Dew Point Depression Research Articles

The impacts of structural parameters on performance and energy loss of the supersonic separator: A sensitivity analysis

The impacts of structural parameters on performance and energy loss of the supersonic separator: A sensitivity analysis

Evaluation of dynamic behaviors in varied swirling flows for high-pressure offshore natural gas supersonic dehydration

Evaluation of dynamic behaviors in varied swirling flows for high-pressure offshore natural gas supersonic dehydration

Different Mechanisms for the Northern and Southern Winter Fog Events over Eastern China

Northern and southern fog events are identified over eastern China across 40 winters from 1981 to 2021. By performing composite analysis on these events, this study reveals that the formation of fog events is controlled by both dynamic and thermodynamic processes. The fog events were induced by Rossby wave trains over the Eurasian continent, leading to the development of surface wind and pressure anomalies, which favor the formation of fog events. The Rossby wave trains in northern and southern fog events are characterized by their occurrence in northern and southern locations, respectively, with different strengths. The water vapor fluxes that contribute to the enhancement of the northern fog events originate from the Yellow Sea and the East China Sea, whereas the southern fog events are characterized by water vapor from the East China Sea and the South China Sea. In both northern and southern fog events, dew point depression and positive A and K index anomalies are found in northern and southern regions of eastern China, which are indicative of supersaturated air and the unstable atmospheric saturation from the low to the middle troposphere, thus providing favorable conditions for the establishment of fog events in northern and southern regions of eastern China.

Decline of a North American rocky intertidal foundation species linked to extreme dry, downslope Santa Ana winds

Foundation species are essential to ecosystem function, but their role as habitat providers is predicated on their spatial dominance. Worldwide, kelps, seagrasses, corals, and other marine foundation species have declined. This is true also for rockweeds, the canopy-forming analog of subtidal kelp forests in temperate rocky intertidal ecosystems. On the west coast of North America, dense beds of the rockweed Silvetia compressa occur across large biogeographic regions, benefitting numerous species by ameliorating physical stress caused by sun exposure, desiccation, heat, and wave disturbance. Like many rockweed species, Silvetia is long-lived, slow-growing, and short-dispersing – characteristics that reduce its resilience to disturbance. Using a generalized additive mixed-effects model with explicit spatial effects, we analyzed canopy cover data from 30 sites spanning 18 years, and we tested the hypothesis that Silvetia population trends are tightly linked to atmospheric climate conditions, particularly Santa Ana wind events (SAWs): strong, hot, and dry downslope winds that originate inland and move offshore. We found that the rockweed had declined markedly, particularly at sites south of the major biogeographic break, Point Conception (PC), including the California Channel Islands and southern California mainland, and a highly significant negative effect of dewpoint depression, a measure of moisture content in the atmosphere, on Silvetia cover across all three regions in this study. Our results suggest that any increases in the frequency or intensity of SAWs are likely to lead to large declines and possible extirpation of Silvetia, as well as the important ecological services the species provides.

Machine learning analysis and nowcasting of marine fog visibility using FATIMA Grand Banks campaign measurements

Introduction: This study presents the application of machine learning (ML) to evaluate marine fog visibility conditions and nowcasting of visibility based on the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island, northeast of Canada.Methods: The measurements were collected using instrumentation mounted on the Research Vessel Atlantic Condor. The collected meteorological parameters were: visibility (Vis), precipitation rate, air temperature, relative humidity with respect to water, pressure, wind speed, and direction. Using all variables, the droplet number concentration was used to qualitatively indicate and assess characteristics of the fog using the t-distributed stochastic neighbor embedding projection method (t-SNE), which clustered the data into groups. Following t-SNE analysis, a correlation heatmap was used to select relevant meteorological variables for visibility nowcasting, which were wind speed, relative humidity, and dew point depression. Prior to nowcasting, the input variables were preprocessed to generate additional time-lagged variables using a 120-minute lookback window in order to take advantage of the intrinsic time-varying features of the time series data. Nowcasting of Vis time series for lead times of 30 and 60 minutes was performed using the ML regression methods of support vector regression (SVR), least-squares gradient boosting (LSB), and deep learning at visibility thresholds of Vis < 1 km and < 10 km.Results: Vis nowcasting at the 60 min lead time was best with LSB and was significantly more skillful than persistence analysis. Specifically, using LSB the overall nowcasts at Vis 1 < km and Vis 10 < km were RMSE = 0.172 km and RMSE = 2.924 km, respectively. The nowcasting skill of SVR for dense fog (Vis ≤ 400 m) was significantly better than persistence at all Vis thresholds and lead times, even when it was less skillful than persistence at predicting high visibility.Discussion: Thus, ML techniques can significantly improve Vis prediction when either observations or modelbased accurate time-dependent variables are available. The results suggest that there is potential for future ML analysis that focuses on modeling the underlying factors of fog formation.

Predictive Population Dynamics of Escherichia coli O157:H7 and Salmonella enterica on Plants: a Mechanistic Mathematical Model Based on Weather Parameters and Bacterial State.

Weather affects key aspects of bacterial behavior on plants but has not been extensively investigated as a tool to assess risk of crop contamination with human foodborne pathogens. A novel mechanistic model informed by weather factors and bacterial state was developed to predict population dynamics on leafy vegetables and tested against published data tracking Escherichia coli O157:H7 (EcO157) and Salmonella enterica populations on lettuce and cilantro plants. The model utilizes temperature, radiation, and dew point depression to characterize pathogen growth and decay rates. Additionally, the model incorporates the population level effect of bacterial physiological state dynamics in the phyllosphere in terms of the duration and frequency of specific weather parameters. The model accurately predicted EcO157 and S. enterica population sizes on lettuce and cilantro leaves in the laboratory under various conditions of temperature, relative humidity, light intensity, and cycles of leaf wetness and dryness. Importantly, the model successfully predicted EcO157 population dynamics on 4-week-old romaine lettuce plants under variable weather conditions in nearly all field trials. Prediction of initial EcO157 population decay rates after inoculation of 6-week-old romaine plants in the same field study was better than that of long-term survival. This suggests that future augmentation of the model should consider plant age and species morphology by including additional physical parameters. Our results highlight the potential of a comprehensive weather-based model in predicting contamination risk in the field. Such a modeling approach would additionally be valuable for timing field sampling in quality control to ensure the microbial safety of produce. IMPORTANCE Fruits and vegetables are important sources of foodborne disease. Novel approaches to improve the microbial safety of produce are greatly lacking. Given that bacterial behavior on plant surfaces is highly dependent on weather factors, risk assessment informed by meteorological data may be an effective tool to integrate into strategies to prevent crop contamination. A mathematical model was developed to predict the population trends of pathogenic E. coli and S. enterica, two major causal agents of foodborne disease associated with produce, on leaves. Our model is based on weather parameters and rates of switching between the active (growing) and inactive (nongrowing) bacterial state resulting from prevailing environmental conditions on leaf surfaces. We demonstrate that the model has the ability to accurately predict dynamics of enteric pathogens on leaves and, notably, sizes of populations of pathogenic E. coli over time after inoculation onto the leaves of young lettuce plants in the field.

Statistical Downscaling in the Tropics and Midlatitudes: A Comparative Assessment over Two Representative Regions

Abstract Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies due to its low computational expense compared to dynamical downscaling, which allows exploration of uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study, several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 at 0.25°) in two regions with very different climates: Spain (midlatitudes) and Central America (tropics). Some key assumptions of SD have been tested: the strength of the predictor–predictand links, the skill of different approaches, and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as is the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where transfer function (TF) methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, model output statistics (MOS) methods have achieved the best results for temperature. In Central America, TF methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the extreme gradient boost machine learning method have achieved the best results in both regions. In addition, it has been found that, although the use of humidity indices as predictors improves results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indices have been compared: relative humidity, specific humidity, and dewpoint depression. The use of the specific humidity has been found to lead to trends given by the downscaled projections that deviate seriously from those given by raw global climate models in both regions.

Skillful prediction of UK seasonal energy consumption based on surface climate information

Climate conditions affect winter heating demand in areas that experience harsh winters. Skillful energy demand prediction provides useful information that may be a helpful component in ensuring a reliable energy supply, protecting vulnerable populations from cold weather, and reducing excess energy waste. Here, we develop a statistical model that predicts winter seasonal energy consumption over the United Kingdom using a multiple linear regression technique based on multiple sources of climate information from the previous fall season. We take the autumn conditions of Arctic sea-ice concentration, stratospheric circulation, and sea-surface temperature as predictors, which all influence North Atlantic oscillation (NAO) variability as reported in a previous study. The model predicts winter seasonal gas and electricity consumption two months in advance with a statistically significant correlation between the predicted and observed time series. To extend the analysis beyond the relatively short time scale of gas and electricity data availability, we also analyze predictability of an energy demand proxy, heating degree days (HDDs), for which the model also demonstrates skill. The predictability of energy consumption can be attributed to the predictability of the NAO and the significant correlation of energy consumption with surface air temperature, dew point depression, and wind speed. We further found skillful prediction of these surface climate variables and HDDs over many areas where the NAO is influential, implying the predictability of energy demand in these regions. The simple statistical model demonstrates the usefulness of fall climate observations for predicting winter season energy demand prediction with a wide range of potential applications across energy-related sectors.

Development of an Objective Methodology for Identifying the Sea-Breeze Circulation and Associated Low-Level Jet in the New York Bight

Abstract In a midlatitude coastal region such as the New York Bight (NYB), the general thermodynamic structure and dynamics of the sea-breeze circulation is poorly understood. The NYB sea-breeze circulation is often amplified by and coterminous with other regional characteristics and phenomena such as complex coastal topology, a low-level jet (LLJ), and coastal upwelling. While typically considered a summertime phenomenon, the NYB sea-breeze circulation occurs year-round. This study creates a methodology to objectively identify sea-breeze days and their associated LLJs from 2010 to 2020. Filtering parameters include surface-based observations of sea level pressure (SLP) gradient and diurnal tendencies, afternoon wind speed and direction tendencies, air temperature gradient, and the dewpoint depression. LLJs associated with the sea-breeze circulation typically occur within 150–300 m MSL and are identified using a coastal New York State Mesonet (NYSM) profiler site. Along coastal Long Island, there are on average 32 sea-breeze days annually, featuring winds consistently backing to the south and strengthening at or around 1800 UTC (1400 EDT). The NYB LLJ is most frequent in the summer months. Sea-breeze days are classified into two categories: classic and hybrid. A classic sea breeze is driven primarily by both cross-shore pressure and temperature gradients, with light background winds; while a hybrid sea breeze occurs in combination with other larger-scale features, such as frontal systems. Both types of sea breeze are similarly distributed with a maximum frequency during July.

A novel strategy for process optimization of a natural gas liquid recovery unit by replacing Joule–Thomson valve with supersonic separator

The natural gas liquid recovery is an important process in a gas plant to correct hydrocarbon dew point and earn profit. In this study, a natural gas liquid recovery unit operated based on the Joule–Thomson process was investigated and its performance was optimized. To improve the system performance, the plant configuration and intermediate pressure ratio were defined as the variables and maximization of the natural gas liquid recovery rate and maximization of exergy efficiency were defined as the objective functions. To improve the plant performance, the amount of natural gas liquid recovery rate should be increased. To achieve this goal, several scenarios for the intermediate pressure ratio and three new configurations were proposed for the investigated gas plant. In the proposed configurations, the supersonic separators with optimized structures were used instead of the Joule–Thomson process. It was observed that all three proposed configurations improved the natural gas liquid recovery rate compared to the existing configuration. For example, by installing two supersonic separators instead of second and third stage Joule–Thomson valve + low temperature separator, at the optimal operating condition, the natural gas liquid recovery rate increased about 390%. The influence of the intermediate pressure ratio on the phase envelope diagram, exergy efficiency, dew point depression and natural gas liquid recovery rate was also investigated. By comparing the influence of intermediate pressure ratio and modifying the plant configuration on the objective functions, it was observed that the system performance can be further improved by modifying the plant configuration.

A modified Euler-Lagrange-Euler approach for modelling homogeneous and heterogeneous condensing droplets and films in supersonic flows

• A novel Euler-Lagrange-Euler model for supersonic swirling condensation annular flow. • Homogeneous and heterogeneous condensation in supersonic flows. • Deposition of supersonic swirl droplets and formation of thin liquid films. • Heat and mass transfer between gas, droplets, and liquid films. • Maximum separation efficiency and dew point depression are 84.74% and 28.28 K. The phase change in supersonic flows is of great interest in many industrial applications including steam turbines, nozzles, ejectors and aircraft. However, the phase change phenomenon is still not fully understood due to the completed flow behavior including nucleation, condensation, film generation and shock waves in supersonic flows. In the present study, we proposed a modified Euler-Lagrange-Euler model to explore the internal flow mechanism within supersonic separators. The mutual heat and mass transfer of the gaseous phase, droplets, and liquid film were simulated in supersonic flows. The homogeneous nucleation and growth model was innovatively added to ensure the model's comprehensiveness. The feasibility of the proposed model was validated by experiments. Then, the interaction of heterogeneous and homogeneous condensation in supersonic condensation flow was excavated for the first time. The results show the heterogeneous droplet diameter's decrease or concentration's increase had a significant inhibitory effect on homogeneous condensation. Subsequently, the supersonic swirl field's generation, the dynamic evolution of the homogeneous/heterogeneous droplet condensation and deposition, the liquid film development, and the heat-mass transfer between them in the supersonic separator were analyzed using the proposed model. Furthermore, the separation capacity of the supersonic separator was evaluated considering the co-action of homogeneous and heterogeneous condensation. Results show that increasing inlet droplet concentration from 0.0001 kg/s to 0.0025 kg/s can increase vapor separation efficiency and dew point depression from 61.39 % to 84.74 % and 19.03 K to 28.28 K, respectively.

Investigation of novel passive methods of generation of swirl flow in supersonic separators by the computational fluid dynamics modeling

In this paper, three passive methods for the generation of swirl flow in the supersonic separator (3S) were investigated, and their structures were optimized by computational fluid dynamics (CFD) modeling. The influence of the structural and operational parameters on the dew point depression, phase envelope diagram, rate of natural gas liquid (NGL) recovery, and separation efficiency have also been evaluated. The collection efficiency was significantly improved for the nozzle equipped with the passive swirler compared with the simple nozzle. The selection of passive swirler type played a crucial role in the natural gas liquefaction and separation. The side injected swirler, and serpentine swirler showed the most significant improvement in separation efficiency than the U-turn swirler. For the side injected swirler at the optimum injection angle, the maximum collection efficiency was about 89% at the pressure loss ratio (PLR) of 0.2. Besides, the simulation results demonstrated that for the serpentine 3S, with the increase in serpentine twist number, the highest improvement on the collection efficiency of the investigated nozzle was obtained. In addition, it was observed that, when the convergent section profile was designed according to the Witoszynski line-type, a larger refrigeration zone was obtained than other considered profiles.

Hygrothermal performance of highly insulated external walls subjected to indoor air exfiltration

The study comprises three laboratory tests in which typical Finnish highly insulated (HI) walls were exposed to concentrated leakages of indoor air under steady outdoor temperatures of 1–5°C. Airflows with a relative humidity of 50% and at rates of 1–3 L/min were directed close to the wooden frames inside the walls. The thermal resistance ratios between the exterior sheathing(s) and the whole wall (Γ) were 20%–22% and 1%–10% for the HI and baseline (BL) walls. The HI walls that presented Γ values of at least 20% were observed to be resistant to air exfiltration, and their durability was not affected by the addition of a gypsum sheathing outside the wooden frame or a more permeable vapor retarder. This is related to the negative linear correlation that exists between the moisture accumulation rate in wood-based material and the dew point depression (DPD) value. The developed approach, called the DPD method, shows that a significant degree of moisture accumulation does not occur even for DPD values of as low as −2°C if the exterior sheathing is vapor permeable. The airflow does not penetrate into the rigid mineral wool sheathing, which helps to avoid interstitial condensation. Regardless of thermal transmittance, the HI and BL walls with maximum Γ values of 1% were exposed to a high relative humidity and even interstitial condensation because the DPD values were often below −2°C. For these walls, the mold index analysis and visual observations confirmed the local risk for mold growth on the opposite side of the leakage point. In practice, long-term mold growth may be limited if the seasonal periods during which the outdoor temperature is 1–5°C last for a maximum of about 1 month every year.

Canopy wetting patterns and the determinants of dry season dewfall in an old growth Douglas-fir canopy

Canopy wetting and drying has a variety of effects on the function of plant foliage, ranging from increased risk of pathogenic infection to reduced diffusion of gases to enhanced leaf water status in plants capable of foliar water uptake (FWU). Projected shifts in rainfall regimes and increases in summertime vapor pressure deficit will likely change the timing and duration of canopy wetting, yet current patterns of wetting are poorly understood. In this study, we investigated patterns of wetting by source (rain, dew, or frost), at different canopy heights, and at annual, seasonal and diurnal time scales using leaf wetness sensor data collected over a 4-year period in an old growth Douglas-fir tree in a temperate wet forest. We found that canopy layers were wet for roughly half the year with strong seasonal variation, staying wet 83% of the cold winter season but only 1.9% of the dry season. Upper canopy layers experienced higher wetting frequency and shorter wetting duration in all seasons compared to lower canopy layers. Outside of the dry season, wetness was predominantly caused by rain, while in the dry season the predominant source was dewfall. Throughout the year and particularly in the dry season, dewfall was restricted to the upper canopy, occurring on 28.5% of dry season nights. Multiple models which use meteorological variables to predict dewfall timing and length were developed and evaluated. Using in-tree observations, dry season dewfall was best predicted with a logistic model using dewpoint depression as a predictor. Using observations from a nearby weather station in a clearing, dry season dewfall was best predicted with the Penman equation, a biophysical model. The most important determinant of dry season dewfall in our study was sufficient nighttime cooling of the air, suggesting that increasing nighttime temperatures will lead to a decrease in dew formation frequency in the future.

Numerical simulation of supersonic condensation flows using Eulerian-Lagrangian and Eulerian wall film models

As a clean and energy-saving natural gas purification and separation device, the supersonic separator's internal gas-liquid separation mechanism needs to be explored. However, the complex three-field (gas, droplet, liquid film) two-phase (gas, liquid) supersonic condensation flow challenges the numerical modeling. Most studies are limited to tracking the gas phase and droplets and ignore the effects of liquid film and phase change on droplets and water vapor removal. In the present study, we established a novel Eulerian-Lagrangian method coupled with the Eulerian wall film model to study the three-field behaviors and phase change for the enhancement of separation efficiency in the supersonic separator. The accuracy of the proposed model was validated by three experiments. The gas, droplet, and liquid film behaviors and three-field heat and mass transfers in the supersonic separator are studied using the proposed three-field two-phase flow model. Then, the sensitivity analysis was carried out, which showed the inlet mass flow rate q p, in of the heterogeneous droplets determines the maximum film thickness. For q p, in = 0.001 kg/s, this value is about 85.2 μm. The result also showed a significant improvement in separation efficiency with a proper inlet droplet diameter d p, in , q p, in , and gas pressure p in . For d p, in , q p, in , and p in are selected as 2.2 μm, 0.0015 kg/s, and 3 atm, better separation efficiency can be obtained with droplet removal rate, water vapor removal rate, and dew point depression being optimized to 100%, 57.4%, and 29.1% respectively. • Gas purification and drying by energy conversion in supersonic separators. • Three-field CFD model for droplet, gas, and film by Eulerian-Lagrangian approach and wall film model. • Phase change of droplet and liquid film, droplet trajectory and film formation were observed. • The optimal droplet diameter is 2.2 μm with an injection rate of 0.0015 kg/s. • Water vapor removal rate achieved 57.4% and dew point depression of 29.1 K.

Integrated damage, visual, remote sensing, and environmental analysis of a strong tornado in southern Brazil

On 20 April 2015 a strong tornado struck the city of Xanxerê located in western Santa Catarina (SC) state, in southern Brazil. The tornado destroyed several buildings, causing almost 100 injuries, four fatalities, and R$ 104 million in damage (approximately USD$ 34.6 million according to 2015 dollar quotation), making it one of the most damaging tornadoes in Brazil's history. This study provides an integrated damage, visual, remote sensing, and environmental analysis of the tornado event. Medium-resolution satellite imagery and in situ photographs of the damage show that the tornado caused high-end F2 damage on the Fujita scale across a 4.9-km-long path in the northern portion of Xanxerê. Videos of the tornado and its parent storm reveal the existence of a low-level mesocyclone and rear-flank downdraft, indicating that the storm was a supercell. Horizontal vortices, which are occasionally observed in strong tornadoes but rarely documented in Brazilian tornadoes, were also observed in the videos. The supercell developed in a warm and moist air mass characterized by moderate buoyancy and weak mid-level lapse rates. The mid- and upper-tropospheric forcing for synoptic-scale ascent was weak but the prevailing zonal flow was moderately strong, providing sufficient deep-layer shear for supercell development. A key component of the synoptic-scale environment was the presence of a northerly low-level jet stream that promoted warm moist advection into western SC in addition to increasing the low-level vertical wind shear in the region. On the mesoscale, the presence of a broad cloud shield from a decaying mesoscale convective system (MCS) preceding the supercell formation hampered daytime radiative heating over western SC resulting in low dewpoint depression and, thus, lower lifting condensation levels around Xanxerê. Remote sensing and surface data suggest that the MCS-induced outflow boundary may have been responsible for the initiation of the supercell. The complex subtropical environment of the Xanxerê tornado differs in many aspects from well-documented North American tornadic environments and highlights the need for more studies addressing distinctions between the atmospheric conditions that are conducive to tornadic activity in the two continents. • A strong tornado impacted a city in southern Brazil, causing significant damage. • Damage, visual, remote sensing, and meteorological data are used to study the event. • Damage caused by the tornado was rated as high-end F2 damage on the Fujita scale. • Instability was due to high low-level moisture, rather than steep lapse rates. • An early convective system fostered the mesoscale environment for the tornadic storm.

In-situ construction of particle-accumulated hydrophobic packing layer from rigid polyurethane wastes for gas pre-dehydration

Rigid polyurethane, a common insulation material, is difficult to degrade naturally and poses significant health and environmental risk. Consequently, the cost-effective and efficient reuse of these solid wastes is a great challenge. Herein, we report an in-situ construction of hexadecyltrimethoxysilane (HDTMS) hydrophobic films on rigid polyurethane waste particle (RPUWP) as an accumulated packing layer in the gas-liquid pre-separator for gas pre-dehydration. The RPUWP grafted with HDTMS (P-HDTMS) packing layer exhibits excellent hydrophobicity in the air, acid (pH = 1), alkali (pH = 14), and high-temperature (over 95 ℃) resistance. Also, the gas pre-dehydration performance of the accumulated P-HDTMS packing layer in the gas-liquid pre-separator was evaluated by the water dew point depression (ΔT d ). It is worth mentioning that the accumulated packing layer can stabilize and greatly reduce the ΔT d by 17.8 ℃ in over 48-hour uninterrupted operation through the coalescence separation mechanism. Therefore, the low-cost accumulated P-HDTMS packing layer is expected to be an ideal candidate material for gas pre-dehydration in large-scale practical applications. This work not only provides a novel method for the resource utilization of plastic solid wastes but also provides a hitherto unexplored perspective for gas dehydration in industrial production. • A waste-to-resource strategy of rigid polyurethane solid waste is introduced. • The mechanism of two steps in-situ grafting process is proposed. • The stacked P-HDTMS packing layer exhibits remarkable gas pre-dehydration performance. • The mechanism of gas-liquid coalescence separation is revealed. • The as-prepared material can withstand strong acid/alkali and high-temperature solutions.

Evaluation of influential parameters for supersonic dehydration of natural gas: Machine learning approach

The supersonic dehydration of natural gas is gaining more attention due to its numerous advantages over the conventional natural gas dehydration technologies. However, supersonic separators have seen minimal field applications despite the multiple benefits over other gas dehydration techniques. This has been mostly attributed to the uncertainty in ascertaining the design and operating parameters that should be monitored to ensure optimum dehydration of the supersonic separation device. In this study, the decision tree machine learning model is employed in investigating the effects of design and operating parameters (inlet and outlet pressures, nozzle length, throat diameter, and pressure loss ratio) on the supersonic separator performance during dehydration of natural gas. The model results show that the significant parameters influencing the shock wave location are the pressure loss ratio and nozzle length. The former was found to have the most significant effect on the dew point depression. The dehydration efficiency is mainly dependent on the pressure loss ratio, nozzle throat diameter, and the nozzle length. Comparing the machine learning model-accuracy with a 1-D iterative model, the machine learning model outperformed the 1-D iterative model with a lower mean average percentage error (MAPE) of 5.98 relative to 15.44 as obtained for the 1-D model.

The droplets and film behaviors in supersonic separator by using three-field two-fluid model with heterogenous condensation

• A novel three-field two-fluid model described by Eulerian-Eulerian approach. • Heat and mass transfer between gas, liquid droplets, and liquid film. • Maximum interfacial wave velocity by cross-correlation algorithm is 1.49 m s −1 . • The optimal value of effective density of foreign droplets is 0.01 kg m −3 . • The maximum separation efficiency and dew point depression are 85.11% and 40.32 °C. Supersonic separator is a kind of natural gas dehydration device with great potential, but its internal mass and heat transfer process has not been fully studied. In this study, a novel three-field two-fluid model described by Eulerian-Eulerian approach for supersonic separator considering the heat and mass transfer between gas, liquid droplets, and liquid film was developed and validated. The interphase slip, latent heat, film heat flux, and film phase change rate were studied. It revealed that the maximum centrifugal slip velocity of droplets can reach 24.9 m s − 1. The maximum latent heat is 5.3 × 10 8 J m −3 from droplets to gas phase during condensation, and the minimum latent heat is -3.4 × 10 8 J m −3 during evaporation. The thickness of swirling liquid film at wet gas outlet is 21 μm, 47 μm, 74 μm and 89 μm, respectively. The liquid film temperature decreases to a minimum 304.1 K due to droplets deposition, where the maximum heat flux is 0.74 MW m −2 . Besides, the frequency and velocity of the interfacial wave of liquid film were obtained by using the cross-correlation algorithm, and their maximum values was 11.07 Hz and 1.49 m s −1 , respectively. In addition, for achieving higher dehydration efficiency, the optimal value of the foreign droplet mass concentration should be 0.01 kg m −3 . The maximum separation efficiency and dew point depression of separator A are 85.11% and 40.32 °C, respectively. The model without considering the liquid film over-predicts the separation efficiency.

The Brazilian Cerrado is becoming hotter and drier.

The Brazilian Cerrado is a global biodiversity hotspot with notoriously high rates of native vegetation suppression and wildfires over the past three decades. As a result, climate change can already be detected at both local and regional scales. In this study, we used three different approaches based on independent datasets to investigate possible changes in the daytime and nighttime temperature and air humidity between the peak of the dry season and the beginning of the rainy season in the Brazilian Cerrado. Additionally, we evaluated the tendency of dew point depression, considering it as a proxy to assess impacts on biodiversity. Monthly increases of 2.2-4.0℃ in the maximum temperatures and 2.4-2.8℃ in the minimum temperatures between 1961 and 2019 were recorded, supported by all analyzed datasets which included direct observations, remote sensing, and modeling data. The warming raised the vapor pressure deficit, and although we recorded an upward trend in absolute humidity, relative humidity has reduced by ~15%. If these tendencies are maintained, gradual air warming will make nightly cooling insufficient to reach the dew point in the early hours of the night. Therefore, it will progressively reduce both the amount and duration of nocturnal dewfall, which is the main source of water for numerous plants and animal species of the Brazilian Cerrado during the dry season. Through several examples, we hypothesize that these climate changes can have a high impact on biodiversity and potentially cause ecosystems to collapse. We emphasize that the effects of temperature and humidity on Cerrado ecosystems cannot be neglected and should be further explored from a land use perspective.