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Lors d’essais de feux d’hydrocarbures liquides dans le dispositif DIVA de l’IRSN, un phénomène oscillatoire basse-fréquence (BF), a été observé. Ce phénomène se manifeste par des fluctuations importantes de la pression dans le local, qui peuvent conduire à une perte de confinement et ainsi favoriser la propagation du feu et le rejet de polluants au-delà du local. Il s’accompagne de déplacements intermittents de la flamme hors du bac. L’étude fine de ce phénomène oscillatoire a tout d’abord consisté à concevoir une maquette à l’échelle 1:4 du dispositif DIVA dans lequel nous avons fait varier différents paramètres. L’analyse des résultats obtenus nous a permis d’identifier différents régimes de combustion, de décrire les mécanismes responsables de l’apparition des oscillations BF et de caractériser les propriétés de ces oscillations (fréquence et amplitude). L’occurrence et la persistance des oscillations BF dépendent essentiellement de l’équilibre, plus ou moins précaire, entre la quantité d’air disponible pour la combustion et le débit d’évaporation du combustible résultant des flux thermiques reçus à sa surface. Une étude numérique exploratoire utilisant le code CFD SAFIR a été ensuite conduite en utilisant le débit d’évaporation mesuré expérimentalement, puis en le calculant à l’aide d’un modèle d’évaporation. Si le code ne permet pas de décrire correctement le déplacement de la flamme hors du bac, il reproduit de façon satisfaisante le comportement oscillatoire BF du feu, en particulier sa fréquence dominante. / During liquid hydrocarbon fire tests in the DIVA device of IRSN, a low-frequency (LF) oscillatory phenomenon, was observed. This phenomenon manifests itself by large variations of the average pressure in the room, which can lead to a loss of confinement and thus promote the spread of fire and the release of pollutants beyond the local. It is accompanied by intermittent displacements of the flame outside the fuel pan. The fine study of this phenomenon consisted in designing a 1:4 scale model of the DIVA device, allowing us to carry out a very large number of tests, varying some parameters. The analysis of the results obtained allowed us to identify different combustion regimes, to describe the mechanisms responsible for the appearance of the LF oscillations, and to characterize the properties of these oscillations (frequency and amplitude). The occurrence and persistence of LF oscillations essentially depend on the precarious equilibrium between the supply of fresh air and the supply of fuel vapors which results from the heat flux received at its surface. An exploratory numerical study using the CFD code SAFIR was then conducted using both the experimentally measured evaporation rate and that calculated using an evaporation model. The model does not correctly describe the displacements of the flame outside the fuel pan. However, it satisfactorily reproduces the LF oscillatory fire behavior, especially its dominant frequency.

<p>    </p> <p>Cerebrovascular diseases, such as stroke, constitute the most common life-threatening neurological disease in the United States. To support normal brain function, maintaining adequate brain perfusion (i.e., cerebral blood flow (CBF)) is important. Therefore, it is crucial to assess the brain perfusion so that early intervention in cerebrovascular diseases can be applied if abnormal perfusion is observed. The goal of my study is to develop metrics to measure the brain perfusion through modeling brain physiology using resting-state and task-based blood-oxygenation-level- dependent (BOLD) functional MRI (fMRI). My first and second chapters focused on deriving the blood arrival time using the resting-state BOLD signal. In the first chapters, we extracted the systemic low-frequency oscillations (sLFOs) in the fMRI signal from the internal carotid arteries (ICA) and the superior sagittal sinus (SSS). Consistent and robust results were obtained across 400 scans showing the ICA signals leading the SSS signals by about 5 seconds. This delay time could be considered as an effective perfusion biomarker that is associate with the cerebral circulation time (CCT). To further explore sLFOs in assessing dynamic blood flow changes during the scan, in my second chapter, a “carpet plot” (a 2-dimensional plot time vs. voxel) of scaled fMRI signal intensity was reconstructed and paired with a developed slope-detection algorithm. Tilted vertical edges across which a sudden signal intensity change took place were successfully detected by the algorithm and the averaged propagation time derived from the carpet plot matches the cerebral circulation time. Given that CO₂ is a vasodilator, controlling of inhaled CO₂ is able to modulate the BOLD signal, therefore, as a follow-up study, we focused on investigating the feasibility of using a CO₂ modulated sLFO signal as a “natural” bolus to track CBF with the tool developed from the second chapter. Meaningful transit times were derived from the CO₂-MRI carpet plots. Not only the timing, the BOLD signal deformation (the waveform change) under CO₂ challenge also reveals very useful perfusion information, representing how the brain react to stimulus. Therefore, my fourth chapter focused on characterizing the brain reaction to the CO₂ stimulus to better measure the brain health using BOLD fMRI. Overall, these studies deepen our understanding of fMRI signal and the derived perfusion parameters can potentially be used to assess some cerebrovascular diseases, such as stroke, ischemic brain damage, and steno-occlusive arterial disease in addition to functional activations. </p>