8,693 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Inferring networks from time series: a neural approach

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    Network structures underlie the dynamics of many complex phenomena, from gene regulation and foodwebs to power grids and social media. Yet, as they often cannot be observed directly, their connectivities must be inferred from observations of their emergent dynamics. In this work we present a powerful and fast computational method to infer large network adjacency matrices from time series data using a neural network. Using a neural network provides uncertainty quantification on the prediction in a manner that reflects both the non-convexity of the inference problem as well as the noise on the data. This is useful since network inference problems are typically underdetermined, and a feature that has hitherto been lacking from network inference methods. We demonstrate our method's capabilities by inferring line failure locations in the British power grid from observations of its response to a power cut. Since the problem is underdetermined, many classical statistical tools (e.g. regression) will not be straightforwardly applicable. Our method, in contrast, provides probability densities on each edge, allowing the use of hypothesis testing to make meaningful probabilistic statements about the location of the power cut. We also demonstrate our method's ability to learn an entire cost matrix for a non-linear model from a dataset of economic activity in Greater London. Our method outperforms OLS regression on noisy data in terms of both speed and prediction accuracy, and scales as N2N^2 where OLS is cubic. Since our technique is not specifically engineered for network inference, it represents a general parameter estimation scheme that is applicable to any parameter dimension

    Statistical Learning for Gene Expression Biomarker Detection in Neurodegenerative Diseases

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    In this work, statistical learning approaches are used to detect biomarkers for neurodegenerative diseases (NDs). NDs are becoming increasingly prevalent as populations age, making understanding of disease and identification of biomarkers progressively important for facilitating early diagnosis and the screening of individuals for clinical trials. Advancements in gene expression profiling has enabled the exploration of disease biomarkers at an unprecedented scale. The work presented here demonstrates the value of gene expression data in understanding the underlying processes and detection of biomarkers of NDs. The value of novel approaches to previously collected -omics data is shown and it is demonstrated that new therapeutic targets can be identified. Additionally, the importance of meta-analysis to improve power of multiple small studies is demonstrated. The value of blood transcriptomics data is shown in applications to researching NDs to understand underlying processes using network analysis and a novel hub detection method. Finally, after demonstrating the value of blood gene expression data for investigating NDs, a combination of feature selection and classification algorithms were used to identify novel accurate biomarker signatures for the diagnosis and prognosis of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Additionally, the use of feature pools based on previous knowledge of disease and the viability of neural networks in dimensionality reduction and biomarker detection is demonstrated and discussed. In summary, gene expression data is shown to be valuable for the investigation of ND and novel gene biomarker signatures for the diagnosis and prognosis of PD and AD

    The Role of Transient Vibration of the Skull on Concussion

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    Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bones’ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury

    FiabilitĂ© de l’underfill et estimation de la durĂ©e de vie d’assemblages microĂ©lectroniques

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    Abstract : In order to protect the interconnections in flip-chip packages, an underfill material layer is used to fill the volumes and provide mechanical support between the silicon chip and the substrate. Due to the chip corner geometry and the mismatch of coefficient of thermal expansion (CTE), the underfill suffers from a stress concentration at the chip corners when the temperature is lower than the curing temperature. This stress concentration leads to subsequent mechanical failures in flip-chip packages, such as chip-underfill interfacial delamination and underfill cracking. Local stresses and strains are the most important parameters for understanding the mechanism of underfill failures. As a result, the industry currently relies on the finite element method (FEM) to calculate the stress components, but the FEM may not be accurate enough compared to the actual stresses in underfill. FEM simulations require a careful consideration of important geometrical details and material properties. This thesis proposes a modeling approach that can accurately estimate the underfill delamination areas and crack trajectories, with the following three objectives. The first objective was to develop an experimental technique capable of measuring underfill deformations around the chip corner region. This technique combined confocal microscopy and the digital image correlation (DIC) method to enable tri-dimensional strain measurements at different temperatures, and was named the confocal-DIC technique. This techique was first validated by a theoretical analysis on thermal strains. In a test component similar to a flip-chip package, the strain distribution obtained by the FEM model was in good agreement with the results measured by the confocal-DIC technique, with relative errors less than 20% at chip corners. Then, the second objective was to measure the strain near a crack in underfills. Artificial cracks with lengths of 160 ÎŒm and 640 ÎŒm were fabricated from the chip corner along the 45° diagonal direction. The confocal-DIC-measured maximum hoop strains and first principal strains were located at the crack front area for both the 160 ÎŒm and 640 ÎŒm cracks. A crack model was developed using the extended finite element method (XFEM), and the strain distribution in the simulation had the same trend as the experimental results. The distribution of hoop strains were in good agreement with the measured values, when the model element size was smaller than 22 ÎŒm to capture the strong strain gradient near the crack tip. The third objective was to propose a modeling approach for underfill delamination and cracking with the effects of manufacturing variables. A deep thermal cycling test was performed on 13 test cells to obtain the reference chip-underfill delamination areas and crack profiles. An artificial neural network (ANN) was trained to relate the effects of manufacturing variables and the number of cycles to first delamination of each cell. The predicted numbers of cycles for all 6 cells in the test dataset were located in the intervals of experimental observations. The growth of delamination was carried out on FEM by evaluating the strain energy amplitude at the interface elements between the chip and underfill. For 5 out of 6 cells in validation, the delamination growth model was consistent with the experimental observations. The cracks in bulk underfill were modelled by XFEM without predefined paths. The directions of edge cracks were in good agreement with the experimental observations, with an error of less than 2.5°. This approach met the goal of the thesis of estimating the underfill initial delamination, areas of delamination and crack paths in actual industrial flip-chip assemblies.Afin de protĂ©ger les interconnexions dans les assemblages, une couche de matĂ©riau d’underfill est utilisĂ©e pour remplir le volume et fournir un support mĂ©canique entre la puce de silicium et le substrat. En raison de la gĂ©omĂ©trie du coin de puce et de l’écart du coefficient de dilatation thermique (CTE), l’underfill souffre d’une concentration de contraintes dans les coins lorsque la tempĂ©rature est infĂ©rieure Ă  la tempĂ©rature de cuisson. Cette concentration de contraintes conduit Ă  des dĂ©faillances mĂ©caniques dans les encapsulations de flip-chip, telles que la dĂ©lamination interfaciale puce-underfill et la fissuration d’underfill. Les contraintes et dĂ©formations locales sont les paramĂštres les plus importants pour comprendre le mĂ©canisme des ruptures de l’underfill. En consĂ©quent, l’industrie utilise actuellement la mĂ©thode des Ă©lĂ©ments finis (EF) pour calculer les composantes de la contrainte, qui ne sont pas assez prĂ©cises par rapport aux contraintes actuelles dans l’underfill. Ces simulations nĂ©cessitent un examen minutieux de dĂ©tails gĂ©omĂ©triques importants et des propriĂ©tĂ©s des matĂ©riaux. Cette thĂšse vise Ă  proposer une approche de modĂ©lisation permettant d’estimer avec prĂ©cision les zones de dĂ©lamination et les trajectoires des fissures dans l’underfill, avec les trois objectifs suivants. Le premier objectif est de mettre au point une technique expĂ©rimentale capable de mesurer la dĂ©formation de l’underfill dans la rĂ©gion du coin de puce. Cette technique, combine la microscopie confocale et la mĂ©thode de corrĂ©lation des images numĂ©riques (DIC) pour permettre des mesures tridimensionnelles des dĂ©formations Ă  diffĂ©rentes tempĂ©ratures, et a Ă©tĂ© nommĂ©e le technique confocale-DIC. Cette technique a d’abord Ă©tĂ© validĂ©e par une analyse thĂ©orique en dĂ©formation thermique. Dans un Ă©chantillon similaire Ă  un flip-chip, la distribution de la dĂ©formation obtenues par le modĂšle EF Ă©tait en bon accord avec les rĂ©sultats de la technique confocal-DIC, avec des erreurs relatives infĂ©rieures Ă  20% au coin de puce. Ensuite, le second objectif est de mesurer la dĂ©formation autour d’une fissure dans l’underfill. Des fissures artificielles d’une longueuer de 160 ÎŒm et 640 ÎŒm ont Ă©tĂ© fabriquĂ©es dans l’underfill vers la direction diagonale de 45°. Les dĂ©formations circonfĂ©rentielles maximales et principale maximale Ă©taient situĂ©es aux pointes des fissures correspondantes. Un modĂšle de fissure a Ă©tĂ© dĂ©veloppĂ© en utilisant la mĂ©thode des Ă©lĂ©ments finis Ă©tendue (XFEM), et la distribution des contraintes dans la simuation a montrĂ© la mĂȘme tendance que les rĂ©sultats expĂ©rimentaux. La distribution des dĂ©formations circonfĂ©rentielles maximales Ă©tait en bon accord avec les valeurs mesurĂ©es lorsque la taille des Ă©lĂ©ments Ă©tait plus petite que 22 ÎŒm, assez petit pour capturer le grand gradient de dĂ©formation prĂšs de la pointe de fissure. Le troisiĂšme objectif Ă©tait d’apporter une approche de modĂ©lisation de la dĂ©lamination et de la fissuration de l’underfill avec les effets des variables de fabrication. Un test de cyclage thermique a d’abord Ă©tĂ© effectuĂ© sur 13 cellules pour obtenir les zones dĂ©laminĂ©es entre la puce et l’underfill, et les profils de fissures dans l’underfill, comme rĂ©fĂ©rence. Un rĂ©seau neuronal artificiel (ANN) a Ă©tĂ© formĂ© pour Ă©tablir une liaison entre les effets des variables de fabrication et le nombre de cycles Ă  la dĂ©lamination pour chaque cellule. Les nombres de cycles prĂ©dits pour les 6 cellules de l’ensemble de test Ă©taient situĂ©s dans les intervalles d’observations expĂ©rimentaux. La croissance de la dĂ©lamination a Ă©tĂ© rĂ©alisĂ©e par l’EF en Ă©valuant l’énergie de la dĂ©formation au niveau des Ă©lĂ©ments interfaciaux entre la puce et l’underfill. Pour 5 des 6 cellules de la validation, le modĂšle de croissance du dĂ©laminage Ă©tait conforme aux observations expĂ©rimentales. Les fissures dans l’underfill ont Ă©tĂ© modĂ©lisĂ©es par XFEM sans chemins prĂ©dĂ©finis. Les directions des fissures de bord Ă©taient en bon accord avec les observations expĂ©rimentales, avec une erreur infĂ©rieure Ă  2,5°. Cette approche a rĂ©pondu Ă  la problĂ©matique qui consiste Ă  estimer l’initiation des dĂ©lamination, les zones de dĂ©lamination et les trajectoires de fissures dans l’underfill pour des flip-chips industriels

    The influence of complex volcanic vent morphology on eruption dynamics

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    VulkanausbrĂŒche gelten als eine der spektakulĂ€rsten Naturgewalten unserer Erde. Gleichzeitig stellen sie jedoch auch eine Gefahr fĂŒr die menschliche Gesundheit und Infrastruktur dar. Aufgrund ihrer Dynamik und ihres unberechenbaren Charakters geht von explosiven VulkanausbrĂŒchen eine besonders große GefĂ€hrdung des Menschen und seiner Umwelt aus. Im Zuge eines explosiven Ausbruchs werden heiße Gase und Pyroklasten in die AtmosphĂ€re ausgeworfen. Obwohl das Monitoring aktiver Vulkane in den letzten Jahren immer weiter verbessert wurde, ist es immer noch schwierig eine konkrete Vorhersage zu den AusbrĂŒchen zu erstellen. Aufgrund ihrer KomplexitĂ€t ist das Verhalten von Vulkanen nicht kalkulierbar. Bis heute ist weder eine Beobachtung, noch eine Messung der unterirdischen Rahmenbedingungen möglich, welche den Ausbruch steuern. Trotz dieser UnwĂ€gbarkeiten unterliegen VulkanausbrĂŒche dennoch physikalischen GesetzmĂ€ĂŸigkeiten, sodass die Möglichkeit besteht, die Prozesse im Untergrund eines Vulkans zu modellieren oder durch Experimente zu beschreiben. Aufgrund der KomplexitĂ€t der Wechselwirkungen innerhalb des Systems Vulkan ist es erforderlich Experimente zunehmend realistischer zu gestalten. Sobald das ausgeworfene Material aus dem Krater austritt können wir den Ausbruch visuell Beobachten. In diesem Bereich ist das Verhalten des Ausbruchs vollstĂ€ndig von den Prozessen im Untergrund und von der Geometrie des Kraters abhĂ€ngig. Im Vergleich zu den symmetrischen Kraterformen, welche in Experimenten und Modellen oft angenommen werden, sind die Krater in der Natur deutlich unregelmĂ€ĂŸiger geformt. Ihre Geometrien sind oft eingekerbt und haben eine schrĂ€ge OberflĂ€che. Zudem können sich die Kratergeometrien innerhalb kĂŒrzester Zeit verĂ€ndern. Um den Einfluss der Prozesse im Untergrund zu verstehen mĂŒssen wir zuerst den Einfluss der beobachtbaren Parameter (z. B. Kratergeometrie) ergrĂŒnden. Schlussendlich wird ein tiefergehendes VerstĂ€ndnis der Parameter, die VulkanausbrĂŒche steuern, zu einem Fortschritt und der Verbesserung der GefĂ€hrdungsanalysen fĂŒhren. Um dies zu erreichen, habe ich Beobachtungen aus Feldkampagnen und Laborexperimenten kombiniert. ZunĂ€chst habe ich die Geometrien von Vulkankratern erfasst und deren zeitliche Entwicklung dokumentiert. Dazu haben ich die Geometrie der Krater in der Kraterterrasse des Strombolis in einer hohen Auflösung vermessen und die jeweils zugehörigen Explosionen beobachtet. Dabei konnte ich feststellen, dass sowohl die IntensitĂ€t, als auch die Art und die Richtung der AusbrĂŒche durch FormverĂ€nderungen der OberflĂ€chentopografie beeinflusst werden. Mittels Drohneneinsatz habe ich innerhalb eines Zeitraums von neun Monaten (Mai 2019–Januar 2020) fĂŒnf topografische DatensĂ€tze erstellt. In diesem Zeitraum war es möglich „normale“ Strombolianische AktivitĂ€t, starke AusbrĂŒche und sogar zwei Paroxysmen zu beobachten (3. Juli und 28. August 2019), sodass es möglich war, die verschiedenen Ausbruchstypen mit den vorherrschenden Ablagerungs- und Abtragungsprozessen zu verknĂŒpfen. Zudem konnte ich die Anzahl der aktiven Krater, deren Positionen sowie deren Umgestaltung nachverfolgen. Da VerĂ€nderungen der Kratergeometrie und der Kraterposition auf eine Modifikation des Ausbruchsgeschehens hinweisen können, sind auch dies wichtige Faktoren fĂŒr eine GefĂ€hrdungsanalyse. Die aus den Feldforschungen gewonnenen Daten zeigen deutlich die KomplexitĂ€t, Vielseitigkeit und VariabilitĂ€t der Formen vulkanischer Krater in einer nie da gewesenen zeitlichen und rĂ€umlichen Auflösung. DarĂŒber hinaus haben die Beobachtungen der VulkanausbrĂŒche deutlich gemacht, wie stark die Beziehung zwischen dem Krater, der Kratergeometrie und dem Auswurf von pyroklastischem Material ist. Diese Erkenntnis hat eine große Bedeutung fĂŒr die GefĂ€hrdungsanalyse, vor allem fĂŒr Gebiete, die potentiell durch vulkanische Bomben und pyroklastischem Fallout bedroht sind. Im Anschluss habe ich eine Reihe von Dekompressionsexperimenten mit Kratergeometrien durchgefĂŒhrt, welche auf den Beobachtungen am Stromboli aufbauen. Durch diese Experimente wurde der Zusammenhang zwischen Kratergeometrie und Ausbruchsdynamik bestĂ€tigt. Die verwendeten Geometrien haben eine geneigte OberflĂ€che mit einem Winkel von 5°, 15° und 30° und jeweils einer zylindrischen und einer trichterförmigen inneren Geometrie. Daraus ergeben sich sechs experimentelle Krater die mit folgenden experimentellen Bedingungen getestet wurden: Vier unterschiedliche StartdrĂŒcke (5, 8, 15 und 25 MPa) und zwei Gasvolumina (127.4cm3, 31.9cm3). Alle Experimente wurden bei Raumtemperatur und mit Argon durchgefĂŒhrt. Trotz des vertikalen Aufbaus konnte man auf beiden Seiten des Kraters unterschiedlich große Winkel des austretenden Gases beobachten. Weiterhin war der Gasstrahl geneigt. Die Richtung der Neigung wurde durch die innere Geometrie be- stimmt. Bei einer zylindrischen Geometrie neigte sich der Gasstrahl in die Einfallsrichtung der geneigten OberflĂ€che. Im Falle einer trichterförmigen inneren Geometrie neigt sich der Gasstrahl entgegen der Einfallsrichtung. Der Winkel des Gasaustritts war bei einer zylindrischen inneren Geometrie immer grĂ¶ĂŸer als bei der trichterförmigen Geometrie. Sowohl die Winkel des Gasaustritts als auch die Neigung des Gasstrahls zeigten eine starke Reaktion auf eine VerĂ€nderung der Druckbedingung und OberflĂ€chenneigung. Dabei zeigten sowohl der Austrittswinkel als auch die Neigung eine positive Korrelation mit dem Druck und der OberflĂ€chenneigung. Hohe Druckbedingungen haben außerdem dafĂŒr gesorgt, dass fĂŒr einen lĂ€ngeren Zeitraum ÜberdruckverhĂ€ltnisse am Kraterausgang herrschten. Ein höheres Gasvolumen hat grĂ¶ĂŸere Gasaustrittswinkel ermöglicht. Zuletzt habe ich die Dekompressionsexperimente durch den Einsatz von Partikeln ergĂ€nzt, um so den Auswurf von Gas und Partikeln wĂ€hrend eines explosiven Vulkanausbruchs nachzustellen. Dabei habe ich die beiden experimentellen Kratergeometrien aus den vorangegangenen Experimenten ausgewĂ€hlt, welche den stĂ€rksten Einfluss auf die Gasdynamik aufgezeigt haben. ZusĂ€tzlich habe ich eine dritte Kratergeometrie verwendet, die dem aktiven Krater S1 auf Stromboli nachempfunden ist. Die Geometrie entspricht der Kratergeometrie aus der Vermessung im Mai 2019. Die S1 Geometrie zeichnet sich durch einen asymmetrischen Öffnungswinkel aus (~10° auf einer Seite, ~40° auf der anderen Seite). ZusĂ€tzlich zu den drei Kratergeometrien wurden unterschiedliche Partikel verwendet (Schlacke und Bims), mit jeweils drei unterschiedlichen KorngrĂ¶ĂŸen (0.125–0.25, 0.5–1 und 1–2mm) und zwei Druckstufen (8 und 15MPa). Die Partikeldynamik, in der NĂ€he des experimentellen Kraters, wurde anhand der Winkel des Partikelauswurfs und der Geschwindigkeit der Partikel definiert und beschrieben. Dabei wurde festgestellt, dass die Geometrie des Kraters die Richtung und Neigung des Partikelauswurfswinkels und die Geschwindigkeit der Partikel bestimmt. Bei allen Kratergeometrien kam es zu einem asymmetrischen Partikelauswurf und im Falle von Bimspartikeln zudem zu einer ungleichmĂ€ĂŸigen Geschwindigkeitsverteilung. Die Kombination aus Daten aus Feldkampagnen, Experimenten mit Gas und Experimenten mit zusĂ€tzlichen Partikeln zeigte deutlich den starken Einfluss der Kratergeometrie auf Eruptionen. In der Natur, fĂŒhrt eine modifizierte Kratergeometrie zu einem verĂ€ndertem Auswurfsmuster der Pyroklasten. Im Labor haben komplexe Kratergeometrien zu geneigten Gasstrahlen, asymmetrischen Auswurfswinkeln von Gas- und Gaspartikeln und einer asymmetrischen Verteilung der Geschwindigkeit von Partikeln gefĂŒhrt. Auf Basis dieser Beobachtungen komme ich zu dem Schluss, dass asymmetrische Vulkankrater eine asymmetrische Verteilung von pyroklastischem Auswurf hervorrufen. Das fĂŒhrt zu einer bevorzugten Richtung fĂŒr vulkanischen Fallout — und falls es zu einer kollabierenden AusbruchsĂ€ule kommt — zu einer bevorzugten Richtung fĂŒr pyroklastische Ströme. Der technische Fortschritt durch Drohnen, Photogrammmetrie und 3D Druck bietet einige Chancen fĂŒr die Vulkanologie. Luftaufnahmen durch Drohnen ermöglichen eine schnelle, gĂŒnstige und sichere Vermessung von Vulkankratern, auch in Zeiten erhöhter AktivitĂ€t. Zusammen mit Photogrammmetrie und 3D Druck lassen sich realitĂ€tsnahe Kratergeometrien erzeugen, fĂŒr zunehmend realistische skalierte Laborexperimente.Volcanic eruptions are among the most violent displays of the Earth’s natural forces and threaten human health and infrastructure. Explosive eruptions are hazardous due to their impulsive and dynamic nature, ejecting gas and pyroclasts at high velocity and temperature into the atmosphere. In recent years, monitoring efforts have increased, but forecasting eruptions is still challenging as volcanoes are complex systems with the potential for inherently unpredictable behaviours. To date, the underlying boundary conditions are beyond observation and quantification. Still, they are constrained by physical laws and can be described through models and experiments. The complexity and interdependency of the parameters governing the dynamics of volcanic eruptions ask for increasingly realistic experiments to investigate the sub-surface conditions driving volcanic eruptions. Above the vent, in the near-vent region, the dynamics of explosive eruptions can first be visually observed. The characteristics at this stage are purely the result of the underlying boundary conditions and the exit (vent) geometry. Volcanic vents are rarely the symmetric features that are often assumed in models and experiments. They often exhibit highly irregular shapes with notched or slanted rims that can be transient. To eventually understand the unobservable boundary conditions, it is necessary to initially gain knowledge about the effect of the observable factors (i.e. vent geometry). This knowledge will ultimately improve the understanding of the parameters affecting an explosive event to develop accurate probabilistic hazard maps. To this end, a combination of field observations and laboratory experiments was used. First, I characterised vent and crater shape changes at a frequently erupting volcano (Stromboli) to collect high-resolution geometric data of volcanic vents and observe the related explosion dynamics. As a result of topographic changes, variable eruption intensity, style and directionality could be detected. Five topographic data sets were acquired by unoccupied aerial vehicles (UAVs) over nine months (May 2019-January 2020). During this period, changes associated with "normal" Strombolian activity, "major explosions" and paroxysmal episodes (3 July and 28 August 2019) occurred. Hence, the topographic data made it possible to link the predominant constructive and destructive processes to these eruption styles. Furthermore, the number and position of active vents changed significantly, which is a critical parameter for hazard assessment as vent geometry and position can be linked to shifts in eruptive mechanisms. These field surveys highlight the geometric complexity and variability of volcanic vents at an unprecedented spatiotemporal resolution. Additionally, the observations of explosions suggested the paramount influence of crater and vent geometry on pyroclast ejection characteristics, a fact that has strong implications for areas potentially affected by bomb impact and pyroclastic fall out. Secondly, I designed a series of shock-tube experiments incorporating the geometry elements observed at Stromboli to quantify the influence of vent geometry and several boundary conditions. These experiments validated the link between vent geometry and explosion dynamics that was observed in the field. The novel geometry element is an inclined exit plane of 5°, 15° and 30° slant angle combined with a cylindrical and diverging inner geometry resulting in six vent geometries. All experiments were conducted with gas-only (Argon) at room temperature, four different starting pressures (5, 8, 15, 25 MPa) and two reservoir volumes (127.4 cm3, 31.9 cm3). Despite the vertical setup, the slanted geometry yielded both a laterally variable gas spreading angle and an inclination of the jets. The inner geometry controlled the jet inclination towards the dip direction of the slanted exit plane (cylindrical) and against the dip direction of the slanted exit plane (diverging). Cylindrical vents produced larger gas spreading angles than diverging vents. Both gas spreading angle and jet inclination were highly sensitive to the experimental pressure and the slant angle. They had a positive correlation with maximum gas spreading angle and jet inclination. Additionally, the pressure was positively correlated with the maximum duration of underexpanded characteristics of the jet. The gas volume only showed a positive correlation with the maximum gas spreading angle. Thirdly, I added particles to the experiments to mimic the ejection of gas-particle jets during explosive volcanic eruptions. For this set of experiments, the two geometries with the 30° slant angle from the previous experimental series were used as they exhibited the strongest effect on the gas ejection dynamics. They were supplemented by a third vent that resembled the "real" geometry of Stromboli’s active S1 vent as it was mapped in May 2019 and fabricated by 3D printing. The S1’s geometry is characterised by a ~ 10° divergence on one side and a ~ 40° divergence on the other side. Besides three vent geometries, two types of particles (scoria and pumice), each with three different grain size distributions (0.125– 0.25, 0.5–1, 1–2 mm) and two starting pressures (8, 15 MPa) were used. The near-vent vent dynamics were characterised as a function of particle spreading angle and particle ejection velocity. The vent geometry governed the direction and the magnitude of particle spreading, and the velocity of particles. All geometries yielded asymmetric particle spreading as well as a non-uniform velocity distribution in experiments with pumice particles. The combination of field observations, gas-only and gas-particle experiments demonstrated the prime control exerted by vent geometry. In nature, a modification of the vent led to modified pyroclast ejection patterns. In the laboratory the complex geometries facilitated inclined gas jets, an asymmetric gas and particle spreading angle, and an asymmetric particle ejection velocity distribution. These findings suggest that the asymmetry of volcanic vents and/or craters can promote the asymmetric distribution of volcanic ejecta.Which, in turn, will lead to a preferred direction of volcanic fallout and — in case a column collapse occurs — to a preferred direction of the ensuing pyroclastic density currents. The availability of new technology like unoccupied aerial vehicles, photogrammetry and 3D printing provides several opportunities for the volcanological community. Aerial observations allow a fast, inexpensive and safe way to collect geometrical data of volcanic vents and craters, even in times of elevated volcanic activity. In combination with photogrammetry and 3D printing, "real" vents can be produced for increasingly realistic scaled laboratory experiments

    Hunting Wildlife in the Tropics and Subtropics

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    The hunting of wild animals for their meat has been a crucial activity in the evolution of humans. It continues to be an essential source of food and a generator of income for millions of Indigenous and rural communities worldwide. Conservationists rightly fear that excessive hunting of many animal species will cause their demise, as has already happened throughout the Anthropocene. Many species of large mammals and birds have been decimated or annihilated due to overhunting by humans. If such pressures continue, many other species will meet the same fate. Equally, if the use of wildlife resources is to continue by those who depend on it, sustainable practices must be implemented. These communities need to remain or become custodians of the wildlife resources within their lands, for their own well-being as well as for biodiversity in general. This title is also available via Open Access on Cambridge Core

    Elasto-plastic deformations within a material point framework on modern GPU architectures

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    Plastic strain localization is an important process on Earth. It strongly influ- ences the mechanical behaviour of natural processes, such as fault mechanics, earthquakes or orogeny. At a smaller scale, a landslide is a fantastic example of elasto-plastic deformations. Such behaviour spans from pre-failure mech- anisms to post-failure propagation of the unstable material. To fully resolve the landslide mechanics, the selected numerical methods should be able to efficiently address a wide range of deformation magnitudes. Accurate and performant numerical modelling requires important compu- tational resources. Mesh-free numerical methods such as the material point method (MPM) or the smoothed-particle hydrodynamics (SPH) are particu- larly computationally expensive, when compared with mesh-based methods, such as the finite element method (FEM) or the finite difference method (FDM). Still, mesh-free methods are particularly well-suited to numerical problems involving large elasto-plastic deformations. But, the computational efficiency of these methods should be first improved in order to tackle complex three-dimensional problems, i.e., landslides. As such, this research work attempts to alleviate the computational cost of the material point method by using the most recent graphics processing unit (GPU) architectures available. GPUs are many-core processors originally designed to refresh screen pixels (e.g., for computer games) independently. This allows GPUs to delivers a massive parallelism when compared to central processing units (CPUs). To do so, this research work first investigates code prototyping in a high- level language, e.g., MATLAB. This allows to implement vectorized algorithms and benchmark numerical results of two-dimensional analysis with analytical solutions and/or experimental results in an affordable amount of time. After- wards, low-level language such as CUDA C is used to efficiently implement a GPU-based solver, i.e., ep2-3De v1.0, can resolve three-dimensional prob- lems in a decent amount of time. This part takes advantages of the massive parallelism of modern GPU architectures. In addition, a first attempt of GPU parallel computing, i.e., multi-GPU codes, is performed to increase even more the performance and to address the on-chip memory limitation. Finally, this GPU-based solver is used to investigate three-dimensional granular collapses and is compared with experimental evidences obtained in the laboratory. This research work demonstrates that the material point method is well suited to resolve small to large elasto-plastic deformations. Moreover, the computational efficiency of the method can be dramatically increased using modern GPU architectures. These allow fast, performant and accurate three- dimensional modelling of landslides, provided that the on-chip memory limi- tation is alleviated with an appropriate parallel strategy
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