Flow Facility Research Articles

Controlling the combustion and agglomeration characteristics of a solid composite propellant via a DC electric field

This study explores the use of a DC electric field to control the combustion and agglomeration characteristics of a solid composite propellant as typically deployed for rocket propulsion. Experiments were performed in a custom-built reacting flow facility that supports high-speed photography and the collection of condensed combustion products (CCPs). The propellant burning rate, aluminum agglomeration process, CCP size distribution, and combustion efficiency were systematically examined under a range of electric-field voltages (−5 to 5 kV) in both the forward and backward directions. It was found that as the electric-field voltage increased, both the propellant burning rate and combustion efficiency increased, while the degree of aluminum agglomeration decreased, reducing the particle size of the CCPs. The introduction of an electric field can bring about profound effects via particle charge acceleration, Rayleigh crushing, and ion excitation. For example, aluminum near the burning surface can split into smaller particles, the diffusion flames around droplets can exhibit a higher stand-off ratio, and more hollow agglomerates can form, as evidenced by an inspection of scanning electron microscopy images of the CCPs. The electric field was found to have a more pronounced effect in the forward direction than in the backward direction. A physical mechanism was proposed for how an electric field can alter propellant combustion. Through this, the combustion and agglomeration characteristics of solid composite propellants can be precisely tuned with an electric field without resorting to changes in the propellant formulation. The findings of this study contribute to a better understanding of how an electric field can be used to control propellant combustion and thrust in solid rocket motors.

Wall Shear Stress Measurements Using A MTV-Based Plenoptic Microscope

In this contribution, we describe the first ever microscopic Molecular Tagging Velocimetry (MTV) measurements using Integral Imaging. This technique permits the measurement of the 3D-2C velocity field in an axi-symmetric stagnation jet and observed profiles agree well with analytical predictions. Standard reconstruction algorithms are compared against more advanced deconvolution algorithms and impressive improvements in reconstructed probe resolution are obtained. Integral imaging is compared with a scanning confocal microscope which serves as a basis of comparison and good agreement is achieved. The flow facility was designed for a low Reynolds number, but with an adjustable viscous length scale comparable with higher Reynolds number flows. Our instrument proves capable of resolving viscous length scales on the order of 30 micrometers which can be improved by increasing the numerical aperture of the microscope objective.

Improved manifold sparse slow feature analysis for process monitoring

Sparse models retain valuable properties to prevent over-fitting issues and improve models’ interpretability, which are important for tasks such as anomaly detection in large-scale processes. In this work, an enhanced sparse algorithm based on slow feature analysis (SFA) is proposed using proximal methods and manifold optimization, which is referred to as manifold sparse SFA (MSSFA). MSSFA utilizes proximal gradient descent to optimize the non-smooth sparsity objective and manifold optimization to fulfill the SFA constraints. A MSSFA based fault monitoring and diagnosis framework is also designed, and its effectiveness is evaluated on the Tennessee Eastman Process (TEP) and the Three Phase Flow Facility (TPFF). MSSFA obtains improved sparsity, interpretability, and computing efficiency, and it shows superior monitoring and diagnosis performance when compared to SFA, sparse SFA and sparse principal component analysis.

A mechanistic model for the prediction of flow pattern transitions during separation of liquid-liquid pipe flows

A one-dimensional mechanistic model that predicts the flow pattern transitions during the separation of dispersed liquid-liquid flows in horizontal pipes was developed. The model is able to capture the evolution along the pipe of the four characteristic layers that develop from initially dispersed flows of either oil-in-water or water-in-oil at a range of mixture velocities: a pure water layer at the bottom, a settling (flotation/sedimentation) layer, a dense-packed zone, and a pure oil layer on the top. Coalescence correlations from literature were included in the model to predict the drop growth due to binary drop coalescence and the coalescence rate of drops with their corresponding interface. The model predictions on the evolution of the heights of the different layers were partly compared against available experimental data obtained in a pilot scale two-phase flow facility in a test section of 0.037 m inner diameter using tap water and an oil of density 828 kg m−3 and viscosity 5.5 mPa s as test fluids, and in a 0.1 m inner diameter test section using water and an oil of density 857 kg m−3 and viscosity 13.6 mPa s. It was shown that the evolution of the four characteristic layers depends on the rates of drop settling and drop-interface coalescence. Oil-in-water dispersions separated faster than water-in-oil ones, while dispersions with smaller drop-sizes were more likely to exhibit depletion of the dense-packed zone.

Electrical resistance tomography-based multi-modality sensor and drift flux model for measurement of oil–gas–water flow

This paper proposes a novel method to measure each constituent of an oil–gas–water mixture in a water continuous flow, typically encountered in many processes. It deploys a dual-plane electrical resistance tomography sensor for measuring dispersed phase volume fraction and velocity; a gradiomanometer flow density meter and a drift flux model to estimate slip velocities; with absolute pressure and temperature measurements. These data are fused to estimate constituent volume flow rates. Other commonly used operational parameters can be further derived: water cut or water liquid ratio (WLR) and gas volume fraction (GVF). Trials are described for flow rates of water 5–10 m3 h−1; oil 2–10 m3 h−1 and gas 1–15 m3 h−1. The comparative results are included with published data from the Schlumberger Gould Research flow facility. The paper proposes the use of the described configuration for measurement of volume flow rates in oil–gas–water flows with an absolute error of ±10% within GVF 9%–85% and WLR > 45%.

Industrial process monitoring based on optimal active relative entropy components

The multivariate statistic method has been widely applied, but no clear mapping relationship exists between the latent variables and the fault information, which leads to various information in different latent variables. The traditional methods cause the loss of useful fault information and the decline of monitoring performance. In this paper, a process monitoring method based on the optimal active relative entropy component is proposed. Based on global and local preserving projection, relative entropy is selected as the monitoring statistic. The influence of latent variable selection on relative entropy calculation is analyzed, and a relative entropy activity index is proposed to select the relative entropy component beneficial to process monitoring. The parameters are optimized to generate the optimal active relative entropy component for process monitoring using the mayfly algorithm. Finally, a numerical case, Tennessee Eastman (TE) process, and three-phase flow facility are used to verify the superiority of the monitoring algorithm.

Intelligent fault diagnosis of machine under noisy environment using ensemble orthogonal contractive auto-encoder

Despite significant advances in auto-encoder (AE) based intelligent fault diagnosis recently, their assumption of high-quality sensor data is unrealistic due to noisy environment. Aiming at such problem, this work proposes a novel ensemble orthogonal contractive auto-encoder (EOCA) method to improve diagnosis accuracy. By designing a new cost function including contractive term and orthogonal constrain, OCA can learn robust and non-redundant features from noisy input. To make fault patterns separable, fisher discriminant analysis is cascaded with OCA for mapping these features into a new coordinate space with maximum class separability. Considering the limitation of a single model, sixteen different OCAs with complementary activation functions as well as a new ensemble strategy based on expert model are developed to produce a stable diagnosis result. Additionally, to prove the effectiveness, error bound of EOCA is analyzed and theoretical basis is found for supporting OCA design. EOCA is verified on three cases including three-phase flow facility, gear box and self-priming centrifugal pump. Experiments are conducted by introducing various strength of white Gaussian noise. Results show that EOCA is better than single OCA, canonical AE, sparse AE, denoising AE and several advanced ensemble methods under strong noise.

An overview of speed model study under different types of facilities in Malaysian roadway system

The present paper reviewed past studies related to speed model studies and the measured traffic parameters used by other researchers. The purpose of the paper is to discover the relationships between speeds with the differences in roadway condition & characteristics, traffic composition, driver behaviour and to propose a research gap related to the future study of Malaysian road conditions. Fundamental relationships were observed over different parameters and were found to be significantly different. However, under uninterrupted flow facilities, using free-flow speed as a prediction for highway capacity analysis and determining the level-of-service (LOS), the time headway threshold, and the speed of observations at certain flow rates must be carefully studied to represent the free-flow speed in ideal conditions. The paper review helps the authors find research gaps for an in-depth investigation of the free-flow speed on Malaysian highways or expressways. Hence, the finding from the paper reviewed will be used in a future research study on developing a free-flow speed model based on the basic section of roadway in Malaysia.

The Needs of Pedestrian Circulation Design for Adolescents In Semarang Old Town Public Space That Responsive To Covid-19 Pandemic

Abstract. Semarang Old Town is a site in Semarang City that provides economically valuable public spaces that teenagers use. Due to irregular pedestrian circulation and packed economic activity points, Semarang Old Town is a sensitive location for the possible spread of the Covid-19 virus. The Covid-19 epidemic has not been addressed in the public spaces of Semarang Old Town. Circulation flow, circulation signage, crossing points, and health protocol facilities have all been identified as issues. This project intends to develop pedestrian circulation needs for teenagers in an economically valuable public place in Semarang Old Town in response to the Covid-19 pandemic. The quantitative method employed in this study was behavior mapping and quantitative descriptive analysis techniques. The stages are as follows: first, identify teenage features; second, analyze design demands for adolescent pedestrian circulation elements. Then, construct the design requirements and placement. The study was conducted on a group of 41 adolescents aged 12 to 25, with observation taking place on Saturday night and Sunday morning (peak hours). According to quantitative descriptive analysis methodologies, adolescents prefer more simple, more adaptable designs and occupy less space by 57 percent..

Reynolds number dependence of inner peak turbulence intensity in pipe flow

Streamwise turbulence statistics in the range from Reτ = 990 to Reτ = 20 750 at the High Reynolds Number Actual Flow Facility at the National Metrology Institute of Japan are presented, specifically focusing on the Reynolds number dependence of the inner peak turbulence intensity. Velocity measurements are conducted using laser Doppler velocimetry (LDV), taking account of problems specific to this method, with the aim of providing reliable experimental results. The control volume and the fringe pattern of LDV, both of which influence turbulence statistics, are directly measured using a rotary wire device, and they are used to correct the measured turbulence intensity using methods developed in this study. The present results for mean velocity and turbulence intensity profiles agree well with direct numerical simulation data. The inner peak turbulence intensity in this pipe experiment increases with the increasing Reynolds number. It is found that the Reynolds number dependence of the inner peak up to Reτ = 20 750 is very similar to that in a turbulent boundary layer (TBL). The slope of the outer logarithmic region in the turbulence intensity profile is twice the slope obtained from the relation between the inner peak and the Reynolds number. This relation is also consistent with that for TBL flow.

Hierarchical Latent Variable Extraction and Multisegment Probability Density Analysis Method for Incipient Fault Detection

The incipient fault is difficult to detect because of its small amplitude and insignificant impact, however, ignoring such fault may cause irreversible damage to the system. In this article, a hierarchical latent variable extraction and multisegment probability density analysis method is proposed to detect the incipient fault. First, three data subspaces are constructed, which are named dominant, intermediate, and residual spaces, and key latent variables which contain more offline variance or online variation information will be retained. Afterward, the expanded data distribution interval and multiple data segmentsare constructed for the probability density estimation. Based on the improved symmetric divergence index, the distribution distance between the online data and offline modeling data can be evaluated, which has achieved 95.3% and 86.8% average detection rates for the faults in numerical case and Tennessee Eastman process. Finally, a real multiphase flow facility is used to demonstrate the effectiveness of the proposed method.

Laser-Based In-Exhaust Gas Sensor for On-Road Vehicles

<div class="section abstract"><div class="htmlview paragraph">A novel laser-absorption gas sensing apparaOn-vehicle Testing at VERtus capable of measuring NO directly within vehicle exhaust was developed and tested. The sensor design was enabled by key advances in the construction of optical probes that are sufficiently compact for deployment in real-world exhaust systems and can survive the harsh, high-temperature, and strongly vibrating environment typical of exhaust streams. Prototype test campaigns were conducted at high-temperature flow facilities intended to simulate exhaust gas conditions and within the exhaust of vehicles mounted on a chassis dynamometer. Results from these tests demonstrated that the sensor prototype is fundamentally free of cross-interference with competing species in the exhaust stream, can achieve a 1 ppmv NO detection limit, and can be operated across the full range of thermodynamic conditions expected for typical vehicle exhausts. These features address the key technological drawbacks associated with electrochemical NO<sub>x</sub> sensors widely used in current aftertreatment systems while enabling an on-board diagnostic capable of determining NO<sub>x</sub> output at the tailpipe at a level of accuracy comparable to laboratory-grade instrumentation. Broadly, this novel sensor provides high-quality emissions data needed to help vehicle manufacturers develop powertrains and aftertreatment systems that meet future, more stringent emissions regulations.</div></div>

Robust probabilistic predictable feature analysis and its application for dynamic process monitoring

Dynamic process monitoring with multivariate statistical methods has been widely researched and applied for anomaly detection in dynamic systems. However, most of them are designed under the assumption that the measurement noise follows a Gaussian distribution, which usually contradicts the actual situations. Alternatively, in this paper, a novel mixture of Gaussian and Student’s t distributions is designed to account for the system noise, and a robust probabilistic predictable feature analysis (RPPFA) algorithm is proposed to capture both the process dynamics and the characteristic of the heavy tail from multivariate temporal data. Moreover, an improved Expectation–Maximization algorithm and a modified Kalman filter method are proposed for parameter optimization. Three monitoring statistics are designed to identify abnormal conditions, which are Hotelling’s T2, squared prediction error and dynamic index. The superiority of RPPFA is demonstrated through two case studies, namely, a simulated three phase flow facility and an industrial medium speed coal mill.

Ultrasound imaging velocimetry in particle-laden flows: counteracting attenuation with correlation averaging

Ultrasound imaging velocimetry (UIV) refers to the technique wherein ultrasound images are analysed with 2D cross-correlation techniques developed originally in the framework of particle image velocimetry. Applying UIV to opaque, particle-laden multiphase flows have long been considered to be an attractive prospect. In this study, we demonstrate how fundamental differences in acoustical imaging, as compared to optical imaging, manifest themselves in the 2D cross-correlation analysis. A chief point of departure from conventional particle image velocimetry is the strong variation in the intensity profile of the acoustic wavefield, primarily caused by the attenuation of ultrasonic waves in particle-laden flows. Attenuation necessitates using a larger ensemble of correlation planes to obtain satisfactory time-averaged velocity profiles. For a given combination of imaging and flow conditions, attenuation sets upper limits on volume fraction, penetration depth, as well as temporal resolutions that may be accessed confidently. This behaviour is demonstrated in two experimental datasets and is also supported by a modified cross-correlation theory. The modification is brought about by incorporating a lumped model of ultrasonic backscattering in suspensions into existing spatial cross-correlation analysis. The two experimental datasets correspond to two distinct particle-laden pipe flows: (1) a neutrally buoyant non-Brownian suspension in a laboratory-scale flow facility, wherein particle sizes are comparable to the ultrasonic wavelength and (2) a non-Newtonian slurry in an industrial-scale flow facility, wherein particle sizes are much smaller than the ultrasonic wavelength. We illustrate how and to what extent correlation averaging can counteract the adversity caused by attenuation. The work herein offers a template for one to evaluate the performance of UIV in particle-laden flows.Graphical abstract

Effects of nanosilica on fine migration and location distribution of blockage at different pH and temperatures: Modelling and experimental studies

AbstractThere are a lot of fine particles in sandstone reservoirs that are not strongly bonded to the rock's surface. These fine particles move with the fluid flow and cause formation damage and facilities' erosion. An effective method for preventing fine migration is using nanoparticles (NPs). In this study, modelling and experimental methods were utilized to investigate NPs' effect on fine migration. The Derjaguin–Landau–Verwey–Overbeek (DLVO) theory was used to model NPs' effect on the stability of fines on the rock's surface. Long sand packs were utilized to study the silicon dioxide NPs' effect on fine migration and location distribution of blockage in experiments at different pH and temperatures. Modelling results showed that the presence of NPs reduces the zeta potential of fine surfaces from −27.6 and −35.6 mV to −1.8 and −6.7 mV at pH = 8 and pH = 12, respectively. Based on the DLVO theory, it was concluded that NPs increase the particles' stability on the rock's surface. The experimental results showed that although fine migration increases with an increase in pH and temperature, the presence of SiO2 decreases it by 58% and 38% at pH = 8 and 12, respectively. The results showed that the performance of NPs reduced with an increase in pH and temperature. It was shown that the presence of NPs influences the location distribution of pressure drop and leads to permeability being more homogeneous in porous media during low salinity water injection.

MAGIC Dataset: Multiple Conditions Unmanned Aerial Vehicle Group-Based High-Fidelity Comprehensive Vehicle Trajectory Dataset

High-fidelity vehicle trajectory data contain rich spatiotemporal characteristics and play a major role in the field of transportation research, for example, driving behavior models, traffic flow models, traffic state identification, and driver assistance strategies. There are some coverage issues with the existing datasets recorded in continuous traffic flow facilities, which may limit the further development of research on the safety and efficiency of continuous traffic flow facilities. For example, the observation areas of these datasets lack different traffic states and section types. Therefore, it is necessary to collect a new trajectory dataset. This paper proposes a detailed plan for aerial photography by unmanned aerial vehicle (UAV) group. An experiment was conducted from 7:40 to 10:40 a.m. on a section of the Shanghai Inner Ring, Shanghai, China, with a total length of 4,000 m in both directions including a large radius curve and six ramps. The trajectory dataset, named MAGIC, is extracted and compared with NGSIM US-101 and HIGH-SIM from the aspects of experiment field, traffic states, and so on. Further, to illustrate the advantages of the MAGIC dataset incorporating different traffic states and section types, the MAGIC dataset is evaluated from the three aspects of traffic congestion state, fundamental diagram, and traffic conflict. Overall, there are significant differences in macroscopic traffic flow and traffic safety characteristics between road sections with different section types or traffic states. Therefore, the proposed method presents some unique advantages and may perform effectively in many fields.

Innovative liner concept using friction powder for increasing of broadband noise absorption. Applications for broadband noise absorption in fan duct

This paper presents research related influence of friction powders on enlarging the absorption band of acoustic liners used for reduction of tonal noise in fan duct of aero-engine. Kundt tube measurements done at COMOTI using fine powders (granules) made of various light-weight materials placed in the honeycomb cells of SDOF liners have shown a considerable broadening of the absorbed band of frequencies without a significant decreasing of absorption at the resonance frequency. Although the phenomenon is generally present for any type of powder, it was observed that for some powders the absorption is higher than for others. On the other hand, it was observed that the effect of the filling percentage of the cavity is important. Then, experiments done at the grazing flow facility of CNRS-Le Mans University have shown that the phenomenon is also present for high acoustic incident levels (up to 140dB) and M=0.13 while it still depends by the nature of powder material. The best results were obtained for the cork powder when the honeycomb is filled with powder at 66% of its height. For this material, the broadening of the transmission loss well was maximum. This phenomenon could be explained by the apparition of friction between powder granules which are taking place at very low scale consuming the noise power on a broader range of frequencies. It is supposed that the friction between the particles of powder have major influence because the best noise absorption was obtained for powders with a large distribution of particles’ dimensions while for particles with a small dimensional distribution (expanded polyester balls, for example) the transmission well broadening was smaller. The friction powders technology is simple and can be easily adapted to existing acoustic liner technologies with small manufacturing costs. This feature is conferred by the fact that powders can be easily poured in the honeycombs at the required height. The friction powder technology can be applied not only for the fan duct. In future, it could also be applied for reduction of jet noise reflected by pressure side of wing and for cabin noise reduction.

FLEET Velocimetry for Aerodynamics

Long-lasting emission from femtosecond excitation of nitrogen-based flows shows promise as a useful mechanism for a molecular tagging velocimetry instrument. The technique, known as femtosecond laser electronic excitation tagging (FLEET), was invented at Princeton a decade ago and has quickly been adopted and used in a variety of high-speed ground test flow facilities. The short temporal scales offered by femtosecond amplifiers permit nonresonant multiphoton excitation, dissociation, and weak ionization of a gaseous medium near the beam's focus without the generation of a laser spark observed with nanosecond systems. Gated, intensified imaging of the resulting emission enables the tracking of tagged molecules, thereby measuring one to three components of velocity. Effects of local heating and acoustic disturbances can be mitigated with the selection of a shorter-wavelength excitation source. This review surveys the development of FLEET over the decade since its inception, as it has been implemented in several test facilities to make accurate, precise, and seedless velocimetry measurements for studying complex high-speed flows.

A Mixed Radiative-Convective Technique for the Calibration of Heat Flux Sensors in Hypersonic Flow

The ability to measure the very high heat fluxes that typically occur during the hypersonic re-entry phase of space vehicles is generally considered a subject of great importance in the aerospace field. Most of the sensors used for these measurements need to be checked periodically and re-calibrated accordingly. Another bottleneck relates to the need to procure thermal sources that are able to generate reliable reference heat fluxes in the range between 100 and 1000 kW/m2 (as order of magnitude). In the present study, a method is presented by which, starting from a calibration system with a capacity of approximately 500 kW/m2 only, heat fluxes in the range of interest for hypersonic applications are generated. The related procedure takes advantage of established standards for the characterization of a radiative heat flux. It also builds on the hybrid radiative-convective nature of typical hypersonic heat fluxes and the yet poorly explored possibility to use convective sources of heat to produce high-intensity fluxes. The reliability of such a strategy has been tested using a high enthalpy supersonic flow facility relying on an electric arc-heater and pure Nitrogen as work gas. Stagnation-point heat fluxes have been successfully measured (with reasonable accuracy) in the range between 600 and 1500 kW/m2 for values of the centerline enthalpy spanning the interval from to 6 to 24 MJ/kg.

Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system

A method for classifying flow regimes is proposed that employs a neural network with inputs of extracted features from Doppler ultrasonic signals of flows using either the Discrete Wavelet Transform (DWT) or the Power Spectral Density (PSD). The flow regimes are classified into four types: annular, churn, slug, and bubbly flow regimes. The neural network used in this work is a feedforward network with 20 hidden neurons. The network comprises four output neurons, each of which corresponds to the target vector's element number. 13 and 40 inputs are used for features extracted from PSD and DWT respectively. Experimental data were collected from an industrial-scale multiphase flow facility. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets in the classification and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thus showing the superiority of the DWT in feature extraction of flow regime classification. The approach demonstrates the applicability of a neural network and DWT for flow regime classification in industrial applications using a clamp-on Doppler ultrasonic sensor. The scheme has significant advantages over other techniques as only a non-radioactive and non-intrusive sensor is used. To the best of our knowledge, this is the first known successful attempt for the classification of liquid-gas flow regimes in an S-shape riser system using an ultrasonic sensor, PSD-DWTs features, and a neural network.