With the significant attention on global climate change, regional integrated energy systems (RIES) in low-carbon city planning has become one of the most important ways for global cities to achieve the goal of energy conservation and emission reduction. However, the planning strategy used in the primary stage of RIES establishment will greatly affect the system economy and environment. In view of the lack of planning guidance mechanisms for the large-scale RIES in China, a method for RIES preliminary overall planning, focused on energy types and use, is proposed in this paper. A graph theory-based mathematical optimal model was established with the lifetime costs of the whole system as the economic goal, and an improved Prim algorithm was put forward to solve the costs of the transmission and distribution network with the dynamic weight set of pipeline flow. The model was solved by an algorithm based on the idea of a dynamic minimum spanning tree and optimal path planning. The model and method were applied in a China–U.S. low-carbon demonstration city to verify feasibility and validity. The results could help us to comprehensively integrate regional energy and accurately plan future cities.

An indoor high and open space is characterized by high mobility of people and uneven temperature distribution, so the conventional design and operation of air conditioning systems makes it difficult to regulate the air conditioning system precisely and efficiently. Thus, a Wireless Sensor Network was constructed in an indoor space located in Hong Kong to monitor the indoor environmental parameters of the space and improve the temperature control effectively. To ensure the continuity of the measurement data, three algorithms for reconstructing temperature, relative humidity and carbon dioxide data were implemented and compared. The results demonstrate the accuracy of support vector regression model and multiple linear regression model is higher than Back Propagation neural network model for reconstructing temperature data. Multiple linear regression is the most convenient from the perspective of program complexity, computing speed and difficulty in obtaining input conditions. Based on the data we collected, the traditional single-input-single-output control, zonal temperature control and the proposed zonal demand control methods were modeled on a Transient System Simulation Program (TRNSYS) control platform, the thermal coupling between the subzones without physical partition was taken into account, and the mass transfer between the virtual boundaries was calculated by an external CONTAM program. The simulation results showed the proposed zonal demand control can alleviate the over-cooling or over-heating phenomenon in conventional temperature control, thermal comfort and energy reduction is enhanced as well.

The random movement of occupants in a high-speed railway station results in a more complex indoor environment. In this study, the indoor thermal environment and the thermal comfort in summer were investigated via field measurements and questionnaires in the waiting hall of a high-speed railway station. The results showed that there was an uneven horizontal temperature distribution in the area, and over 30% of the passengers were dissatisfied with the air conditioning system. In order to improve the control of the indoor temperature as well as reduce the energy consumption of the air conditioning system, an improved zonal control strategy and AMPC control optimization algorithm based on real-time people are proposed, and different control strategies are modeled and simulated using MATLAB/Simulink. It is concluded that the improved zonal control method proposed in this paper can save 28.04% of the fan energy consumption compared with the traditional control strategy.

Spanwise heterogeneity in surface roughness generates secondary mean flows in a rough-wall turbulent boundary layer. This study investigates the influence of roughness spanwise wavelength on the \textcolor{blue}{arrangement} of these secondary flows using Direct Numerical Simulation (DNS). We systematically vary the spanwise wavelength, $S/\delta$, from $\pi/16$ to $2\pi$, while maintaining constant roughness height and surface coverage density. Here, $S$ represents the roughness spanwise wavelength, and $\delta$ denotes \textcolor{blue}{the outer length scale, which in this case is the half-channel} height. Secondary flows are observed in all DNS cases, but their configurations depend on the spanwise wavelength. Specifically, small wavelengths result in low-momentum pathways above the roughness elements, whereas large wavelengths lead to high-momentum pathways at these locations. To elucidate the mechanisms behind this rearrangement, we analyze the mean streamwise vorticity transport equation. The findings indicate that shear stress anisotropy induces another pair of secondary vortices above the roughness elements as the spanwise wavelength increases. Given that secondary flows are features of the mean flow, we further examine the budgets of the dispersive kinetic energy (DKE). The analyses reveal that at small spanwise wavelengths, shear production primarily generates DKE, while at large wavelengths, wake production becomes the dominant energy source. Building on these insights, we propose a refined classification for surfaces with spanwise heterogeneity.