abstract: An accurate knowledge of the complex microstructure of a heterogeneous material is crucial for quantitative structure-property relations establishment and its performance prediction and optimization. X-ray tomography has provided a non-destructive means for microstructure characterization in both 3D and 4D (i.e., structural evolution over time). Traditional reconstruction algorithms like filtered-back-projection (FBP) method or algebraic reconstruction techniques (ART) require huge number of tomographic projections and segmentation process before conducting microstructural quantification. This can be quite time consuming and computationally intensive. In this thesis, a novel procedure is first presented that allows one to directly extract key structural information in forms of spatial correlation functions from limited x-ray tomography data. The key component of the procedure is the computation of a “probability map”, which provides the probability of an arbitrary point in the material system belonging to specific phase. The correlation functions of interest are then readily computed from the probability map. Using effective medium theory, accurate predictions of physical properties (e.g., elastic moduli) can be obtained. Secondly, a stochastic optimization procedure that enables one to accurately reconstruct material microstructure from a small number of x-ray tomographic projections (e.g., 20 - 40) is presented. Moreover, a stochastic procedure for multi-modal data fusion is proposed, where both X-ray projections and correlation functions computed from limited 2D optical images are fused to accurately reconstruct complex heterogeneous materials in 3D. This multi-modal reconstruction algorithm is proved to be able to integrate the complementary data to perform an excellent optimization procedure, which indicates its high efficiency in using limited structural information. Finally, the accuracy of the stochastic reconstruction procedure using limited X-ray projection data is ascertained by analyzing the microstructural degeneracy and the roughness of energy landscape associated with different number of projections. Ground-state degeneracy of a microstructure is found to decrease with increasing number of projections, which indicates a higher probability that the reconstructed configurations match the actual microstructure. The roughness of energy landscape can also provide information about the complexity and convergence behavior of the reconstruction for given microstructures and projection number. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2017

付記する学位プログラム名: 充実した健康長寿社会を築く総合医療開発リーダー育成プログラム / 京都大学 / 新制・課程博士 / 博士(医学) / 甲第23092号 / 医博第4719号 / 新制||医||1050(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 黒田 知宏, 教授 上杉 志成, 教授 藤渕 航 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Planning, scheduling and real time optimization (RTO) are currently implemented by using different types of models, which causes discrepancies between their results. This work presents a single model of a crude distillation unit (preflash, atmospheric, and vacuum towers) suitable for all of these applications, thereby eliminating discrepancies between models used in these decision processes. Hybrid model consists of volumetric and energy balances and partial least squares model for predicting product properties. Product TBP curves are predicted from feed TBP curve, operating conditions (flows, pumparound heat duties, furnace coil outlet temperatures). Simulated plant data and model testing have been based on a rigorous distillation model, with 0.5% RMSE over a wide range of conditions. Unlike previous works, we do not assume that (i) midpoint of a product TBP curve lies on the crude distillation curve, and (ii) midpoint between the back-end and front-end of the adjacent products lies on the crude distillation curves, since these assumptions do not hold in practice. Associated properties (e.g. gravity, sulfur) are computed for each product based on its distillation curve. Model structure makes it particularly amenable for development from plant data. High model accuracy and its linearity make it suitable for optimization of production plans or schedules. / Thesis / Master of Applied Science (MASc)

Molecular property prediction has a vast range of applications in the chemical industry. A powerful molecular property prediction model can promote experiments and production processes. The idea behind this degree program lies in the use of transfer learning to predict molecular properties. The project is divided into two parts. The first part is to build and pre-train the model. The model, which is constructed with pure attention-based Transformer Layer, is pre-trained through a Masked Edge Recovery task with large-scale unlabeled data. Then, the performance of this pre- trained model is tested with different molecular property prediction tasks and finally verifies the effectiveness of transfer learning.The results show that after self-supervised pre-training, this model shows its excellent generalization capability. It is possible to be fine-tuned with a short period and performs well in downstream tasks. And the effectiveness of transfer learning is reflected in the experiment as well. The pre-trained model not only shortens the task- specific training time but also obtains better performance and avoids overfitting due to too little training data for molecular property prediction. / Prediktion av molekylers egenskaper har en stor mängd tillämpningar inom kemiindustrin. Kraftfulla metoder för att predicera molekylära egenskaper kan främja vetenskapliga experiment och produktionsprocesser. Ansatsen i detta arbete är att använda överförd inlärning (eng. transfer learning) för att predicera egenskaper hos molekyler. Projektet är indelat i två delar. Den första delen fokuserar på att utveckla och förträna en modell. Modellen består av Transformer-lager med attention- mekanismer och förtränas genom att återställa maskerade kanter i molekylgrafer från storskaliga mängder icke-annoterad data. Efteråt utvärderas prestandan hos den förtränade modellen i en mängd olika uppgifter baserade på prediktion av molekylegenskaper vilket bekräftar fördelen med överförd inlärning.Resultaten visar att modellen efter självövervakad förträning besitter utmärkt förmåga till att generalisera. Den kan finjusteras med liten tidskostnad och presterar väl i specialiserade uppgifter. Effektiviteten hos överförd inlärning visas också i experimenten. Den förtränade modellen förkortar inte bara tiden för uppgifts-specifik inlärning utan uppnår även bättre prestanda och undviker att övertränas på grund otillräckliga mängder data i uppgifter för prediktion av molekylegenskaper.

A knowledge of thermophysical properties is necessary for the design of all process units. Reliable property prediction methods are essential because reliable experimental data are often not available due to concerns about measurement difficulty, cost, scarcity, safety, or environment. In particular, there is a lack of prediction methods for solid properties. Predicted property values can also be used to fill holes in property databases to understand more fully compound characteristics. This work is a comprehensive analysis of the prediction methods available for five commonly needed solid properties. Where satisfactory methods are available, recommendations are made; where methods are unsatisfactory in scope or accuracy, improvements have been made or new methods have been developed. In the latter case, the following general scheme has been used to develop correlations: extraction of a training set of experimental data of a specific accuracy from the DIPPR 801 database, selection of a class of equations to use in the correlation, refinement of the form of the equation through least squares regression, selection of the chemical groups and/or molecular descriptors to be used as independent variables, calculation of coefficient values using the training set, addition of groups where refinement is needed, and a final testing of the resultant correlation against an independent test set of experimental data. Two new methods for predicting crystalline heat capacity were created. The first is a simple power law method (PL) that uses first-order functional groups. The second is derived as a modification of the Einstein-Debye canonical partition function (PF) that uses the same groups as the PL method with other descriptors to account for molecule size and multiple halogens. The PL method is intended for the temperature range of 50 to 250 K; the PF method is intended for temperatures above 250 K. Both the PL and PF methods have been assigned an uncertainty of 13% in their preferred temperature ranges based on comparisons to experimental data. A method for estimating heat of sublimation at the triple point was created using the same groups as used in the heat capacity PF method (estimated to have an error of 13%). This method can be used in conjunction with the Clausius-Clapeyron equation to predict solid vapor pressure. Errors in predicted solid vapor pressures averaged about 44.9%. As most solid vapor pressures are extremely small, on the order of one Pascal, this error is small on an absolute scale. An improvement was developed for an existing DIPPR correlation between solid and liquid densities at the triple point. The new correlation improves the prediction of solid density at the triple point and permits calculation of solid densities over a wide range of temperatures with an uncertainty of 6.3%. Based on the analysis of melting points performed in this study, Marrero and Gani's method is recommended as the primary method of predicting melting points for organic compounds (deviation from experimental values of 12.5%). This method can be unwieldy due to the large number of groups it employs, so the method of Yalkowsky et al. (13.9% deviation) is given a secondary recommendation due to its broad applicability with few input requirements.

Pavement subsurface drainage systems (PSDS) are designed to drain the entrapped water out of pavement. To investigate the effects of various factors on the performance of PSDS, three dimensional models were developed using the finite element method to simulate the unsaturated drainage process in pavement. The finite element models were calibrated using the field information on outflow, peak flow, layer saturations, and time to drain. Through a series of parametric analyses, the factors that significantly influence the performance of PSDS were screened out, and a set of recommendations were made to improve our current drainage practices.The effects of pavement geometry on drainage were studied in this research. The analysis results indicate that edgedrain system can significantly improve the drainage efficiency of a pavement. The drainage performance of a pavement is mainly affected by the geometric factors that related to the edgedrain itself and the geometric factors related to the driving lanes have very limited effects.To investigate the influences of the properties of various pavement materials, some physical-empirical equations were developed in this research. These equations were used to predict the material hydraulic properties from their grain-size distributions and aggregate/asphalt contents. The analysis results of the models with various material properties indicate that the use of permeable base is effective in improving the drainage ability of a pavement. The performance of PSDS is not only affected by material permeability but also by their waterretention ability. The pavement works as an integrated hydraulic system and the hydraulic compatibility of materials must be considered in the PSDS design.The effects of climatic factors on pavement drainage were also studied in this research. A method was developed in this research to numerically describe the rainfall events. The analysis results of the models under various rainfall events indicate that rainfall duration is a more important parameter than the rainfall quantity in influencing the pavement drainage. Based on the analysis results, regression equations were developed for the estimation of pavement drainage. Finally, for design application purpose, a series of tables were included in this report to help with proper selected of pavement drainage options.

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Organic light-emitting diodes (OLED) are and have been the future of display technology for a minute. Looking back, display technology has moved from cathode-ray tube displays (CRTs) to liquid crystal displays (LCDs). Whereas CRT displays were clunky and had quite high powerconsumption, LCDs were thinner, lighter and consumed less energy. This technological shift has made it possible to create smaller and more portable screens, aiding in the development of personal electronics. Currently, however, LCDs place at the top of the display hierarchy is being challenged by OLED displays, providing higher pixel density and overall higher performance.OLED displays consist of thin layers of organic semiconductors, and are instrumental in the development of folding displays; small displays for virtual reality and augmented reality applications; as well as development of displays that are energy-efficient. In the creation of OLED displays, the organic semiconducting material is vaporized and adhered to a thin film through vapor deposition techniques. One way of aiding in the creation of organic electroluminescent (OEL) materials and OLEDs is through in silico analysis of sublimationtemperatures through machine learning. This master’s thesis inhabits that space, aiming to create a deeper understanding of the OEL materials through sublimation temperature prediction using ensemble learning (light gradient-boosting machine) and deep learning (convolutional neural network) methods. Through analysis of experimental OEL data, it is found that the sublimation temperatures of OLED materials can be predicted with machine learning regression using molecular descriptors, with an R2 score of ~0.86, Mean Absolute Error of ~13°C, Mean Absolute Percentage Error of ~3.1%, and Normalized Mean Absolute Error of ~0.56.