No description available.

Tsunami and storm surge are two of the main destructive and costly natural hazards faced by coastal communities around the world. To enhance coastal resilience and to develop effective risk management strategies, accurate and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy, with respect to high-fidelity physics-based models. First, a tsunami run-up response function (TRRF) model is developed that can rapidly predict a tsunami run-up distribution from earthquake fault parameters. This new surrogate modeling approach reduces the number of simulations required to build a surrogate model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Secondly, a TRRF-based inversion (TRRF-INV) model is developed that can infer a tsunami source and its impact from tsunami run-up records. Since this new tsunami inversion model is based on the TRRF model, it can perform a large number of tsunami forward simulations in tsunami inversion modeling, which is impossible with physics-based models. And lastly, a one-dimensional convolutional neural network combined with principal component analysis and k-means clustering (C1PKNet) model is developed that can rapidly predict the peak storm surge from tropical cyclone track time series. Because the C1PKNet model uses the tropical cyclone track time series, it has the advantage of being able to predict more diverse tropical cyclone scenarios than the existing surrogate models that rely on a tropical cyclone condition at one moment (usually at or near landfall). The surrogate models developed in this dissertation have the potential to save lives, mitigate coastal hazard damage, and promote resilient coastal communities. / Doctor of Philosophy / Tsunami and storm surge can cause extensive damage to coastal communities; to reduce this damage, accurate and fast computer models are needed that can predict the water level change caused by these coastal hazards. The problem is that existing physics-based computer models are either accurate but slow or less accurate but fast. In this dissertation, three new computer models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy compared to the accurate physics-based computer models. Three computer models are as follows: (1) A computer model that can rapidly predict the maximum ground elevation wetted by the tsunami along the coastline from earthquake information, (2) A computer model that can reversely predict a tsunami source and its impact from the observations of the maximum ground elevation wetted by the tsunami, (3) A computer model that can rapidly predict peak storm surges across a wide range of coastal areas from the tropical cyclone's track position over time. These new computer models have the potential to improve forecasting capabilities, advance understanding of historical tsunami and storm surge events, and lead to better preparedness plans for possible future tsunamis and storm surges.

This study goal was to first determine the baseline Global Forecast System (GFS) skill in forecasting borderline (non-bomb:0.75-0.95, bomb: 1.-1.25) bomb events, and second to determine if machine learning (ML) techniques as a post-processor can improve the forecasts. This was accomplished by using the Tempest Extreme cyclone tracking software and ERA5 analysis to develop a case list during the period of October to March for the years 2008-2021. Based on the case list, GFS 24-hour forecasts of atmospheric base state variables in 10-degree by 10-degree cyclone center subdomains was compressed using S-mode Principal Component Analysis. A genetic algorithm was then used to determine the best predictors. These predictors were then used to train a logistic regression as a baseline ML skill and a Support Vector Machine (SVM) model. Both the logistic regression and SVM provided an improved bias over the GFS baseline skill, but only the logistic regression improved skill.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第25124号 / 理博第5031号 / 新制||理||1717(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 竹見 哲也, 教授 榎本 剛, 教授 向川 均 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Helicopter turboshaft engines may ingest large amounts of foreign particles (most commonly sand/dust), which can cause significant compressor blade damage and even engine failure. In many helicopters, this issue is mitigated by separating the particles from the intake airstream. An effective device for engine air-particle separation is the vortex tube separator (VTS), which uses centrifugal forces in a vortical flow to radially filter foreign particles from a duct with an annular exit. Dozens or hundreds of these devices are linked together on a shared manifold known as a VTS array. There is a distinct lack of scientific literature regarding these arrays, which likely feature significantly more complex flowfields than singular VTSs due to aerodynamic interactions between the devices. The research presented in this dissertation identifies and explains flow features unique to arrays by means of an experimental investigation downstream of various VTS configurations in a wind tunnel. Mean PIV flowfields reveal that the VTS array rapidly generates a strong central recirculation zone while a single VTS does not, implying the existence of axial flow gradients within associated separators that could affect filtration efficiency. The key factor here is the global swirl intensity, which is increased in array flows due to high angular momentum contributions from separators that are radially distant from the duct center. A preliminary momentum integral model is constructed to predict the onset of recirculation in VTS flows. Analysis is then extended to the unsteady flowfield, where it is shown that VTS-generated turbulence contains only low levels of anisotropy. Spectral proper orthogonal decomposition is conducted on the array flow; it reveals the existence of low-frequency harmonic behavior composed of back-and-forth pumping motions downstream of the central VTS. Additionally, a unique precession motion is found in the same region at a slightly higher frequency. Similar precessing vortex cores have been shown to reduce separation efficiency in other cyclone separators. Both of these coherent structures may be associated with the central recirculation zone and may interfere with VTS array filtration given their timescales relative to potential particle relaxation timescales. This dissertation opens the door for future experimental and computational studies of fluid and particle dynamics in VTS flows with the goal of improving VTS array-specific design philosophies. / Doctor of Philosophy / Vortex tube separators (VTSs) help protect helicopter engines by filtering harmful particles (sand, dust, snow, ash, sea spray, etc.) they would otherwise ingest. This is done by creating a vortex in which centrifugal forces eject particles outwards, separating them from the main airstream. These devices are effective when dozens are grouped together into VTS arrays, but little is understood of the complex air and particle dynamics that result from the many interacting vortices both in and around such arrays. This dissertation describes an early effort to study these aerodynamics and open the door for subsequent particle dynamics research. A laser-based measurement technique called particle image velocimetry is used to determine flow velocities downstream of a VTS array placed in a wind tunnel. When velocities are averaged together over time, they reveal a central recirculation zone (a known feature of intensely swirling flows) downstream of the VTS array that vanishes when only a single separator in the array is active. A mathematical model is developed to predict such recirculation. It demonstrates that a VTS array comprises many separators that are far from the center of the duct they are contained within, and these contribute greatly to the overall swirl intensity. Other data analysis techniques are used to investigate the instantaneous velocity flowfield, which differs significantly from averaged quantities. One such technique is spectral proper orthogonal decomposition, which extracts so-called "coherent structures" from the flow - correlated high-energy motions that exist at certain frequencies and may not be visible in the raw data. This analysis finds two interesting structures at the very center of the duct, possibly associated with the recirculation zone: a back-and-forth pumping motion at a very low frequency (and some of its harmonic frequencies), and a "precessing" (unsteadily rotating) vortex at a slightly higher frequency. These motions, as well as the central recirculation zone itself, are impactful because they may affect the filtration process within the VTS upstream of where they were measured. Such effects will be investigated in future experiments and, if confirmed, may influence the design of VTS arrays.

Les procédés conventionnels de transformation thermique de la biomasse sont conçus pour la production d’huiles ou de gaz riches en CO, CO2, H2 et hydrocarbures légers à des fins énergétiques ou chimiques. Le pilote de pyrolyse rapide utilisé dans cette étude comporte un réacteur cyclone, chauffé à ses parois, et capable de mettre en oeuvre la pyroliquéfaction ou la pyrogazéification par le simple jeu des conditions opératoires. Les produits de réaction (charbon, huiles et gaz) sont récupérés et analysés. Les bilans de matière massiques et élémentaires (C, H, O) bouclent de façon très satisfaisante. Les basses températures de paroi et faibles débits de gaz vecteur favorisent la pyroliquéfaction. La production d’huiles augmente avec le débit de biomasse. La taille des particules a une faible influence sur les sélectivités en gaz, liquides et charbon. Un modèle de fonctionnement du cyclone est établi en tenant compte de l’hydrodynamique des phases gaz et solide ainsi que des lois de transferts de chaleur paroi-gaz et paroi-solides dans le cyclone. Ce modèle inclut également un schéma cinétique de pyrolyse rapide intégré dans un modèle de décomposition de la particule, ainsi qu’un modèle de craquage des vapeurs. Validé successivement sur la cellulose puis sur le bois, il permet de prédire les variations des sélectivités en fonction des conditions opératoires. Le modèle montre que les réactions de craquage se déroulent majoritairement dans une mince couche limite proche des parois chaudes. On propose deux lois générales (pyrogazéification et pyroliquéfaction) regroupant les différents paramètres opératoires contrôlant les performances du réacteur / The usual processes of biomass thermal upgrading are designed for the production of bio-oils or of gases rich in CO, CO2, H2 and light hydrocarbons for energy or chemical productions. The laboratory-scaled set-up used in this study includes a cyclone reactor, heated at its walls and able to carry out the fast pyroliquefaction or pyrogazeification by simply changing the operating conditions. The reaction products (charcoal, liquids and gases) are recovered and analyzed. The masses and elementary (C, H, O) balances closures are very accurate. Pyroliquefaction conditions are favoured by low walls temperatures and small carrier gas flowrates. The bio-oils fractions increase with the biomass flowrate. The particles size has only a weak influence on gas, liquids and charcoal selectivities. A model representing the cyclone behaviour is established by taking into account the hydrodynamics of the gases and solids, and the wall-gas and wall-solids heat transfer laws inside the cyclone. This general model includes also a model of particle decomposition (scheme of fast pyrolysis in competition with heat transfers) and a model of vapours cracking. Validated successively with cellulose and then with wood, it allows to predict the variations of the selectivities according to the operating conditions. The model shows that the cracking reactions occur mainly inside a thin boundary layer close to the hot walls. Two laws (pyrogazeification and pyroliquefaction) gathering the various operational parameters that control the performances of the reactor are finally proposed

No description available.

L'action humanitaire a connu un développement exponentiel au sein de la société internationale au cours de ces dernières années. Multiplication des résolutions humanitaires votées par les Nations unies, mise en place d'une justice pénale internationale chargée de réprimer les violations du droit international humanitaire, émergence de la responsabilité de protéger impliquant un recours à la force à des fins humanitaires, développement des organisations non gouvernementales en sont les manifestations majeures. Toutefois, la portée de ces évolutions récentes de l'action humanitaire tant sur le plan de la normativité que de l'opérationnalité est à relativiser. Si les insuffisances du droit de Genève ont pleinement justifié l'émergence d'un droit de New York, ce droit de nature essentiellement déclaratoire éprouve de réelles difficultés à palier les lacunes du droit de Genève. En outre, la mise en oeuvre contemporaine de l'action humanitaire, soulève de nombreux questionnements juridiques liés aux modalités et aux conditions de mise en oeuvre. La présente étude a pour objet d'analyser les évolutions et les limites du cadre juridique de l'action humanitaire afin de faire des propositions pour améliorer la condition juridique des victimes des catastrophes humanitaires. / Humanitarian action has seen such an exponential growth in international society in recent years that humanitarianism seems to be carrying increasing weight in international relations. Some of the main examples of this phenomenon are the increased number of humanitarian resolutions passed by the United Nations, the creation of an international court of justice to reprimand violations of international humanitarian law, the emergence of a sense of responsibility to ensure protection by means of force for humanitarian purposes, and the development of non-governmental organizations. However, the scope of these recent developments in humanitarianism, on both the normative and operational levels, needs to be put into perspective. Indeed, while the shortcomings of Geneva law fully justify the emergence of New York law, this essentially declaratory law faces real challenges in overcoming the short comings in Geneva law. Further more, humanitarian action as it has been carried out in recent years gives rise to a number of legal questions relating to the conditions under which such action is taken. This study aims at analyzing the developments and limits of the humanitarian action legal framework, in order to put forward proposals for improving the legal position of the victims of humanitarian disasters.

The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like cyclone tracking and earthquake modeling require high-fidelity and high- performance simulations involving large-scale computations and generate huge amounts of data. Faster simulations and simultaneous online data analysis and visualization enable scientists provide real-time guidance to policy makers. In this thesis, we present a set of techniques for efficient high-fidelity simulations, online data analysis and visualization in environments with varying resource configurations. First, we present a strategy for improving throughput of weather simulations with multiple regions of interest. We propose parallel execution of these nested simulations based on partitioning the 2D process grid into disjoint rectangular regions associated with each subdomain. The process grid partitioning is obtained from a Huffman tree which is constructed from the relative execution times of the subdomains. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. We observe up to 33% gain over the default strategy in weather models. Second, we propose a processor reallocation heuristic that minimizes data redistribution cost while reallocating processors in the case of dynamic regions of interest. This algorithm is based on hierarchical diffusion approach that uses a novel tree reorganization strategy. We have also developed a parallel data analysis algorithm to detect regions of interest within a domain. This helps improve performance of detailed simulations of multiple weather phenomena like depressions and clouds, thereby in- creasing the lead time to severe weather phenomena like tornadoes and storm surges. Our method is able to reduce the redistribution time by 25% over a simple partition from scratch method. We also show that it is important to consider resource constraints like I/O bandwidth, disk space and network bandwidth for continuous simulation and smooth visualization. High simulation rates on modern-day processors combined with high I/O bandwidth can lead to rapid accumulation of data at the simulation site and eventual stalling of simulations. We show that formulating the problem as an optimization problem can deter- mine optimal execution parameters for enabling smooth simulation and visualization. This approach proves beneficial for resource-constrained environments, whereas a naive greedy strategy leads to stalling and disk overflow. Our optimization method provides about 30% higher simulation rate and consumes about 25-50% lesser storage space than a naive greedy approach. We have then developed an integrated adaptive steering framework, InSt, that analyzes the combined e ect of user-driven steering with automatic tuning of application parameters based on resource constraints and the criticality needs of the application to determine the final parameters for the simulations. It is important to allow the climate scientists to steer the ongoing simulation, specially in the case of critical applications. InSt takes into account both the steering inputs of the scientists and the criticality needs of the application. Finally, we have developed algorithms to minimize the lag between the time when the simulation produces an output frame and the time when the frame is visualized. It is important to reduce the lag so that the scientists can get on-the- y view of the simulation, and concurrently visualize important events in the simulation. We present most-recent, auto-clustering and adaptive algorithms for reducing lag. The lag-reduction algorithms adapt to the available resource parameters and the number of pending frames to be sent to the visualization site by transferring a representative subset of frames. Our adaptive algorithm reduces lag by 72% and provides 37% larger representativeness than the most-recent for slow networks.

Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means. Aim: The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning systems, and second, investigating if biologically based Artificial Neural Networks (ANNs) are feasible to solve some of the identified problems. Method: First, for evaluating the efficiency of cyclone early warning systems, Bangladesh was selected as study area, where a questionnaire survey and an in-depth interview were administered. Second, a review of currently operational TC track forecasting techniques was conducted to gain a better understanding of various techniques’ prediction performance, data requirements, and computational resource requirements. Third, a technique using biologically based ANNs was developed to produce TC track and intensity forecasts. Systematic testing was used to find optimal values for simulation parameters, such as feature-detector receptive field size, the mixture of unsupervised and supervised learning, and learning rate schedule. Five types of 2D data were used for training. The networks were tested on two types of novel data, to assess their generalization performance. Results: A major problem that is identified in the thesis is that the meteorologists at the Bangladesh Meteorological Department are currently not capable of providing accurate TC forecasts. This is an important contributing factor to residents’ reluctance to evacuate. To address this issue, an ANN-based TC track and intensity forecasting technique was developed that can produce early and accurate forecasts, uses freely available satellite images, and does not require extensive computational resources to run. Bidirectional connections, combined supervised and unsupervised learning, and a deep hierarchical structure assists the parallel extraction of useful features from five types of 2D data. The trained networks were tested on two types of novel data: First, tests were performed with novel data covering the end of the lifecycle of trained cyclones; for these test data, the forecasts produced by the networks were correct in 91-100% of the cases. Second, the networks were tested with data of a novel TC; in this case, the networks performed with between 30% and 45% accuracy (for intensity forecasts). Conclusions: The ANN technique developed in this thesis could, with further extensions and up-scaling, using additional types of input images of a greater number of TCs, improve the efficiency of cyclone early warning systems in countries with less economic and technical means. The thesis work also creates opportunities for further research, where biologically based ANNs can be employed for general-purpose weather forecasting, as well as for forecasting other severe weather phenomena, such as thunderstorms.