Founded in 1895 as Peiyang University,Tianjin University is the oldest institution of higher education in modern Chinese history.After 130 years of growth and development,it has now become one of the leading research and teaching univer-sities,pioneering various aspects of engineering education and applications.As an integral part of the university,the Department of Mechanics has grown from a mechanics lab-oratory in the 1920s to its current status as one of the leading research and teaching departments,covering solid mechan-ics,fluid mechanics,engineering mechanics,and dynamics and control.Its distinctive research in solid mechanics and interdisciplinary fields ranges from elastic waves and con-trol,experimental mechanics,nonlinear dynamics,contact mechanics and tribology to nanomechanics,contributing both to the fundamental understanding of nature and to engi-neering applications.
Iron and steel industry is a significant basic industry,where steelmaking-refining-continuous casting(SRCC)is a bot-tleneck.Efficient SRCC schedules can enhance the iron and steel production productivity greatly.SRCC scheduling problems are important and challenging industrial scheduling problems,as well as well-known nondeterministic poly-nomial time-hard problems.In the realistic SRCC process,the last production stage's processing times are controllable,and the corresponding scheduling problems are named SRCC scheduling problems with controllable processing times(CPTs).To deal with the SRCC scheduling problems with CPTs efficiently,a new neighborhood structure(named restricted multiswap)and a new decoding method based on domain knowledge,and then a hybrid evolutionary algorithm(HEA)are proposed.The proposed HEA integrates several distinguished features:the aforementioned neighborhood structure and decoding method,local search,biased probability-guided crossover,and two diversification strategies including pertur-bation and mutation.The HEA achieves the lowest average relative percentage increase(RPI)in comparison with seven state-of-the-art scheduling algorithms,with the average RPI of 0.02％,0.05％,0.09％under time limits of 10,20,and 30 s,respectively.
A novel image-based corrosion assessment method for low-alloy steels by integrating deep learning with quantitative metallography is proposed. The critical challenges of in situ corrosion monitoring in marine environments are addressed by introducing three key innovations: (1) a five-stage corrosion classification scheme based on combined macro- and micro-scale morphological analysis; (2) a high-quality annotated dataset (LASCD-UHD-2025) containing 1000 ultra-high-resolution images (5472 pixel × 3648 pixel) of Q420 and Q420RE steels under 3.5 wt.
As a critical pillar of China’s dual-carbon strategy, decarbonizing the steel industry requires innovative alternatives to conventional carbon-intensive iron ore agglomeration methods. A key challenge in enhancing the mechanical strength of cold-bonded pellets (CBPs), a sustainable low-temperature agglomeration technology, was addressed through binder optimization and structural reinforcement. Forming and consolidation regimes of CBPs were systematically optimized. Optimal parameters were identified as 7
With the development of railway transportation toward heavy-haul and high-speed directions, the service environment for wheel–rail contact steels has become increasingly severe, placing higher demands on its comprehensive performance, especially corrosion resistance. Current research predominantly focuses on in-depth analysis of corrosion phenomena, while comprehensive performance evaluation under various corrosive environments, systematic analysis of failure mechanisms, and integrated studies on anti-corrosion mechanisms for different types of wheel–rail contact steels are still lacking. In light of this, the classification of wheel–rail contact steels is introduced, analyzes corrosion types and mechanisms, domestic and international research progress on the influence of factors such as alloying elements, microstructure, and interfaces on the corrosion performance of wheel–rail contact steels is reviewed, and the application of computational simulations in corrosion research of these materials is summarized. Furthermore, measures to enhance corrosion resistance through improving inherent material properties, surface treatment technologies, and electrochemical protection are discussed. Existing research findings are summarized, and future research directions for improving the corrosion resistance of wheel–rail contact steels are outlooked, aiming to provide a reference for the development and production of wheel–rail contact steels with superior corrosion resistance.
