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The utilization of thermal insulation is one of the most effective methods to reduce the heating and cooling loss through building envelopes. However, moisture ingress driven by the pressure gradient across the insulation may lead to a variation of the effective thermal conductivity, which highly deteriorates the thermal performance of building envelopes. In order to investigate moisture transport mechanism and its impact on the heat transfer procedure, a novel simultaneous test method based on the modified guarded hot box is proposed to investigate the variations of the moisture content and the effective thermal conductivity of thermal insulation, besides, several possible visual identification methods are applied to analyze the moisture transport paths. Based on the proposed methods, both aerogel blanket and organic foam are investigated on the variations of hygrothermal properties. Results indicate that compared to the foam insulation, the moisture content has a greater impact on aerogel blankets with the thermal conductivity increase to 3 times of the initial values. Both scanning electron microscopy (SEM) combined with crystal tracer and magnetic resonance imaging (MRI) are utilized to identify the moisture transport paths of phenolic and polyisocyanurate foams. Pinholes and capillary channels are two main paths for liquid permeation into phenolic. • Propose a simultaneous test method to measure the heat and moisture transfer. • Analyze the hygrothermal performance based on the material structure. • Visualize the moisture transport paths in typical thermal insulation for the first time. • Pinholes and capillary channels are main paths for liquid water to permeate into phenolic. [ABSTRACT FROM AUTHOR]

Closed-cell thermal insulation is one typical type of thermal insulation applied in the engineering field. However, it is observed that moisture may gradually accumulate in the insulation and highly decrease the system thermal behavior. The hygrothermal behavior depends on the mesoscopic structure and moisture distribution of the closed-cell materials. In order to investigate the impact of mesoscopic structure on the hygrothermal behavior of closed-cell insulation, a multi-scale simulation study is reported in this work. An improved random reconstruction method is first proposed to describe the structure of closed-cell insulation, and then a multi-scale thermal model is proposed to study the impact of mesoscopic structure at both mesoscopic and macroscopic scales. The trend of the results derived from the proposed multi-scale model is consistent with the experimental results, and the relevant error is lower than 10% when the degree of saturation is low. Based on the proposed models, it is found that the mesoscopic structures have significant impacts on the hygrothermal behavior of closed-cell thermal insulation at multiple scales. Both the porosity related parameters and other structure parameters are considered and discussed in detail, with sensitivity analysis provided at the end. [ABSTRACT FROM AUTHOR]

Abstract The Global Satellite Mapping of Precipitation (GSMaP) product is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission with high-precision and high-resolution. To quantify the error features and correction of GSMaP estimates and understand their hydrological potentials, the statistical indices were calculated to investigate the errors of three widely used GSMaP products (GSMaP_NRT, GSMaP_MVK and GSMaP_Gauge) at the daily and 0.1° × 0.1° resolutions over Hanjiang River Basin. Assessment results show that both GSMaP_NRT and GSMaP_MVK apparently overestimate the low rainy events (<10 mm/d), while underestimate the mid and high rainy events (>35 mm/d). False bias and hit bias are the main source of the total errors which mainly emerged in the upstream of Hanjiang River Basin. GSMaP_Gauge are in a position to substantially monitor the precipitation events with better error stability and distribution. But its unexpected systematic biases and high false alarm ratio, which mainly come from the overestimation of lower rain rates especially in the Tangbai river basin, still need to strengthen further in future developments. In addition, its mean errors are strongly related to rainfall intensity of rain gauge measurements that could be characterized by a quadratic polynomial fitting, which provides useful information on calibration. In practice, the errors of GSMaP_Gauge estimates have been reduced after correction by the polynomial fitting, while it causes some rain events to be lost. Overall, our study reveals the GSMaP_Gauge dataset can well reflect the precipitation characteristics of Hanjiang River Basin and offer the opportunity to be used in hydrometeorological applications. We expect the results documented here can provide a deep insight to the GSMaP estimates' error features correction and potential utilization for various hydrological applications. Highlights • GSMaP_NRT and GSMaP_MVK both overestimate the rainy events of <10 mm/d, while underestimate the rainy events of >35 mm/d. • GSMaP_Gauge has better error stability. But its systematic biases and high false alarm ratio still need to been strengthen. • Errors of GSMaP_Gauge are related to rainfall intensity that could be characterized by a quadratic polynomial fitting. • Document provides a deep insight to errors correction of GSMaP and its potential utilization for hydrological applications. [ABSTRACT FROM AUTHOR]

Urban green roofs have emerged as a significant trend in urban architecture worldwide, offering numerous benefits, including enhanced energy performance, improved urban microclimate, and public health. This study proposes a holistic framework for assessing the green roofs' energy-saving potential at the city scale, achieved by scaling up building-scale energy simulations to city-scale energy demands. Firstly, massive building information is collected using Geometric Information System (GIS) technologies. Subsequently, prototype buildings are generated to accurately represent the geometric characteristics of buildings at the city scale. Building performance simulation is further conducted considering three types of plants on roofs. A case study in Xiamen, China, demonstrates the effectiveness of the proposed framework to efficiently quantify the city-scale energy-saving potential of green roofs. By implementing green roofs in Xiamen, energy savings of 1.62–1.83% and peak load shaving of 1.10–1.63% can be achieved for the whole city. Overall, the proposed framework has the potential for widespread application in other cities with minor adjustments to accommodate variations in climate and building parameters. • Holistic framework for evaluating city-scale potential of green roofs. • Prototype buildings to capture various building characteristics at city scale. • GIS-informed scaling up of building-scale energy simulation toward city-scale. • Ophiopogon japonicus performing the best on city-scale green roofs in Xiamen. • Up to 1.83% energy saving and 1.63% peak load shaving by green roofs in Xiamen. [ABSTRACT FROM AUTHOR]

Highlights • Provide a mesoscopic model to study the heat transfer in soil-like materials. • Perform thermal probe tests to study the fractal quartz sand in mesoscopic scale. • Clarify the impacts of mesoscopic structural parameters in dry and moist materials. • Verify that the randomness of the porous structure does have considerable impact. Abstract The ground heat exchanger (GHE) is a key component in the ground coupled heat pump (GCHP) system. The design of GHE significantly depends on the effective thermal conductivity of the ambient soil, which is derived from thermal response tests in the field. However, the test results normally indicate large differences in value, even at the same location. Such variations might be caused by the mesoscopic structure of soil. In order to clarify the impacts of the mesoscopic structural parameters on the effective thermal conductivity of soil-like materials, this paper presents and analyzes the findings from thermal probe tests and a modified random fractal model. It is found that porosity and degree of saturation are still the dominant factors, while the impacts of fractal dimension and size ratio are minor. The randomness of the porous structure in the soil-like materials does have considerable impact on the effective thermal conductivity. [ABSTRACT FROM AUTHOR]

• Build a co-optimization model to optimize supply and demand sides of IES. • Modify stochastic programming to capture both demand and supply sides' scenarios. • Co-optimization model achieves more cost-efficient design considering carbon tax. • Basic and Standard demand-side upgrading are cost-efficient while Premium is not. • Carbon tax accelerates both renewable penetration and demand-side upgrading. Integrated energy systems (IES) can help achieve greater energy efficiency, and then ultimately promote a climate-neutral economy by utilizing local renewable resources. Demand-side energy-saving measures can reduce operational costs associated with energy usage. Most existing IES models, however, focus on supply-side optimization, while the demand-side energy-saving potential and its impacts on whole-system performance are still not clear. The increasing carbon tax makes it even more important to understand the interactions between supply and demand sides to achieve a sustainable system with a minimal carbon charge. Hence, this study proposes a co-optimization model to simultaneously optimize the supply and demand sides of an IES considering the impact of the carbon tax. A selection tree is developed to describe various demand-side envelope upgrading technologies, and a binary tree is established by generating a set of supply-side scenarios with corresponding probabilities. Based on these results, an improved two-stage stochastic programming model is proposed. The robustness of the modeling results was further validated by a simulation–optimization-based uncertainty analysis addressing price uncertainties. A case study in Shanghai indicates that the proposed co-optimization model achieves more cost-efficient solutions than supply-side-only optimization considering carbon tax. Introducing carbon tax can reduce the installed capacity of fuel-based energy technologies by up to 24% and greatly accelerate the penetration of renewables. The increasing carbon tax also promotes the adoption of more advanced energy-saving technologies. Uncertainty analysis reveals acceptable robustness of the optimal demand-side scheme and supply-side configuration with a deviation of less than 5% and a coefficient of variation of 7%. Overall, the observations of the proposed model and case study provide valuable insights for IES design considering an emerging charge of carbon tax. [ABSTRACT FROM AUTHOR]

• Model community-level energy by combining GIS, simulation, and data-driven approaches. • Propose a GIS-based urban building energy modeling method with low computational cost. • Generate various prototype buildings through two-stage clustering process to describe buildings' diversity. • Integrate urban building energy modeling with global sensitivity analysis to discovery urban planning factors. • Identify FAR and BCR as the most influential factors affecting communities' energy use. The residential sector accounts for an increasing amount of global energy use with continued urbanization. Residential energy-informed urban planning offers an economical and easy-to-operate approach to achieve more efficient urban energy utilization. However, quantifying the interactions between residential energy and urban planning remains an open challenge. This study proposes a holistic approach integrating GIS techniques, building energy modeling, and a global sensitivity analysis to prioritize eight key urban planning factors on the community energy performance based on a building energy dataset. The dataset, including urban planning and building information, was first established using GIS techniques and validated using survey data. The residential energy performance model at the community scale was developed using the clustering tree structure of residential building prototypes and building performance simulations. A combined data-driven and global sensitivity analysis approach was further applied to prioritize the impacts of eight vital urban planning factors on energy use intensity and peak load intensity. A case study of 1963 communities in Shanghai revealed that, for the energy performance of residential communities, the floor area ratio and building coverage ratio are the most influential factors, followed by the maximum height and high-rise proportion having a relatively low impact but higher than other factors. Overall, the proposed holistic approach generates robust insights into urban-scale residential energy performance, which can effectively inform urban planners to achieve more energy-efficient regulatory planning. [ABSTRACT FROM AUTHOR]