Examining Airflow Velocity Distribution Measurement in Large Ducts: Introducing an Innovative Automatic Positioning-Based Concept for Multipoint Single Probe Hot-Wire Anemometry

doi:10.21203/rs.3.rs-3116877/v1

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Examining Airflow Velocity Distribution Measurement in Large Ducts: Introducing an Innovative Automatic Positioning-Based Concept for Multipoint Single Probe Hot-Wire Anemometry

https://doi.org/10.21203/rs.3.rs-3116877/v1

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This paper presents a case study on the application of an automated multipoint single probe hot-wire anemometry in the context of a stenter machine, which is used for textile processing. The study focuses on capturing the airflow velocity distribution within the stenter machine ducts to ensure consistent and uniform drying of fabrics. The research explores the effectiveness and practicality of the automated multipoint single probe hot-wire anemometry in capturing airflow velocity profiles within the ducts. The article discusses various traditional techniques used for airflow velocity measurement, such as Pitot Tube Traverse, Hot-Wire Anemometry, Ultrasonic Anemometry, Laser Doppler Anemometry, Particle Image Velocimetry, Thermal Imaging, Constant Temperature Anemometry, and Volumetric Velocimetry. However, recent advancements in automation and sensing technologies have paved the way for more sophisticated and efficient measurement methods. The case study introduces the concept of automated multipoint single probe hot-wire anemometry and its potential in steady-state conditions. The methodology includes a detailed presentation of the experimental setup, including device configuration and calibration methods. The obtained results are presented and discussed, followed by an evaluation of the measurement uncertainty associated with airflow and mass flow measurements. The experimental setup utilizes a combination of hardware components controlled by an Arduino microcontroller for precise positioning of the hot-wire anemometer probe. The study also discusses the characteristics of the hot-wire anemometer used in the experiment. The data acquisition system involves an Arduino Mega 2560 microcontroller board and customized code for precise positioning of the anemometer probe at multiple locations. The acquired data is recorded using PLX-DAQ, an Excel add-in, and further analyzed using OriginPro software. The article emphasizes the importance of sensor calibration and discusses the calibration by comparison method used in the experiment. The findings of the case study contribute to the broader understanding of airflow dynamics in industrial duct systems and highlight the potential benefits of using automated multipoint single probe hot-wire anemometry for enhancing process efficiency in textile processing.

Multipoint hot-wire anemometry

Airflow velocity distribution

Stenter machine

Textile processing

Measurement uncertainty

Sensor calibration

In the field of industrial manufacturing, understanding and quantifying airflow characteristics in large-sized duct are essential for ensuring efficient and reliable processes (Ower & Pankhurst, 1977). One crucial aspect of fluid flow analysis is the measurement of airflow velocity distribution in complex duct systems (Spahn et al., 2021)(Mcwilliams, 2002). Accurate and comprehensive data regarding airflow patterns can aid in identifying potential bottlenecks, optimizing energy consumption, and enhancing overall operational efficiency. Traditionally, obtaining such measurements required manual techniques that were time-consuming, prone to human error, and limited in their ability to capture detailed velocity profiles or were very expensive and difficult to implement (Mcwilliams, 2002).

The mainly used are: Pitot Tube Traverse: This technique involves moving a pitot tube along predetermined measurement points within the airflow to measure the static pressure and calculate the velocity at each location. The measurements can be used to generate a contour map of air velocities(Cui et al., 2018). Hot-Wire Anemometry: By using multiple hot-wire probes positioned at different locations within the airflow, the technique measures the local air velocities. These measurements can be combined to create a contour map of air velocities (Charles G. Lomas et al n.d. ,1987). Ultrasonic Anemometry: Ultrasonic anemometers utilize multiple ultrasonic transducers to measure the time-of-flight or frequency shift of sound waves in different directions within the airflow. By positioning the transducers at various locations, a contour map of air velocities can be obtained (Han et al., 2008; Lynnworth & Liu, 2006; Nakai & Shimoyama, 2012a, 2012b; Raine et al., 2015). Laser Doppler Anemometry (LDA): LDA uses laser light to measure the velocity of particles or scatterers in the airflow (Flack et al., 1979; Puharic et al., 2007). By scanning the laser beam across the measurement area, a contour map of air velocities can be constructed. Particle Image Velocimetry (PIV): PIV involves seeding the airflow with small tracer particles and capturing images using a laser sheet. By analyzing the displacement of the particles between consecutive images, a contour map of air velocities can be generated(Buckley & Veron, 2017). Thermal Imaging: Thermal imaging cameras can be used to visualize and measure temperature differences in the airflow. By applying techniques such as thermal anemometry, which relates temperature variations to air velocities, contour maps of air velocities can be produced(Nguyen, 2004). Constant temperature anemometry (CTA): CTA, also known as Thermal Anemometry, is a technique for the measurement of turbulence in 1D, 2D, or 3D gas and liquid flows using hot-wire or hot-film probes inserted into the flow it is particularly suitable for the measurement of flows with very fast fluctuations at a point (high turbulence) and the study of flow microstructures, where there is a need to resolve small flow eddies down to the order of tenths of a millimeter (Hultmark & Smits, 2010; Itsweire & Helland, 1983; Ligȩza, 2008; Perry A N & Morrison, 1971).

Volumetric velocimetry: Volumetric velocimetry, also known as three-dimensional (3D) velocimetry or volumetric flow measurement, is a technique used to measure the velocity distribution of a fluid flow within a three-dimensional space. It aims to capture the complete flow field information, including the velocity vectors, in a volume rather than at a single point or plan (Discetti & Coletti, 2018; Freudenhammer et al., 2014; Joao et al., 2018; Jux et al., 2018; Schneiders et al., 2018).

However, recent advancements in automation and sensing technologies have paved the way for more sophisticated and or efficient measurement methods. One such innovation is the automated multipoint single probe hot-wire anemometry, which offers the capability to rapidly and accurately assess airflow velocity distributions in intricate duct systems. Furthermore, air velocity contour measurements serve as an essential tool in the validation of numerical simulations and computational fluid dynamics (CFD) models. By comparing experimental results obtained from contour measurements with simulation predictions, researchers can validate the accuracy and reliability of numerical models, enhancing confidence in their predictions. This validation process helps improve the understanding and accuracy of fluid flow simulations, which in turn facilitates better design and optimization of fluid systems.

This case study focuses on the application of an automated multipoint single probe hot-wire anemometry in the context of a stenter machine, a widely used device in textile processing that ensures the proper drying and setting of fabrics (Repository, 2018; Wilmer Parra Llanos et al., 2020). The stenter machine ducts, which serve as conduits for hot air circulation, play a critical role in achieving optimal drying conditions. Understanding the airflow velocity distribution within these ducts is vital for maintaining consistent and uniform drying across the fabric surface (Juraeva et al., 2017).

By utilizing an automated multipoint single probe hot-wire anemometry, this study aims to explore the effectiveness and practicality of this advanced measurement technology in capturing airflow velocity profiles within the stenter machine ducts. The investigation will involve analyzing a range of operational parameters and their impact on airflow distribution, including duct design, air pressure, and centrifugal fan frequency.

Having examined the existing techniques for measuring velocity contours in fluid mechanics, this research will introduce the fundamental concept of automated multipoint single probe hot-wire anemometry in steady-state conditions. We will delve into its unique characteristics and discuss the potential it holds for experimental investigations in the field of fluid mechanics. Our methodology will encompass a detailed presentation of the experimental setup, including the configuration steps of the devices, along with an emphasis on the calibration method we have developed. Subsequently, we will present the obtained results, followed by a comprehensive discussion. Finally, the conclusion will summarize the findings and their implications. The findings of this case study will contribute to the broader understanding of airflow dynamics in industrial duct systems and provide insights into the potential benefits of employing automated multipoint single probe hot-wire anemometry for enhancing process efficiency.

Measurement concepts

A versatile system was built to cater to different measurement needs and develop an innovative hot-wire probe automatic positioning-based concept for a multipoint duct airflow measurement device. Figure 1 illustrates the system's overview, which comprises four distinct subsystems. These subsystems are further explained in next Sections, providing detailed insights into the final configuration method employed during testing. Additionally, the subsequent section provides a comprehensive outline of the system's operation and the equipment utilized.

The assembly was programmed to perform the airflow velocity measurement of 5032 points. In order to deduct the uniformity index based on measured velocity at standard air condition, the uniformity index is given by Eq. 1. and the standard deviation is given by Eq. 2 (Zhang et al., 2017)(Sharma et al., 2015).

$${\gamma }_{u}=1-\frac{1}{\text{2n}}\sum _{i=1}^{n}\frac{\sqrt{{\left({u}_{i}-\stackrel{-}{u}\right)}^{2}}}{\stackrel{-}{u}} \left(1\right)$$

where ${{\gamma }}_{{u}}$ is the experimental uniformity index; $\stackrel{-}{{u}}$ the measured outlet average velocity ${n}$ the number of measurement points.

$$\stackrel{-}{u}= \frac{\sum _{i=1}^{n}{u}_{i}}{n} \left(2\right)$$

$$Std=\sqrt{\frac{\sum _{i=1}^{n}{\left({u}_{i}-\stackrel{-}{u}\right)}^{2}}{n}} \left(3\right)$$

Measurement uncertainty

This study also aimed to evaluate the type A and B uncertainties associated with airflow and mass flow measurements. It was acknowledged that no measurement can be perfectly precise, necessitating the expression of uncertainty in the estimated values. While the study assumed that the uncertainties related to air density calculation and cross-section measurement had minimal impact on the results, the primary focus was on assessing the uncertainty associated with airspeed measurements.

Type A uncertainty was quantified using the statistically estimated sample standard deviation of the mean, denoted as ${u}_{A}\left(x\right)$ (4) (Biol et al., 2010)(Fojtlín et al., n.d.). Formula (4) was considered valid under the following conditions: 1. The variable $x$ follows a normal probability distribution, and 2. The data were derived from independent observations conducted under the same conditions. Our previous experience provided evidence supporting the assumption of a normal probability distribution for the data.

$${{u}_{A}\left(x\right)=k}_{uA}.{\left[\sum _{i=1}^{n}{\frac{1}{n\left(n-1\right)}\left({x}_{i}- \stackrel{-}{x}\right)}^{2}\right]}^{\frac{1}{2}} \left(4\right)$$

The type A uncertainty of variable x, denoted as ${u}_{A}\left(x\right)$, is determined using the statistically estimated sample standard deviation. It takes into account the number of independent observations $\left(n\right)$, the individual values of ${x}_{i}$, the mean value of $x$, and the safety factor ${k}_{uA}$.

In this study, the outlets measurements involved 5032 observations, resulting in a safety factor ${k}_{uA}=4.36$ .

On the other hand, the type B uncertainty, ${u}_{B}\left(x\right)$, is evaluated using methods other than statistical data analysis. For this study, only the uncertainty arising from the limited precision of the anemometer W410D2 (which ensures accuracy with a rating of 0.2 + 2% FS (Full Scale) in m/s) was considered, while other sources of type B uncertainty were omitted.

The expanded combined uncertainty was expressed as follows.

$$U=k.u=k.\sqrt{{ {u}_{A}\left(x\right)}^{2}+{ {u}_{b}\left(x\right)}^{2}} \left(5\right)$$

Where $k$ is the coverage factor, $k=2$ was applied to achieve 95% level of confidence; $u$ is the combined uncertainty

he uncertainty of an indirect independent measurement depends on the individual uncertainties associated with each quantity involved in the calculation. When determining the final uncertainty $\left({u}_{x}\right)$ of a quantity $x$, which is a function of various quantities $\left(a, b,c,\dots ,\right)$, the calculation follows the following procedure.

$$x=f\left(a,b,c\dots \right) \left(6\right)$$

$${u}_{x}=\sqrt{{\left(\frac{\partial f}{\partial a}{u}_{a}\right)}^{2}+{\left(\frac{\partial f}{\partial b}{u}_{b}\right)}^{2}+{\left(\frac{\partial f}{\partial c}{u}_{c}\right)}^{2}+\dots } \left(7\right)$$

Materials

In our experimental setup, we utilized a combination of hardware components controlled by an Arduino microcontroller ATMEGA 2560 for precise positioning of a Chinese-made W410D2 analog hot-wire anemometer. The device consisted of a 3-meter stroke Belt Linear Motion Slide and a 600 mm stroke worm screw linear motion slide, which provided robust and accurate movement along the desired axes. The Arduino microcontroller served as the control unit, executing custom code to command the stepper motors responsible for the linear motion slides, ensuring precise positioning of the anemometer probe. The experiment was conducted with the support of a personal computer equipped with an i7 processor and 16GB of RAM, running under the Microsoft Windows operating system. The computer played a crucial role in data acquisition, analysis, and providing a graphical user interface for controlling the positioning system. Figures 4 and 5 visually depict the configuration of the experimental device, highlighting the interconnection between the various components and their respective roles in the setup.

-The hot-wire anemometer probe with his inbuild analogic to digital electronic board.

The W410D2 analog hot-wire anemometer, a China-made model, is a versatile airflow measurement device offering a range of desirable characteristics (Li et al., 2018). Powered by a DC supply of 10 to 30VDC, it consumes a maximum of 0.6W. Capable of measuring airflow in various mediums such as air, nitrogen, and smoke exhaust, the W410D2 ensures accuracy with a rating of 0.2 + 2% FS (Full Scale) in m/s. It operates reliably in humid conditions ranging from 0 to 95% RH (Relative humidity) and can withstand temperatures between − 10 to + 50 degrees Celsius. The anemometer provides both a current signal output (4 to 20 mA) and a voltage signal output (0 to 5V) for easy integration with analog systems. With a high resolution of 0.1 m/s, it covers a wide range of airflow velocities from 0 to 30 m/s. The W410D2 boasts a quick response time of 2 seconds and exceptional long-term stability, maintaining a deviation of less than 0.1 m/s per year. Overall, the JT400 analog hot-wire anemometer combines reliable performance, versatility, and accurate airflow measurements, making it an excellent choice for this research.

- Probe positioning actuator

The 2D positioning device is a versatile and precise system designed to enable accurate movement along the X and Y axes. It incorporates a 3-meter stroke Belt Linear Motion Slide Stage Guide Actuator, specifically equipped with a powerful Nema 34 Motor (Figure***), to control the movement along the X axis. The Nema 34 Motor provides substantial power and torque, ensuring efficient and reliable positioning(Silva et al., 2017). Its belt-driven mechanism guarantees precise control and repeatability, allowing for precise and smooth movement. For the Y axis, the device features a 600 mm stroke worm screw linear motion slide actuator powered by a compact Nema 16 motor. This actuator enables fine adjustments and micro-movements along the Y axis, enhancing overall precision and resolution. The Nema 16 motor ensures smooth and controlled motion, contributing to the device's accuracy. With these combined components, the 2D positioning device offers exceptional control and performance for a wide range of applications requiring precise and reliable positioning along both axes.

- Data acquisition system

For data acquisition from the hot-wire probe, we utilize an Arduino Mega 2560 microcontroller board, which is based on the ATmega2560 microcontroller. The Arduino Mega 2560 offers an array of features including 54 digital input/output pins, with 15 of them capable of functioning as PWM outputs. It also provides 16 analog inputs, 4 UARTs for hardware serial communication, a 16 MHz crystal oscillator, a USB connection, a power jack, an ICSP header, and a reset button. With these components, the Arduino Mega 2560 is a comprehensive platform that supports the microcontroller's functionality. It can be easily connected to a computer using a USB cable or powered by an AC-to-DC adapter or battery. Furthermore, the Mega 2560 is compatible with shields designed for the Uno, Duemilanove, or Diecimila boards, offering additional flexibility and expandability for various applications (Munitxa, 2016).

Methods

- Computer and data analysis software

To acquire velocities data, we developed a customized code utilizing the Arduino programming language to interface with the microcontroller and control the stepper motors accurately. The code incorporated an algorithm that allowed us to position the anemometer probe at 5032 different locations based on specified abscissa and ordinate coordinates. To capture the measured velocity data, we utilized PLX-DAQ, an Excel add-in that facilitated real-time data acquisition (Fig. 5). PLX-DAQ allowed us to record and save the measured velocity values into an Excel table, along with the corresponding coordinates of each measurement point (Shreesha & Gudi, n.d.). This enabled us to automate the precise positioning of the anemometer probe at multiple locations and efficiently collect and organize velocity data for further analysis and interpretation as shown in Fig. 4. The acquired data was plotted using OriginPro and can be further analyzed and compared with the numerical results obtained using Ansys Fluent, providing valuable insights into the airflow characteristics.

Sensor calibration

Calibration by comparison consists of comparing the indications given by a measuring instrument with the values provided by a standard instrument probe placed under the same measurement conditions. It is obvious that, in this calibration method, it is assumed that the sensitive elements of the standard probe and the probe being calibrated are subjected to the same flow of air in steady state(Takahashi et al., 2020).

Before conducting our experiment, we carried out a preliminary calibration of our probe to ensure its alignment with the highly accurate TESTO 425 hotwire anemometer, which had undergone calibration according to ISPP 9001:2008 and ISO/IEC 17025:2005 standards(Calibration-Flow Meters for Liquids and Gases, n.d.). To perform the calibration, we adjusted a centrifugal fan that supplied air to the blowing chamber of the stenter machine. We selected a specific point at the outlet of the chamber for calibration purposes. Initially, we took three velocity measurements using the high-quality TESTO 425 probe, followed by three measurements using the Chinese-manufactured probe. The difference in mean values between the two sets of measurements was then incorporated into our algorithm as an adjustment constant. Subsequently, we conducted three additional speed measurements using each probe. The obtained results confirmed that both probes exhibited nearly identical measurement consistency and precision. This calibration process ensured that our probe provided accurate and reliable measurements comparable to the industry-standard TESTO 425 hotwire anemometer.

Table 1

Calibration measurements results
Before Calibration
	Measure 1	Measure 2	Measure 3
TESTO 425 probe	15.32	15.33	15.34
W410D2 China-made	15.17	15.15	15.14
After Calibration
TESTO 425 probe	15.34	15.32	15.33
W410D2 China-made	15.31	15.33	15.32

The air velocity contour measurement device was successfully applied to the air chamber outlet of a stenter machine to validate the computational fluid dynamics (CFD) prediction of the chamber flow pattern. The device provided valuable insights into the actual airflow distribution and allowed for a comprehensive comparison with the CFD simulation as shown in Fig. 6.

The inlet velocity at the fan outlet was consistent between the simulation and experimental measurements, both set at an average value of 7 m/s. Figure 7 illustrates the outlet velocity distribution of the stenter machine's hot air supply chamber, comparing it to the experimental velocity distribution obtained through multi-point measurements. The outlet can be divided into three distinct areas:

The straight area, ranging from 0 to 800 mm starting from right to left, exhibits a concentrated and intense flow with the highest speeds (11 to 14 m/s). This can be attributed to the proximity to the fan (Fig. 4).
The middle area, spanning from 800 mm to 2100 mm, experiences a significant lack of airflow, with nearly zero velocities. This phenomenon can be explained by the centrifugal effect associated with the centrifugal fan principle.
Area 3, located between 2100 mm and 2850 mm, represents the exit door of the wall flow, where the airflow adheres to the chamber's lower wall due to viscosity.

Table 2

Experiments and CFD results value
	Baseline geometry CFD	Baseline geometry experiment
UI	72.89	71.87
S.D	4.82	4.36
A.V	6.97	6.39

Furthermore, under these conditions, the measurement data yielded a uniformity index of 71.87%, standard deviation of 4.36 m/s, and average velocity of 6.39 m/s. On the other hand, the simulation results yielded a uniformity index of 71.89%, standard deviation of 4.82 m/s, and average velocity of 6.97 m/s (Table 2). The difference between the simulation and experimental results was less than 5%, indicating a relatively good agreement. This agreement supports the reliability of the CFD model, confirming that it accurately predicts the airflow behavior in the stenter machine's air chamber.

The contour plots showed a similar airflow pattern, with regions of high and low velocities accurately captured by both methods. This agreement indicates the reliability and accuracy of the CFD model in simulating the airflow within the stenter machine's air chamber.

Furthermore, the measurement device allowed for a detailed analysis of the flow characteristics and providing parameters such as Uniformity index and Standard deviation. It revealed specific areas where the airflow exhibited deviations from the CFD prediction, highlighting potential areas for improvement in the computational model. By identifying these disparities, the measurement device contributed to the iterative refinement of the CFD simulation, enhancing its accuracy and predictive capability.

Additionally, the measurement device provided a quantitative assessment of the air velocity distribution, enabling a comprehensive understanding of the airflow dynamics within the air chamber. This information is crucial for optimizing the stenter machine's performance, as it allows for adjustments in the design and operation parameters to achieve desired airflow conditions.

In conclusion, the application of the air velocity contour measurement device on the stenter machine's air chamber outlet proved to be highly effective in validating the CFD prediction of the chamber flow pattern. The device facilitated a thorough comparison between the experimental measurements and the computational simulation, leading to improved understanding and refinement of the airflow dynamics. The successful validation of the CFD model enhances its reliability and enables its use as a predictive tool for optimizing the performance of the stenter machine's air chamber.

In conclusion, this research paper investigates the application of an automated multipoint single probe hot-wire anemometry in capturing airflow velocity distribution within the ducts of a stenter machine used in textile processing. The study highlights the limitations of traditional airflow velocity measurement techniques and introduces the concept of automated multipoint single probe hot-wire anemometry as a more sophisticated and efficient method. The experimental setup, including device configuration and calibration methods, is detailed, and the obtained results are presented and discussed. The research emphasizes the importance of sensor calibration and evaluates the measurement uncertainty associated with airflow and mass flow measurements. The combination of hardware components controlled by an Arduino microcontroller enables precise positioning of the hot-wire anemometer probe, while data acquisition and analysis are performed using PLX-DAQ and OriginPro software. The findings of the case study contribute to the understanding of airflow dynamics in industrial duct systems and highlight the potential benefits of using automated multipoint single probe hot-wire anemometry for enhancing process efficiency in textile processing. The automated multipoint single probe hot-wire anemometry offers a rapid and accurate assessment of airflow velocity distributions, which can aid in identifying bottlenecks, optimizing energy consumption, and improving overall operational efficiency. Moreover, the research findings provide insights into the validation of numerical simulations and computational fluid dynamics models by comparing experimental results with simulation predictions. Overall, this research paper demonstrates the effectiveness and practicality of the automated multipoint single probe hot-wire anemometry in capturing airflow velocity profiles and its potential for enhancing process efficiency in textile processing and other industrial applications. The advanced measurement technology presented in this study opens new possibilities for accurate and comprehensive airflow velocity measurements in large-sized ducts, leading to improved process control and optimization.

Competing interests

The authors declare that they have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Authors' contributions

ADANKPO Miguel T.: Methodology, Visualization, Investigation, Writing

Zhong Xiang: Supervision, Conceptualization, Methodology, Data curation, Writing - original draft preparation.

Miao Qian: Validation, Methodology, Writing - original draft.

Funding

This work was supported by the Central Guidance on Local Science and Technology Development Fund of Zhejiang Province under Grant 2022ZYYDSA300214.

Availability of data and materials

The raw/processed data required to reproduce these findings will be given as required privately.

Biol, A., Giroud, C., Arnaud, J., Vassault, A., Vassault, A., Adjidé, V., Arnaud, J., Bailly, P., Barbier, F., Baudin, B., Bourcier, C., Chapuzet, É., Combe, P., De Graeve, J., Dhondt, J.-L., Drouard, L., Fournier, P., Giroud, C., Guezenec, L., … Szymanowicz, A. (2010). Qualité et accréditation en biologie médicale Incertitude de mesure. In Clin (Vol. 68, Issue 1).
Buckley, M. P., & Veron, F. (2017). Airflow measurements at a wavy air–water interface using PIV and LIF. Experiments in Fluids, 58(11). https://doi.org/10.1007/s00348-017-2439-2
Calibration-Flow meters for liquids and gases. (n.d.).
Cui, C., Cai, W., & Chen, H. (2018). Airflow measurements using averaging Pitot tube under restricted conditions. Building and Environment, 139, 17–26. https://doi.org/10.1016/j.buildenv.2018.05.014
Discetti, S., & Coletti, F. (2018). Volumetric velocimetry for fluid flows. In Measurement Science and Technology (Vol. 29, Issue 4). Institute of Physics Publishing. https://doi.org/10.1088/1361-6501/aaa571
Flack, R. D., Assistant Professor, J., Witt Engineer Hayes, C. L., & Assoc Mem ASME, V. (1979). Velocity Measurements in Two Natural Convection Air Flows Using a Laser Velocimeter. http://heattransfer.asmedigitalcollection.asme.org/
Fojtlín, M., Planka, M., Fišer, J., Pokorný, J., & Jícha, M. (n.d.). Airflow Measurement of the Car HVAC Unit Using Hot-wire Anemometry.
https://doi.org/10.1051/C
Freudenhammer, D., Baum, E., Peterson, B., Böhm, B., Jung, B., & Grundmann, S. (2014). Volumetric intake flow measurements of an IC engine using magnetic resonance velocimetry. Experiments in Fluids, 55(5). https://doi.org/10.1007/s00348-014-1724-6
Fundamentals of hot wire anemometry_ Charles G. Lomas (International Journal of Heat and Fluid Flow, vol. 8, issue 2) (1987). (n.d.).
Han, D., Kim, S., & Park, S. (2008). Two-dimensional ultrasonic anemometer using the directivity angle of an ultrasonic sensor. Microelectronics Journal, 39(10), 1195–1199. https://doi.org/10.1016/j.mejo.2008.01.090
Hultmark, M., & Smits, A. J. (2010). Temperature corrections for constant temperature and constant current hot-wire anemometers. Measurement Science and Technology, 21(10). https://doi.org/10.1088/0957-0233/21/10/105404
Itsweire, E. C., & Helland, K. N. (1983). A high-performance low-cost constant-temperature hot-wire anemometer. In J. Phys. E: Sci. Instrum (Vol. 16). http://iopscience.iop.org/0022-3735/16/6/022
Joao, A., Figueiredo, C., Jones, R., Pountney, O. J., Scobie, J. A., Lock, G. D., Sangan, C. M., & Cleaver, D. J. (2018). VOLUMETRIC VELOCIMETRY MEASUREMENTS OF FILM COOLING JETS. http://proceedings.asmedigitalcollection.asme.org
Juraeva, M., Ryu, K. J., & Song, D. J. (2017). Optimum design of the injection duct system of a stenter machine. Journal of Mechanical Science and Technology, 31(5), 2279–2285. https://doi.org/10.1007/s12206-017-0424-6
Jux, C., Sciacchitano, A., Schneiders, J. F. G., & Scarano, F. (2018). Robotic volumetric PIV of a full-scale cyclist. Experiments in Fluids, 59(4). https://doi.org/10.1007/s00348-018-2524-1
Li, Y., Xu, L., Zhou, Y., Li, B., Liang, Z., & Li, Y. (2018). Effects of throughput and operating parameters on cleaning performance in air-and-screen cleaning unit: A computational and experimental study. Computers and Electronics in Agriculture, 152, 141–148. https://doi.org/10.1016/j.compag.2018.07.019
Ligȩza, P. (2008). Construction and experimental testing of the constant-bandwidth constant-temperature anemometer. Review of Scientific Instruments, 79(9). https://doi.org/10.1063/1.2976039
Lynnworth, L. C., & Liu, Y. (2006). Ultrasonic flowmeters: Half-century progress report, 1955–2005. Ultrasonics, 44(SUPPL.). https://doi.org/10.1016/j.ultras.2006.05.046
Mcwilliams, J. (2002). Review of Airflow Measurement Techniques.
Munitxa, M. L. (2016). Robot-aided interactive design for wind tunnel experiments. Advances in Intelligent Systems and Computing, 418, 365–377. https://doi.org/10.1007/978-3-319-27149-1_29
Nakai, T., & Shimoyama, K. (2012a). Ultrasonic anemometer angle of attack errors under turbulent conditions. Agricultural and Forest Meteorology, 162–163, 14–26. https://doi.org/10.1016/j.agrformet.2012.04.004
Nakai, T., & Shimoyama, K. (2012b). Ultrasonic anemometer angle of attack errors under turbulent conditions. Agricultural and Forest Meteorology, 162–163, 14–26. https://doi.org/10.1016/j.agrformet.2012.04.004
Nguyen, N. T. (2004). A novel wind sensor concept based on thermal image measurement using a temperature sensor array. Sensors and Actuators, A: Physical, 110(1–3), 323–327. https://doi.org/10.1016/j.sna.2003.10.041
Ower, E. (Ernest), & Pankhurst, R. C. (1977). The measurement of air flow. Pergamon Press.
Perry A N, A. E., & Morrison, D. G. L. (1971). A study of the constant-temperature hot-wire anemometer. In J. Fluid Mech (Vol. 47).
Puharic, M., Ristic, S., Kutin, M., & Adamovic, Z. (2007). LASER DOPPLER ANEMOMETRY IN HYDRODYNAMIC TESTING. In Journal of Russian Laser Research (Vol. 28, Issue 6).
Raine, A. B., Aslam, N., Underwood, C. P., & Danaher, S. (2015). Development of an ultrasonic airflow measurement device for ducted air. Sensors (Switzerland), 15(5), 10705–10722. https://doi.org/10.3390/s150510705
Repository, D. I. (2018). Study on stenter machine in Knit fabric finishing process.
Schneiders, J. F. G., Scarano, F., Jux, C., & Sciacchitano, A. (2018). Coaxial volumetric velocimetry. Measurement Science and Technology, 29(6). https://doi.org/10.1088/1361-6501/aab07d
Sharma, P. K., Venkatachalam, S., Paulraj, P., Halbe, V. G., & Pandian, S. (2015). An experimental comparison of the uniformity index for two dual stage plate mixers in a SCR system. SAE Technical Papers, 0109(2). https://doi.org/10.4271/2015-26-0109
Shreesha, C., & Gudi, R. D. (n.d.). Lecture Notes in Electrical Engineering 581 Control Instrumentation Systems Proceedings of CISCON 2018. http://www.springer.com/series/7818
Silva, W., Moura, D., Carvalho-Curi, T., Seber, R., & Massari, J. (2017). Evatuation system of exhaust fans used on ventilation system in commercial broiler house. Engenharia Agricola, 37(5), 887–899. https://doi.org/10.1590/1809-4430-eng.agric.v37n5p887-899/2017
Spahn, C., Hipp, A. M., Schubert, B., Axt, M. R., Stratmann, M., Schmölder, C., & Richter, B. (2021). Airflow and Air Velocity Measurements While Playing Wind Instruments, with Respect to Risk Assessment of a SARS-CoV-2 Infection. Public Health, 18, 5413. https://doi.org/10.3390/ijerph
Takahashi, H., Kurita, M., Iijima, H., & Koga, S. (2020). Simplified calibration method for constant-temperature hot-wire anemometry. Applied Sciences (Switzerland), 10(24), 1–10. https://doi.org/10.3390/app10249058
Wilmer Parra Llanos, J., Mailde Santos, R., Bastos Quadri, M., & Oliveira Martins, I. (2020). Phenomenological modeling and simulation of a textile stenter. Textile Research Journal. https://doi.org/10.1177/0040517520918231
Zhang, H., Li, X., Wang, Y., Yao, J., Kang, Y., & Wang, Y. (2017). Evaluation and analysis of internal flow field uniformity in grain stack based on multi zone model of porous media. Procedia Engineering, 205, 2164–2170. https://doi.org/10.1016/j.proeng.2017.10.036

No competing interests reported.

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Examining Airflow Velocity Distribution Measurement in Large Ducts: Introducing an Innovative Automatic Positioning-Based Concept for Multipoint Single Probe Hot-Wire Anemometry

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