Pressure Variation Research Articles

Multiple lubricated joints with long and short bearings in multibody mechanical systems - Modeling, simulation, and performance analysis

This study examines the effect of multiple lubricated imperfect long and short bearings on the performance of a multibody mechanical system. Unlike the typical assumption of a single perfect or imperfect lubricated joint due to modeling difficulties, this research considers the practical impacts of clearances in multiple joints. While unlubricated joints generally cause significant performance issues, lubricants reduce these effects, creating more localized peaks. The findings show that as the number of lubricated joints increases, both the magnitude and frequency of these peaks rise. In systems with multiple journal bearings, torque peaks become more noticeable due to the additional degrees of freedom introduced by the clearances. These degrees of freedom amplify acceleration, leading to higher lubricant reaction forces, which in turn require greater motor torque peaks to maintain the system's kinematics. Shorter lubricated joints exhibit more severe peaks than longer ones, mainly due to side leakage causing axial pressure variation and reduced damping capacity. The study highlights the need to replace idealized joints with imperfect ones for more accurate modeling of practical systems.

Rapid-response, low-detection-limit, positive-negative air pressure sensing: GaN chips integrated with hydrophobic PDMS films

Despite the importance of positive and negative pressure sensing in numerous domains, the availability of a single sensing unit adept at handling this dual task remains highly limited. This study introduces a compact optical device capable of swiftly and precisely detecting positive and negative pressures ranging from −35 kPa to 35 kPa. The GaN chip, which serves as a core component of the device, is monolithically integrated with light-emitting and light-detecting elements. By combining a deformable PDMS film coated with a hydrophobic layer, the chip can respond to changes in optical reflectance induced by pressure fluctuations. The integrated sensing device has low detection limits of 4.3 Pa and −7.8 Pa and fast response times of 0.14 s and 0.22 s for positive and negative pressure variations, respectively. The device also demonstrates adaptability in capturing distinct human breathing patterns. The proposed device, characterized by its compactness, responsiveness, and ease of operation, holds promise for a variety of pressure-sensing applications.

Research on aerodynamic characteristics of high-velocity train bogies

The bogie is a critical component of high-velocity trains. As train velocity rises, the operational quality is increasingly influenced by aerodynamic forces. The aerodynamic characteristics of bogies significantly impact the safety of train operations. This article examines a real high-velocity train model, constructing a 3-group train model, and developing a mathematical model of high-velocity train operation on open lines. Using three-dimensional steady-state Navier–Stokes equations, the flow field characteristics, pressure spread, aerodynamic forces, and torque characteristics on the bogie and body of high-velocity locomotives operating at velocities of 200 km/h, 250 km/h, 300 km/h, and 350 km/h are calculated, with measurement spots set to study the trend of pressure variation with train velocity. The research findings indicate that pressure at the same location on the body surface rises with vehicle velocity, with a quadratic link between the two. The uneven pressure spread between two bogies of the same vehicle and between two axles of the same bogie indicates that aerodynamic forces can cause axle load transfer in high-velocity trains. The highest values of aerodynamic resistance and lift happen at the front bogie, and the resistance and lift of both a single carriage and the entire vehicle increase with velocity, following a quadratic relationship. The highest nodding torque of the train body occurs on one vehicle, while the highest nodding torque of the bogie occurs on the 6th bogie, with both torques increasing with velocity, also in a quadratic relationship.

Fabrication of Highly Sensitive Porous Polydimethylsiloxane Pressure Sensor Through Control of Rheological Properties.

In order to enhance the sensitivity of elastomers, pores were integrated into their structure. These pores facilitate the adjustment of thickness in response to external pressure variations, thereby improving the sensitivity of pressure sensors. Pores were introduced by emulsifying immiscible polydimethylsiloxane (PDMS) and water with a surfactant. By controlling the water content in the PDMS and water emulsion, we controlled the size, density, uniformity, and spatial distribution (2D or 3D) of the pores within the PDMS matrix. The presence of these pores significantly improved the sensitivity of PDMS under low external pressure conditions compared to high pressures. Specifically, porous PDMS exhibited approximately 10-times greater sensitivity under low-pressure conditions than non-porous PDMS. The effectiveness of porous PDMS was demonstrated through dynamic loading and unloading detection of a small Lego toy and monitoring of human heartbeats. These results highlight the efficacy of our pressure sensor based on porous PDMS, which is fabricated through a simple and cost-effective process using a PDMS and water emulsion. This approach is highly suitable for developing the ability to detect applied pressures or contact forces.

Study of Nanomaterials Under Second Order Bulk Modulus

In this study, we analyze the variation of pressure under different values of (𝑽𝑽𝟎) for nanomaterials using secondorder pressure derivative of bulk modulus equation of state. Second order pressure derivative of bulk modulus show fine studies of nanomaterials. The Gupta & Gupta EOS is used to calculate the resultant data of ε-Fe (Hexagonal iron), α-Fe2O3 and Ni-filled MWCNT nanomaterials and compared with other equations of state, like Tait EOS, Mie–Gruneisen EOS, Vinet EOS and experimental data available in literature. The resultant data show good agreement with experimental data and other EOSs.

Mitigation and Optimization of Induced Seismicity Using Physics‐Based Forecasting

AbstractFluid injection can induce seismicity by altering stresses on pre‐existing faults. Here, we investigate minimizing induced earthquake potential by optimizing injection operations in a physics‐based forecasting framework. We built a 3D finite element model of the poroelastic crust for the Raton Basin, Central US, and used it to estimate time dependent Coulomb stress changes due to 25 years of wastewater injection in the region. Our finite element model is complemented by a statistical analysis of the seismogenic index (SI), a proxy for critically stressed faults affected by variations in the pore pressure. Forecasts of seismicity rate from our hybrid physics‐based statistical model suggest that induced seismicity in the Raton Basin, from 2001 to 2022, is still driven by wastewater injection despite declining injection rates since 2011. Our model suggests that pore pressure diffusion is the dominant cause of Coulomb stress changes at seismogenic depth, with poroelastic stress changes contributing about 5% to the driving force. Linear programming optimization for the Raton Basin reveals that it is feasible to reduce earthquake potential for a given amount of injected fluid (safety objective) or maximize fluid injection for a prescribed earthquake potential (economic objective). The optimization tends to spread out high‐rate injectors and shift them to regions of lower SI. The framework has practical importance as a tool to manage injection rate per unit field area to reduce induced earthquake potential. Our optimization framework is both flexible and adaptable to mitigate induced earthquake potential in other regions and for other types of subsurface fluid injection.

Smart Compression Sock for Early Detection of Diabetic Foot Ulcers.

The prevention of diabetic foot ulcers remains a critical challenge. This study evaluates a smart compression sock designed to address this issue by integrating temperature, plantar pressure, and blood oxygen sensors and monitoring data recorded by these sensors. The smart sock, developed with input from a certified Pedorthist, was tested on 20 healthy adult participants aged 16 to 53. It includes four temperature sensors and pressure sensors at common ulcer sites (first and fifth metatarsal heads, calcaneus, and hallux), and a blood oxygen sensor on the hallux. The sensors are monitored, and their transduced data are collected and stored through an app installed on a personal cell phone. The mobile app interface is user-friendly, providing intuitive navigation and easy access to the sensors' data. Using repeated measures ANOVA and post hoc tests, we analyzed the impact of various physical activities on physiological changes in the foot. The device effectively detected significant variations in blood oxygen, temperature, and pressure across six activities. Statistical analyses revealed significant differences based on activity type and sensor location. These results highlight the smart sock's sensitivity and accuracy, suggesting its potential to prevent diabetic foot ulcers. Further clinical trials are needed to evaluate its efficacy in a larger, more diverse population.

Development of Electrically Conductive Wood-Based Panels for Sensor Applications.

This study investigates the development of electrically conductive panels for application as emergency detection sensors in smart house systems. These panels, composed of wood chips coated with polymeric methylene diphenyl isocyanate, were modified with carbon black and carbon fibers to enable detection of moisture, temperature, and pressure variations. Manufactured via hot pressing, the panels retained standard mechanical properties and exhibited stable performance under diverse environmental conditions. Carbon black-filled panels achieved electrical percolation at a lower filler concentration (5%) compared to carbon fiber-filled panels. The incorporation of carbon black reduced the electrical resistivity to 8.6 ohm·cm, while the addition of carbon fibers further decreased it to 7.7 ohm·cm. In terms of sensor capabilities, panels containing carbon fibers demonstrated superior sensitivity to moisture and pressure changes. However, carbon black was ineffective for temperature sensing. Among the carbon fiber-filled panels, those with 20 wt.% concentration exhibited the best performance for moisture and pressure detection, whereas panels with 40 wt.% carbon fiber content displayed the most reliable and consistent temperature-sensing properties.

Experimental and numerical study on cavitation pulsating pressure of water-jet propulsion axial-flow pump.

Cavitation may occur in water-jet pump during operation of water-jet propulsion vessel, and once cavitation occurs, the tip clearance pulsating pressure of the impeller may be intensified, resulting in increased vibration of the water-jet propulsion unit. In this paper, the cavitation pulsating pressure characteristics at different positions in the pump are studied by experiment and numerical simulation, and the pulsating pressure characteristics in tip clearance are mainly researched. Based on Star-CCM+ commercial software, unsteady Reynolds-averaged Navier-Stokes equations(RANS) numerical simulation is carried out, and the feasibility of the numerical simulation method is verified by uncertainty analysis. The results show that the cavitation pulsating pressure near the leading edge of the impeller in the tip clearance is the largest. The variation of the tip clearance pulsating pressure with the intensification of cavitation is studied by numerical simulation, and its mechanism is revealed. A dimensionless coefficient of net positive suction head (CNPSH) is proposed, and the study shows that the cavitation pulsation pressure coefficients of pumps of different scales are equal when the working conditions are similar and the CNPSH are equal, which indicates that the cavitation pulsating pressure performance of full scale pump can be predicted by model scale. It is of great significance to evaluate the vibration performance of the full scale water-jet propulsion.

Distance-Based Fluorescent Immunosensor for Point-of-Care Test of Illegal Additives through the Gas-Producing Nanozyme.

The colorimetric point-of-care test (POCT) offers a rapid and efficient method for detecting specific targets in real samples. However, traditional colorimetric methods often rely on complex signal amplification techniques or electronic devices to enhance detection sensitivity, which can inadvertently increase both cost and time, thus contradicting the fundamental goals of visual detection methods. Here, we presented a distance-based fluorescent immunosensor that utilized a gas-producing nanozyme for continuous gas production reaction as a signal. Specifically, the SOM-ZIF-8@Pt nanozyme catalyzed the production of O2 from H2O2 to cause an obvious increase in the pressure within a sealed chamber, thus driving the production of H2S to quench the fluorescence of CsPbBr3 on the walls of the capillaries. Based on the competitive immunoassay, the fluorescence quenching lengths were relative with the concentration of aminopyrine in the range from 0.2 to 20 ng/L; thus, the fluorescent POCT-based homemade device was realized through the amplification of distance-based signals facilitated by the continuous gas production reaction. This strategy provides an effective way to realize POCT assays in resource-limited areas by transforming pressure variations into directly observable signals. Furthermore, distinguished by its high sensitivity, ease of operation, and portability, it also represents a significant advancement in biomedical diagnostics, particularly within home healthcare and clinical POCT.

Simulation study on the cavitation flow characteristics of liquid ammonia in the nozzle under high-pressure injection conditions

In this study, a non-isothermal compressible cavitating flow simulation model for liquid ammonia in high-pressure injector, accounting for the phase change properties, was established. The model was validated using experimental data from existing literature. The cavitation phase change process of liquid ammonia within the injector nozzle was investigated, and the differences in cavitation distribution between ammonia and diesel were explored. The results indicate that with increasing injection pressure, the vapor volume fraction of ammonia increases linearly, while that of diesel fuel shows a rapid initial rise followed by a decelerating trend. In terms of flow coefficient, ammonia is lower than diesel, though the two values converge at higher injection pressures. Under low injection pressure, the heat absorbed by the cavitation phase change of ammonia exceeded the heat generated by the high-speed friction of ammonia flow against the nozzle wall, leading to a temperature drop at the nozzle inlet. Conversely, at high injection pressure, the temperature of ammonia on the nozzle wall increases by more than 20 K, resulting in a rapid rise in saturated vapor pressure and significantly intensifying the cavitation effect. Compared to ammonia, diesel experienced a higher temperature rise within the nozzle but exhibited smaller variations in saturated vapor pressure, making the temperature effect on cavitation less pronounced. Diesel’s higher density and viscosity resulted in lower flow velocities compared to ammonia, leading to more developed cavitation in the lower wall region of the nozzle and creating a higher pressure region on the upper wall of the nozzle, which was more distant from the nozzle inlet and inhibited cavitation development there. The research results provide theoretical support for the design of liquid ammonia injectors.

Quantitative detection of refrigerant charge faults in multi-unit air conditioning systems based on machine learning algorithms

Refrigerant charging discrepancies constitute the predominant malfunctions in air conditioning systems. Achieving the optimal charging level is crucial for system performance, underscoring the importance of precise refrigerant level prediction. This study introduces an algorithm designed for the quantitative detection of refrigerant charging errors by integrating the Markov Transition Field (MTF), Convolutional Neural Networks (CNN), and Multi-head Self-Attention (MSA) mechanisms. A high-precision enthalpy difference chamber was employed to establish a Variable Refrigerant Flow (VRF) refrigerant charging test bench. This setup facilitated the analysis of system parameter sensitivity to charging faults and aided in the creation of a training dataset for the algorithm. Comparative analysis was conducted against Support Vector Machines (SVM), Random Forests (RF), CNN with Self-Attention (AT), and MTF-CNN-MSA. The findings reveal that our method adeptly captures temporal dependencies and dynamic shifts in time series as visual representations, offering novel insights for discerning fault patterns within such data. Notably, the maximum pressure variations at high-pressure and low-pressure points were 0.25 MPa and 0.07 MPa, respectively, with temperature shifts of 12 °C and 3.5 °C at the high and low-temperature points. The high-pressure and high-temperature points are particularly sensitive to changes in refrigerant charging, and parameters from these sections were utilized to construct the dataset. The CNN-MSA algorithm demonstrates consistent performance across various fault types, effectively delineating fault characteristics. The accuracies achieved by SVM, RF, CNN-AT, and MTF-CNN-MSA were 84.38 %, 73.75 %, 88.13 %, and 93.75 %, respectively. In comparison, the CNN-MSA algorithm was able to more accurately detect refrigerant charge faults at different levels.

Thermo-economic analysis on trans-critical compressed CO2 energy storage system integrated with the waste heat of liquid-cooled data center

Compressed CO2 energy storage (CCES) technology has the advantages of high energy storage density, low economic cost, low carbon emission, which is suitable for the construction of large-scale and long-time energy storage system. Besides, as a scene with massive heat, the electricity consumption of servers in data center is mostly converted into heat. Thus, the purpose of this work is to integrate a trans-critical compressed CO2 energy storage system with the waste heat of data centers, so as to improve the system performance and reduce the operating cost of data center. Based on different operating principles of compressors, a single-stage compression system (System-CP) and a double-stage compression system (System-VP) are constructed. To analyze and compare the energy and exergy performance of these two systems under design conditions, a quasi-dynamic model is developed by considering the variations of pressure and temperature in the storage tank. On this basis, an economic model is developed to obtain the economy performance for the systems. The obtained results show that the round-trip efficiency (RTE) of System-CP and System-VP are 64.67 % and 67.41 %, respectively. For the energy storage capacity 15 MW × 5 h, volumes of high-pressure S-CO2 storage tank are 314,387.51 m3 and 287,982.91 m3 for System-CP and System-VP, respectively. Thereby, the energy storage density (ESD) of System-CP and System-VP are 0.24 kW·h/m3 and 0.26 kW·h/m3, respectively. Meanwhile, the total capital cost (TCC) is $477.84 × 106 for System-CP, and $437.41 × 106 for System-VP, and the corresponding payback period (PBP) are 14.76 years and 12.39 years respectively. Further, the effect of key parameters on systems performance is revealed. The results show that with the decrease of slip-pressure range decreases, RTE, TCC and the volume of storage tanks increase linearly.

Seasonal variation in home blood pressure during pregnancy and frequency of hypertensive disorders of pregnancy: a multicenter prospective study of home blood pressure measurements in pregnant women using information technology.

This multicenter prospective study investigated seasonal variation in home blood pressure (HBP) during pregnancy and its association with the frequency of hypertensive disorders of pregnancy (HDP) and developed trimester-specific upper limits of HBP according to season. Low-risk women with singleton pregnancies were recruited at six university hospitals and three maternity clinics. HBP was measured by a sphygmomanometer twice daily and recorded online using a mobile application. HBP was evaluated according to season (winter, spring, summer, and autumn) and gestational age. Of 705 women recruited, 603 were finally included. Mean diastolic HBP values were lowest in summer and highest in winter throughout pregnancy (mean difference between summer and winter, 3.5-4.6 mmHg). Mean systolic HBP values were also lowest in summer and highest in winter except during the early period of the second trimester (mean difference between summer and the season with the highest value, 2.5-3.4 mmHg). HDP was diagnosed in 32 women (5.3%). The risk of HDP was significantly higher in women who delivered in spring and autumn but not in winter compared with summer. The rounded upper limit of HBP was 120/80 mmHg in the first trimester, 120/75 mmHg in the second trimester, and 125/80 mmHg in the third trimester for summer and 125/85 mmHg, 125/80 mmHg, and 130/85 mmHg, respectively, for other seasons. Systolic and diastolic HBP were lowest in summer throughout pregnancy. Seasonal variation should be considered when interpreting HBP and the risk of HDP during pregnancy.

Towards smart and secure batteries: Linking pressure and temperature profiles with electrochemical behavior through hybrid optical fiber sensors

A hybrid sensing configuration combining fiber Bragg grating (FBG) with a Fabry-Perot interferometer is proposed for highly sensitive detection of pressure and temperature inside a commercial lithium-ion battery (LiB). The battery is also instrumented externally with FBGs to monitor its surface temperature variations. The LiB is operated under several cycling tests at different C-rates and environmental temperatures. High pressure and temperature resolutions of ±0.4 mbar and ±0.5 °C, respectively, are attained for the developed hybrid sensor, along with a minimum response time of 1.7 s. When the LiB is subjected to higher environmental temperatures, reduced thermal variations and higher pressure variations are registered by the optical sensor. Overall, during the cycling tests, the external temperature sensors closely follows the internal sensors behavior. Incremental capacity analysis derivative curves were systematically applied throughout the battery cycling experiments to unveil relevant insights into the interplay between pressure and temperature variations and the LiB electrochemical behavior. It was found that the pressure fluctuations present a “breathing profile” directly linked with the volume electrode lithiation and delithiation processes.

Numerical Investigations of a Single Loop Pulsating Heat Pipe for Cryogenic Applications

Abstract The thermohydraulics of a single-loop pulsating heat pipe (PHP) for cryogenic applications have been simulated. The 120 mm long PHP tube is made of a 1.5 mm diameter inner tube of thickness 0.83 mm. The CFD analysis performed with the ANSYS Fluent software is a two-dimensional numerical study using pure nitrogen as the working fluid in binary phases. The boundary condition on the evaporator is of constant heat flux, while the same on the condenser is of constant temperature. The phase behaviour of the liquid and vapour and their interactions are accounted for through the volume of fluid (VOF) method and the Lee model. The numerical model is validated using the existing experimental data, with an agreement of less than 8% between them. The thermo-hydraulic variations of temperature, pressure, and velocity are simulated for different heat loads and fractional liquid contents (fill ratios). The temperature and pressure oscillations set in the PHP-fluid increase with the heat added to the evaporator while the fluid velocity remains independent. The heat load and the fill ratio dictate the effective thermal conductivity - attaining nearly 3400 W/mK for a fill ratio of 70% in the chosen PHP geometry. An alteration has been made in the Jacob number to predict the dominance of sensible heat over latent heat in a PHP, postulated by other researchers. The constant fill ratio assumption is not truly valid as it indicates a small yet finite variation with the change in the heat load.

Short-Term and Mid-Term Blood Pressure Variability and Long-Term Mortality

Until recently, a focus has been made on exploring the influence of average blood pressure on risk of mortality. We go beyond average blood pressure to also investigate mortality risk with respect to variation in blood pressure over two timescales – short-term variation among multiple measures at one visit, and medium-term variation between the measures at two visits several months apart. We present an application of Bayesian hierarchical modeling to the problem of estimating the effect of blood pressure (BP) variability on all-cause and cardiovascular (CV) mortality. We use data from the Third National Health and Nutrition Examination Survey linked with up to 27 years of mortality follow-up. We find that medium-term systolic BP variability had a very significant predictive value for CV and all-cause mortality, around one-third as large as the well-established impact of mean systolic BP. Medium-term diastolic variability had an additional, though smaller, predictive effect. Short-term variability, on the other hand, had little or no measurable predictive value. The medium-term variability effect persisted when controlling for Framingham risk score.

Evaluation of a Physiologic-Driven Closed-Loop Resuscitation Algorithm in an Animal Model of Hemorrhagic Shock.

Appropriate resuscitation from hemorrhagic shock is critical to restore tissue perfusion and to avoid over-resuscitation. The objective of this study was to test the ability of a closed-loop diagnosis and resuscitation algorithm called resuscitation from shock using functional hemodynamic monitoring using invasive monitoring (ReFit1) and minimally invasive monitoring (ReFit2) to identify, treat, and stabilize a porcine model of severe hemorrhagic shock. We created a ReFit algorithm using dynamic hemodynamic parameters of pulse pressure variation (PPV), stroke volume variation (SVV), dynamic arterial elastance (Eadyn = PPV/SVV), driven by mean arterial pressure (MAP), mixed venous oxygen saturation, and heart rate targets to define the need for fluids, vasopressors, and inotropes. University-based animal laboratory. Twenty-seven female pigs. Anesthetized, intubated, and ventilated (8 mL/kg) pigs were bled at 10 mL/min until a MAP of less than 40 mm Hg, held for 30 minutes, then resuscitated. The ReFit algorithm used the above dynamic parameters to drive computer-controlled infusion pumps to deliver blood, lactated Ringer's solution, norepinephrine, and in ReFit1 dobutamine. In four animals, after initial resuscitation from hemorrhagic shock, the ability of the ReFit1 algorithm to treat acute air embolism-induced pulmonary hypertension and right heart failure was also tested. In 10 ReFit1 and 17 ReFit2 animals, the time to stabilization from shock was not dissimilar to open controlled resuscitation performed by an expert physician (52 ± 12, 50 ± 13, and 60 ± 15 min, respectively) with similar amounts of fluids and norepinephrine needed. In four ReFit1 animals after initial stabilization, the algorithm successfully resuscitated the animals after inducing an acute air embolism right heart failure, with all animals recovering stability within 30 minutes. Our physiologically based functional hemodynamic monitoring-centered closed-loop resuscitation system can effectively diagnose and treat cardiovascular shock due to hemorrhage and air embolism.

Quality of Recovery in Pediatrics After Tonsillectomy Under Total Intravenous Anesthesia Versus Sevoflurane Anesthesia

Introduction: Inhalational anesthetics are often the first choice in pediatric anesthesia. However, the most recent literature demonstrates possible advantages of total intravenous anesthesia (TIVA). The goal of this study was to compare the postoperative quality in children submitted to tonsillectomy under TIVA versus sevoflurane anesthesia. Materials and Methods: Prospective observational case-control study (Sevoflurane vs TIVA) including children submitted to (adeno) tonsillectomy to compare the quality of anesthesia considering intra and postoperative parameters. Data was collected from clinical records, anesthetic monitoring and interviews. Results: From the 52 children included, 25% received TIVA. TIVA group had a shorter time for liquid diet reintroduction (42 minutes vs. 95 minutes) (p<0.05) and the Sevoflurane group had a lower mean blood pressure variation (p=0.027). Mean wake up time was similar for both groups (11.3±6.2 minutes for TIVA and 11.6±5.3 minutes for Sevoflurane). In PACU, TIVA group demonstrated, although not statistically significant, a shorter time of stay (40 minutes vs. 41 minutes), less emergence delirium (7.7% vs. 12.5%) and less nausea and vomiting (0% vs. 5.1%). At 24 hours, TIVA group presented, although not statistically significant, less nausea and vomiting (7.7% vs 13.5%), lower pain (69.2% with <5 on the numerical pain scale vs 56.8%) and higher parental satisfaction (10 vs 8). Conclusion: This study suggests that TIVA for tonsillectomy may be associated with better recovery quality. However, given the limited sample size of this study, it should be repeated in a larger sample, or in a multicentric study, for more robust conclusions.

Rayleigh wave ellipticity from ambient noise: A practical method for monitoring seismic velocity variations in the near-surface

This study explores the feasibility and limitations of using Rayleigh wave (Rg) ellipticity for noise-based seismic monitoring at near-surface depths (4–70 m). We use the degree of polarization (DOP) method to extract the Rayleigh wave ellipticity from seismic noise recordings, employing normalized cross-correlation and cross-covariance coefficients to quantify ellipticity variations over time. Synthetic models and field data from three distinct case studies—Garner Valley, California; Riotinto mine, Spain; and the 2011 submarine volcanic eruption on El Hierro Island, Canary Islands—validate our approach. In these field applications, our method effectively tracks the seasonality of the shallow groundwater levels in Garner Valley, monitors pore pressure variations at the tailings dam of Riotinto mine, and detects volcanic induced changes on El Hierro, demonstrating robust performance even with variable noise sources. Our results indicate that Rayleigh wave ellipticity is a versatile tool for subsurface monitoring, capable of detecting velocity changes across a broad depth range. Our methodology represents a new independent and non-interferometric approach that enhances the detection of subsurface changes while improving resolution and exploration depth in seismic monitoring techniques.