Flow Rate Data Research Articles

Adaptive thresholds‐based leak detection using real‐time transient modeling of two‐phase flows

AbstractPipelines are widely regarded as the most cost‐effective mode of transportation for oil and gas, particularly when it comes to covering long distances. Regrettably, pipelines are not impervious to accidents, which can result in colossal material damage and fatalities. As the demand for oil and gas rises, so does the need for pipelines. Therefore, issues in pipeline malfunctions cannot be overlooked. Leakage and blockage are the two main faults in pipelines that lead to pipeline incidents if not addressed timely. Detecting pipeline leaks is a major hurdle for industry, and various techniques have been developed to tackle this. This article focuses on the modeling of leakage detection in pipelines in two‐phase transient flows. Initially, OLGA, a multiphase software, is used for simulating the transient flows in a black oil case study. A leak was induced after 30 min of the simulation and leak cases were designed with varying parameters. Using the simulated data of mass flow rate, and pressure at the inlets and outlets, the model will be identified. The adaptive thresholds‐based model was able to predict the leaks accurately. The performance of the leak detection model was analyzed. The model was able to detect leaks of 2%–10% sizes successfully.

Analysis of the percolation flow of 133 earth and rockfill dams and control values for flow meters

Worldwide, legal measures have been intensified with the aim of reducing dam accidents through government management and oversight. However, it is important to emphasize the evaluation of damages and anomalies caused by dam breaches. Geotechnical engineering currently benefits from a vast collection of data obtained through geotechnical instrumentation monitoring, which provides valuable information for the verification and validation of new projects. Utilizing this data helps in interpreting the static and dynamic behaviour of geotechnical structures, as well as supporting the adjustment and calibration of physical and computational models regarding the infiltration behaviour through dam embankments. The primary objective of this research is to consolidate real data from the monitoring history of executed and operational works in Brazil, contributing to the analysis of instrumentation through the inclusion of real flow rate values observed in flow meters. Moreover, it aims to enrich existing data in bibliographic references that assist in analysing the sensitivity of instrumentation readings. The accessed data were selected from the database of a design company, typically consisting of construction reports and historical data of instrumentation readings related to the monitoring of Brazilian dams. The results comprise consolidated real data of specific infiltration flow rates in earth and rockfill dams.

Fault Condition Indicators along the Demand Side of a Sustainable Compressed Air System

Compressed air is a vital form of energy storage and transfer in the manufacturing industry. Pneumatic system efficiency is typically low due to faults such as leakages and pressure drops which are often left unattended due to the downtime required to maintain them. The goal of this study was to identify parameters which can be used to computationally detect the presence of faults in a pneumatic pick-and-place system within an industrial automation setup. Leakages and pressure drops were systematically induced at three locations along the demand side of the system. Pneumatic data was collected from two separate locations simultaneously, and this work presents the systematic approach utilised to investigate the impacts of each fault. Indicators, namely the mean, standard deviation and impulse factor for pressure and flow rate data were identified as being able to identify the presence of faults together with their severity, type and location. This study concludes that by solely using pressure data, all three fault characteristics could be identified. These indicators can form part of data monitoring within smart machines, allowing for autonomous analyses.

Direct Numerical Simulation of Atomization Characteristics of ECN Spray-G Injector: In-Nozzle Fluid Flow and Breakup Processes

AbstractIn order to sustain applications dealing with various liquid fuels in internal combustion engine (ICE), it is essential to make available prediction methodologies that allow an early evaluation of the potential usefulness of such fuels in terms of favourable mixture preparation process already in realistic configurations. Since the air-mixture formation and subsequent processes are predominantly governed by the fuel injection, a DNS based numerical investigation coupled with VOF as an interface tracking method is carried out in this paper to gain better insight on the fuel injection from an industrial injector "Spray G" configuration. Chosen from Engine Combustion Network (ECN), this is a gasoline direct injector (GDI) featuring 8-holes orifices and operating with high injection pressure (200 bar). Under consideration of the required computational cost associated with DNS, only the 1/8 of the nozzle geometry including one orifice is used. The numerical simulation is accomplished for the quasi-steady injection condition with nozzle needle fully opened. The obtained results are first validated with available experimental data for nozzle mass flow rate and spray spread angle showing a good agreement. Then, a detailed numerical analysis is provided for the in/near nozzle flow evolution especially for flow turbulence, primary and secondary atomization. Furthermore, droplet statistics in terms of droplet shape, and droplet size-velocity distribution together with a breakup regime map are reported. Finally, a 2-D data curation technique is proposed to extract the droplet statistics along selected planes and evaluated by direct comparison with three-dimensional droplet data, which may allow handling of the DNS data in more feasible and economical way especially for time series data with higher frequency. The comprehensive DNS data generated by this DNS-VOF approach enable not only to carry out detailed numerical analysis of in- and near-nozzle physical phenomena for which experimental data are still scarce, but also to provide a hint of more reliable injector boundary conditions useful for lower order spray injection method based on Lagrangian particle tracking.

DESIGN AND OPTIMIZATION OF MODULAR PRODUCTION SYSTEM PICK & PLACE STATION

In the industrial era 4.0 where the development of sophisticated technology, the development of technology has been increasingly rapid. The industry itself began to use this sophisticated technology with the aim of seeking maximum profit with minimum capital, so for that reason the industry began to switch to "Industrial Automation". With the aim of understanding the complicated production process, MPS (Modular Production System) is made with the aim of facilitating understanding of the related production process where the MPS to be studied is Pick & Place Station. The method to be used is an experimental method where what will be studied is the effect of flowcontrol openings on cycle time, the effect of flowcontrol openings on flowsensor data, vacuum lift force, vacuum force using the venturi principle. Where the greater the flowcontrol opening, the faster the cycle time will be with the risk of cylinder damage where the first entry group produces a slower cycle time than the cycle time produced by the second entry group. The flowcontrol opening itself also affects the data in the flowsensor where the data for average mass flowrate, average flow velocity, average volumetric flowrate, average temperature, and average pressure shown by data entry 1 is smaller than data entry 2. The vacuum lift force for entry 1 and entry 2 has the same value or there is a very small change for both the distribution station and the pick & place station. For vacuum force with the venturi principle, data entry group 1 produces a smaller force than data entry group 2.

Fracture Spacing Optimization Method for Multi-Stage Fractured Horizontal Wells in Shale Oil Reservoir Based on Dynamic Production Data Analysis

In order to improve the shale oil production rate and save fracturing costs, based on dynamic production data, a production-oriented optimization method for fracture spacing of multi-stage fractured horizontal wells is proposed in this study. First, M. Brown et al.’s trilinear seepage flow models and their pressure and flow rate solutions are applied. Second, deconvolution theory is introduced to normalize the production data. The data of variable pressure and variable flow rate are, respectively, transformed into the pressure data under unit flow rate and the flow rate data under unit production pressure drop; and the influence of data error is eliminated. Two kinds of typical curve of the normalized data are analyzed using the pressure and flow rate solutions of M. Brown et al.’s models. The two fitting methods constrain each other. Thus, reservoir and fracture parameters are interpretated. A practical model has been established to more accurately describe the seepage flow behavior in shale oil reservoirs. Third, using Duhamel’s principle and the rate solution, the daily and cumulative production rate under any variable production pressure can be obtained. The productivity can be more accurately predicted. Finally, the analysis method is applied to analyze the actual dynamic production data. The fracture spacing of a shale oil producing well in an actual block is optimized from the aspects of production life, cumulative production, economic benefits and other influencing factors, and some significant conclusions are obtained. The research results show that with the goal of maximum cumulative production, the optimal fracture spacing is 5.5 m for 5 years and 11.4 m for 10 years. All in all, the fracture spacing optimization and design theory of multi-stage fractured horizontal wells is enriched.

A novel, benchtop model for quantitative analysis of resistance in ventricular catheters.

The mechanisms of catheter obstruction are still poorly understood, but the literature suggests that resistance to fluid flow plays a significant role. We developed and assessed a gravity-driven device that measures flow through ventricular catheters. We used this device to quantitatively analyze the resistances of unused ventricular catheters used in the treatment of hydrocephalus; failed hydrocephalus catheters from our catheter biorepository were also evaluated quantitatively. Catheters of three manufacturing companies were inserted into the benchtop model, which records time, flow rate, and pressure data using sensors. The relative resistances of catheters across six design models were evaluated. Experiments were performed to evaluate changes in the relative resistance of a catheter when the catheter's holes were progressively closed. The relative resistance of explanted catheters from our catheter biorepository was also measured. Experimental results showed significant differences (P<0.05) between the relative resistances of different catheter models just after being removed from their packaging. A non-linear trend of increasing resistance was observed in experiments on catheters with artificially obstructed holes. Data from five individual benchtop models were compared, and the differences in measured data between the models were found to be negligible. A significant increase (P < 0.05) in relative resistance was observed in explanted catheters. The current study sought to propose a novel in-vitro model and use it to examine data on differences in relative resistance among catheter models. From these experiments, we can rapidly correlate clinical patient cohorts to identify mechanisms of luminal shunt obstruction.

Simultaneous Pipe Leak Detection and Localization Using Attention-Based Deep Learning Autoencoder

Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions, such as fluctuating water demands, data noise, and the presence of leaks, are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to two baselines: a multichannel CNN encoder with LSTM decoder (MC-CNN-LSTM) and a random forest and model based on supervised learning with a false positive rate of 8% and 15%, respectively. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in the operational conditions of water supply networks using inline sensor data.

Occurrence Assessment of Pharmaceuticals in Various Sewage Treatment Plants and Effluent-Receiving Streams in Korea

The occurrence of micropollutants, including pharmaceuticals, personal care products, pesticides, and hormones in various aquatic ecosystems is a matter of grave concern due to their possible repercussions on human and wildlife endocrine systems. The wastewater containing pharmaceuticals from various sites is usually introduced to sewage treatment plants (STPs); therefore, monitoring of pharmaceuticals in STPs is crucial. In this study, we determined the occurrence of 58 pharmaceuticals in the influent and effluent of 13 STPs based on regional and linked wastewater differences and investigated their removal rates. Furthermore, we assessed the contribution rates of some STP effluents on pharmaceutical concentration in the upstream and downstream areas of the discharge source. Different kinds of pharmaceuticals were measured in the STPs. The top five pharmaceuticals with high concentrations in the influent of each STP were similar due to the dominance of domestic sewage in the influent. The average concentration of acetaminophen, caffeine, acetylsalicylic acid, naproxen, and ibuprofen in the influent of the STPs was higher than that of other pharmaceuticals, and their removal was 94–100%. In contrast, iopamidol, cimetidine, diphenhydramine, and carbamazepine showed a high average concentration in the effluent. The monitoring results of nine streams near STPs indicated that the effluent could contribute to the increase in the types of pharmaceuticals in the receiving streams. The detected pharmaceuticals’ types were 9–29 and 17–33 in the upstream and downstream areas, respectively, of STP discharge channels. Based on flowrate data, the contribution rate of the STP effluent on the stream was −69–326%.

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Pneumatic systems are widely used in industrial production sectors. Increasing penetrations of Intelligent Manufacturing and Green Manufacturing are highlighting the drawbacks of pneumatic technology in terms of particularly low energy efficiency and low-level fault diagnosis intelligence. Here we propose that a combined energy-based maintenance and fault diagnostic approach for pneumatic systems could be a game-changer for pneumatics. In this study, a pneumatic cylinder with internal and external leakages is examined and a typical pneumatic experimental system is built. Exergy is adopted for evaluating the available energy of compressed air. Data-driven machine learning models, SAE + SoftMax neural network model and SAE + SVM model, are developed for fault detection and diagnosis. By comparing different machine learning methods with various pressure, flowrate, and exergy data, it is found that the diagnostic accuracy when using pressure and flowrate data is highly dependent on operating conditions, while the diagnostic accuracy when using exergy data is always high regardless of operating conditions. This indicates the promise of developing an exergy-based maintenance paradigm in pneumatic systems. Besides, with exergy and machine learning, more downstream faults can be detected and diagnosed with fewer upstream sensors. This study is the first attempt to develop an exergy-based maintenance paradigm in pneumatic systems. We hope it could inspire the following investigations in other energy domains.

A New Methodology to Evaluate the Sealing Process Based on Pressure Drop and Fluid Loss in Fractures during the Drilling Operation

Summary Natural or artificial fractures are common in the wellbore during drilling operations. These fractures allow the flow of drilling fluid into the rock formation. The loss of circulation increases the operation’s cost and nonproductive time, which may threaten the well’s structural integrity. To overcome this problem, it is necessary to understand the flow of fluids through fractures and develop methods to mitigate the loss of circulation. This work’s main contributions are expanding the knowledge on the flow of drilling fluids through fractured channels, conducting an experimental study on the flow of suspensions of lost circulation materials (LCM) in fractures, and performing a theoretical analysis to obtain mathematical models describing fractured channels’ sealing. This work proposes a correlation between the pressure drop and the volumetric flow rate of fluid through fractures. To validate the model, a physical simulator collected fluid flow data and pressure drop in fractures with 2-mm, 5-mm, and 10-mm apertures and 1.02-m length. A blend of polymers and calcium borate was used in suspension in water viscosified with xanthan gum (XG). Density and rheological behavior tests were performed to characterize the studied fluids. The LCM had a bimodal particle-size distribution, and the formulated fluids had a Herschel-Bulkley rheological behavior. Pressure drop, flow rate, and rheological data were used to propose a correlation between pressure drop and volumetric flow rate through the fracture. The proposed correlation was used to monitor the sealing of fractures by calculating their hydraulic diameter throughout the sealing process. The LCM suspensions underwent filtration tests to observe the effects of sealing particles on the mudcake and filtrate volume. The proposed correlation fitted the experimental data with less than 10% deviation. The fracture hydraulic diameter was estimated using experimental data of volumetric flow rate and pressure drop, which made it possible to monitor the sealing process of fractures through time. The sealing and filtration tests showed that the borate and polymer blends are effective as filtration control agents and LCM.

A Hydraulic Online Monitoring System for Forestry Harvesters Based on LabVIEW

The hydraulic system is a key component of intelligent forestry harvesters. In the testing of a forestry harvester, researchers need to analyze the operating efficiency and energy consumption of the forestry harvester based on the pressure and flow rate data in the hydraulic system of the forestry harvester and formulate energy-efficient control strategies. In order to enable researchers to monitor and extract the parameters of the hydraulic system of an intelligent forestry harvester in real time, this paper designs a hydraulic online monitoring system for forestry harvesters based on the LabVIEW 2019 software platform. This system realizes the following functions by reading the CAN (controller area network, a serial communication protocol for multi-host localized networks) bus of the harvester: (1) it collects and stores hydraulic system pressure, flow, and other data and displays the value curve in the system interface in real time, and (2) it monitors the control signals received by the hydraulic system and displays the control signal status and value received by the system interface in real time. This paper used this system to carry out on-site testing of an actual machine, and compared and analyzed the online monitoring data of the hydraulic system and the theoretical pressure data of the main hydraulic valve manifold. The average value of the relative error between the two was 1.65%, and the maximum value of the relative error was 2.75%. The results show that the designed system has good accuracy and stability, and can effectively realize online monitoring of the working status of the hydraulic system of forestry harvesters during their operation.

A LoRaWAN-based IoT System for Leakage Detection in Pipelines

Leakages in a pipeline are an important problem due to the potential economic and environmental hazard they present. In this study, we proposed a LoRaWAN-based approach for detecting and localizing leakages in pipelines. Our study includes an experimental setup that simulates a pipeline network with pressure and flow rate sensors attached. The flow rate and pressure data were transmitted through LoRaWAN to a receiver, which in turn uploads the data to a cloud server using a cellular network. The receiver compares the flow rate reading from all the monitoring nodes attached to the pipeline network. If flow rate reading from successive nodes presents a percentage variation of more than 1.5%, a leak is confirmed to have taken place. The flow rate readings can also be used to localize the leak. The resolution of the leak detection is dependent on the number of monitoring nodes on the pipeline network. In our study, the pressure readings were found to be insufficient to provide reliable evidence of leakages. In our specific situation, due to the relatively short length of the experimental pipeline network, a pressure drop of up to 38.2% was recorded between successive nodes with an overall pressure loss of 62%, making pressure data unsuitable for leak detection in the short pipeline network.

Acoustic Flow Sensor Using a Passive Bell Transducer

Sensing based on a passive transducer that is wirelessly linked to a nearby data collection node can offer an attractive solution for use in remote, inaccessible, or harsh environments. Here we report a pipe flow sensor based on this principle. A transducer mounted inside the pipe generates an acoustic signal that is picked up by an external microphone. The passive transducer comprises a cavity with a trapped ball that can oscillate in response to flow. Its collisions generate an acoustic signal correlated to the flow speed. The transducer is implemented on a 6 mm diameter probe and characterized as a water flow meter. The time - average microphone voltage output is calculated by an analogue circuit, without any further signal processing. With the microphone mounted on the probe, and for flow rates in the range 0.35 m/s to 6.5 m/s, correlation between the sensor voltage output and flow rate data from a commercial flow meter is demonstrated with a worst-case accuracy of 2%. This was achieved by simple averaging of the acoustic pulse train over a 5-second time interval. Consistent correlation with the microphone mounted on the pipe wall at distances up to 150 mm from the probe location is also reported. These results demonstrate the viability of remote acoustic flow sensing using passive structures and offer a simple and minimally invasive flow monitoring method.

Machine learning model for detecting hydrogen leakage from hydrogen pipeline using physical modeling

Hydrogen pipelines (HPL) are one of the hydrogen transportation systems for realizing a hydrogen society. Hydrogen leakage from HPL is a challenge because hydrogen has a wide flammable range and low minimum ignition energy. Thus, hydrogen leakage from the HPL must be rapidly detected, and appropriate actions should be taken. Leakage detection is important for safe operation of HPL. The basic leakage detection method for HPL involves monitoring the pressure and flow rate values of the sensors. However, in some cases, it is difficult to distinguish between non-leakage and leakage conditions using this method. In this study, we focus on a leakage detection method using machine learning (ML) based on the relationship between pressure and flow rate data. There are two challenges in applying the ML- based leak detection method to an HPL. First, there are insufficient operational data for ML during the process- design stage. Secondly, it is difficult to obtain the pressure and flow rate behaviors during hydrogen leakage because leakage does not occur frequently. Consequently, this study employed an unsupervised ML method based on data simulated using a physical model of the HPL. First, a physical model of the HPL (HPL model) was constructed, and an ML model for leak detection was constructed based on the data simulated by the HPL model. The leak detection capability of the ML model was verified by comparing the anomaly scores of the non-leakage and leakage conditions. From the results, the ML model can distinguish between non-leakage and leakage behaviors and identify leakage points under certain conditions. This method can contribute to the optimization of the sensors required for leak detection during the process design stage.

Hydrodynamic performance assessment of photocatalytic reactor with baffles and roughness in the flow path: A modelling approach with experimental validation

Purification of wastewater is essential for human being as well as for the flora and fauna, and sustainable environment. Photocatalytic reactor with TiO2 coated layer can be used to degrade the pollutants but without proper pollutant mass transfer in the reactive surface, photocatalytic reactor decreases its effectiveness. The baffles and rough surface in the flow path can improve the fluid mixing to enhance pollutant mass transfer to improve the reactor's performance. In this study, a computational fluid dynamics (CFD) model has been developed to investigate the effect of four top baffles and three rough surfaces (semi-circular, triangle, and rectangle) on pressure drops, mass transfer and the hydrodynamic performance of the reactor. The experimental investigation was carried out using Formic Acid (FA) as pollutant in feed water for model validation. The simulated result varies only within 5% with the experimental data of FA concentration versus feed flow rate and fluid velocity. The model was run at fluid velocity of 0.15 m/s and 0.5 m/s (Reynolds number of 2150 (laminar flow) and 7500 (turbulent flow), respectively. The simulation result shows that the addition of baffles and roughness on the reactive surfaces increases the turbulent kinetic energy (minimum increase 8%) and consequently increases the mass transfer (maximum increase 37%) of the pollutant. The highest wall shear was observed to be 40 Pa when both square and triangular elements were used as roughness elements at turbulent flow condition. The results also shows that the highest pressure-drop of 8 kPa was found when the square roughness element was used at turbulent flow condition. Overall, the photocatalytic reactor performance is significantly enhanced by the application of combined baffles and roughness elements in the reactive surface.

A practical workflow to design inflow control devices in SAGD projects to increase production and lower fresh water usage

This paper discusses the significance of inflow control devices (ICDs) in steam-assisted gravity drainage (SAGD) operations and their impact on well performance in different reservoir heterogeneities and qualities. The study focuses on investigating the specifications of ICD design using a numerical flow simulation model and flow rate versus pressure data obtained from a flow-loop experiment. The key advantage of employing flow-loop experiment data in the simulation would be a physics-based mechanistic model rather than using empirical correlations.The research collects core analysis data from three wells and particle size distribution (PSD) data from four wells in the same location. Permeability estimation is conducted for each PSD using a previously developed correlation. These data and other real data are used to construct the reservoir model, and the performance of liner deployed (LD) ICDs is compared by assigning flow-loop data to the simulation.By employing a relatively conservative production approach with subcooling between 10 °C and 15 °C, the cases with LDICDs demonstrate higher oil production rates, improved steam conformance, and lower cumulative steam oil ratios (cSOR) compared to the case without LDICDs. However, in a relatively challenging production scenario with subcooling between 1 °C and 5 °C, the case without ICDs cannot be simulated at the desired subcooling temperature and the cases with LDICDs improved the well productivity. LDICD#1 is identified in both scenarios as the best case due to its enhanced steam conformance and higher oil production rate.Compared to the case without ICDs, using LDICD#1 at higher subcooling temperatures leads to a 17 % increase in oil production rate, while reducing cSOR and natural gas usage by 8 % and 10 % respectively. Similarly, at lower subcooling temperatures, the case with LDICD#1 shows a 21 % increase in oil production rate and reductions of 12 % and 17 % in cSOR and consumed natural gas respectively, compared to the case without ICDs.The findings highlight the effectiveness of LDICDs at various subcooling levels and their potential application in SAGD projects to reduce freshwater usage and greenhouse gas emissions. Completion and production engineers can benefit from a better understanding of relative production performance to develop more effective operational designs.

Natural convection in vertical solar heat collector nanotextured with reduced graphene oxide and silver nanowires

In this study, a vertical shaft is coated using silver nanowires (AgNWs) together with reduced graphene oxide (rGO) via the supersonic spray-coating technique to enhance the absorption of solar radiative heat for use in solar air heaters. The rGO/AgNW-coated surface induces multiple light reflections, similar to that in a perfect black body inside a Helmholtz jar, thus enhancing the collection of solar radiation. Air fed into the vertical heated shaft ascends owing to buoyancy. The air temperature difference, ΔT, between the outlet and inlet is measured for various mass flow rates to quantify the heat transferred to the air from the solar-heated vertical shaft. The longitudinal air velocity and temperature distributions inside the shaft are numerically simulated using the fire dynamics simulator. Both two- and three-dimensional simulations are performed, and the results are compared with experimental data for various mass flow rates. The results confirm that the trends observed in both the experiments and simulations agree well for all cases. Multiple metal meshes are installed inside the vertical shaft to induce turbulence, which enhances the heat transfer intensity. This turbulence enhancement is confirmed via smoke visualization and infrared images.

Intravenous Fluid Resuscitation Capabilities in Simulated Reduced Gravity.

BACKGROUND: Critical care for exploration space missions may require intravenous (IV) fluid resuscitation therapy. Resource constraints may limit availability of standard, Earth-based infusion technologies. The effect of variable acceleration on infusion flow rates using simple fluid resuscitation supplies was investigated.METHODS: Infusions of water or blood analog (40% glycerol) from a 1 L IV bag were performed using pressure bag augmentation at 0, 150, or 300 mmHg. The solution bag rested on an adjustable mount, configured to different heights to simulate relevant gravitational accelerations (1 G, Martian G, lunar G, and 0 G). The bag emptied through an IV line with a 14- or 20-gauge angiocath into a 3-mmHg venous pressure reservoir. Flow rates were measured using an in-line flow probe. Three determinations were made for each test condition.RESULTS: Temporal flow rate data for all test conditions displayed one-phase exponential decay. At 300 mmHg pressurization, maximum infusion rates ranged from 92-222 mL ⋅ min-1 for water and from 21-49 mL ⋅ min-1 for blood analog. All reduced gravity conditions had significantly longer infusion times in comparison to 1 G for both test solutions.DISCUSSION: Reduced acceleration significantly altered flow rates and infusion times for fluid resuscitation. Fluid resuscitation protocols specify a desired volume to infuse for a target time (e.g., 20-30 mL ⋅ min-1 for a 75-kg adult). This data demonstrates that this protocol parameter can be achieved with infusion pressure bag augmentation alone and provides information for the refinement of fluid resuscitation protocols for exploration space missions.Pantalos GM, Heidel JS, Jain IM, Warner SE, Barefoot TL, Baker RO, Hailey M. Intravenous fluid resuscitation capabilities in simulated reduced gravity. Aerosp Med Hum Perform. 2023; 94(8):596-603.

Leakage Fault Diagnosis of Two Parallel Cylinders in Pneumatic System with a Minimal Number of Sensors

The low investment cost is one of the core competitiveness advantages of pneumatic power systems. With increasingly pressing intelligent manufacturing, it is meaningful to investigate the feasibility of implementing fault diagnoses of pneumatic systems with a minimal number of low-cost sensors. In this study, a typical pneumatic circuit with two parallel-installed cylinders is taken as an example. The pressure, flow rate, and exergy data collected from upstream sensors are used for diagnosing the leakage faults in two downstream cylinders with the help of different machine learning methods. The features of data are extracted with stacked auto-encoders. Gaussian process classifier, support vector machine, and k-nearest neighbor are used for classifying faults. The results show that it is feasible to detect and diagnose downstream multi-faults with one or two upstream sensors. In terms of the working conditions presented in this study, the average accuracy of diagnosis with exergy data is the highest, followed by flow-rate data and pressure data. The support vector machine performs the best among the three machine learning methods.