Gas turbines
Researchers from Harbin Engineering University have developed a novel dual-drive agent combining data-driven and thermodynamic models to accurately identify gas turbine degradation states. The proposed method significantly reduces the number of required training samples while enhancing identification accuracy, achieving a classification accuracy of over 96.55% for individual components. This
Thermodynamics
Physicists have made significant research findings in the field of thermodynamics, specifically focusing on the Schwarzschild black hole embedded in an anti-de Sitter background. This study, conducted by researchers from the Department of Physics, explores the thermodynamic properties of the black hole, including its mass, pressure, and specific heat capacity.
Thermodynamics
Researchers from the National Institute of Technology have made significant advancements in understanding the behavior of black holes within the framework of Einstein-Yang-Mills (EYM) theory. By employing a higher-dimensional Wu-Yang ansatz, they have thoroughly examined the effects of quantum fluctuations on the black hole's thermodynamic properties, including its
Alloys
Researchers from the Northwest Institute for Nonferrous Metal Research, in collaboration with other institutions, have made a groundbreaking discovery in understanding the mechanisms governing Martensitic Transformation (MT) in advanced high-strength steel alloys, particularly those undergoing quenching and partitioning treatments. By integrating thermodynamics and kinetics in multi-solute systems, the team has
Thermodynamics
Researchers at Pennsylvania State University (Penn State) have conducted a study on the microscopic origins of enhanced conductivity at ferroelectric domain walls in BiFeO3. According to the study, oxygen vacancies at domain walls significantly contribute to the observed conductivity, which is a crucial aspect of ferroelectric materials. The study used
Thermodynamics
Research conducted at North China Electric Power University has shed new light on the catalytic hydrogenation of CO2 to methanol, a promising low-carbon approach. Investigators utilized density functional theory calculations to elucidate the CO2 interaction mechanisms on the metakaolin surface doping with Ni and Zn. The findings indicate that the
Thermodynamics
A new research study has shed light on the kinetics of proton-coupled electron transfer (PCET) and hydrogen-atom transfer (HAT) reactions, collectively called H-atom abstraction (HAA) reactions, which play crucial roles in biological processes and modern organic synthesis. The study, conducted by researchers at the Academy of Sciences of the Czech
Thermodynamics
A new report from Duy Tan University has investigated the sulfate radical anion (SO4 circle-)-initiated oxidation of the herbicide metazachlor (MTZ) in aqueous and gaseous phases using the density functional theory. The research aimed to elucidate mechanisms and kinetics of the oxidation process. The study revealed that most oxidation
Emissions (Pollution)
Amid the growing global urgency to reduce carbon emissions, researchers have proposed an innovative solution to achieve carbon neutrality in aviation by developing an ammonia-blended turboprop engine with a chemical regeneration cycle. Funded by the National Natural Science Foundation of China, China Postdoctoral Science Foundation, and Heilongjiang Provincial Postdoctoral Fund,
Artificial neural networks
Researchers at Friedrich-Alexander-University Erlangen-Nurnberg (FAU) have developed a novel machine learning framework called Generalized Standard Material Networks, which utilizes convex neural networks to learn the mechanical behavior of complex materials. This framework, supported by the European Research Council (ERC), SNF, Switzerland, and the German Research Foundation (DFG), aims to provide
Artificial neural networks
Researchers at the University of Lorraine have developed a novel approach to predict the nonlinear, anisotropic response of recycled glass fiber-reinforced polyamide 6 composites. The Multiscale Thermodynamics-Informed Neural Network (MuTINN) framework integrates thermodynamic principles with artificial neural networks to capture the evolution of internal state variables and Helmholtz free energy.
Artificial neural networks
Researchers from the Qingdao University of Technology have made a significant breakthrough in thermodynamics by developing a neural network method to solve time-fractional diffusion equations. This innovative approach combines machine learning techniques with the Method of Lines to provide an efficient and accurate solution to complex diffusion problems. The researchers&
