Real-time Pressure Research Articles

Automated Gas Influx Handling Model and Mechanisms During Deep High-Temperature and High-Pressure Well Drilling

The exploration and development of oil and gas resources in deep formations is a key strategic priority for national energy production. However, manual methods for handling gas kicks suffer from low operating accuracy and inefficiency during high-temperature and high-pressure deep well drilling. To address the need for real-time bottomhole pressure prediction and control, an efficient gas–liquid–solid computing model was developed based on the gas slip model and cuttings settling velocity model. By integrating this model with an automatic choke adjustment system, an automatic gas kick attenuation model for deep well drilling was established. Results show that, compared to the driller’s and wait-and-weight methods, the automatic gas kick attenuation method significantly reduces peak choke pressure due to its larger frictional pressure drop and higher cuttings hydrostatic pressure. The automatic attenuation method not only leads to an average reduction of 28.42% in maximum choke/casing pressure but also accelerates gas removal, achieving gas kick attenuation ten times faster than the driller’s method and seven times faster than the wait-and-weight method. The study also investigates the influence of gas solubility, well depth, gas influx volume, formation permeability, and drilling fluid volumetric flow rate on gas kick attenuation characteristics. The findings provide a solid foundation for improving the efficiency of gas kick management in deep well drilling operations.

A High-Stability Pressure-Sensitive Implantable Memristor for Pulmonary Hypertension Monitoring.

Pulmonary hypertension (PH) significantly affects the quality of life and lifespan of humans and has promoted the development of flexible implantable electronic devices for PH diagnosis and prevention. Traditional implantable devices based on the von Neumann architecture face insurmountable challenges in processing large amounts of biological data due to computational bottlenecks. Memristors, with integrated in-memory sensing and computing capabilities, can effectively eliminate computational bottlenecks and become one of the most promising products in implantable devices for health monitoring. Here, a memristor with the Ag/MnO2/BaTiO3/FTO structure is implemented and implanted into Sprague‒Dawley (SD) rats. With polydimethylsiloxane (PDMS) packaging, the device can be continuously worked in vivo for up to four weeks, demonstrating excellent stability and biocompatibility. Furthermore, a memristive sensor array is designed for pulmonary artery blood pressure monitoring based on the pressure-responsive characteristics of the as-prepared memristive device. The front-end memristive sensor array can collect and feedback pressure signal, while noise reduction is achieved through memristive logic circuits, and ultimately the memristor neural network processes and classifies the information. Therefore, this work demonstrates the potential of implantable memristors for pulmonary artery pressure monitoring and provides new inspiration for the design of efficient, real-time, and reliable implantable pressure monitoring devices in medical health monitoring.

An entropy measure-based study on flow pattern of gas–liquid two-phase flow in a U-Tube

U-tubes are widely applied in gas–liquid two-phase transportation in chemical engineering. The diverse flow patterns within these tubes significantly affect the pressure loss, heat transfer efficiency, and even the fluid-induced vibration amplitude of the tubes. This study explores the complex flow pattern features in a U-tube in a vertical plane and focuses on recognizing them. For the acquisition and classification of flow patterns, a Computational Fluid Dynamics (CFD) model for gas–liquid two-phase flow is first established, and its quantitative calculation error is ensured to be less than 5%. Then, the spatiotemporal evolution characteristics of flow patterns is analyzed. The real-time pressure drop response is chosen as the representation signal, and its nonlinear features in the time and frequency domain under different flow patterns are explored. A nonlinear time series is constructed by extracting a segment from the real-time pressure drop data, and six entropy measures are applied to analyze and identify them. Finally, the sensitivity of entropy measures to both the time series lengths and the tested sections are evaluated. Results show that there are six typical flow patterns in a U-tube. According to most entropy measures, the bubble flow has the highest complexity; however, the plug flow presents the lowest complexity. In the U-bend, pressure drop signals for the bubble and annular flows show random fluctuations within a specific range, in contrast to the marked periodicity in plug flow signals, while wavy and slug flows exhibit intermittent peak values. Including the upstream and downstream straight pipes in the analysis, rather than focusing solely on the U-bend, significantly increases the complexity of the stratified, plug, and slug flows. Fuzzy entropy is an effective tool for identifying the six flow patterns, demonstrating good resilience to variations in the length of the data series. This characteristic makes it highly useful for real-time identification of flow patterns in the U-bend sections of non-transparent U-tubes, offering considerable potential in chemical equipment.

A feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach for PEMFC air supply system

With the application of proton exchange membrane fuel cell (PEMFC) in the field of transportation and energy, the requirements for air flow and pressure tracking performance, efficiency and stability of PEMFC air supply system become higher and higher. To improve PEMFC cathode air flow and pressure control performance under load variation and avoid surge of air compressor, a novel feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach is proposed. Firstly, optimal references of oxygen excess ratio (OER) and cathode air pressure under different load are calculated based on net output power of PEMFC system analysis. Then, an extended state observer is designed to estimate the cathode air pressure and flow in real time, a feedback linearization sliding mode based decoupling controller is designed to enhance OER and air pressure tracking control performance and robustness. Considering on the operating trajectory and surge line of air compressor, a fuzzy logic based anti-surge compensator is designed to prevent air compressor from surge by compensating both air flow and pressure simultaneously. The proposed control approach is implemented in PEMFC air supply control experiments and the results demonstrates its effectiveness.

Human Anterior Segment Perfusion Organ Culture.

The human anterior segment perfusion organ culture is an ex vivo model system for studying the human conventional outflow pathway with reference to pressure regulation. In this model, anterior segments dissected from human donor eyes can be fixed to a modified petri dish and perfused with media along with various study agents at the physiological flow rate of 2.5μL/min. The model mimics the one-way flow of aqueous humor in human eyes and can be used to evaluate the effects of various drugs on eye pressure in real time. Using this model, cells and tissues of the anterior segment can be maintained for up to 28days, enabling histological and molecular evaluations.

Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution.Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O(nlogn) instead of the typical O(n3) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.

Real-time Blood Pressure Prediction on Wearable Devices using Edge Based Deep Neural Networks: A Hardware-software Co-design Approach

This paper presents the hardware realization of a real-time blood pressure (BP) prediction model for wearable devices, utilizing long short-term memory (LSTM) deep neural networks (DNNs). The proposed system uses both electrocardiogram (ECG) and photoplethysmogram (PPG) signal values for BP prediction. It aims to address the limitations of traditional BP measurement methods, providing a low error, minimal computational overhead, more accurate and convenient alternative system for individuals with hypertension or at risk for cardiovascular diseases. The utilization of split matrix approach leads to a reduction in hardware complexity across the entire system. This technique involves breaking down the larger weight matrices used in the computations of DNNs into smaller matrices. This fragmentation results in a decrease in the complexity of the hardware responsible for performing matrix vector multiplications (MVMs) within LSTMs. The resultant architecture of the predictive model gains several advantages, including a lowered level of complexity in terms of the space occupied by individual cells, decreased processing delay, and reduced power consumption. Furthermore, this approach enables the achievement of a notably improved minimum achievable clock period of 2.972 ns. This prediction model can operate locally on wearable devices, reducing the reliance on cloud computing and improving privacy and security. The performance evaluations are carried out using both analytical and implementation results. The results indicate that the proposed model can be practically applied to real-world problems and can potentially enhance the accuracy of various machine-learning tasks.

Application of digital twin technology in monitoring system of pump turbine

The advent of advanced productive forces has catalyzed the ongoing evolution of intelligent and digital technologies within the pump-turbine sector. The integration of digital technologies has not only enhanced production efficiency but also facilitated intelligent operation management, offering novel solutions for pump-turbine design and operation, thereby accelerating the digital transformation in fluid machinery. This study first established the theoretical framework of the digital twin system for pump-turbines, followed by the development of a mathematical model and the construction of a twin virtual model utilizing Proper Orthogonal Decomposition (POD) reduction theory. The model’s accuracy was validated through real-time pressure pulsation data, and exploratory investigations into cavitation prediction were subsequently carried out. To enable the visualization of the digital twin system, this study incorporated Open3D (Three-dimensional computer graphics tools) point cloud technology, effectively rendering the state cloud map of the pump-turbine. Finally, by synthesizing the proposed theories and technologies, the digital twin system’s visualization and monitoring for pump-turbines were successfully implemented through the Unity3D simulation platform. This study offers novel insights into intelligent monitoring and prediction of pump-turbines, holding significant implications for the modernization and intelligent advancement of the hydropower industry.

Research on Automatic Pipe Cleaning Technology for Natural Gas Medium and Low Pressure Pipelines

Abstract As the number of low-production and low-efficiency wells in Chang Qing gas field increases gradually, and the problems of pipeline liquid accumulation, freezing plugging and leakage due to production with liquid are worsening, in view of the low efficiency of manual analysis of abnormal pipeline status, the difficulty of timely detection of potential problems and the labor intensity of manual blockage clearing operation, we study an automatic pipeline clearing technology applicable to medium- and low-pressure gas pipelines in the gas field, which mainly includes pipeline operation status analysis and early warning system and automatic pipeline clearing device. An analysis and warning algorithm module is proposed to analyze, warn and locate the frozen plugging point based on pipeline pressure, differential pressure and flow parameters in real time. A low and medium pressure balling device is designed to achieve the goals of intelligent control, pressure isolation balling and safe operation through mechanical structure design, automatic control system and safety protection system. The device and system are applied in the well field at the end of 1 and 3# trunk pipelines of Shen Mu Gas Field Station 7, which verifies the feasibility, accuracy and stability of the technology. Based on the joint operation of the pipeline analysis and warning system and the automatic pipeline clearing device, it is able to meet the remote intelligent pipeline clearing operation and pipeline maintenance tasks in the process of gas gathering and transmission in the gas field.

Integrating Petrophysical, Hydrofracture, and Historical Production Data with Self-Attention-Based Deep Learning for Shale Oil Production Prediction

Summary As the energy industry increasingly turns to unconventional shale reservoirs to meet global demands, the development of advanced predictive models for shale oil production has become imperative. The inherent complexity of shale formations, coupled with the intricacies of hydraulic fracturing, poses significant challenges to efficient resource extraction. Our study leverages a substantial data set from the Ordos Basin to develop an advanced predictive model, integrating 18 parameters that blend static petrophysical attributes and dynamic factors, including hydraulic fracturing parameters and real-time pump pressure data. This holistic approach enables our self-attention (SA) model to accurately forecast future production rates by processing the complex interplay between reservoir characteristics and operational inputs. In testing across three wells, the model achieved average accuracies of 99.28% for daily oil production (DOP) and 99.25% for daily liquid production (DLP) over 20 days, surpassing traditional long short-term memory (LSTM) and gated recurrent unit (GRU) models, proving its efficacy in fractured well production forecasting. Furthermore, using the initial 30 days of production data as input, the model demonstrated its capability to predict DOP and DLP over a one-year period, achieving prediction accuracies of 96.2% for DOP and 99.6% for DLP rates. Our model’s profound implications for the shale industry include establishing a quantifiable link between key factors and production forecasts, guiding the optimization of controllable aspects, and serving as a decision-support tool for more efficient and cost-effective oil recovery.

Ultrasensitive and wide-range MXene/PDMS piezoresistive sensors inspired by rose petals

Flexible pressure sensors are crucial in a variety of applications, including artificial intelligence (AI), the Internet of Things (IoT), and electronic skin (e-Skin), due to their ability to convert mechanical stimuli into electrical signals. However, achieving both high sensitivity and a wide detection range simultaneously remains challenging, often limiting their practical applications. To address this, we designed a three-dimensional contact interface with Ti3C2 MXene/polydimethylsiloxane (MP) inspired by rose petals, which allows for significant strain and resistance changes across a broad range of stress levels. The innovative design significantly extends the pressure detection range (0.033–535 kPa), enhances pressure sensitivity (0.432 kPa−1), offers a fast response/recovery times (57/4 ms), and ensures exceptional long-term durability (up to 2950 cycles). The structured MP sensor demonstrates the capability to detect human physiological movements, distinguish various voices and map real-time pressure distributions. Notably, it achieves real-time tracking of handwriting processes with high recognition accuracy through a machine learning module. The work offers a new strategy and inspiration for designing and manufacturing innovative structured pressure sensors with reliable performance, paving the way for their application in next-generation wearable and portable electronics.

Catastrophe Information Characteristics and Prevention Measures of Water Inrush in Tunnel Approaching Fault with Different Water Pressure

In order to ensure the safety of the tunnel approaching the fault and prevent water inrush disasters, and then take reasonable protective measures, a fault-tunnel-surrounding rock is established by using a three-dimensional (3D) discrete element numerical analysis method, which takes into account the fluid-structure coupling effect. Based on the method of control variables, the catastrophe information characteristics of displacement and water pressure of the surrounding rock of the tunnel face and the corresponding characteristics of changes before the occurrence of water inrush disasters were studied under different fault water pressures during the excavation of the tunnel approaching the water-rich fault. The results show that, during excavation at the same step, displacement and its magnitude in the surrounding rock escalate as fault water pressure increases. The maximum pressure of the water in the surrounding rock is also constantly increasing. As tunnel excavation progresses, at constant fault water pressure, longer excavation distances result in greater axial displacement of the surrounding rock mass and increased water pressure at corresponding positions within the surrounding rock, leading to higher magnitude increases. As excavation proceeds, the displacement and water pressure in the surrounding rock and the increase of its amplitude continue to increase. Pre-reinforcement grouting techniques and pipe umbrella support systems that are very effective protective measures can be determined by a comprehensive approach integrating advanced geological forecasting methods, real-time water pressure detection, and the analysis of stress-strain and seepage pressure field variations in the surrounding rock mass.

Scientific preparation for JRT: Wind pressure prediction model for large radio telescope based on real data from multi-sensors

Jingdong 120-meter radio telescope (JRT) is poised to become the world's largest single-aperture fully steerable medium-low frequency radio telescope. However, like other large-aperture radio telescopes, the JRT is vulnerable to wind loads, which can cause structural deformation and pointing errors. Addressing this challenge requires the ability to predict dynamic winds in real-time. This study developed a wind pressure preprocessing and prediction model using sensor data collected from the Kunming 40-meter radio telescope (KRT), enabling real-time prediction of wind pressure on the telescope. The model employs adaptive noise and Variational Mode Decomposition (VMD) techniques to eliminate random noise from the original wind pressure data. Subsequently, wind pressure predictions are made using a Bidirectional Long Short-term Memory (BiLSTM) model. By conducting predictions under various stabilization conditions and conducting a thorough analysis of measurement data from five sensors, the study has achieved impressive results in predicting wind pressure on the KRT reflector surface. The proposed model demonstrates the lowest MAE, RMSE, and MAPE, while achieving the highest R2 across various data sets. Where the average R2 of the proposed model is 0.9392 at 45° pitch angle attitude and the RMSE, MAE and MAPE values are 1.4923, 1.2377 and 1.82% respectively. This model helps wind load monitoring of real-time wind pressure monitoring of the telescope surface, to study the effects of wind load on pointing accuracy. By adjusting the control parameters to reduce wind load interference, to ensure the high-precision work of a large radio telescope, such as JRT.

Automated Workflow Uses Pressure, Rate Measurements for Well Monitoring

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218470, “A New Automated Workflow for Well Monitoring Using Permanent Pressure and Rate Measurements,” by Anton Shchipanov, SPE, NORCE; Boyu Cui, SPE, University of Stavanger; and Vitaly Starikov, SPE, Heriot-Watt University, et al. The paper has not been peer reviewed. _ The complete paper describes an integrated workflow for automated well monitoring using pressure and rate measurements obtained with permanent gauges and flowmeters. The workflow is based on time-lapse pressure transient analysis (PTA) and integrates the following components: virtual flowmetering (VFM), transient identification, feature extraction and pattern recognition in transient pressure responses, and assessment of well performance based on PTA metrics. The methodology behind the workflow combines different physics-informed and data-driven methods described in the paper. A New Workflow for Automated Well Monitoring The concept of the automated workflow described in the complete paper has been suggested in the literature. The workflow consists of four main steps (Fig. 1), and integrates the following components: - VFM - Pressure transient identification - PTA feature extraction and time-lapse pattern recognition - PTA metrics to obtain the well-performance profile VFM. The well flow rate is often measured more sparsely than its bottomhole or wellhead pressure for many reasons. However, the permanent downhole or wellhead gauges, when installed in a well, provide high-frequency real-time pressure and temperature measurements. This disparity in the pressure- and rate-measurement sampling rates is a problem for PTA, which requires accurate and synchronized input for flow rates in line with the pressure time series. VFM helps reconstruct missing flow-rate measurements using available pressure measurements. The automated well-monitoring workflow described in this paper would benefit from integration of a relatively simple but robust and scalable VFM model having modest input-data requirements. The steady-state vertical-lift-performance-based VFM model was chosen for use by the authors after testing. The model uses only the bottomhole and wellhead pressure difference, which fits well with the automated workflow requirements. Put simply, the VFM-developed algorithm identifies the suitable pressure and flow-rate samples to train the VFM model; estimates, filters, and verifies the evolution of the model coefficients; and predicts the missing flow-rate values using the pressure measurements where available. This prediction applies only to the time when the well is flowing; as for shut-in periods, the algorithm assigns zero rate values. Transient Identification. Proper transient identification is crucial for PTA because accurate identification of the starting break point of a transient and sequential synchronization with corresponding rate governs the reliability of PTA interpretation results. This is important not only for the transient in question but also for the well history preceding the transient. In the latter case, proper transient identification may help in reliable rate time allocation throughout the well history for common cases of uncertain rates. The workflow of the extended method consists of two main stages. First, the breakpoints indicating start and end of shut-in transients are detected using the topographic prominence max rotation (TPMR) method. This automatically provides detection of flowing transients between the shut-ins identified. Second, the breakpoints of multirate periods (assuming stepwise rate changes) are detected within the long flowing transients using the local minimum in rotation (LMIR) method. As a result, the well pressure history is divided into sequential flowing and shut-in transients, also differentiating flowing transients governed by stepwise rate changes. Neither TPMR nor LMIR methods require denoising, so raw data from permanent gauges may be used without any preprocessing.

Incipient instability real-time warning via adaptive wavelet synchrosqueezed transform: Onboard applications from compressors to gas turbine engines

To address the limitations of existing stall/surge warning methods—including variations in research subjects, sensor configurations, computational complexity, and the universality of thresholds—this study proposes a novel incipient instability warning approach utilizing adaptive wavelet synchrosqueezed transform. Each detection cycle begins with the acquisition of a low-pass filtered and downsampled total pressure signal from the compressor outlet, captured via a sliding window to represent the pressure sample. Then, the adaptive wavelet synchrosqueezed transform (AWSST), employing dynamic discretization with scales and synchrosqueezing technology, analyzes the real-time pressure sample to extract precise instantaneous frequency changes at low computational costs. Features of instability severity are derived from the amplitude, phase, and pressure drop within the time-frequency spectrum, leading to the formulation of a warning logic. The proposed method's effectiveness and universality are validated through hardware-in-loop (HIL) tests using multiple surge test data. Results show that the proposed method outperforms traditional real-time warning methods, which rely on the pressure change rate, by enhancing accuracy and advancing warning times by 15 ms–50 ms. Notably, the threshold is universal for compressors or engines of the same model. By increasing the acceptable performance decline criteria, compressors, turbofan engines, and turboshaft engines can employ a common set of thresholds.

Recognizing the Most Accurate Herschel-Bulkley Fluid Pressure Estimation Method for Annulus: An Update to API RP 13D, 7th Edition

Summary The Herschel-Bulkley (HB) model is widely regarded as highly accurate for modeling non-Newtonian fluid flow, applicable across diverse fields such as chemical, mechanical, and petroleum engineering. The American Petroleum Institute and other references recognize the HB model as the most accurate and recommend the industry use it for drilling hydraulics. Pressure estimation models are beneficial for applying the HB model in field settings, as numerical solutions may not be feasible due to the computational demands and time constraints, especially challenging for high-frequency real-time pressure loss estimation (at least 1 Hz). Past literature has presented four estimation methods for the HB fluid, consisting of Zamora and Lord (1974), Merlo et al. (1995), Gjerstad and Time (2015), and the generalized method. However, the recommended practice API RP 13D (2023), “Rheology and Hydraulics of Oilwell Drilling Fluids,” has considered the most traditional Zamora and Lord (1974) as their chosen estimation method for industry use while neglecting to identify the most accurate model. Therefore, to update API RP 13D (2023), this work addresses this shortcoming by investigating the performance of different methods in estimating pressure losses while providing user-friendly, straightforward procedures applicable for field use. To recognize the most accurate estimation method for industry use, different methods’ pressure losses were estimated and compared with the numerical solution as well as experimental data to find the errors. The results showed that the Merlo et al. (1995) method provides the most accurate estimates, with the least average absolute percent error (APE) of maximum 4% underestimating the numerical solution, followed by Zamora and Lord’s error overestimating the numerical solution. The errors may slightly vary depending on the input parameters used, including diameters, mud properties, and circulation rate. The effect of the circulation rate on the accuracy of different methods was also investigated; except for the generalized method, the accuracy of the other methods increases with increasing the circulation rate. This work is a very innovative practical work for finding annular pressure losses accurately without using complex numerical solutions.

Influence of Lubrication Cycle Parameters on Hydrodynamic Linear Guides through Simultaneous Monitoring of Oil Film Pressure and Floating Heights

Hydrodynamic linear guides in machine tools offer a high load capacity and excellent damping characteristics, improving stability, precision, and vibration reduction. This study builds on previous research where floating heights were verified with a simulation model limited to measured floating heights. Advancements include incorporating pressure sensors into a fixed steel rail, enabling simultaneous measurement of oil film pressure and floating heights for a comprehensive understanding of lubrication conditions within the lubrication gap. The experimental results explore the effects of different lubrication methods, providing valuable insights into cavitation and lubrication adequacy. The results demonstrate the feasibility of utilizing pressure sensors to measure oil film pressure within the lubrication gap, providing a nuanced understanding of lubrication dynamics. By measuring both floating heights and pressure measurement, distinctions between hydrodynamic lubrication, mixed friction regions, and instances of lubricant deficiency become readily discernible. The variations in real-time oil film pressure and floating heights help to optimize the lubrication cycle for hydrodynamic linear guides, enhancing system performance and longevity.

Wearable Pressure Sensing System Appliedin Sports Assistive Monitoring and Technological Rehabilitation

In the aging society, chronic diseases have become a very common problem. Many elderly people have at least one chronic disease. Most chronic diseases are caused by poor diet and lack of physical activity. As the body ages, chronic diseases will develop. Therefore, it is important to establish a healthy lifestyle. The best way to prevent chronic diseases is to diagnose them early and self-check your health. In terms of self-check, most hospitals provide good testing equipment for the public to use. However, in Taiwan, most elderly people with chronic diseases are not aware of any abnormalities in their bodies. They often go to the hospital for a doctor's check only after experiencing a serious injury. For example, diabetic patients are tend to foot pressure distribution imbalance and foot deformation due to peripheral nerve damage caused by diabetes. In severe cases, long-term friction can cause wound infection and lead to amputation. Parkinson's disease, which commonly occurs in the elderly, is the main cause of falls in the elderly. Therefore, early self-check is very important.We used TENG technology to develop high-performance pressure sensors. The sensors are made of biocompatible silicone and conductive silicone, which are soft, flexible, and durable. We installed the sensors in insoles. We hope that by allowing patients to wear corrective insoles to detect the current foot pressure in real time, we can first collect data on the pressure distribution and walking frequency of healthy people. Then, we can compare the data to abnormal walking patterns. Finally, we can use machine learning to analyze and classify the data to predict the risk of patients developing complications of chronic diseases. This allows patients to do a simple self-check at home without having to go to a large hospital for a complex examination, which also avoids wasting unnecessary medical resources.

Self-Powered Wearable Pressure Sensor System for Smart Healthcare Applications

Wearable sensors mark a groundbreaking advancement in the realm of health monitoring technology. However, traditional wearable devices lack comprehensive solutions for tracking sports exercise behaviors and providing feedback on rehabilitation for those undergoing home-based recovery. To bridge this gap, a self-powered triboelectric nanogenerator (TENG)-based wearable sensing system has been developed with in-house monitoring capabilities. The wearable sensor system is composed of triboelectric pressure sensors with an electro-conductive silicone sheet and patterned Ecoflex film as the contact layers. Highly sensitive TENG sensors have been successfully embedded into a bicycle saddle and human shoe insole for real-time pressure monitoring. Experimental results demonstrate that biomechanical energy, originating from both the pelvic-saddle interactions on bicycle and human gait movements, has been successfully converted into electrical energy. The generated sensor voltage remains stable under frequency variations and external environmental conditions, such as temperature and humidity variations. Utilizing distinctive information contained in individual body mass imbalances and gait characteristics, a smart healthcare monitoring system is envisioned to extract essential biomechanical insights via artificial intelligence, facilitating a pivotal role in in-house monitoring and tracking of sports rehabilitation exercises. The as-designed low-cost, highly scalable self-powered wearable sensor system offers an accessible, user-friendly point-of-care solution for gait detection and rehabilitation monitoring for individuals with various physical impairments, with considerable potential for commercialization. Figure 1

Few-mode optical fiber-based flexible pressure feedback tentacle doped with fluorescence temperature pointer

Abstract In this paper, a flexible fiber pressure feedback whisker is proposed, which consists of a water droplet shaped polydimethylsiloxane (PDMS) elastomer with an embedded balloon shaped few mode fiber. The mechanical sensing performance of the device was analyzed and optimized using a combination of finite element method and beam propagation method (BPM). The built-in cladding corroded few-mode fiber increases pressure sensitivity by more than four times. The collection efficiency of fluorescence signal is improved by cladding corrosion. The PDMS elastomer was doped with upconversion nanoparticles NaYF4:Yb, Er@NaYF4 in order to achieve temperature measurement by fluorescence intensity ratio technology. The combination of fluorescence signal and interference spectrum can not only achieve real-time and accurate pressure detection at different temperatures, but also incorporate fluorescent materials into flexible bionic skin for temperature self-compensation, which has potential application value for the development of bionic fiber micro-nano sensing and control devices.