsorption and filtration of both dissolved and particulate components of an effluent. The soil matrix is divided into mobile and immobile flow domains with convective flow of solutes occurring in the mobile fraction only. Diffusion is considered to occur between the micropore and mesopore domains both between and within a soil layer, allowing dissolved material to move into the immobile zone. To select an appropriate sub-model to simulate the water fluxes within CaNS-Eff, the measured drainage volumes and water table heights from the lysimeters were compared to simulated values over four years. Two different modelling approaches were compared, a simpler water balance model, DRAINMOD, and a solution to Richards' equation, SWIM. Both models provided excellent estimation of the total amount of drainage and water table height. The greatest errors in drainage volume were associated with rain events over the summer and autumn, when antecedent soil conditions were driest. When soil water and interlayer fluxes are required at small time steps such as during infiltration under DFE-irrigation, SWIM's more mechanistic approach offered more flexibility and consequently was the sub-model selected to use within CaNS-Eff. Measured bromide leaching from the lysimeters showed that on average 18% of the bromide from an irrigation event bypassed the soil matrix and was leached in the initial drainage event. This bypass mechanism accounted for the high amount of organic N leached under DFE-irrigation onto these soils and a description of this bypass process needed to be included in CaNS-Eff. Between 80 and 90% of the N and C leached from the lysimeters was particulate (> 0.2 µm in size), demonstrating the need to describe transport of particulate material in CaNS-Eff. The filtration behaviour of four soil horizons was measured by characterising the size of C material in a DFE, applying this DFE onto intact soil cores, and collecting and analyzing the resulting leachate using the same size characterisation. After two water flushes, an average of 34% of the applied DFE-C was leached through the top 0-50 mm soil cores, with a corresponding amount of 27% being leached from the 50-150 mm soil cores. Most of the C leaching occurred during the initial DFE application onto the soil. To simulate the transport and leaching of particulate C, a sub-model was developed and parameterised that describes the movement of the effluent in terms of filtering and trapping the C within a soil horizon and then washing it out with subsequent flow events. The microbial availability of the various organic fractions within the soil system are described in CaNS-Eff by availability spectra of multiple first-order decay functions. The simulation of microbial dynamics is based on actual consumption of available C for three microbial biomass populations: heterotrophs, nitrifiers and denitrifiers. The respiration level of a population is controlled by the amount of C that is available to that population. This respiration rate can vary between low level maintenance requirements, when very little substrate is available, and higher levels when excess substrate is available to an actively growing population. The plant component is described as both above and below-ground fractions of a rye grass-clover pasture. The parameter set used in CaNS-Eff to simulate the fate of DFE irrigated onto the conventionally drained lysimeter treatments over three years with a subsequent 10 months non-irrigation period was derived from own laboratory studies, field measurements, experimental literature data and published model studies. As no systematic calibration exercise was undertaken to optimise these parameters, the parameter set should be considered as "initial best estimates" and not as a calibrated data set on which a full validation of CaNS-Eff could be based. Over the 42 months of simulation, the cumulative drainage from CaNS-Eff for the conventionally drained DFE lysimeter was always within the 95% CI of the measured value. On the basis of individual drainage bulking periods, CaNS-Eff was able to explain 92% of the variation in the measured drainage volumes. On an event basis the accuracy of the simulated water filled pore space (WFPS) was better than that of the drainage volume, with an average of 70% of the simulated WFPS values being within the 95% CI for the soil layers investigated, compared to 44% for the drainage volumes. Overall the hydrological component of CaNS-Eff, which is based on the SWIM model, could be considered as satisfactory for the purposes of predicting the soil water status and drainage volume from the conventionally drained lysimeter treatment for this study. The simulated cumulative nitrate leaching of 4.7 g NO₃-N m⁻² over the 42 months of lysimeter operation was in good agreement to the measured amount of 3.0 (± 2.7) g NO₃-N m⁻². Similarly, the total simulated ammonium leaching of 2.7g NH₄- N m⁻² was very close to the measured amount of 2.5 (± 1.35) g NH₄- N m⁻² , however the dynamics were not as close to the measured values as with the nitrate leaching. The simulated amount of organic N leached was approximately double that measured, and most of the difference originated from the simulated de-adsorption of the dissolved fraction of organic N during the l0-month period after the final DFE irrigation. The 305 g C m⁻² of simulated particulate C leached was close to the measured amount of 224 g C m⁻² over the 31 months of simulation. The dissolved C fraction was substantially over-predicted. There was good agreement in the non-adsorbed and particulate fractions of the leached C and N in DFE. However, the isothermic behaviour of the adsorbed pools indicated that a non-reversible component needed to be introduced or that the dynamics of the de-adsorption needed to be improved. Taking into account that the parameters were not calibrated but only "initial best estimates", the agreement in the dynamics and the absolute amounts between the measured and simulated values of leached C and N demonstrated that CaNS-Eff contains an adequate description of the leaching processes following DFE irrigation onto the soil. The simulated pasture N production was in reasonable agreement with the measured data. The simulated dynamics and amounts of microbial biomass in the topsoil layers were in good agreement with the measured data. This is an important result as the soil microbial biomass is the key transformation station for organic materials. Excepting the topsoil layer, the simulated total C and N dynamics were close to the measured values. The model predicted an accumulation of C and N in the topsoil layer as expected, but not measured. Although no measurements were available to compare the dynamics and amounts of the soil NO₃-N and NH₄-N, the simulated values appear realistic for an effluent treatment site and are consistent with measured pasture data. Considering the large amount of total N and C applied onto the lysimeters over the 42 months of operation (4 t ha⁻¹ of N and 42 t ha⁻¹0f C), the various forms of C and N in dissolved and particulate DFE as well as in returned pasture, and that the parameters used in the test have not been calibrated, the simulated values from CaNS-Eff compared satisfactorily to the measured data.

Waste Load Allocation (WLA) in rivers refers to the determination of required pollutant fractional removal levels at a set of point sources of pollution to ensure that water quality standards are maintained throughout the system. Optimal waste load allocation implies that the selected pollution treatment vector not only maintains the water quality standards, but also results in the best value for the objective function defined for the management problem. Waste load allocation problems are characterized by uncertainties due to the randomness and imprecision. Uncertainty due to randomness arises mainly due to the random nature of the variables influencing the water quality. Uncertainty due to imprecision or fuzziness is associated with setting up the water quality standards and goals of the Pollution Control Agencies (PCA), and the dischargers (e.g., industries and municipal dischargers). Many decision problems in water resources applications are dominated by natural, extreme, rarely occurring, uncertain events. However usually such events will be absent or be rarely present in the historical records. Due to the scarcity of information of these uncertain events, a realistic decision-making becomes difficult. Furthermore, water resources planners often deal with imprecision, mostly due to imperfect knowledge and insufficient or inadequate data. Therefore missing data is very common in most water resources decision problems. Missing data introduces inaccuracy in analysis and evaluation. For instance, the sample mean of the available data can be an inaccurate estimate of the mean of the complete data. Use of sample statistics estimated from inadequate samples in WLA models would lead to incorrect decisions. Therefore there is a necessity to incorporate the uncertainty due to missing data also in WLA models in addition to the uncertainties due to randomness and imprecision. The uncertainty in the input parameters due to missing or inadequate data renders the input parameters (such as mean and variance) as interval grey parameters in water quality decision-making. In a Fuzzy Waste Load Allocation Model (FWLAM), randomness and imprecision both can be addressed simultaneously by using the concept of fuzzy risk of low water quality (Mujumdar and Sasikumar, 2002). In the present work, an attempt is made to also address uncertainty due to partial ignorance due to missing data or inadequate data in the samples of input variables in FWLAM, considering the fuzzy risk approach proposed by Mujumdar and Sasikumar (2002). To address the uncertainty due to missing data or inadequate data, the input parameters (such as mean and variance) are considered as interval grey numbers. The resulting output water quality indicator (such as DO) will also, consequently, be an interval grey number. The fuzzy risk will also be interval grey number when output water quality indicator is an interval grey number. A methodology is developed for the computation of grey fuzzy risk of low water quality, when the input variables are characterized by uncertainty due to partial ignorance resulting from missing or inadequate data in the samples of input variables. To achieve this, an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system to address uncertainties due to randomness, fuzziness and also due to missing data or inadequate data. Monte Carlo Simulation (MCS) incorporating a water quality simulation model is performed two times for each set of randomly generated input variables: once for obtaining the upper bound of DO and once for the lower bound of DO, by using appropriate upper or lower bounds of interval grey input variables. These two bounds of DO are used in the estimation of grey fuzzy risk by substituting the upper and lower values of fuzzy membership functions of low water quality. A backward finite difference scheme (Chapra, 1997) is used to solve the water quality simulation model. The goal of PCA is to minimize the bounds of grey fuzzy risk, whereas the goal of dischargers is to minimize the fractional removal levels. The two sets of goals are conflicting with each other. Fuzzy multiobjective optimization technique is used to formulate the multiobjective model to provide best compromise solutions. Probabilistic Global Search Lausanne (PGSL) method is used to solve the optimization problem. Finally the results of the model are compared with the results of risk minimization model (Ghosh and Mujumdar, 2006), when the methodology is applied to the case study of the Tunga-Bhadra river system in South India. The model is capable of determining a grey fuzzy risk with the corresponding bounds of DO, at each check point, rather than specifying a single value of fuzzy risk as done in a Fuzzy Waste Load Allocation Model (FWLAM). The IFWLAM developed is based on fuzzy multiobjective optimization problem with ‘max-min’ as the operator, which usually may not result in a unique solution and there exists a possibility of obtaining multiple solutions (Karmakar and Mujumdar, 2006b). Karmakar and Mujumdar (2006b) developed a two-phase Grey Fuzzy Waste Load Allocation Model (two-phase GFWLAM), to determine the widest range of interval-valued optimal decision variables, resulting in the same value of interval-valued optimal goal fulfillment level as obtained from GFWLAM (Karmakar and Mujumdar 2006a). Following Karmakar and Mujumdar (2006b), two optimization models are developed in this study to capture all the decision alternatives or multiple solutions: one to maximize and the other to minimize the summation of membership functions of the dischargers by keeping the maximum goal fulfillment level same as that obtained in IFWLAM to obtain a lower limit and an upper limit of fractional removal levels respectively. The aim of the two optimization models is to obtain a range of fractional removal levels for the dischargers such that the resultant grey fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision-making. The models are applied to the case study of Tunga-Bhadra river system. A range of upper and lower limits of fractional removal levels is obtained for each discharger; within this range, the discharger can select the fractional removal level so that the resulting grey fuzzy risk will also be within specified bounds. In IFWLAM, the membership functions are subjective, and lower and upper bounds are arbitrarily fixed. Karmakar and Mujumdar (2006a) developed a Grey Fuzzy Waste Load Allocation Model (GFWLAM), in which uncertainty in the values of membership parameters is quantified by treating them as interval grey numbers. Imprecise membership functions are assigned for the goals of PCA and dischargers. Following Karmakar and Mujumdar (2006a), a Grey Optimization Model with Grey Fuzzy Risk is developed in the present study to address the uncertainty in the memebership functions of IFWLAM. The goals of PCA and dischargers are considered as grey fuzzy goals with imprecise membership functions. Imprecise membership functions are assigned to the fuzzy set of low water quality and fuzzy set of low risk. The grey fuzzy risk approach is included to account for the uncertainty due to missing data or inadequate data in the samples of input variables as done in IFWLAM. Randomness and imprecision associated with various water quality influencing variables and parameters of the river system are considered through a Monte-Carlo simulation when input parameters (such as mean and variance) are interval grey numbers. The model application is demonstrated with the case study of Tunga-Bhadra river system in South India. Finally the results of the model are compared with the results of GFWLAM (Karmakar and Mujumdar, 2006a). For the case study of Tunga Bhadra River system, it is observed that the fractional removal levels are higher for Grey Optimization Model with Grey Fuzzy Risk compared to GFWLAM (Karmakar and Mujumdar, 2006a) and therefore the resulting risk values at each check point are reduced to a significant extent. The models give a set of flexible policies (range of fractional removal levels). Corresponding optimal values of goal fulfillment level and the grey fuzzy risk are all in terms of interval grey numbers. The IFWLAM and Grey Fuzzy Optimization Model with Grey Fuzzy Risk, developed in the study do not limit their application to any particular pollutant or water quality indicator in the river system. Given appropriate transfer functions for spatial distribution of the pollutants in water body, the models can be used for water quality management of any general river system.