4,342 research outputs found
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
CFD Modelling of the Mixture Preparation in a Modern Gasoline Direct Injection Engine and Correlations with Experimental PN Emissions
A detailed 3D CFD analysis of a modern gasoline direct injection (GDI) engine is carried
out to reveal the connections between pre-combustion mixture indicators and PN emissions.
Firstly, a novel calibration methodology is introduced to accurately predict the widely
used characteristics of the high-pressure fuel spray. The methodology utilised the Siemens
STAR-CD 3D CFD software environment and employed a combination of statistical and
optimization methods supported by experimental data. The calibration process identified dominant
factors influencing spray properties and established their optimal levels. The two most
used models for fuel atomisation were investigated. The Kelvin–Helmholtz/Rayleigh–Taylor
(KH–RT) and Reitz–Diwakar (RD) break-up models were calibrated in conjunction with
the Rosin–Rammler (RR) mono-modal droplet size distribution. RD outperformed KH–RT
in terms of prediction when comparing numerical spray tip penetration and droplet size
characteristics to the experimental counterparts. Then, the modelling protocol incorporated
droplet-wall interaction models and a multi-component surrogate fuel blend model. The
comprehensive digital model was validated using published data and applied to a modern
small-capacity GDI engine. The study explored various engine operating conditions and
highlights the contribution of fuel mal-distribution and liquid film retention at spark timing
to Particle Number (PN) emissions. Finally, a novel surrogate model was developed to
predict the engine-out PN. An extensive CFD analysis was conducted considering part-load
operating conditions and variations of engine control variables. The PN surrogate model
was developed using an Elastic Net (EN) regression technique, establishing relationships
between experimental PN emission levels and modelled, pre-combustion, air-fuel mixture
quality indicators. The approach enabled the reliable prediction of engine sooting tendencies
without relying on complex measurements of combustion characteristics. These research
efforts aim to enhance engine efficiency, reduce emissions, and contribute to the development
of a reliable and cost-effective digital toolset for engine development and diagnostics
Accelerating Particle-in-Cell Kinetic Plasma Simulations via Reduced-Order Modeling of Space-Charge Dynamics using Dynamic Mode Decomposition
We present a data-driven reduced-order modeling of the space-charge dynamics
for electromagnetic particle-in-cell (EMPIC) plasma simulations based on
dynamic mode decomposition (DMD). The dynamics of the charged particles in
kinetic plasma simulations such as EMPIC is manifested through the plasma
current density defined on the edges of the spatial mesh. We showcase the
efficacy of DMD in modeling the time evolution of current density through a
low-dimensional feature space. Not only do such DMD-based predictive
reduced-order models help accelerate EMPIC simulations, they also have the
potential to facilitate investigative analysis and control applications. We
demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current
density, and speed-up in EMPIC simulations involving electron beams under the
influence of magnetic fields and virtual cathode oscillations
Understanding and controlling structural distortions underlying superconductivity in lanthanum cuprates
The suppression of superconductivity in layered lanthanum cuprates near x = 1/8 coincides with a structural phase transition from a low-temperature orthorhombic to a low-temperature tetragonal phase. The low-temperature phases are characterised by a static tilt of the CuO6 octahedra away from the layering axis in distinct directions. It remained an open question whether the orthorhombic-to-tetragonal phase transition would only occur in the context of competing electronic orders in the lanthanum cuprates.
This thesis proposes a novel approach to studying the orthorhombic-to-tetragonal phase transition using the novel La2MgO4 system. La2MgO4 adopts the layered Ruddlesden-Pepper structure of the lanthanum cuprates but lacks the strong electron correlations and octahedral distortions associated with the Jahn-Teller active Cu site. Combining first-principles simula- tions using density-functional theory with experimental data on the novel La2MgO4 system, the context in which these structural phases can occur is detailed, outlining the key param- eters determining the stability of the phase which suppresses bulk superconductivity. The same sequence of structural phase transitions occurs in La2MgO4 as in La1.875Ba0.125CuO4, and the tetragonal phase is stabilised via steric effects beyond a critical octahedral tilt magnitude. Larger Jahn-Teller distortions favour the orthorhombic phase.
The effect of isotropic and anisotropic pressure on La2MgO4 and La2CuO4 is explored. These form the basis for a structural mechanism to understand the experimental trends of the bulk superconducting transition temperature under uniaxial pressure. Finally, the justification for the methodology used throughout this thesis to simulate these systems is provided, highlighting that DFT+U accurately describes their atomic and electronic structure.Open Acces
Introduction to Psychology
Introduction to Psychology is a modified version of Psychology 2e - OpenStax
Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics
It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been
emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations
Insights into temperature controls on rockfall occurrence and cliff erosion
A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 m². A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls.
The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development
ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE
In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph
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Rapid Neutron-Capture Nucleosynthesis from the Births and Deaths of Neutron Stars
The astrophysical origins of the rapid neutron-capture process (r-process), which gives rise to roughly half of the elements heavier than iron, has remained a mystery for almost 70 years. The likely violent events, which seed the r-process abundances in our solar system and galaxy, remain uncertain to this day. This is in part due to nuclear physics uncertainties associated with the r-process itself, but mainly due to uncertainties in astrophysics modeling. The discovery of the radioactively-powered kilonova emission from the neutron star merger event GW170817 confirmed the violent deaths of neutron stars as one key site of the r-process in the universe. However, other evidence appears to favor an additional r-process channel that more promptly follows star formation in the universe, such as core-collapse supernovae (CCSNe), i.e. the brilliant births of neutron stars.
The two viable sites for the r-process are (1) core-collapse supernovae (CCSNe), which are explosions of massive stars at the end of their lives and (2) compact object mergers, which are violent collisions of stellar remnants formed at the endpoints of stellar evolution.
Chapters 2 and 3 of this dissertation present general relativistic magnetohydrodynamic simulations of one potential r-process site associated with CCSNe: the neutrino-driven wind. These outflows are launched from the hot proto-neutron star (PNS) remnant by neutrino-heating above their surfaces, within seconds after the collapse of a massive star. However, previous work has shown that spherically symmetric winds from non-rotating PNS fail to achieve the requisite conditions for a robust r-process. Chapter 2 explores for the first time the combined effects of rapid rotation and strong gravity of the PNS on the wind properties. Chapter 3 explores the impact of a dynamically strong ordered magnetic field on the properties of non-rotating PNS winds. The wind in both cases is simulated in a controlled environment rather than as a part of a self-consistent global CCSNe simulation, to assess the viability of r-process nucleosynthesis as a function of PNS properties (neutrino energies/luminosities, rotation rate, magnetization).
We find that rapid rotation allows for outflows that are ~10% more neutron-rich in the equatorial region, where the mass loss rate is roughly an order of magnitude higher than that of otherwise equivalent non-rotating models. The birth of very rapidly spinning neutron stars may thus be a site for the production of light r-process nuclei (38 < Z < 47). For PNSs with sufficiently strong magnetic fields (such that magnetic pressure exceeds gas pressure above the PNS surface), we find that equatorial outflows are trapped by the magnetic field in a region near the surface, and therefore receive additional neutrino heating relative to a freely-expanding unmagnetized wind. This allows a modest fraction of the wind material to achieves entropies high enough to synthesize 2nd peak r-process elements via an alpha-rich freeze-out mechanism.
The final chapter explores the interplay between the r-process and the dynamics of compact object merger ejecta. Gravitational wave observatories are expected to detect several additional binary neutron star (BNS) and black hole-neutron star (BHNS) mergers in current and future observing runs, some of which may be accompanied by electromagnetic counterparts such as kilonovae. However, distinguishing more distant BNS from BHNS mergers based on their associated gamma-ray bursts (GRB), has proven tricky.
This chapter presents a calculation of the effects of r-process heating on the dynamics of tidal ejecta from BNS and BHNS mergers. In particular we explore whether late-time fall-back of weakly bound debris created during the merger to the central black hole remnant, can explain the temporally extended X-ray emission observed following several merger GRB on timescales of several seconds to minutes. As a result of the different impact that r-process heating has depending on the composition of the ejecta and the mass of the black hole, a method to differentiate BHNS from BNS mergers, based on their extended X-ray emission, is proposed
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