More Like this

1. Interactions between urban heat islands and heat waves

   https://doi.org/10.1088/1748-9326/aa9f73  

   Zhao, Lei; Oppenheimer, Michael; Zhu, Qing; Baldwin, Jane W; Ebi, Kristie L; Bou-Zeid, Elie; Guan, Kaiyu; Liu, Xu (March 2018, Environmental Research Letters)
2. Heat purchase agreements could lower barriers to heat pump adoption

   https://doi.org/10.1016/j.apenergy.2021.116489  

   Kircher, Kevin J.; Zhang, K. Max (March 2021, Applied Energy)
3. Modeling pseudo-turbulent heat flux in gas-solid heat transfer

   https://doi.org/10.1016/j.ces.2023.119371  

   Zhou, Jiazhong; Sun, Bo; Subramaniam, Shankar (January 2024, Chemical Engineering Science)

   Flow past disperse solid particles or bubbles induces fluctuations in carrier fluid velocity, which correlate with temperature fluctuations in non-isothermal flows resulting in the pseudo-turbulent heat flux (PTHF). In the Eulerian-Eulerian (EE) two-fluid (TF) model, the transport of PTHF is shown to be an important contributor to the overall energy budget, and is modeled using a pseudo-turbulent thermal diffusivity (PTTD). The PTHF and PTTD were originally quantified using particle-resolved direct numerical simulation (PR-DNS) data, and correlations were developed over a range of solid volume fraction (0.1 ≤ 𝜀𝑠 ≤ 0.5) and mean slip Reynolds number (1 ≤ 𝑅𝑒𝑚 ≤ 100) for a Prandtl number of 0.7. However, the original PTTD correlation diverges to infinity as the solid volume fraction goes to zero, which is physically unrealistic. This singular behavior is problematic for EE TF simulations at particle material fronts where solid volume fraction values can fall below the lower limit of existing data (𝜀𝑠 =0.1) to zero in the pure carrier phase. In this work, additional PR-DNS data are reported for 𝜀𝑠 < 0.1, and improved correlations are developed for the PTHF and PTTD. The new PTTD correlation is non- singular, and both the PTHF and PTTD decay exponentially to zero as the solid volume fraction approaches zero, which is physically reasonable. This improves prediction of PTHF transport in dilute flow using EE TF heat transfer simulations. 

   more » « less
4. Inversion for Fire Heat-Release Rate Using Heat Flux Measurements

   https://doi.org/10.1115/1.4046264  

   Kurzawski, Andrew J.; Ezekoye, Ofodike A. (May 2020, Journal of Heat Transfer)

   Abstract In fire hazard calculations, knowledge of the heat-release rate (HRR) of a burning item is imperative. Typically, room-scale calorimetry is conducted to determine the HRRs of common combustible items. However, this process can be prohibitively expensive. In this work, a method is proposed to invert for the HRR of a single item burning in a room using transient heat flux measurements at the walls and ceiling near the item. The primary device used to measure heat flux is the directional flame thermometer (DFT). The utility of the inverse method is explored on both synthetically generated and experimental data using two so-called forward models in the inversion algorithm: fire dynamics simulator (FDS) and the consolidated model of fire and smoke transport (CFAST). The fires in this work have peak HRRs ranging from 200 kW to 400 kW. It was found that FDS outperformed CFAST as a forward model at the expense of increased computational cost and that the error in the inverse reconstruction of a 400 kW steady fire was on par with room-scale oxygen consumption calorimetry. 

   more » « less
5. Riverine heat waves on the rise, outpacing air heat waves

   https://doi.org/10.1073/pnas.2503160122  

   Sadayappan, Kayalvizhi; Li, Li (September 2025, Proceedings of the National Academy of Sciences)

   While air heat waves often grab headlines, riverine heat waves have gone quietly unnoticed because rivers are commonly perceived as cool refuges. Analysis of riverine heat waves has been hindered by fragmented datasets, despite a proliferation in water-temperature monitoring with sensors and satellites. Here, we analyze riverine heat wave events by training one single deep learning (long short-term memory) model and reconstructing consistent and continuous daily water temperatures (WT) in 1471 sites in the Contiguous United States (1980–2022). We show that riverine heat waves occur at about half the frequency (2.3 versus 4.6 events/year), a third intensity (2.6 versus 7.7 °C/event), but almost double the duration (7.2 versus 4.0 d/event) of air heat waves. Riverine heat wave events have increased at double to quadruple rates of air heat wave events, amounting to an additional 1.8 events/year in frequency, 0.43 °C/event in intensity, 3.4 d/event in duration, and 7 to 15 additional thermal stress days for aquatic ecosystems in 2022 compared to 1980. Rising riverine heat waves have outpaced those of air heat waves in 65 to 76% of the sites, particularly in regions experiencing accelerated warming (e.g., the Rockies). Riverine heat wave trends are driven predominantly by climate-induced changes such as warming and dwindling snowpacks and water flow. Human activities do play important roles: large dams elongate, whereas agriculture reduces heat waves. These results highlight anthropogenic climate change as the primary external driver, whereas human-induced structural changes as the secondary internal modulators of river response to heat disturbance. The widespread rise of riverine heat waves threatens aquatic ecosystems and water-energy-food security, underscoring the need for their global characterization and risk assessment. 

   more » « less