Search

The energy consumption of air conditioners (ACs) in buildings is particularly high during summer, thus placing high demands on indoor thermal comfort. However, previous studies have only focused on a single factor either AC energy consumption or indoor thermal comfort, and only a few comprehensive analyses have attempted to optimize the AC energy consumption and indoor thermal comfort in buildings during summer. In this study, the comprehensive evaluation model was introduced to quantitatively evaluate the performance of different ventilation and cooling systems. A total of 48 orthogonal tests were conducted to evaluate cooling efficiency under different working conditions. The degree of door and window opening/closing and the AC setpoint temperature were considered in the test, and the degree–power index was proposed as an evaluation metric. The effects of multiple working conditions on the energy consumption and thermal comfort of a single AC office were estimated using the Gray-Technique for Order Preference by Similarity to an Ideal Solution (GRA-TOPSIS) model. Results show that, AC energy consumption is positively correlated with the degree of door and window opening/closing, especially at an AC setpoint temperature of 28 ℃. The increase in energy consumption is especially significant, thereby highlighting the importance of window and door management in controlling the energy consumption of buildings at high summer temperatures. Meanwhile, reducing the AC setpoint temperature improved the comfort of the indoor environment. Opening one window at an AC setpoint temperature of 26 ℃ provides the best balance between energy saving and thermal comfort. The experiments demonstrate that optimizing the degree of door and window opening/closing and setting rational AC temperatures not only ensure thermal comfort but also effectively reduce energy consumption in buildings. [ABSTRACT FROM AUTHOR]

[Objective] This paper reveals the heterogeneity of physical and chemical properties in soils under different land use patterns in the lower reaches of Fenhe River basin, and expounds its influence on the development of preferential flow. [Methods] Three land use types in the lower reaches of the Fenhe River basin were selected in the field staining and tracing experiments to evaluate the development degree of preferential flow and analyze the differences of soil properties between preferential flow areas and non-preferential flow areas and their effects on preferential flow characteristics. [Results] The development degrees of preferential flow indicated by the index of preferential flow under different land use types were grassland (1.18), cropland (1.12) and orchard (0.88), respectively. Soil properties other than pH were significantly affected by land use types, soil layers, and preferential flow areas (p＜0.05). The increase of soil organic matter, total porosity, capillary porosity and saturated hydraulic conductivity, and the decrease of bulk density contributed to the development of preferential flow. The multiple stepwise regression analysis showed that the organic matter and total porosity in preferential flow areas were the main factors affecting the dyeing area ratio, while the organic matter, bulk density and sand particles in non-preferential flow areas were the main factors affecting the non-dyeing area ratio. [Conclusion] Compared with farmland and orchard, the soil physical and chemical properties of grassland have higher heterogeneity in soil layers and preferential flow areas, which has a greater impact on the development of preferential flow. [ABSTRACT FROM AUTHOR]

In western high altitude areas, strong ultraviolet radiation and high frequency freeze-thaw cycles and their coupling effects are the direct causes of asphalt concrete bridge pavement cracks. The purpose of this paper is to analyze the attenuation laws of cracking resistance of asphalt mixture under high altitude environment with multiple indices, and to propose a new comprehensive index. Firstly, laboratory tests were designed to simulate the aging effects of high altitude environment on asphalt mixture. Secondly, semi-circular bending (SCB) tests were used to investigate the performance attenuation of asphalt mixture under different coupling schemes. Finally, the monotonic relationship between each anti-cracking evaluation index and independent variables was explained by statistical analysis, and the cracking resistance index in high altitude region (HAR-CRI) was proposed. The results showed that the reduction of cracking resistance of asphalt mixture per unit of freeze-thaw cycle is 4.6 times higher than that of ultraviolet radiation, due to the surface property of ultraviolet aging; the maximum damage deepening values of G f , R C , BCI, FEI, FI and CRI can reach 5.6 %, 7.6 %, 5.0 %, 4.3 %, 3.6 % and 2.0 % respectively, indicating that mutually deepening environmental factors had the greatest reduction in R C. According to correlation and difference analysis: FEI, R C and BCI were recommended to study the effects of temperature, freeze-thaw cycles frequency and ultraviolet radiation duration on cracking resistance of asphalt mixture respectively; ultraviolet radiation duration only had significant effects on FI and CRI in the three-factor interaction. Based on the principal component analysis, a comprehensive index (HAR-CRI) covering more than 98 % of multiple-index information was proposed. It can effectively reflect the strength properties of materials, and can be used to predict and rank the cracking resistance of asphalt mixtures under different environmental conditions in high altitude areas. • FEI, R C and BCI were recommended to study the effects of temperature, freeze-thaw cycles frequency and ultraviolet radiation duration. • Mutually deepening environmental factors had the greatest reduction in R C. • A new SCB index (HAR-CRI) was established through statistical analysis. • HAR-CRI can predict and rank the cracking resistance of asphalt mixture. [ABSTRACT FROM AUTHOR]

With development of grid-connected wind power, data-driven wind turbine power curve (WTPC) becomes vital for many valuable applications whereas it is hugely affected by the potential outliers in supervisory control and data acquisition (SCADA) system. In this paper, high-fidelity WTPC modeling and its application on condition monitoring are deeply studied. Firstly, through irregular space-division and nonlinear space-mapping, stepwise data cleaning procedure is proposed. On this basis, the cleaned data are used for high-fidelity modeling where optimized least square support vector machine (LSSVM) is chosen for deterministic WTPC modeling and conditional kernel density estimation (CKDE) is selected for uncertainty modeling after comparison where the models are updated via the sliding time window mechanism. Using the consistency of historical data-driven models in a certain future time window, a daily condition monitoring system, including daily multi-index system and improved fuzzy comprehensive evaluation method (FCEM), is established to monitor future wind turbine condition. Finally, measured data from a wind farm in north China are acquired for validation. The results show that the outliers are effectively cleaned for high-fidelity WTPC modeling. The daily condition monitoring system including daily raw data preprocessing, multi-index system and FCEM algorithm can provide efficient daily rating score. • A versatile way for outlier identification in two-dimensional space is proposed. • Periodically updated deterministic and uncertainty WTPC modeling are presented. • A daily multi-index system is provided to capture different features of WTPC. • Daily comprehensive rating system of wind turbine power generation is realized. [ABSTRACT FROM AUTHOR]

As the tunnel excavation and resource exploitation go deeper into the Earth, increasing number of rockburst occur all over the world. However, the understanding of rockburst prediction and rockburst proneness evaluation is still at its infancy. In this paper, to accurately and efficiently predict rockburst proneness, a multi-index evaluation method for rockburst proneness of deep underground rock openings is established, verified and applied. The multi-index evaluation method is put forward based on attribute recognition model and combined weighting method. Eight discriminate indexes are taken into account, i.e., in-situ stress, rock brittleness, elastic deformation energy, maximum storage elastic strain energy, rock integrity, groundwater condition, section design size and site construction status. Through analyzing a variety of practical engineering cases, the weight values of each index are evaluated by combined method of analytic hierarchy process and random forest algorithm. It is found that the groundwater condition, in-situ stress, rock integrity and elastic deformation energy are the most important evaluation indexes. The attribute recognition model of rockburst proneness is established with weight evaluation and the attribute measure function of each index, where the attribute measure function is developed by analyzing the rockburst proneness of each index and setting up the standards for risk classes. The accuracy and applicability of the proposed multi-index evaluation method are verified through analyzing rockburst cases of twenty engineering projects. It is then applied to the rockburst hazard evaluation of the Caoguoshan Tunnel. Results showed that the predictions for rockburst proneness are consistent with the actual rockburst occurrence. The findings in this paper could facilitate evaluating of rockburst proneness for deep underground rock opening and be of great significance for rockburst risk prediction. [ABSTRACT FROM AUTHOR]

The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate. [ABSTRACT FROM AUTHOR]