Industrial flaring can result in atmospheric emissions that have significant impact on regional air quality. This study investigates the impact on one-hour average ozone concentrations due to industrial flaring, using the region around Houston, Texas as a case study. Specifically, this study examined the impact on ozone formation of different flare destruction efficiencies. There are some concerns about whether flare destruction efficiency is reduced from design conditions (98 to 99% destruction) at low flare flow rates. Some studies have reported very low flares destruction efficiencies under low flow, so it is possible that ozone precursor emissions may be underestimated by an order of magnitude or more at low flow conditions. In this thesis, 100 different destruction efficiency scenarios have been constructed where destruction efficiency depends on the ratio of flare flow rate to the maximum flow rate (turndown ratio). The scenarios differ in the assumed destruction efficiency at near zero flow and the turndown ratio at which destruction efficiency returns to the design value. These destruction scenarios are applied to hourly mass flow data for twenty-five flares in Houston, Texas. The scenarios have very different impacts on air quality. The air quality implications of these results for possible modifications to flare operation are explored. / text

Zugl.: Zürich, Techn. Hochsch., Diss., 2008

“Flare for orchestra based on poetry by Mary Oliver” is a three-movement work based on the poem “Flare,” by Mary Oliver. In “Flare,” the emotional and expressive content of the poetry is captured in the programmatic narrative of the music. Oliver’s poem also influences structural elements of the music. Another important feature of the music is the use of a dialectic approach to the many contrasting aspects of the poetry. The goal of “Flare” is to infuse numerous elements of the poetry into the music programmatically and structurally to create a shared creative experience of both the music and poetry. / text

University, Diss., 2003--München.

High resolution spectroscopic techniques are used to investigate the magnetic topology of the young, rapidly rotating star, AB Dor. Doppler images of AB Dor are produced using the Li I 6708 Å line and compared to images produced using Ca I and Fe i lines. A Li abundance of 2.9±0.3 dex is measured using LTE profile synthesis. By evaluating the effects of enhanced spot Li abundances we find that starspots are unlikely to lead to an overestimation of the photospheric Li abundance. While the Li I 6708 Å line is strengthened in the presence of starspots, the extent to which the Li I line equivalent width displays rotational modulation is diluted. A Zeeman Doppler imaging code, which maps the surface magnetic flux distributions on rapid rotators, has been developed and tested in this thesis. Reliability tests indicate that this technique is robust with respect to small errors in line and stellar parameters. Spectropolarimetric observations of AB Dor from three years are presented here. The technique of least squares deconvolution, combines the signal from over 1500 lines, enhancing the S/N of the observed dataset. Brightness and magnetic maps of AB Dor are reconstructed using the deconvolved profiles and show excellent agreement with maps produced using independently developed codes by J.-F. Donati. This points to the consistency of Doppler imaging techniques. Magnetic field maps show the presence of strong radial and azimuthal fields at almost all latitudes. This non-solar like distribution of magnetic flux may indicate the presence of a distributed dynamo component operating in the convection zones of rapid rotators. By tracing the positions of absorption transients in Ha, we find prominence complexes at almost all longitudes. Footpoint locations remain difficult to ascertain. Prominences are ejected over much shorter timescales than the timescale over which differential rotation should act further observations with closer time sampling are necessary to investigate the coronal topology further.

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京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第22253号 / 理博第4567号 / 新制||理||1656(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 柴田 一成, 准教授 浅井 歩, 教授 一本 潔 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 101-105).

Nowadays, space weather has become an international issue to the world's countries because of its catastrophic effect on space-borne and ground-based systems, and industries, impacting our lives. One of the main solar activities that is considered as a major driver of space weather is solar flares. Solar flares can be defined as an enormous eruption in the sun's atmosphere. This phenomenon happens when magnetic energy stored in twisted magnetic fields, usually near sunspots, is suddenly released. Yet, their occurrence is not fully understood. These flares can affect the Earth by the release of massive quantities of charged particles and electromagnetic radiation. Investigating the associations between solar flares and sunspot groups is helpful in comprehending the possible cause and effect relationships among solar flares and sunspot features. 01 This thesis proposes a new approach developed by integrating advances in image processing, machine learning, and deep learning with advances in solar physics to extract valuable knowledge from historical solar data related to sunspot regions and flares. This dissertation aims to achieve the following: 1) We developed a new prediction algorithm based on the Automated Solar Activity Prediction system (ASAP) system. The proposed algorithm updates the ASAP system by extending the training process and optimizing the learning rules to the optimize performance better. Two neural networks are used in the proposed approach. The first neural network is used to predict whether a specific sunspot class at a particular time is likely to produce a significant flare or not. The second neural network is used to predict the type of this flare, X or M-class. 2) We proposed a new system called the ASAP_Deep system built on top of the ASAP system introduced in [6] but improves the system with an updated deep learning-based prediction capability. In addition, we successfully apply Convolutional Neural Network (CNN) to the sunspot group image without any pr-eprocessing or feature extraction. Moreover, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. In addition, the proposed system achieves a relatively high score of True Skill Statistic (TSS) and Heidke Skill Score (HSS). 3) We presented a novel system that used the Deep Belief Networks (DBNs) to predict the solar flares occurrence. The input data are SDO/HMI Intensitygram and Magnetogram images. The model outputs are "Flare or No-Flare" of significant flare occurrence (M and X-class flares). In addition, we created a dataset from the sunspots groups extracted from SDO HMI Intensitygram images. We compared the results obtained from the complete suggested system with those of three previous flare forecast models using several statistical metrics. In our view, these developed methods and results represent an excellent initial step toward enhancing the accuracy of flare forecasting, enhance our understanding of flare occurrence, and develop efficient flare prediction systems. The systems, implementation, results, and future work are explained in this dissertation.