The mechanisms of thermal conductivity enhancement and the factors influencing viscosity are of great interest in the study of nanofluids, while molecular dynamics (MD) simulations considering nanofluids provide more accurate predictions of microscopic properties than conventional experimental studies. MD simulations of non-equilibrium molecular dynamics and reversing perturbation non-equilibrium molecular dynamics methods were used to study thermal conductivity and viscosity, taking into account a variety of influencing factors, as well as nanoparticle material and volume fraction. Through the analysis of the number density distribution, radial distribution function (RDF), and mean square displacement (MSD), the influences of different nanoparticles (Ag, Cu, Au, and Fe) were described and investigated: Ag particles contribute to 47.0% increase in thermal conductivity of 2.5 vol% nanofluids; Au particles improved the viscosity of 2.5 vol% nanofluids by 20.2%; the number density distribution showed positive linear relationship with the atomic mass; the results of MSD and RDF (mean square displacement and radial distribution function) in combination indicated a positive effect of interfacial nanolayer. The results of this research provide important perspectives for comprehending the impacts of multiple nanoparticles on the micro-thermal properties of nanofluids and also highlight the simulation potential of Au–Ar nanofluids.

In this study, a new method of combining lance–liquid flow characteristics and mixing uniformity is proposed to evaluate the stirring characteristics in the bottom blowing copper molten pool. A fluid simulation model of a bottom blowing molten pool was established, water was used to simulate the melt environment, and an experimental platform was set up for verification. The effects of swirl, multi-channel, and straight pipe spray on the lance–liquid stirring characteristics of the bottom-blown copper molten pool are compared through quantifying the flow characteristics and mixing uniformity. In addition, digital image processing technologies, such as image entropy variance and eddy current map entropy increase, are introduced. Through numerical simulation research, it is found that the transverse velocity of the swirl spray lance is the largest, which makes the rise time of the bubble increase to the greatest extent. Compared with the straight pipe spray, the swirl spray reduces the liquid splash height by 0.054 m, and the degree of vortex flow is higher. The lance phase stability is increased by 37.87%, and the maximum turbulent kinetic energy can be increased by 8.73%. The spray effect of the multi-channel spray is between the two. It is shown that the swirling spray lance can improve the stability of gas in the molten pool, enhance the uniformity of gas–liquid mixing, and improve the operation cycle and the smelting efficiency of the molten pool.

With the continuous development of China’s economy, the phenomenon of energy scarcity has become more and more prominent, for which China has put forward the strategic goal of carbon peak and carbon neutrality (double carbon target). As densely populated areas, the demand for energy is especially tight in universities. In combination with the work of “conservation-oriented colleges” carried out by the Ministry of Education, the accurate monthly electrical and water energy consumption of Kunming University of Science and Technology from 2018–2021 was counted, and the data were plotted into an energy consumption analysis chart to determine its compliance with the prediction range of the smoothing index prediction model. The corresponding smoothing indices were calculated by writing smoothing formulas through Excel, and, finally, the overall energy consumption indexes for 2022 and 2023 were successfully predicted using the exponential smoothing method. The errors between the real and forecasted values of electricity and water consumption in 2021 are reduced to 2.61% and 2.44%. The smoothing index predicts that the baseline discounted electricity energy consumption in 2022 is 5,423,658.235 kgce and in 2023 is 5,758,865.224 kgce; on the other hand, the baseline discounted water energy consumption in 2022 is predicted to be 632,654.321 kgce, while in 2023 it is predicted to be 652,321.238 kgce. The projected values can be used as an early warning line for the energy consumption index, and long-term management approaches and data support for energy conservation and carbon emission reduction can be effectively provided. The mentioned research provides an important reference for the proposal and implementation of efficient management measures, and provides strong theoretical technical support for the implementation of the carbon peak and neutrality in universities.