Using the method of computational fluid dynamics (CFD), numerically simulates office building with chilling ceiling /displacement ventilation (CC/DV) and analyzes indoor airflow velocity field, temperature field and thermal stratification height of the building. Consider that the CC/DV system can improve indoor air quality and save energy. And the CC/DV system could solve many constraints of displacement ventilation system. when the cooling load is large, due to the limit of air supply and temperature supply the displacement ventilation system can not be used, but the CC/DV system could well satisfied the requirement of body. The CC/DV system also has the problem of lower thermal stratification height because the chilling ceiling has a lower temperature.
This paper adopted computational fluid dynamics (CFD) method, used k-ε RNG turbulence model-closed control differential equations for numerical simulation. Through numerical simulation and analysis of wind environment in a middle school campus, the round wind field under dominant wind direction was got in the summer and winter. According to the results of velocity field and pressure field, analysis the wind environment, compared the influence of wind direction and surrounding buildings space to the natural ventilation, provided guidance introduce for the layout of the school.
Modeling of the turbulent convective heat transfer to supercritical pressure fluids in horizontal circular tubes is achieved through an integral approach, and a traditional mixing length turbulence model is employed into the numerical scheme. Based on this model, heat transfer of supercritical carbon dioxide cooled in circular tubes was investigated numerically. The effects of mass flux, pressure, heat flux and tube diameter on heat transfer coefficient were simulated, and the simulation results were then compared with the experimental data. It is shown that the present model can provide fast and accurate predictions for the heat transfer behavior in the turbulent boundary layer of supercritical fluid flows under cooling conditions.
Recently with the rapid growth of electric power literatures, it is hard to artificially track and process hot electric scientific researches. In the past most, professionals use simple statistics to get high-frequency words, which is time-consuming and ignores the similarity between words. Moreover, different researchers have different requirements for prediction time span. In the paper we propose a prediction system for hot electric scientific researches and gives its implementation. It is based on our previous work and we improve it to suit indefinite prediction period. The proposed embedded RNN prediction model is flexible for heterogeneous time spans and can return prediction results rapidly and accurately. Our extensive experiments demonstrated that our approach has acceptable precision ratio as well as training time in comparison to SVM, RNN and linear regression algorithms. It also performs better when the embedded layers are multiple.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.