Air Pressure Measurements Research Articles

Self-powered wireless sensor node enabled by ultra-high-output swinging hybrid generator toward real-time and in-situ marine meteorological observations

With the intensification of global ocean exploration, there is an increasing need for extensive and dispersed wireless sensor networks (WSNs) to meet the requirements of real-time, in-situ, and high-spatial-temporal-density marine meteorological observations. Nevertheless, providing a reliable power source and ensuring the ongoing maintenance of these WSNs pose significant challenges. Here, an innovative self-powered wireless sensor node is developed to address these challenges, featuring an ultra-high-output triboelectric-electromagnetic hybrid generator (T-EHG) with a swinging mechanism. The T-EHG, which combines triboelectric (TENG) and electromagnetic (EMG) generation, assisted by a universal power management system (UPMS), efficiently converts wave energy into a consistent voltage output between 1.5 V to 3.3 V. Additionally, the self-powered WSNs integrated with T-EHG, UPMS circuits, meteorological sensors, and microcontroller units (MCUs) with LoRa wireless transmission functionality are capable of executing data collection and transmission operations every 10 s under simulated wave conditions. These systems can achieve a maximum wireless transmission range of 1.5 km. Notably, a marine meteorological monitoring system utilizes these autonomous sensor nodes, along with base stations, cloud servers, and terminal devices to effectively implement real-time, on-site measurements of temperature, humidity, and air pressure within the monitored zone. This research underscores the substantial practical utility and extensive application prospects of these advanced self-powered WSNs for marine meteorological surveillance.

ANALYSIS OF METHODS FOR EVALUATION ESSENTIAL PERFORMANCE OF MECHANICAL TOURNIQUETS

The article is devoted to the analysis of methods for evaluating essential performance of mechanical tourniquets to improve their safety in use. According to the legislation of Ukraine and the European Union, a tourniquet is a medical device that, regardless of design, provides relief to a patient in the event of an injury by stopping bleeding in the limb by applying mechanical pressure. Tourniquets are classified as pneumatic and mechanical depending on the method of creating external pressure. The article considers a typical design of mechanical tourniquets and identifies the main structural elements: a pressure generating mechanism, a pressure element, a fixing device, and a place for recording the time of application. The analysis of scientific articles shows a significant number of studies focusing on the characteristics of pneumatic tourniquets, while methods for testing the effectiveness and safety of mechanical tourniquets have been insufficiently investigated. It is noted that the safety of medical devices is defined as the absence of an unacceptable level of risk for the operator and the patient, where risk means the effect of uncertainty in achieving the goals in the process of development, production, design, and use of a medical device. A list of essential performance of mechanical tourniquets is provided: overlapping pressure, pressure loss after application during the time of use, pressure gradient. It has been established that to date, four methods are known for obtaining measurement information on essential performance of mechanical tourniquets: a method based on converting the pressure value generated by the tourniquet during application into an electrical signal using strain sensors; a method based on measuring air pressure in a test cuff; an indirect method for determining pressure using Lamé's equation; a method for directly measuring pressure in the limb under the tourniquet using an invasive pressure sensor. Based on the identified essential performance of mechanical tourniquets, the requirements for methods of their determination are presented: measurement of overlapping pressure, determination of pressure loss after the tourniquet is applied during the time of use, determination of pressure gradient, reproducibility, repeatability, evaluation of metrological uncertainty, and evaluation of the essential performance of mechanical tourniquets based on mathematical models. Ways to improve the existing methods for evaluating essential performance are considered.

Calculation study of ion flow field in ±800 kV transmission lines under the effects of air pressure, temperature, humidity and improvement measures

In line operation, Ultra-High Voltage Direct Current (UHVDC) transmission lines must meet the corresponding electromagnetic environment requirements; the ion flow field is a crucial environmental index of ultra-high-voltage DC transmission lines. Under the combined effects of humidity, air pressure, and temperature, the ion flow field is easily influenced. A calculation method for the ion flow field of UHVDC transmission lines that considers the combined effects of humidity, air pressure, and temperature is proposed to investigate the impact on the ion flow field. Based on this, when the humidity exceeds 60%, the influence of the charging characteristics of airborne droplets on the ion flow field is considered to align the calculations more closely with actual conditions. An improved finite element method is used to calculate the ground synthetic electric field strength and ion flow density of the ±800 kV line under these combined effects, and its effectiveness is validated through empirical testing. The results show that the synthetic electric field strength under the positive line decreases with increasing humidity, peaks at 90% humidity under the negative line and reaches a minimum at 50% humidity for the negative line. When considering the charging characteristics of droplets with humidity above 60%, the maximum synthetic electric field strength under the positive line is increased by 5.46%, and under the negative line by 5.5%, while the ground ion flow density remains largely unchanged. Finally, in conjunction with the electromagnetic environmental parameter limits for UHVDC transmission lines promulgated by China, improvement measures are proposed for the ground ion flow field.

3D-printed magnetic-based air pressure sensor for continuous respiration monitoring and breathing rehabilitation

The rapid development of point-of-care testing has made prompt diagnosis, monitoring and treatment possible for many patients suffering from chronic respiratory diseases. Currently, the biggest challenge is further optimizing testing devices to facilitate more functionalities with higher efficiency and performance, along with specificity toward patient needs. By understanding that patients with chronic respiratory diseases may have difficulty breathing within a normal range, a respiration sensor is developed focusing on sensitivities in the lower air pressure range. In contrast to the simpler airflow data, the sensor can provide respiratory air pressure as an output using a magnetic-based pressure sensor. This unconventional but highly reliable approach, combined with the rest of the simple 3D-printed design of the sensor, offers a wide range of tunability and functionalities. Due to the detachable components of the respiration sensor, the device can be easily transformed into other respiratory uses such as an inspiratory muscle training device or modified to cater for higher-ranged deep breathing. Therefore, not only does it reach very low air pressure measurement (0.1 cmH2O) for normal, tidal breathing, but the sensor can also be manipulated to detect high levels of air pressure (up to 35 cmH2O for exhalation and 45 cmH2O for inhalation). With its excellent sensitivities (0.0456 mV/cmH2O for inhalation, -0.0940 mV/cmH2O for exhalation), impressive distinction between inhalation and exhalation, and fully reproducible and convenient design, we believe that this respiration sensor will pave the way for developing multimodal and multifunctional respiration sensors within the biomedical field.

Hollow microsphere probes formed by hollow core optical fiber discharging for monitoring gas pressure and temperature.

A hollow core fiber (HCF) is spliced with a single-mode fiber, and then, the end face of the HCF is etched to form a microsphere interferometer for measuring gas pressure and environmental temperature. The total length of each microsphere is less than 200μm. We fabricated two such structures and used femtosecond laser pulses to drill micro-holes on the HCF walls of both structures. One of the structures is directly used to measure air pressure, achieving a sensitivity of up to 2.857 nm/MPa while being almost insensitive to temperature. This structure is capable of assessing pressure down to 3.4kPa within the range of 0-0.5 MPa. Another structure is filled with thermally sensitive material dimethyl silicone oil through a micro-hole, and then, it is sealed with AB adhesive to form a harmonic Vernier effect temperature sensor, with a sensitivity of up to -5.16 nm/°C. This structure is capable of assessing temperature down to 0.38 °C within the range of 30-60 °C. Additionally, the sensors have good repeatability and stability and compact structure and simple manufacturing and can be used as a sensing probe for monitoring gas pressure and temperature under extreme environments.

Magnetic field and gas pressure sensor based on Mach–Zehnder interferometer with micro-groove fabricated by femtosecond laser pulse

A microgroove type Mach–Zehnder interferometer (MZI) for gas pressure and magnetic field measurements was prepared using a femtosecond laser pulse in a single mode fiber. Due to the interference light passing through the microgroove, changing the refractive index (RI) of the medium in the microgroove will change the optical path difference between the two channels, leading to significant drift of the MZI transmission spectrum, thus achieving the measurement of environmental parameters. When the gas pressure inside the microgroove changes, the RI of the air changes, achieving the measurement of environmental gas pressure. When the microgroove is filled with magnetic fluid, changes in the environmental magnetic field will cause changes in the RI of the magnetic fluid, thus achieving measurement of the magnetic field. The experimental results show that the gas pressure sensitivity of the sensor is −5.03 nm/MPa, and the temperature cross-sensitivity is only 1.31 kPa/°C. This sensor achieves a magnetic field sensitivity of −0.3505 nm/mT in the magnetic field range of 6.4–13.6 mT. The sensor is small in size and easy to manufacture, making it a good choice for measuring air pressure and magnetic field.

Sphygmomanometer Dynamic Pressure Measurement Using a Condenser Microphone.

There is a worldwide need to improve blood pressure (BP) measurement error in order to correctly diagnose hypertension. Cardiovascular diseases cause 17.9 million deaths annually and are a substantial monetary strain on healthcare. The current measurement uncertainty of 3 mmHg should be improved upon. Dynamic pressure measurement standards are lacking or non-existing. In this study we propose a novel method of measuring air pressure inside the sphygmomanometer tubing during BP measurement using a condenser microphone. We designed, built, and tested a system that uses a radiofrequency (RF) modulation method to convert changes in capacitance of a condenser microphone into pressure signals. We tested the RF microphone with a low-frequency (LF) sound source, BP simulator and using a piezoresistive pressure sensor as a reference. Necessary tests were conducted to assess the uncertainty budget of the system. The RF microphone prototype has a working frequency range from 0.5 Hz to 280 Hz in the pressure range from 0 to 300 mmHg. The total expanded uncertainty (k = 2, p = 95.5%) of the RF microphone was 4.32 mmHg. The proposed method could establish traceability of BP measuring devices to acoustic standards described in IEC 61094-2 and could also be used in forming dynamic BP standards.

Experimentally Validated Neural Networks for Sensors Redundancy Purposes in Spark Ignition Engines

<div>In the aeronautical field, aircraft reliability is strictly dependent on propulsion systems. Indeed, a reliable propulsion system ensures the safety of pilots and passengers and the possibility of making comfortable flights. Typically, on aircraft equipped with spark ignition (SI) engines, one of the principal requirements to make them reliable is the correct balancing between the intake air mass and fuel flows. Advances in the implementation of sophisticated control and estimation strategies on SI engines allow realizing engines with improved features in terms of performance, reducing pollution emissions, and fuel consumption. Approaches based on sensor redundancy are applied to improve the reliability in measurements of the manifold air pressure (MAP) and flow (MAF) to avoid issues related to possible faults of sensors vital for the correct functioning of SI engines. Model-based estimation techniques, based on the speed–density and alpha-speed methods for determining the MAF in engine control units, are employed to obtain sensor-less redundancy. The prediction of MAP and MAF, for sensors redundancy purposes, can be made through neural networks, allowing the avoidance of effects due to unmodeled dynamical behaviors. A sensor redundancy approach based on feedforward neural networks (FNNs) is proposed in this work for MAP and MAF prediction of a SI engine. The present work focuses on the possibility of estimating the physical quantities related to SI engines, such as the MAP and the MAF, fundamental for their monitoring using neural networks trained by means of a model-based approach avoiding expensive experimental tests for producing training data. A well-known intake manifold dynamical model (IMDM), parametrized based on the CMD 22 aeronautical engine, is employed for generating synthetic training data in steady-state conditions functional for making the chosen FNNs able to predict both MAP and MAF even in transient behavior.</div> <div>The MAP and MAF are predicted through two virtual sensors based on two independent FNNs, having the same inputs, constituted by the engine speed and the throttle angle. An experimental investigation based on an aircraft endurance test of two hours proposed by the European Aviation Safety Agency (EASA) has been made on a controlled and monitored CMD 22 engine for comparing the experimentally measured MAP and MAF with the predicted ones by the FNNs. The results demonstrate the suitability of the proposed approach for sensor redundancy purposes in SI engines to increase their reliability.</div>

Highly sensitive air pressure sensor based on harmonic Vernier effect by Fabry-Perot and Sagnac interferometers cascading

A high sensitivity harmonic Vernier effect air pressure sensor is proposed based on Fabry-Perot interferometer (FPI) and Sagnac interferometer (SI) cascading. FPI consists of an F-P cavity with micro-pore, detecting the changes of environmental air pressure. The reference interferometer SI is composed of panda fiber rings and cascaded with FPI to generate a harmonic Vernier effect. In harmonic Vernier effect sensor, using inner envelope intersection to measure air pressure can achieve more accurate sensitivity and measurement accuracy. Meanwhile, the harmonic Vernier effect has lower requirements for free spectral range matching between two interferometers, making it easier to achieve harmonic Vernier effect and its sensitivity is i + 1 times that of the fundamental Vernier effect. The sensitivity of the first-order harmonic Vernier effect sensor obtained in the experiment is 132.32 nm/MPa, while the corresponding the fundamental Vernier effect sensor has the sensitivity of only 68.78 nm/MPa, almost doubling. The experimental results also confirm that the proposed sensor has excellent long-term stability and repeatability. Meanwhile, the temperature cross-sensitivity of the proposed air pressure sensor is only 0.0124 MPa/°C, which is small. This sensor is expected to play a crucial role in precise and highly sensitive air pressure measurement applications.

Applicability and evaluation of airtightness prediction method using pressure differences under actual climate conditions

The blower door test and tracer gas test methods are commonly used to measure airtightness and infiltration in buildings. Nonetheless, these methods can be costly and time-consuming. To address these issues, the air pressure measurement and prediction system (APMPS) was proposed. This method predicts the airtightness of a building by measuring the differential pressure across its components. However, the effectiveness of this method has yet to be verified under various conditions. The aim of this study is to analyze the effectiveness of the APMPS under different building and weather conditions. Three residential buildings of varying heights were analyzed, and measurement of differential pressure and temperature differences were taken. The actual airtightness was determined using blower door tests, and the results were compared to the predicted airtightness calculated by the APMPS. The results indicate that APMPS is effective in predicting building airtightness, with the majority of predicted values falling within the range of 2-3 h−1 at 50 Pa. The accuracy of the predictions improved as the building height, differences in indoor and outdoor temperatures, and differential pressure increased. Furthermore, a significant correlation was observed between the error rate, height difference, and differential pressure at a significance level of 1%. The APMPS has demonstrated its value as reliable tool for accurately predicting the airtightness of multi-story buildings under various conditions. This method simplifies the measurement of airtightness in building components, which is crucial for evaluating building energy demand and air quality.

Compressor-Powered Tyre Pressure System for Monitoring

Abstract: In the Indian subcontinent, where one in every three houses owns a two-wheeler, around fifteen million motorcycles are sold each year. Every year, the country manufactures 20 million two-wheelers. Severe underinflation can produce mechanical and thermal stress caused by overheating, which can cause the tyre to blow out soon. Failures of this type can lead to hazards and catastrophes that endanger not just the operator but also people around them or her. In accordance with median nation estimations, under-inflated tyres cause separation of the tread and tyre breakdown, leading to forty thousand mishaps, more than 33,000 damage, and over 700 fatal injuries per year. However, when the population of India has been taken into consideration, these percentages may rise due to an abundance of individuals possessing numerous 2-wheelers, getting little opportunity to personally check, as well as being unable to consistently get their automobiles examined at a maintenance facility. The difficulties will occur with greater regularity if the riders are unaware. The goal of this project is to develop and build an immediate tyre pressure monitoring system (TPMS), that utilises a pressure sensor in order to measure air pressure directly from the tyre.

Tyre Pressure Monitoring System with Compressor

Abstract: Around 15 million two-wheelers are sold annually in India, where one in three homes has a two-wheeler. The country produces 20 million units of two-wheelers annually. Severe underinflation can result in mechanical and thermal stress brought on by overheating, which can very quickly cause the tyre to blow out. Failures of this nature may result in dangers and mishaps that jeopardise not only the rider but also those around him or her. Under-inflated tyres cause tread separation and tyre failure, resulting in 40,000 accidents, 33,000 injuries, and over 700 fatalities each year, according to average country estimates. However, when taking into account India's population, these figures may soar due to a high number of people owning more 2- wheelers, having less time to physically check, and being unable to routinely get their cars inspected at a repair facility. Without the riders' knowledge, the aforementioned issues will occur more frequently. This project focuses on designing and creating a direct tyre pressure monitoring system (TPMS), which uses a pressure sensor to measure air pressure taken straight from the tyre. The rider does not need to get out of his vehicle and manually pump air into his tyres when the TPMS detects a reduced tyre pressure because the TPMS and the compressor unit are working together to supply the air to the tyre. Energy is provided to the gadget either by a dynamo or a backup battery. To ensure consistency and accuracy in reporting the pressure and providing enough air to the tyre, the key components must be calibrated. Although there are numerous challenges and constraints, this prototype is a promising product for use in the real world.

Flexible Wearable Capacitive Sensors Based on Ionic Gel with Full-Pressure Ranges.

Flexible positive pressure sensors have been studied extensively and have been used in a lot of scenarios. However, negative pressure detection is also in demand in some scenarios, such as fluid mechanics analysis, air pressure sensing, and so on. Flexible wearable sensors that can detect both positive and negative pressures will greatly broaden the application field. In this paper, we report a flexible highly sensitive ionic gel (IG) pressure sensor, which is simple and of low cost to prepare and can reliably detect a large pressure range from -98 to 100 kPa under an atmospheric pressure of about 982 hPa. The IG dielectric layer is composed of polyvinyl alcohol and phosphoric acid with a random microstructure of sandpaper inversion. The sensor exhibits flexibility, cycling stability, and high sensitivity under both negative and positive pressures (S = 84.45 nF/kPa for the negative pressure section, S = 25.61 nF/kPa for the positive pressure section). These sensors could be worn on the body not only to test breathing and pulse but also to measure air pressure for estimating the altitude, showing that the flexible full-pressure sensors have a wider application range in wearable electronics.

Experimental Measurement of Enhanced and Hindered Particle Settling in Turbulent Gas‐Particle Suspensions, and Geophysical Implications

AbstractThe dynamics of geophysical dilute turbulent gas‐particles mixtures depends to a large extent on particle concentration, which in turn depends predominantly on the particle settling velocity. We experimentally investigate air‐particle mixtures contained in a vertical pipe in which the velocity of an ascending air flux matches the settling velocity of glass particles. To obtain local particle concentrations in these mixtures, we use acoustic probing and air pressure measurements and show that these independent techniques yield similar results for a range of particle sizes and particle concentrations. Moreover, we find that in suspensions of small particles (78 μm) the settling velocity increases with the local particle concentration due to the formation of particle clusters. These clusters settle with a velocity that is four times faster than the terminal settling velocity of single particles, and they double settling speeds of the suspensions. In contrast, in suspensions of larger particles (467 μm) the settling velocity decreases with increasing particle concentration. Although particle clusters are still present in this case, the settling velocity is decreased by 30%, which is captured by a hindered settling model. These results suggest an interplay between hindered settling and cluster‐induced enhanced settling, which in our experiments occur respectively at Stokes number O(100) and O(1). We discuss implications for volcanic plumes and pyroclastic currents. Our study suggests that clustering and related enhanced or hindered particle settling velocities should be considered in models of volcanic phenomena and that drag law corrections are needed for reliable predictions and hazard assessment.

Sensor BMP280 Statistical Analysis for Barometric Pressure Acquisition

BMP280 sensor is widely used to measure air pressure. Accuracy and precision are parameters used to indicate the performance of a sensor. This study examined the BMP280 sensor performance by measuring the air pressure on 15 sensors compared with BMKG devices as a reference standard. This measurement was conducted in BMKG class III Tanjungpinang from 20-22 May 2022. BMP280 Sensor performance analysis was performed using a statistical analysis one-way ANOVA method, Tukey test, linear regression, and root mean square error. Based on the results of one-way ANOVA, it is found that there is a BMP280 sensor that is different from the others. The Tukey test found that ID sensors 6 & 14 are not significantly different, ID sensors 1 & 4 are not significantly different, sensors 15 & BMKG are not significantly different, and sensors 3 and 9 are not significantly different. In addition, the results of linear regression and RMSE show an increase in the accuracy of the BMP280 sensor reading. Linear regression makes sensor measurement more accurate due to the small RMSE value.

Using the MPX5500DP Sensor for Monitoring Microcontroller-Based HVAC Systems and IOT

MPX5500DP is a sensor designed to measure how much air pressure is traveling in a channel or piping system. The MPX5500DP sensor has an analog output value that can be converted into a digital signal to display data on how much air pressure is being generated. By utilizing the MPX5500DP sensor the purpose of this study is to measure air pressure in HVAC systems with Micro SD data storage and IOT (Internet Of Things) based Web Browsers. The method used in implementing the MPX5500DP sensor is, selecting materials, making tools, installing sensors and testing. The test results obtained from Micro SD storage are the average air pressure value of 15.76 kPa. The IOT test produced an average air pressure of 15.67 kPa. At the time of testing, there were problems where data transmission was very slow so that for 30 minutes the test only received 14 air pressure data that were read on the Web Browser, namely ThingSpeak. However, the IOT test on an air pressure gauge using the MPX 5500DP sensor was successful. The success of the IOT system in monitoring HVAC air pressure can assist in monitoring the HVAC system in multi-storey buildings.

Paediatric sleep apnea event prediction using nasal air pressure and machine learning.

Sleep-disordered breathing is an important health issue for children. The objective of this study was to develop a machine learning classifier model for the identification of sleep apnea events taken exclusively from nasal air pressure measurements acquired during overnight polysomnography for paediatric patients. A secondary objective of this study was to differentiate site of obstruction exclusively from hypopnea event data using the model. Computer vision classifiers were developed via transfer learning to either normal breathing while asleep, obstructive hypopnea, obstructive apnea or central apnea. A separate model was trained to identify site of obstruction as either adeno-tonsillar or tongue base. In addition, a survey of board-certified and board-eligible sleep physicians was completed to compare clinician versus model classification performance of sleep events, and indicated very good performance of our model relative to human raters. The nasal air pressure sample database available for modelling comprised 417 normal, 266 obstructive hypopnea, 122 obstructive apnea and 131 central apnea events derived from 28 paediatric patients. The four-way classifier achieved a mean prediction accuracy of 70.0% (95% confidence interval [67.1-72.9]). Clinician raters correctly identified sleep events from nasal air pressure tracings 53.8% of the time, whereas the local model was 77.5% accurate. The site of obstruction classifier achieved a mean prediction accuracy of 75.0% (95% confidence interval [68.7-81.3]). Machine learning applied to nasal air pressure tracings is feasible and may exceed the diagnostic performance of expert clinicians. Nasal air pressure tracings of obstructive hypopneas may "encode" information regarding the site of obstruction, which may only be discernable by machine learning.

Identification of Soil Features Suitable for Barley, Maize and Sugarcane Cultivation using Precision Agriculture

Agriculture plays a crucial role in a nation's economy by significantly contributing to its Gross Domestic Product (GDP). Improved methods of farming not only benefit farmers by lowering their workload but also boost crop yields. Identifying the optimal soil for each crop is essential for producing harvests of the highest quality and quantity. Precision farming in agriculture uses Internet of Things (IoT) technology to ensure the most efficient use of available resources to increase crop yields and reduce operational costs. It reduces instances of crop failure and aids in averting catastrophic losses. Variations in soil temperature and soil moisture have the most significant influence on crop yield. The Soil Moisture Sensors calculate the soil's total volumetric water content. Using the BMP280 absolute barometric pressure sensor, it is possible to get accurate temperature and air pressure measurements. In the paper, the primary purpose of precision agriculture usage is to determine the optimal soil characteristics for the production of Barely, Maize and Sugarcane crops.

Strength in Numbers: The Tail End of Typhoon Songda Combines with Local Cyclones to Generate Extreme Sea Level Oscillations on the British Columbia and Washington Coasts during Mid-October 2016

Abstract From 12 to 16 October 2016, a series of three major low pressure systems, including the tail end of Typhoon Songda, crossed the coasts of British Columbia (BC) and the state of Washington (WA). Songda was generated on 2 October and, after traveling northward along the coast of Japan, turned eastward toward North America. Once there, it merged with two extratropical cyclones moving along the coast of Vancouver Island. The combined lows generated pronounced storm surges, seiches, and infragravity waves off southern BC and northern WA. Here, we examine the event in terms of sea levels measured by tide gauges and offshore bottom pressure recorders, together with reanalysis data, and high-resolution air pressure and wind measurements from 182 meteorological stations. Surge heights during the event typically exceeded 80 cm, with maximum heights of over 100 cm observed at La Push (WA) and New Westminster (BC). At Tofino, on the west coast of Vancouver Island, there was a sharp 40-cm increase in sea level on 14 October in response to a marked air pressure disturbance; slightly lower sea level peaks were also observed at other outer coast locations. In all cases, the sea level response was 1.5–2.5 times as great as that expected from the inverted barometer effect, consistent with local topographic amplification. The sea level oscillations at Tofino had the form of a forced solitary wave (“meteorological tsunami,” or meteotsunami), whereas those on the southwestern shelf off Vancouver Island are well described by classical standing-wave theory. A numerical model closely reproduces the observed meteotsunami peaks and standing-wave oscillations.

Global propagation of air pressure waves and consequent ocean waves due to the January 2022 Hunga Tonga-Hunga Ha'apai eruption

On January 15, 2022, the Hunga Tonga-Hunga Ha'apai volcano's huge eruption generated tsunami waves globally due to an unusual source in this unique event: fast-traveling air pressure disturbances originated from the explosion of the volcano. Here we investigate the global propagation of the pressure waves (first cycle) and the consequent ocean waves. The analysis of the measured air pressure waves shows that the speed of air pressure linearly increases with time. The peak and trough amplitudes of the pressure wave exponentially decrease with the traveled distance until half the overall distance; then, it restarts to increase, reaching a peak towards the apogee. Two different modeling approaches are followed to solve the air pressure waves and the resulting ocean waves: i) a synthetic pressure forcing model based on barometric measurements and ii) a numerical model based on nonlinear shallow water theory using an initial disturbance at the volcano. We forced the hydrodynamic model by the produced pressure fields to compute the resulting tsunami waves globally. We present the modeling results, discussing the possible sea level amplification mechanisms and affecting factors. Fairly well agreement between the computed and measured air pressure waves and sea levels around the globe is promising.