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While equation-based object-oriented modeling language Modelica can evaluate practical energy improvements for district cooling systems, few have adopted Modelica for this type of large-scale thermo-fluid system. Further, to our best knowledge, district cooling modeling studies have yet to include hydraulics in piping networks alongside plant models featuring realistic mechanical systems and controls. These are critical details to include when looking to make energy and control improvements in many physical system installations. To fill these gaps, this study released new open-source district cooling models at the Modelica Buildings Library and leveraged these models for a real-world case study at the University of Colorado Boulder. The site includes six buildings connected to a central chiller plant featuring a waterside economizer. Several energy saving strategies are pursued based on the validated model, including control setpoint optimization, equipment modification, and pump setpoint adjustments. Results indicate that a combination of the studied measures can save the campus annually 84.6 MWh of energy, 8.9% of electricity costs, 58.0 metric tons of carbon dioxide emissions, while the waterside economizer cuts down chillers’ run times by 201 days/year, reducing maintenance costs and extending chiller life.

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the evaluation and optimization of those controls is limited. It is possible to maximize energy savings by selecting different sequences and related parameters based on the plant configuration, load, and climate. This paper addresses this gap by developing near-optimal advanced control sequences for chiller plants with WSEs. First, advanced control sequences for chiller plants with WSEs are categorized into condenser water, chilled water, and hybrid controls and representative sequences from each category are identified. Next, 504 different scenarios are optimized. These scenarios represent all possible combinations of two plant configurations, a constant or variable load profile, three advanced control sequences, and seven optimization parameter combinations in six climate zones. The results show the recommended near-optimal sequences can reduce energy consumption by up to 15% relative to the baseline depending on the configuration, load profile, and climate. Specifically, the CW-CHW sequence is recommended for the majority of systems because it is often the most energy efficient and/or reduces the runtime of chillers. The methodology in this paper provides practical guidance for achieving energy savings through near-optimal control of chiller plants with WSEs. [ABSTRACT FROM AUTHOR]

Safety monitoring sensors in smart railways need a sustainable onboard power supply. This article proposes a counter‐rotating gear energy harvester (CG‐EH) to convert the longitudinal vibration energy of trains into electricity for onboard sensors.CG‐EH consists of a vibration input module, a motion conversion module, and an energy conversion module. The vibration input module converts the longitudinal displacement of the coupler into the rotational motion of the gears. The motion conversion module realizes the conversion of the reciprocating input displacement into the unidirectional rotation based on a counter‐rotating gear set, multi‐stage spur gear sets can effectively mitigate the effects of excitation on CG‐EH. The energy conversion module transforms the kinetic energy of the unidirectional rotation into electrical energy through a generator. Experimental results show that the energy outputs of CG‐EH are improved with longitudinal vibration compared with the usual onboard energy harvester. From the result, the peak output power of CG‐EH is 14.59 W, the peak efficiency reaches 39.2%, enough to power relevant onboard sensors. Moreover, CG‐EH can monitor the running status of trains based on deep learning. From the experiment results and application prospects, CG‐EH is a favorable solution for the power supply problems of onboard sensors in smart railways. [ABSTRACT FROM AUTHOR]

The increasing challenges of urbanization and climate change have driven the need for innovative stormwater management solutions. Rain gardens, as a nature-based solution (NBS), have emerged as a critical component in urban water management, particularly in enhancing hydrological regulation, water quality, and ecosystem services. This bibliometric review examines the application of rain gardens in urban environments, focusing on their roles in stormwater management, pollutant removal, and ecological enhancement. Data from 728 academic papers published between 2000 and 2023 were analyzed using the Web of Science (WoS) Core Collection, employing bibliometric tools such as the "Bibliometrix" R package and CiteSpace. The analysis highlights the increasing global interest in rain gardens, particularly since 2015, with China and the United States leading research efforts. Key findings reveal that rain gardens significantly reduce runoff, improve water quality, and contribute to urban biodiversity. In addition, their integration into public spaces offers landscape esthetics and social benefits, enhancing the quality of life in urban areas. However, challenges remain in optimizing their design for diverse climates and long-term performance. The study underscores the need for further research on plant–soil interactions, pollutant removal mechanisms, and the broader ecological and social contributions of rain gardens. This review provides insights into the evolution of rain garden research and identifies future directions for advancing sustainable urban stormwater management. [ABSTRACT FROM AUTHOR]

Rapid urbanization has altered the natural surface properties and spatial patterns, increasing the risk of urban waterlogging. Assessing the probability of urban waterlogging risk is crucial for preventing and mitigating the environmental risks associated with urban waterlogging. This study aims to evaluate the impact of different urban spatial morphologies on the probability of urban waterlogging risk. The proposed assessment framework was demonstrated in Guangzhou, a high-density city in China. Firstly, a spatial weight naive Bayes model was employed to map the probability of waterlogging risk in Guangzhou. Secondly, the World Urban Database and Access Portal Tools (WUDAPT)-based method was used to create a local climate zone (LCZ) map of Guangzhou. Then, the range of waterlogging risk and the proportion of risk levels were analyzed across different LCZs. Finally, the Theil index was used to measure the disparity in waterlogging risk exposure among urban residents. The results indicate that 16.29% of the area in Guangzhou is at risk of waterlogging. Specifically, 13.06% of the area in LCZ 2 is classified as high risk, followed by LCZ 1, LCZ 8, and LCZ 10, with area proportions of 11.42%, 8.37%, and 6.26%, respectively. Liwan District has the highest flood exposure level at 0.975, followed by Haizhu, Yuexiu, and Baiyun. The overall disparity in waterlogging exposure in Guangzhou is 0.30, with the difference between administrative districts (0.13) being smaller than the difference within the administrative districts (0.17). These findings provide valuable insights for future flood risk mitigation and help in adopting effective risk reduction strategies at urban planning level. [ABSTRACT FROM AUTHOR]

In this study, a multi-stage planning framework was constructed by using SWMM simulation modeling and NSGA-II and applied to optimize the layout of integrated grey–green infrastructure (IGGI) under land use change and climate change scenarios. The land use change scenarios were determined based on the master plan of the study area, with imperviousness of 50.7% and 62.0% for stage 1 and stage 2, respectively. Rainfall trends for stage 1 and stage 2 were determined using Earth-E3 from the CMIP6 model. The rainfall in stage 2 increased by 14.9% from stage 1. Based on these two change scenarios, the spatial configuration of IGGI layouts with different degrees of centralization of the layout (DCL) under the two phases was optimized, with the lowest life cycle cost (LCC) as the optimization objective. The results showed that the layout with DCL = 0 had better performance in terms of LCC. The LCC of the layout with DCL = 0 was only 66.9% of that of the layout with DCL = 90.9%. In terms of Tech-R, stage 2 had better performance than stage 1. Furthermore, the average technological resilience (Tech-R) index of stage 2 was 0.8–3.4% higher than that of stage 1. Based on the LCC and Tech-R indices of all of the layouts, TOPSIS was used to compare the performance of the layouts under the two stages, and it was determined that the layout with DCL = 0 had the best economic and performance benefits. The results of this study will be useful in exploring the spatial configuration of urban drainage systems under land use change and climate change for sustainable stormwater management. [ABSTRACT FROM AUTHOR]

Urbanization and climate change have led to rising urban temperatures, increasing heat-related health risks. Assessing urban heat risk is crucial for understanding and mitigating these risks. Many studies often overlook the impact of block types on heat risk, which limits the development of mitigation strategies during urban planning. This study aims to investigate the influence of various spatial factors on the heat risk at the block scale. Firstly, a GIS approach was used to generate a Local Climate Zones (LCZ) map, which represents different block types. Secondly, a heat risk assessment model was developed using hazard, exposure, and vulnerability indicators. Thirdly, the risk model was demonstrated in Guangzhou, a high-density city in China, to investigate the distribution of heat risk among different block types. An XGBoost model was used to analyze the impact of various urban spatial factors on heat risk. Results revealed significant variations in heat risk susceptibility among different block types. Specifically, 33.9% of LCZ 1–4 areas were classified as being at a high-risk level, while only 23.8% of LCZ 6–9 areas fell into this level. In addition, the pervious surface fraction (PSF) had the strongest influence on heat risk level, followed by the height of roughness elements (HRE), building surface fraction (BSF), and sky view factor (SVF). SVF and PSF had a negative impact on heat risk, while HRE and BSF had a positive effect. The heat risk assessment model provides valuable insights into the spatial characteristics of heat risk influenced by different urban morphologies. This study will assist in formulating reasonable risk mitigation measures at the planning level in the future. [ABSTRACT FROM AUTHOR]

An attempt was made to develop an improved autoregressive with exogenous (ARX) model for office buildings cooling load prediction in five major climates of China. The cooling load prediction methods can be arranged into three categories: regression analysis, energy simulation, and artificial intelligence. Among them, the regression analysis methods using regression models are much simple and practical for real applications. However, traditional regression models are often helpless to manage multiparameter dynamic changes, making it not accurate as the other two categories. Many of the existing cooling load prediction studies use piecewise linearization to manage nonlinearity. To improve the prediction accuracy of regression analysis methods, higher order and interaction terms are included in improved ARX based on traditional ARX model. The improved ARX model consists of eight variables, with eleven coefficients accessed at a time. For applications and evaluations, an office building in major cities within each climatic zone was selected as a representation. These cities were Harbin, Beijing, Nanjing, Kunming and Guangzhou respectively. The coefficient of determination R2 is greater than 0.9 in five cities. The prediction results show that the improved ARX model can adapt to different climatic conditions, including those nonlinearity cases.