The Rise of a Magnetic Flux Tube in a Background Field: Solar Helicity Selection Rules

The buoyant transport of magnetic fields from the solar interior towards the surface plays an important role in the emergence of active regions, the formation of sunspots and the overall solar dynamo. Observations suggest that toroidal flux concentrations often referred to as "flux tubes", rise from their region of initiation likely in the solar tachocline towards the solar surface due to magnetic buoyancy. Many studies have assumed the existence of such magnetic structures and studied the buoyant rise of an isolated flux tube in a quiescent, field-free environment. Here, motivated by their formation (Cline et al. 2003; Brummell et al. 2002), we relax the latter assumption and study the rise of a toroidal flux tube embedded in a large-scale poloidal background magnetic field. We find that the presence of the large-scale background field severely affects the dynamics of the rising tube. A relatively weak background field, as low as 6% of the tube strength, can destroy the rise of a tube that would otherwise rise in the absence of the background field. Surprisingly, the rise of tubes with one sign of the twist is suppressed by a significantly weaker background field than the other. This demonstrates a potential mechanism for the selection of the preferred helicity of rising and emerging tubes for the solar case that is commensurate with many features of the hemispherical rule.Comment: 9 pages, 5 figures, accepted for publication in ApJ

Learning-based system-level power modeling of hardware IPs

Accurate power models for hardware components at high levels of abstraction are a critical component to enable system-level power analysis and optimization. Virtual platform prototypes are widely utilized to support early system-level design space exploration. There is, however, a lack of accurate and fast power models of hardware components at such high-levels of abstraction. In this dissertation, we present novel learning‑based approaches for extending fast functional simulation models of white-, gray-, and black-box custom hardware intellectual property components (IPs) with accurate power estimates. Depending on the observability, we extend high-level functional models with the capability to capture data-dependent resource, block, or I/O activity without a significant loss in simulation speed. We further leverage state-of-the-art machine learning techniques to synthesize abstract power models that can predict cycle-, block-, and invocation-level power from low-level hardware implementations, where we introduce novel structural decomposition techniques to reduce model complexities and increase estimation accuracy. Our white-box approach integrates with existing high-level synthesis (HLS) tools to automatically extract resource mapping information, which is used to trace data-dependent resource-level activity and drive a cycle-accurate online power-performance model during functional simulation. Our gray-box approach supports power estimation at coarser basic block granularity. It uses only limited information about block inputs and outputs to extract light-weight block-level activity from a functional simulation and drive a basic block-level power model that utilizes a control flow decomposition to improve accuracy and speed. It is faster than cycle-level models, while providing a finer granularity than invocation-level models, which allows to further navigate accuracy and speed trade-offs. We finally propose a novel approach for extending behavioral models of black-box hardware IPs with an invocation-level power estimate. Our black-box model only uses input and output history to track data-dependent pipeline behavior, where we introduce a specialized ensemble learning that is composed out of individually selected cycle-by-cycle models with reduced complexity and increased accuracy. The proposed approaches are fully automated by integrating with existing, commercial HLS tools for custom hardware synthesized by HLS. Results of applying our approaches to various industrial‑strength design examples show that our power models can predict cycle‑, basic block-, and invocation-level power consumption to within 10%, 9%, and 3% of a commercial gate-level power estimation tool, respectively, all while running at several order of magnitude faster speeds of 1-10Mcycles/sec.Electrical and Computer Engineerin

Amorphous Metal-Free Organic Phosphors for Sensor Applications

Phosphorescent organic light-emitting diodes are promising for many applications such as display and solid-state lighting because they can reach 100% theoretical internal quantum efficiency. In order to realize bright purely organic phosphors, efficiently promoting intersystem crossing from singlet to triplet and suppressing vibrational dissipation of triplets must be achieved. In this dissertation, I systematically investigated the two critical processes and devised some strategies to achieve bright room temperature purely organic phosphorescence in amorphous films and optical ozone sensors based on phosphorescence phenomena. Embedding organic phosphors into amorphous glassy polymer was investigated as the first strategy to efficiently suppress the triplet vibration by restricting molecular motion of the embedded organic phosphors. This system showed temperature dependent phosphorescence attributed to changing vibrational characteristics of the matrix polymer. An optical temperature sensor integrated in a microfluidic device was devised and demonstrated. Incorporating strong hydrogen bonding between a newly devised purely organic phosphor and hydrogen bonding capable matrix polymer was the second strategy, resulting in much brighter phosphorescence. Modulation of hydrogen bonding by water showed unique reversible phosphorescence-to-fluorescence switching behavior, which was utilized to develop a ratiometric water sensor. Based on the finding that the phosphorescence intensity of the purely organic phosphors is sensitive to environmental ozone concentration, I revealed that the origin of the ozone sensitivity is oxidation of the aldehyde moiety of the organic phosphors and devised highly sensitive and convenient optical ozone sensors by utilizing the observed inverse linear correlation between the phosphorescence emission intensity and the ozone concentration. Since manipulating conjugation length of organic phosphors is a powerful tool to tune the emission color, establishing an understanding on the conjugation length effects on the phosphorescent emission intensity is important. The effects of the conjugation length of the purely organic phosphors on their phosphorescence intensity were systematically studied using a combined experimental and computational approach. The obtained knowledge regarding the role of intermolecular interactions for vibration suppression was adapted to achieve high thermal conductivity in amorphous polymers by designing hydrogen bonding donating and accepting polymer pairs, providing uniformly distributed strong interpolymer linkage, and leading to high thermal conductivity of 1.5 Wm-1K-1.PHDMacromolecular Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111449/1/dongwook_1.pd

Multidimensional adaptive order GP-WENO via kernel-based reconstruction

This paper presents a fully multidimensional kernel-based reconstruction scheme for finite volume methods applied to systems of hyperbolic conservation laws, with a particular emphasis on the compressible Euler equations. Non-oscillatory reconstruction is achieved through an adaptive order weighted essentially non-oscillatory (WENO-AO) method cast into a form suited to multidimensional stencils and reconstruction. A kernel-based approach inspired by Gaussian process (GP) modeling is presented here. This approach allows the creation of a scheme of arbitrary order with simply defined multidimensional stencils and substencils. Furthermore, the fully multidimensional nature of the reconstruction allows a more straightforward extension to higher spatial dimensions and removes the need for complicated boundary conditions on intermediate quantities in modified dimension-by-dimension methods. In addition, a new simple-yet-effective set of reconstruction variables is introduced, as well as an easy-to-implement effective limiter for positivity preservation, both of which could be useful in existing schemes with little modification. The proposed scheme is applied to a suite of stringent and informative benchmark problems to demonstrate its efficacy and utility.Comment: Submitted to Journal of Computational Physics April 202