抵抗ネットワークに基づく多点温度計測システムの開発とその固体酸化物形燃料電池への応用

京都大学新制・課程博士博士(工学)甲第24227号工博第5055号京都大学大学院工学研究科機械理工学専攻(主査)教授 岩井 裕, 教授 中部 主敬, 教授 黒瀬 良一学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDGA

Design of Virtual Anchor Based on 3Dmax

With the rapid development of virtual reality and live streaming technologies, virtual anchors have become increasingly popular in recent years. In this paper, we propose a design method of virtual anchors based on 3DMAX. Through the use of modeling, rigging, and animation techniques, virtual anchors with realistic appearances and human-like movements can be created. We also explore the application of machine learning technologies in improving the interaction between virtual anchors and users. In addition, we provide a case study on the design and implementation of a virtual anchor for a popular live streaming platform. Our results show that the use of 3DMAX in virtual anchor design can greatly enhance user engagement and improve the overall user experience.Virtual anchor design technology based on 3DMAX is a highly complex design work, which requires designers to have a variety of skills and creative capabilities, and needs to fully consider the needs of the audience and the development trend of the industry. Designers should also have certain cultural accumulation and creative ability, and be able to design an attractive and valuable virtual anchor image from the perspective of the audience.This paper analyzes the production and design of the current virtual anchors, in order to provide some reference significance for the production, operation and commercial realization of the virtual anchors in the future

Cross-Coupling of Alkyl Redox-Active Esters with Benzophenone Imines: Tandem Photoredox and Copper Catalysis

Alkyl amines are an important class of organic compounds in medicinal and materials chemistry. Until now very have been very few methods for the synthesis of alkyl amines by metal‐catalyzed cross‐coupling of alkyl electrophiles with nitrogen nucleophiles. Described here is an approach to employ tandem photoredox and copper catalysis to enable the cross‐coupling of alkyl N‐hydroxyphthalimide esters, readily derived from alkyl carboxylic acids, with benzophenone‐derived imines. Hydrolysis of the coupling products furnish alkylated primary amines. Primary, secondary, and tertiary alkyl groups can be transferred, and the coupling tolerates a diverse set of functional groups. The method allows rapid functionalization of natural products and drugs, and can be used to expedite syntheses of pharmaceuticals from readily available chemical feedstocks

Decarboxylative C(sp3)–N cross-coupling via synergetic photoredox and copper catalysis

Amines are a quintessential moiety in bioactive molecules, pharmaceuticals and organic materials. Transition-metal-catalysed C–N coupling of aryl electrophiles has been established as a powerful and reliable method for amine synthesis. However, the analogous C–N coupling of alkyl electrophiles is largely under-developed due to the decomposition of metal alkyl intermediates by β-hydrogen elimination and difficulty in C(sp3)–N reductive elimination. Here, we provide a general strategy for amination of alkyl electrophiles by merging photoredox and copper catalysis. Photoredox catalysis allows the use of alkyl redox-active esters, recently established as a superior class of alkyl electrophiles, whereas copper catalysis enables C(sp3)–N cross-coupling. Decarboxylative amination can be used for the synthesis of a diverse set of alkyl anilines with high chemoselectivity and functional-group compatibility. Rapid functionalization of amino acids, natural products and drugs is demonstrated

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware

Detailed simulation of LOX/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning

Accurate and affordable simulation of supercritical reacting flow is of practical importance for developing advanced engine systems for liquid rockets, heavy-duty powertrains, and next-generation gas turbines. In this work, we present detailed numerical simulations of LOX/GCH4 flame-vortex interaction under supercritical conditions. The well-established benchmark configuration of three-dimensional Taylor-Green vortex (TGV) embedded with a diffusion flame is modified for real fluid simulations. Both ideal gas and Peng-Robinson (PR) cubic equation of states are studied to reveal the real fluid effects on the TGV evolution and flame-vortex interaction. The results show intensified flame stretching and quenching arising from the intrinsic large density gradients of real gases, as compared to that for the idea gases. Furthermore, to reduce the computational cost associated with real fluid thermophysical property calculations, a machine learning-based strategy utilising deep neural networks (DNNs) is developed and then assessed using the three-dimensional reactive TGV. Generally good prediction accuracy is achieved by the DNN, meanwhile providing a computational speed-up of 13 times over the convectional approach. The profound physics involved in flame-vortex interaction under supercritical conditions demonstrated by this study provides a benchmark for future related studies, and the machine learning modelling approach proposed is promising for practical high-fidelity simulation of supercritical combustion

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Aliphatic Carboxylic Acids as Coupling Partners in Carbon-Heteroatom Bond-Forming Reactions

Carboxylic acids are one of the most suitable starting materials for synthesis. They are readily available and therefore inexpensive, stable and non-toxic. Many carboxylic acids can be obtained directly from natural resources, thus avoiding the extraction of fossil resources, such as oil or natural gas, in the production of chemicals. Furthermore, renewable carboxylic acids derived from biomass have a wide structural diversity (e.g., amino acids, fatty acids and sugar acids), which not only holds greater potential for the synthesis of complex molecules, but also advances the development of green chemistry. In this context, this thesis aims at establishing new carbon-heteroatom coupling reactions with carboxylic acids as versatile coupling partners. These reactions are hitherto unknown, which not only fill some of the gaps in synthetic organic chemistry, but also provide solutions for the rapid assembly of bioactive molecules. In order to overcome the challenge of forming C(sp3)-heteroatom bonds, Chapters 4, 5 and 6 detail the development of photoredox/copper bi-catalytic systems and their applications in decarboxylative C(sp3)-N and C(sp3)-O coupling reactions. These methods are not only compatible with numerous functional groups, but also allow for rapid late-stage functionalization. More importantly, these methods can simplify the pathway for building drug core skeletons, highlighting the potential of these methods to expedite drug discovery. To bridge the disconnection in the synthesis of trifluoromethylthioesters from carboxylic acids, Chapter 7 describes the establishment of a "umpolung" strategy and its application in the conversion of carboxylic acids to trifluoromethylthioesters. This approach provides the most concise synthetic pathway to date for the synthesis of trifluoromethylthioesters, which not only accommodates a variety of functional groups, but also allows for the rapid functionalization of carboxylic acid-containing natural products and drug molecules. In summary, this thesis illustrates how novel catalytic methods can be used to convert inexpensive, readily available and environmentally benign aliphatic carboxylic acids into high value-added compounds