1,635 research outputs found
Ein Forschungsneubau in Freiberg für 41,5 Mio. Euro - Zentrum für effiziente Hochtemperatur-Stoffumwandlung (ZeHS)
No full textIm Zeitraum der Jahre 2012 bis 2015 beteiligte sich die TU Bergakademie Freiberg mit einem Antrag für ein \\\"Zentrum für effiziente Hochtemperatur- Stoffwandlung\\\" (ZeHS) am Wettbewerb um eine Förderempfehlung für Forschungsbauten an Hochschulen gemäß Art. 91b GG. Nach der erfolgreichen Verteidigung vor dem Wissenschaftsrat und der Bestätigung durch die gemeinsame Wissenschaftskonferenz des Bundes und der Länder stehen der Universität in den Jahren 2015 bis 2020 41,5 Mio. Euro für die Baukosten und die Beschaffung ausgewählter Großgeräte zur Verfügung. Der Forschungsbau, der für Wissenschaftler aller Fakultäten der TU Bergakademie Freiberg offen ist, ermöglicht die strukturelle Bündelung der an der Universität in den Bereichen Hochtemperatur-Prozesse und -Materialien in einzigartiger Weise vorhandenen Kompetenzen. Der Fokus des ZeHS liegt auf der Entwicklung innovativer, ressourcen- und energieeffizienter Technologien im Bereich der Grundstoffindustrie, wobei Prozess- und Materialanforderungen in der Chemischen Industrie, der Metallurgie sowie der Keramik-, Glas- und Baustoffindustrie zusammenhängend betrachtet werden und die Ergebnisse auch auf andere Branchen übertragbar sind
Gemeinsame Nutzung von Radarinterferometrie und Geostatistik zur räumlichen Analyse von Bergsenkungen
No full textThe development of modern mostly satellite-based monitoring methods in the last two decades, especially of the SAR interferometry allows, particularly in built-up urban areas, a spatially dense collection of movements in relative short time intervals. Due to the almost extensive existence of measurement results, in contrast to the line-like levelling, one has the opportunity of spatial statistical analysis. Within the framework of a diploma thesis at the TU Bergakademie Freiberg, the applicability of geostatistical methods for analyzing radar interferometric height change data was investigated. In the spatial analysis of the measured values especially the high local variability in the data stood out and a direct relationship between the spatial structure in the measurements and the ongoing mining could be detected.Geoscience & EngineeringCivil Engineering and Geoscience
Mechanisches Verhalten von kohlenstoffgebundenen Feuerfestwerkstoffen bis 1500°C
No full textDie Arbeit führt Methoden zur Durchführung von Zugversuchen und bruchmechanischen Versu-chen ein und liefert mechanische Kennwerte für zwei kohlenstoffgebundene FFW im Bereich von RT bis 1500°C. Dafür standen ein grobkörniges MgO-C und ein feinkörniges Al2O3-C zur Ver-fügung. Die Werkstoffe zeigten bis 1200°C keine Duktilität und sprachen spröde. Die Schädigung erfolgte ausschließlich durch Risswachstum. Dieses fand beim MgO-C temperaturunabhängig auf Grund der rissbehafteten Mikrostruktur durch stabiles Risswachstum bereits vorhandener Risse statt. Es kam dabei zur Bildung von Rissnetzwerken sowie zu zahlreichen energiedissipierenden Prozessen. Beim Al2O3-C trat be RT instabiles Risswachstum auf. Bei hohen Temperaturen kam es durch thermisch aktivierte Prozesse zu duktilem Verhalten und stabilem Risswachstum. Beim grobkörnigen MgO-C wurden große Verformungen durch das starre Oxidgerüst verhindert. Zu-sätzlich zeigten die Werkstoffe auf Grund ihrer Mikrostruktur eine Zunahme der Festigkeit mit steigender Temperatur. Aus den Versuchen wurde ein Heißpressverfahren zur Herstellung von gradierten Werkstoffen abgeleitet.:1 Einleitung
2 Grundlagen
2.1 Feuerfestwerkstoffe
2.1.1 Einsatz und Beanspruchung von FFW
2.1.2 Einteilung von FFW
2.2 Kohlenstoffgebundene FFW
2.2.1 Kohlenstoff und seine Terminologie
2.2.2 Grundlegende Eigenschaften kohlenstoffgebundener FFW
2.2.3 Anwendungen kohlenstoffgebundener FFW
2.2.4 Aufbau und Mikrostruktur kohlenstoffgebundener FFW
2.2.5 Herstellungsparameter kohlenstoffgebundener FFW
2.2.6 Chemische Eigenschaften kohlenstoffgebundener FFW
2.3 Mechanische Eigenschaften kohlenstoffgebundener FFW
2.3.1 Mechanische Eigenschaften kohlenstoffgebundener FFW bei RT
2.3.2 Mechanische Eigenschaften kohlenstoffgebundener FFW bei HT
2.4 Grundlagen zur Werkstoffprüfung bei RT und hohen Temperaturen
2.4.1 Streuung und Einfluss der Probengröße
2.4.2 Belastungsrate
2.4.3 Zugversuche
2.4.4 Druckversuche
2.4.5 Biegeversuche
2.4.6 Bruchmechanische Untersuchungen
2.4.7 Temperaturwechselbeständigkeit
2.4.8 Kriechen
2.4.9 Spannungsrelaxation
2.4.10 Härtemessung
2.4.11 Hochtemperaturprüfung in Kaltkammerofen mit induktiver Heizung
2.4.12 Temperaturmessung mit Thermoelement, Pyrometrie, Thermographie
2.4.13 Bestimmung elastischer Konstanten mittels akustischer Methoden
2.4.14 Optische in situ Schadensbeschreibung mittels Mikroskopie und DIC
2.5 Heißpressverfahren
3 Experimentelles
3.1 Werkstoffe
3.1.1 Kohlenstoffgebundenes Magnesiumoxid (MgO-C)
3.1.2 Kohlenstoffgebundenes Aluminiumoxid (Al2O3-C)
3.1.3 Graphit (ISEM 8)
3.2 Mechanische Tests
3.2.1 Prüfmaschine für Druck- und Biegeversuche
3.2.2 Prüfmaschine für Zug-Druck-Versuche
3.2.3 Probengeometrien
3.2.4 Druckversuche
3.2.5 Biegeversuche
3.2.6 Bruchmechanische Versuche
3.2.7 Versuche mit Zugbeanspruchung
3.2.8 Versuchsabläufe der Hochtemperaturversuche
3.2.9 Temperaturmessung mittels Thermographie
3.3 Weitere Versuchsmethoden
3.3.1 Mikrostrukturuntersuchung mittels Mikroskopie und Röntgenbeugung
3.3.2 Porositäts- und Dichtemessung
3.3.3 Härtemessung
3.3.4 Dynamischer E-Modul
4 Methodische Erkenntnisse und Voruntersuchungen
4.1 Temperaturmessung und -verteilung
4.1.1 Temperaturmessung mittels Thermoelement und Pyrometer
4.1.2 Emissionskoeffizient und Probenbeschichtung
4.1.3 Temperaturverteilung
4.2 Dehnungsmessung
4.3 Zugversuche an Keramiken
4.3.1 Übertragung von Zugkräften
4.3.2 Axialität in Zugversuchen
4.4 Bruchmechanische Versuche
4.4.1 Kerbeinbringung
4.4.2 Überprüfung des optischen Messsystems
4.4.3 Bestimmung der Risslänge während des Versuchs
4.5 Überprüfung der Messmethodik mit dem Referenzwerkstoff Graphit ISEM-8
5 Ergebnisse
5.1 Mikrostrukturbeschreibung der untersuchten FFW
5.1.1 Mikrostruktur des MgO-C’s
5.1.2 Mikrostruktur des Al2O3-C’s
5.2 Mechanisches Verhalten bei RT
5.2.1 Mechanisches Verhalten von MgO-C bei RT
5.2.2 Mechanisches Verhalten von Al2O3-C bei RT
5.3 Mechanische Eigenschaften bei HT
5.3.1 Mechanisches Verhalten von MgO-C bei HT
5.3.2 Mechanisches Verhalten von Al2O3-C bei HT
5.4 Heißpressverfahren für kohlenstoffgebundene FFW
5.4.1 Beschreibung des Heißpressverfahrens
5.4.2 Physikalische Eigenschaften und Mikrostruktur des Presslings
5.4.3 Mechanische Eigenschaften des Presslings
6 Diskussion
7 Zusammenfassung und Ausblick
Literatur
Anhan
Measurement of thermodynamic data at elevated pressure and temperature conditions with a microfluidic setup
No full textWith this thesis, I present an experimental study focusing on the provision of thermodynamic data of fluids at elevated pressure and temperature conditions. Hereby a microcapillary setup that is equipped with an in situ Raman Spectroscopy unit as well as with a high-speed camera, was further improved within the scientific employment of the author. The setup consists in principle of a fused-silica microcapillary embedded in a heating block, which is furthermore connected to high pressure syringe pumps.
Pure compounds and mixtures were studied with the microfluidic setup and different thermodynamic properties were determined. For instance, vapor pressures of Poly(oxymethylene) Dimethyl Ethers (OME3 and OME4), a potential class of renewable diesel fuels, were the first time measured for temperatures exceeding the atmospheric boiling temperature. Hereby the regarded compound is pressurized at constant temperature, from what the vapor pressure is determined optically by detecting bubble or film formation, indicating the transition from vapor to liquid state.
The main results of this thesis were however the vapor-liquid equilibria (VLE) of fuel/air-systems that were determined by in situ Raman Spectroscopy, whereby the Stokes-scattered Raman signal can be successfully separated phase-dependently by light barrier technology. A further task was the determination of saturated mixture densities of the validation system ethanol/CO2.
With this study, I intend to contribute to the scarce literature data for the studied systems and properties. Therewith I want to help to enhance the understanding of microprocesses such as the evaporation and mixing formation in diesel combustion engines
Katalog der Bibliothek der Königlich sächsischen Bergakademie Freiberg.
No full text"Erläuternde Bemerkungen," by Bergrath Professor C.G. Kreischer.T. 1. Alphabetischer Katalog.Mode of access: Internet
Energy forms on a C^1 diffeomorphic images of the Sierpinski gasket
No full textThe energy form on a conformal C^1 diffeomorphic image G of the Sierpinski gasket K is constructed by iterating the Lagrangian L_G on G, which is given in terms of a pullback of the Lagrangian L_k on K and of the differential of the diffeomorphism. The extension of this approach to the class of nested fractals is outlined
Cutting force component-based rock differentiation utilising machine learning
No full textThis dissertation evaluates the possibilities and limitations of rock type identification in rock cutting with conical picks. For this, machine learning in conjunction with features derived from high frequency cutting force measurements is used. On the basis of linear cutting experiments, it is shown that boundary layers can be identified with a precision of less than 3.7 cm when using the developed programme routine. It is further shown that rocks weakened by cracks can be well identified and that anisotropic rock behaviour may be problematic to the classification success. In a case study, it is shown that the supervised algorithms artificial neural network and distributed random forest perform relatively well while unsupervised k-means clustering provides limited accuracies for complex situations. The 3d-results are visualised in a web app. The results suggest that a possible rock classification system can achieve good results—that are robust to changes in the cutting parameters when using the proposed evaluation methods.:1 Introduction...1
2 Cutting Excavation with Conical Picks...5
2.1 Cutting Process...8
2.1.2 Cutting Parameters...11
2.1.3 Influences of Rock Mechanical Properties...17
2.1.4 Influences of the Rock Mass...23
2.2 Ratios of Cutting Force Components...24
3 State of the Art...29
3.1 Data Analysis in Rock Cutting Research...29
3.2 Rock Classification Systems...32
3.2.1 MWC – Measure-While-Cutting...32
3.2.2 MWD – Measuring-While-Drilling...34
3.2.3 Automated Profiling During Cutting...35
3.2.4 Wear Monitoring...36
3.3 Machine learning for Rock Classification...36
4 Problem Statement and Justification of Topic...38
5 Material and Methods...40
5.1 Rock Cutting Equipment...40
5.2 Software & PC...42
5.3 Samples and Rock Cutting Parameters...43
5.3.1 Sample Sites...43
5.3.2 Experiment CO – Zoned Concrete...45
5.3.3 Experiment GN – Anisotropic Rock Gneiss...47
5.3.4 Experiment GR – Uncracked and Cracked Granite...49
5.3.5 Case Study PB and FBA – Lead-Zinc and Fluorite-Barite Ores...50
5.4 Data Processing...53
5.5 Force Component Ratio Calculation...54
5.6 Procedural Selection of Features...57
5.7 Image-Based Referencing and Rock Boundary Modelling...60
5.8 Block Modelling and Gridding...61
5.9 Correlation Analysis...63
5.10 Regression Analysis of Effect...64
5.11 Machine Learning...65
5.11.2 K-Means Algorithm...66
5.11.3 Artificial Neural Networks...67
5.11.4 Distributed Random Forest...70
5.11.5 Classification Success...72
5.11.6 Boundary Layer Recognition Precision...73
5.12 Machine Learning Case Study...74
6 Results...75
6.1 CO – Zoned Concrete...75
6.1.1 Descriptive Statistics...75
6.1.2 Procedural Evaluation...76
6.1.3 Correlation of the Covariates...78
6.1.4 K-Means Cluster Analysis...79
6.2 GN – Foliated Gneiss...85
6.2.1 Cutting Forces...86
6.2.2 Regression Analysis of Effect...88
6.2.3 Details Irregular Behaviour...90
6.2.4 Interpretation of Anisotropic Behaviour...92
6.2.5 Force Component Ratios...92
6.2.6 Summary and Interpretations of Results...93
6.3 CR – Cracked Granite...94
6.3.1 Force Component Results...94
6.3.2 Spatial Analysis...97
6.3.3 Error Analysis...99
6.3.4 Summary...100
6.4 Case Study...100
6.4.1 Feature Distribution in Block Models...101
6.4.2 Distributed Random Forest...105
6.4.3 Artificial Neural Network...107
6.4.4 K-Means...110
6.4.5 Training Data Required...112
7 Discussion...114
7.1 Critical Discussion of Experimental Results...114
7.1.1 Experiment CO...114
7.1.2 Experiment GN...115
7.1.3 Experiment GR...116
7.1.4 Case Study...116
7.1.5 Additional Outcomes...117
7.2 Comparison of Machine Learning Algorithms...118
7.2.1 K-Means...118
7.2.2 Artificial Neural Networks and Distributed Random Forest...119
7.2.3 Summary...120
7.3 Considerations Towards Sensor System...121
7.3.1 Force Vectors and Data Acquisition Rate...121
7.3.2 Sensor Types...122
7.3.3 Computation Speed...123
8 Summary and Outlook...125
References...128
Annex A Fields of Application of Conical Tools...145
Annex B Supplements Cutting and Rock Parameters...149
Annex C Details Topic-Analysis Rock Cutting Publications...155
Annex D Details Patent Analysis...157
Annex E Details Rock Cutting Unit HSX-1000-50...161
Annex F Details Used Pick...162
Annex G Error Analysis Cutting Experiments...163
Annex H Details Photographic Modelling...166
Annex I Laser Offset...168
Annex J Supplements Experiment CO...169
Annex K Supplements Experiment GN...187
Annex L Supplements Experiment GR...191
Annex M Preliminary Artificial Neural Network Training...195
Annex N Supplements Case Study (CD)...201
Annex O R-Codes (CD)...203
Annex P Supplements Rock Mechanical Tests (CD)...204Die Dissertation evaluiert Möglichkeiten und Grenzen der Gebirgserkennung bei der schneidenden Gewinnung von Festgesteinen mit Rundschaftmeißeln unter Nutzung maschinellen Lernens – in Verbindung mit aus hochaufgelösten Schnittkraftmessungen abgeleiteten Kennwerten. Es wird auf linearen Schneidversuchen aufbauend gezeigt, dass Schichtgrenzen mit Genauigkeiten unter 3,7 cm identifiziert werden können. Ferner wird gezeigt, dass durch Risse geschwächte Gesteine gut identifiziert werden können und dass anisotropes Gesteinsverhalten möglicherweise problematisch auf den Klassifizierungserfolg wirkt. In einer Fallstudie wird gezeigt, dass die überwachten Algorithmen Künstliches Neurales Netz und Distributed Random Forest teils sehr gute Ergebnisse erzielen und unüberwachtes k-means-Clustering begrenzte Genauigkeiten für komplexe Situationen liefert. Die Ergebnisse werden in einer Web-App visualisiert. Aus den Ergebnissen wird abgeleitet, dass ein mögliches Sensorsystem mit den vorgeschlagenen Auswerteroutinen gute Ergebnisse erzielen kann, die gleichzeitig robust gegen Änderungen der Schneidparameter sind.:1 Introduction...1
2 Cutting Excavation with Conical Picks...5
2.1 Cutting Process...8
2.1.2 Cutting Parameters...11
2.1.3 Influences of Rock Mechanical Properties...17
2.1.4 Influences of the Rock Mass...23
2.2 Ratios of Cutting Force Components...24
3 State of the Art...29
3.1 Data Analysis in Rock Cutting Research...29
3.2 Rock Classification Systems...32
3.2.1 MWC – Measure-While-Cutting...32
3.2.2 MWD – Measuring-While-Drilling...34
3.2.3 Automated Profiling During Cutting...35
3.2.4 Wear Monitoring...36
3.3 Machine learning for Rock Classification...36
4 Problem Statement and Justification of Topic...38
5 Material and Methods...40
5.1 Rock Cutting Equipment...40
5.2 Software & PC...42
5.3 Samples and Rock Cutting Parameters...43
5.3.1 Sample Sites...43
5.3.2 Experiment CO – Zoned Concrete...45
5.3.3 Experiment GN – Anisotropic Rock Gneiss...47
5.3.4 Experiment GR – Uncracked and Cracked Granite...49
5.3.5 Case Study PB and FBA – Lead-Zinc and Fluorite-Barite Ores...50
5.4 Data Processing...53
5.5 Force Component Ratio Calculation...54
5.6 Procedural Selection of Features...57
5.7 Image-Based Referencing and Rock Boundary Modelling...60
5.8 Block Modelling and Gridding...61
5.9 Correlation Analysis...63
5.10 Regression Analysis of Effect...64
5.11 Machine Learning...65
5.11.2 K-Means Algorithm...66
5.11.3 Artificial Neural Networks...67
5.11.4 Distributed Random Forest...70
5.11.5 Classification Success...72
5.11.6 Boundary Layer Recognition Precision...73
5.12 Machine Learning Case Study...74
6 Results...75
6.1 CO – Zoned Concrete...75
6.1.1 Descriptive Statistics...75
6.1.2 Procedural Evaluation...76
6.1.3 Correlation of the Covariates...78
6.1.4 K-Means Cluster Analysis...79
6.2 GN – Foliated Gneiss...85
6.2.1 Cutting Forces...86
6.2.2 Regression Analysis of Effect...88
6.2.3 Details Irregular Behaviour...90
6.2.4 Interpretation of Anisotropic Behaviour...92
6.2.5 Force Component Ratios...92
6.2.6 Summary and Interpretations of Results...93
6.3 CR – Cracked Granite...94
6.3.1 Force Component Results...94
6.3.2 Spatial Analysis...97
6.3.3 Error Analysis...99
6.3.4 Summary...100
6.4 Case Study...100
6.4.1 Feature Distribution in Block Models...101
6.4.2 Distributed Random Forest...105
6.4.3 Artificial Neural Network...107
6.4.4 K-Means...110
6.4.5 Training Data Required...112
7 Discussion...114
7.1 Critical Discussion of Experimental Results...114
7.1.1 Experiment CO...114
7.1.2 Experiment GN...115
7.1.3 Experiment GR...116
7.1.4 Case Study...116
7.1.5 Additional Outcomes...117
7.2 Comparison of Machine Learning Algorithms...118
7.2.1 K-Means...118
7.2.2 Artificial Neural Networks and Distributed Random Forest...119
7.2.3 Summary...120
7.3 Considerations Towards Sensor System...121
7.3.1 Force Vectors and Data Acquisition Rate...121
7.3.2 Sensor Types...122
7.3.3 Computation Speed...123
8 Summary and Outlook...125
References...128
Annex A Fields of Application of Conical Tools...145
Annex B Supplements Cutting and Rock Parameters...149
Annex C Details Topic-Analysis Rock Cutting Publications...155
Annex D Details Patent Analysis...157
Annex E Details Rock Cutting Unit HSX-1000-50...161
Annex F Details Used Pick...162
Annex G Error Analysis Cutting Experiments...163
Annex H Details Photographic Modelling...166
Annex I Laser Offset...168
Annex J Supplements Experiment CO...169
Annex K Supplements Experiment GN...187
Annex L Supplements Experiment GR...191
Annex M Preliminary Artificial Neural Network Training...195
Annex N Supplements Case Study (CD)...201
Annex O R-Codes (CD)...203
Annex P Supplements Rock Mechanical Tests (CD)...20
Solid-liquid equilibria of Sorel phases and Mg (OH)2 in the system Na-Mg-Cl-OH-H2O. Part I: experimental determination of OH− and H+ equilibrium concentrations and solubility constants at 25°C, 40°C, and 60°C
No full textSorel phases are the binder phases of the magnesia building material (Sorel cement/concrete) and of special concern for the construction of long-term stable geotechnical barriers in repositories for radioactive waste in rock salt, as potentially occurring brines are expected to contain MgCl2. Sorel phases, in addition to Mg(OH)2, are equally important as pH buffers to minimize solubility and potential mobilization of radionuclides in brine systems. In order to obtain a detailed database of the relevant solid-liquid equilibria and the related pHm values of the equilibrium solutions, extensive experimental investigations were carried out. Solid phase formation was studied by suspending MgO and Mg(OH)2 in NaCl saturated MgCl2-solutions at 25°C. Mg(OH)2 and the 3-1-8 Sorel phase were identified as the stable solid phases, while the 5-1-8 Sorel phase is metastable. Equilibration at 40°C did not lead to any solid phase changes. Both OH− and H+ equilibrium concentrations were analyzed as a function of MgCl2 concentration at 25°C and 40°C. In addition to our already published solid-liquid equilibria for the ternary system Mg-Cl-OH-H2O (25°C–120°C), the equilibrium H+ concentrations (pHm) determined at 25°C, 40°C and 60°C are now reported. Analyzing these data together with known ion-interaction Pitzer coefficients, the solubility constants for Mg(OH)2 and the 3-1-8 phase at these three temperatures, for the metastable 5-1-8 phase at 25°C and for the 2-1-4 phase at 60°C have been consistently calculated
Processing and properties of bulk and cellular carbon-bonded refractory materials
No full textIn this manuscript, distinct problems concerning carbon-bonded materials processing and characterization were analyzed, from bulk to cellular samples. The main motivation was to address central topics that would enhance the comprehension of the material’s behavior, as well as trigger targeted improvements. Some of the topics this thesis covers are: Non-linear Young’s modulus behavior of carbon-bonded alumina at high temperatures; Influence of the processing route on the cold crushing strength of carbon-bonded alumina foam filters; Geometric characterization of ceramic foam filters as a tool to understand processing parameters; Use of advanced techniques such as computer tomography and finite element modelling to correlate processing parameters and mechanical behavior. In most of the analyses, non-standard computational strategies were adopted. In those cases, algorithms were written to facilitate the evaluations, or even enable it in the first place. All the algorithms’ concepts are described in this thesis and their codes are available in the Appendices. The current work was carried out within the framework of the Collaborative Research Center 920 (CRC 920) “Multifunctional filters for metal melt filtration - a contribution to zero defect materials” at the Technische Universität Bergakademie Freiberg funded by the German Research Foundation (DFG)
Zur Palökologie und Biostratinomie der Randfazies der Wettiner Schichten (Stefan C) bei Dobis nördlich Halle (Saale)
No full text- …
