High-resolution Pressure Research Articles

A data assimilation pressure field measurement method for linear turbine cascades based on compressed sensing

One of the most dominant measuring techniques for linear turbine cascades is to obtain the high spatial resolution pressure field by a discrete point array. In this work, a compressed sensing (CS) based data assimilation methodology is proposed, by which a set of optimal sparse measuring points can be derived through an optimization procedure. Combined with numerical simulations and the data-driven modal decomposition, the high spatial resolution pressure distribution can be reconstructed accurately with sparse random sampling. To this end, detailed comparative research is conducted. First, the impacts of different sparse bases, including the discrete Fourier transform (DFT), the discrete cosine transform (DCT), and the proper orthogonal decomposition (POD) matrices, and the compression ratio on the reconstruction performance are compared and analyzed systematically under different incidence angles and cascade exit isentropic Mach numbers. Results reveal that a CS approach on POD subspace (CS-POD) performs remarkably better than the CS-DFT and CS-DCT in capturing the spatially continuous pressure distribution, even with a small number of measuring points. Furthermore, effects of the order of truncated POD modes and the number of training dataset required to conduct POD on the error are also investigated that exhibits a downward trend with the rise in these two elements. To overcome the deficiency of randomly selected sparse observation sites with this methodology and the resulting high measuring cost under different conditions, a vectorized CS-POD (Vec-CS-POD) model is constructed to obtain one set of measuring distribution that could satisfy the multi-conditional measurements simultaneously, and its reliability and robustness are validated through cascade experiments. With the aid of the Vec-CS-POD based data fusion framework, spatially resolved end wall pressure fields can be acquired by only a few measuring ports, the number of which can be reduced by 77% compared to the conventional uniform arrangement. The generalization ability of the proposed framework is also validated and evaluated; thus, it exhibits broad potential in other flow field measurements.

Arrayed multi-layer piezoelectric sensor based on electrospun P(VDF-TrFE)/ZnO with enhanced piezoelectricity

Flexible pressure sensors can maintain good pressure sensing capabilities even under arbitrary degrees of deformation, which are widely used in fields such as robotic perception, physiological signal detection and wearable electronics. Currently, piezoelectric-based flexible pressure sensors face significant limitations in terms of large-area, high-sensitivity, and high-resolution pressure detection. Targeted research efforts primarily focus on enhancing the performance of piezoelectric sensors by optimizing the properties of the sensitive layer piezoelectric material and modulating device structural design. This study introduces a novel multilayer array design for flexible composite piezoelectric sensors. A high-voltage electrospinning selective deposition process is proposed to fabricate arrayed nanofiber mats. Patterned electrospun films can be automatically patterned by patterned AgNW electrodes, which allows manual alignment of patterns for subsequent stacking. Compared to sensor based on P(VDF-TrFE) prepared under the same conditions, the sensitivity of the multi-layer P(VDF-TrFE)/ZnO sensor increased from 2.82 mV/kPa to 8.30 mV/kPa, providing an effective approach for highly sensitivity wearable devices with short response time (∼5 ms).

Impact of Alaska atmospheric blocking on the carbon flux in the Northeast Pacific Ocean

The Northeast Pacific Ocean (NEP) is one of the important carbon sinks in the global ocean. The causes of carbon flux changes in this region have been widely studied, but the physical processes associated with large scale climate variability remain controversial primarily due to scarcity of spatially and temporally continuous observations. In this study, we constructed a high-resolution sea surface partial pressure of CO2 (pCO2) from satellite observations for the NEP from 2003 to 2020 using the machine learning based XGBoost model. By analyzing the interannual large-scale high-latitude atmospheric dynamics and ocean physical conditions over the NEP, we find that the CO2 flux density (FCO2) anomalies have a strong correlation with the Alaskan atmospheric blocking events. In the region north of 48°N, anomalous cyclones triggered by atmospheric blocking increased sea surface height (SSH), which reduced the replenishment of dissolved inorganic carbon (DIC) from deep seawater, leading to enhanced carbon uptake. By contrast, in the region south of 48°N, the increase in sea surface temperature (SST) triggered by atmospheric blocking reduced the solubility of CO2 in seawater, resulting in a decrease in regional carbon flux. These results provide new perspectives for better understanding and predicting the effects of high-latitude atmospheric dynamics on regional ocean carbon fluxes.

Performance characterization of a wireless sensors network system (WSNS) for measurements of hurricane wind effects on structures

This paper presents a new wireless sensors network system (WSNS) designed for high-resolution absolute pressure measurements, wind speed, and direction. The system is tailored to assess the impact of hurricane winds on residential structures, both in laboratory settings and field environments. Importantly WSNS stands out for its unique ability to provide waterproof, surface-mounted external pressure measurements. The system's performance is evaluated during deployment on a full-scale house model at the Wall of Wind facility. The WSNS sensors were installed on different surfaces of a single-story residential building model. The sensor locations mirrored the locations of surface pressure taps connected to a Scanivalve differential pressure measurement system. Due to the size and shape of the WSNS pressure module, a casing effect was observed, which may result in pressure offsets under certain wind speeds and directions, depending on the sensor's location and the conditions, including dry and light rain (50 mm per hour). The comparison between WSNS and Scanivalve indicates that the sensor's casing geometry does not cause significant differences in the time-averaged measurements for low-turbulence regions. Conversely, this is not true for high-turbulence regions, which should be marked for future deployments using smaller surface-mounted pressure taps.

The effectiveness and safety of the anti gastroesophageal reflux system in the treatment of gastroesophageal reflux disease: a single center study of 23 cases

Objective: To evaluate the safety and efficacy of domestically produced magnetic sphincter augmentation (MSA) for gastroesophageal reflux disease. Method: This study is a prospective cohort study. Patients with typical heartburn and reflux symptoms (at least partial response to proton pump inhibitors), abnormal esophageal acid exposure and normal esophageal peristalsis were included, prospectively in the Department of Gastroesophageal Surgery, Rocket Force Characteristic Medical Center from June 2019 to September 2022. Patients with hiatal hernia >2 cm and severe esophagitis were excluded. The MSA was wrapped around the distal esophagus after esophageal hiatus repair by laparoscopy. A postoperative questionnaire survey was conducted to assess the relief of symptom, complications, the discontinuation rate of proton pump inhibitor, and surgical satisfaction. Gastroscopy, high-resolution esophageal pressure measurement, and pH value impedance monitoring were also reviewed. The pre- and postoperative rates were compared using the McNeinar χ2 test. Result: Currently, 23 patients with gastroesophageal reflux disease were enrolled and underwent MSA surgery. There were 20 males and 3 females, aged (M (IQR)) 48 (14) years (range: 25 to 64 years). All cases were successfully implanted with MSA. Subjective indicators were followed for 17 (18) months (range: 14 to 53 months), while objective indicators were followed for 17 (1) months (range: 12 to 23 months). The postoperative gastrointestinal and extraesophageal symptom scores showed a significant decrease compared to preoperative levels as follows: the degree of subjective relief of overall digestive symptoms was 90 (20)% (range:0~100%), the degree of subjective relief of overall respiratory symptoms was 100(10)% (range: 10%~100%), the overall satisfaction rate was 83% (19/23), the proton pump inhibitor discontinuation rate was 70% (16/23). The proportion of esophagitis has decreased from 44% (10/23) to 9% (2/23) (κ=0.169, P=0.039), The Hill grade of gastroesophageal valve morphology improved from 1 case of grade Ⅰ, 5 cases of grade Ⅱ, 10 cases of grade Ⅲ, and 7 cases of grade Ⅲ preoperative to 22, 1, 0, and 0 cases postoperative. The proportion of lower esophageal sphincter pressure below normal has decreased from 70% (16/23) to 35% (8/23) (κ=0.170, P=0.012). There were 21 patients who restored normal esophageal acid exposure. Eleven patients had mild long-term dysphagia, but it didn't affect their daily life. No postoperative device migration, erosion, or secondary surgical removal occurred. Conclusions: Laparoscopic implantation of the MSA device was safe and well tolerated. It can effectively control the symptoms of gastroesophageal reflux disease, reduce medication, restore normal cardia morphology and function, and esophageal acid exposure. The main postoperative complication was dysphagia, but it was relatively mild.

Junction Piezotronic Transistor Arrays Based on Patterned ZnO Nanowires for High-Resolution Tactile and Photo Mapping.

Recently, a great deal of interest has been focused on developing sensors that can measure both pressure and light. However, traditional sensors are difficult to integrate into silicon (Si)-based integrated circuits. Therefore, it is particularly important to design a sensor that operates on a new principle. In this paper, junction piezotronic transistor (JPT) arrays based on zinc oxide (ZnO) nanowire are demonstrated. And the JPT arrays show high spatial resolution pressure and light mapping with 195 dpi. Because ZnO nanowires are arranged vertically above the p-type Si channel's center of the transistor, the width of the heterojunction depletion region is constricted by the positive piezoelectric potential generated by strained ZnO. In addition, photogenerated charge carriers can be created in the Si channel when JPT is stimulated by light, which increases its electrical conductivity. Consequently, the external pressure and light distribution information can be obtained from the variation in the output current of the device. The prepared JPT arrays can be compatible with Si transistors, which make them highly competitive and make it possible to incorporate both pressure and light sensors into large integrated circuits. This work will contribute to many applications, such as intelligent clothing, human-computer interaction, and electronic skin.

Collateral flow and pulsatility during large vessel occlusions: insights from a quantitative in vitro study.

Acute ischemic stroke caused by large vessel occlusions is being increasingly treated with neurovascular interventions. The hemodynamics within the collateral system of the circle of Willis (CoW) hemodynamics play a fundamental role in therapy success. However, transient in vivo data on pathological collateral flow during large vessel occlusions are not available. Moreover, there are no flow models that accurately simulate the hemodynamic conditions in the CoW during large vessel occlusions. We used a circulatory loop to generate highly reproducible cerebrovascular-like flows and pressures and used non-invasive flow visualization and high-resolution flow and pressure measurements to acquire detailed, time-dependent hemodynamics inside an anatomical phantom of the CoW. After calibrating a physiological reference case, we induced occlusions in the 1. middle cerebral artery, 2. terminal carotid artery, and 3. basilar artery; and measured the left posterior communicating artery flow. Mean arterial pressure and pulse pressure remained unchanged in the different occlusion cases compared to the physiological reference case, while total cerebral flow decreased by up to 19%. In all three occlusion cases, reversed flow was found in the left posterior communicating artery compared to the reference case with different flow magnitudes and pulsatility index values. The experimental results were compared with clinical findings, demonstrating the capability of this realistic cerebrovascular flow setup. This novel cerebrovascular flow setup opens the possibility for investigating different topics of neurovascular interventions under various clinical conditions in controlled preclinical laboratory studies.

Association of Postarrest Hypotension Burden With Unfavorable Neurologic Outcome After Pediatric Cardiac Arrest.

Quantify hypotension burden using high-resolution continuous arterial blood pressure (ABP) data and determine its association with outcome after pediatric cardiac arrest. Retrospective observational study. Academic PICU. Children 18 years old or younger admitted with in-of-hospital or out-of-hospital cardiac arrest who had invasive ABP monitoring during postcardiac arrest care. None. High-resolution continuous ABP was analyzed up to 24 hours after the return of circulation (ROC). Hypotension burden was the time-normalized integral area between mean arterial pressure (MAP) and fifth percentile MAP for age. The primary outcome was unfavorable neurologic status (pediatric cerebral performance category ≥ 3 with change from baseline) at hospital discharge. Mann-Whitney U tests compared hypotension burden, duration, and magnitude between favorable and unfavorable patients. Multivariable logistic regression determined the association of unfavorable outcomes with hypotension burden, duration, and magnitude at various percentile thresholds from the 5th through 50th percentile for age. Of 140 patients (median age 53 [interquartile range 11-146] mo, 61% male); 63% had unfavorable outcomes. Monitoring duration was 21 (7-24) hours. Using a MAP threshold at the fifth percentile for age, the median hypotension burden was 0.01 (0-0.11) mm Hg-hours per hour, greater for patients with unfavorable compared with favorable outcomes (0 [0-0.02] vs. 0.02 [0-0.27] mm Hg-hr per hour, p < 0.001). Hypotension duration and magnitude were greater for unfavorable compared with favorable patients (0.03 [0-0.77] vs. 0.71 [0-5.01]%, p = 0.003; and 0.16 [0-1.99] vs. 2 [0-4.02] mm Hg, p = 0.001). On logistic regression, a 1-point increase in hypotension burden below the fifth percentile for age (equivalent to 1 mm Hg-hr of burden per hour of recording) was associated with increased odds of unfavorable outcome (adjusted odds ratio [aOR] 14.8; 95% CI, 1.1-200; p = 0.040). At MAP thresholds of 10th-50th percentiles for age, MAP burden below the threshold was greater in unfavorable compared with favorable patients in a dose-dependent manner. High-resolution continuous ABP data can be used to quantify hypotension burden after pediatric cardiac arrest. The burden, duration, and magnitude of hypotension are associated with unfavorable neurologic outcomes.

Aerodynamic conditions measured at a rotor blade of large wind turbine prototype

In this paper we present high resolution pressure distributions measured on the surface of a rotor blade of an 8 MW offshore prototype. We investigate two time intervals of approximately 10 minute duration acquired in April 2022. During the first time interval the turbine is operated at rated power, during the second time interval the turbine is operated below rated power. We see a clear increase and decrease of pressure values on the pressure side and suction side of the rotor blade, respectively. Also, 1P frequency oscillations are found in all sensors except during idling state. We find a decreasing signal to noise ratio in these oscillations with increasing distance from leading edge towards trailing edge, indicating higher turbulence in the air flow. Additionally, pressure changes during pitching movements can be observed. These data present the first data set of pressure distribution measured over a longer time period on a state-of-the-art sized offshore wind turbine prototype and therefore, provide important input for model validation and further understanding of the aerodynamic conditions at a rotor blade.

From Pressure to Water Consumption: Exploiting High-Resolution Pressure Data to Investigate the End Uses of Water

HighlightsA method to investigate water consumption based on pressure data is developed.The method exploits the headloss-flowrate equation to obtain water-consumption data.The method is validated on a real case study, resulting in an average error of 2.3%.Limitations affecting the installation of domestic flow meters are overcome.Insights into the features of individual water-consumption events are provided.

Deep ocean hydrographic variability estimated from distributed geodetic sensor arrays off northern Chile

Observations of spatio-temporal variability of the deep ocean are rare and little is known about occurrence of deep ocean mesoscale dynamics. Here, we make use of 2.5 years of time series data from three distributed sensor arrays, which acquired high-resolution temperature, pressure and sound speed data of the bottom layer offshore northern Chile. Estimating salinity and density from the direct observations enable access to the full spectrum of hydrographic variability from a multi-hourly to annual time scale and with average inter-station distances of less than 1 km. Analyses revealed interannual warming over the continental slope of 0.002 °C yr−1–0.003 °C yr−1, and could trace periodic hydrographic anomalies, likely related to coastal-trapped waves, as far as to the lower continental slope. A concurrent change in the shape of the warm anomalies and the rate of deep-sea warming that occurs with the crossing of the deep-sea trench suggests that the abyssal part of the eastern boundary current system off Chile does not extend past the deep sea trench. Furthermore, the comparison of anomaly timing and shape in between stations implies southwards flow over the mid to lower continental slope, centred closer to the trench.

Extreme wave impacts on a suspended box above free surface

A systematic approach incorporating precision-controlled experiments and high-fidelity numerical simulations has been employed to delve deeper into the physics of extreme wave impact pressures. Using a suite of high-speed imaging and high-resolution pressure sensing techniques that are accurately synchronized with the wave generation, a consistent and well-correlated set of data providing details of the focusing incident wave front, flow velocities, and impact pressure time histories has been successfully obtained. The numerical simulations using a validated incompressible smoothed particle hydrodynamics (ISPH) provided further details that were not tractable in the experiments. Based on the results obtained, there were five broad impact scenarios identified, ranging from focusing crest impacts to plunging jet impacts with varying entrapped air. Among these impact types, the highest impact pressures were those associated with a focusing wave crest. It has been found that high impact pressures were correlated with a convergence of the horizontally impinging overturning crest and the vertical surge of the wall boundary water mass, but with the overturning crest impingement just ahead of the arrival of the wall boundary water mass, which in essence amounted to the impingement of a focusing wave front. Depending on the relative arrival times of the impinging crest and the wall boundary upsurge, the impact scenarios could vary from an up-slosh to a jet impingement with a clear entrapped air pocket, followed by a flip-through of the incident crest. For the scenario with the highest impact pressures, broadly classified as type II impact in this paper, the peak pressures could reach as high as 85ρC2, an order of magnitude higher than impact scenarios without the focusing effect, such as the scenario with a plunging jet impact with entrapped air (classified as types III and IV impact in this study). As the volume of entrapped air for this scenario was relatively small, the one-phase ISPH model used in this study was able to capture the peak pressure characteristics, including the peak pressure amplitudes. However, in scenarios with significantly highly entrapped air during impact, a two-phase model would be necessary.

Identifying steady and pulsatile two-phase flow regimes with pressure drop signals and acoustic emissions

Identifying two-phase flow regimes is vital for understanding phase-change heat and mass transfer processes. Traditionally, flow regime identification has relied on subjective flow visualization studies, which are then converted to flow regime maps applicable to specific operating conditions. However, these conventional, largely subjective, maps fall short in predicting abrupt changes in flow patterns that often occur during regular operation of vapor compression and absorption heat pumps and other thermal systems. Conventional flow regime maps are also inadequate for addressing the transient, intermittent, or periodic flow regimes that occur in boiling and condensation enhanced using acoustic and other emerging techniques. Therefore, there is a pressing need for alternative flow regime identification techniques that can adapt and reliably track rapid and local changes in two-phase hydrodynamics. Promising candidates for dynamic flow pattern classification include high-resolution pressure drop signals and acoustic emission spectra, which can provide insights into the local hydrodynamics within a flow channel. To assess the feasibility of these measurement techniques, differential pressure and condenser microphone measurements are recorded alongside high-speed videos of a two-phase flow of saturated R134a. The total mass flux and vapor quality are varied to understand the effects of phase velocity and liquid inventory on wave propagation in the channel. Additionally, forced oscillations are introduced to a steady two-phase flow to analyze their impact on flow patterns and their corresponding pressure drop and acoustic signals. Statistical analyses, including Gaussian mixture modeling, are employed to reveal characteristic pressure drop probability associated with each flow pattern, which form the basis for developing a model capable of predicting flow regimes in both steady and oscillating flows. The resulting framework introduces a new flow regime identification technique that can adapt to dynamic operating conditions, benefiting a wide range of thermal systems and phase-change processes.

Evaluating the accuracy of cerebrovascular computational fluid dynamics modeling through time-resolved experimental validation

Accurate modeling of cerebral hemodynamics is crucial for better understanding the hemodynamics of stroke, for which computational fluid dynamics (CFD) modeling is a viable tool to obtain information. However, a comprehensive study on the accuracy of cerebrovascular CFD models including both transient arterial pressures and flows does not exist. This study systematically assessed the accuracy of different outlet boundary conditions (BCs) comparing CFD modeling and an in-vitro experiment. The experimental setup consisted of an anatomical cerebrovascular phantom and high-resolution flow and pressure data acquisition. The CFD model of the same cerebrovascular geometry comprised five sets of stationary and transient BCs including established techniques and a novel BC, the phase modulation approach. The experiment produced physiological hemodynamics consistent with reported clinical results for total cerebral blood flow, inlet pressure, flow distribution, and flow pulsatility indices (PI). The in-silico model instead yielded time-dependent deviations between 19–66% for flows and 6–26% for pressures. For cerebrovascular CFD modeling, it is recommended to avoid stationary outlet pressure BCs, which caused the highest deviations. The Windkessel and the phase modulation BCs provided realistic flow PI values and cerebrovascular pressures, respectively. However, this study shows that the accuracy of current cerebrovascular CFD models is limited.

226 Advancing Neurovascular Diagnostics for Abnormal Hemodynamic Conditions Through AI-Driven Physics-informed Neural Networks

INTRODUCTION: Many studies have explored the application of machine learning and neural networks in extracting critical diagnostic information from structural and functional medical imaging. While these methods show potential for improving efficiency, concerns arise when interpreting subtle imaging features, especially when training data is limited. Physics-informed neural networks (PINNs) address these issues by incorporating governing equations from physical and mechanical models into the analysis. METHODS: We examined the use of a PINN that enforces the convection equation, relating contrast media propagation to blood velocity, the Navier-Stokes equations for velocity and pressure distributions, and the conservation of mass requirement to calculate velocity and pressure distributions within patient-specific blood vessels. We also implemented a boundary condition that accounts for real contrast-media propagation from 1000 fps high-speed angiographic (HSA) image sequences, allowing for an assumption-free problem space within a medical imaging framework. Velocity fields and pressure gradients were calculated for flow in aneurysms and carotid bifurcations. RESULTS: Our method demonstrates comparable results to computational fluid dynamics (CFD) without requiring manual data processing, significantly improving the efficiency of calculating high-resolution velocity and pressure fields. By integrating AI with physics modeling, this novel approach holds the potential for advancing neurovascular diagnostics and providing more accurate, personalized treatment plans for patients with neurovascular pathologies. CONCLUSIONS: This innovative approach, integrating physics modeling and artificial intelligence, offers more accurate and personalized diagnostics for patients with neurovascular pathologies. The PINN method not only achieves results comparable to computational fluid dynamics without the need for manual data processing but also greatly enhances the efficiency of calculating high-resolution velocity and pressure fields.

A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.

In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications. In their 2-center retrospective cohort study, the authors used labeled ICP data from 40 patients in the first neurocritical care unit (University Hospital Zurich) for model development. The authors created 94 descriptive features that were used to train the model. They compared histogram-based gradient boosting with extremely randomized trees after building pipelines with principal component analysis, hyperparameter optimization via grid search, and sequential feature selection. Performance was measured with nested 5-fold cross-validation and multiclass area under the receiver operating characteristic curve (AUROC). Data from 20 patients in a second, independent neurocritical care unit (Charité - Universitätsmedizin Berlin) were used for external validation with bootstrapping technique and AUROC. In cross-validation, the best-performing model achieved a mean AUROC of 0.945 (95% CI 0.92-0.969) on the development dataset. On the external validation dataset, the model performed with a mean AUROC of 0.928 (95% CI 0.908-0.946) in 100 bootstrapping validation cycles to classify normal signals, artifacts, and drainages. Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.

Dynamic Simulation Platform Aids Deep Transient Tests, Well-Control Safety

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215454, “Enhancing Well-Control Safety With Dynamic Well-Control Cloud Solutions: Case Studies of Successful Deep Transient Tests in Southeast Asia,” by M. Ashraf Abu Talib, SPE, M. Shahril Ahmad Kassim, and Izral Izarruddin Marzuki, SPE, Petronas, et al. The paper has not been peer reviewed. _ The complete paper addresses challenges related to well control and highlights the successful implementation of deep transient tests (DTT) in an offshore well performed with the help of a dynamic well-control simulation platform. The paper aims to provide insights into the prejob simulation process, which ensured a safer operation from a well-control perspective. Additionally, a comparison between simulated and actual sensor measurements during the DTT operation is presented. DTT DTT is a formation-testing (FT) method that allows pressure transient tests that reach deeper into the formation compared with conventional interval pressure transient tests (IPTT). DTT enables the testing of formations with higher permeability, greater thickness, and lower viscosity and real-time measurement of crucial parameters. During a DTT, formation fluid is pumped from the reservoir; upon stopping the pump, the formation pressure begins to recover as fluid further from the wellbore replaces the extracted fluid. By analyzing the resulting pressure transient, properties such as formation permeability, permeability anisotropy, and other characteristics can be determined. DTT allows for a better understanding of reservoir characteristics and rock heterogeneity. When properly designed and executed, DTT can reveal potential baffles and boundaries within the radius of investigation. A further advantage of DTT over drillstem tests (DST) is its minimal fluid flow, which allows for the attainment of objectives while contributing to the United Nations sustainable development goals. In DTT operations, the FT tool is connected to the drillpipe through a circulating sub and a slip joint. The circulating sub plays a critical role in DTT operations because it enables the continuous mixing of pumped formation fluid with circulated mud and facilitates its transportation to the surface (Fig. 1). Typically, a constant circulation rate ranging from 100 to 250 gal/min is maintained. During circulation, the annular preventer is closed and the mud/hydrocarbon mixture is directed through the choke line to the mud/gas separator (MGS) once it reaches the surface. No formation fluids are flared during DTT operations. Instead, the circulated oil is retained in the mud and only small amounts of gas are vented. By use of a slip joint, the FT remains anchored to the borehole wall. A high-resolution pressure gauge is used to capture and interpret even minor pressure fluctuations during the pressure transient buildup.

All-Optical High-Resolution On-Chip Pressure Sensing Method Based on Tunable Liquid-Core/Liquid-Cladding Waveguide

All-Optical High-Resolution On-Chip Pressure Sensing Method Based on Tunable Liquid-Core/Liquid-Cladding Waveguide

Physics-informed neural networks modelling for systems with moving immersed boundaries: Application to an unsteady flow past a plunging foil

Physics informed neural networks (PINNs) have been explored extensively in the recent past for solving various forward and inverse problems for facilitating querying applications in fluid mechanics. However, investigations on PINNs for unsteady flows past moving bodies, such as flapping wings are scarce. Earlier studies mostly relied on transferring the problems to a body-attached frame of reference, which could be restrictive towards handling multiple moving bodies/deforming structures. The present study attempts to couple the benefits of PINNs with a fixed Eulerian frame of reference, and proposes an immersed boundary aware framework for developing surrogate models for unsteady flows past moving bodies. Specifically, high-resolution velocity reconstruction and pressure recovery as a hidden variable are the main goals. The framework has been developed by using downsampled velocity data obtained from prior simulations to train the PINNs model. The efficacy of the velocity reconstruction has been tested against high resolution IBM simulation data, whereas the efficacy of the pressure recovery has been tested against high resolution simulation data from an arbitrary Lagrange Eulerian (ALE) solver. Under the present framework, two PINN variants, (i) a moving-boundary-enabled standard Navier–Stokes based PINN (MB-PINN), and, (ii) a moving-boundary-enabled IBM based PINN (MB-IBM-PINN) have been formulated.Relaxation of physics constraints in PINNs models has been identified to be a useful strategy in improving the predictions. A fluid-solid partitioning of the physics losses in MB-IBM-PINN has been allowed, in order to investigate the effects of solid body points while training. This strategy enables MB-IBM-PINN to match with the performance of MB-PINN under certain loss-weighting conditions. Interestingly, MB-PINN is found to be superior to MB-IBM-PINN when a priori knowledge of the solid body position and velocity is available. To improve the data efficiency of MB-PINN, a physics based data sampling technique has also been investigated. It is observed that a suitable combination of physics constraint relaxation and physics based sampling can achieve a model performance comparable to the case of using all the data points, under a fixed training budget.

A High-Resolution Film-Resistance Plantar Pressure Sensor

A High-Resolution Film-Resistance Plantar Pressure Sensor