Pressure Variation Research Articles

Stimulation of tight basalt reservoirs using supercritical carbon dioxide: Implications for large-scale carbon sequestration

Although supercritical carbon dioxide (SC-CO2) fracturing shows tremendous potential for maximizing injection efficiency and enhancing storage volumes, few investigations have been reported on the SC-CO2 fracturing characteristics of tight basalts and the reactions between fractured basalt and SC-CO2. In this study, hydraulic fracturing experiments were conducted on cylindrical basalt specimens using water and SC-CO2 as fracturing fluids. Geometric parameters were proposed to characterize the fracture morphologies based on the three-dimensional (3D) reconstructions of fracture networks. The rock slices with induced fractures after SC-CO2 fracturing were then processed for fluid (deionized water/SC-CO2)-basalt reaction tests. The experimental results demonstrate that SC-CO2 fracturing can induce complex and tortuous fractures with spatially dispersed morphologies. Other fracturing behaviors accompanying the acoustic emission (AE) signals and pump pressure changes show that the AE activity responds almost simultaneously to variation in the pump pressure. The fractured basalt blocks exposed to both SC-CO2 and water exhibit rough and uneven surfaces, along with decreased intensities in the element peaks, indicating that solubility trapping predominantly occurs during the early injection stage. The above findings provide a laboratory research basis for understanding the fracturing and sequestration issues related to effective CO2 utilization.

Dynamic characteristics of high-pressure and high-density carbon dioxide leakage process

The leakage of high-pressure and high-density carbon dioxide (CO2) during storage and transportation can result in the rapid release of a substantial amount of CO2, presenting significant risks to Carbon Capture and Storage (CCS) technology. Investigating the dynamic characteristics of high-pressure and high-density CO2 leakage in storage tank, as well as understanding the variations in CO2 pressure, temperature, density, and phase during the leakage process, is crucial for the design of tank and pipeline leakage prevention. Existing research mainly focuses on leakage at the top or side of storage tank, there is a lack of attention to leakage from the bottom of storage tank. To simulate the bottom leakage processes, a depressurized experimental system with a 0.065 m3 CO2 storage tank volume was constructed. The range of CO2 parameter conditions for filling density is 811 ∼ 998 kg/m3, with initial pressure ranging from 5.72 ∼ 16.50 MPa. According to the change in the slope of the pressure drop in the storage tank, the leakage can be divided into three processes. The closer to the top of the storage tank, the liquid–gas phase transition of CO2 occurs earlier. The pressure shows two inflection points (point I and point II) representing the upper and lower limits of CO2 saturation pressure, respectively. The lower filling density results in a higher saturation pressure, while the higher initial pressure also leads to a higher saturation pressure. The current work can help to better understand pressure, temperature, density, and phase state changes of CO2 during leakage.

An all-purpose method for optimal pressure sensor placement in water distribution networks based on graph signal analysis

Many researchers have addressed the challenge of optimal pressure sensor placement for different purposes, such as leakage detection, model calibration, state estimation, etc. However, pressure data often need to serve multiple purposes, and a method to optimize sensor locations with versatility for various objectives is still lacking. In this paper, a graph-based optimal sensor placement (GOSP) framework is proposed, which aims to provide a robust and all-purpose approach to identify critical points for pressure monitoring. By analysing the spatial variation frequencies of WDN pressures, the relationship between measurements and the global variation of original pressures is established. On this basis, the D-optimality criterion is adopted to formulate the objective of GOSP, which aims to maximize the information on the spatial distribution of pressures that can be obtained from measurements. The new-proposed objective ensures that the sensor locations are compatible with various application scenarios. The proposed method was applied to a real-life distribution network, and was compared with other optimal sensor placement methods oriented towards burst detection and pipe roughness calibration. Based on comparative studies in different scenarios including unknown pressure estimation, burst detection, and model calibration, the effectiveness and robustness of the proposed method have been proved.

Assessment of the impact of agricultural tractor wheels on soil compaction

Abstract This article presents the results of an experimental study on the impact of leading wheel tires 15.5R38 and 16.9R38 on soil. Specifically, the study analyzed the influence of vertical load on the maximum tire pressure (MTA) and internal air pressure on soil. The variations in maximum soil pressures of 15.5R38 and 16.9R38 tires at different internal air pressures depending on corresponding loads were investigated. The characteristics of changes in permissible pressure and loading of tires on soil are graphically depicted, and the permissible standards for their use in agricultural work are determined. Recommendations are provided for using tires at the minimum and maximum permissible internal air pressures. Using values smaller than the recommended values can lead to a decrease in the useful life of the tire.

Preliminary Observations of the Loke Microdialysis in an Experimental Pig Model: Are We Ready for Continuous Monitoring of Brain Energy Metabolism?

Brain energy metabolism is often disturbed after acute brain injuries. Current neuromonitoring methods with cerebral microdialysis (CMD) are based on intermittent measurements (1-4 times/h), but such a low frequency could miss transient but important events. The solution may be the recently developed Loke microdialysis (MD), which provides high-frequency data of glucose and lactate. Before clinical implementation, the reliability and stability of Loke remain to be determined in vivo. The purpose of this study was to validate Loke MD in relation to the standard intermittent CMD method. Four pigs aged 2-3 months were included. They received two adjacent CMD catheters, one for standard intermittent assessments and one for continuous (Loke MD) assessments of glucose and lactate. The standard CMD was measured every 15 min. Continuous Loke MD was sampled every 2-3 s and was averaged over corresponding 15-min intervals for the statistical comparisons with standard CMD. Intravenous glucose injections and intracranial hypertension by inflation of an intracranial epidural balloon were performed to induce variations in intracranial pressure, cerebral perfusion pressure, and systemic and cerebral glucose and lactate levels. In a linear mixed-effect model of standard CMD glucose(mM), there was a fixed effect value (± standard error [SE]) at 0.94 ± 0.07 (p < 0.001) for Loke MD glucose(mM), with an intercept at - 0.19 ± 0.15 (p = 0.20). The model showed a conditional R2 at 0.81 and a marginal R2 at 0.72. In a linear mixed-effect model of standard CMD lactate(mM), there was a fixed effect value (± SE) at 0.41 ± 0.16 (p = 0.01) for Loke MD lactate(mM), with an intercept at 0.33 ± 0.21 (p = 0.25). The model showed a conditional R2 at 0.47 and marginal R2 at 0.17. The established standard CMD glucose thresholds may be used as for Loke MD with some caution, but this should be avoided for lactate.

Pressure prediction of water supply network based on 1DCNN-GRU-multi-head attention

ABSTRACT The water supply network is a crucial component of urban construction and plays a pivotal role in the normal operation of the city. The complexity and periodicity of pressure variations in the water supply networks pose significant challenges to traditional prediction models. In this study, we introduce a novel short-term pressure prediction model, termed the 1DCNN-GRU-multi-head attention (CGMA) model, which incorporates a multi-head attention mechanism alongside an integrated network consisting of one-dimensional convolutional neural networks (1DCNNs) and gated recurrent units (GRUs). Initially, the model employs a 1DCNN network to extract features from the pressure data. Subsequently, the extracted features are input into the GRU neural network, leveraging its long-term dependency capabilities to improve prediction accuracy. Then, the attention mechanism is incorporated into the GRU component to highlight key information, enabling the model to focus on more important data features, thereby improving the prediction performance. We implemented this model in a real urban water distribution system equipped with five pressure sensors. The model achieved a mean absolute error of 0.00197 and a root mean square error of 0.00262. When compared with alternative approaches, our method demonstrated superior predictive performance for pressure data, thereby confirming its efficacy in practical applications.

An efficient pressure sensor based on environmental-friendly CNTs-graphene-PDMS film

Given the rapid advancements in artificial intelligence, there is an escalating demand for wearable sensors. An efficient graphene-based material synthesized from the mesophase pitch of waste slurry oil was integrated into a cost-effective piezoresistive pressure sensor consisting of a conductive film made of carbon nanotubes (CNTs), graphene, and Polydimethylsiloxane (PDMS). A simple fabrication approach has been suggested to infuse PDMS with CNTs-graphene, resulting in a pressure sensor exhibiting superior conductivity, enhanced sensitivity, and quick responsiveness to diverse pressure variations. Moreover, films containing varying percentages of graphene were compared. Scanning electron microscopy was utilized to examine the surface and structural characteristics of the CNTs-graphene-PDMS film, alongside studying the pressure sensor's sensing capabilities. Various applications were examined for both the individual sensor and the array of sensors. The findings demonstrate the successful detection of diverse human motions, Morse code recognition, and effective discernment of various pressures by the fabricated pressure sensor, indicating its potential for applications in smart devices, robotics, and wearable sensors.

Exploiting Nonlinearities of Electrostatic MEMS Resonators for Tunable Low Pressure Sensing

In this paper, we demonstrate the potential functionalization of inherent nonlinearities associated with electrostatic MEMS resonators for low air pressure applications, with operating ranges as low as 0.65-295 mbar. The proposed pressure sensor is made of a polysilicon microplate subject to electrostatic actuation via an underneath electrode and attached to two cantilever microbeams. The pressure sensing mechanism relies on the motion-induced current method, a transduction mechanism that converts the dynamic alteration of the microplate due to pressure variations into an electrical signal detected via a lock-in amplifier. The experimental study showed evidence of the possible tunability of the pressure sensor via the deployment of different detection mechanisms to achieve superior performance in terms of sensitivity and low pressure operating range. These mechanisms rely on nonlinear dynamic features that result from the strong coupling between the microplate and the surrounding fluid along with the electrostatic actuation. These include dynamic pull-in instability, bifurcation and softening behavior associated with the elastic effect of the squeeze-film damping. The experimental results revealed that tracking the bifurcation frequency allows for the detection of low pressure levels, in the range of a few millibars. This capability is not provided by the resonance frequency shift method, which, while offering higher sensitivity, does not achieve the same low-pressure detection.

Time-dependent variations of groundwater radon: Insights from a twelve-year study in the Baikal region, East Siberia, Russia

Time-dependent variations of 222Rn concentration (Q) in groundwater have been monitored for twelve years (2012–2023) at eight sites of groundwater discharge within the Baikal region in East Siberia, Russia. The concentrations of radioactive gas at different sites vary from 30% to 60% of average values (Qav). The sampled waters are of three groups with Qav ≈ 15 Bq/l (I), ≈30 Bq/l (II1), and ≈50 Bq/l (II2). Cluster analysis shows closest linkage between the two subgroups of group II due to similarity in the discharge mechanisms. Fourier analysis of periodic 222Rn behavior reveals major cycles of 365, 180, 126, and 30 days correlated with variations of air temperature and pressure, as well as with the patterns of groundwater discharge. In addition, radon anomalies are related with seismicity. Earthquakes are reflected in the radon field as three distinct anomaly types, occurring either subsequent to or prior to the seismic event. The anomalies responding to earthquake nucleation can be considered as precursors and used in earthquake prediction. The revealed trends make basis for a model designed to predict Q variations in groundwater of the area to 80% average efficiency. The external and internal factors that affect the concentration of radioactive gas in groundwater are linked in a hierarchic system and are classified according to the degree and type of their influence.

Fluidic Multichambered Actuator and Multiaxis Intrinsic Force Sensing.

Soft robots have morphological characteristics that make them preferred candidates, over their traditionally rigid counterparts, for executing physical interaction tasks with the environment. Therefore, equipping them with force sensing is essential for ensuring safety, enhancing their controllability, and adding autonomy. At the same time, it is necessary to preserve their inherent flexibility when integrating sensory units. Soft-fluidic actuators (SFAs) with hydraulic actuation address some of the challenges posed by the compressibility of pneumatic actuation while maintaining system compliance. This research further investigates the feasibility of utilizing the incompressible actuation fluid as the means of actuation and of multiaxial sensing. We have developed a hyperelastic model for the actuation pressure, acting as a baseline pressure. Any disparities from the baseline have been mapped to external forces, using the principle of pressure-based fluidic soft sensor. Computed tomography imaging has been used to examine inner deformation and validate the analytically derived actuation-pressure model. The induced stresses within the SFA are examined using COMSOL simulations, contributing to the development of a calibration algorithm, which accounts for geometric and cross-sectional nonlinearities and maps pressure variations with tip forces. Two force types (concentrated and distributed) acting on our SFA under different configurations are examined, using two experimental setups described as "Point Load" and "Distributed Force." The force sensing algorithm achieves high accuracy with a maximum absolute error of 0.32N for forces with a magnitude of up to 6N.

Predicting Wall Pressure in Shock Wave/Boundary Layer Interactions with Convolutional Neural Networks

Within the dynamic realm of variable-geometry shock wave/boundary layer interactions, the wall parameters of the flow field undergo real-time fluctuations. The conventional approach to sensing these changes in wall pressure through sensor measurements is encumbered by a cumbersome process, leading to diminished efficiency and an inability to provide swift predictions of wall parameters. This paper introduces a data-driven methodology that leverages non-contact schlieren imaging to predict wall pressure within the flow field, a technique that holds promise for informing the optimized design of variable-geometry systems. A sophisticated deep learning framework, predicated on Convolutional Neural Networks (CNN), has been engineered to anticipate alterations in wall pressure stemming from high-speed shock wave/boundary layer interactions. Utilizing an impulsive wind tunnel with a Mach number of 6, we have procured a sequence of schlieren images and corresponding wall pressure measurements, capturing the continuous variations induced by an attack angle from a shock wave generator. These data have been instrumental in compiling a comprehensive dataset for the training and evaluation of the CNN. The CNN model, once trained, has adeptly deduced the distribution of wall pressure from the schlieren imagery. Notwithstanding, it was observed that the CNN’s predictive prowess is marginally diminished in regions where pressure variations are most pronounced. To assess the model’s generalization capabilities, we have segmented the dataset according to different temporal intervals for network training. Our findings indicate that while the generalization of all models crafted was less than optimal, Model 4 demonstrated superior generalization. It is thus suggested that augmenting the training set with additional samples and refining the network architecture will be a worthwhile endeavor in subsequent research initiatives.

Meteorological factors and risk of ischemic stroke, intracranial hemorrhage, and subarachnoid hemorrhage: A time-stratified case-crossover study.

Stroke risks associated with rapid climate change remain controversial due to a paucity of evidence. To examine the risk of subarachnoid hemorrhage (SAH), intracranial hemorrhage (ICH), and ischemic stroke (IS) associated with meteorological parameters. In this time-stratified case-crossover study, adult patients hospitalized for their first stroke between 2011 and 2020 from the insurance claims data in Taiwan were identified. The hospitalization day was designated as the case period, and three or four control periods were matched by the same day of the week and month of each case period. Daily mean and 24-h variations in ambient temperature, relative humidity, air pressure, and apparent temperature were measured. Conditional logistic regression models were applied to assess the risk of stroke associated with exposure to weather variables, using the third quintile as a reference, controlling for air pollutant levels. There were 7161 patients with SAH, 40,426 patients with ICH, and 107,550 patients with IS. There was an inverse linear relationship between mean daily temperature and apparent temperature with ICH. Elevated mean daily atmospheric pressure was associated with an increased risk of ICH. A greater decrease in apparent temperature over a 24-h period was associated with increased risk of ICH but decreased risk of IS (odds ratio (95% confidence interval) for the first vs. third quintile of changes in apparent temperature, 1.141 (1.053-1.237) and 0.946 (0.899-0.996), respectively). There were considerable differences in short-term associations between meteorological parameters and three main pathological types of strokes. The authors have no permission to share the data.

Preoperative intravenous mannitol administration and its rationale before cataract surgery.

The purpose of this study is to assess the variations in anterior chamber depth (ACD), axial length (AXL), and intraocular pressure (IOP) in both phakic and pseudophakic eyes undergoing preoperative intravenous mannitol infusion. The study was conducted at the Ophthalmology Department of IRCSS Humanitas in Milan, Italy. This is a prospective and non-randomized study. 40 patients with phakic eyes and 40 patients with pseudophakic eyes scheduled for cataract surgery in their fellow eye were included. Prior to the surgery, comprehensive ophthalmic examinations were conducted, including IOP measurement, and bilateral biometry performed with Anterion® (Heidelberg Engineering GmbH, 69,115, Heidelberg, Germany). All patients received intravenous infusion of mannitol before the surgery. One hour after cataract extraction, the patients underwent the same set of examinations. We analyzed the changes AXL, ACD and IOP in the eye opposite to the one that underwent surgery. The comparison between preoperative and postoperative parameters showed no differences in AXL (p = 0.34 and p = 0.53) and in ACD (p = 0.38 and p = 0.31) in the phakic and in the pseudophakic group, respectively. Instead, a statistically significant difference was found between the mean preoperative and postoperative IOP (p = 0.02) for the phakic group and (p = 0.03) for the pseudophakic group. The administration of mannitol does not lead to any changes in the ACD and AXL, regardless of whether the eyes are phakic or pseudophakic. However, there is a statistically significant reduction in IOP. Nonetheless, it is important to consider the various side effects associated with mannitol infusion.

A retrospective study of the effects of a vasopressor bolus on systolic slope (dP/dt) and dynamic arterial elastance (Eadyn)

BackgroundTo enhance the utility of functional hemodynamic monitoring, the variables systolic slope (dP/dt) and dynamic arterial elastance (Eadyn) are calculated by the Hypotension Prediction Index (HPI) Acumen® Software. This study was designed to characterize the effects of phenylephrine and ephedrine on dP/dt and Eadyn.MethodsThis was a retrospective, non-randomized analysis of data collected during two clinical studies. All patients required intra-operative controlled mechanical ventilation and had an indwelling radial artery catheter connected to an Acumen IQ sensor. Raw arterial pressure waveform data was downloaded from the patient monitor and all hemodynamic measurements were calculated off-line. The anesthetic record was reviewed for bolus administrations of either phenylephrine or ephedrine. Cardiovascular variables prior to drug administration were compared to those following vasopressor administrations. The primary outcome was the difference for dP/dt and Eadyn at baseline compared with the average after the bolus administration. All data sets demonstrated non-normal distributions so statistical analysis of paired and unpaired data followed the Wilcoxon matched pairs signed-rank test or Mann-Whitney U test, respectively.Results201 doses of phenylephrine and 100 doses of ephedrine were analyzed. All data sets are reported as median [95% CI]. Mean arterial pressure (MAP) increased from 62 [54,68] to 78 [76,80] mmHg following phenylephrine and from 59 [55,62] to 80 [77,83] mmHg following ephedrine. Stroke volume and cardiac output both increased. Stroke volume variation and pulse pressure variation decreased. Both drugs produced significant increases in dP/dt, from 571 [531, 645] to 767 [733, 811] mmHg/sec for phenylephrine and from 537 [509, 596] to 848 [779, 930] mmHg/sec for ephedrine. No significant changes in Eadyn were observed.ConclusionBolus administration of phenylephrine or ephedrine increases dP/dt but does not change Eadyn. dP/dt demonstrates potential for predicting the inotropic response to phenylephrine or ephedrine, providing guidance for the most efficacious vasopressor when treating hypotension.Trial registrationData was collected from two protocols. The first was deemed to not require written, informed consent by the Institutional Review Board (IRB). The second was IRB-approved (Effect of Diastolic Dysfunction on Dynamic Cardiac Monitors) and registered on ClinicalTrials.gov (NCT04177225).

Mechanical and structural properties of monatomic zirconium metallic glass under pressure variations and annealing processes: A molecular dynamics study

The aim of this study is to investigate how different pressure levels and annealing times affect the local atomic structure and mechanical properties of pure zirconium metallic glasses. Using molecular dynamics simulations with the embedded atom method, we explored how these changes influence the material's inherent characteristics. Analysis of the radial distribution function, coordination number, and Voronoi tessellation revealed a spectrum of structural arrangements in metallic glass formed across a pressure range of 0–70 GPa. Additionally, the glass transition temperature increased with increasing pressure, accompanied by reduced free volume. The annealing process, ranging from 0 to 5 ns on metallic glass synthesized under 0 GPa pressure, showed the coordination number's significance in achieving a glassy state. Regarding mechanical behavior, both elastic constants and moduli showed a progressive increase with rising pressure, while Young's modulus and hardness displayed enhanced values with longer annealing times.

Effect of protein supplements on cardiovascular health and exercise performance of young adult males.

The modern fitness culture, propelled by celebrity influence and media exposure, has led to widespread acceptance and consumption of protein supplements, particularly among young adult males aspiring for an ideal physique. Despite the surge in usage, limited research has explored the impact of these supplements on cardiovascular health and exercise performance. This study aims to bridge this gap by clinically examining the cardiovascular system using heart rate variability (HRV) and comparing outcomes between those using protein supplements and a control group. This study was conducted at the Department of Physiology, AIIMS Raipur. The cross-sectional study involved 60 young adult males aged 18-25 years. Participants were divided into two groups: group I (n = 30), comprising individuals using commercially available protein supplements for a minimum of three months, and group II (n = 30) as age-matched controls. Cardiovascular assessments, including HRV analysis, were performed at rest and after the Harvard step test. Pre-exercise and post-exercise parameters were statistically analyzed, and participants in the test group provided information on supplement labels. Participants in both groups exhibited comparable pre-exercise cardiovascular parameters. However, post-exercise results did not reveal significant variations in systolic blood pressure, diastolic blood pressure, mean blood pressure, low frequency/high frequency (LF/HF) ratio, Standard deviation of the successive differences between adjacent NNs (SDNN), and standard deviation of successive differences (SDSD) between the test and control groups. The average exercise duration for the control group was longer than that of the test group. Amino acid profiles and nutritional content varied among 13 different protein supplement brands. The study found no statistically significant positive influence of protein supplements on cardiovascular health or exercise performance in young adult males.

Effects of Relative Humidity and Phase on the Molecular Detection of Nascent Sea Spray Aerosol Using Extractive Electrospray Ionization.

Online mass spectrometry techniques, such as extractive electrospray ionization mass spectrometry (EESI-MS), present an attractive alternative for analyzing aerosol molecular composition due to reduced aerosol sample collection and handling times and improved time resolution. Recent studies show a dependence of EESI-MS sensitivity on particle size and mixing state. This study measured authentic sea spray aerosol (SSA) components generated during a phytoplankton bloom, specifically glycerol, palmitic acid, and potassium ions. We demonstrate temporal variability and trends dependent on specific biological processes occurring in seawater. We found that the EESI-MS sensitivity, after adjusting for pressure variations at the inlet and normalizing to the reagent ion, critically depends on the sample's relative humidity. Relevant SSA species exhibited heightened sensitivity at an elevated relative humidity near the deliquescence relative humidity of sea salt and poorer sensitivity with sparse detection below the efflorescence relative humidity. Modeling the reagent ion's diffusive depth demonstrates that the sample aerosol particle viscosity governs the relative humidity dependence because it modulates the particle's coagulation efficiency and distance the reagent ion diffuses and reacts with components in the particle bulk. The effects of particle size and mixing state are discussed, revealing improved sensitivity of phase-separated components present along the particle surface. This work highlights the importance of the particle phase state in detecting and quantifying molecular components within authentic and complex aerosol particles and the utility of EESI-MS for measuring SSA composition.

Association between oral microbial nitrate metabolism and poor prognosis in acute ischemic stroke patients with a history of hypertension

ABSTRACT Background Oral microbes mediate the production of nitric oxide (NO) through the denitrification pathway. This study aimed to investigate the association between oral microbial nitrate metabolism and prognosis in acute ischemic stroke (AIS) patients. Methods This prospective, observational, single-center cohort study included 124 AIS patients admitted within 24 hours of symptom onset, with 24-hour ambulatory blood pressure data. Oral swabs were collected within 24 hours. Hypertensive AIS patients were stratified by the coefficient of variation (CV) of 24-hour systolic blood pressure. Microbial composition was analyzed using LEfSe and PICRUSt2 for bacterial and functional pathway identification. Results Significant differences in oral microbiota composition were observed between hypertensive AIS patients with varying CVs. Lower CV groups showed enrichment of nitrate-reducing bacteria and “Denitrification, nitrate => nitrogen” pathways. The TAX score of oral nitrate-reducing bacteria, derived from LASSO modeling, independently correlated with 90-day modified Rankin Scale scores, serving as an independent risk factor for poor prognosis. Mediation analyses suggested indirect that the TAX score not only directly influences outcomes but also indirectly affects them by modulating 24-hour systolic blood pressure CV. Conclusions AIS patients with comorbid hypertension and higher systolic blood pressure CV exhibited reduced oral nitrate-reducing bacteria, potentially worsening outcomes.

Temperature and pressure prediction of offshore high CO2-content associated gas lift and analysis of influencing factors

With the continuous injection of CO2 into the reservoir, a portion of the sequestered CO2 is co-produced with crude oil, resulting in a significant concentration of CO2 in the associated gas. Utilizing high CO2-content associated gas for offshore gas lift operations improves the reutilization of such gas and reduces costs related to sourcing and transporting it. To ensure smooth activation of the gas lift valve, conducting a thorough study on temperature and pressure within the wellbore during the gas lift process is essential. This study developed a dynamic model for calculating the transient temperature and pressure within offshore gas lift wellbores and fitted equations to calculate the physical properties of high CO2-content associated gas for specific compositions. This refinement minimized computational discrepancies arising from fluctuations in physical property parameters. When applying this model to oilfield scenarios, the maximum error observed was 3.83%, which aligned well with the accuracy standards required for oilfield computations. By integrating oilfield production data, this research explored the influence of injection parameters on temperature and pressure dynamics within the gas lift wellbore. The results revealed that variations in injection temperature and pressure had minimal impact on the temperature at the gas lift valve. Yet, they induced significant changes in the pressure at that point. An increase in CO2 concentration within the gas source composition was associated with considerable shifts in temperature and pressure gradients throughout the wellbore. At high mass flow rates, the temperature gradient of the wellbore was notably reduced, while the pressure gradient was significantly enhanced. These research findings provided a theoretical basis for optimizing gas lift operations using high CO2-content associated gas in offshore settings.

A global plasma and surface model of hydrogen/methane inductively coupled discharge to analyze hydrocarbon plasma–surface interactions in extreme-ultraviolet lithography machines

Hydrocarbon contamination is associated with light transmission losses in modern lithography machines, which contain extreme-ultraviolet-induced plasma. A volume-averaged global and deposition/etch surface model of a reference hydrogen/methane inductive discharge is developed to investigate the plasma–surface interactions. The simulation results are validated against a wide variety of experiments and verified with respect to multiple sets of computational data. The deposition rate is calculated for a variation in methane impurity (10–10 000 ppm), power, pressure, and net mass flow. The simulations conclude that the hydrocarbon plasma deposition can be minimized by reducing methane impurity and excluding solid organic structures.