Heat-induced air turbulence produces complex, depth-dependent image distortions that are challenging to reproduce interactively because thermally driven flow must be coupled with refractive light transport. Existing real-time methods often rely on single-view 2D screen-space warps that break multi-view coherence and do not model a 3D refractive volume. We present a real-time, fully 3D Lagrangian framework that models the full pipeline from thermal transport to density variation to optical refraction. Our system augments compressible Smoothed Particle Hydrodynamics (SPH) with temperature transport, buoyancy, and pressure-driven motion to capture rising plumes and turbulent mixing. We render the resulting continuous refractive-index field via curved ray tracing to model light bending in 3D. To reconcile physical fidelity with interactive performance, we introduce spatially adaptive step-size integration for curved-ray tracing, refining steps near strong refractive-index gradients while relaxing them in smooth regions to preserve temporal stability and high-frequency distortion detail without uniform oversampling. The system runs at interactive rates (about 40 fps in our prototype) and matches depth-dependent, multi-view-consistent distortions observed in real video captures more closely than image-based baselines.
Jonathan Stéphano, Michaël Brunengo, Riccardo Di Dio
et al.
We propose a multi-scale lung model to investigate spatio-temporal distributions of ventilation variables. Lung envelope and large airway geometries are derived from CT scans; smaller airways are generated using a physiologically consistent algorithm. Tissue mechanics is modeled using nonlinear elasticity under small deformations, coupled with local air pressure from fluid dynamics within the bronchial tree. Airflow accounts for inertia and static airway compliance. Simulations employ finite elements. Using this model, we explore spatio-temporal airflows and shear stresses distributions.
Chen Guanghao He Yunyun Chen Jianyong Chen Ying Luo Xianglong, Liang Yingzong He Jiacheng
Vapor-liquid separation technology can enhance heat transfer while reducing pressure drop. The vapor-liquid separation unit is key to achieving efficient vapor-liquid separation. A visualization experiment of the header-orifice separator is conducted in this study using the zeotropic mixture R1234ze（E）/R32 （mass fraction ratio， 80/20） to investigate the vapor-liquid separation characteristics under different conditions and obtain the effective separation range. The results show that increasing the inlet dryness vapor quality， reducing the inlet mass-flow rate， increasing the flow cross-sectional area of the lower outlet branch， and expanding the separation aperture can improve the separation efficiency， among which the inlet mass-flow rate contributes the most significantly to the separation efficiency. In the effective separation area， when the flow rate increases from 18 to 12 g/s， the separation efficiency increases by 14.0%. The inlet mass-flow rate， valve opening， and separation aperture minimally affects the size of the effective separation dryness range； however， for the deviation of the effective separation area dryness range， the inlet mass-flow rate exerts the greatest impact， followed by the separation aperture， whereas the valve opening exerts the least impact.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Heating systems in a building’s mechanical infrastructure account for a significant share of global building energy consumption, underscoring the need for improved efficiency. This study evaluates 31 predictive models—including neural networks, gradient boosting (XGBoost), bagging, and multiple linear regression (MLR) as a baseline—to estimate heating-coil performance. Experiments were conducted on a water-based air-handling unit (AHU), and the dataset was cleaned to eliminate illogical and missing values before training and validation. Among the evaluated models, neural networks, gradient boosting, and bagging demonstrated superior accuracy across various error metrics. Bagging offered the best balance between outlier robustness and pattern recognition, while neural networks showed strong capability in capturing complex relationships. An input-importance analysis further identified key variables influencing model predictions. Future work should focus on refining these modeling techniques and expanding their application to other HVAC components to improve adaptability and efficiency.
Intensifying heatwaves driven by climate change are accelerating the adoption of mobile air conditioning (AC) systems. A rapid mass adoption of such AC systems could create additional stress on electricity grids and the power system. This study presents a novel method to estimate the electricity demand from AC systems both at the system level and at high temporal and spatial granularity. We apply the method to a near-future heatwave scenario in Germany in which household AC adoption increases from the current 19% to 35% during a heatwave similar to the one of July 2025. We analyze the effects for 196,428 grid cells of one square kilometer across Germany, by combining weather data, census data, socio-demographic assumptions, mobility patterns, and temperature-dependent AC activation functions. We find that electricity demand of newly purchased mobile AC systems could increase the peak load by over 12.9 GW, with urban hot-spots reaching 5.2 MW per square kilometer. The temporal pattern creates a pronounced afternoon peak that coincides with lower photovoltaic generation, potentially exacerbating power system stability challenges. Our findings underscore the urgency for proactive energy system planning to manage emerging demand peaks.
In this study, we investigate the impact of heating or cooling in a suspension experiencing phototactic bioconvection. The suspension is illuminated by collimated irradiation from the top and subjected to heating or cooling from the bottom. The governing equations include the Navier Stokes equations with the Boussinesq approximation, the diffusion equation for motile microorganisms, and the energy equation for temperature. Employing linear perturbation theory, we analyse the linear stability of the suspension. The findings predict that the suspension undergoes destabilization when heated from below and stabilization when cooled from below. This suggests a sensitive dependence of the system's stability on the thermal conditions, providing valuable insights into the behavior of phototactic bioconvection under different heating or cooling scenarios.
We investigate the effect of humidity on the propagation of streamers in air. We present a minimal set of chemical reactions that takes into account the presence of water in a nonthermal air plasma and considers ionization, attachment, detachment, recombination and ion conversion including water cluster formation. We find differences in streamer propagation between dry and humid air that we attribute mostly to an enhanced effective attachment rate in humid air, leading to higher breakdown electric field and threshold field for propagation. This higher effective attachment rate in humid conditions leads to a faster decay of the conductivity in the streamer channel, which hinders the accumulation of charge in the streamer head. In some cases a propagating streamer solution still exists at the expense of a smaller radius and lower velocity. In other cases a high humidity leads to the stagnation of the streamer. We finally discuss how all these statements may affect streamer branching and the dimensions and lifetime of a streamer corona.
A passenger-centered Heating, Ventilation, and Air Conditioning (HVAC) system is an urgent need with the innovative development of intelligent vehicles. A prerequisite for such a system is precise real-time estimation of human thermal comfort. This paper introduces a new method based on deep learning to predict the personal thermal comfort in the vehicle via facial thermal image. Specifically, a deep learning-based ResNet34 coupled with a spatial attention mechanism (SAM-ResNet34) is designed for feature extraction from the facial thermal image. Unlike the existing methods, this method extracts the gray features from the different areas of facial thermal images instead of measuring the facial skin temperature to predict the thermal comfort states. The thermal comfort data were collected from the experiment with 22 subjects. It has highlighted that the model trained with the thermal image dataset can achieve an accuracy as high as 93.75% in the test set. The result suggests that the non-invasive method proposed in this study could accurately predict personal thermal comfort in the vehicular environment and holds great potential to be applied to the HVAC control system of a vehicle in the future.
In this study, the efficient preservation of fresh frozen plasma was investigated. Improving the quick freezing efficiency was achieved by adding a magnetic field to assist the freezing process. The changes in plasma ice crystal structure and size in the quick-freezing experiment under a magnetic field of 0–100 Gs were analyzed. Considering magnetic field-assisted quick-freezing accelerates the mass and heat transfer rate, the growth rate of ice crystals is faster than the speed of water migration, resulting in changes in the shape and distribution of the ice crystals. Therefore, small, round, and uniform ice crystals were formed in the plasma, which reduced the damage to the structure of the plasma by the ice crystals and thus improved the quality. The plasma coagulation factor Ⅷ content in the quick-freezing experiment under 0–100 Gs magnetic field intensity was reduced by different amounts depending on the magnetic field intensity when compared with that for fresh frozen plasma. The maximum reduction of 3.56% occurred at a magnetic field of 20 Gs. The active concentration under magnetic fields of 60 Gs and 80 Gs were higher than those atother field strengths, and the plasma quality effect after quick-freezing storage was better.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Ata Nazari, Jiarong Hong, Farzad Taghizadeh-Hesary
et al.
Abstract Transmission via virus-carrying aerosols inside enclosed spaces is an important transmission mode for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as supported by growing evidence. The urban subway is one of the most commonly used enclosed spaces. The subway is a utilitarian and low-cost transit system in today’s society. However, studies are yet to demonstrate patterns of viral transmission in subway heating, ventilation, and air conditioning (HVAC) systems. To fill this gap, we performed a computational investigation of the airflow (and the associated aerosol transmission) in an urban subway cabin equipped with an HVAC system. We employed a transport equation for aerosol concentration, which was added to the basic buoyant solver to resolve aerosol transmission inside the subway cabin. This was achieved by considering the thermal, turbulence, and induced ventilation flow effects. Using the aerosol encounter probability over sampling lines crossing the passenger breathing zones, we can detect the highest infection risk zones inside the urban subway under different settings. We proposed a novel HVAC system that can impede aerosol spread, both vertically and horizontally, inside the cabin. In the conventional model, the maximum aerosol encounter probability from an infected individual breathing near the fresh-air ducts was equal to 15%. This decreased to 0.36% in the proposed HVAC model. Overall, using the proposed HVAC system for urban subways decreased the mean value of the aerosol encounter probability by approximately 79% compared to that for the conventional system.
Xuecheng Yin, I. Esra Buyuktahtakin, Bhumi P. Patel
This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling the COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. The results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.
Air pollution has been linked to elevated levels of risk aversion. This paper provides the first evidence showing that such effect reduces life-threatening risky behaviors. We study the impact of air pollution on traffic accidents caused by risky driving behaviors, using the universe of accident records and high-resolution air quality data of Taiwan from 2009 to 2015. We find that air pollution significantly decreases accidents caused by driver violations, and that this effect is nonlinear. In addition, our results suggest that air pollution primarily reduces road users' risky behaviors through visual channels rather than through the respiratory system.
Marvin G. Pizon, Ronald R. Baldo, Ruthlyn N. Villarante
et al.
Coronavirus disease 2019 (COVID-19) is one of the most infectious diseases and one of the greatest challenge due to global health crisis. The virus has been transmitted globally and spreading so fast with high incidence. While, the virus still pandemic, the government scramble to seek antiviral treatment and vaccines to combat the diseases. This study was conducted to investigate the influence of air pressure, air temperature, and relative humidity on the number of confirmed cases in COVID-19. Based on the result, the calculation of reproduced correlation through path decompositions and subsequent comparison to the empirical correlation indicated that the path model fits the empirical data. The identified factor significantly influenced the number of confirmed cases of COVID-19. Therefore, the number of daily confirmed cases of COVID-19 may reduce as the amount of relative humidity increases; relative humidity will increase as the amount of air temperature decreases; and the amount of air temperature will decrease as the amount of air pressure decreases. Thus, it is recommended that policy-making bodies consider the result of this study when implementing programs for COVID-19 and increase public awareness on the effects of weather condition, as it is one of the factors to control the number of COVID-19 cases.
Air pollution has long been a serious environmental health challenge, especially in metropolitan cities, where air pollutant concentrations are exacerbated by the street canyon effect and high building density. Whilst accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models fail to fully address the complex interaction between air pollution and urban dynamics. Our Deep-AIR, a novel hybrid deep learning framework that combines a convolutional neural network with a long short-term memory network, aims to address this gap to provide fine-grained city-wide air pollution estimation and station-wide forecast. Our proposed framework creates 1x1 convolution layers to strengthen the learning of cross-feature spatial interaction between air pollution and important urban dynamic features, particularly road density, building density/height, and street canyon effect. Using Hong Kong and Beijing as case studies, Deep-AIR achieves a higher accuracy than our baseline models. Our model attains an accuracy of 67.6%, 77.2%, and 66.1% in fine-grained hourly estimation, 1-hr, and 24-hr air pollution forecast for Hong Kong, and an accuracy of 65.0%, 75.3%, and 63.5% for Beijing. Our saliency analysis has revealed that for Hong Kong, street canyon and road density are the best estimators for NO2, while meteorology is the best estimator for PM2.5.