Flow Rate Sensor Research Articles

Linear regression and R-squared correlation analysis on major nuclear online plant cooling system

The primary cooling system is an integral part of a nuclear reactor that maintains reactor operational safety. It is essential to investigate the effects of the cooling system parameter before implementing predictive maintenance techniques in the reactor monitoring system. This paper presents a linear regression and R-squared correlation analysis of the nuclear plant cooling system parameter in the TRIGA PUSPATI Reactor in Malaysia. This research examines the primary cooling system's temperature, conductivity, and flow rate in maintaining the nuclear reactor. Data collection on the primary coolant system has been analyzed, and correlation analysis has been derived using linear regression and R-squared analysis. The result displays the correlation matrix for all sensors in the primary cooling system. The R-squared value for TT5 versus TT2 is 89%, TT5 versus TT3 is 94%, and TT5 against TT4 is 66% which shows an excellent correlation to the linear regression. However, the conductivity sensor CT1 does not correlate with other sensors in the system. The flow rate sensor FT1 positively correlates with the temperature sensor but does not correlate with the conductivity sensor. This finding can help to better develop the predictive maintenance strategy for the reactor monitoring program.

The Characterisation of Differential Pressure Based Flow Sensor in a Microfluidic Environment

Flows in a microfluidic environment differ in their flow parameter changes compared to macro flows and should be handled carefully. Recently microfluidic innovations have been drawn into much consideration in biomedical, chemical synthesis, and cooling industries. There exists a need for a flow rate sensor for measuring tiny fluid flows in developing microfluidic Lab-on-chip products, Organ-on-chip, and other products for biological, chemical, and other flow analysis. Working with microfluidic products mainly revolves around flow measurement. The control of these frameworks is just conceivable with sensors that estimate flow rate. In this work, we understand the dynamics of microfluidic flow and try to determine its influence on sensor behavior. The paper presents the construction of flow-sensing techniques for microflows. The design of the microchannel opted for the study is also discussed. A microfluidic flow rate sensing technique is put forward using the physical principle of fluid dynamics; based on pressure in a rectangular microchannel. For rectangular microchannel, the variation in pressure had an impact on the flow rate measurement of the microflow sensor.

Digital monitoring system for anaerobic bioreactor conditions based on information on biogas volume, biogas pressure and methane gas content

The operational conditions of the anaerobic bioreactor can be predicted using three parameters, such as biogas pressure, biogas mass flow rate, and methane gas content. This research will present a monitoring system for the operational conditions of an anaerobic bioreactor through the measurement of those operational parameters based on knowledge data. The knowledge-based monitoring system is equipped with an interface for connectivity with pressure sensor, mass flow rate and methane gas content sensor, so that it can monitor the condition of the bioreactor in real time. The implementation of bioreactor monitoring is applied at bioreactors in Pakal Benowo Forest, Surabaya. The trial test was carried out under three different conditions: In the morning (around 08.00 AM) indicated information on the methane gas content of 72.5% LEL, biogas pressure 7.3 kPa and mass flow rate biogas 0.3 liters per minute. The second one during the day (around 11.00 AM) indicates information on the methane gas content of 56.5% LEL, biogas pressure 1.1 kPa and biogas mass flow rate of 0.3 liters per minute. The last, in the afternoon (around 03.00 PM) indicates information on the methane gas content of 51.1% LEL, biogas pressure of 1.1 kPa and biogas mass flow rate of 0.3 liters per minute. From those results, our monitoring system is able to identify the operational conditions of the anaerobic bioreactor.

Evaluation of a Soft Sensor Concept for Indirect Flow Rate Estimation in Solenoid-Operated Spool Valves

Concepts, such as power and condition monitoring or smart systems, are becoming increasingly important but require extensive insight into the process or machine, which is mostly gained by additional sensors. However, this development is contrasted by growing global competition and cost pressure. The measurement concept of a soft flow rate sensor presented here, addresses this discrepancy by means of a cost-effective software-based sensor, utilizing the dependence between flow rate and flow force in hydraulic spool valves. In the presented work, the feasibility of the introduced approach and the anticipated challenges, such as the modeling of disturbances in the mechanical, fluidic and electromagnetic subdomain, are assessed. For this purpose, important phenomena influencing measurement accuracy are identified on the basis of previous work. With the help of these findings, a greatly simplified version of the soft sensor is built and evaluated with a commercial valve to perform an initial test of the concept. Regardless of future implementations, the soft sensor concept presented here may have limitations in terms of dynamics and accuracy. However, the aim is not to replace a classic flow rate sensor, such as a gear sensor, but rather to create a cost-effective way to determine the flow rate without any additional integration effort.

Tunable MEMS-Based Terahertz Metamaterial for Pressure Sensing Application.

In this study, a tunable terahertz (THz) metamaterial using the micro-electro-mechanical system (MEMS) technique is proposed to demonstrate pressure sensing application. This MEMS-based tunable metamaterial (MTM) structure is composed of gold (Au) split-ring resonators (SRRs) on patterned silicon (Si) substrate with through Si via (TSV). SRR is designed as a cantilever on the TSV structure. When the airflow passes through the TSV from bottom to up and then bends the SRR cantilever, the SRR cantilever will bend upward. The electromagnetic responses of MTM show the tunability and polarization-dependent characteristics by bending the SRR cantilever. The resonances can both be blue-shifted from 0.721 THz to 0.796 THz with a tuning range of 0.075 THz in transverse magnetic (TM) mode and from 0.805 THz to 0.945 THz with a tuning range of 0.140 THz in transverse electric (TE) mode by changing the angle of SRR cantilever from 10° to 45°. These results provide the potential applications and possibilities of MTM design for use in pressure and flow rate sensors.

Using parallel plates capacitor as a volumetric flow rate sensor and direction detection for microfluidic/nanofluidic and extra smaller applications

This paper shows how a single parallel plate capacitor can be used to sense volumetric flow rate in micro/nano and smaller scale fluidic conduits and how a double parallel plate capacitor can be used to sense both volumetric flow rate and direction of flow. The concept was developed based on the principles of conservation of energy with some fundamentals of electricity and electromagnetics. Furthermore, the concept was validated experimentally to be accurate. Upon validation the measurement of the sensor was found to be in good agreement with experimental measurement with more accuracy obtained as the supplied voltage for the sensor is increased. Also, the accuracy of the developed concept increases as the characteristic length of the conduit decreases which makes the developed concept even more suitable for smaller scales applications. The concept consumes extremely low power and can be used directly without calibration provided that accurate value for magnetic permittivity of fluid is available. Moreover, the concept can be used for any type of fluid of any conduit shape with easy incorporation onto lab-on-a-chip or MEMS technology. The sensitivity of the sensor is approximately 0.03 A/(m3/s) which is good compared to other sensors.

Highly Sensitive Flow Rate Sensor Based on High Quality Graded Hollow-Core Microcavity

Highly Sensitive Flow Rate Sensor Based on High Quality Graded Hollow-Core Microcavity

Estimation of wastewater flowrate in a gravitational sewer line based on a low-cost distance sensor

Abstract Wastewater flowrate exhibits valuable information on the conditions in a sewer line. However, existing hardware flowrate sensors are rather expensive, vulnerable to fouling and breakages, and require frequent and laborious maintenance. Therefore, they are typically only mounted in a few key locations of a sewer system, leading to a lack of important information in a major part of the network. Utilizing more cost-effective sensors and a soft sensor approach is advisable for estimating the flowrate at locations where hardware sensors are lacking. Here, the development and testing of a data-driven soft sensor for estimating the wastewater flowrate based on the water level information obtained from a low-cost ultrasonic distance sensor are presented. The research included a long-term functionality testing period of the sensor in a cold region. The soft sensor-based flowrate was applied to estimate inflow and infiltration, indicating the conditions of the sewer line. The harsh conditions inside the sewer manhole caused challenges for the reliability of the distance measurement based on an ultrasonic principle. With the developed model-based soft sensor, it seems possible to accurately estimate the wastewater flowrate. Together with additional information, it might also enable accurate monitoring of inflows and infiltrations.

Sensitivity-Enhanced Flow Rate Sensor Based on Vernier Effect With a Virtual Reference Cell

A novel scheme for the measurement of an ultralow flow rate based on a fiber Fabry–Perot interferometer (FPI) and sensitivity-enhanced Vernier effect is proposed. The impact of water at different flow rates varies the cavity length of the sensor, resulting in a change of the free spectrum range (FSR) of the interferometric spectrum. The theoretical relation between the FSR and flow rate has been established by numerical simulation. The sensitivity can be further enhanced via the Vernier effect with a virtual reference cell and experimentally verified in the flow rate range of 0– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$80\,\mu \text{m}$ </tex-math></inline-formula> /s. The proposed interferometric sensor with modified Vernier effect is used for ultralow flow rate measurements for the first time, which shows the potential for applications in microfluidic technology and biochemical reaction.

Real-Time Leak Diagnosis in Water Distribution Systems Based on a Bank of Observers and a Genetic Algorithm

The main contribution of this paper is to present a novel solution for the leak diagnosis problem in branched pipeline systems considering the availability of pressure head and flow rate sensors on the upstream (unobstructed) side and the downstream (constricted) side. This approach is based on a bank of Kalman filters as state observers designed on the basis of the classical water hammer equations and a related genetic algorithm (GA) which includes a fitness function based on an integral error that helps obtaining a good estimation despite the presence of noise. For solving the leak diagnosis problem, three stages are considered: (a) the leak detection is performed through a mass balance; (b) the region where the leak is occurring is identified by implementing a reduced bank of Kalman filters which localize the leak by sweeping all regions of the branching pipeline through a GA that reduces the computational effort; (c) the leak position is computed through an algebraic equation derived from the water hammer equations in steady-state. To assess this methodology, experimental results are presented by using a test bed built at the Tuxtla Gutiérrez Institute of Technology, Tecnológico Nacional de México (TecNM). The obtained results are then compared with those obtained using a classic extended Kalman filter which is widely used in solving leak diagnosis problems and it is highlighted that the GA approach outperforms the EKF in two cases whereas the EKF is better in one case.

Sensor fault detection in a biohydrogen production process based on an interval observer and adaptive thresholds

In this paper, an interval observer-based fault detection strategy is proposed to deal with faults in the hydrogen flow rate sensor of a biohydrogen production process. A control Hinf-based robust interval observer is designed to (i) estimate the glucose and biomass concentrations from measurements of the hydrogen flow rate produced, (ii) attenuate the effect of the unknown inlet glucose concentration, and (iii) detect faults in the hydrogen flow rate sensor through adaptive thresholds. The gain is designed by solving a semi-definite optimization problem subject to LMIs. The feasibility of the proposed strategy is demonstrated by simulations.

Non-invasive thermal energy flow rate sensor for turbulent pipe flows

A non-invasive thermal energy flow rate sensor based on a combination of transient heat flux and temperature measurements has been developed. A heat flux sensor and a thin-film thermocouple are covered by a thin-film electric heater and clamped onto the outer surface of a pipe. A one-dimensional transient thermal model is applied before and during activation of the external heater to provide estimates of the fluid heat transfer coefficient and thermal resistance. A parameter estimation code is used to estimate the unknown parameters by using the minimum root mean square error between the analytical and experimental sensor temperature values. The sensor was tested with two different fluids (water and diesel oil) and with two different pipe diameters (d=25.4mm and d=12.7mm). Experiments were completed over a range of Reynolds number from 2000 to 25,000 and a range of fluid temperature from 20 °C to 50 °C. Correlations for the fluid temperature and the fluid velocity were within ±0.13 K and ±5% of the direct measurements. The resulting non-invasive thermal energy flow rate sensor can be used to estimate a fluid velocity and a fluid temperature in heating and cooling pipe applications.

Partial Obstruction of Ventricular Catheters Affects Performance in a New Catheter Obstruction Model of Hydrocephalus.

Objective: One of the major causes of cerebral ventricular shunt failure is proximal catheter occlusion. We describe a novel ventricular cerebrospinal fluid (CSF) flow replicating system that assesses pressure and flow responses to varying degrees of catheter occlusion. Methods: Ventricular catheter performance was assessed during conditions of partial and complete occlusion. The catheters were placed into a three-dimensionally-printed phantom ventricular replicating system. Artificial CSF was pumped through the ventricular system at a constant rate of 1 mL/min to mimic CSF flow, with the proximal end of the catheter in the phantom ventricle. Pressure transducer and flow rate sensors were used to measure intra-phantom pressure, outflow pressure, and CSF flow rates. The catheters were also inserted into silicone tubing and pressure was measured in the same manner for comparison with the phantom. Results: Pressure measured in the ventricle phantom did not change when the outflow of the ventricular catheter was partially occluded. However, the intraventricular phantom pressure significantly increased when the outflow catheter was 100% occluded. The flow through the catheter showed no significant difference in rate with any degree of partial occlusion of the catheter. At the distal end of the partially occluded catheters, there was less pressure compared with the nonoccluded catheters. This difference in pressure in partially occluded catheters correlated with the percentage of catheter hole occlusion. Conclusions: Our model mimics the physiological dynamics of the CSF flow in partially and completely obstructed ventricular catheters. We found that partial occlusion of the catheter had no effect on the CSF flow rate, but did reduce outflow pressure from the catheter.

Pipe leakage detection system with artificial neural network

&lt;span lang="EN-US"&gt;This project aims to develop a system that can monitor to detect leaks in water distribution networks. It has been projected that leakage from pipelines may lead to significant economic losses and environmental damage. The loss of water from leaks in pipeline systems accounts for a large portion of the water supply. Pipelines are maintained throughout their lives span; however, it is difficult to avoid a leak occurring at some point. A tremendous amount of water could be saved globally if automated leakage detection systems were introduced. An embedded system that monitors water leaks can efficiently aid in water conservation. This project focuses on developing a real-time water leakage detection system using a few types of sensors: water flow rate sensor, vibration sensor, and water pressure sensor. The data from the sensors is uploaded and stored by the microcontroller (NodeMCU V3) to the database cloud (Google Sheets). The data that is stored in the database is analyzed by artificial neural network (ANN) by using Matlab software. An application is developed based on results from ANN training to detect the leakage event. Implementing the proposed system can increase operations efficiency, reduce delay times, and reduce maintenance costs after leaks are detected.&lt;/span&gt;

Towards breath sensors that are self-powered by design.

Piezoelectric materials are widely used to generate electric charge from mechanical deformation or vice versa. These strategies are increasingly common in implantable medical devices, where sensing must be done on small scales. In the case of a flow rate sensor, a sensor’s energy harvesting rate could be mapped to that flow rate, making it ‘self-powered by design (SPD)’. Prior fluids-based SPD work has focused on turbulence-driven resonance and has been largely empirical. Here, we explore the possibility of sub-resonant SPD flow sensing in a human airway. We present a physical model of piezoelectric sensing/harvesting in the airway, which we validated with a benchtop experiment. Our work offers a model-based roadmap for implantable SPD sensing solutions. We also use the model to theorize a new form of SPD sensing that can detect broadband flow information.

A novel, fast and clean 3D permeability measurement method for multi-layer textiles and fabrics

Permeability quantifies flow conductance of textile reinforcements, is required for process simulations, and can be used for a range of materials and process monitoring by industry. Current permeability measurement techniques are destructive, either because a liquid penetrates the sample or the sample must be cut from products to a specific size. A non-destructive measurement concept is introduced based on air flow established between flat circular patterns containing flow rate and pressure sensors. The presented experimental studies show that from flow rate and pressure distribution data, the technique clearly distinguishes changes in volume fraction and layup while being applicable to a range of woven and non-crimp textiles. The concept interprets permeability from a neural network trained using a large set of air flow simulations. A preliminary set of 250,000 simulated cases was applied to train seven different neural network types. Limited predictive accuracy has been achieved, utilizing a CGB neural net structure.

Data-Driven Virtual Flow Rate Sensor Development for Leakage Monitoring at the Cradle Bearing in an Axial Piston Pump

The leakage of the tribological contact in axial piston pumps significantly impacts the pump efficiency. Leakage observations can be used to optimize the pump design and monitor the behavior of the tribological contact. However, due to assembly limitations, it is not always feasible to observe the leakage of each tribological contact individually with a flow rate sensor. This work developed a data-driven virtual flow rate sensor for monitoring the leakage of cradle bearings in axial piston pumps under different operating conditions and recess pressures. The performance of neural network, support vector regression, and Gaussian regression methods for developing the virtual flow rate sensor was systematically investigated. In addition, the effect of the number of datasets and label distribution on the performance of the virtual flow sensor were systematically studied. The findings are verified using a data-driven virtual flow rate sensor to observe the leakage. In addition, they show that the distribution of labels significantly impacts the model’s performance when using support vector regression and Gaussian regression. Neural network is relatively robust to the distribution of labeled data. Moreover, the datasets also influence model performance but are not as significant as the label distribution.

Experimental validation of an interval observer-based sensor fault detection strategy applied to a biohydrogen production dark fermenter

In this paper, the problem of designing an interval observer applied to a biohydrogen production dark fermenter is addressed, in order to: (i) estimate the glucose and biomass concentrations, and the hydrogen flow rate produced, (ii) reduce the influence of the unknown inlet glucose concentration and the model uncertainties, and (iii) detect the presence of faults in the hydrogen flow rate sensor. Based on literature reports, we propose an interval observer structure, constituted by a Luenberger observer and an interval predictor, for a class of linear systems. The interval observer offers three estimations: the upper and lower estimations provided by the interval predictor, and one estimation given by the Luenberger observer. The upper and lower estimations provide bounds at any instant of time for the state vector of the perturbed/uncertain biohydrogen production bioreactor model from an appropriate initial condition. The design conditions presented in this work are based on control H∞ in combination with pole placement and polytopic parametric uncertainties. The observer design is proposed as a semi-definite optimization problem subject to Linear Matrix Inequalities, which do not depend on the cooperativity property. Based on the proposed observer structure, we also present two fault detection schemes for the same class of uncertain systems to detect the occurrence of sensor faults: one makes use of the logic comparison between the set threshold and the residual produced by the Luenberger observer; the other considers adaptive thresholds for the output signal by using the upper and lower estimates of the interval predictor. Finally, the proposed approaches are validated with experimental data from a biohydrogen production dark fermenter.

A Nonintrusive Flow Rate Sensor Based on Microwave Split-Ring Resonators and Thermal Modulation

Microwave sensors offer noncontact solutions for flow rate measurements, central to various applications in pharmaceutics, chemical analysis, and biomedicine. The challenge, however, remains for microwave flow rate sensing of uniform liquids, where different flow rates do not lead to any permittivity or conductivity variations. In this work, we propose a noncontact, nonintrusive microwave sensor, capable of measuring the flow rate of uniform liquids. This sensor comprises a microwave split-ring resonator (SRR) integrated with a temperature modulation technique. The sensor operates based on the change in the dielectric properties of the liquid inside the channel, induced by local delivery of thermal energy. Different flow rates affect the exposure time of the liquid to thermal energy, which alters its permittivity and loss tangent, consequently changing the response of the SRR. The SRR with the integrated fluidic channel is designed to operate at 4.6 GHz with a resonant amplitude of &#x2212;6.8 dB and a <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor (&#x2212;3 dB) of 98. By monitoring the <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> (dB) response, flow rates ranging from 1 to 40 mL/h are differentiated. The <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> (dB) response&#x2019;s achieved sensitivity to different flow rates is <inline-formula> <tex-math notation="LaTeX">$\vert 0.02\vert $ </tex-math></inline-formula> dB/mL/h. The proof of concept is successfully evaluated using water and isopropanol as two standard liquids. The results indicate the potential of the proposed nonintrusive flow rate sensing method in various applications, such as drug delivery and chemical sample preparation.

Optimization of the CRTM process by means of monitoring techniques

In this work the feasability of the monitoring system, composed of flow-rate and injection pressure sensors, in addition to the pressure sensors integrated in the mold, is demonstrated to determine the optimal process window of the CRTM (Compression Resin Transfer Molding). The sensors of the injector allow monitoring of the injection phase, while the pressure sensors integrated in the mold allow the phases of the compression and the curing to be followed. In order to relate the pressure sensor variations with the curing phase, an electric current sensor has been used. The increase of the resistance, associated with the beginning of curing, corresponds in time with the pressure drop, which in turn is justified by the contraction generated during curing. This pressure drop reaches a horizontal asymptote which corresponds to a conversion rate of 40%. Based on this behavior of the pressure sensor, it is possible to determine the minimum time for the demoulding of each of the pieces. Finally, the system has been used to monitor the manufacturing of prototypes with curing temperatures between 80-120 &ordm;C, and it has been confirmed that the sensors identify the parts that are inside the process window and those that are not.