Zhihao Wang

Directly irradiated phase change materials (PCMs) are governed by coupled radiative absorption, internal heat transfer, and heat-extraction conditions, making filler effects strongly stage-dependent. Paraffins with nominal phase-change temperatures of 46, 58, and 70 °C were formulated as neat paraffin, 2 wt% expanded graphite (EG), 1 wt% multi-walled carbon nanotubes (MWCNTs), and a hybrid composite containing 2 wt% EG and 1 wt% MWCNTs. All 12 samples were charged under direct irradiation at 1000 W·m⁻², while the 58 °C series was characterized in detail. During discharge, units with different phase-change temperatures were assembled into filler-specific cascaded systems and tested under once-through water flow at 0.7 L·min⁻¹. During early charging (0–60 min), the EG and hybrid systems showed mean heating rates of 0.406 and 0.437 °C·min⁻¹, respectively, exceeding those of neat paraffin and the MWCNT system. During 60–300 min, the MWCNT system achieved the highest mean heating rate (0.148 °C·min⁻¹) and the shortest interval from local melting onset to substantial melting of the main region (198 min). Over the first 90 min of discharge, the hybrid and EG cascaded systems delivered mean heat-extraction powers of 347.4 and 332.8 W, corresponding to 44.1% and 38.0% increases over neat paraffin. Thus, EG mainly improved heat diffusion and water-side output, whereas MWCNTs better sustained temperature rise and phase-change progression during middle-to-late charging.

Flow monitoring is increasingly used in urban sewer networks to support inflow and infiltration assessment and operational management, making sensor placement strategy (SPS) optimization important for effective monitoring under limited budgets. Although existing placement criteria consider information content, detection sensitivity, or spatial coverage, the resulting SPSs may lack direct physical transparency or provide limited guidance for diagnosing and refining existing expert-driven SPSs. To address these limitations, this study proposes a physically interpretable monitoring-load-uniformity framework for optimizing and refining flow sensor placement in urban sewer networks. Monitoring load is defined by coupling net monitored pipe length and sensor-assigned flow, thereby integrating spatial monitoring scope and hydraulic contribution into a single measure of sensor-level monitoring responsibility. The proposed criterion is compared with three benchmark criteria, namely pipe-length uniformity, flow uniformity, and average detection threshold (ADT) minimization. Applied to two real-world sewer networks, the proposed load-balanced SPS achieves the lowest monitoring-load variability while maintaining competitive ADT performance. In the larger network, it obtains a load uniformity index (LUI) of 0.98 and a maximum-to-minimum monitoring-load ratio of 1.96, while its ADT is only about 5% higher than that of the ADT-minimized solution. For an expert-driven layout, local refinement guided by monitoring-load diagnosis increases LUI from 0.87 to 0.96 and reduces ADT from 17.33 to 10.58 L/s. These results demonstrate that monitoring-load uniformity provides an interpretable and engineering-friendly basis for both optimized SPS design and practical refinement of existing sewer-network monitoring layouts.

Conventional rectangular encapsulation structures for latent heat thermal energy storage (LHTES) systems suffer from thermal hysteresis at bottom corners in the late phase change stage, where conductive "dead zones" form due to weakened natural convection, causing local melting delays and limiting energy storage performance. To address this issue, this study proposes a structural optimization strategy by regulating the top-bottom thickness ratio of the PCM cavity to realize differentiated spatial distribution of phase change material (PCM), with the cavity volume and heating surface area kept constant. Three PCM distribution configurations (uniform thickness S=1, thick-top-thin-bottom S=0.5, thicker-top-even-thinner-bottom S=0) were designed and experimentally investigated under heating surface inclinations of 30°, 60° and 90°.The results indicate that a smaller top-bottom thickness ratio accelerates the overall melting process, shortens the total melting duration significantly, and improves the temperature uniformity of PCM, thus alleviating issues of localized sensible heat accumulation. Thermodynamic analysis reveals that the optimized structure extends the active period of natural convection. Specifically, the accelerated thermal storage is achieved by prioritizing latent heat utilization in the upper PCM region, a mechanism that effectively weakens the corner hysteresis phenomenon. This work proposes a novel structural design concept for LHTES units and provides experimental validation for its thermal performance enhancement, offering a feasible approach for the structural optimization of phase change energy storage systems.

Amid increasingly frequent extreme climate events, studies on lake-induced cooling effects exhibit a clear spatial bias—focusing mainly on low-elevation urban areas and the high (>3,000 m) Tibetan Plateau—while mid-elevation lakes (1,000 2,000 m) on the Yunnan Plateau remain largely understudied. To address this research gap, this study develops a multi-scale thermal-effect assessment framework for mid-elevation Yunnan Plateau lakes based on multi-source remote sensing data, integrating comparative analyses of lake attributes with RF SHAP–based modeling of lakeshore environmental factors to identify key drivers and cooling typologies. Results show that lake induced cooling is most pronounced during the rainy season (mean cooling intensity, CI = 14.3°C. cooling distance, CD = 712 m) and exhibits distinct lag effects during transitional periods. Water depth and volume primarily determine cooling intensity, whereas regional humidity and precipitation govern the horizontal extent of cooling. Among lakeshore variables, NDVI, slope, and vegetation height significantly influence cooling performance, exhibiting nonlinear thresholds that vary across lake types. Overall, the proposed dual-scale framework effectively reveals the spatial heterogeneity and nonlinear dynamics of lake cooling effects in mid-elevation Yunnan Plateau lakes, providing a scientific basis for optimizing landscape configuration, enhancing climate-resilient planning, and integrating nature-based solutions in vulnerable highland regions.

Understanding the seasonal dynamics of urban thermal environments requires high–spatiotemporal-resolution thermal infrared remote sensing data. Based on these data, we develop an explainable machine-learning framework to quantify the nonlinear interactions between urban form and climatic forcing that drive urban thermal patterns during the dry and wet seasons. The results reveal two contrasting thermal regimes: in the dry season, strong solar radiation and low humidity produce a compact high-temperature core, while surrounding mountains provide persistent topographic cooling and constrain thermal expansion; in the wet season, background temperatures rise, but land cover heterogeneity and urban–rural gradients limit contiguous extreme heat, resulting in a fragmented thermal mosaic. Cross-seasonal analysis shows that elevation and nighttime light intensity dominate land surface temperature variability, whereas tree canopy height consistently provides cooling. A significant warming threshold emerges when the sky view factor exceeds 0.85. Interaction effects vary seasonally: the coupling between topography and human activity strengthens during the wet season, and canopy structure more effectively mitigates extreme heat in densely built-up areas. The integrated high-resolution thermal remote sensing and explainable AI approach provides a transferable framework for clarifying the seasonal modulation pathways of urban form on thermal environments. It also offers a quantitative basis for climate-adaptive planning in temperate cities—such as optimizing blue-green spaces based on morphological thresholds and refining heat-risk zoning.

This study compares high-frequency mobile observation data collected in the same area of Kunming under two different meteorological conditions—15 January 2020, and 8 January 2023—to analyze changes in the micro-scale urban thermal environment. Vehicle-mounted temperature and humidity sensors, combined with GPS tracking, were used to conduct real-time, high-resolution data collection across various urban functional areas. The results show that in the two tests, the maximum temperature differences were 10.4 °C and 16.5 °C, respectively, and the maximum standard deviations were 0.34 °C and 2.43 °C, indicating a significant intensification in thermal fluctuations. Industrial and commercial zones experienced the most pronounced cooling, while green spaces and water bodies exhibited greater thermal stability. The study reveals the sensitivity of densely built-up areas to cold extremes and highlights the important role of green infrastructure in mitigating urban thermal instability. Furthermore, this research demonstrates the advantages of mobile observation over conventional remote sensing methods in capturing fine-scale, dynamic thermal distributions, offering valuable insights for climate-resilient urban planning.

This research addresses urban heat island intensification driven by urbanization using Dianchi Lake in Kunming, China, as a case study, aiming to quantitatively evaluate the spatial extent, intensity, and land cover sensitivity differences in the cooling effects of large urban water bodies across dry/wet seasons and complex urban landscapes (forest, cropland, and impervious surfaces) to provide a scientific basis for optimizing thermal environments in low-latitude plateau cities. Based on Landsat 8/9 satellite data from dry (January) and wet (May) seasons in 2020 and 2023 used for land surface temperature (LST) retrieval combined with land use data, buffer zone gradient analysis was adopted to quantify the spatial heterogeneity of key cooling indicators within 0–1500 m lakeshore buffers. The results demonstrated significant seasonal differences. The wet season showed a greater cooling extent (600 m) and higher intensity (6.0–6.6 °C) compared with the dry season (400 m; 2.4–3.9 °C). The land cover responses varied substantially, with cropland having the largest influence (600 m), followed by impervious surfaces (400 m), while forest exhibited a minimal effective cooling range (100 m) but localized warming anomalies at 200–400 m. Sensitivity analysis confirmed that impervious surfaces were the most sensitive to water-cooling, followed by cropland, whereas forest showed the lowest sensitivity.