Dew Point Research Articles

METHODS FOR INCREASING THE CONDENSATE RECOVERY COEFFICIENT WHEN DEVELOPING GAS CONDENSATE FIELDS

This article will discuss methods for increasing the condensate recovery coefficient and the concept of condensate recovery. Gas condensate is a cold hydrocarbon reservoir that includes natural gas as well as liquid hydrocarbons, often in the form of liquid condensate. During reservoir extraction, liquids are mixed from mixtures of light hydrocarbons (eg methane, ethane, propane) and denser liquid hydrocarbons (known as condensates). Gas condensates are found in underground reservoirs, where temperature and pressure conditions allow the gas to condense into liquid form as it moves from the formation to the surface. Such situations are often observed in tanks where there is a combination of increased pressure, high temperatures and a specific gas composition. The condensate production indicator from the reservoir describes the level of gas condensate extraction from gas condensate and oil and gas condensate fields. A distinction is made between instantaneous condensate production (determined at a certain point in time) and final production (at the end of commercial exploitation of the field). To quantify condensate production, the condensate production indicator is used - this is the ratio between the given definition of condensate and the reserves taken from the reserves of the field based on its study (expressed as a percentage or fraction of units). This indicator is used to extract recoverable condensate reserves. In addition, it is taken into account when developing a project for a gas condensate or oil and gas condensate field, taking into account the efficiency of the technology used. The completeness of condensate removal in some cases determines the reasonableness of developing the selected system. The main features of the behavior of gas condensate systems are associated with the corresponding phenomena reflected in the phase diagrams of reverse condensation and evaporation. These features lead to the fact that when the pressure in the gas condensate system decreases to a level below the saturation pressure, condensation of heavier hydrocarbons (condensate) occurs. If the pressure in the gas condensate formation during development is maintained at the initial level (or the pressure at the beginning of condensation), then phase transitions occur only in the formation zones adjacent to the well. This requires taking into account changes over time, such as changes in filtration characteristics in the bottomhole zones of wells. When developing a gas condensate field to the point of depletion, condensate begins to fall out everywhere in the reservoir. However, the condensate that falls often has little effect on the gas saturation coefficient of the entire formation. Consequently, when developing a gas condensate field until depletion (with a low condensate content in the gas), filtration flows can be considered as single-phase, since the precipitated condensate remains motionless. Low condensate saturation of the formation leads to slight changes in its capacitive and filtration characteristics. In the zone of the bottomhole space of the formation, two-phase filtration occurs. Keywords: gas, gas condensate, heavier hydrocarbons, dew point, the condensate recovery coefficient.

Improving the estimation of atmospheric water vapor pressure using interpretable long short-term memory networks

Atmospheric water vapor pressure is an essential meteorological control on land surface and hydrologic processes. As it is not as frequently observed as other meteorologic conditions, it is often inferred through the August–Roche–Magnus formula by simply assuming dew point and daily minimum temperatures are equivalent or by empirically correlating the two temperatures using an aridity correction. The performance of both methods varies considerably across different regions and during different time periods; obtaining consistently accurate estimates across space and time remains a great challenge. Here, an interpretable Long Short-Term Memory (iLSTM) network conditioned on static, location specific attributes is proposed to estimate the daily vapor pressure. This approach allows for training a single transferable model using ensemble data from multiple sites and exploring the quantitative dependency of vapor pressure prediction on multiple environmental variables and their histories. To evaluate this approach, three iLSTM model configurations were developed, each considering different site attributes as static variables. For each configuration, multiple model realizations were trained using 83 FLUXNET sites in the United States and Canada, where each realization corresponds to different withheld groups of sites used for model evaluation. Results show that the iLSTM networks noticeably improve the estimation accuracy in comparison with the two assumption-based methods for most sites, reducing the failure rate from 32 % to 10.9 % for the best iLSTM model configuration. Additionally, this network provides reasonable insights into both the relative importance of the time-series input variables and their temporal importance. This method is found to be effective for imputing vapor pressure across space and time.

Experimental research and numerical analysis for effect of drainage structure on separation performance and the “blockage” of fluid in nozzle

ABSTRACT Supersonic separation technology is a cutting-edge method for efficient dehydration and hydrocarbon removal, yet challenges arise from the intricate flow dynamics within a supersonic nozzle. To address these challenges, a novel cascade drainage structure is proposed in this paper, combining the high swirl intensity of flush-type drainage with the extended drainage section, larger opening area, and reduced flow velocity of cylindrical drainage with oblique openings. Through comprehensive physical modeling and numerical analysis, this design is shown to enhance separation performance and mitigate fluid blockage within the nozzle. Through physical modeling and numerical analysis, we demonstrate its effectiveness in enhancing separation and mitigating fluid blockage. Experimental results confirm the cascade drainage structure’s superiority, particularly at an optimal pressure ratio ( R np = 1.4). Ethanol vapor removal rate ( δ steam ) and dew point drop ( Δ T d ) reach 53.94% and 18.32°C, respectively, surpassing alternatives. Maintaining a 2 mm discharge clearance yields an outstanding dry gas ratio of approximately 85%. The findings contribute to advancements in dehydration and hydrocarbon removal processes, showcasing the potential for enhanced performance in supersonic separation technology within a concise framework.

Solar driven liquid desiccant dehumidification system: Measurements and annual system simulations

Liquid desiccant air conditioning (LDAC) systems are used for dehumidification of air to low dew point temperatures. In the presented study a TRNSYS model of a LDAC unit is developed and validated with laboratory measurements. This model is coupled with a solar thermal system that provides heating water for the regeneration process. With this model dynamic annual system simulations can be carried out to simulate the dehumidification of a building. The solar fraction is evaluated for various infiltration rates of the building and solar collector areas with an internally cooled as well as an adiabatic absorption process.The simulation results reveal that high solar fractions of 70% to 81% can be achieved when dimensioning the solar thermal system to cover the regeneration heat demand on a good summer day. This is due to the good correlation of solar irradiance and dehumidification load. Without internal cooling of the absorption process (adiabatic conditions), the solar fraction is reduced by about 10percent points for the same size and operating conditions.

Efficient harvesting of renewable evaporative energy from atmospheric air through hierarchical nano/microscale shaping of air-water interface

Renewable evaporative energy from atmospheric air is a recently emerged research field that demonstrates the great potential for significant energy savings in the air conditioning of buildings, data/big-data/integrated-big-data centers, agricultural storage facilities, and greenhouses. Here, we develop a method for an essential increase in the harvesting efficiency of renewable air evaporative energy by enhancing the water evaporation rate through the shaping of the air-water interface (AWI) by the menisci formed in superhydrophilic hierarchical surface nano/microstructures. Using this method, we achieve a 10-fold rise in the evaporation rate for the microscale AWI shaping. For the first time, we discover the superevaporation effect that provides an unprecedented 130-fold rise in the evaporation rate at the hierarchical nano/microscale AWI shaping. Furthermore, based on these findings, for the first time, we elaborate a novel material with an engineered AWI for a significant increase in the harvesting of renewable air evaporative energy in the dew point (Maisotsenko cycle) indirect evaporative air conditioning systems whose operation is fundamentally based on utilizing renewable air evaporative energy. The dew point cooling device fabricated using this novel material shows significantly superior cooling performance, validating the substantially enhanced harvesting of the air's evaporative energy. Our findings can essentially advance the emerging field of renewable air evaporative energy and, besides the air conditioning technology, can provide significant efficiency enhancements in water purification/desalination, thermal management, gas turbine power generation, and waste heat recovery technologies.

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.

Numerical Investigation of Influence of Extent and Position of a Bypass on the Performance of a Single Unit Liquid Desiccant Based Evaporative Cooler

Air-conditioning of buildings is more energy-consuming with widely used vapour compression refrigeration systems in hot and humid areas. Alternatively, evaporative cooling does not show much effect in sultry regions because of increased humidity despite the minimum energy consumption. Indirect evaporative cooling is a promising alternative to direct evaporative cooling systems, but it suffers from low cooling effectiveness and is unsuitable for highly humid regions. Considering the above facts an alternative liquid desiccant-assisted modified M-cycle cooling system is proposed and examined numerically in the present work. The proposed cooling and dehumidification system is a simple unit incorporating chemical dehumidification and regenerative evaporative cooling in a single unit. A simultaneous heat and mass transfer model is developed for the numerical analysis. The numerical code is written in MATLAB and is validated with results available in the literature. Further, numerical analysis was carried out for the effect of various parameters on the performance of the proposed cooling system. The study shows an improvement of 19.2% in dew point effectiveness by incorporating an intermediate opening in the plate separating the product air and working air channels.

Multi-objective optimization of dew point indirect evaporative coolers for data centers

This study introduces a novel counter-flow dew point indirect evaporative cooler (DPIEC) tailored for data center cooling. A numerical model for DPIEC was developed in COMSOL and validated experimentally, followed by the formulation of regression models using response surface methodology. Eight critical design parameters (five operational and three geometrical) were linked to three performance evaluation indexes (outlet primary air temperature T1,out, coefficient of performance COP, and cooling capacity per unit volume Qv). The magnitude of parameter influence on the evaluation indexes was assessed. Employing the three regression models as objective functions, a genetic algorithm, implemented in MATLAB, was adopted to perform multi-objective optimization for DPIEC under four climatic conditions, yielding an array of optimal parameter combinations. The results underscored the robust predictive accuracy of the established regression models, with R2 and Adeq Precision exceeding 0.96 and 53.7, respectively. The ideal point method achieved design solutions of the DPIEC with T1,out below 21.6 °C, COP above 61.3, and Qv over 15.5 kW/m3. Compared to the original design, the optimized design featured reductions of over 13.84 %, elevations of over 23.50 %, and enhancements of over 104.10 % in these three indexes, respectively. In addition, the confirmation study of the Pareto front proved the reliability of the obtained DPIEC design reference.

Stability Study Through Water Activity Measurements for Dispensed Powdered Raw Materials.

Holding times for raw materials are relevant since they enable us to understand the allowable time that a raw material can be kept under ideal storage conditions before the start of the manufacturing process without its quality attributes being affected. The quantification of water activity can be used as an indicator of the microbiological, physicochemical, and organoleptic stability of a specimen, since low water activity retards autohydrolysis and microbiological growth. The main purpose of this investigation was to test the stability of powdered raw materials for a maximum holding time of 8 days through water activity measurements. Thus microbiological, physicochemical, and organoleptic measurements were carried out in parallel and simultaneously to experimentally establish a relationship between the status of the water activity of processed raw materials and the microbiological, physicochemical, and organoleptic results. The raw materials were stored for a maximum holding time of 8 days, in accordance with United States Pharmacopeia monographs. For all the raw materials tested, water activity measurements were performed using the dew point chilled-mirror method on days 0, 3, and 8. On days 0 and 8, microbiological, physicochemical, and organoleptic assessments were performed. It was established that under these storage conditions, the processed raw materials exhibited water activity below 0.60 during the entire holding time. However, there were statistically significant differences in water activity levels between days 0, 3, and 8 (ANOVA P < 0.05). Despite observing statistically significant differences between days, the microbiological, physicochemical, and organoleptic features were within specification at those water activity levels below 0.60. Water activity below 0.60 does not allow the growth of microorganisms, and the organoleptic and physicochemical features remain unperturbed. The results indicate that water activity can be used as an indicator of the microbiological load and chemical stability of the raw materials tested. This research provides evidence that corroborates that water activity status may be used as a reliable indicator for the microbiological burden and physicochemical features of pharmaceutical material during stability studies.

New insights into the effect of Al on the selective oxidation of advanced high-strength steels prior to hot-dip galvanizing

Advanced high-strength steels (AHSSs) enable the manufacture of lighter, safer and more energy-saving body-in-white. However, the poor galvanizability induced by the surface selective oxidation during annealing makes the production of high-grade galvanized AHSS quite challenging. It is generally believed that replacing Si with Al enhances the galvanizability of AHSS, but a recent industrial practice shows the opposite for a high Al steel grade, which indicates complex competing mechanisms introduced by Al. Here, we study the selective oxidation of a model system Fe-Mn-Si-Al systematically by varying the Al content and dew point of annealing atmosphere. We suggest that Al has a dual-side effect on the galvanizability of AHSS via: i) promoting the outward diffusion and subsequent external oxidation of Mn and Si, ii) deteriorating the surface chemistry of galvanizing by enhancing the Si/Mn ratio of surface oxides, iii) facilitating the formation of amorphous film-like external oxides, and iv) a comparatively low trend for Al to segregate to the surface, where the first three function negatively while the last positively. Our work would change the perspective away from a focus solely on the deceleration effect of Al to also consider its acceleration on the surface selective oxidation, insights critical for developing strategies against galvanizing problems of AHSSs.

Study of the cooling performance of an anti-condensation radiant cooling unit with an infrared-transparent silicon wafer

Radiant cooling systems consume a low amount of energy and provide high thermal comfort; however, the risk of condensation in hot and humid areas limits their cooling capacity and is not conducive to their application. To solve the contradiction between condensation problems and the cooling demand, this study proposes a novel anti-condensation radiant cooling unit (RCU) that uses an infrared-transparent silicon wafer (ISW) with a good mechanical strength to separate the cooling radiant panel from the humid air, thus avoiding condensation. A heat transfer model of the anti-condensation RCU was established, and experimental tests were conducted under different indoor air temperatures, humidities, and vacuum degrees in the interlayer. At a temperature of 26 °C in 50 % relative humidity, the maximum cooling capacity of the anti-condensation RCU reached 103.66 W/m2, which was 60.88 % higher than the performance of conventional RCU. When the vacuum degree of the interlayer was 0.06 Mpa, the air-contacting surface outside the ISW was approximately 1.2 °C higher than that of atmospheric pressure. The results demonstrate that the anti-condensation RCU overcomes the limitation of the dew-point temperature on the cooling capacity to a certain extent and expands its applicability in high-humidity environments.

Analysis of thermal discomfort associated with synoptic conditions in the city of Pelotas, southernmost region of Brazil.

Here, we evaluated the influence of outdoor environmental conditions (synoptic weather conditions) on human thermal discomfort in the five macro-regions of Pelotas city, located in the southernmost region of Brazil. To do this, meteorological sensors (HOBO MX2301A) were installed outside the residences to measure the air temperature, dew point temperature, and relative humidity between 18 January and 20 August 2019. Two well-established simplified biometeorological indices were examined seasonally: (i) humidex for the summer months and (ii) effective temperature as a function of wind for the autumn and winter months. Our findings showed seasonal differences related to human thermal discomfort and outdoor environmental conditions. The thermal discomfort was highest in the afternoons during the summer months and at night during the winter months. The seasonal variation in human thermal discomfort was highly associated with the meteorological conditions. In summer, the presence of the South Atlantic Subtropical Anticyclone (SASA) contributed to heat stress. The SASA combined with the continent's low humidity contributed to the perceived sensation of thermal discomfort. In the winter, thermal discomfort was associated with the decrease in air humidity caused by high atmospheric pressure systems, which led to a decrease in both air temperature and air moisture content. Our findings suggest that a better understanding of the complex interplay between outdoor environmental factors and human thermal comfort is needed in order to mitigate the negative effects of thermal discomfort.

Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework

Developing reliable streamflow forecasting models is critical for hydrological tasks such as improving water resource management, analyzing river patterns, and flood forecasting. In this research, for the first time, an emerging multi-level TOPSIS (technique for order preference by similarity to the ideal solution) -based hybridization comprised of the Boruta classification and regression tree (Boruta-CART) feature selection, multivariate variational mode decomposition (MVMD), and a hybrid Convolutional Neural Network (CNN) Bidirectional Gated Recurrent Unit (CNN-BiGRU) deep learning was adopted to multi-temporal (one and three days ahead) forecast the daily streamflow in the Rivers of Prince Edward Island, Canada. For this aim, in the first step, the Boruta-CART feature selection technique determines the most effective lagged components among all the antecedent two-day information (i.e., t-1 and t-2) of hydro-meteorological features (from 2015 to 2020), including the water level, mean air temperature, heat degree days, total precipitation, dew point temperature, and relative humidity in the Bear and Winter Rivers of Prince Edward Island, Canada. Afterwards, a multivariate variational mode decomposition (MVMD) decomposes the input time series to decrease the complexity and non-linearity of the non-stationary ones before feeding the deep learning (DL) models. Here, the CNN-GRU was employed as the primary DL model, along with the kernel extreme machine method (KELM), random variational function link (RVFL), and hybrid CNN bidirectional recurrent neural network (CNN-BiRNN) as the comparative models. A TOPSIS scheme applying several performance measures like the correlation coefficient (R), root mean square error (RMSE), and reliability was designed for the robustness assessment of the hybrid (MVM-CNN-BiGRU, MVM-CNN-BiRNN, MVM-RVFL, and MVM-KELM) and standalone models. The computational outcomes revealed that in the Bear River, the MVM-CNN-BiGRU, owing to its best forecasting performance (one day ahead: TOPSIS score 1, R = 0.960, RMSE = 0.098, and reliability = 65.082; three days ahead: TOPSIS score = 0.999, R = 0.924, and RMSE = 0.33) outperformed the other hybrid models, followed by the MVM-CNN-BiRNN, MVM-RVFL, and MVM-KELM, respectively. Moreover, in the Winter River, the MVM-CNN-BiGRU in terms of (one-day ahead: TOPSIS score = 0.890, R = 0.955, RMSE = 0.274, and reliability = 34.004; three-days ahead: TOPSIS score = 0.686, R = 0.924, and RMSE = 0.330) was superior to the other models. The provided expert system could be vital in the local flood decision-making process, in the absence of streamflow information as input modeling, during the flood seasons to reduce flood damage in residential areas.

Dense collection areas and terrestrial alteration of meteorites in the Atacama Desert

AbstractIn the last 15 years, more than 2700 meteorites have been recovered and officially classified from the Atacama Desert. Although the number of meteorites collected in the Atacama has risen, the physical and climatic properties of the dense collection areas (DCAs) have not been fully characterized. In this article, we compiled the published data of all classified meteorites found in the Atacama Desert to (i) describe the distribution by meteorite groups, (ii) compare the weathering degree of chondrites among different Atacama DCAs and other hot and cold deserts, and (iii) determine the preservation conditions of chondrites in the main Atacama DCAs in relation with the local climatic conditions. The 35 DCAs so far identified in the Atacama Desert are located in three main morphotectonic units: The Coastal Range (CR), Central Depression (CD), and Pre‐Andean Range/Basement. A comparison with reported weathering data from other cold and hot deserts indicates that the mean terrestrial weathering of Atacama chondrites (W1–2), displays less alteration than other hot deserts (W2–3) and resembles the weathering distribution of the Antarctic meteorites (W1–2). The highest abundance of Atacama chondrites with low weathering (≤W2) is localized in the CD (78.8%, N = 1435), which is protected from the coastal fog influence and seasonal rainfalls and displays the oldest surfaces in the Atacama Desert. The morphogenetic classification based on present‐day temperatures and precipitations of the main Atacama DCAs reveals similar regional/subregional climatic conditions in the most productive areas and a truly productive surface for meteorite recovery between 5% and 58% of the quadrangles formally defined for each Atacama DCA. Our morphogenetic classification lacks consideration of some meteorological parameters such as the coastal fog, so it cannot fully explain the differences in weathering patterns among CR chondrites. Future studies of chondrite preservation in the Atacama DCAs should consider other meteorological variables such as relative humidity, specific humidity, or dew point, in combination with exposure ages of meteorites and its surfaces.

Using a Model to Compare Cooling System Design Factors For Lactating Cows

Highlights Cow mass, milk yield, solar load, air velocity, dry-bulb and dew-point temperature impact lactating cow respiration rates. Modeled respiration rates align with published studies dealing with milk yield, solar load, and evaporative cooling. The model can be used to assess cooling systems for conditions that might be difficult to capture in field measurements. Abstract. The risk of lactating cows experiencing heat stress is increasing because of increased productivity. A steady-state process-based model described by Nelson and Janni (2023) and assessed by Janni et al. (2023) was used to compare additional combinations of environmental conditions (e.g., air dry-bulb and dew-point temperatures, air velocity, solar load) and lactating cow characteristics (e.g., body mass and daily milk yield) on modeled respiration rate. Respiration rate was linked to qualitative heat stress levels, based on work by Renaudeau et al. (2012). The model demonstrates how cows with greater body mass have larger inspired volumes of respired air (i.e., liters per minute) because they have larger tidal volumes (i.e., liters per breath) while they have lower respiration rates (i.e., breaths per minute) compared to cows with smaller body masses. The modeled results support previous studies that demonstrated heat stress increases with greater daily milk yields, solar load, and air dry-bulb and dew-point temperatures. The model finds conditions where increasing air velocity at cow level can help cows reduce heat stress levels due to solar load, and high air dry-bulb and dew-point temperatures. A comparison to a field study demonstrates the need to align field data collection and model input needs to explore the actual or potential impacts of mitigation methods like shade, evaporative cooling, and air velocity (i.e., via fans). This report demonstrates how the model can be used by engineers to investigate the impact of ventilation system designs on respiration rates using current and future technologies. Keywords: Air velocity, Cow mass, Dairy, Dew-point temperature, Dry-bulb temperature, Heat stress, Milk yield, Respiration rate, Solar load.

Research on Risk Assessment and Prevention of Dewing on the Surface of the Thousand-handed Avalokitesvara Statue Based on COMSOL and CFD

The Thousand-handed Avalokitesvara statue in Dazu Stone Carving has been in a high temperature and humidity environment with serious air pollution for a long time. Under the combined effect of various environmental factors, its surface disease is extremely serious. Among them, condensation disease is one of the main diseases of the Thousand-handed Avalokitesvara statue, and also the root cause of other diseases. The surface temperature of the Thousand-handed Avalokitesvara statue was simulated by COMSOL simulation technology, and compared with the dew point temperature of the air near the wall at the same time, the seasonal and daily variation laws of the condensation risk on the image surface were analysed. To effectively prevent surface condensation, a three-level condensation risk assessment level was established based on the difference between the surface temperature and the dew point temperature near the wall. Summer is a period of high risk of condensation, during field investigation, it was found that under the condition that dehumidification measures have been added, a large area of condensation still occurs on the statue surface in summer. According to the actual measurement of the core area where the Thousand-handed Avalokitesvara statue is located, it was found that there were obvious differences between the near wall measuring points and those far away, the near wall air fluidity was poor, and the actual application effect of dehumidification equipment was poor. In order to better create a suitable storage environment and avoid condensation, based on CFD simulation technology, the distribution law of the internal environmental parameters of the Dabei Pavilion under different optimization prevention and control schemes was studied, and the optimal optimization scheme of closing the steam inlet and adding dehumidifiers was proposed, which could reduce the relative humidity and dew point temperature of the air near the wall of the Thousand-handed Avalokitesvara statue by 12.6% and 3ºC respectively, effectively reducing the risk of condensation.

Prediction of Clausius–Clapeyron Scaling of Daily Precipitation Extremes over China

Abstract Observations and model simulations have confirmed that the higher percentiles of precipitation intensity of all wet days have increased with the local daily dewpoint temperature at the Clausius–Clapeyron (C-C) rate of 7% °C−1 using the binning technique over China. It is uncertain whether the binning scaling rates will remain constant in the future climate. Here, we use daily precipitation and dewpoint temperature (DPT) over China from 20 CMIP6 models to examine and project the change of binning scaling of precipitation extremes. The results of the study suggest that the multimodel ensemble median (MEM) generally captures the spatial distribution of binning scaling rates and binning curves in different climate zones across China. The binning scaling rates have stability in the future but with a large increasing trend (more than 30%) in southeast China in the far future (2061–2100) under the SSP5-8.5 scenario. CMIP6 models project that both the peak of extreme precipitation and the temperature for the plateau mountain zone will increase in the far future under the SSP5-8.5 scenario, implying that the peak structure does not provide an upper limit for future precipitation extremes. Significance Statement The binning scaling rates describe the relationship between precipitation extremes and temperature based on their day-to-day variations. However, it is still unclear whether the binning scaling rates of daily precipitation extremes with daily temperature will change in the future. We use 20 CMIP6 models to project the scaling rates of precipitation extremes with dewpoint temperature over China in the future. The results show that the sensitivity of precipitation extremes to dewpoint temperature will generally be stable, except that southeast China shows an increasing trend of up to 30%. Moreover, the peak of extreme precipitation and the temperature will increase with warming in the plateau mountain zone. Our results have implications for precipitation prediction and risk assessment.

Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires

With the increase in the number and severity of California wildfires, firefighters need more efficient tools to predict potential wildfires so that they can efficiently cease these devastating events, preserve the safety of the human population, and the environment. With nearly eight million acres burned between 2010 and 2019, there is a clear and immediate issue. We hypothesized that a machine learning model could be developed to accurately predict the severity of California wildfires and determine the most influential meteorological factors. In this study, we used machine learning to analyze historical fire behaviors and then accurately predict the severity (defined by acres burned) of California wildfires. We created a custom dataset by using a combination of information from the World Weather Online API and a Kaggle dataset of wildfires in California from 20132020. The resulting dataset consisted of information (including temperature, humidity, dew point, and wind speeds) on 1,462 wildfires. We used three classification algorithms—logistic regression, support vector machine, and random forest regression—to classify these fires. Devising a system categorizing fires based on acres burned, our algorithms were able to classify fires into seven categories with promising accuracy (around 55 percent). We generated a correlation matrix, providing insight into the most concerning factors and allowing firefighters to choose the best course of action. We found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This machine learning tool could vastly improve the efficiency and preparedness of firefighters as they deal with these crises.

Increase the internal surface temperature to eliminate surface condensation on the window

One of the most critical places in buildings in terms of physical thermal properties are windows. This is the most frequent place where the temperature drops below the dew point and surface condensation, or mould, develops. The work deals with the study of the surface temperature courses at the critical points of window glazing and connection joints and the subsequent improvement of the initial unsatisfactory condition by using a patented technology, a conductive material without further intervention in the structure. In the case of a very unsatisfactory condition, a heating resistance wire was attached to the bar, which in combination with the bar distributes the heat to a wider area. The evaluation of the surface temperature increase was performed in a steady-state thermal analysis with fixed boundary conditions and then with the influence of the flow in a room with the heater placed under the window.

MODELING OF THREE-LAYER FLOWS WITH NON-UNIFORM EVAPORATION BASED ON THE EXACT SOLUTION OF CONVECTION EQUATIONS

Mathematical modeling of three-layer convective flows of two liquids and a gas-vapor mixture in a horizontal channel is based on the exact solution of the Navier-Stokes equations in the Boussinesq approximation. Thermocapillary interfaces are assumed to be non-deformable. The inhomogeneous mass transfer of the light liquid to the upper layer is considered. The Soret and Dufour effects are taken into account in the upper layer, and the gas flow rate is given. The influence of thermal load and flow geometry on the main characteristics of the flow was studied using the example of the water–benzine–air system. It is shown that the Soret effect has an impact on the nature of the flow. The influence of the effect of thermodiffusion, changes in the longitudinal temperature gradients, and thicknesses of the layers of the system on the dew point is revealed.