Nowadays, RNA is extensively acknowledged an important role in the functions of information transfer, structural components, gene regulation and etc. The secondary structure of RNA becomes a key to understand structure-function relationship. Computational prediction of RNA secondary structure does not only provide possible structures, but also elucidates the mechanism of RNA folding. Conventional prediction programs are either derived from evolutionary perspective, or aimed to achieve minimum free energy. In vivo, RNA folds during transcription, which indicates that native RNA structure is a result from both thermodynamics and kinetics. In this thesis, I first reviewed the current leading kinetic folding programs and demonstrate that these programs are not able to predict secondary structure accurately. Upon that, I proposed a new sequential folding program called GTkinetics. Given an RNA sequence, GTkinetics predicts a secondary structure and a series of RNA folding trajectories. It treats the RNA as a growing chain, and adds stable local structures sequentially. It is featured with a Z-score to evaluate stability of local structures, which is able to locate native local structures with high confidence. Since all stable local structures are captured in GTkinetics, it results in some false positives, which prevents the native structure to form as the chain grows. This suggests a refolding model to melt the false positive hairpins, probable intermediate structures, and to fold the RNA into a new structure with reliable long-range helices. By analyzing suboptimal ensemble along the folding pathway, I suggested a refolding mechanism, with which refolding can be evaluated whether or not to take place. Another way to favor local structures over long-distance structures, we introduced a distance penalty function into the free energy calculation. I used a sigmoidal function to compute the energy penalty according to the distance in the primary sequence between two nucleotides of a base pair. For both the training dataset and the test dataset, the distance function improves the prediction to some extent. In order to characterize the differences between local and long-range helices, I carried out analysis of standardized local nucleotide composition and base pair composition according to the two groups. The results show that adenine accumulates on the 5' side of local structure, but not on that of long-range helices. GU base pairs occur significantly more frequent in the local helices than that in the long-range helices. These indicate that the mechanisms to form local and long range helices are different, which is encoded in the sequence itself. Based on all the results, I will draw conclusions and suggest future directions to enhance the current sequential folding program.

Motivation: Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Current alignment-based prediction algorithms only find the consensus structure, and their alignments can come from structure-based alignment algorithms, which is more reliable, but come with an increased cost compared to sequence-based alignment algorithms. This step can be removed; however, non-alignment based algorithms neglect structural information that can be found within similar sequences. Results: We present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes an RNA sequence alignment and uses covariation and thermodynamic energy minimization to predict secondary structures for each individual sequence in the alignment. We compared KnotAli's performance to that of three other alignment-based algorithms, on a large data set of 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT). We found KnotAli to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. We find KnotAli's predictions to be less dependent on alignment quality. In particular, KnotAli is shown to have more accurate predictions compared to other leading methods as alignment quality deteriorates. Availability: The algorithm can be found online on Github at https://github.com/mateog4712/KnotAli / Graduate / 2022-08-16

Divertors are in-vessel, plasma-facing, components in magnetic-confinement fusion reactors. Their main function is to remove the fusion reaction ash (α-particles), unburned fuel, and eroded particles from the reactor, which adversely affect the quality of the plasma. A significant fraction (~15 %) of the total fusion thermal power is removed by the divertor coolant and must, therefore, be recovered at elevated temperature in order to enhance the overall thermal efficiency. Helium is the leading coolant because of its high thermal conductivity, material compatibility, and suitability as a working fluid for power conversion systems using a closed high temperature Brayton cycle. Peak surface heat fluxes on the order of 10 MW/m^2 are anticipated with surface temperatures in the region of 1,200°C to 1,500°C. Recently, several helium-cooled divertor designs have been proposed, including a modular T-tube design and a modular finger configuration with jet impingement cooling from perforated end caps. Design calculations performed using the FLUENT® CFD software package have shown that these designs can accommodate a peak heat load of 10 MW/m^2. Extremely high heat transfer coefficients (~50,000 W/(m^2 K)) were predicted by these calculations. Since these values of heat transfer coefficient are considered to be outside of the experience base for gas-cooled systems, an experimental investigation has been undertaken to validate the results of the numerical simulations. Attention has been focused on the thermal performance of the T-tube and the finger divertor designs. Experimental and numerical investigations have been performed to support both divertor geometries. Excellent agreement has been obtained between the experimental data and model predictions, thereby confirming the predicted performance of the leading helium-cooled divertor designs for near- and long-term magnetic fusion reactor designs. The results of this investigation provide confidence in the ability of state-of-the-art CFD codes to model gas-cooled high heat flux plasma-facing components such as divertors.

They analyzed the various factors affecting the design or evaluation of structural glass. Within evaluation of structural glass element is checked selected concepts of linear elastic fracture mechanics. For modeling is used programme system ANSYS based finite element method. The results are compared with the analytical solution.